path: root/scraper/reports/stats/unknown_papers.csv

add85ee833e2a1c5cdbcd206d5423d63f20cda24,International Journal of Advanced Robotic Systems Embedded Face Detection and Recognition Regular Paper,"International Journal of Advanced Robotic Systems
Embedded Face Detection
nd Recognition
Regular Paper
Göksel Günlü
Department of Electrical and Electronics Engineering Turgut Özal University, Ankara, Turkey
* Corresponding author E-mail:
Received 07 May 2012; Accepted 28 Jun 2012
DOI: 10.5772/51132
© 2012 Günlü; licensee InTech. This is an open access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work is properly cited."
ad2afeb4c1975c637291bc3f7087d665c3f501c8,WebVision Challenge: Visual Learning and Understanding With Web Data,"WebVision Challenge: Visual Learning and
Understanding With Web Data
Wen Li, Limin Wang, Wei Li, Eirikur Agustsson, Jesse Berent, Abhinav Gupta, Rahul Sukthankar,
nd Luc Van Gool"
ada73060c0813d957576be471756fa7190d1e72d,VRPBench: A Vehicle Routing Benchmark Tool.,"VRPBench: A Vehicle Routing Benchmark Tool
October 19, 2016
Guilherme A. Zeni1 , Mauro Menzori1, P. S. Martins1, Luis A. A. Meira1"
ad9d1fb6d39f2c42f9070032c2f8a4a4da1c7128,Ear Segmetation using Topographic Labels,"EAR SEGMETATION USING TOPOGRAPHIC LABELS
Department of Electrical Engineering and Robotics, Shahrood University of Technology, Shahrood, Iran
Milad Lankarany and Alireza Ahmadyfard
Keywords:
Ear Biometrics, Ear Segmentation, Topographic Features."
ad7a7f70e460d4067d7170bcc0f1ea62eedd7234,CBinfer: Exploiting Frame-to-Frame Locality for Faster Convolutional Network Inference on Video Streams,"CBinfer: Exploiting Frame-to-Frame Locality for Faster
Convolutional Network Inference on Video Streams
Lukas Cavigelli, Luca Benini"
ad30152944a42975f16a53cf0e0666e9937e9d73,Dyadic Interaction Detection from Pose and Flow,"Dyadic interaction detection from pose and flow
Anonymous ECCV submission
Paper ID 17"
adca02d4b34a9851d1c9c0a7c1bb8d5178b59b85,Modeling the dynamics of individual behaviors for group detection in crowds using low-level features,"Modeling the dynamics of individual behaviors for group
detection in crowds using low-level features
Omar Adair Islas Ram´ırez
Giovanna Varni
Mihai Andries
Mohamed Chetouani
Raja Chatila"
ad9b3dc6c0e54070cec79df86458ed38566da1ff,Automated Image Captioning for Rapid Prototyping and Resource Constrained Environments,"Automated Image Captioning for Rapid Prototyping
nd Resource Constrained Environments
Department of Computer Science, The University of Georgia, Athens, Georgia 30602-7404, USA
Karan Sharma Arun CS Kumar
Emails:
Suchendra M. Bhandarkar"
ade32e04dceeaed72c3e99a9f3698b2fe01c9863,Learning confidence measures in the wild,"F. TOSI ET AL.: LEARNING CONFIDENCE MEASURES IN THE WILD
Learning confidence measures in the wild
University of Bologna
Department of Computer Science and
Engineering
Bologna, Italy
Fabio Tosi
http://vision.disi.unibo.it/~ftosi
Matteo Poggi
http://vision.disi.unibo.it/~mpoggi
Alessio Tonioni
Luigi Di Stefano
Stefano Mattoccia
http://vision.disi.unibo.it/~smatt"
adba4fe9640c03d8a98bf7604edb32cb868df655,Large Scale Hard Sample Mining with Monte Carlo Tree Search,"Large Scale Hard Sample Mining with Monte Carlo Tree Search
– Supplementary material –
Olivier Can´evet1,2 and Franc¸ois Fleuret1
Idiap Research Institut, Switzerland
´Ecole Polytechnique F´ed´erale de Lausanne (EPFL), Switzerland"
ad109cfebcb512ce83b0a6b2fe640466ccb8a1d9,Reliability-Based Voting Schemes Using Modality-Independent Features in Multi-classifier Biometric Authentication,"Reliability-Based Voting Schemes Using
Modality-Independent Features in
Multi-classifier Biometric Authentication
Jonas Richiardi and Andrzej Drygajlo
Laboratory of IDIAP, Signal Processing Institute
Swiss Federal Institute of Technology Lausanne
http://scgwww.epfl.ch"
ade18cf978e4b00fb74352a7eba90b4f4509d645,Articulated Multi-body Tracking under Egomotion,"Articulated Multi-body Tracking Under Egomotion
S. Gammeter1, A. Ess1, T. J¨aggli1, K. Schindler1, B. Leibe1,2, and L. Van Gool1,3
ETH Z¨urich
RWTH Aachen
KU Leuven, IBBT"
addab6e00b0c79d89c5ba177ac316e7d175e4427,An Optimized Sliding Window Approach to Pedestrian Detection,
ade1034d5daec9e3eba1d39ae3f33ebbe3e8e9a7,Multimodal Caricatural Mirror,"Multimodal Caricatural Mirror
Martin O.(1), Adell J.(2), Huerta A.(3), Kotsia I.(4), Savran A.(5), Sebbe R.(6)
(1)  : Université catholique de Louvain, Belgium
(2)  Universitat Polytecnica de Barcelona, Spain
(3)  Universidad Polytècnica de Madrid, Spain
(4)  Aristotle University of Thessaloniki, Greece
(5)  Bogazici University, Turkey
(6)  Faculté Polytechnique de Mons, Belgium"
adf62dfa00748381ac21634ae97710bb80fc2922,ViFaI : A trained video face indexing scheme Harsh,"ViFaI: A trained video face indexing scheme
Harsh Nayyar
Audrey Wei
. Introduction
With the increasing prominence of inexpensive
video recording devices (e.g., digital camcorders and
video recording smartphones),
the average user’s
video collection today is increasing rapidly. With this
development, there arises a natural desire to rapidly
ccess a subset of one’s collection of videos. The solu-
tion to this problem requires an effective video index-
ing scheme. In particular, we must be able to easily
process a video to extract such indexes.
Today, there also exist large sets of labeled (tagged)
face images. One important example is an individual’s
Facebook profile. Such a set of of tagged images of
one’s self, family, friends, and colleagues represents
n extremely valuable potential training set.
In this work, we explore how to leverage the afore-"
ad75879082132a73fe173a890a0f414f2c279739,A comparison of CNN-based face and head detectors for real-time video surveillance applications,"A Comparison of CNN-based Face and Head Detectors for
Real-Time Video Surveillance Applications
Le Thanh Nguyen-Meidine1, Eric Granger 1, Madhu Kiran1 and Louis-Antoine Blais-Morin2
´Ecole de technologie sup´erieure, Universit´e du Qu´ebec, Montreal, Canada
Genetec Inc., Montreal, Canada"
adf7ccb81b8515a2d05fd3b4c7ce5adf5377d9be,Apprentissage de métrique appliqué à la détection de changement de page Web et aux attributs relatifs,"Apprentissage de métrique appliqué à la
détection de changement de page Web et
ux attributs relatifs
Marc T. Law* — Nicolas Thome* — Stéphane Gançarski* — Mat-
thieu Cord*
* Sorbonne Universités, UPMC Univ Paris 06, UMR 7606, LIP6, F-75005, Paris,
France
RÉSUMÉ. Nous proposons dans cet article un nouveau schéma d’apprentissage de métrique.
Basé sur l’exploitation de contraintes qui impliquent des quadruplets d’images, notre approche
vise à modéliser des relations sémantiques de similarités riches ou complexes. Nous étudions
omment ce schéma peut être utilisé dans des contextes tels que la détection de régions impor-
tantes dans des pages Web ou la reconnaissance à partir d’attributs relatifs."
adefabe194863b4f764ec982e3120554165c841c,Radius based Block Local Binary Pattern on T-Zone Face Area for Face Recognition,"Journal of Computer Science 11 (1): 96-108, 2015
ISSN: 1549-3636
© 2015 Science Publications
RADIUS BASED BLOCK LOCAL BINARY PATTERN ON T-
ZONE FACE AREA FOR FACE RECOGNITION
Md. Jan Nordin, 2Abdul Aziz K. Abdul Hamid,
Sumazly Ulaiman and 2R.U. Gobithaasan
Center for Artificial Intelligent Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
School of Informatics and App. Maths, Universiti Malaysia Terengganu, Terengganu, Malaysia
Received 2014-02-20; Revised 2014-04-29; Accepted 2014-08-04"
ad88fcfd12b62d607259db8d98e2a1a0a9642ca0,Real-time tracking-with-detection for coping with viewpoint change,"Real-Time Tracking-with-Detection for Coping With Viewpoint Change
Shaul Oron · Aharon Bar-Hillel · Shai Avidan
Received: 11 May 2014 / Revised: 02 Nov 2014 / Accepted: 09 Mar 2015"
ad3caae50feee550b047e17699cfe7bb9e243cf5,Sparse similarity-preserving hashing,"Sparse similarity-preserving hashing
Jonathan Masci
Alex M. Bronstein
Michael M. Bronstein
Pablo Sprechmann
Guillermo Sapiro"
ad6bcf4384a7604b6252a6eeefade4c486b01240,Cluster-Based Distributed Face Tracking in Camera Networks,"Cluster-Based Distributed Face Tracking in Camera
Networks
Josiah Yoder, Henry Medeiros, Johnny Park, and Avinash C. Kak"
ad8642e186c5c81d06934d4e6fc249b7cbca40e8,Learning Transferable Architectures for Scalable Image Recognition,"Learning Transferable Architectures for Scalable Image Recognition
Barret Zoph
Google Brain
Vijay Vasudevan
Google Brain
Jonathon Shlens
Google Brain
Quoc V. Le
Google Brain"
ad8b8eb07491b3e771e873703bac23568f134bad,Monocular Depth Estimation with Augmented Ordinal Depth Relationships,"Monocular Depth Estimation with Augmented
Ordinal Depth Relationships
Yuanzhouhan Cao, Tianqi Zhao, Ke Xian, Chunhua Shen, Zhiguo Cao"
adaf2b138094981edd615dbfc4b7787693dbc396,Statistical methods for facial shape-from-shading and recognition,"Statistical Methods For Facial
Shape-from-shading and Recognition
William A. P. Smith
Submitted for the degree of Doctor of Philosophy
Department of Computer Science
0th February 2007"
adbdb6f3c6c79c29f3af1cf39d24c0dccf2d6b2d,Robust Face Recognition by Hierarchical Kernel Associative Memory Models Based on Spatial Domain Gabor Transforms,"Robust Face Recognition by Hierarchical Kernel
Associative Memory Models Based on Spatial
Domain Gabor Transforms
Bai-ling Zhang, Pietro Cerone
School of Computer Science and Mathematics, Victoria University
Email: (cid:0)bzhang,
Yongsheng Gao
School of Engineering, Griffith University"
ad01c5761c89fdf523565cc0dec77b9a6ec8e694,Global and Local Consistent Wavelet-domain Age Synthesis,"Global and Local Consistent Wavelet-domain Age
Synthesis
Peipei Li†, Yibo Hu†, Ran He Member, IEEE and Zhenan Sun Member, IEEE"
040033d73d1efe316c8f0a8ed702b833a0550d83,Generating Expressions that Refer to Visible Objects,"Atlanta, Georgia, 9–14 June 2013. c(cid:13)2013 Association for Computational Linguistics
Proceedings of NAACL-HLT 2013, pages 1174–1184,"
04743c503620baffd75f93f8e4583fcba369ac9d,Proofread Sentence Generation as Multi-Task Learning with Editing Operation Prediction,"Proceedings of the The 8th International Joint Conference on Natural Language Processing, pages 436–441,
Taipei, Taiwan, November 27 – December 1, 2017 c(cid:13)2017 AFNLP"
04241ba56d4499a00beb6991d2460d571a218d85,Learning appearance in virtual scenarios for pedestrian detection,"Learning Appearance in Virtual Scenarios for Pedestrian Detection
Javier Mar´ın, David V´azquez, David Ger´onimo and Antonio M. L´opez
Computer Vision Center and Computer Science Dpt. UAB, 08193 Bellaterra, Barcelona, Spain
{jmarin, dvazquez, dgeronimo,"
044da4715e439b4f91cee8eec55299e30a615c56,Inducing a Concurrent Motor Load Reduces Categorization Precision for Facial Expressions,"Journal of Experimental Psychology:
Human Perception and Performance
016, Vol. 42, No. 5, 706 –718
0096-1523/16/$12.00
© 2015 The Author(s)
http://dx.doi.org/10.1037/xhp0000177
Inducing a Concurrent Motor Load Reduces Categorization Precision for
Facial Expressions
Alberta Ipser and Richard Cook
City University London
Motor theories of expression perception posit that observers simulate facial expressions within their own
motor system, aiding perception and interpretation. Consistent with this view, reports have suggested that
locking facial mimicry induces expression labeling errors and alters patterns of ratings. Crucially,
however, it is unclear whether changes in labeling and rating behavior reflect genuine perceptual
phenomena (e.g., greater internal noise associated with expression perception or interpretation) or are
products of response bias. In an effort to advance this literature, the present study introduces a new
psychophysical paradigm for investigating motor contributions to expression perception that overcomes
some of the limitations inherent in simple labeling and rating tasks. Observers were asked to judge
whether smiles drawn from a morph continuum were sincere or insincere, in the presence or absence of
motor load induced by the concurrent production of vowel sounds. Having confirmed that smile"
04cb43806ca57040100b33af0781e4331f8daa56,Long-term Multi-granularity Deep Framework for Driver Drowsiness Detection,"Long-term Multi-granularity Deep Framework
for Driver Drowsiness Detection
Jie Lyu
Zejian Yuan
Dapeng Chen
Xi’an Jiaotong University
Xi’an Jiaotong University
Xi’an Jiaotong University
Email:
Email:
Email:"
047d7cf4301cae3d318468fe03a1c4ce43b086ed,Co-Localization of Audio Sources Using Binaural Features and Locally-Linear Regression,"Co-Localization of Audio Sources in Images Using
Binaural Features and Locally-Linear Regression
Antoine Deleforge, Radu Horaud, Yoav Y. Schechner, Laurent Girin
To cite this version:
Antoine Deleforge, Radu Horaud, Yoav Y. Schechner, Laurent Girin. Co-Localization of Audio
Sources in Images Using Binaural Features and Locally-Linear Regression. IEEE Transactions
on Audio Speech and Language Processing, 2015, 15p. <hal-01112834>
HAL Id: hal-01112834
https://hal.inria.fr/hal-01112834
Submitted on 3 Feb 2015
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,
´emanant des ´etablissements d’enseignement et de"
04379f40d2a26dd769c53488b7b08a5123f89347,3D Facial Expression Recognition Based on Histograms of Surface Differential Quantities,"D Facial Expression Recognition Based on
Histograms of Surface Differential Quantities
Huibin Li1,2, Jean-Marie Morvan1,3,4, and Liming Chen1,2
Universit´e de Lyon, CNRS
Ecole Centrale de Lyon, LIRIS UMR5205, F-69134, Lyon, France
Universit´e Lyon 1, Institut Camille Jordan,
3 blvd du 11 Novembre 1918, F-69622 Villeurbanne - Cedex, France
King Abdullah University of Science and Technology, GMSV Research Center,
Bldg 1, Thuwal 23955-6900, Saudi Arabia"
048eb50c398fa01bd15329945113341102d96454,Addressing perceptual insensitivity to facial affect in violent offenders: first evidence for the efficacy of a novel implicit training approach.,"doi:10.1017/S0033291713001517
O R I G I N A L A R T I C L E
Addressing perceptual insensitivity to facial affect
in violent offenders: first evidence for the efficacy
of a novel implicit training approach
M. Schönenberg*, S. Christian, A.-K. Gaußer, S. V. Mayer, M. Hautzinger and A. Jusyte
Department of Clinical Psychology and Psychotherapy, University of Tübingen, Germany
Background. Although impaired recognition of affective facial expressions has been conclusively linked to antisocial
ehavior, little is known about the modifiability of this deficit. This study investigated whether and under which circum-
stances the proposed perceptual insensitivity can be addressed with a brief implicit training approach.
Method. Facial affect recognition was assessed with an animated morph task, in which the participants (44 male incar-
erated violent offenders and 43 matched controls) identified the onset of emotional expressions in animated morph clips
that gradually changed from neutral to one of the six basic emotions. Half of the offenders were then implicitly trained to
direct attention to salient face regions (attention training, AT) using a modified dot-probe task. The other half underwent
the same protocol but the intensity level of the presented expressions was additionally manipulated over the course of
training sessions (sensitivity to emotional expressions training, SEE training). Subsequently, participants were reassessed
with the animated morph task.
Results. Facial affect recognition was significantly impaired in violent offenders as compared with controls. Further, our
results indicate that only the SEE training group exhibited a pronounced improvement in emotion recognition.
Conclusions. We demonstrated for the first time that perceptual insensitivity to facial affect can be addressed by an"
0449b56b6b19a3c42766962782bfb88576b5bd62,Spontaneous and cued gaze-following in autism and Williams syndrome,"Spontaneous and cued gaze-following in autism
nd Williams syndrome
Riby et al.
Riby et al. Journal of Neurodevelopmental Disorders 2013, 5:13
http://www.jneurodevdisorders.com/content/5/1/13"
0470b0ab569fac5bbe385fa5565036739d4c37f8,Automatic face naming with caption-based supervision,"Automatic Face Naming with Caption-based Supervision
Matthieu Guillaumin, Thomas Mensink, Jakob Verbeek, Cordelia Schmid
To cite this version:
Matthieu Guillaumin, Thomas Mensink, Jakob Verbeek, Cordelia Schmid. Automatic Face Naming
with Caption-based Supervision. CVPR 2008 - IEEE Conference on Computer Vision
Pattern Recognition,
iety,
<10.1109/CVPR.2008.4587603>. <inria-00321048v2>
008,
pp.1-8,
008, Anchorage, United
<http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4587603>.
IEEE Computer
States.
HAL Id: inria-00321048
https://hal.inria.fr/inria-00321048v2
Submitted on 11 Apr 2011
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-"
04f6a747cba48be1cabbf5efe6ce3eb85e061395,Discriminative Detection and Alignment in Volumetric Data,"Discriminative Detection
nd Alignment in Volumetric Data
Dominic Mai1,2, Philipp Fischer1, Thomas Blein4, Jasmin D¨urr3,
Klaus Palme2,3, Thomas Brox1,2, and Olaf Ronneberger1,2
Lehrstuhl f¨ur Mustererkennung und Bildverabeitung, Institut f¨ur Informatik
BIOSS Centre of Biological Signalling Studies
Institut f¨ur Biologie II, Albert-Ludwigs-Universit¨at Freiburg
INRA Versailles"
04b29b6f1210f4309f3d5ab9e6bd2c8a026ce244,Face Recognition in the Presence of Expressions,"Journal of Software Engineering and Applications, 2012, 5, 321-329
http://dx.doi.org/10.4236/jsea.2012.55038 Published Online May 2012 (http://www.SciRP.org/journal/jsea)
Face Recognition in the Presence of Expressions
Xia Han1*, Moi Hoon Yap2, Ian Palmer3
Centre for Visual Computing, University of Bradford, Bradford, UK; 2School of Computing, Mathematics, and Digital Technology,
Manchester  Metropolitan  University  (MMU),  Manchester,  UK;  3School  of  Computing,  Informatics  and  Media,  University  of
Bradford, Bradford, UK.
Email:
Received February 21st, 2012; revised March 25th, 2012; accepted April 27th, 2012"
04d71c17c46f25ce3087792ab995c19f22d3e4e9,Automatic Person Verification Using Speech and Face Information,"Automatic Person Verification
Using Speech and Face Information
A Dissertation
Presented to
The School of Microelectronic Engineering
Faculty of Engineering and Information Technology
Grif‌f‌ith University
Submitted in Fulfillment
of the Requirements of the Degree of
Doctor of Philosophy
Conrad Sanderson, BEng (Hons)
August 2002
[revised February 2003]"
04e5d374c10b70a74d79070103cab1f362b113ba,"DeepHash: Getting Regularization, Depth and Fine-Tuning Right","DeepHash: Getting Regularization, Depth and Fine-Tuning Right
Jie Lin∗,1,3, Olivier Mor`ere∗,1,2,3, Vijay Chandrasekhar1,3, Antoine Veillard2,3, Hanlin Goh1,3
I2R1, UPMC2, IPAL3"
0485e96bb0c1276fe2a27271b939b6e67997acfc,Active Learning for Structured Probabilistic Models,"Active Learning for Structured Probabilistic Models
Qing Sun
Virginia Tech
Ankit Laddha ∗
Virginia Tech
Dhruv Batra
Virginia Tech"
042510b39c6cdb463610fdda2081b36ff469a353,Human Pose Estimation from Video and IMUs,"Human Pose Estimation from Video and IMUs
Timo von Marcard, Gerard Pons-Moll, and Bodo Rosenhahn"
0468b2b98f6bc190a84daa5902b094ca23122ff6,Low-drift and real-time lidar odometry and mapping,"Auton Robot
DOI 10.1007/s10514-016-9548-2
Low-drift and real-time lidar odometry and mapping
Ji Zhang1 · Sanjiv Singh1
Received: 25 October 2014 / Accepted: 7 February 2016
© Springer Science+Business Media New York 2016"
0431e8a01bae556c0d8b2b431e334f7395dd803a,Learning Localized Perceptual Similarity Metrics for Interactive Categorization,"Learning Localized Perceptual Similarity Metrics for Interactive Categorization
Catherine Wah ∗
Google Inc.
google.com"
04a88ab3ee6314997a51f8ce60da6111226e0f37,Locally Supervised Deep Hybrid Model for Scene Recognition,"Locally-Supervised Deep Hybrid Model
for Scene Recognition
Sheng Guo, Weilin Huang, and Yu Qiao"
0464b56c5beee717b074ed950abcc959372256a6,Fast and Robust Optimization Approaches for Pedestrian Detection,"Fast and Robust Optimization Approaches for
Pedestrian Detection
Victor Hugo Cunha de Melo∗, David Menotti (Co-advisor)†, William Robson Schwartz (Advisor)∗
Computer Science Department, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
Computer Science Department, Universidade Federal de Ouro Preto, Ouro Preto, Brazil
Email:"
041d3eedf5e45ce5c5229f0181c5c576ed1fafd6,How to Take a Good Selfie?,"How to Take a Good Selfie?
Mahdi M. Kalayeh(cid:63) Misrak Seifu◦ Wesna LaLanne(cid:5) Mubarak Shah(cid:63)
(cid:63)Center for Research in Computer Vision at University of Central Florida
◦Jackson State University
(cid:5)University of Central Florida"
045fbe21ea8e501d443fa2d297c1292264712c62,Links between multisensory processing and autism,"Exp Brain Res
DOI 10.1007/s00221-012-3223-4
R E S E A R C H A R T I C L E
Links between multisensory processing and autism
Sarah E. Donohue • Elise F. Darling •
Stephen R. Mitroff
Received: 1 June 2012 / Accepted: 7 August 2012
Ó Springer-Verlag 2012"
046f1c194a09fc84f535c27a3373622223a80c67,Memory-efficient groupby-aggregate using compressed buffer trees,"Memory-Efficient GroupBy-Aggregate using
Compressed Buffer Trees
Hrishikesh Amur†, Wolfgang Richter(cid:63), David G. Andersen(cid:63),
Michael Kaminsky‡, Karsten Schwan†, Athula Balachandran(cid:63), Erik Zawadzki(cid:63)
(cid:63)Carnegie Mellon University, †Georgia Institute of Technology, ‡Intel Labs Pittsburgh"
047f6afa87f48de7e32e14229844d1587185ce45,An Improvement of Energy-Transfer Features Using DCT for Face Detection,"An Improvement of Energy-Transfer Features
Using DCT for Face Detection
Radovan Fusek, Eduard Sojka, Karel Mozdˇreˇn, and Milan ˇSurkala
Technical University of Ostrava, FEECS, Department of Computer Science,
7. listopadu 15, 708 33 Ostrava-Poruba, Czech Republic"
044600cc4b93bb0504e8d72a5476d16f1a61a107,Discriminant Analysis of Principal Components for Face Recognition,"DiscriminantAnalysisofPrincipalComponentsforFace
Recognition(cid:3)
W.Zhao
R.Chellappa
A.Krishnaswamy
CenterforAutomationResearch
ElectricalEngineeringDept
UniversityofMaryland
StanfordUniversity
CollegePark,MD-
Stanford,CA	
LDAofPrincipalComponentsface"
04cdc847f3b10d894582969feee0f37fbd3745e5,Compressed Sensing with Deep Image Prior and Learned Regularization,"Compressed Sensing with Deep Image Prior
nd Learned Regularization
David Van Veen∗†
Ajil Jalal∗†
Eric Price ‡
Sriram Vishwanath †
Alexandros G. Dimakis †
June 19, 2018"
040806bc41c0dd50273921d8d839fda58d20b01e,Socio-affective touch expression database,"RESEARCH ARTICLE
Socio-affective touch expression database
Haemy Lee Masson*, Hans Op de Beeck*
Department of Brain and Cognition, KU Leuven, Leuven, Belgium
* (HLM); (HOB)"
0447bdb71490c24dd9c865e187824dee5813a676,Manifold Estimation in View-based Feature Space for Face Synthesis Across Pose Paper 27,"Manifold Estimation in View-based Feature
Space for Face Synthesis Across Pose
Paper 27"
04ff060369c86ccb07414935bd3e3b85e4896261,Object detection can be improved using human-derived contextual expectations,"Object detection can be improved using
human-derived contextual expectations
Harish Katti, Marius V. Peelen, and S. P. Arun"
04dca7c7f85d607cba64ca56de3364a4085effa1,ExprGAN: Facial Expression Editing With Controllable Expression Intensity,"ExprGAN: Facial Expression Editing with Controllable Expression Intensity
Hui Ding,1 Kumar Sricharan2, Rama Chellappa3
,3University of Maryland, College Park
PARC, Palo Alto"
040eb316cec08b36ae0b57fede86043ee0526686,Learning Reliable and Scalable Representations Using Multimodal Multitask Deep Learning,"Learning Reliable and Scalable Representations
Using Multimodal Multitask Deep Learning
Abhinav Valada, and Wolfram Burgard
Department of Computer Science, University of Freiburg, Germany
I. INTRODUCTION
Modality 1
Modality 2
Unimodal Seg.
Multimodal Seg.
Fifties - in 5 years robots would be everywhere.
Sixties - in 10 years robots would be everywhere.
Seventies - in 20 years robots would be everywhere.
Eighties - in 40 years robots would be everywhere.
-Marvin Minsky
Those were the words from one of the pioneers of AI
when asked to comment on the progress of robotics in the
twentieth century. This shows the high expectations and
unforeseen challenges that we are faced with for deploying
robots in complex real-world environments. One of the primary
impediments has been the robustness of scene understanding"
04bf170753cee3d1da1b9ab41a5b0874685142fa,Casualty Detection for Mobile Rescue Robots via Ground-Projected Point Clouds,"TAROS2018, 037, v5 (final): ’Casualty Detection for Mobile Rescue Robots via Ground- . . ."
0462aa8b7120a34f111e81f77acd1cc7d81680a6,Color Emotions in Large Scale Content Based Image Indexing,"Link¨oping Studies in Science and Technology
Dissertations, No. 1362
Color Emotions in Large Scale Content Based
Image Indexing
Martin Solli
Department of Science and Technology
Link¨oping University, SE-601 74 Norrk¨oping, Sweden
Norrk¨oping, March 2011"
044fdb693a8d96a61a9b2622dd1737ce8e5ff4fa,Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions,"Dynamic Texture Recognition Using Local Binary
Patterns with an Application to Facial Expressions
Guoying Zhao and Matti Pietik¨ainen, Senior Member, IEEE"
0407866be9938f24acc44afd6760e27e15e6e160,Simplest representation yet for gait recognition: averaged silhouette,"Simplest Representation Yet for Gait Recognition: Averaged Silhouette
Zongyi Liu and Sudeep Sarkar
University of South Florida; Tampa; FL 33620
Computer Science and Engineering
{zliu4,"
048dc2682fd7b4fbef5b8c30cf75d422fe1a4108,Part I Face Recognition in General,"Face Recognition
Jens Fagertun
Kongens Lyngby 2005
Master Thesis IMM-Thesis-2005-74"
04616814f1aabe3799f8ab67101fbaf9fd115ae4,Spécialité : Informatique Et Applications Description Sémantique Des Humains Présents Dans Des Images Vidéo (semantic Description of Humans in Images),"UNIVERSIT´EDECAENBASSENORMANDIEU.F.R.deSciences´ECOLEDOCTORALESIMEMTH`ESEPr´esent´eeparM.GauravSHARMAsoutenuele17D´ecembre2012envuedel’obtentionduDOCTORATdel’UNIVERSIT´EdeCAENSp´ecialit´e:InformatiqueetapplicationsArrˆet´edu07aoˆut2006Titre:DescriptionS´emantiquedesHumainsPr´esentsdansdesImagesVid´eo(SemanticDescriptionofHumansinImages)TheworkpresentedinthisthesiswascarriedoutatGREYC-UniversityofCaenandLEAR–INRIAGrenobleJuryM.PatrickPEREZDirecteurdeRechercheINRIA/Technicolor,RennesRapporteurM.FlorentPERRONNINPrincipalScientistXeroxRCE,GrenobleRapporteurM.JeanPONCEProfesseurdesUniversit´esENS,ParisExaminateurMme.CordeliaSCHMIDDirectricedeRechercheINRIA,GrenobleDirectricedeth`eseM.Fr´ed´ericJURIEProfesseurdesUniversit´esUniversit´edeCaenDirecteurdeth`ese"
04c606e8e33cccc69060e42db53738ec6a0f1d03,Evaluation of speech quality measures for the purpose of speaker verification,"Evaluation of speech quality measures for the purpose of speaker verification
Jonas Richiardi, Andrzej Drygajlo
Signal Processing Institute
Swiss Federal Instiute of Technology (EPFL)"
040dc119d5ca9ea3d5fc39953a91ec507ed8cc5d,Large-scale Bisample Learning on ID vs. Spot Face Recognition,"Noname manuscript No.
(will be inserted by the editor)
Large-scale Bisample Learning on ID vs. Spot Face Recognition
Xiangyu Zhu∗ · Hao Liu∗ · Zhen Lei · Hailin Shi · Fan Yang · Dong
Yi · Stan Z. Li
Received: date / Accepted: date"
04bb0a1ccca86a4c1084fc7472ea07189c110aa7,Tracking Interacting Objects Using Intertwined Flows,"Tracking Interacting Objects Using
Intertwined Flows
Xinchao Wang∗ , Engin T¨uretken∗, Franc¸ois Fleuret, and Pascal Fua, Fellow, IEEE"
042a71e5c19bfce4e6f7b98492e68192b471a449,Towards speaker independent continuous speechreading,"TOWARDS SPEAKER INDEPENDENT CONTINUOUS SPEECHREADING
Juergen Luettin
IDIAP
CP 	, 
	 Martigny, Switzerland"
04b08a2735eff524f17d3f1a63eb7fc6484d4f83,Facial emotion detection using deep learning,IT 16 040Examensarbete 30 hpJuni 2016Facial emotion detection using deep learning Daniel Llatas SpiersInstitutionen för informationsteknologiDepartment of Information Technology
04bd29ec1ae0b64367ec37ddde51a0d8f8b7f670,Few-shot Object Detection,"SUBMITTED TO IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2017.
Few-shot Object Detection
Xuanyi Dong, Liang Zheng, Fan Ma, Yi Yang, Deyu Meng"
04f4679765d2f71576dd77c1b00a2fd92e5c6da4,Part Detector Discovery in Deep Convolutional Neural Networks,"Part Detector Discovery in Deep Convolutional
Neural Networks
Marcel Simon, Erik Rodner, and Joachim Denzler
Computer Vision Group, Friedrich Schiller University of Jena, Germany
www.inf-cv.uni-jena.de"
04f7eab5d03ac6ad678f2fc8adf29bc1a84a2084,Tree based object matching using multi-scale covariance descriptor,"Tree based object matching using multi-scale covariance
descriptor
Walid AYEDI1,2, Hichem SNOUSSI1, Fethi SMACH2 and Mohamed ABID2
Charles Delaunay Institute (FRE CNRS 2848), University of Technology of Troyes, 10010 Troyes, France
Sfax University, National Engineering School of Sfax, 3052 Sfax, Tunisia"
047f8d5d5134dd12c67038623417f05ab9885056,Motion Synthesis In : Static Scan + Expression Out : Best Fitting Sequence + Angry Out : Animated Sequence Statistical Analysis Expression Recognition,"D Faces in Motion: Fully Automatic Registration and Statistical Analysis
Timo Bolkarta,∗, Stefanie Wuhrera
Saarland University, Saarbr¨ucken, Germany"
044ae9738c2445d4fda30fcd6c289eddf8b3add9,Multiple Instance Learning: A Survey of Problem Characteristics and Applications,"Multiple Instance Learning: A Survey of Problem
Characteristics and Applications
Marc-Andr´e Carbonneau∗
Veronika Cheplygina†
Eric Granger∗
Ghyslain Gagnon‡"
0480b458439069687ec41c90178ba7e9a056bcca,Gender Classification Using Gradient Direction Pattern,"Sci.Int(Lahore),25(4),797-799,2013
ISSN 1013-5316;  CODEN: SINTE 8
GENDER CLASSIFICATION USING GRADIENT DIRECTION PATTERN
Department of Computer Science, School of Applied Statistics,
National Institute of Development Administration, Bangkok, Thailand.
Mohammad Shahidul Islam"
041ac91c85276f61bec3f0f3c42782e4f9a31f88,Detailed Dense Inference with Convolutional Neural Networks via Discrete Wavelet Transform,"Detailed Dense Inference with Convolutional Neural Networks
via Discrete Wavelet Transform
Lingni Ma1, J¨org St¨uckler2, Tao Wu1 and Daniel Cremers1"
04dcdb7cb0d3c462bdefdd05508edfcff5a6d315,Assisting the training of deep neural networks with applications to computer vision,"Assisting the training of deep neural networks
with applications to computer vision
Adriana Romero
tesi  doctoral  està  subjecta  a
Aquesta
CompartirIgual  4.0. Espanya de Creative Commons.
Esta tesis doctoral está sujeta a la licencia  Reconocimiento - NoComercial – CompartirIgual
.0.  España de Creative Commons.
This  doctoral  thesis  is  licensed  under  the Creative  Commons  Attribution-NonCommercial-
ShareAlike 4.0. Spain License.
llicència Reconeixement-  NoComercial  –"
04b851f25d6d49e61a528606953e11cfac7df2b2,Optical Flow Guided Feature: A Fast and Robust Motion Representation for Video Action Recognition,"Optical Flow Guided Feature: A Fast and Robust Motion Representation for
Video Action Recognition
Shuyang Sun1,2, Zhanghui Kuang2, Lu Sheng3, Wanli Ouyang1, Wei Zhang2
The University of Sydney 2SenseTime Research 3The Chinese University of Hong Kong
{shuyang.sun
{wayne.zhang"
045b45adbcb83a34d087c917b79274858a878937,A Methodology for Extracting Standing Human Bodies From Single Images,"Invention Journal of Research Technology in Engineering & Management (IJRTEM)                                                  ISSN: 2455-3689
www.ijrtem.com ǁ Volume 1 ǁ Issue 8 ǁ
A Methodology for Extracting Standing Human Bodies from Single Images
Dr. Y. Raghavender Rao1, N. Devadas Naik2
Head ECE JNTUHCEJ Jagtityal
Asst professor Sri Chaitanya engineering college"
04ceb15dfe33884ac38fa9ec0abb1e19ab090679,Resolving Referring Expressions in Images With Labeled Elements,"RESOLVING REFERRING EXPRESSIONS IN IMAGES WITH LABELED ELEMENTS
Nevan Wichers, Dilek Hakkani-T¨ur, Jindong Chen
Google AI, Mountain View, CA, USA"
9d35d4fba9217404a7aab84a7d09e53c324710be,Biometrics Project : Bayesian Face Recognition,"Biometrics Project: Bayesian Face Recognition
Jinwei Gu
Computer Science Department"
9d1940f843c448cc378214ff6bad3c1279b1911a,Shape-aware Instance Segmentation,"Shape-aware Instance Segmentation
Zeeshan Hayder1,2, Xuming He2,1
Australian National University & 2Data61/CSIRO ∗
Mathieu Salzmann2,3
CVLab, EPFL, Switzerland"
9da2b79c6942852e8076cdaa4d4c93eb1ae363f1,Constraint-Based Visual Generation,"Constraint-Based Visual Generation
Giuseppe Marra
Francesco Giannini
Marco Gori
Michelangelo Diligenti
Department of Information Engineering and Mathematical Sciences
http://sailab.diism.unisi.it/
October 9, 2018"
9d1e32f6af50354b64ca8f004746073473559056,A visual surveillance system for person re-identification,"International Conference on Quality Control by Artificial Vision 2017, edited by Hajime Nagahara,Kazunori Umeda, Atsushi Yamashita, Proc. of SPIE Vol. 10338, 103380D · © 2017 SPIECCC code: 0277-786X/17/$18 · doi: 10.1117/12.2266509Proc. of SPIE Vol. 10338  103380D-1"
9d24179aa33a94c8c61f314203bf9e906d6b64de,Searching for People through Textual and Visual Attributes,"Searching for People through
Textual and Visual Attributes
Junior Fabian, Ramon Pires, Anderson Rocha
Institute of Computing
University of Campinas (Unicamp)
Campinas-SP, Brazil
Fig. 1. The proposed approach aims at searching for people using textual and visual attributes. Given an image database of faces, we extract the points of
interest (PoIs) to construct a visual dictionary that allow us to obtain the feature vectors by a quantization process (top). Then we train attribute classifiers to
generate a score for each image (middle). Finally, given a textual query (e.g., male), we fusion obtained scores to return a unique final rank (bottom)."
9d2ad0b408bddc9c5a713e250b52aa48f1786a46,Visual Recognition Using Local Quantized Patterns,"Visual Recognition using Local Quantized Patterns
Sibt Ul Hussain, Bill Triggs
To cite this version:
Sibt Ul Hussain, Bill Triggs. Visual Recognition using Local Quantized Patterns. Andrew Fitzgibbon,
Svetlana Lazebnik, Pietro Perona, Yoichi Sato, and Cordelia Schmid. ECCV 2012 - 12th European
Conference on Computer Vision, Oct 2012, Florence, Italy. Springer, 7573, pp.716-729, 2012, Lecture
Notes in Computer Science. <10.1007/978-3-642-33709-3_51>. <hal-00695627>
HAL Id: hal-00695627
https://hal.archives-ouvertes.fr/hal-00695627
Submitted on 9 May 2012
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
9d58e8ab656772d2c8a99a9fb876d5611fe2fe20,Beyond Temporal Pooling: Recurrence and Temporal Convolutions for Gesture Recognition in Video,"Beyond Temporal Pooling: Recurrence and Temporal
Convolutions for Gesture Recognition in Video
Lionel Pigou, A¨aron van den Oord∗ , Sander Dieleman∗ ,
{lionel.pigou,aaron.vandenoord,sander.dieleman,
Mieke Van Herreweghe & Joni Dambre
mieke.vanherreweghe,
Ghent University
February 11, 2016"
9d6a2180a5f452356526edd8b4833180fa09cb3f,Photo Aesthetics Analysis via DCNN Feature Encoding,"Photo Aesthetics Analysis
via DCNN Feature Encoding
Hui-Jin Lee, Ki-Sang Hong, Henry Kang, and Seungyong Lee"
9dc263210770e7e836040c8e9d0edff40814254b,A track before detect approach for sequential Bayesian tracking of multiple speech sources,"978-1-4244-4296-6/10/$25.00 ©2010 IEEE
ICASSP 2010"
9d743bbef448e7c145aeb11e55cc05fdbafe9d6d,Person tracking and gesture recognition in challenging visibility conditions using 3D thermal sensing,"Person Tracking and Gesture  Recognition
in Challenging Visibility Conditions
Using 3D Thermal Sensing
Ariel Kapusta and Patrick Beeson
IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)
August, 30, 2016"
9d0ceae3747467488ba914cb2ca5b30ac3032286,Modèles graphiques probabilistes pour la reconnaissance de formes. (Probabilistic graphical models for shape recognition),"Modèles graphiques probabilistes pour la reconnaissance
de formes
Sabine Barrat
To cite this version:
Sabine Barrat. Modèles graphiques probabilistes pour la reconnaissance de formes. Interface homme-
machine [cs.HC]. Université Nancy II, 2009. Français. <tel-00530755>
HAL Id: tel-00530755
https://tel.archives-ouvertes.fr/tel-00530755
Submitted on 29 Oct 2010
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de
recherche français ou étrangers, des laboratoires"
9d357bbf014289fb5f64183c32aa64dc0bd9f454,Face Identification by Fitting a 3D Morphable Model Using Linear Shape and Texture Error Functions,"Face Identification by Fitting a 3D Morphable Model
using Linear Shape and Texture Error Functions
Sami Romdhani, Volker Blanz, and Thomas Vetter
University of Freiburg, Instit¨ut f¨ur Informatik,
Georges-K¨ohler-Allee 52, 79110 Freiburg, Germany,
fromdhani, volker,"
9d3ac3d29164c2665c371a3c71de75bea753eb47,Skeleton-Aided Articulated Motion Generation,"Skeleton-aided Articulated Motion Generation
Yichao Yan, Jingwei Xu, Bingbing Ni, Xiaokang Yang"
9d8978ee319d671283a90761aaed150c7cc9154b,Fader Networks: Manipulating Images by Sliding Attributes,"Fader Networks:
Manipulating Images by Sliding Attributes
Guillaume Lample1,2, Neil Zeghidour1,3, Nicolas Usunier1,
Antoine Bordes1, Ludovic Denoyer2, Marc’Aurelio Ranzato1"
9d9166e1d9e80bbe772423384af53a3d5da898ae,Object Geolocation Using MRF Based Multi-Sensor Fusion,"OBJECT GEOLOCATION USING MRF BASED MULTI-SENSOR FUSION
Vladimir A. Krylov and Rozenn Dahyot
ADAPT Centre, School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland"
9d5db7427b44d83bf036ff4cff382c23c6c7b6d8,Video redaction: a survey and comparison of enabling technologies,"Downloaded From: https://biomedicaloptics.spiedigitallibrary.org/journals/Journal-of-Electronic-Imaging on 10/14/2018
Terms of Use: https://biomedicaloptics.spiedigitallibrary.org/terms-of-use
Videoredaction:asurveyandcomparisonofenablingtechnologiesShaganSahAmeyaShringiRaymondPtuchaAaronBurryRobertLoceShaganSah,AmeyaShringi,RaymondPtucha,AaronBurry,RobertLoce,“Videoredaction:asurveyandcomparisonofenablingtechnologies,”J.Electron.Imaging26(5),051406(2017),doi:10.1117/1.JEI.26.5.051406."
9df3c81ce84b027d9cda37c754250d31a5561005,Semantic scene modeling and retrieval,"DISS. ETH NO. 15751
Semantic Scene Modeling and Retrieval
A dissertation submitted to the
SWISS FEDERAL INSTITUTE OF TECHNOLOGY ZURICH
for the degree of
Doctor of Technical Sciences
presented by
JULIA VOGEL
Dipl. Ing. Elektrotechnik, M.S. Electrical and Computer Engineering
orn November 23, 1973
itizen of Germany
ccepted on the recommendation of
Prof. Dr. Bernt Schiele, examiner
Prof. Dr. Andrew Zisserman, co-examiner"
9df7ea3eed6b0c9c067521119698cfa79cc1f91d,Representations and Matching Techniques for 3 D Free-form Object and Face Recognition,"Representations and Matching
Techniques for 3D Free-form Object and
Face Recognition
Ajmal Saeed Mian
This thesis is presented for the degree of
Doctor of Philosophy
of The University of Western Australia
School of Computer Science and Software Engineering.
March 2006"
9da2abae3072fd9fcff0e13b8f00fc21f22d0085,NOKMeans: Non-Orthogonal K-means Hashing,"NOKMeans: Non-Orthogonal K-means Hashing
Xiping Fu, Brendan McCane, Steven Mills, and Michael Albert
Dep. of Computer Science, University of Otago, Dunedin, NZ"
9d6e386a83dab99232b7b519761894a9f9b3bb41,Wide and deep volumetric residual networks for volumetric image classification,"Wide and deep volumetric residual networks for volumetric image classification
Varun Arvind 1, Anthony Costa 2, Marcus Badgeley2, Samuel Cho1, Eric Oermann 2
Department of Orthopedics, Icahn School of Medicine at Mount Sinai, 1 Gustave Levy Pl. New York, NY 10029
Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, 1 Gustave Levy Pl. New York, NY 10029"
9d0bf3b351fb4d80cee5168af8367c5f6c8b2f3a,"The Tromso Infant Faces Database (TIF): Development, Validation and Application to Assess Parenting Experience on Clarity and Intensity Ratings","METHODS
published: 24 March 2017
doi: 10.3389/fpsyg.2017.00409
The Tromso Infant Faces Database
(TIF): Development, Validation and
Application to Assess Parenting
Experience on Clarity and Intensity
Ratings
Jana K. Maack†, Agnes Bohne†, Dag Nordahl, Lina Livsdatter, Åsne A. W. Lindahl,
Morten Øvervoll, Catharina E. A. Wang and Gerit Pfuhl*
Department of Psychology, UiT – The Arctic University of Norway, Tromsø, Norway
Newborns and infants are highly depending on successfully communicating their needs;
e.g., through crying and facial expressions. Although there is a growing interest in
the mechanisms of and possible influences on the recognition of facial expressions in
infants, heretofore there exists no validated database of emotional infant faces. In the
present article we introduce a standardized and freely available face database containing
Caucasian infant face images from 18 infants 4 to 12 months old. The development
nd validation of the Tromsø Infant Faces (TIF) database is presented in Study 1. Over
700 adults categorized the photographs by seven emotion categories (happy, sad,
disgusted, angry, afraid, surprised, neutral) and rated intensity, clarity and their valance."
9d8747468f0fed8e335656d7fe9737e4dc21c798,RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free,"RetinaMask: Learning to predict masks improves state-of-the-art single-shot
detection for free
Cheng-Yang Fu Mykhailo Shvets Alexander C. Berg
Computer Science Department of
UNC at Chapel Hill
{cyfu, mshvets,"
9d8fd639a7aeab0dd1bc6eef9d11540199fd6fe2,EARNING TO C LUSTER,"Workshop track - ICLR 2018
LEARNING TO CLUSTER
Benjamin B. Meier, Thilo Stadelmann & Oliver D¨urr
ZHAW Datalab, Zurich University of Applied Sciences
Winterthur, Switzerland"
9dc70aa3d51a9403e1894a7fa535ace99b527861,3 Bayesian Tracking by Online Co-Training and Sequential Evolutionary Importance Resampling,"We are IntechOpen,
the world’s leading publisher of
Open Access books
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9d17e897e8344d1cf42a322359b48d1ff50b4aef,Learning to Fuse Things and Stuff,"Learning to Fuse Things and Stuff
Jie Li*, Allan Raventos*, Arjun Bhargava*, Takaaki Tagawa, Adrien Gaidon
Toyota Research Institute (TRI)"
9d839dfc9b6a274e7c193039dfa7166d3c07040b,Augmented faces,"Augmented Faces
Matthias Dantone1
Lukas Bossard1
Till Quack1,2
Luc van Gool1,3
ETH Z¨urich
Kooaba AG
K.U. Leuven"
9d518344d5c7d889f9c90c6193be4757fa584770,3 D registration based on a multi-references local parametrisation : Application to 3 D faces,"D registration based on a multi-references local parametrisation:
Application to 3D faces
Wieme Gadacha1, Faouzi Ghorbel1
CRISTAL laboratory, GRIFT research group
National School of Computer Sciences (NSCS), La Manouba 2010, Tunisia"
9d138bc60593c2770d968ba56172332773e02fa5,GPLAC: Generalizing Vision-Based Robotic Skills Using Weakly Labeled Images,
9d67af2158807aa815b5a4485b076f7a18ce6ab4,Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding,"Model Adaptation with Synthetic and Real Data
for Semantic Dense Foggy Scene Understanding
Christos Sakaridis1(), Dengxin Dai1, Simon Hecker1, and Luc Van Gool1,2
ETH Z¨urich, Z¨urich, Switzerland
KU Leuven, Leuven, Belgium"
c27f64eaf48e88758f650e38fa4e043c16580d26,Title of the proposed research project: Subspace analysis using Locality Preserving Projection and its applications for image recognition,"Title of the proposed research project: Subspace analysis using Locality Preserving
Projection and its applications for image recognition
Research area: Data manifold learning for pattern recognition
Contact Details:
Name: Gitam C Shikkenawis
Email Address:
University:  Dhirubhai  Ambani  Institute  of  Information  and  Communication  Technology
(DA-IICT), Gandhinagar."
c2b8b49526e3dd537b641a6495e49a3d1a0ebbf2,Extended Feature-Fusion Guidelines to Improve Image-Based Multi-Modal Biometrics,"Extended Feature-Fusion Guidelines to Improve
Image-Based Multi-Modal Biometrics
Dane Brown
Council for Scientific and Industrial Research
Information Security
Pretoria, South Africa"
c274a4428e81f49c2395f4b3888e768e2dec9ee9,CPU / GPGPU / HW comparison of an Eigenfaces face recognition system,"Universidad Politécnica de Madrid
Escuela Técnica Superior de Ingenieros Industriales
Departamento de Automática, Ingeniería Electrónica e Informática
Industrial
Master on Industrial Electronics
CPU/GPGPU/HW comparison of
n Eigenfaces face recognition
system
Author: Julio Camarero Mateo
Advisor: Eduardo de la Torre Arnanz
March 2014
Master Thesis"
c26735fb53c54b7319857797dc16786123626d14,Model Cards for Model Reporting,"Model Cards for Model Reporting
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben
Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, Timnit Gebru"
c2e6daebb95c9dfc741af67464c98f1039127627,Efficient Measuring of Facial Action Unit Activation Intensities using Active Appearance Models,"MVA2013 IAPR International Conference on Machine Vision Applications, May 20-23, 2013, Kyoto, JAPAN
Ef‌f‌icient Measuring of Facial Action Unit Activation Intensities
using Active Appearance Models
Daniel Haase1, Michael Kemmler1, Orlando Guntinas-Lichius2, Joachim Denzler1
Computer Vision Group, Friedrich Schiller University of Jena, Germany
Department of Otolaryngology, University Hospital Jena, Germany"
c219244bf27ed16c5c710f3a7c7d92b1ea16e8cc,An Independent Evaluation of Subspace Face Recognition Algorithms,"An Independent Evaluation of Subspace Face Recognition Algorithms
Dhiresh R. Surajpal and Tshilidzi Marwala"
c2cb401f73ee13f35368127939c6db8654aa422a,Learning Optical Flow from Real Robot Data,"Learning Optical Flow from Real Robot Data
Parth Shah"
c2d35b387518496d8100f70e82597b002eba600e,Online Multi-player Tracking in Monocular Soccer Videos,"Available online at www.sciencedirect.com
AASRI Procedia 00 (2014) 000–000
014 AASRI Conference on Sports Engineering and Computer Science (SECS 2014)
Online Multi-player Tracking in Monocular Soccer Videos
Michael Herrmanna,*, Martin Hoerniga, Bernd Radiga
Technische Universität München, Image Understanding and Knowledge-Based Systems, Boltzmannstr. 3, D-85748 Garching, Germany"
c21db705a33212768c63be11747d075371c7307f,A Content-Based Late Fusion Approach Applied to Pedestrian Detection,"A Content-Based Late Fusion Approach Applied to
Pedestrian Detection
Jessica Sena, Artur Jord˜ao, William Robson Schwartz
Smart Surveillance Interest Group
Department of Computer Science, Universidade Federal de Minas Gerais
Av. Presidente Antˆonio Carlos, 6627 - Pampulha, Belo Horizonte, Brazil"
c2cb4da617168c76c4560a01de8b5e68b5250749,FineTag: Multi-attribute Classification at Fine-grained Level in Images,"FineTag: Multi-attribute Classification at
Fine-grained Level in Images
Roshanak Zakizadeh, Michele Sasdelli, Yu Qian and Eduard Vazquez
Cortexica Vision Systems, London, UK"
c259693737ce52e2e37972e15334cbe78b653e69,Image Processing Supports HCI in Museum Application,"Image Processing Supports HCI in Museum Application
Niki Martinel, Marco Vernier, Gian Luca Foresti and Elisabetta Lamedica
Department of Mathematics and Computer Science, University of Udine, Via Delle Scienze 206, Udine, Italy
{niki.martinel, marco.vernier,
Keywords:
Augmented Reality: Information Visualization: User Interface Design: Mobile HCI."
c29487c5eb0cdb67d92af1bc0ecbcf825e2abec3,3-D Face Recognition With the Geodesic Polar Representation,"-D Face Recognition With the
Geodesic Polar Representation
Iordanis Mpiperis, Sotiris Malassiotis, and Michael G. Strintzis, Fellow, IEEE
therefore,"
c2b9d6742e504491800cee44adb05d2d706fc209,Semantic-Based Web Mining For Image Retrieval Using Enhanced Support Vector Machine,"International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 5 (2016) pp 3276-3281
© Research India Publications.  http://www.ripublication.com
Semantic-Based Web Mining For Image Retrieval Using Enhanced Support
Vector Machine
Ph.D Research Scholar, Research Department of Computer Science,
NGM College, Pollachi, Coimbatore, Tamil Nadu, India.
P. Sumathi
R. Manickachezian
Associate Professor, Research Department of Computer Science,
NGM College, Pollachi, Coimbatore, Tamil Nadu, India."
c231d8638e8b5292c479d20f7dd387c53e581a1a,Multi-View Data Generation Without View Supervision,"MULTI-VIEW DATA GENERATION WITHOUT VIEW
SUPERVISION
Micka¨el Chen, Ludovic Denoyer
Sorbonne Universit´es, UPMC Univ Paris 06, UMR 7606, LIP6, F-75005, Paris, France
Thierry Arti`eres
Ecole Centrale Marseille - Laboratoire d’Informatique Fondamentale (Aix-Marseille Univ.), France."
c254b4c0f6d5a5a45680eb3742907ec93c3a222b,A Fusion-based Gender Recognition Method Using Facial Images,"A Fusion-based Gender Recognition Method
Using Facial Images
Benyamin Ghojogh, Saeed Bagheri Shouraki, Hoda Mohammadzade*, Ensieh Iranmehr"
c28f57d0a22e54fdd3c4a57ecb1785dda49f0e5e,From Scores to Face Templates: A Model-Based Approach,"From Scores to Face Templates:
A Model-Based Approach
Pranab Mohanty, Student Member, IEEE, Sudeep Sarkar, Senior Member, IEEE, and
Rangachar Kasturi, Fellow, IEEE"
c2adfc55e0ab9be6e8f5e4ebeb20770dca307cef,"The effect of diagnosis, age, and symptom severity on cortical surface area in the cingulate cortex and insula in autism spectrum disorders.","http://jcn.sagepub.com/
The Effect of Diagnosis, Age, and Symptom Severity on Cortical Surface Area in the Cingulate Cortex
nd Insula in Autism Spectrum Disorders
Krissy A.R. Doyle-Thomas, Azadeh Kushki, Emma G. Duerden, Margot J. Taylor, Jason P. Lerch, Latha V. Soorya, A.
Ting Wang, Jin Fan and Evdokia Anagnostou
J Child Neurol
2013 28: 729 originally published online 25 July 2012
DOI: 10.1177/0883073812451496
The online version of this article can be found at:
http://jcn.sagepub.com/content/28/6/729
Published by:
http://www.sagepublications.com
Additional services and information for
can be found at:
Email Alerts:
http://jcn.sagepub.com/cgi/alerts
Subscriptions:
http://jcn.sagepub.com/subscriptions
Reprints:
http://www.sagepub.com/journalsReprints.nav"
c2fb2cb5487ad404b8e66daf74198496c40bef32,Learning to Transfer Privileged Information,"Learning to Transfer Privileged Information
Viktoriia Sharmanska1∗, Novi Quadrianto2, and Christoph Lampert1,
Institute of Science and Technology Austria, Austria
SMiLe CLiNiC, University of Sussex, UK"
c2b1007824fa7ce3a7a94209f0be0902a3454bae,Project Description 1 Introduction,"Project Description
Introduction
Recognizing human action is a key component in many vision applications, such as video surveil-
lance, 3D human pose estimation and video indexing. From the human-centered computing (HCC)
point of view, an automatic action recognition system can provide an interface between artificial
gents and human users accounting for perception and action in a novel interaction paradigm.
Although significant progress has been made in action recognition [1], the problem remains inher-
ently challenging due to significant intra-class variations, viewpoint change, partial occlusion and
ackground dynamic variations. A key limitation of many action-recognition approaches is that
their models are learned from single 2D view video features on individual datasets and thus un-
ble to handle arbitrary view change or scale and background variations. Also, since they are not
generalizable across different datasets, retraining is necessary for any new dataset.
Our research is motivated by the requirement of view-invariant action recognition and the fact that
the existing human motion capture data provides useful knowledge to understand the intrinsic motion
structure (Fig. 2). In particular, we address the problem of modeling and analyzing human motion
in the joint-trajectories space. Our view-invariant recognition system has the following functions
(Fig. 1),
(1) Given a labeled Mocap sequences with M markers in 3D, which is a 3M -dimensional sequential
data, the low dimensional manifold structure (i.e., geodesics distance, intrinsic dimensionality, etc)
is learnt by using Tensor Voting. This is an offline process, as shown in Fig. 1."
c2f2c89d7615df07b540748d6c53485c4cbfa9c0,An Experience Report on Requirements-Driven Model-Based Synthetic Vision Testing,"An Experience Report on Requirements-Driven
Model-Based Synthetic Vision Testing
Markus Murschitz and Oliver Zendel and Martin Humenberger
nd Christoph Sulzbachner and Gustavo Fern´andez Dom´ınguez 1"
c2eed73654b544a705b194ade58cd82488c6c5b9,Pixels labeled with a scene ’ s semantics and geometry let computers describe what they see,"ontributed articles
DOI:10.1145/2629637
Pixels labeled with a scene’s semantics and
geometry let computers describe what they see.
BY STEPHEN GOULD AND XUMING HE
Scene
Understanding
y Labeling
Pixels
PROGRAMMING COMPUTERS TO automatically interpret
the content of an image is a long-standing challenge in
rtificial intelligence and computer vision. That difficulty
is echoed in a well-known anecdote from the early years
of computer-vision research in which an undergraduate
student at MIT was asked to spend his summer getting a
omputer to describe what it “saw” in images obtained
from a video camera.35 Almost 50 years later researchers
re still grappling with the same problem.
A scene can be described in many ways and include
details about objects, regions, geometry, location,"
c2cb38fc68b877a96be99b814e8ee437e585f5b2,Mining on Manifolds: Metric Learning without Labels,"Mining on Manifolds: Metric Learning without Labels
Ahmet Iscen1 Giorgos Tolias1 Yannis Avrithis2 Ondˇrej Chum1
VRG, FEE, CTU in Prague
Inria Rennes"
c2e9300b0e72dca0b95ccd4181fc2a7a5178dea7,Improving Bilayer Product Quantization for Billion-Scale Approximate Nearest Neighbors in High Dimensions.,"Improving Bilayer Product Quantization
for Billion-Scale Approximate Nearest Neighbors in High
Dimensions
Artem Babenko
Yandex
Moscow Institute of Physics and Technology
Victor Lempitsky
Skolkovo Institute of Science and Technology"
c223b2b7d38dc4e0ad418c404b2d3c43c62213bc,Trade-off Between GPGPU based Implementations of Multi Object Tracking Particle Filter,"Trade-off between GPGPU based implementations of
multi object tracking particle filter
Petr Jecmen, Frédéric Lerasle, Alhayat Ali Mekonnen
To cite this version:
Petr Jecmen, Frédéric Lerasle, Alhayat Ali Mekonnen. Trade-off between GPGPU based implemen-
tations of multi object tracking particle filter. International Conference on Computer Vision Theory
nd Applications, Feb 2017, Porto, Portugal. 10p., 2017. <hal-01763095>
HAL Id: hal-01763095
https://hal.laas.fr/hal-01763095
Submitted on 10 Apr 2018
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
c20b2ec72ebf798e9567a145465e37a755fc34d8,Fully Automatic Multi-person Human Motion Capture for VR Applications,"Fully Automatic Multi-person Human Motion Capture
for VR Applications
Ahmed Elhayek1,2, Onorina Kovalenko1, Pramod Murthy1,2, Jameel Malik1,2, and
Didier Stricker1,2
German Research Centre for Artificial Intelligence (DFKI), Kaiserslautern, Germany
University of Kaiserslautern, Germany
{ahmed.elhayek, onorina.kovalenko, pramod.murthy,
jameel.malik,"
3f7723ab51417b85aa909e739fc4c43c64bf3e84,Improved Performance in Facial Expression Recognition Using 32 Geometric Features,"Improved Performance in Facial Expression
Recognition Using 32 Geometric Features
Giuseppe Palestra1(B), Adriana Pettinicchio2, Marco Del Coco2,
Pierluigi Carcagn`ı2, Marco Leo2, and Cosimo Distante2
Department of Computer Science, University of Bari, Bari, Italy
National Institute of Optics, National Research Council, Arnesano, LE, Italy"
3f9210830e31f42103c6550f75cb37fde18e5af1,HeadFusion: 360° Head Pose Tracking Combining 3D Morphable Model and 3D Reconstruction,"PAMI SPECIAL ISSUE
HeadFusion: 360◦Head Pose tracking combining
D Morphable Model and 3D Reconstruction
Yu Yu, Kenneth Alberto Funes Mora, Jean-Marc Odobez"
3f848d6424f3d666a1b6dd405a48a35a797dd147,Is 2D Information Enough For Viewpoint Estimation?,"GHODRATI et al.: IS 2D INFORMATION ENOUGH FOR VIEWPOINT ESTIMATION?
Is 2D Information Enough For Viewpoint
Estimation?
Amir Ghodrati
Marco Pedersoli
Tinne Tuytelaars
KU Leuven, ESAT - PSI, iMinds
Leuven, Belgium"
3f10b9d98a276fb9e21e5742ce88bc7f48629715,Imparare a Quantificare Guardando (Learning to Quantify by Watching),"Imparare a quantificare guardando
Sandro Pezzelle
CIMeC
Ionut Sorodoc
Aurelie Herbelot
CIMeC
EM LCT
Universit`a degli Studi di Trento
Raffaella Bernardi
CIMeC, DISI"
3fd90098551bf88c7509521adf1c0ba9b5dfeb57,Attribute-Based Classification for Zero-Shot Visual Object Categorization,"Page 1 of 21
*****For Peer Review Only*****
Attribute-Based Classification for Zero-Shot
Visual Object Categorization
Christoph H. Lampert, Hannes Nickisch and Stefan Harmeling"
3f06d445371c252d5a6ba977181987094148d6de,Fast Single Shot Detection and Pose Estimation,"Fast Single Shot Detection and Pose Estimation
Patrick Poirson1, Phil Ammirato1, Cheng-Yang Fu1, Wei Liu1, Jana Koˇseck´a2, Alexander C. Berg1
UNC Chapel Hill 2George Mason University
201 S. Columbia St., Chapel Hill, NC 27599 24400 University Dr, Fairfax, VA 22030"
3f6a6050609ba205ec94b8af186a9dca60a8f65e,Harmonizing Maximum Likelihood with Gans,"Under review as a conference paper at ICLR 2019
HARMONIZING MAXIMUM LIKELIHOOD WITH GANS
FOR MULTIMODAL CONDITIONAL GENERATION
Anonymous authors
Paper under double-blind review"
3f60b1f800178841f4e0ecb79b64fe60b48ed03b,Video Scene Parsing with Predictive Feature Learning,"Video Scene Parsing with Predictive Feature Learning
Xiaojie Jin1 Xin Li2 Huaxin Xiao2 Xiaohui Shen3 Zhe Lin3 Jimei Yang3
Yunpeng Chen2 Jian Dong4 Luoqi Liu4 Zequn Jie2 Jiashi Feng2 Shuicheng Yan4,2
NUS Graduate School for Integrative Science and Engineering, NUS
360 AI Institute
Department of ECE, NUS
Adobe Research"
3f0f3c2bc151ef91959b06442b9ad80d405387a5,Evidential combination of pedestrian detectors,"XU ET AL.: EVIDENTIAL COMBINATION OF PEDESTRIAN DETECTORS
Evidential combination of pedestrian
detectors
Philippe Xu1
https://www.hds.utc.fr/~xuphilip
Franck Davoine12
Thierry Denœux1
https://www.hds.utc.fr/~tdenoeux
UMR CNRS 7253, Heudiasyc,
Université de Technologie de
Compiègne, France
CNRS, LIAMA,
Beijing, P. R. China"
3f8e481ea845aa20704d8c93f6a3a72025219f64,Data mapping by probabilistic modular networks and information-theoretic criteria,"IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 46, NO. 12, DECEMBER 1998
Data Mapping by Probabilistic Modular
Networks and Information-Theoretic Criteria
Yue Wang, Shang-Hung Lin, Huai Li, and Sun-Yuan Kung, Fellow, IEEE"
3f600008dd9745e8357f5b7b3c1a69b8be6b7767,Atypical reflexive gaze patterns on emotional faces in autism spectrum disorders.,"The Journal of Neuroscience, September 15, 2010 • 30(37):12281–12287 • 12281
Behavioral/Systems/Cognitive
Atypical Reflexive Gaze Patterns on Emotional Faces in
Autism Spectrum Disorders
Dorit Kliemann,1,2,3 Isabel Dziobek,2 Alexander Hatri,1,2 Rosa Steimke,2,4 and Hauke R. Heekeren1,2,3
Department of Educational Science and Psychology, and 2Cluster of Excellence, “Languages of Emotion,” Freie Universita¨t Berlin, 14195 Berlin, Germany,
nd 3Max Planck Institute for Human Development, 14195 Berlin, Germany, and 4Department of Psychiatry and Psychotherapy, Charité University
Medicine, 10117 Berlin, Germany
Atypical scan paths on emotional faces and reduced eye contact represent a prominent feature of autism symptomatology, yet the reason
for these abnormalities remains a puzzle. Do individuals with autism spectrum disorders (ASDs) fail to orient toward the eyes or do they
ctively avoid direct eye contact? Here, we used a new task to investigate reflexive eye movements on fearful, happy, and neutral faces.
Participants (ASDs: 12; controls: 11) initially fixated either on the eyes or on the mouth. By analyzing the frequency of participants’ eye
movements away from the eyes and toward the eyes, respectively, we explored both avoidance and orientation reactions. The ASD group
showed a reduced preference for the eyes relative to the control group, primarily characterized by more frequent eye movements away
from the eyes. Eye-tracking data revealed a pronounced influence of active avoidance of direct eye contact on atypical gaze in ASDs. The
ombination of avoidance and reduced orientation into an individual index predicted emotional recognition performance. Crucially, this
result provides evidence for a direct link between individual gaze patterns and associated social symptomatology. These findings thereby
give important insights into the social pathology of ASD, with implications for future research and interventions.
Introduction
Recent reports from the social-cognitive neurosciences have em-"
3f9d1e2dd09a4f2ab939693e91fcfd77d51d90c9,Reducing drift in visual odometry by inferring sun direction using a Bayesian Convolutional Neural Network,"Reducing Drift in Visual Odometry by Inferring Sun Direction
Using a Bayesian Convolutional Neural Network‡
Valentin Peretroukhin†, Lee Clement†, and Jonathan Kelly"
3f9a7d690db82cf5c3940fbb06b827ced59ec01e,VIP: Finding important people in images,"VIP: Finding Important People in Images
Clint Solomon Mathialagan
Virginia Tech
Andrew C. Gallagher
Google Inc.
Dhruv Batra
Virginia Tech
Project: https://computing.ece.vt.edu/~mclint/vip/
Demo: http://cloudcv.org/vip/"
3f5b20c35f55417823f0201862d85af1f31e9348,Salience Biased Loss for Object Detection in Aerial Images,"Salience Biased Loss for Object Detection
in Aerial Images
Peng Sun
Guerdan Luke
Guang Chen
University of Missouri-Columbia
Yi Shang
over regular and dense sampling of object scales, locations,
nd aspect ratios, such as YOLO [8], SSD [11], and RetinaNet
[18]. Each of these demonstrates promising results with faster
speed, a simpler network, and similar accuracy of two-stage
object detectors. RetinaNet [18] even outperforms one of the
est two-stage detectors, Faster R-CNN [5], with a relative 4.0
mAP improvement in COCO data [17]."
3fdcc1e2ebcf236e8bb4a6ce7baf2db817f30001,A Top-Down Approach for a Synthetic Autobiographical Memory System,"A top-down approach for a synthetic
utobiographical memory system
Andreas Damianou1,2, Carl Henrik Ek3, Luke Boorman1, Neil D. Lawrence2,
nd Tony J. Prescott1
Shef‌f‌ield Centre for Robotics (SCentRo), Univ. of Shef‌f‌ield, Shef‌f‌ield, S10 2TN, UK
Dept. of Computer Science, Univ. of Shef‌f‌ield, Shef‌f‌ield, S1 4DP, UK
CVAP Lab, KTH, Stockholm, Sweden"
3fac7c60136a67b320fc1c132fde45205cd2ac66,Remarks on Computational Facial Expression Recognition from HOG Features Using Quaternion Multi-layer Neural Network,"Remarks on Computational Facial Expression
Recognition from HOG Features Using
Quaternion Multi-layer Neural Network
Kazuhiko Takahashi1, Sae Takahashi1, Yunduan Cui2,
nd Masafumi Hashimoto3
Information Systems Design, Doshisha University, Kyoto, Japan
Graduate School of Doshisha University, Kyoto, Japan
Intelligent Information Engineering and Science, Doshisha University, Kyoto, Japan"
3fa9bf4649ff5e0d63ee20a546e8814f3a93ca4d,Digital Image Technique using Gabor Filter and SVM in Heterogeneous Face Recognition,"Research Inventy: International Journal of Engineering And Science
Vol.4, Issue 4 (April 2014), PP 45-52
Issn (e): 2278-4721, Issn (p):2319-6483, www.researchinventy.com
Digital Image Technique using Gabor Filter and SVM in
Heterogeneous Face Recognition
M.Janani#1, K.Nandhini*2, K.Senthilvadivel*3,S.Jothilakshmi*4,
PG Student#1,*2*3, Assistant Professor*4,, Dept of CSE#1,*2,*3,*4
S.V.S College of Engineering#1,*4,, PPG Institute of Technology*2,*3,
Coimbatore, Tamilnadu"
3f0e0739677eb53a9d16feafc2d9a881b9677b63,Efficient Two-Stream Motion and Appearance 3D CNNs for Video Classification,"Efficient Two-Stream Motion and Appearance 3D CNNs for
Video Classification
Ali Diba
ESAT-KU Leuven
Ali Pazandeh
Sharif UTech
Luc Van Gool
ESAT-KU Leuven, ETH Zurich"
3f55d26dd638c849745b95e912c28d88445ba5e1,Supervised Learning of Universal Sentence Representations from Natural Language Inference Data,"Supervised Learning of Universal Sentence Representations from
Natural Language Inference Data
Alexis Conneau
Facebook AI Research
Douwe Kiela
Facebook AI Research
Holger Schwenk
Facebook AI Research
Lo¨ıc Barrault
LIUM, Universit´e Le Mans
Antoine Bordes
Facebook AI Research"
3fa738ab3c79eacdbfafa4c9950ef74f115a3d84,DaMN - Discriminative and Mutually Nearest: Exploiting Pairwise Category Proximity for Video Action Recognition,"DaMN – Discriminative and Mutually Nearest:
Exploiting Pairwise Category Proximity
for Video Action Recognition
Rui Hou1, Amir Roshan Zamir1, Rahul Sukthankar2, and Mubarak Shah1
Center for Research in Computer Vision at UCF, Orlando, USA
Google Research, Mountain View, USA
http://crcv.ucf.edu/projects/DaMN/"
3f9c09e2fbefc9aeba6505f49317f9a2fc03a615,Understanding fundamental design choices in single-ISA heterogeneous multicore architectures,"Understanding Fundamental Design Choices in Single-ISA
Heterogeneous Multicore Architectures
KENZO VAN CRAEYNEST and LIEVEN EECKHOUT, Ghent University
Single-ISA heterogeneous multicore processors have gained substantial interest over the past few years
ecause of their power efficiency, as they offer the potential for high overall chip throughput within a
given power budget. Prior work in heterogeneous architectures has mainly focused on how heterogeneity
an improve overall system throughput. To what extent heterogeneity affects per-program performance
has remained largely unanswered. In this article, we aim at understanding how heterogeneity affects both
hip throughput and per-program performance; how heterogeneous architectures compare to homogeneous
rchitectures under both performance metrics; and how fundamental design choices, such as core type, cache
size, and off-chip bandwidth, affect performance.
We use analytical modeling to explore a large space of single-ISA heterogeneous architectures. The ana-
lytical model has linear-time complexity in the number of core types and programs of interest, and offers a
unique opportunity for exploring the large space of both homogeneous and heterogeneous multicore proces-
sors in limited time. Our analysis provides several interesting insights: While it is true that heterogeneity
an improve system throughput, it fundamentally trades per-program performance for chip throughput;
lthough some heterogeneous configurations yield better throughput and per-program performance than
homogeneous designs, some homogeneous configurations are optimal for particular throughput versus per-
program performance trade-offs. Two core types provide most of the benefits from heterogeneity and a larger
number of core types does not contribute much; job-to-core mapping is both important and challenging for"
3f44352b857f2fc18c18c5ebb2cbf994ee22f44c,Humanist computing for knowledge discovery from ordered datasets,"HumanistComputingforKnowledgeDiscovery
fromOrderedDatasets
JonathanMichaelRossiter
DepartmentofEngineeringMathematics
UniversityofBristol
AdissertationsubmittedtotheUniversityofBristol
inaccordancewiththerequirementsofthedegreeof
DoctorofPhilosophyintheFacultyofEngineering
January"
3f5158ea65bb483c6797462faffa16fea9f0b004,"Lie-X: Depth Image Based Articulated Object Pose Estimation, Tracking, and Action Recognition on Lie Groups","Lie-X : Depth Image Based Articulated Object Pose Estimation,
Tracking, and Action Recognition on Lie Groups
Chi Xu1, Lakshmi Narasimhan Govindarajan1, Yu Zhang1, and Li Cheng∗1
Bioinformatics Institute, A*STAR, Singapore"
3fea412361b2d14cb3c6723968b421c1c8cb38e8,Shake-Shake regularization,"Shake-Shake regularization
Xavier Gastaldi"
3f63f9aaec8ba1fa801d131e3680900680f14139,Facial Expression Recognition using Local Binary Patterns and Kullback Leibler Divergence,"Facial Expression Recognition using Local Binary
Patterns and Kullback Leibler Divergence
AnushaVupputuri, SukadevMeher
divergence."
3f0e00188d751829c4548f9aacb939b982425ebd,Template Protection For 3 D Face Recognition 315 Template Protection For 3 D Face Recognition,"Template Protection For 3D Face Recognition
Template Protection For 3D Face Recognition
Xuebing Zhou, Arjan Kuijper and Christoph Busch
Fraunhofer Institute for Computer Graphics Research IGD
Germany"
3faebe9d5c47fc90998811c4ac768706283d605c,Semi-Supervised Detection of Extreme Weather Events in Large Climate Datasets,"Under review as a conference paper at ICLR 2017
SEMI-SUPERVISED DETECTION OF EXTREME WEATHER
EVENTS IN LARGE CLIMATE DATASETS
Evan Racah1, Christopher Beckham2, Tegan Maharaj2
Prabhat1, Christopher Pal2
Lawrence Berkeley National Lab, Berkeley, CA,
´Ecole Polytechnique de Montr´eal,"
3faff93758fe7fc58b3832055cb15c6ca3f306a7,Evaluation of multi feature fusion at score-level for appearance-based person re-identification,"Evaluation of Multi Feature Fusion at Score-Level
for Appearance-based Person Re-Identification
Markus Eisenbach
Ilmenau University of Technology
98684 Ilmenau, Germany
Alexander Kolarow
Alexander Vorndran
Julia Niebling
Horst-Michael Gross
Ilmenau University of Technology
Ilmenau University of Technology
98684 Ilmenau, Germany
98684 Ilmenau, Germany"
3f2270762ff68d6771d93d800683ae6bc76855e7,Human Motion Tracking and Pose Estimation using Probabilistic Activity Models,"MANCHESTER METROPOLITAN UNIVERSITY
D Human Motion Tracking and
Pose Estimation using
Probabilistic Activity Models
John Darby
A thesis submitted in partial fulfillment for the
degree of Doctor of Philosophy
Faculty of Science and Engineering
The Department of Computing and Mathematics
October 2010"
e5eb7fa8c9a812d402facfe8e4672670541ed108,Performance of PCA Based Semi-supervised Learning in Face Recognition Using MPEG-7 Edge Histogram Descriptor,"Performance of PCA Based Semi-supervised
Learning in Face Recognition Using MPEG-7
Edge Histogram Descriptor
Shafin Rahman, Sheikh Motahar Naim, Abdullah Al Farooq and Md. Monirul Islam
Department of Computer Science and Engineering
Bangladesh University of Engineering and Technology(BUET)
Dhaka-1000, Bangladesh
Email: {shafin buet, naim sbh2007,"
e5d13afe956d8581a69e9dc2d1f43a43f1e2f311,Automatic Facial Feature Extraction for Face Recognition,"We are IntechOpen,
the world’s leading publisher of
Open Access books
Built by scientists, for scientists
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08,500
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in Web of Science™ Core Collection (BKCI)
Interested in publishing with us?
Contact"
e5320955580401d5a5b2ae8b507e8f0b47e08118,Deep Supervision with Intermediate Concepts,"Deep Supervision with Intermediate Concepts
Chi Li, M. Zeeshan Zia, Quoc-Huy Tran, Xiang Yu, Gregory D. Hager, and Manmohan Chandraker"
e58f08ad6e0edd567f217ef08de1701a8c29fcc8,Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing - and Back,"Pseudo-task Augmentation: From Deep Multitask
Learning to Intratask Sharing—and Back
Elliot Meyerson 1 2 Risto Miikkulainen 1 2"
e59a68c328c69c294991f87b741a5d4e952defba,NISTIR 7972 Performance Metrics for Evaluating Object and Human Detection and Tracking Systems,"This publication is available free of charge from http://dx.doi.org/10.6028/NIST.IR.7972
NISTIR 7972
Performance Metrics for Evaluating
Object and Human Detection and
Tracking Systems
Afzal Godil
Roger Bostelman
Will Shackleford
Tsai Hong
Michael Shneier
http://dx.doi.org/10.6028/NIST.IR.7972"
e577847c36251dc31282ad57ea969ea8297369be,Face scanning and spontaneous emotion preference in Cornelia de Lange syndrome and Rubinstein-Taybi syndrome,"Crawford et al. Journal of Neurodevelopmental Disorders  (2015) 7:22
DOI 10.1186/s11689-015-9119-4
R ES EAR CH
Face scanning and spontaneous emotion
preference in Cornelia de Lange syndrome
nd Rubinstein-Taybi syndrome
Hayley Crawford1,2*, Joanna Moss2,3, Joseph P. McCleery4, Giles M. Anderson5 and Chris Oliver2
Open Access"
e596a4aedb5cda6f0df35d38549564a0dd5546a7,Public Document Document Evolution Executive Summary,"Project N° IST-2002-507634 - BioSecure
D 9.1.3 – Revision: b2
09 June 2006
Contract Number :
Project Acronym :
Project Title :
Instrument :
Start Date of Project :
Duration :
Deliverable Number :
Title of Deliverable :
Contractual Due Date :
Actual Date of Completion :
IST-2002-507634
BioSecure
Biometrics for Secure Authentication
Network of Excellence
01 June, 2004
6 months
D 9.1.3"
e596753824ed56f17927984f78f51713b321588d,3DOF Pedestrian Trajectory Prediction Learned from Long-Term Autonomous Mobile Robot Deployment Data,"DOF Pedestrian Trajectory Prediction Learned from Long-Term
Autonomous Mobile Robot Deployment Data
Li Sun1 and Zhi Yan2 and Sergi Molina Mellado1 and Marc Hanheide1 and Tom Duckett1"
e5563a0d6a2312c614834dc784b5cc7594362bff,Real-Time Demographic Profiling from Face Imagery with Fisher Vectors,"Noname manuscript No.
(will be inserted by the editor)
Real-Time Demographic Profiling from Face Imagery with
Fisher Vectors
Lorenzo Seidenari · Alessandro Rozza · Alberto Del Bimbo
Received: ... / Accepted: ..."
e524f222a117890126bd9597934d0504adce85ec,Error Correction for Dense Semantic Image Labeling,"Yu-Hui Huang1∗ Xu Jia2∗ Stamatios Georgoulis1
Tinne Tuytelaars2
Luc Van Gool1,3
KU-Leuven/ESAT-PSI, Toyota Motor Europe (TRACE)
ETH/DITET-CVL
KU-Leuven/ESAT-PSI, IMEC"
e58434a01c45505995b000f5e631843a2f2ea582,Scale coding bag of deep features for human attribute and action recognition,"Noname manuscript No.
(will be inserted by the editor)
Scale Coding Bag of Deep Features for Human Attribute
nd Action Recognition
Fahad Shahbaz Khan, Joost van de Weijer, Rao Muhammad Anwer,
Andrew D. Bagdanov, Michael Felsberg, Jorma Laaksonen
Received:"
e5918229f44f0215d73a0b9d5eb13eb56764a2e4,Counting Vehicles with Cameras,"Counting Vehicles with Cameras
Luca Ciampi1, Giuseppe Amato1, Fabrizio Falchi1, Claudio Gennaro1, and
Fausto Rabitti1
Institute of Information, Science and Technologies of the National Research Council
of Italy (ISTI-CNR), via G. Moruzzi 1, 56124 Pisa, Italy"
e5c468c859faf03954d9440fa33b913d01c65141,Retina alapú mintavételezés arckomponens detektálási feladaton,"-JLI
6AH	=H
AJE= ==Fœ EJ=L	JAA	I =H?FAI
5=> J

)==JJ =JA=JEKI D=C=J
6	=LAAJfi 5=J
HO
BH=JE= 2D, D=C=J
-6- 66 1BH
?EI 6=I	"
e525ba29497bab9b530ea7b056dd0128be22c48a,Tencent ML-Images: A Large-Scale Multi-Label Image Database for Visual Representation Learning.,"Tencent ML-Images: A Large-Scale Multi-Label
Image Database for Visual Representation
Learning
Baoyuan Wu, Weidong Chen, Yanbo Fan, Yong Zhang, Jinlong Hou, Jie Liu, Junzhou Huang, Wei Liu,
Tong Zhang"
e5799fd239531644ad9270f49a3961d7540ce358,Kinship classification by modeling facial feature heredity,"KINSHIP CLASSIFICATION BY MODELING FACIAL FEATURE HEREDITY
Ruogu Fang1, Andrew C. Gallagher1, Tsuhan Chen1, Alexander Loui2
Dept. of Elec. and Computer Eng., Cornell University 2Eastman Kodak Company"
e5604c3f61eb7e8b80bf423f7828d8c1fa0f1d32,Towards Image Understanding from Deep Compression without Decoding,"Published as a conference paper at ICLR 2018
TOWARDS IMAGE UNDERSTANDING FROM
DEEP COMPRESSION WITHOUT DECODING
Robert Torfason
ETH Zurich, Merantix
Fabian Mentzer
ETH Zurich
Eirikur Agustsson
ETH Zurich
Michael Tschannen
ETH Zurich
Radu Timofte
ETH Zurich, Merantix
Luc Van Gool
ETH Zurich, KU Leuven"
e56c4c41bfa5ec2d86c7c9dd631a9a69cdc05e69,Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-Art,"Human Activity Recognition Based on Wearable
Sensor Data: A Standardization of the
State-of-the-Art
Artur Jord˜ao, Antonio C. Nazare Jr., Jessica Sena and William Robson Schwartz
Smart Surveillance Interest Group, Computer Science Department
Universidade Federal de Minas Gerais, Brazil
Email: {arturjordao, antonio.nazare, jessicasena,"
e564268a03b21fa092390db0c11ba1c33d2323f9,Multi-view Stereo with Single-View Semantic Mesh Refinement,"Multi-View Stereo with Single-View Semantic Mesh Refinement
Andrea Romanoni Marco Ciccone
Francesco Visin Matteo Matteucci
{andrea.romanoni, marco.ciccone, francesco.visin,
Politecnico di Milano, Italy"
e5dcec59afdab7c15e3a874e9b602b8fc42b9019,Nonparametric Video Retrieval and Frame Classification using Tiny Videos,"International Conference on Recent Trends in Computational Methods, Communication and Controls (ICON3C 2012)
Proceedings published in International Journal of Computer Applications® (IJCA)
Nonparametric Video Retrieval and Frame Classification
using Tiny Videos
A.K. M. Shanawas Fathima,
PG Student,
Department of CSE
GCE, Tirunelveli.
R. Kanthavel,
Department of CSE,
Government College of Engineering,
Tirunelveli."
e502dad3aa196a47ed3cfb727b6b75c65be8a871,A Baseline for Multi-Label Image Classification Using Ensemble Deep CNN.,"A Baseline for Multi-Label Image Classification Using Ensemble Deep CNN
Qian Wang
Toby Breckon
Ning Jia
Durham University
{qian.wang,ning.jia,"
e56c99e8a94d3e585166fcd66f2ab6da60932f09,Semantic Speech Retrieval With a Visually Grounded Model of Untranscribed Speech,"Semantic speech retrieval with a
visually grounded model of untranscribed speech
Herman Kamper, Gregory Shakhnarovich, and Karen Livescu"
e592f6dc3bf1d53044cd59ce4a75fdacd0ecc80d,Hand Vein Infrared Image Segmentation for Biometric Recognition,"Hand Vein Infrared Image Segmentation for Biometric
Recognition
Ignacio Irving Morales-Montiel1, J. Arturo Olvera-López1, Manuel Martín-Ortíz1, and
Eber E. Orozco-Guillén2
Facultad de Ciencias de la Computación
Benemérita Universidad Autónoma de Puebla
Av. San Claudio y 14 sur. Ciudad Universitaria.
Puebla, Pue., Mexico
Mazatlán, Sin., Mexico
Programa de Ingeniería en Informática
Universidad Politécnica de Sinaloa
Carretera Municipal Libre Mazatlán Higueras Km. 3."
e5bcbfd346121769b674a7ad35e594758de5553f,A Dataset for Lane Instance Segmentation in Urban Environments,"A Dataset for Lane Instance Segmentation in
Urban Environments
Brook Roberts, Sebastian Kaltwang, Sina Samangooei,
Mark Pender-Bare, Konstantinos Tertikas, and John Redford
FiveAI Ltd., Cambridge CB2 1NS, U.K."
a472d59cff9d822f15f326a874e666be09b70cfd,VISUAL LEARNING WITH WEAKLY LABELED VIDEO A DISSERTATION SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE AND THE COMMITTEE ON GRADUATE STUDIES OF STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY,"VISUAL LEARNING WITH WEAKLY LABELED VIDEO
A DISSERTATION
SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE
AND THE COMMITTEE ON GRADUATE STUDIES
OF STANFORD UNIVERSITY
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
Kevin Tang
May 2015"
a493a731dadababb6f2ae0b4b6233d861206345b,Studio2Shop: from studio photo shoots to fashion articles,"Studio2Shop: from studio photo shoots to fashion articles
Julia Lasserre1, Katharina Rasch1 and Roland Vollgraf
Zalando Research, Muehlenstr. 25, 10243 Berlin, Germany
Keywords:
omputer vision, deep learning, fashion, item recognition, street-to-shop"
a4dd2ff517ae61d7f39ec176915d8da5cafe8323,Lip Processing and Modeling based on Spatial Fuzzy Clustering in Color Images,"International Journal of Fuzzy Systems, Vol. 13, No. 2, June 2011                                                                                                65
Lip Processing and Modeling based on Spatial Fuzzy Clustering in Color
Images
R. Rohani, F. Sobhanmanesh, S. Alizadeh, and R. Boostani"
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Nonlinear and Optimal Control Theory, P. Nistri and G. Stefani, Eds.
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[21] H. K. Khalil, Nonlinear Systems, third ed. Upper Saddle River, NJ:
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[22] R. A. Horn and C. R. Johnson, Matrix Analysis. Cambridge, U.K.:
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[23] W. Ren and R. W. Beard, “Consensus seeking in multiagent systems"
a4ee9f089ab9a48a6517a6967281247339a51747,Resembled Generative Adversarial Networks: Two Domains with Similar Attributes,"DUHYEON BANG, HYUNJUNG SHIM: RESEMBLED GAN
Resembled Generative Adversarial Networks:
Two Domains with Similar Attributes
School of Integrated Technology, Yonsei
University, South Korea
Duhyeon Bang
Hyunjung Shim"
a42eb9e4c2506640446f07df3a9a0134752b00da,Domain Adaptive Transfer Learning with Specialist Models,"Domain Adaptive Transfer Learning with Specialist Models
Jiquan Ngiam, Daiyi Peng, Vijay Vasudevan, Simon Kornblith, Quoc V. Le, Ruoming Pang
Google Brain"
a46b950e1aa97ab3033d8a21fabb1952fb7eb5ce,Mixtures of boosted classifiers for frontal face detection,"SIViP (2007) 1:29–38
DOI 10.1007/s11760-007-0003-x
O R I G I NA L PA P E R
Mixtures of boosted classifiers for frontal face detection
Julien Meynet · Vlad Popovici ·
Jean-Philippe Thiran
Received: 12 October 2006 / Revised: 12 January 2007 / Accepted: 12 January 2007 / Published online: 3 March 2007
© Springer-Verlag London Limited 2007"
a4f345a8a7b3d5933282cb7fa641b2957ca89113,Comparison of focus measures in face detection environments,"+2)415 . .+75 -)574-5 1 .)+- ,-6-+61
-814-65
 HA  ,AE  +=IJHE + /KAHH=
IJ B 1JAECAJ 5OIJAI KAHE?= )FF E -CEAAHEC 1751)1
7ELAHIEJO B =I 2==I /H= +==HE= +=FKI 7EL 6=H= !#% )I 2==I 5F=E
+=FKI 7EL 6=H= !#% )I 2==I 5F=E
)>IJH=?J
0K=+FKJAH 1JAH=?JE +FKJAH 8EIE )KJB?KI A=IKHAI
6DEI MH FHAIAJI = ?F=HEI =C B?KI A=IKHAI E JDA EJAH=JKHA BH =KJB?KIEC E =
 FHALEKIO =FFE?=JE B B=?A 6DEI =FFE?=JE D=I ?D=H=?JAHEIJE?I J JDIA
MDAHA =KJB?KI D=LA >AA EA E?HI?FO H BH B?KI 6DA =E B JDA
MH EI J EB JDA >AIJ B?KI A=IKHAI E =FFE?=JEI B =KJB?KI D=LA JDA I=A FAHBH=?A
E B=?A =FFE?=JEI 6 JD=J IEN B?KI A=IKHAI D=I >AA E BKH IAJJECI BH
JDA J HA HA?AJ AI
 B=?A B=?A HA?CEJE
AJ = ''"" 4MAO AJ = ''& /HII
AJ =   0A=I M   ;=C AJ =
 D= AJ =  ! F=O = EFHJ=J
HA >A?=KIA JDAO JDA B=?E= BA=JKHAI JD=J =FFA=H
E JDA B=?A IK?D =I AOAI KJD IA MDE?D =HA"
a4c430b7d849a8f23713dc283794d8c1782198b2,Video Concept Embedding,"Video Concept Embedding
Anirudh Vemula
Rahul Nallamothu
Syed Zahir Bokhari
. Introduction
In the area of natural language processing, there has been
much success in learning distributed representations for
words as vectors. Doing so has an advantage over using
simple labels, or a one-hot coding scheme for representing
individual words. In learning distributed vector representa-
tions for words, we manage to capture semantic relatedness
of words in vector distance. For example, the word vector
for ”car” and ”road” should end up being closer together in
the vector space representation than ”car” and ”penguin”.
This has been very useful in NLP areas of machine transla-
tion and semantic understanding.
In the computer vision domain, video understanding is a
very important topic.
It is made hard due to the large
mount of high dimensional data in videos. One strategy"
a49b661e42aea6f205e543a80106fc9c6ff0f9d4,Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry,"Deep Virtual Stereo Odometry:
Leveraging Deep Depth Prediction for
Monocular Direct Sparse Odometry
Nan Yang1,2, Rui Wang1,2, J¨org St¨uckler1, and Daniel Cremers1,2
Technical University of Munich
Artisense"
a422c2bd9030c8a2c89b6db79be2743c4a4609fb,Auto Deep Compression by Reinforcement Learning Based Actor-Critic Structure,"Auto Deep Compression by Reinforcement Learning Based
Actor-Critic Structure
Hamed Hakkak1"
a44b91f46ba66c8279b93caab6842444de0c9343,Frequency-domain Tracking Spatial-domain Detection Generic Object Proposal Histogram based Representation Detection Result Tracking State Estimation Spatial Regressor Correlation Model IFFT Search Space Feature Extraction Correlation Map Correlation Model FFT,"Monocular Long-term Target Following on UAVs
Rui Li ∗
Minjian Pang†
Cong Zhao ‡
Guyue Zhou ‡
Lu Fang †§"
a432f815d753121267ffb524f8fabac21be32733,Proyecto Aguará : Automatic Face Recognition,"Proyecto Aguar´a: Automatic Face Recognition
C. AGUERREBERE, G. CAPDEHOURAT, M. DELBRACIO AND M. MATEU
Instituto de Ingenier´ıa El´ectrica, Facultad de Ingenier´ıa,
Universidad de la Rep ´ublica, Montevideo, Uruguay
We present a biometric system performing both, verification and identification, implementing automatic face recognition. The algorithm is based on Elastic Bunch
Graph Matching [1]. EBGM is a technique that uses local information extracted with Gabor filters for discrimination. CSU implementation [2] was used as the
main reference of this work. The results are comparable with those of the state of the art."
a4bab165158b9627280fb3052b1c731210f2a901,"Pedestrian Localization, Tracking and Behavior Analysis from Multiple Cameras","Pedestrian Localization, Tracking and Behavior Analysis
from Multiple Cameras
THÈSE NO 4629 (2010)
PRÉSENTÉE LE 9 AVRIL 2010
À LA FACULTÉ INFORMATIQUE ET COMMUNICATIONS
LABORATOIRE DE VISION PAR ORDINATEUR
PROGRAMME DOCTORAL EN INFORMATIQUE, COMMUNICATIONS ET INFORMATION
ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE
POUR L'OBTENTION DU GRADE DE DOCTEUR ÈS SCIENCES
Jérôme BERCLAZ
cceptée sur proposition du jury:
Prof. P. Thiran, président du jury
Prof. P. Fua, Dr F. Fleuret, directeurs de thèse
Prof. M. Bierlaire, rapporteur
Prof. H. Bischof, rapporteur
Dr J. Ferryman, rapporteur
Suisse"
a48ac2ebade8a0c77c936e45756bccef9668d8e6,Scale-Space Techniques for Fiducial Points Extraction from 3D Faces,"Scale-Space Techniques for Fiducial Points
Extraction from 3D Faces
Nikolas De Giorgis(B), Luigi Rocca, and Enrico Puppo
Department of Informatics, Bio-engineering, Robotics and System Engineering,
University of Genova, Via Dodecaneso 35, 16146 Genova, Italy"
a427ee25ef515ddd9cf50b4cc3a7376f57d58926,Human-Drone-Interaction: A Case Study to Investigate the Relation Between Autonomy and User Experience,"Human-Drone-Interaction: A Case Study to
Investigate the Relation between Autonomy and
User Experience
Patrick Ferdinand Christ1,3(cid:63), Florian Lachner2,3(cid:63), Axel H¨osl3, Bjoern Menze1,
Klaus Diepold3, and Andreas Butz2
Image-based Biomedical Modeling Group,
Technical University of Munich (TUM)
{patrick.christ,
Chair for Human-Computer-Interaction,
University of Munich (LMU)
{florian.lachner, axel.hoesl,
Center for Digital and Technology Management,
TUM and LMU
Chair for Data Processing,
Technical University of Munich (TUM)"
a43f460f6c1abbe8eb0097594df6eafc0f651d49,Saliency-based object recognition in video,"Saliency-based object recognition in video
Iv´an Gonz´alez-D´ıaz, Hugo Boujut, Vincent Buso, Jenny Benois-Pineau,
Jean-Philippe Domenger
To cite this version:
Iv´an Gonz´alez-D´ıaz, Hugo Boujut, Vincent Buso, Jenny Benois-Pineau, Jean-Philippe
Domenger. Saliency-based object recognition in video. 10 pages. 2013. <hal-00799127>
HAL Id: hal-00799127
https://hal.archives-ouvertes.fr/hal-00799127
Submitted on 1 Jan 2014
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,
´emanant des ´etablissements d’enseignement et de
recherche fran¸cais ou ´etrangers, des laboratoires"
a47ac8569ab1970740cff9f1643f77e9143a62d4,Associative Compression Networks for Representation Learning,"Associative Compression Networks for Representation Learning
Alex Graves 1 Jacob Menick 1 A¨aron van den Oord 1"
a47e51dd3f73817679ff0e987a0064d43db25060,Grad-CAM: Why did you say that? Visual Explanations from Deep Networks via Gradient-based Localization,"Visual Explanations from Deep Networks via Gradient-based Localization
Grad-CAM: Why did you say that?
Ramprasaath R. Selvaraju
Abhishek Das
Devi Parikh
Ramakrishna Vedantam
Dhruv Batra
Virginia Tech
Michael Cogswell
{ram21, abhshkdz, vrama91, cogswell, parikh,
(a) Original Image
(b) Guided Backprop ‘Cat’
(c) Grad-CAM for ‘Cat’
(d) Guided Grad-CAM ‘Cat’
(e) Occlusion Map ‘Cat’
(f) ResNet Grad-CAM ‘Cat’
(g) Original Image
(h) Guided Backprop ‘Dog’
(i) Grad-CAM for ‘Dog’
(l) ResNet Grad-CAM ‘Dog’"
a4a0b5f08198f6d7ea2d1e81bd97fea21afe3fc3,cient Recurrent Residual Networks Improved by Feature Transfer MSc Thesis,"Ecient Recurrent Residual Networks Improved by
Feature Transfer
MSc Thesis
written by
Yue Liu
under the supervision of Dr. Silvia-Laura Pintea, Dr. Jan van Gemert,
nd Dr. Ildiko Suveg and submitted to the Board of Examiners for the
degree of
Master of Science
t the Delft University of Technology.
Date of the public defense: Members of the Thesis Committee:
August 31, 2017
Prof. Marcel Reinders
Dr. Jan van Gemert
Dr. Julian Urbano Merino
Dr. Silvia-Laura Pintea
Dr. Ildiko Suveg (Bosch)
Dr. Gonzalez Adrlana (Bosch)"
a4f37cfdde3af723336205b361aefc9eca688f5c,Recent Advances in Face Recognition,"Recent Advances
in Face Recognition"
a4ce0f8cfa7d9aa343cb30b0792bb379e20ef41b,Facial Landmark Machines: A Backbone-Branches Architecture with Progressive Representation Learning,"Facial Landmark Machines: A Backbone-Branches
Architecture with Progressive Representation
Learning
Lingbo Liu, Guanbin Li, Yuan Xie, Yizhou Yu, Qing Wang and Liang Lin"
a45450824c6e8e6b42fd9bbf52871104b6c6ce8b,Optimizing the Latent Space of Generative Networks,"Optimizing the Latent Space of Generative Networks
Piotr Bojanowski, Armand Joulin, David Lopez-Paz, Arthur Szlam
{bojanowski, ajoulin, dlp,
Facebook AI Research"
a49acd70550c209965a6d39d7ff92d11f0a5b1b6,"YouTube Scale, Large Vocabulary Video Annotation","YouTube Scale, Large Vocabulary
Video Annotation
Nicholas Morsillo, Gideon Mann and Christopher Pal"
a4a90a2db209db2d5c49adfd2091ede2d4130f60,Interactive Grounded Language Acquisition and Generalization in a 2D World,"Published as a conference paper at ICLR 2018
INTERACTIVE GROUNDED LANGUAGE ACQUISITION
AND GENERALIZATION IN A 2D WORLD
Haonan Yu1, Haichao Zhang1 & Wei Xu1,2
Baidu Research, Sunnyvale USA
National Engineering Laboratory for Deep Learning Technology and Applications, Beijing China"
a40f8881a36bc01f3ae356b3e57eac84e989eef0,"End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks","End-to-end semantic face segmentation with conditional
random fields as convolutional, recurrent and adversarial
networks
Umut Güçlü*, 1, Yağmur Güçlütürk*, 1,
Meysam Madadi2, Sergio Escalera3, Xavier Baró4, Jordi González2,
Rob van Lier1, Marcel van Gerven1"
a40f614499dab76b477ca5bbb4614d6f1a5c8b4b,Highly Efficient Compact Pose SLAM with SLAM++,"Highly Efficient Compact Pose SLAM with SLAM++1
Viorela Ila, Lukas Polok, Marek Solony and Pavel Svoboda"
a416513aaf97060287bf3e64ccdc1ccf85106c07,Seasonal Separation of African Savanna Components Using Worldview-2 Imagery: A Comparison of Pixel- and Object-Based Approaches and Selected Classification Algorithms,"Article
Seasonal Separation of African Savanna Components
Using Worldview-2 Imagery: A Comparison of Pixel-
nd Object-Based Approaches and Selected
Classification Algorithms
˙Zaneta Kaszta 1,2,*, Ruben Van De Kerchove 1,3, Abel Ramoelo 4, Moses Azong Cho 4,
Sabelo Madonsela 4, Renaud Mathieu 4,5 and Eléonore Wolff 1
Institut de Gestion de l’Environnement et d’Aménagement de Territoire (IGEAT),
Université Libre de Bruxelles, Brussels 1050, Belgium;
School of Applied Environmental Sciences, Pietermaritzburg 3209, South Africa
Mol 2400, Belgium;
Council for Scientific and Industrial Research, Pretoria 0001, South Africa; (A.R.);
(M.A.C.); (S.M.); (R.M.)
5 Department of Geography, Geoinformatics and Meteorology, University of Pretoria,
Pretoria 0028, South Africa
* Correspondence: Tel.: +32-02-650-68-20
Academic Editors: Giles M. Foody, Magaly Koch, Clement Atzberger and Prasad S. Thenkabail
Received: 15 May 2016; Accepted: 8 September 2016; Published: 16 September 2016"
a453863082a7fb42c9b402023294390eb4167fbe,Identifying Where to Focus in Reading Comprehension for Neural Question Generation,"Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2067–2073
Copenhagen, Denmark, September 7–11, 2017. c(cid:13)2017 Association for Computational Linguistics"
a4f38e32c23fd1f5a1e1157a4e62b38731f2e5d8,Online Learning for Ship Detection in Maritime Surveillance,"Online Learning for Ship Detection
in Maritime Surveillance
Rob Wijnhoven1
ViNotion1
, Kris van Rens1, Egbert G. T. Jaspers1, Peter H. N. de With2
University of Technol. Eindhoven2 CycloMedia Technol.3
P.O. Box 2346
5600 CH Eindhoven
The Netherlands
P.O. Box 513
5600 MB Eindhoven
The Netherlands"
a481de6e9a7303784a7492ede7a7f055be7bc831,Egocentric Audio-Visual Scene Analysis. A Machine Learning and Signal Processing Approach. (Analyse Égocentrique de Scènes Audio-Visuelles. Une approche par Apprentissage Automatique et Traitement du Signal),"Egocentric Audio-Visual Scene Analysis : a machine
learning and signal processing approach
Xavier Alameda-Pineda
To cite this version:
Xavier Alameda-Pineda. Egocentric Audio-Visual Scene Analysis : a machine learning and signal pro-
essing approach. General Mathematics [math.GM]. Université de Grenoble, 2013. English. <NNT :
013GRENM024>. <tel-00880117v2>
HAL Id: tel-00880117
https://tel.archives-ouvertes.fr/tel-00880117v2
Submitted on 31 Mar 2015
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
bc749f0e81eafe9e32d56336750782f45d82609d,Combination of Texture and Geometric Features for Age Estimation in Face Images,
bc8373b3d4110786a597b21f3ae9c8e5ffd34a2e,Optimal Gabor kernel location selection for face recognition,"OPTIMAL GABOR KERNEL LOCATION SELECTION FOR FACE RECOGNITION
B. G¨okberk, M. O. Irfanoglu, L. Akarun, and E. Alpaydın
Bo˘gazic¸i University, Computer Engineering Dept.
{gokberk,irfanogl,akarun,
Istanbul, TURKEY"
bc9af4c2c22a82d2c84ef7c7fcc69073c19b30ab,MoCoGAN: Decomposing Motion and Content for Video Generation,"MoCoGAN: Decomposing Motion and Content for Video Generation
Sergey Tulyakov,
Snap Research
Ming-Yu Liu, Xiaodong Yang,
NVIDIA
Jan Kautz"
bcac3a870501c5510df80c2a5631f371f2f6f74a,Structured Face Hallucination,"#1387
CVPR 2013 Submission #1387. CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE.
#1387
Structured Face Hallucination
Anonymous CVPR submission
Paper ID 1387"
bc995457cf5f4b2b5ef62106856571588d7d70f2,Comparison of Maximum Likelihood and GAN-based training of Real NVPs,"Comparison of Maximum Likelihood and GAN-based training of Real NVPs
Ivo Danihelka 1 2 Balaji Lakshminarayanan 1 Benigno Uria 1 Daan Wierstra 1 Peter Dayan 3"
bcaa5fab589d95890d539a3119657fa253176f0d,"THE PROBLEM : MID-RANGE FR AT NIGHT No Active Illumination Night Time 120 meters Evaluating the Efficiency of a Night-Time , Middle-Range Infrared Sensor for Applications in Human Detection and Recognition","THE PROBLEM: MID-RANGE FR AT NIGHT
No Active Illumination
NIR Led Illuminator
Night Time 120 meters
eters
Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXIII, edited by Gerald C. Holst, Keith A. Krapels,
Proc. of SPIE Vol. 8355, 83551B · © 2012 SPIE · CCC code: 0277-786X/12/$18 · doi: 10.1117/12.917831
Proc. of SPIE Vol. 8355  83551B-1
From: http://proceedings.spiedigitallibrary.org/ on 04/30/2013 Terms of Use: http://spiedl.org/terms"
bcf73131c2be397fa2105ac45df3ce1a55c07c2f,Automated markerless extraction of walking people using deformable contour models,"This is a preprint of an article published in Computer Animation and Virtual
Worlds, 15(3-4):399-406, 2004.
This journal may be found at:
http://www.interscience.wiley.com"
bc1fa3efa43dfb79f6f8243d29327c8ee06e8a97,No 275 Learning object classes with generic knowledge,"ETH Zurich, D-ITET, BIWI
Technical Report No 275
Learning object classes with generic knowledge
Thomas Deselaers, Bogdan Alexe, and Vittorio Ferrari"
bca09d92a25e5cc96df5c8d2eb87e2854cdc02b1,Pose Invariant 3 D Face Authentication based on Gaussian Fields Approach,"To the Graduate Council:
I  am  submitting  herewith  a  thesis  written  by  Venkat  Rao  Ayyagari  entitled  “Pose
Invariant 3D Face Authentication based on Gaussian Fields Approach”. I have examined
the  final  electronic  copy  of  this  thesis  for  form  and  content  and  recommend  that  it  be
ccepted  in  partial  fulfillment  of  the  requirements  for  the  degree  of  Master  of  Science,
with a major in Electrical Engineering.
Mongi A. Abidi
Major Professor
We have read this thesis and
recommend its acceptance:
Andreas Koschan
Seong G. Kong
Accepted for the Council:
Anne Mayhew
Vice Chancellor and Dean of
Graduate Studies
(Original signatures are on file with official student records.)"
bc7f431c4c5cecfc7bf95b2f0704d81469f23580,AN INTELLIGENT APPAREL RECOMMENDATION SYSTEM FOR ONLINE SHOPPING USING STYLE CLASSIFICATION,"I J A B E R, Vol. 13, No. 2, (2015): 671-686
AN INTELLIGENT APPAREL RECOMMENDATION
SYSTEM FOR ONLINE SHOPPING USING STYLE
CLASSIFICATION
C. Perkinian* and P. Vikkraman**"
bcf7fb98ab0137d8a8b8a952819f5e13ec4648aa,FACE RECOGNITION WITH SINGLE SAMPLE PER CLASS USING CS-LBP AND GABOR FILTER 1,"Journal of Theoretical and Applied Information Technology
31st October 2014. Vol. 68 No.3
© 2005 - 2014 JATIT & LLS. All rights reserved.
ISSN: 1992-8645                                                       www.jatit.org                                                          E-ISSN: 1817-3195
FACE RECOGNITION WITH SINGLE SAMPLE PER
CLASS USING CS-LBP AND GABOR FILTER
A.USHA RUBY,
DR.J.GEORGE CHELLIN CHANDRAN
Research Scholar, Department of CSE, Bharath University
Principal, CSI College of Engineering, Ketti
E-mail:   ,"
bc843c35530e38396e8ba55b8891dbe8324054a8,Group Visual Sentiment Analysis,"Group Visual Sentiment Analysis
Zeshan Hussain, Tariq Patanam and Hardie Cate
June 6, 2016"
bc4537bc5834b41a631d9a807500d199b438fb27,Perceptual Integration Deficits in Autism Spectrum Disorders Are Associated with Reduced Interhemispheric Gamma-Band Coherence.,"6352 • The Journal of Neuroscience, December 16, 2015 • 35(50):16352–16361
Neurobiology of Disease
Perceptual Integration Deficits in Autism Spectrum
Disorders Are Associated with Reduced Interhemispheric
Gamma-Band Coherence
Ina Peiker,1* Nicole David,1* X Till R. Schneider,1 Guido Nolte,1 Daniel Scho¨ttle,2 and XAndreas K. Engel1
Departments of 1Neurophysiology and Pathophysiology and 2Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, 20246
Hamburg, Germany
The integration of visual details into a holistic percept is essential for object recognition. This integration has been reported as a key deficit
in patients with autism spectrum disorders (ASDs). The weak central coherence account posits an altered disposition to integrate features
into a coherent whole in ASD. Here, we test the hypothesis that such weak perceptual coherence may be reflected in weak neural coherence
cross different cortical sites. We recorded magnetoencephalography from 20 adult human participants with ASD and 20 matched
ontrols, who performed a slit-viewing paradigm, in which objects gradually passed behind a vertical or horizontal slit so that only
fragments of the object were visible at any given moment. Object recognition thus required perceptual integration over time and, in case
of the horizontal slit, also across visual hemifields. ASD participants were selectively impaired in the horizontal slit condition, indicating
specific difficulties in long-range synchronization between the hemispheres. Specifically, the ASD group failed to show condition-related
enhancement of imaginary coherence between the posterior superior temporal sulci in both hemispheres during horizontal slit-viewing
in contrast to controls. Moreover, local synchronization reflected in occipitocerebellar beta-band power was selectively reduced for
horizontal compared with vertical slit-viewing in ASD. Furthermore, we found disturbed connectivity between right posterior superior
temporal sulcus and left cerebellum. Together, our results suggest that perceptual integration deficits co-occur with specific patterns of"
bcc172a1051be261afacdd5313619881cbe0f676,A fast face clustering method for indexing applications on mobile phones,"978-1-5090-4117-6/17/$31.00 ©2017 IEEE
ICASSP 2017"
bc4627e1bc3bbe21c46c4011ec4f9bd377ec83a4,Towards recognition of degraded words by probabilistic parsing,"Towards Recognition of Degraded Words by Probabilistic
Parsing
Karthika Mohan
IIIT, Hyderabad
AP, India 500 032
K. J. Jinesh
IIIT, Hyderabad
AP, India 500 032
C. V. Jawahar
IIIT, Hyderabad
AP, India 500 032"
bca52740ba679b67a508894e68a0e52f6bf62079,Development of an Efficient Face Recognition System Based on Linear and Nonlinear Algorithms,"Development of an Efficient Face Recognition System based on Linear and Nonlinear Algorithms
{tag}                                                 {/tag}
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 134
Number 7
Year of Publication: 2016
Authors:
Filani Araoluwa S., Adetunmbi Adebayo O.
10.5120/ijca2016907932
{bibtex}2016907932.bib{/bibtex}"
bc4e86b6d2d386805466b822a04ea0c015debfff,Robust 3D Face Recognition from Expression Categorisation,"Cook, Jamie A and Cox, Mark and Chandran, Vinod and Sridharan,
Sridha (2007) Robust 3D Face Recognition from Expression
Categorisation. In Proceedings International Conference on Biometrics
642, pages pp. 271-280, Seoul, Korea.
This is the author-manuscript version of this work - accessed from
http://eprints.qut.edu.au
Copyright 2007 Springer"
bc15e0ebe7ff84e090aa2d74d753d87906d497f7,The Impact of Preprocessing on Deep Representations for Iris Recognition on Unconstrained Environments,"The Impact of Preprocessing on Deep
Representations for Iris Recognition on
Unconstrained Environments
Luiz A. Zanlorensi∗, Eduardo Luz†, Rayson Laroca∗, Alceu S. Britto Jr.‡, Luiz S. Oliveira∗, David Menotti∗
Department of Informatics, Federal University of Paran´a (UFPR), Curitiba, PR, Brazil
Computing Department, Federal University of Ouro Preto (UFOP), Ouro Preto, MG, Brazil
Postgraduate Program in Informatics, Pontifical Catholic University of Paran´a (PUCPR), Curitiba, PR, Brazil"
bcee40c25e8819955263b89a433c735f82755a03,Biologically Inspired Vision for Human-Robot Interaction,"Biologically inspired vision for human-robot
interaction
M. Saleiro, M. Farrajota, K. Terzi´c, S. Krishna, J.M.F. Rodrigues, and J.M.H.
du Buf
Vision Laboratory, LARSyS, University of the Algarve, 8005-139 Faro, Portugal,
{masaleiro, mafarrajota, kterzic, jrodrig,"
bcf52629788e210f9c87945fa9cf792609e9154a,Ð ×ýñññøöý Éùùòøø¬¬¬øøóò Óö Üôöö×××óò Áòúöööòø Àùññò Áááòøø¬¬¬øøóò £,"Facia Ay

ey 		ai(cid:12)cai f Exei 
vaia
	
a 
dei(cid:12)cai
Y. i	 .ed	 .. Sch
id ad .F. Ch
Dea
e f ychgy
Uiveiy f ib	gh
Caegie e Uiveiy
The Rbic 
i	e
R.. Weave
Saiic De
Caegie e Uiveiy
ib	gh A 15213
ib	gh A 15260
ib	gh A 15213"
bc8bd3f5653165809af3d4525f266219add4d132,Symbolic principal component for interval-valued observations,
bc40057335e589c30e0b6c7b71685ef0433b5af5,"Recounting ( MER ) , Surveillance Event Detection ( SED ) , and Semantic Indexing ( SIN ) Systems","IBM Research and Columbia University TRECVID-2013 Multimedia Event Detection (MED),
Multimedia Event Recounting (MER), Surveillance Event Detection (SED), and Semantic
Indexing (SIN) Systems
Lisa Browny, Liangliang Caoy, Shih-Fu Chang(cid:3), Yu Chengy, Alok Choudharyy, Noel Codellay, Courtenay Cotton(cid:3), Dan Ellis,(cid:3)
Quanfu Fany, Rogerio Ferisy, Leiguang Gongy, Matthew Hilly, Gang Huay, John Kenderz, Michele Merlery, Yadong Mu(cid:3),
Sharath Pankantiy, John R. Smithy, Felix X. Yu(cid:3)x"
bc8e1c2284008319ee325ff7ea19916726235f55,Autonomic responses to social and nonsocial pictures in adolescents with autism spectrum disorder.,"RESEARCH ARTICLE
Autonomic Responses to Social and Nonsocial Pictures in
Adolescents With Autism Spectrum Disorder
Anneke Louwerse, Joke H. M. Tulen, Jos N. van der Geest, Jan van der Ende, Frank C. Verhulst, and
Kirstin Greaves-Lord
It remains unclear why individuals with autism spectrum disorder (ASD) tend to respond in an atypical manner in social
situations. Investigating autonomic and subjective responses to social vs. nonsocial stimuli may help to reveal underlying
mechanisms of these atypical responses. This study examined autonomic responses (skin conductance level and heart
rate) and subjective responses to social vs. nonsocial pictures in 37 adolescents with an ASD and 36 typically developing
(TD) adolescents. Thirty-six pictures from the International Affective Picture System were presented, divided into six
ategories based on social content (social vs. nonsocial) and pleasantness (pleasant, neutral, and unpleasant). Both in
dolescents with ASD as well as TD adolescents, pictures with a social content resulted in higher skin conductance
responses (SCRs) for pleasant and unpleasant pictures than for neutral pictures. No differences in SCRs were found for
the three nonsocial picture categories. Unpleasant pictures, both with and without a social content, showed more heart
rate deceleration than neutral pictures. Self-reported arousal ratings were influenced by the social and affective content
of a picture. No differences were found between individuals with ASD and TD individuals in their autonomic and
subjective responses to the picture categories. These results suggest that adolescents with ASD do not show atypical
utonomic or subjective responses to pictures with and without a social content. These findings make it less likely that
impairments in social information processing in individuals with ASD can be explained by atypical autonomic responses
to social stimuli. Autism Res 2013, (cid:129)(cid:129): (cid:129)(cid:129)–(cid:129)(cid:129). © 2013 International Society for Autism Research, Wiley Periodicals, Inc."
bcd299eb32f17b531fa281cb750a89895cb4feb5,Computer Vision Research at the Computational Vision Laboratory of the Universidad de Chile,"Computer Vision Research at the Computational Vision
Laboratory of the Universidad de Chile
Javier Ruiz-del-Solar
Department of Electrical Engineering, Universidad de Chile"
8a866bc0d925dfd8bb10769b8b87d7d0ff01774d,WikiArt Emotions: An Annotated Dataset of Emotions Evoked by Art,"WikiArt Emotions: An Annotated Dataset of Emotions Evoked by Art
Saif M. Mohammad and Svetlana Kiritchenko
National Research Council Canada"
8a12ee3c98b76d99531d5965f15bb77a10ec2569,Holistic Face Recognition through Multivariate Analysis and Genetic Algorithms,"Holistic Face Recognition through Multivariate Analysis and Genetic
Algorithms"
8acb55a72c4d6eae528a99e571b4a24d51f57fe6,Toward Crowd-Sensitive Path Planning,"Toward Crowd-Sensitive Path Planning
Anoop Aroor1 and Susan L. Epstein1,2
The Graduate Center1 and Hunter College2 of The City University of New York
New York, NY 10065"
8abfda3c1e1599bed454661f15ee0bbe7f6b8c12,Who is Mistaken?,"Who is Mistaken?
Benjamin Eysenbach
Carl Vondrick
Antonio Torralba"
8a02a0517b841b53fba478a851948b86869a0582,Fast Neural Networks with Circulant Projections,"Fast Neural Networks with Circulant Projections
Yu Cheng∗, Felix X. Yu∗, Rogerio S. Feris,
Sanjiv Kumar, Alok Choudhary, Shih-Fu Chang"
8a05d0a98570be20bccd106602f3e981d9b05334,A Unified Framework for Manifold Landmarking,"A unified framework for manifold landmarking
Hongteng Xu, Licheng Yu, Mark A. Davenport, Senior Member, IEEE, Hongyuan Zha"
8a4119c2898f611a6ffa0b4b72acf322d1b455b1,A Diagram Is Worth A Dozen Images,"A Diagram Is Worth A Dozen Images
Aniruddha Kembhavi†, Mike Salvato†(cid:63), Eric Kolve†(cid:63), Minjoon Seo§,
Hannaneh Hajishirzi§, Ali Farhadi†§
Allen Institute for Artificial Intelligence, §University of Washington"
8ad4742e656c409e5a813c1a6d5f21fd2e3a9225,A Novel Algorithm for Face Recognition From Very Low Resolution Images,"J Electr Eng Technol Vol. 10, No. ?: 742-?, 2015
http://dx.doi.org/10.5370/JEET.2015.10.1.742
ISSN(Print)    1975-0102
ISSN(Online)  2093-7423
A Novel Algorithm for Face Recognition From Very Low Resolution
Images
C. Senthilsingh† and M. Manikandan*"
8aed6ec62cfccb4dba0c19ee000e6334ec585d70,Localizing and Visualizing Relative Attributes,"Localizing and Visualizing Relative Attributes
Fanyi Xiao and Yong Jae Lee"
8ae02cef563120be51f8655e199a54af856059b7,Three-Dimensional Anthropometric Database of Attractive Caucasian Women: Standards and Comparisons,"SCIENTIFIC FOUNDATION
Three-Dimensional Anthropometric Database of
Attractive Caucasian Women: Standards
nd Comparisons
Luigi Maria Galantucci, PhD, MSE,
Alberto Laino, PhD, DS,
Eliana Di Gioia, DS, MD,§jj Raoul D’Alessio, DS, MD,ô Fulvio Lavecchia, PhD, MSE,#
Roberto Deli, PhD, DS,
Gianluca Percoco, PhD, MSE,# and Carmela Savastano, DS, MD"
8aa6c3601924c99ca420c7c37ffcffe00db1eb78,3D facial expression recognition via multiple kernel learning of Multi-Scale Local Normal Patterns,"1st International Conference on Pattern Recognition (ICPR 2012)
November 11-15, 2012. Tsukuba, Japan
978-4-9906441-0-9 ©2012 ICPR"
8ab2bc0f298cf595d50064a5bce57065d5b69c59,Development of Multimedia Application for Smartphones,"International Journal of Computer Applications (0975 – 8887)
Volume 95 – No 2, June 2014
Development of Multimedia Application for Smartphones
M. A. Mohamed
Assoc. Prof. Mansoura
University Mansoura
Egypt
A. I. Abdel-Fatah
Prof. Mansoura University
Mansoura
Egypt
Bassant M. El-Den
Demonstrator Delta University
Mansoura
Egypt"
8a14dfe0e11e03505db9c0d84bce96f165223cae,Learning from Demonstration in the Wild,"Learning from Demonstration in the Wild
Feryal Behbahani1, Kyriacos Shiarlis1, Xi Chen1, Vitaly Kurin1,2, Sudhanshu Kasewa1,2, Ciprian Stirbu1,2,
Jo˜ao Gomes1, Supratik Paul1,2, Frans A. Oliehoek1,3, Jo˜ao Messias1, Shimon Whiteson1,2"
8ac2736683dac9a467602ee19f5a290096259148,HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection,"HyperNet: Towards Accurate Region Proposal Generation
nd Joint Object Detection
Tao Kong1
Anbang Yao2 Yurong Chen2 Fuchun Sun1
State Key Lab. of Intelligent Technology and Systems
Tsinghua National Laboratory for Information Science and Technology (TNList)
Department of Computer Science and Technology, Tsinghua University 2Intel Labs China
{anbang.yao,"
8aae23847e1beb4a6d51881750ce36822ca7ed0b,Comparison between geometry-based and Gabor-wavelets-based facial expression recognition using multi-layer perceptron,"Comparison Between Geometry-Based and Gabor-Wavelets-Based
Facial Expression Recognition Using Multi-Layer Perceptron
Zhengyou Zhang

Shigeru Akamatsu
 Michael Lyons Michael Schuster
 ATR Human Information Processing Research Laboratories
 ATR Interpreting Telecommunications Research Laboratories
-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-02, Japan
INRIA, 2004 route des Lucioles, BP 93, F-06902 Sophia-Antipolis Cedex, France
e-mail:"
8ac074829b55bb6b4c67f062ca9ec62bb79f865f,Person re-identification based on deep multi-instance learning,"Person Re-identification based on Deep
Multi-instance Learning
Domonkos Varga∗†, Tam´as Szir´anyi∗‡
MTA SZTAKI, Institute for Computer Science and Control
{varga.domonkos,
Budapest University of Technology and Economics, Department of Networked Systems and Services
Budapest University of Technology and Economics, Department of Material Handling and Logistics Systems"
8a91cb96dd520ba3e1f883aa6d57d4d716c5d1c8,Low Cost Eye Tracking: The Current Panorama,"Hindawi Publishing Corporation
Computational Intelligence and Neuroscience
Volume 2016, Article ID 8680541, 14 pages
http://dx.doi.org/10.1155/2016/8680541
Review Article
Low Cost Eye Tracking: The Current Panorama
Onur Ferhat1,2 and Fernando Vilariño1,2
Computer Vision Center, Edifici O, Campus UAB, 08193 Bellaterra, Spain
Computer Science Department, Universitat Aut`onoma de Barcelona, Edifici Q, Campus UAB, 08193 Bellaterra, Spain
Correspondence should be addressed to Onur Ferhat;
Received 27 November 2015; Accepted 18 February 2016
Academic Editor: Ying Wei
Copyright © 2016 O. Ferhat and F. Vilari˜no. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
ited.
Despite the availability of accurate, commercial gaze tracker devices working with infrared (IR) technology, visible light gaze
tracking constitutes an interesting alternative by allowing scalability and removing hardware requirements. Over the last years, this
field has seen examples of research showing performance comparable to the IR alternatives. In this work, we survey the previous
work on remote, visible light gaze trackers and analyze the explored techniques from various perspectives such as calibration
strategies, head pose invariance, and gaze estimation techniques. We also provide information on related aspects of research such"
8ab16c26678245ef009cbbf87d750cfd18e21572,A Wearable Ultrasonic Obstacle Sensor for Aiding Visually Impaired and Blind Individuals,"A Wearable Ultrasonic Obstacle Sensor for Aiding Visually Impaired and Blind Individuals
{tag}                                                                                   {/tag}
IJCA Proceedings on National Conference on
Growth of Technologies in Electronics, Telecom and Computers - India Perception
© 2014 by IJCA Journal
GTETC-IP
Year of Publication: 2014
Authors:
V.  Diana Earshia
S.  M.  Kalaivanan
Angel Dayana
{bibtex}gtetc1314.bib{/bibtex}"
8aaa97c686c60f611fe5a979d9afbc29dde3d33f,Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent,"Published as a conference paper at ICLR 2018
MASTERING THE DUNGEON: GROUNDED LANGUAGE
LEARNING BY MECHANICAL TURKER DESCENT
Zhilin Yang, Saizheng Zhang, Jack Urbanek, Will Feng, Alexander H. Miller
Arthur Szlam, Douwe Kiela & Jason Weston
Facebook AI Research"
8afe84f915d3dbc45c57011e62f5dbf9003dfb4c,Adaptive Binary Quantization for Fast Nearest Neighbor Search,"Adaptive Binary Quantization for Fast Nearest Neighbor
Search
Zhujin Li1 and Xianglong Liu∗2 and Junjie Wu3 and Hao Su4"
8aac66d15e0903257ec3abe6f126bf6316779011,Constructive Autoassociative Neural Network for Facial Recognition,"RESEARCH ARTICLE
Constructive Autoassociative Neural
Network for Facial Recognition
Bruno J. T. Fernandes1*, George D. C. Cavalcanti2, Tsang I. Ren2
. Escola Polite´ cnica, Universidade de Pernambuco, Recife-PE, Brazil, 2. Centro de Informa´ tica,
Universidade Federal de Pernambuco, Recife-PE, Brazil"
8aea75940c90fac8c1e5d7ece7d04a61555c3bf6,Divide and Grow: Capturing Huge Diversity in Crowd Images with Incrementally Growing CNN,
8ae470fba004309d3dd107fb201940324f400654,Finding and Archiving the Internet Footprint ∗,"Finding and Archiving the Internet Footprint∗
Simson Garfinkel† and David Cox
Naval Postgraduate School
Monterey, CA, USA
February 10, 2009"
8ad407142de84b66144029845587c77ae94fd240,Multi-class speed-density relationship for pedestrian traffic,"Multi-class speed-density relationship for
pedestrian traffic
Marija Nikoli´c ∗
Matthieu de Lapparent ∗
Michel Bierlaire ∗
Riccardo Scarinci ∗
January 15, 2017
Report TRANSP-OR 170115
Transport and Mobility Laboratory
School of Architecture, Civil and Environmental Engineering
Ecole Polytechnique Fédérale de Lausanne
transp-or.epfl.ch
Transport and Mobility Laboratory, School of Architecture, Civil and Environmental Engi-
neering, École Polytechnique Fédérale de Lausanne, Switzerland,
{marija.nikolic, michel.bierlaire, matthieu.delapparent,"
8a29378973987bdb040f35349d1c5a86a538c0fc,Hierarchical Temporal Memory Using Memristor Networks: A Survey,"Hierarchical Temporal Memory using Memristor
Networks: A Survey
Olga Krestinskaya, Graduate Student Member, IEEE, Irina Dolzhikova, Graduate Student Member, IEEE, and
Alex Pappachen James, Senior Member, IEEE"
8aa5f1b2639da73c2579ea9037a4ebf4579fdc4f,A Steerable multitouch Display for Surface Computing and its Evaluation,"December
S0218213013600166
013 14:51 WSPC/INSTRUCTION
st Reading
International Journal on Artificial Intelligence Tools
Vol. 22, No. 6 (2013) 1360016 (29 pages)
(cid:13) World Scientific Publishing Company
DOI: 10.1142/S0218213013600166
A STEERABLE MULTITOUCH DISPLAY FOR SURFACE
COMPUTING AND ITS EVALUATION
PANAGIOTIS KOUTLEMANIS, ANTONIOS NTELIDAKIS, XENOPHON ZABULIS,
DIMITRIS GRAMMENOS and ILIA ADAMI
Foundation for Research and Technology – Hellas (FORTH )
Institute of Computer Science, N. Plastira 100
Vassilika Vouton, GR-700 13 Heraklion, Crete, Greece
{koutle, ntelidak, zabulis, grammenos,
Received 28 January 2013
Accepted 19 March 2013
Published 20 December 2013
In this paper, a steerable, interactive projection display that has the shape of a disk is"
8a48904820b6cfffe8bd951877a3e6e0d5dd6eaa,Dynamic machine learning for supervised and unsupervised classification. (Apprentissage automatique dynamique pour la classification supervisée et non supervisée),"Dynamic machine learning for supervised and
unsupervised classification
Adela-Maria Sîrbu
To cite this version:
Adela-Maria Sîrbu. Dynamic machine learning for supervised and unsupervised classification. Machine
Learning [cs.LG]. INSA de Rouen, 2016. English. <NNT : 2016ISAM0002>. <tel-01402052>
HAL Id: tel-01402052
https://tel.archives-ouvertes.fr/tel-01402052
Submitted on 24 Nov 2016
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de
recherche français ou étrangers, des laboratoires"
8af0854c652c90d4004e1868bc5fafec3e4ce724,Etiquetage du comportement de descripteurs locaux pour une recherche sélective de contenus vidéo,"INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET EN AUTOMATIQUE
Labelling the Behaviour of Local Descriptors for
Selective Video Content Retrieval
Julien Law-To — Valerie Gouet-Brunet — Olivier Buisson — Nozha Boujemaa
N° 5821
January 2006
Thème COG
p p o r t  (cid:13)
(cid:13) d e  r e c h e r c h e (cid:13)"
8a158fb9380a6666b922bc7a00121b6bf4a5ab0b,UnDEMoN 2.0: Improved Depth and Ego Motion Estimation through Deep Image Sampling,"UnDEMoN 2.0: Improved Depth and Ego Motion Estimation through Deep
Image Sampling
Madhu Babu V, Swagat Kumar, Anima Majumder and Kaushik Das
TATA Consultancy Services, Bangalore, India.
Technical Report
November 28, 2018
(madhu.vankadari, swagat.kumar, anima.majumder,"
8a382f000f98cdab7f7b79e543c75c6b8f93b6f9,Learning Semantic Image Representations at a Large Scale,"Learning Semantic Image Representations at a Large
Scale
Yangqing Jia
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2014-93
http://www.eecs.berkeley.edu/Pubs/TechRpts/2014/EECS-2014-93.html
May 16, 2014"
8a7726e58c2e24b0a738b48ae35185aaaacb8fe9,PILOT ASSESSMENT OF NONVERBAL PRAGMATIC ABILITY IN PEOPLE WITH ASPERGER SYNDROME,"Psychology of Language and Communication 2013, Vol. 17, No. 3
DOI: 10.2478/plc-2013-0018
FRANCISCO J. RODRÍGUEZ MUÑOZ
University of Almería
PILOT ASSESSMENT OF NONVERBAL PRAGMATIC ABILITY
IN PEOPLE WITH ASPERGER SYNDROME
The purpose of this study is to present a diagnostic tool to assess the nonverbal pragmatic
ehaviors of people with Asperger syndrome, with the intent to give an account of the
severity of symptoms in the area of nonverbal interaction, as well as providing a profile
of nonverbal behaviors that may be targeted for intervention. Through this communica-
tion profile, overall nonverbal ability is calculated in a group of 20 subjects with Asperger
syndrome. The proposed scale also includes the measurement of the following nonverbal
dimensions: (1) eye gaze, (2) facial expression, (3) body language and posture, (4) proxemics,
(5) gestures, and (6) paralanguage. The results of this assessment suggest low nonverbal
pragmatic ability in these subjects, show specific deficits in nonverbal communication, and
apture variability in nonverbal behavior in individuals with AS.
Key words: Asperger syndrome, autism spectrum disorders, communication profile, non-
verbal communication, pragmatic assessment, speech-language pathology
Introduction
Nobody can deny that nonverbal behavior, understood as a communication"
8acdc4be8274e5d189fb67b841c25debf5223840,Improving clustering performance using independent component analysis and unsupervised feature learning,"Gultepe and Makrehchi
Hum. Cent. Comput. Inf. Sci.  (2018) 8:25
https://doi.org/10.1186/s13673-018-0148-3
RESEARCH
Improving clustering performance
using independent component analysis
nd unsupervised feature learning
Open Access
Eren Gultepe* and Masoud Makrehchi
*Correspondence:
Department of Electrical
nd Computer Engineering,
University of Ontario Institute
of Technology, 2000 Simcoe
St N, Oshawa, ON L1H 7K4,
Canada"
8aa41170a9591ff2e5e56ed218d955a4222101b8,Towards Accurate Task Accomplishment with Low-Cost Robotic Arms,"Towards Accurate Task Accomplishment with Low-Cost Robotic Arms
Yiming Zuo1∗, Weichao Qiu2, Lingxi Xie2, Fangwei Zhong3, Yizhou Wang3, Alan L. Yuille2
Tsinghua University 2The Johns Hopkins University 3Peking University"
8a56adc9605a894c513537f1a2c8d9459573c0a8,EFFECT OF IDENTITY ON TRUST LEARNING,"This is an author produced version of Incidental learning of trust from eye-gaze: Effects of
race and facial trustworthiness.
White Rose Research Online URL for this paper:
http://eprints.whiterose.ac.uk/119885/
Article:
Strachan, James, Kirkham, Alexander James orcid.org/0000-0001-9286-9448, Manssuer,
Luis et al. (2 more authors) (2017) Incidental learning of trust from eye-gaze: Effects of
race and facial trustworthiness. VISUAL COGNITION. pp. 1-13. ISSN 1350-6285
https://doi.org/10.1080/13506285.2017.1338321
promoting access to
White Rose research papers
http://eprints.whiterose.ac.uk/"
8a336e9a4c42384d4c505c53fb8628a040f2468e,Detecting Visually Observable Disease Symptoms from Faces,"Wang and Luo EURASIP Journal on Bioinformatics
nd Systems Biology  (2016) 2016:13
DOI 10.1186/s13637-016-0048-7
R ES EAR CH
Detecting Visually Observable Disease
Symptoms from Faces
Kuan Wang* and Jiebo Luo
Open Access"
6d2b633743178bd5aac1073b60d81ceb41933a4a,Carried Object Detection Based on an Ensemble of Contour Exemplars,"Carried Object Detection based on an Ensemble
of Contour Exemplars
Farnoosh Ghadiri1, Robert Bergevin1, Guillaume-Alexandre Bilodeau2
LVSN-REPARTI, Universit(cid:19)e Laval
LITIV lab., Polytechnique Montr(cid:19)eal"
6dc17e91c0b02ff3b9e5c9283924279c28641db7,A Methodology for Extracting Standing Human Bodies from Single Images,"Invention Journal of Research Technology in Engineering & Management (IJRTEM)                                                  ISSN: 2455-3689
www.ijrtem.com ǁ Volume 1 ǁ Issue 8 ǁ
A Methodology for Extracting Standing Human Bodies from Single Images
Dr. Y. Raghavender Rao1, N. Devadas Naik2
Head ECE JNTUHCEJ Jagtityal
Asst professor Sri Chaitanya engineering college"
6dd5dbb6735846b214be72983e323726ef77c7a9,A Survey on Newer Prospective Biometric Authentication Modalities,"Josai Mathematical Monographs
vol. 7 (2014), pp. 25-40
A Survey on Newer Prospective
Biometric Authentication Modalities
Narishige Abe, Takashi Shinzaki"
6d7ba173121edd5defadfde04f7c1e7bc72859c2,The study of autism as a distributed disorder.,"MENTAL RETARDATION AND DEVELOPMENTAL DISABILITIES
RESEARCH REVIEWS 13: 85 – 95 (2007)
THE STUDY OF AUTISM AS A
DISTRIBUTED DISORDER
Brain Development Imaging Laboratory, Department of Psychology, San Diego State University, San Diego, California
Department of Cognitive Science, University of California, San Diego, California
Ralph-Axel Mu¨ ller1,2*
Past autism research has often been dedicated to tracing the
auses of the disorder to a localized neurological abnormality, a single
functional network, or a single cognitive-behavioral domain.
In this
review, I argue that autism is a ‘‘distributed disorder’’ on various levels of
study (genetic, neuroanatomical, neurofunctional, behavioral). ‘‘Localizing’’
models are therefore not promising. The large array of potential genetic
risk factors suggests that multiple (or all) emerging functional brain net-
works are affected during early development. This is supported by wide-
spread growth abnormalities throughout the brain. Interactions during
development between affected functional networks and atypical experi-
ential effects (associated with atypical behavior) in children with autism
further complicate the neurological bases of the disorder, resulting in"
6ddcc4ce66f301954132c13e629899a27f729112,Unsupervised Deep Network Pretraining via Human Design,"MITSUBISHI ELECTRIC RESEARCH LABORATORIES
http://www.merl.com
Unsupervised Network Pretraining via Encoding Human
Design
Liu, M.-Y.; Mallya, A.; Tuzel, C.O.; Chen, X.
TR2016-022 March 2016"
6d994076a6ef3b6e74e2a0149af759e48b71f9a0,Could dynamic attractors explain associative prosopagnosia?,"http://intl.elsevierhealth.com/journals/mehy
Could dynamic attractors explain associative
prosopagnosia?
Ali Zifan a,1, Shahriar Gharibzadeh a,*, Mohammad Hassan Moradi b
Neuromuscular Systems Laboratory, Faculty of Biomedical Engineering, Amirkabir University of
Technology, Tehran 15875-4413, Iran
Biological Signal Processing Laboratory, Faculty of Biomedical Engineering, Amirkabir University of
Technology, Tehran 15875-4413, Iran
Received 21 June 2006; accepted 28 June 2006
Summary Prosopagnosia is one of the many forms of visual associative agnosia, in which familiar faces lose their
distinctive association. In the case of prosopagnosia, the ability to recognize familiar faces is lost, due to lesions in the
medial occipitotemporal region. In ‘‘associative’’ prosopagnosia, the perceptual system seems adequate to allow for
recognition, yet recognition cannot take place. Our hypothesis is that a possible cause of associative prosopagnosia
might be the occurrence of Dynamic attractors in the brain’s auto-associative circuits. We present a biologically
plausible model composed of two stages: Pre-processing and face recognition. In the first stage, the face image is
passed through Gabor filters which model the kind of visual processing carried out by the simple and complex cells of
the primary visual cortex of higher mammals and the resulting features are fed into a Pseudo-inverse associative neural
network for the recognition task. Next, we damage the network by reducing self-connections below a certain threshold
in order to create dynamic attractors and hence hinder the networks ability to recognize familiar faces (faces already
learned). Results obtained from simulations show that the resulting network responses are very similar to those of"
6db59b031406546682a773baed2caed529aaf37c,Inferring the semantics of direction signs in public places,"Inferring the Semantics of Direction Signs in Public Places
J´erˆome Maye∗, Luciano Spinello∗†, Rudolph Triebel∗, and Roland Siegwart∗
Autonomous Systems Lab, ETH Zurich, Switzerland
email: {jerome.maye, rudolph.triebel,
Social Robotics Lab, Department of Computer Science, University of Freiburg, Germany
email:"
6dd850acb928457ffd44e5d9dceb7946a7f0c6ee,Template-based matching using weight maps,"


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     "
6d79999f8dc0cb9f86a87eaa2eb313a4eaeb2e5a,Instructions for use Title Bregman pooling : feature-space local pooling for imageclassification,"Title
Bregman pooling : feature-space local pooling for image
lassification
Author(s)
Najjar, Alameen; Ogawa, Takahiro; Haseyama, Miki
Citation
International Journal of Multimedia Information Retrieval
Issue Date
015-09-04
Doc URL
http://hdl.handle.net/2115/62753
Right
The final publication is available at link.springer.com
rticle (author version)
Additional
Information
Information BP.pdf
Instructions for use
Hokkaido University Collection of Scholarly and Academic Papers : HUSCAP"
6dfa82f00ec6faee1db319c1e306ae779cfc1c36,"The Role of Methodology and Spatiotemporal Scale in Understanding Environmental Change in Peri-Urban Ouagadougou, Burkina Faso","Remote Sens. 2013, 5, 1465-1483; doi:10.3390/rs5031465
OPEN ACCESS
ISSN 2072-4292
www.mdpi.com/journal/remotesensing
Article
The Role of Methodology and Spatiotemporal Scale in
Understanding Environmental Change in Peri-Urban
Ouagadougou, Burkina Faso
Yonatan Kelder 1,*, Thomas Theis Nielsen 1 and Rasmus Fensholt 2
Roskilde University, Universitetsvej 1, ENSPAC House 0.2, Roskilde 4000, Denmark;
E-Mail:
Copenhagen University, Institute for Geography and Geology, Øster Voldgade 10,
Copenhagen K 1350, Denmark; E-Mail:
*  Author to whom correspondence should be addressed; E-Mail:
Tel.: +45-30-49-14-92.
Received: 18 January 2013; in revised form: 24 February 2013 / Accepted: 15 March 2013 /
Published: 19 March 2013"
6d902439b736a7546dd8872b307fb760087ca629,SIFT Meets CNN: A Decade Survey of Instance Retrieval,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
SIFT Meets CNN:
A Decade Survey of Instance Retrieval
Liang Zheng, Yi Yang, and Qi Tian, Fellow, IEEE"
6d6bb981bc8470de23e30890bd96a76ffd2b7ced,The Eyes Are the Windows to the Mind: Direct Eye Gaze Triggers the Ascription of Others' Minds.,"669124 PSPXXX10.1177/0146167216669124Personality and Social Psychology BulletinKhalid et al.
research-article2016
Article
The Eyes Are the Windows to
the Mind: Direct Eye Gaze Triggers
the Ascription of Others’ Minds
Saara Khalid1, Jason C. Deska1, and Kurt Hugenberg1
Personality and Social
Psychology Bulletin
016, Vol. 42(12) 1666 –1677
© 2016 by the Society for Personality
nd Social Psychology, Inc
Reprints and permissions:
sagepub.com/journalsPermissions.nav
DOI: 10.1177/0146167216669124
pspb.sagepub.com"
6d8057ce549db311b7ddaeed8dfd934b58c1c281,A RELIEF Based Feature Extraction Algorithm,"A RELIEF Based Feature Extraction Algorithm
Yijun Sun∗
Dapeng Wu†"
6dd007b6e518a3aa96111028c4664f2647e5e81a,3D Face Synthesis Driven by Personality Impression,"D Face Synthesis Driven by Personality Impression
Yining Lang1 Wei Liang1 Yujia Wang1 Lap-Fai Yu2
Beijing Institute of Technology
University of Massachusetts Boston"
6ddb65ce430f8db2eb66b0a98ed8981049b3f520,PML-SLAM : a solution for localization in large-scale urban environments,"PML-SLAM: a solution for localization
in large-scale urban environments
Zayed Alsayed∗†, Guillaume Bresson∗, Fawzi Nashashibi† and Anne Verroust-Blondet†
Institut VEDECOM
Versailles, France
Inria Paris-Rocquencourt
Le Chesnay, France"
6d76eefecdcaa130a000d1d6c93cf57166ebd18e,Resource Aware Person Re-identification Across Multiple Resolutions,"Resource Aware Person Re-identification across Multiple Resolutions
Yan Wang∗ †, Lequn Wang∗ †, Yurong You∗ ‡, Xu Zou§, Vincent Chen†
Serena Li†, Gao Huang†, Bharath Hariharan†, Kilian Q. Weinberger†"
6d88fb85fe5c61bd65e0a373cd39fac81a19596a,DC-Image for Real Time Compressed Video Matching,"DC-Image for Real Time Compressed
Video Matching
Saddam Bekhet, Amr Ahmed and Andrew Hunter"
6d7dabc58f53c0233d6d593a8fee76d1c7f44033,Robust Observation Detection for Single Object Tracking: Deterministic and Probabilistic Patch-Based Approaches,"Sensors 2012, 12, 15638-15670; doi:10.3390/s121115638
OPEN ACCESS
sensors
ISSN 1424-8220
www.mdpi.com/journal/sensors
Article
Robust Observation Detection for Single Object Tracking:
Deterministic and Probabilistic Patch-Based Approaches
Mohd Asyraf Zulkifley 1,*, David Rawlinson 2 and Bill Moran 2
Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built
Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Malaysia
Department of Electrical and Electronic Engineering, The University of Melbourne, VIC 3010,
Australia; E-Mails: (D.R.); (B.M.)
* Author to whom correspondence should be addressed; E-Mail:
Tel.: +603-8921-6335.
Received: 18 September 2012; in revised form: 5 November 2012 / Accepted: 5 November 2012 /
Published: 12 November 2012
the problems of blurring, moderate deformation,"
6da06fc70f32454f7841b153c582e65aed7047e9,Deep pipelined one-chip FPGA implementation of a real-time image-based human detection algorithm,"NAOSITE: Nagasaki University's Academic Output SITE
Title
Deep pipelined one-chip FPGA implementation of a real-time image-based
human detection algorithm
Author(s)
Negi, Kazuhiro; Dohi, Keisuke; Shibata, Yuichiro; Oguri, Kiyoshi
Citation
011, Article number6132679; 2011
Issue Date
011-12
Right
http://hdl.handle.net/10069/29887
© 2011 IEEE. Personal use of this material is permitted. Permission from
IEEE must be obtained for all other uses, in any current or future media,
including reprinting/republishing this material for advertising or
promotional purposes, creating new collective works, for resale or
redistribution to servers or lists, or reuse of any copyrighted component of
this work in other works.
This document is downloaded at: 2018-12-08T05:46:10Z
http://naosite.lb.nagasaki-u.ac.jp"
6dc3b8a5fdceaea4b32df8552cbb5a22ef83c197,Speech-Based Visual Question Answering,"Speech-Based Visual Question Answering
Ted Zhang
KU Leuven
Dengxin Dai
ETH Zurich
Tinne Tuytelaars
KU Leuven
Marie-Francine Moens
KU Leuven"
6d618657fa5a584d805b562302fe1090957194ba,Human Facial Expression Recognition based on Principal Component Analysis and Artificial Neural Network,"Full Paper
NNGT Int. J. of Artificial Intelligence , Vol. 1, July 2014
Human Facial Expression Recognition based
on Principal Component Analysis and
Artificial Neural Network
Laboratory of Automatic and Signals Annaba (LASA) , Department of electronics, Faculty of Engineering,
Zermi.Narima, Ramdani.M, Saaidia.M
Badji-Mokhtar University, P.O.Box 12, Annaba-23000, Algeria.
E-Mail :"
6d500b0c342c1cf23efff049ef121bcf5e606ea1,Real-Time Category-Based and General Obstacle Detection for Autonomous Driving,"Real-time category-based and general obstacle detection for autonomous driving
Noa Garnett
Uri Verner
Ariel Ayash
Shai Silberstein
Vlad Goldner
Shaul Oron
Rafi Cohen
Ethan Fetaya
Kobi Horn
Dan Levi
Advanced Technical Center Israel, General Motors R&D
Hamada 7, Herzlyia, Israel"
6dd052df6b0e89d394192f7f2af4a3e3b8f89875,A literature survey on Facial Expression Recognition using Global Features,"International Journal of Engineering and Advanced Technology (IJEAT)
ISSN: 2249 – 8958, Volume-2, Issue-4, April 2013
A literature survey on Facial Expression
Recognition using Global Features
Vaibhavkumar J. Mistry, Mahesh M. Goyani"
6d96bf377c96e1dd9b43e9f12e0ee2a66543edbe,Viewpoint invariant 3D landmark model inference from monocular 2D images using higher-order priors,"011 IEEE International Conference on Computer Vision
978-1-4577-1102-2/11/$26.00 c(cid:13)2011 IEEE"
6d6a106caef228b3eee1f5765740938a534db828,Density-based clustering: A ‘landscape view’ of multi-channel neural data for inference and dynamic complexity analysis,"RESEARCH ARTICLE
Density-based clustering: A ‘landscape view’ of
multi-channel neural data for inference and
dynamic complexity analysis
Gabriel Baglietto1,2*, Guido Gigante3,4, Paolo Del Giudice1,3
INFN-Roma1, Italian National Institute for Nuclear Research (INFN), Rome, Italy, 2 IFLYSIB Instituto de
Fı´sica de Lı´quidos y Sistemas Biolo´gicos (UNLP-CONICET), La Plata, Argentina, 3 Italian Institute of Health
(ISS), Rome, Italy, 4 Mperience srl, Rome, Italy"
6d84d92d9ed6c226f0cc6401bc425a23432c9f96,Autism spectrum disorders: clinical and research frontiers.,"Downloaded from
dc.bmj.com
on 22 May 2008
Autism spectrum disorders: clinical and research
frontiers
E B Caronna, J M Milunsky and H Tager-Flusberg
Arch. Dis. Child.
doi:10.1136/adc.2006.115337
2008;93;518-523; originally published online 27 Feb 2008;
Updated information and services can be found at:
http://adc.bmj.com/cgi/content/full/93/6/518
These include:
References
This article cites 70 articles, 25 of which can be accessed free at:
http://adc.bmj.com/cgi/content/full/93/6/518#BIBL
Rapid responses
You can respond to this article at:
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service"
6d432962055a8c521e6b388d5a0a2140a0019a5e,Sensor network reconfiguration and big multimedia data fusion for situational awareness in smart environments,"Sensor network reconfiguration and big multimedia data fusion for situational
wareness in smart environments
Z. Akhtar, C. Drioli, M. Farinosi, G. Ferrin, G.L. Foresti, N. Martinel, C. Micheloni, C. Piciarelli, D.
Salvati, L. Snidaro and M. Vernier
AVIRES Lab - Department of Mathematics and Computer Science, Università degli Studi di Udine
Via delle Scienze, 206, 33100 Udine - Italy
last  years,  an
INTRODUCTION
increasing  number  of
environments  have  been  enhanced  with  smart
sensors and have become more and more smart and
self-organizing  [1].  Situational  awareness  (SA)  in
these  wide  areas  covers  a  huge  range  of  topics  and
hallenges  [2].  As  matter  of  fact,  understanding
ctivities
for  situation  assessment  cannot  be
chieved locally but  it requires to widen as much as
possible  the  monitored  area.  Several  different  and
new  problems must  be  investigated from the  use  of
single  sensors  able  to  adapt  internal  or  external"
6d8c9a1759e7204eacb4eeb06567ad0ef4229f93,"Face Alignment Robust to Pose, Expressions and Occlusions","Face Alignment Robust to Pose, Expressions and
Occlusions
Vishnu Naresh Boddeti†, Myung-Cheol Roh†, Jongju Shin, Takaharu Oguri, Takeo Kanade"
6dead19a89cbcbb71350a19925cb2c6f71261dfc,Fair k-Center Clustering for Data Summarization,"Fair k-Center Clustering for Data Summarization
Matth¨aus Kleindessner 1 Pranjal Awasthi 1 Jamie Morgenstern 2"
6d10beb027fd7213dd4bccf2427e223662e20b7d,User Adaptive and Context-Aware Smart Home Using Pervasive and Semantic Technologies,"Publishing CorporationJournal of Electrical and Computer EngineeringVolume 2016, Article ID 4789803, 20 pageshttp://dx.doi.org/10.1155/2016/4789803"
6d77b214a39b8592cd6ce48c9945e8a2466b22ba,Videana: A Software Toolkit for Scientific Film Studies,"Ralph Ewerth, Markus Mühling, Thilo Stadelmann,
Julinda Gllavata, Manfred Grauer, Bernd Freisleben
Videana: A Software Toolkit for Scientific Film Studies"
309e17e6223e13b1f76b5b0eaa123b96ef22f51b,Face recognition based on a 3D morphable model,"Face Recognition based on a 3D Morphable Model
Volker Blanz
University of Siegen
H¤olderlinstr. 3
57068 Siegen, Germany"
30fa389899ab0577779ce0aa19d2a69702d251a1,Sensor Selection by Linear Programming,"Noname manuscript No.
(will be inserted by the editor)
Sensor Selection by Linear Programming
Joseph Wang · Kirill Trapeznkov ·
Venkatesh Saligrama
Received: date / Accepted: date"
3046baea53360a8c5653f09f0a31581da384202e,Deformable Face Alignment via Local Measurements and Global Constraints,"Deformable Face Alignment via Local
Measurements and Global Constraints
Jason M. Saragih"
30a68bea6a43c239d899d7f02bb8ef9f3c5a8f47,Cross-Media Similarity Evaluation for Web Image Retrieval in the Wild,"Cross-Media Similarity Evaluation for
Web Image Retrieval in the Wild
Jianfeng Dong, Xirong Li, and Duanqing Xu"
30fd7b1f8502b1c1d7a855946d99d2d5323ec973,Big Data Analysis for 2 Media Production,"I N V I T E D
P A P E R
Big Data Analysis for
Media Production
By Josep Blat, Alun Evans, Hansung Kim, Evren Imre, Luka`sˇ Polok,
Viorela Ila, Nikos Nikolaidis, Senior Member IEEE, Pavel Zemcˇı´k, Anastasios Tefas,
Pavel Smrzˇ, Adrian Hilton, Member IEEE, and Ioannis Pitas, Fellow IEEE"
30f113d985d876a3974838b2ead49a069b474e57,Guided Upsampling Network for Real-Time Semantic Segmentation,"MAZZINI: GUN FOR REAL-TIME SEMANTIC SEGMENTATION
Guided Upsampling Network for Real-Time
Semantic Segmentation
Davide Mazzini
Department of Informatics, Systems
nd Communication
University of Milano-Bicocca
viale Sarca 336 Milano, Italy"
306ae56a4fc8f090e58a237749950e1607382ed7,Spatio-Temporal Matching for Human Pose Estimation in Video,"Spatio-temporal Matching for
Human Pose Estimation in Video
Feng Zhou and Fernando De la Torre"
300b819bbbe857f5fe89d0895f907073fc288719,"Towards a Robust People Tracking Framework for Service Robots in Crowded , Dynamic Environments","Towards a Robust People Tracking Framework
for Service Robots in Crowded, Dynamic Environments
Timm Linder
Fabian Girrbach
Kai O. Arras"
305c4d91b0f70853a1cb0ed2a60a466b84e5c13d,Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs,"Multi-Modal Trajectory Prediction of Surrounding Vehicles with
Maneuver based LSTMs
Nachiket Deo and Mohan M. Trivedi"
300fd9ae3bb33fba3e48b605e49d59b3ce3957ff,Recurrent Neural Networks for Emotion Recognition in Video,"Recurrent Neural Networks
for Emotion Recognition in Video
Samira Ebrahimi Kahou
École Polytechnique de
Montréal, Canada
samira.ebrahimi-
Vincent Michalski
Université de Montréal,
Montréal, Canada
Kishore Konda
Goethe-Universität Frankfurt,
Germany
Roland Memisevic
Université de Montréal,
Montréal, Canada
Christopher Pal
École Polytechnique de
Montréal, Canada"
30f78071ac2bc965ffbf452a7b315d6dfddae30e,Lingusitic Analysis of Multi-Modal Recurrent Neural Networks,"Proceedings of the 2015 Workshop on Vision and Language (VL’15), pages 8–9,
Lisbon, Portugal, 18 September 2015. c(cid:13)2015 Association for Computational Linguistics."
30ad88ca5b834ca78619f3938b5f8ee534fd3ea8,Random Forest with Learned Representations for Semantic Segmentation,"Random Forest with Learned Representations
for Semantic Segmentation
Byeongkeun Kang and Truong Q. Nguyen, Fellow, IEEE"
305346d01298edeb5c6dc8b55679e8f60ba97efb,Fine-Grained Face Annotation Using Deep Multi-Task CNN,"Article
Fine-Grained Face Annotation Using Deep
Multi-Task CNN
Luigi Celona *
, Simone Bianco
nd Raimondo Schettini
Department of Informatics, Systems and Communication, University of Milano-Bicocca,
viale Sarca, 336 Milano, Italy; (S.B.); (R.S.)
* Correspondence:
Received: 3 July 2018; Accepted: 13 August 2018; Published: 14 August 2018"
30ff70a3afea6b6b46bde883ca1ade0e932bbe71,"Image Parsing: Unifying Segmentation, Detection, and Recognition","Int’l J. of Computer Vision, Marr Prize Issue, 2005.
Image Parsing: Unifying Segmentation, Detection, and
Recognition
Zhuowen Tu1, Xiangrong Chen1, Alan L. Yuille1,2, and Song-Chun Zhu1,3
Departments of Statistics1, Psychology2, and Computer Science3,
University of California, Los Angeles,
Los Angeles, CA 90095.
emails:"
309011919f45062ceefcd2275ded5171762baa59,"Principles "" Generic Object Recognition Via Integrating Distinct Features with SVM "" by","Notice of Violation of IEEE Publication Principles
""Generic Object Recognition Via Integrating Distinct Features with SVM""
y Tong-Cheng Huang and You-Dong Ding
in Proceedings of 2006 International Conference on Machine Learning and Cybernetics,
pp 3897-3902.
After careful and considered review of the content and authorship of this paper by a duly
onstituted expert committee, this paper has been found to be in violation of IEEE's
Publication Principles.
This paper is a near duplication of the original text from the papers cited below. The
original text was copied without attribution and without permission.
Due to the nature of this violation, reasonable effort should be made to remove all past
references to this paper, and future references should be made to the following articles:
""Generic Object Recognition by Combining Distinct Features in Machine
Learning,""
y Hongying Meng, David R. Hardoon, John Shawe-Taylor, Sandor Szedmak,
in the Proceedings of the 17th Annual Symposium on Electronic Imaging, January 2005,
Authorized licensed use limited to: University College London. Downloaded on June 6, 2009 at 03:32 from IEEE Xplore.  Restrictions apply."
30d4d6bf4bd09f5b5a4f3631aa1ef18fe07efef8,Subtraction with Dirichlet Process Mixture Models,"Background Subtraction with Dirichlet Process Mixture Models.
Haines, TS; Xiang, T
For additional information about this publication click this link.
http://qmro.qmul.ac.uk/xmlui/handle/123456789/11343
Information about this research object was correct at the time of download; we occasionally
make corrections to records, please therefore check the published record when citing. For
more information contact"
30eed14dfdee78279536e680871bed4f128d5f46,A Study of Calorie Estimation in Pictures of Food,
30b103d59f8460d80bb9eac0aa09aaa56c98494f,Enhancing Human Action Recognition with Region Proposals,"Enhancing Human Action Recognition with Region Proposals
Fahimeh Rezazadegan, Sareh Shirazi, Niko Sünderhauf, Michael Milford, Ben Upcroft
Australian Centre for Robotic Vision(ACRV), School of Electrical Engineering and Computer Science
Queensland University of Technology(QUT)"
302fee58f8c9498e8a5e543312e7c11baf7e0827,Robust voting algorithm based on labels of behavior for video copy detection,"Robust Voting Algorithm Based on Labels of Behavior
for Video Copy Detection
Julien Law-To, Olivier Buisson
Valerie Gouet-Brunet, Nozha Boujemaa
INRIA Institut National
de la Recherche et de l’Informatique
Rocquencourt, France
Institut National de l’Audiovisuel
Bry Sur Marne, France
(jlawto,obuisson)"
300b8caf79783a7eba5608b5819b6fed14273d2d,Unsupervised Joint Mining of Deep Features and Image Labels for Large-Scale Radiology Image Categorization and Scene Recognition,"Unsupervised Joint Mining of Deep Features and Image Labels
for Large-scale Radiology Image Categorization and Scene Recognition
Xiaosong Wang, Le Lu, Hoo-chang Shin, Lauren Kim, Mohammadhadi Bagheri,
Isabella Nogues, Jianhua Yao, Ronald M. Summers
Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center,
0 Center Drive, Bethesda, MD 20892"
30861d747c87e2e838c1c30eed334b17cc93cdb6,Bootstrapping Face Detection with Hard Negative Examples,"Bootstrapping Face Detection with Hard
Negative Examples
Shaohua Wan
Zhijun Chen Tao Zhang Bo Zhang Kong-kat Wong
{wanshaohua, chenzhijun, tao.zhang, zhangbo,
Xiaomi Inc.
August 9, 2016"
30962cf6f47396df88bf1c8827ebda8f0a6ff516,A Convolutional Neural Network Approach for Assisting Avalanche Search and Rescue Operations with UAV Imagery,"Article
A Convolutional Neural Network Approach for
Assisting Avalanche Search and Rescue Operations
with UAV Imagery
Mesay Belete Bejiga 1, Abdallah Zeggada 1, Abdelhamid Nouffidj 2 and Farid Melgani 1,*
Department of Information Engineering and Computer Science University of Trento, 38123 Trento, Italy;
(M.B.B.); (A.Z.)
Département des Télécommunications, Faculté d’Electronique et d’Informatique, USTHB BP 32, El-Alia,
Bab-Ezzouar, 16111 Algiers, Algeria;
* Correspondence: Tel.: +39-046-128-1573
Academic Editors: Francesco Nex, Xiaofeng Li and Prasad S. Thenkabail
Received: 11 November 2016; Accepted: 14 January 2017; Published: 24 January 2017"
30bb582c2c09abc7eb9dda7d9f80804eeb89f9d7,Research Problems and Opportunities in Memory Systems,"ResearchProblemsandOpportunitiesinMemorySystemsOnurMutlu1,LavanyaSubramanian1c(cid:13)TheAuthors2014.ThispaperispublishedwithopenaccessatSuperFri.orgThememorysystemisafundamentalperformanceandenergybottleneckinalmostallcom-putingsystems.Recentsystemdesign,application,andtechnologytrendsthatrequiremoreca-pacity,bandwidth,ef‌f‌iciency,andpredictabilityoutofthememorysystemmakeitanevenmoreimportantsystembottleneck.Atthesametime,DRAMtechnologyisexperiencingdif‌f‌iculttech-nologyscalingchallengesthatmakethemaintenanceandenhancementofitscapacity,energy-ef‌f‌iciency,andreliabilitysignificantlymorecostlywithconventionaltechniques.Inthisarticle,afterdescribingthedemandsandchallengesfacedbythememorysystem,weexaminesomepromisingresearchanddesigndirectionstoovercomechallengesposedbymemoryscaling.Specifically,wedescribethreemajornewresearchchallengesandsolutiondirections:1)enablingnewDRAMarchitectures,functions,interfaces,andbetterintegrationoftheDRAMandtherestofthesystem(anapproachwecallsystem-DRAMco-design),2)designingamemorysystemthatemploysemergingnon-volatilememorytechnologiesandtakesadvantageofmultipledifferenttechnologies(i.e.,hybridmemorysystems),3)providingpredictableperformanceandQoStoapplicationssharingthememorysystem(i.e.,QoS-awarememorysystems).WealsobrieflydescribeourongoingrelatedworkincombatingscalingchallengesofNANDflashmemory.Keywords:memorysystems,scaling,DRAM,flash,non-volatilememory,QoS,reliability.IntroductionMainmemoryisacriticalcomponentofallcomputingsystems,employedinserver,em-bedded,desktop,mobileandsensorenvironments.Memorycapacity,energy,cost,performance,andmanagementalgorithmsmustscaleaswescalethesizeofthecomputingsysteminordertomaintainperformancegrowthandenablenewapplications.Unfortunately,suchscalinghasbe-comedif‌f‌icultbecauserecenttrendsinsystems,applications,andtechnologygreatlyexacerbatethememorysystembottleneck.1.MemorySystemTrendsInparticular,onthesystems/architecturefront,energyandpowerconsumptionhavebecomekeydesignlimitersasthememorysystemcontinuestoberesponsibleforasignificantfractionofoverallsystemenergy/power[112].Moreandincreasinglyheterogeneousprocessingcoresandagents/clientsaresharingthememorysystem[11,36,39,60,78,79,178,181],leadingtoincreasingdemandformemorycapacityandbandwidthalongwitharelativelynewdemandforpredictableperformanceandqualityofservice(QoS)fromthememorysystem[129,137,176].Ontheapplicationsfront,importantapplicationsareusuallyverydataintensiveandarebecomingincreasinglyso[17],requiringbothreal-timeandof‌f‌linemanipulationofgreatamountsofdata.Forexample,next-generationgenomesequencingtechnologiesproducemassiveamountsofsequencedatathatoverwhelmsmemorystorageandbandwidthrequirementsoftoday’shigh-enddesktopandlaptopsystems[9,111,186,196,197]yetresearchershavethegoalofenablinglow-costpersonalizedmedicine,whichrequiresevenlargeramountsofdataandtheireffectiveanalyses.Creationofnewkillerapplicationsandusagemodelsforcomputerslikelydependsonhowwellthememorysystemcansupporttheef‌f‌icientstorageandmanipulationofdatainsuch1CarnegieMellonUniversityDOI:10.14529/jsfi1403022014,Vol.1,No.319"
30f84c48bdf2f6152075dd9651a761a84b2f2166,"No fear, no panic: probing negation as a means for emotion regulation.","doi:10.1093/scan/nss043
SCAN (2013) 8, 654 ^661
No fear, no panic: probing negation as a means for
emotion regulation
Cornelia Herbert,1 Roland Deutsch,2 Petra Platte,1 and Paul Pauli1
Department of Psychology, Biological Psychology, Clinical Psychology and Psychotherapy, University of Wu¨rzburg, 97070 Wu¨rzburg and
Department of Psychology, Technische Universita¨t Dresden, Dresden, Germany
This electroencephalographic study investigated if negating one’s emotion results in paradoxical effects or leads to effective emotional downregulation.
Healthy participants were asked to downregulate their emotions to happy and fearful faces by using negated emotional cue words (e.g. no fun, no fear).
Cue words were congruent with the emotion depicted in the face and presented prior to each face. Stimuli were presented in blocks of happy and fearful
faces. Blocks of passive stimulus viewing served as control condition. Active regulation reduced amplitudes of early event-related brain potentials (early
posterior negativity, but not N170) and the late positive potential for fearful faces. A fronto-central negativity peaking at about 250 ms after target face
onset showed larger amplitude modulations during downregulation of fearful and happy faces. Behaviorally, negating was more associated with
reappraisal than with suppression. Our results suggest that in an emotional context, negation processing could be quite effective for emotional
downregulation but that its effects depend on the type of the negated emotion (pleasant vs unpleasant). Results are discussed in the context of
dual process models of cognition and emotion regulation.
Keywords: emotion regulation; event-related brain potentials; negation; reappraisal; suppression
INTRODUCTION
Emotion regulation is an important aspect of everyday life (Gross and
John, 2003; Nezlek and Kuppens, 2008). Imagine the following situ-"
30ccfd2b4b6d5b30581356ccefcf96fd77c1766a,Overview of the ImageCLEF 2014 Scalable Concept Image Annotation Task,"Overview of the ImageCLEF 2016 Scalable
Concept Image Annotation Task
Andrew Gilbert, Luca Piras, Josiah Wang, Fei Yan, Arnau Ramisa, Emmanuel
Dellandrea, Robert Gaizauskas, Mauricio Villegas and Krystian Mikolajczyk"
3035bcbad93767570d444c136f4036f357648d60,Feature Extraction for Incomplete Data Via Low-Rank Tensor Decomposition With Feature Regularization.,"This is a repository copy of Feature extraction for incomplete data via low-rank tensor
decomposition with feature regularization.
White Rose Research Online URL for this paper:
http://eprints.whiterose.ac.uk/138784/
Version: Accepted Version
Article:
Shi, Q., Cheung, Y.-M., Zhao, Q. et al. (1 more author) (2018) Feature extraction for
incomplete data via low-rank tensor decomposition with feature regularization. IEEE
Transactions on Neural Networks and Learning Systems. ISSN 2162-237X
https://doi.org/10.1109/TNNLS.2018.2873655
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be
obtained for all other users, including reprinting/ republishing this material for advertising or
promotional purposes, creating new collective works for resale or redistribution to servers
or lists, or reuse of any copyrighted components of this work in other works. Reproduced
in accordance with the publisher's self-archiving policy.
Reuse
Items deposited in White Rose Research Online are protected by copyright, with all rights reserved unless
indicated otherwise. They may be downloaded and/or printed for private study, or other acts as permitted by
national copyright laws. The publisher or other rights holders may allow further reproduction and re-use of
the full text version. This is indicated by the licence information on the White Rose Research Online record"
30a319d6e1472c81a1987133afb01f524df459dc,Pedestrian Detection Based on Adaptive Selection of Visible Light or Far-Infrared Light Camera Image by Fuzzy Inference System and Convolutional Neural Network-Based Verification,"Article
Pedestrian Detection Based on Adaptive Selection of
Visible Light or Far-Infrared Light Camera Image by
Fuzzy Inference System and Convolutional Neural
Network-Based Verification
Jin Kyu Kang, Hyung Gil Hong and Kang Ryoung Park *
Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu,
Seoul 100-715, Korea; (J.K.K.); (H.G.H.)
* Correspondence: Tel.: +82-10-3111-7022; Fax: +82-2-2277-8735
Received: 16 June 2017; Accepted: 5 July 2017; Published: 8 July 2017"
30256c10cb7ec139b4245855850998c39b297975,Functional magnetic resonance imaging of autism spectrum disorders,"C l i n i c a l   r e s e a r c h
Functional magnetic resonance imaging of
utism spectrum disorders
Gabriel S. Dichter, PhD
Introduction
utism was first described by Leo Kanner1 and
Hans Asperger2 in a series of clinical case studies. Both
linicians suggested that the conditions now referred to
s autism spectrum disorders (ASDs) may have a neu-
robiological basis. With the relatively recent advent of
modern brain imaging techniques, translational psychi-
tric  research  has  embraced  the  systematic  study  of
This review presents an overview of functional magnetic resonance imaging findings in autism spectrum disorders
(ASDs). Although there is considerable heterogeneity with respect to results across studies, common themes have
emerged, including: (i) hypoactivation in nodes of the “social brain” during social processing tasks, including regions
within the prefrontal cortex, the posterior superior temporal sulcus, the amygdala, and the fusiform gyrus; (ii) aber-
rant frontostriatal activation during cognitive control tasks relevant to restricted and repetitive behaviors and inter-
ests, including regions within the dorsal prefrontal cortex and the basal ganglia; (iii) differential lateralization and
ctivation of language processing and production regions during communication tasks; (iv) anomalous mesolimbic
responses to social and nonsocial rewards; (v) task-based long-range functional hypoconnectivity and short-range"
300eb15b819ecc9668be26735e5038efc4e05281,Object-based Place Recognition for Mobile Robots Using Panoramas,"Object-based Place Recognition for
Mobile Robots Using Panoramas
Arturo RIBES a,1, Arnau RAMISA a and Ramon LOPEZ DE MANTARAS a and
Ricardo TOLEDO b
Artificial Intelligence Research Institute (IIIA-CSIC), Campus UAB, 08193 Bellaterra,
Computer Vision Center (CVC), Campus UAB, 08193 Bellaterra, Spain
Spain"
30870ef75aa57e41f54310283c0057451c8c822b,Overcoming catastrophic forgetting with hard attention to the task,"Overcoming Catastrophic Forgetting with Hard Attention to the Task
Joan Serr`a 1 D´ıdac Sur´ıs 1 2 Marius Miron 1 3 Alexandros Karatzoglou 1"
3099615de73bb1d6442ce29dc9959ddb91cfc282,Detecting and Tracking Sports Players with Random Forests and Context-Conditioned Motion Models,"Detecting and Tracking Sports Players with
Random Forests and Context-Conditioned
Motion Models
Jingchen Liu and Peter Carr"
301474a50a39b24917ad79bd2493f1168c4c1227,Eigen-disfigurement model for simulating plausible facial disfigurement after reconstructive surgery,"Lee et al. BMC Medical Imaging  (2015) 15:12
DOI 10.1186/s12880-015-0050-7
R ES EAR CH A R T I C LE
Open Access
Eigen-disfigurement model for simulating plausible
facial disfigurement after reconstructive surgery
Juhun Lee1,2, Michelle C Fingeret2,3, Alan C Bovik1, Gregory P Reece2, Roman J Skoracki2,
Matthew M Hanasono2 and Mia K Markey4,5*"
301b0da87027d6472b98361729faecf6e1d5e5f6,HEAD POSE ESTIMATION IN FACE RECOGNITION ACROSS POSE SCENARIOS,"HEAD POSE ESTIMATION IN FACE RECOGNITION ACROSS
POSE SCENARIOS
M. Saquib Sarfraz and Olaf Hellwich
Computer vision and Remote Sensing, Berlin university of Technology
Sekr. FR-3-1, Franklinstr. 28/29, D-10587, Berlin, Germany.
Keywords:
Pose estimation, facial pose, face recognition, local energy models, shape description, local features, head
pose classification."
305dccd4004560572af2e849a36faf5626990517,Comparative Analysis of Face Recognition Approaches : A Survey,"Comparative Analysis of Face Recognition Approaches:
International Journal of Computer Applications (0975 – 8887)
Volume 57– No.17, November 2012
A Survey
Ripal Patel, Nidhi Rathod, Ami Shah
Electronics & Telecommunication Department,
BVM Engineering College,
Vallabh Vidyanagar-388120, Gujarat, India."
30c8a2b6a505645b9f93dcc4d365eee6f46c4c37,Using Curvilinear Features in Focus for Registering a Single Image to a 3D Object,"Using Curvilinear Features in Focus for Registering
Single Image to a 3D Object
Hatem A. Rashwan, Sylvie Chambon, Pierre Gurdjos, G´eraldine Morin and Vincent Charvillat"
307a810d1bf6f747b1bd697a8a642afbd649613d,An affordable contactless security system access for restricted area,"An affordable contactless security system access
for restricted area
Pierre Bonazza1, Johel Mitéran1, Barthélémy Heyrman1, Dominique Ginhac1,
Vincent Thivent2, Julien Dubois1
Laboratory Le2i
University Bourgogne Franche-Comté, France
Odalid compagny, France
Contact
Keywords – Smart Camera, Real-time Image Processing, Biometrics, Face Detection, Face Verifica-
tion, EigenFaces, Support Vector Machine,
We  present  in  this  paper  a  security  system  based  on
identity verification process and a low-cost smart cam-
era, intended to avoid unauthorized access to restricted
rea.  The  Le2i  laboratory  has  a  longstanding  experi-
ence in smart cameras implementation and design [1],
for  example in the  case of  real-time classical  face de-
tection [2] or human fall detection [3].
The principle of the system, fully thought and designed
in  our  laboratory,  is  as  follows:  the  allowed  user  pre-
sents a RFID card to the reader based on Odalid system"
30a059872d0fff3442504c24880c93738036e6aa,Distributed neural computation for the visual perception of motion. (Calcul neuronal distribué pour la perception visuelle du mouvement),"UFRmath´ematiquesetinformatique´EcoledoctoraleIAEMLorraineD´epartementdeformationdoctoraleeninformatiqueCalculneuronaldistribu´epourlaperceptionvisuelledumouvementTH`ESEpr´esent´eeetsoutenuepubliquementle14Octobre2011pourl’obtentionduDoctoratdel’universit´eNancy2(sp´ecialit´einformatique)parMauricioDavidCerdaVillablancaCompositiondujuryPr´esident:Lepr´esidentRapporteurs:MathiasQUOYProfesseur,Universit´edeCergy-Pontoise,FranceAdrianPALACIOSProfesseur,UniversidaddeValparaiso,ChiliExaminateurs:HeikoNEUMANNProfesseur,UniversityofUlm,AllemagneAnneBOYERProfesseur,Universit´eNancy2,FranceRachidDERICHEDirecteurdeRecherche,INRIA,Sophia-Antipolis,FranceBernardGIRAU(directeur)Professeur,Universit´eHenriPoincar´e,Nancy1LaboratoireLorraindeRechercheenInformatiqueetsesApplications—UMR7503"
30aac3becead355545b5ab7f0c3158040360021e,ACD: Action Concept Discovery from Image-Sentence Corpora,"ACD: Action Concept Discovery from
Image-Sentence Corpora
Jiyang Gao
Univ. of Southern California
Chen Sun
Univ. of Southern California
Ram Nevatia
Univ. of Southern California"
308647f22e3f1c80b7416b3c53fd56f9abfa904f,Robust Real-Time Tracking with Diverse Ensembles and Random Projections,"Robust Real-Time Tracking with Diverse Ensembles and Random Projections
Center for Informatics Science,
Center for Informatics Science,
Sara Maher
Nile University
Giza, Egypt
Mohamed El Helw
Center for Informatics Science,
Nile University
Giza, Egypt
Ahmed Salaheldin
Nile University
Giza, Egypt"
309e5ae1554d2afc3b94eaea66b8f31ba85c434a,"Bian, Xiao. Sparse and Low-rank Modeling on High Dimensional Data: a Geometric Perspective. (under the Direction of Dr. Hamid Krim.) Sparse and Low-rank Modeling on High Dimensional Data: a Geometric Perspective",
300fb25626bebfc84cf2f6458784b5cdf5c3ffc2,Cross-Dataset Adaptation for Visual Question Answering,"Cross-Dataset Adaptation for Visual Question Answering
Wei-Lun Chao∗
Hexiang Hu∗
Fei Sha
U. of Southern California
U. of Southern California
U. of Southern California
Los Angeles, CA
Los Angeles, CA
Los Angeles, CA"
3b9a2f0fd429016b531ed398017da029fe5154da,Gabor Parameter Selection for Local Feature Detection,"Gabor Parameter Selection for Local Feature
Detection ⋆
Plinio Moreno, Alexandre Bernardino, and Jos´e Santos-Victor
{plinio, alex,
Instituto Superior T´ecnico & Instituto de Sistemas e Rob´otica
049-001 Lisboa - Portugal"
3b54cf5eb7fe173def6e81eefeff17a1b9960cb2,Efficient Computation of Collision Probabilities for Safe Motion Planning,"Efficient Computation of Collision Probabilities
for Safe Motion Planning∗
Andrew Blake, Alejandro Bordallo, Majd Hawasly,
Svetlin Penkov, Subramanian Ramamoorthy†, Alexandre Silva"
3bf66814817f582510e0f0a717112b78aca075a0,UNIVERSITY OF CALIFORNIA RIVERSIDE Bio-Image Analysis for Understanding Plant Development and Mosquito Behaviors A Dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy,"UNIVERSITY OF CALIFORNIA
RIVERSIDE
Bio-Image Analysis for Understanding Plant Development and Mosquito Behaviors
A Dissertation submitted in partial satisfaction
of the requirements for the degree of
Doctor of Philosophy
Computer Science
Katya Mkrtchyan
March 2017
Dissertation Committee:
Professor Amit Roy-Chowdhury, Chairperson
Professor Eamonn Keogh
Professor Stefano Lonardi
Professor Tamar Shinar"
3bd63bea64c770df5049879f4398e65f958ebd23,Predicting an Object Location Using a Global Image Representation,"Predicting an Object Location using a Global Image Representation
Jose A. Rodriguez-Serrano and Diane Larlus
Computer Vision Group, Xerox Research Centre Europe"
3b410ae97e4564bc19d6c37bc44ada2dcd608552,Scalability Analysis of Audio-Visual Person Identity Verification,"Scalability Analysis of Audio-Visual Person
Identity Verification
Jacek Czyz1, Samy Bengio2, Christine Marcel2, and Luc Vandendorpe1
Communications Laboratory,
Universit´e catholique de Louvain, B-1348 Belgium,
IDIAP, CH-1920 Martigny,
Switzerland"
3beb94f61b5909fca8917b0475983ea2c66f1df2,Shape model fitting algorithm without point correspondence,"0th European Signal Processing Conference (EUSIPCO 2012)
© EURASIP, 2012  -  ISSN 2076-1465
. INTRODUCTION"
3baa3d5325f00c7edc1f1427fcd5bdc6a420a63f,Enhancing convolutional neural networks for face recognition with occlusion maps and batch triplet loss,"Enhancing Convolutional Neural Networks for Face Recognition with
Occlusion Maps and Batch Triplet Loss
Daniel S´aez Triguerosa,b, Li Menga,∗, Margaret Hartnettb
School of Engineering and Technology, University of Hertfordshire, Hatfield AL10 9AB, UK
IDscan Biometrics (a GBG company), London E14 9QD, UK"
3ba3ef6d8394055d43bf4fe62227fbae8ab9b195,Finding images of difficult entities in the long tail,"Finding Images of Difficult Entities in the Long Tail
Bilyana Taneva
Max-Planck Institute for
Informatics
Saarbrücken, Germany
Mouna Kacimi
Free University of
Bozen-Bolzano
Italy
Gerhard Weikum
Max-Planck Institute for
Informatics
Saarbrücken, Germany"
3b304585d5af0afe98a85d6e0559315fbf3a7807,An Improved Labelling for the INRIA Person Data Set for Pedestrian Detection,"An Improved Labelling for the INRIA Person
Data Set for Pedestrian Detection
Matteo Taiana, Jacinto Nascimento, and Alexandre Bernardino(cid:63)
Institute for Systems and Robotics, IST, Lisboa, Portugal,
WWW home page: http://users.isr.ist.utl.pt/~mtaiana"
3b2acab4fc2bcf86ceaf3526c75a7a9eb01a589a,Separating conditional and unconditional cooperation in a sequential Prisoner’s Dilemma game,"RESEARCH ARTICLE
Separating conditional and unconditional
ooperation in a sequential Prisoner’s
Dilemma game
Raoul Bell*, Laura Mieth, Axel Buchner
Department of Experimental Psychology, Heinrich Heine University Du¨sseldorf, Du¨sseldorf, Germany"
3b4177556f1c9f5a8f8e1b2e8d824dee20e388e4,Spatial Weighting for Bag-of-Features,"Spatial Weighting for Bag-of-Features
Marcin Marsza(cid:7)ek
Cordelia Schmid
INRIA Rh(cid:136)one-Alpes, LEAR - GRAVIR
665 av de l’Europe, 38330 Montbonnot, France"
3b08ef7aa0cf9528da42b2b594b66e4a6f7fdb7f,Active Learning for Delineation of Curvilinear Structures,"Active Learning for Delineation of Curvilinear Structures
Agata Mosinska
Raphael Sznitman
University of Bern
Przemysław Głowacki
Pascal Fua
{agata.mosinska, przemyslaw.glowacki,"
3b6310052026fc641d3fa639647342c45d8f5bd5,Eye Contact Modulates Cognitive Processing Differently in Children With Autism,"Child Development, xxxx 2014, Volume 00, Number 0, Pages 1–11
Eye Contact Modulates Cognitive Processing Differently in
Children With Autism
Terje Falck-Ytter
Karolinska Institutet and Uppsala University
Christoffer Carlstr€om and Martin Johansson
Uppsala University
In humans, effortful cognitive processing frequently takes place during social interaction, with eye contact
eing an important component. This study shows that the effect of eye contact on memory for nonsocial infor-
mation is different in children with typical development than in children with autism, a disorder of social
ommunication. Direct gaze facilitated memory performance in children with typical development (n = 25,
6 years old), but no such facilitation was seen in the clinical group (n = 10, 6 years old). Eye tracking con-
ducted during the cognitive test revealed strikingly similar patterns of eye movements, indicating that the
results cannot be explained by differences in overt attention. Collectively, these findings have theoretical sig-
nificance and practical implications for testing practices in children.
Being looked at is a strong signal, indicating that
the other person is attending to you and processing
information about you. In many nonhuman species,
direct gaze functions as an aversive stimulus, likely
ecause of the threat value associated with eye con-"
3bc776eb1f4e2776f98189e17f0d5a78bb755ef4,View Synthesis from Image and Video for Object Recognition Applications,
3b14bdb0b1a7353d94973ef4c1578e1bd4a4e35e,Three dimensional binary edge feature representation for pain expression analysis,"Three Dimensional Binary Edge
Feature Representation for Pain
Expression Analysis
Xing Zhang1, Lijun Yin1, Jeffrey F. Cohn2
State University of New York at Binghamton; 2University of Pittsburgh"
3b92916dd9d772cf1d167461a548115013a954a8,Unsupervised Framework for Interactions Modeling between Multiple Objects,
3b1b94441010615195a5c404409ce2416860508c,Image Captioning and Visual Question Answering Based on Attributes and External Knowledge,"MANUSCRIPT, 2016
Image Captioning and Visual Question
Answering Based on Attributes
nd External Knowledge
Qi Wu, Chunhua Shen, Peng Wang, Anthony Dick, Anton van den Hengel"
3b557c4fd6775afc80c2cf7c8b16edde125b270e,Face recognition: Perspectives from the real world,"Face Recognition: Perspectives from the
Real-World
Bappaditya Mandal
Institute for Infocomm Research, A*STAR,
Fusionopolis Way, #21-01 Connexis (South Tower), Singapore 138632.
Phone: +65 6408 2071; Fax: +65 6776 1378;
E-mail:"
3b8ad690f8d43d189ea2f2559c41b6eebac8dcc8,Mobile 3D object detection in clutter,"Mobile 3D Object Detection in Clutter
David Meger and James J. Little"
3b8ad1f2335fc755e5cd75ee5922b8a0d432018a,A Fast and Compact Saliency Score Regression Network Based on Fully Convolutional Network,"A Fast and Compact Saliency Score Regression
Network Based on Fully Convolutional Network
Xuanyang Xi, Yongkang Luo, Fengfu Li, Peng Wang and Hong Qiao"
3b15a48ffe3c6b3f2518a7c395280a11a5f58ab0,On knowledge transfer in object class recognition,"On Knowledge Transfer in
Object Class Recognition
A dissertation approved by
TECHNISCHE UNIVERSITÄT DARMSTADT
Fachbereich Informatik
for the degree of
Doktor-Ingenieur (Dr.-Ing.)
presented by
MICHAEL STARK
Dipl.-Inform.
orn in Mainz, Germany
Prof. Dr.-Ing. Michael Goesele, examiner
Prof. Martial Hebert, Ph.D., co-examiner
Prof. Dr. Bernt Schiele, co-examiner
Date of Submission: 12th of August, 2010
Date of Defense: 23rd of September, 2010
Darmstadt, 2010"
3b1aaac41fc7847dd8a6a66d29d8881f75c91ad5,Sparse Representation-Based Open Set Recognition,"Sparse Representation-based Open Set Recognition
He Zhang, Student Member, IEEE and Vishal M. Patel, Senior Member, IEEE"
3b9ee03255eb5a0040676eead1767db431e83562,2013 Ieee Conference on Computer Vision and Pattern Recognition 2013 Ieee Conference on Computer Vision and Pattern Recognition 2013 Ieee Conference on Computer Vision and Pattern Recognition,"013 IEEE Conference on Computer Vision and Pattern Recognition
013 IEEE Conference on Computer Vision and Pattern Recognition
013 IEEE Conference on Computer Vision and Pattern Recognition
063-6919/13 $26.00 © 2013 IEEE
063-6919/13 $26.00 © 2013 IEEE
063-6919/13 $26.00 © 2013 IEEE
DOI 10.1109/CVPR.2013.236
DOI 10.1109/CVPR.2013.236
DOI 10.1109/CVPR.2013.236"
3b2697d76f035304bfeb57f6a682224c87645065,ImageNet Large Scale Visual Recognition Challenge,"Noname manuscript No.
(will be inserted by the editor)
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky* · Jia Deng* · Hao Su · Jonathan Krause ·
Sanjeev Satheesh · Sean Ma · Zhiheng Huang · Andrej Karpathy ·
Aditya Khosla · Michael Bernstein · Alexander C. Berg · Li Fei-Fei
Received: date / Accepted: date"
3b1ba9818e2ee6a54e7ec033c5b2ec8bdbe2935f,Social Signaling Descriptor for Group Behaviour Analysis,"Social Signaling Descriptor for Group
Behaviour Analysis
Eduardo M. Pereira1,2(B), Lucian Ciobanu1, and Jaime S. Cardoso1,2
Faculty of Engineering of the University of Porto, Rua Dr. Roberto Frias, 378,
INESC TEC, Porto, Portugal
200 - 465 Porto, Portugal"
3b38dc6d4f676ace52672f6788b66c9abb10d702,Ph . D . Showcase : Measuring Terrain Distances Through Extracted Channel Networks,"Ph.D. Showcase: Measuring Terrain Distances Through
Extracted Channel Networks
PhD Student:
Christopher Stuetzle
Dept. Computer Science
PhD Superviser:
W. Randolph Franklin
Dept. Electrical Engineering
PhD Superviser:
Barbara Cutler
Dept. Computer Science
Mehrad Kamalzare
Dept. Civil Engineering
Zhongxian Chen
Dept. Computer Science
Thomas Zimmie
Dept. Civil Engineering"
3b9d94752f8488106b2c007e11c193f35d941e92,"Appearance , Visual and Social Ensembles for Face Recognition in Personal Photo Collections","#2052
CVPR 2013 Submission #2052. CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE.
#2052
Appearance, Visual and Social Ensembles for
Face Recognition in Personal Photo Collections
Anonymous CVPR submission
Paper ID 2052"
3b2f78a4edf5da876e52513d0e3960da7d3a253f,Qualitative Evaluation of Detection and Tracking Performance,"Qualitative Evaluation of Detection and Tracking
Performance
Swaminathan Sankaranarayanan, Francois Bremond, David Tax
To cite this version:
Swaminathan Sankaranarayanan, Francois Bremond, David Tax. Qualitative Evaluation of Detection
nd Tracking Performance. 9th IEEE International Conference On Advanced Video and Signal Based
Surveillance (AVSS 12), Sep 2012, Beijing, China. IEEE, pp.362-367, 2012, 2012 IEEE Ninth Inter-
national Conference on Advanced Video and Signal-Based Surveillance. <10.1109/AVSS.2012.57>.
<hal-00763587>
HAL Id: hal-00763587
https://hal.inria.fr/hal-00763587
Submitted on 14 Dec 2012
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents"
3bb80697a28b9876e668de2228003dbbdcb84a25,Face Recognition with semi-supervised learning and Multiple Classifiers,"Face Recognition with semi-supervised learning and Multiple
Classifiers
NEAMAT EL GAYAR*, SHABAN A. SHABAN† SAYED HAMDY†
Institute of Statistical Studies and Research
*Faculty of Computers and Information
Cairo University
5 Ahmed Zewel St., 12613 Orman, Giza
EGYPT"
3bfb3db230b429423dcbdc623ac55b63d038bba8,A new model for Gabor coefficients' magnitude in face recognition,"978-1-4244-4296-6/10/$25.00 ©2010 IEEE
ICASSP 2010
Γ(3β)Γ(1β)!β/2."
3b319645bfdc67da7d02db766e17a3e0a37be47b,On the relationship between visual attributes and convolutional networks,"On the Relationship between Visual Attributes and Convolutional Networks
Victor Escorcia1,2, Juan Carlos Niebles2, Bernard Ghanem1
King Abdullah University of Science and Technology (KAUST), Saudi Arabia. 2Universidad del Norte, Colombia.
The seminal work of Krizhevsky et al. [3] that trained a large convo-
lutional network (conv-net) for image-level object recognition on the Ima-
geNet challenge is considered a major stepping stone for subsequent work in
onv-net based visual recognition. Such a network is able to automatically
learn a hierarchy of nonlinear features that richly describe image content as
well as discriminate between object classes. Recent work [4] has shown that
features extracted from a conv-net trained on ImageNet are general purpose
(or black-box) enough to achieve state-of-the-art results in various other
recognition tasks, including scene, fine-grained, and even action recogni-
tion. However, unlike hand-crafted features, those learned by a conv-net
re usually not visually intuitive and straightforward to interpret. Despite
their excellent recognition performance, understanding and interpreting the
inner workings of conv-nets remains mostly elusive to the community. It
is this lack of deep understanding that is currently motivating researchers
to look under the hood and comprehend how and why these deep networks
work so well in practice. Inspired by recent observations on the analysis of
onv-nets [1], this paper takes another step in a similar direction, namely"
3b996a2e641be7bd395620d30364a27d1558cbad,Tracking Related Multiple Targets in Videos DISSERTATION submitted in partial fulfillment of the requirements for the degree of Doktor / in der technischen Wissenschaften,"Tracking Related Multiple Targets
in Videos
DISSERTATION
zur Erlangung des akademischen Grades
Doktor/in der technischen Wissenschaften
eingereicht von
Nicole M. Artner
Matrikelnummer 0727746
n der
Fakultät für Informatik der Technischen Universität Wien
Betreuung: O.Univ.Prof. Dipl.Ing. Dr.techn. Walter G. Kropatsch
Diese Dissertation haben begutachtet:
(O.Univ.Prof. Dipl.Ing. Dr.techn.
(Prof. Em. Dr. Horst Bunke)
Walter G. Kropatsch)
Wien, 10.10.2013
(Nicole M. Artner)
A-1040 Wien (cid:2) Karlsplatz 13 (cid:2) Tel. +43-1-58801-0 (cid:2) www.tuwien.ac.at
Technische Universität Wien"
47f8ba44fde1f8a3a621b20cabb7e84515fb8313,Superpixel-based Road Segmentation for Real-time Systems using CNN,
475de283dad61a8a9ed231dce0d8d62a54f4d062,Person Following by Autonomous Robots: A Categorical Overview,"Islam et al.
Person Following by Autonomous
Robots: A Categorical Overview
Md Jahidul Islam, Jungseok Hong and Junaed Sattar
Preprint Version I
XX(X):1–25
(cid:13)The Author(s) 2018
Reprints and permission:
sagepub.co.uk/journalsPermissions.nav
DOI: 10.1177/ToBeAssigned
www.sagepub.com/"
474b6593d37c9c6547e2f0fcbfa8a9866b5cccd6,The Iteration-Tuned Dictionary for sparse representations,"The Iteration-Tuned Dictionary for Sparse
Representations
Joaquin Zepeda #1, Christine Guillemot #2, Ewa Kijak ∗3
# INRIA Centre Rennes - Bretagne Atlantique
Campus de Beaulieu, 35042 Rennes Cedex, FRANCE
Universit´e de Rennes 1, IRISA
Campus de Beaulieu, 35042 Rennes Cedex, FRANCE"
47be79c0ecb598e1af44e57f386f79adf491f82b,Scenes categorization based on appears objects probability,"016 IEEE 6th International Conference on System Engineering and Technology (ICSET)
Oktober 3-4, 2016 Bandung – Indonesia
Scenes Categorization based on Appears Objects
Probability
Marzuki1, Egi Muhamad Hidayat2, Rinaldi Munir3, Ary Setijadi P4 ,Carmadi Machbub5
School of Electrical Engineering and Informatics, Institut Teknologi Bandung
Bandung, Indonesia
lskk.ee.itb.ac.id"
47719d391417a237701c5e275ebb1034418e20f2,Human Face Processing with 1.5D Models,"Human Face Processing with 1.5D Models
Gin´es Garc´ıa-Mateos1, Alberto Ruiz1, and Pedro E. L´opez-de-Teruel2
Dept. de Inform´atica y Sistemas
Dept. Ing. y Tecn. de Computadores
Universidad de Murcia, 30.100 Espinardo, Murcia, Spain"
47c0c7f1a27d467e00a6fa7ea2ca0af2e3328b9e,Predicting Scene Parsing and Motion Dynamics in the Future,"Predicting Scene Parsing and Motion Dynamics
in the Future
Xiaojie Jin1, Huaxin Xiao2, Xiaohui Shen3, Jimei Yang3, Zhe Lin3
Yunpeng Chen2, Zequn Jie4, Jiashi Feng2, Shuicheng Yan5,2
NUS Graduate School for Integrative Science and Engineering (NGS), NUS
Department of ECE, NUS
Adobe Research
Tencent AI Lab
5Qihoo 360 AI Institute"
474b461cd12c6d1a2fbd67184362631681defa9e,Multi-resolution fusion of DTCWT and DCT for shift invariant face recognition,"014 IEEE International
Conference on Systems, Man
nd Cybernetics
(SMC 2014)
San Diego, California, USA
5-8 October 2014
Pages 1-789
IEEE Catalog Number:
ISBN:
CFP14SMC-POD
978-1-4799-3841-4"
47bc34ae6f5dc104bc289ae3bb4fa75ef75fbc21,Unsupervised Deep Learning for Optical Flow Estimation,"Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17)
Unsupervised Deep Learning
for Optical Flow Estimation
Zhe Ren,1 Junchi Yan,2,3∗ Bingbing Ni,1 Bin Liu,4 Xiaokang Yang,1 Hongyuan Zha5
Shanghai Jiao Tong University 2East China Normal University 3IBM Research 4Moshanghua Tech 5Georgia Tech"
4753a125469da7649e9f58fb0db781622dff41f8,Single-View Semantic Mesh Refinement,"Multi-View Stereo with Single-View Semantic Mesh Refinement
Andrea Romanoni Marco Ciccone
Francesco Visin Matteo Matteucci
{andrea.romanoni, marco.ciccone, francesco.visin,
Politecnico di Milano, Italy"
47fc921add1421ff8adb730df7aa9e7f865bfdeb,Toward Practical Smile Detection,"Towards Practical Smile Detection
Jacob Whitehill, Gwen Littlewort, Ian Fasel, Marian Bartlett, and Javier Movellan"
47e8db3d9adb79a87c8c02b88f432f911eb45dc5,MAGMA: Multilevel Accelerated Gradient Mirror Descent Algorithm for Large-Scale Convex Composite Minimization,"MAGMA: Multi-level accelerated gradient mirror descent algorithm for
large-scale convex composite minimization
Vahan Hovhannisyan
Panos Parpas
Stefanos Zafeiriou
July 15, 2016"
47e225ad6293ebd589c3c1268bcb70730cfeb8f6,Unsupervised Video Indexing based on Audiovisual Characterization of Persons. (Indexation vidéo non-supervisée basée sur la caractérisation des personnes),"TTHHÈÈSSEE
En vue de l'obtention du
DDOOCCTTOORRAATT  DDEE  LL’’UUNNIIVVEERRSSIITTÉÉ  DDEE  TTOOUULLOOUUSSEE
Délivré par l'Université Toulouse III - Paul Sabatier
Discipline ou spécialité : Science Informatique
Présentée et soutenue par EL-KHOURY Elie
Le 3 juin 2010
Titre : Unsupervised video indexing based on audiovisual characterization of persons
Shih-Fu CHANG…………….…………………Columbia University, United States of America
Bernard MERIALDO……………………………………….…………..……………………Eurocom, France
Sylvain MEIGNIER………………………………………………..….University of Le Maine, France
Rémi LANDAIS………………………………………………………………………………..…Exalead, France
Philippe JOLY…………………………………………………………University of Toulouse III, France
Christine SENAC……………………………………………………University of Toulouse III, France
Ecole doctorale : Mathématiques, Informatique, Télécommunications de Toulouse
Unité de recherche :          I.R.I.T. --- UMR 5505
Directeur(s) de Thèse : Régine ANDRE-OBRECHT………University of Toulouse III, France"
477236563c6a6c6db922045453b74d3f9535bfa1,Attribute Based Image Search Re-Ranking Snehal,"International Journal of Science and Research (IJSR)
ISSN (Online): 2319-7064
Index Copernicus Value (2013): 6.14 | Impact Factor (2014): 5.611
Attribute Based Image Search Re-Ranking
Snehal S Patil1, Ajay Dani2
Master of Computer Engg, Savitribai Phule Pune University, G. H. Raisoni Collage of Engg and Technology, Wagholi, Pune
2Professor, Computer and Science Dept, Savitribai Phule Pune University, G. H .Raisoni Collage of Engg and Technology, Wagholi, Pune
integrating
images  by"
4707175ebc50e4036412f441a7cec6673c4ad31f,Analysis and Comparison of Eigenspace-Based Face Recognition Approaches,"Analysis and Comparison of Eigenspace-based
Face Recognition Approaches
Pablo Navarrete and Javier Ruiz-del-Solar
Department of Electrical Engineering, Universidad de Chile, CHILE
Email: {pnavarre,"
479f44f9b4c401327a721550334b8d491f6b3f16,OR-PCA with MRF for Robust Foreground Detection in Highly Dynamic Backgrounds,"OR-PCA with MRF for Robust Foreground
Detection in Highly Dynamic Backgrounds
Sajid Javed1, Seon Ho Oh1, Andrews Sobral2,
Thierry Bouwmans2 and Soon Ki Jung1
School of Computer Science and Engineering, Kyungpook National University,
80 Daehak-ro, Buk-gu,Daegu, 702-701, Republic of Korea
{sajid,
Laboratoire MIA (Mathematiques, Image et Applications)- Universit´e de La
Rochelle, 17000, France, {andrews.sobral,"
47980c6e42f1a3381e6c5f3db7230e6a64c40218,Finding People in Images and Videos,"INSTITUTNATIONALPOLYTECHNIQUEDEGRENOBLENum´eroattribu´eparlabiblioth`equeTH`ESEpourobtenirlegradedeDOCTEURDEL’INSTITUTNATIONALPOLYTECHNIQUEDEGRENOBLESp´ecialit´e:Imagerie,VisionetRobotiquedanslecardedel’´EcoleDoctoraleMath´ematiques,SciencesetTechnologiedel’Informationpr´esent´eeetsoutenuepubliquementparNavneetDALALle17Juillet,2006FindingPeopleinImagesandVideosJURYM.JamesL.CROWLEYPr´esidentM.MartialHEBERTRapporteurM.LucVanGOOLRapporteurM.ShaiAVIDANExaminateurMme.CordeliaSCHMIDDirecteurdeth`eseM.WilliamJ.TRIGGSDirecteurdeth`eseTh`esepr´epar´eedanslelaboratoireGRAVIR–IMAGauseinduProjetLEAR,INRIARhˆone-Alpes655avenuedel’Europe,38334SaintIsmier,France."
47f2088afb616bde5468818e23d79e1ae5a562cd,Multi-view gender classification based on local Gabor binary mapping pattern and support vector machines,"Multi-view Gender Classification based on Local Gabor Binary
Mapping Pattern and Support Vector Machines
Bin Xia, He Sun and Bao-Liang Lu∗ Senior Member, IEEE"
470b89e2c5248eb58e09129aa9b4d8bc77497e7e,Cortical folding abnormalities in autism revealed by surface-based morphometry.,"The Journal of Neuroscience, October 24, 2007 • 27(43):11725–11735 • 11725
Neurobiology of Disease
Cortical Folding Abnormalities in Autism Revealed by
Surface-Based Morphometry
Christine Wu Nordahl,1 Donna Dierker,2 Iman Mostafavi,1 Cynthia M. Schumann,1,3 Susan M. Rivera,4
David G. Amaral,1 and David C. Van Essen2
The Medical Investigation of Neurodevelopmental Disorders (M.I.N.D.) Institute and the Department of Psychiatry and Behavioral Sciences, University of
California, Davis, Sacramento, California 95817, 2Department of Anatomy and Neurobiology, Washington University in St. Louis, St. Louis, Missouri 63110,
Department of Neurosciences, University of California, San Diego, La Jolla, California 92093, and 4The M.I.N.D. Institute and the Department of
Psychology, University of California, Davis, Davis, California 95616
We tested for cortical shape abnormalities using surface-based morphometry across a range of autism spectrum disorders (7.5–18 years
of age). We generated sulcal depth maps from structural magnetic resonance imaging data and compared typically developing controls
to three autism spectrum disorder subgroups: low-functioning autism, high-functioning autism, and Asperger’s syndrome. The low-
functioning autism group had a prominent shape abnormality centered on the pars opercularis of the inferior frontal gyrus that was
ssociated with a sulcal depth difference in the anterior insula and frontal operculum. The high-functioning autism group had bilateral
shape abnormalities similar to the low-functioning group, but smaller in size and centered more posteriorly, in and near the parietal
operculum and ventral postcentral gyrus. Individuals with Asperger’s syndrome had bilateral abnormalities in the intraparietal sulcus
that correlated with age, intelligence quotient, and Autism Diagnostic Interview-Revised social and repetitive behavior scores. Because of
evidence suggesting age-related differences in the developmental time course of neural alterations in autism, separate analyses on
hildren (7.5–12.5 years of age) and adolescents (12.75–18 years of age) were also carried out. All of the cortical shape abnormalities"
47096e7103a2fbb6f6ede05e996209497d41db6a,Implementation of Artificial Intelligence Methods for Virtual Reality Solutions: a Review of the Literature,"Implementation of Artificial Intelligence Methods for
Virtual Reality Solutions: a Review of the Literature
Rytis Augustauskas
Department of Automation
Aurimas Kudarauskas
Department of Automation
Kaunas University of Technology,
Kaunas University of Technology,
Kaunas, Lithuania
Kaunas, Lithuania
Cenker Canbulut
Department of Multimedia Engineering
Kaunas University of Technology,
Kaunas, Lithuania"
4701112bfe9946a97a60c2bbb2d47dc784942c3f,Understanding classifier errors by examining influential neighbors,"Understanding Classifier Errors by Examining Influential Neighbors
Mayank Kabra, Alice Robie, Kristin Branson
Janelia Research Campus of the Howard Hughes Medical Institute
Ashburn, VA, 20147, USA"
47440f514318b438ebf04d9932f5dafdb488a536,EMOTION RECOGNITION FROM FACIAL IMAGES USING BINARY FACE RELEVANCE MAPS,"STUDIA INFORMATICA
Volume 36
Number 4 (122)
Tomasz HERUD, Michal KAWULOK
Silesian University of Technology, Institute of Informatics
Future Processing, Gliwice, Poland
Bogdan SMOLKA
Silesian University of Technology, Institute of Automatic Control
EMOTION  RECOGNITION  FROM  FACIAL  IMAGES  USING
BINARY FACE RELEVANCE MAPS1
Summary.  This  paper is focused on automatic emotion recognition from static
grayscale images. Here, we propose a new approach to this problem, which combines
few  other  methods.  The  facial  region  is  divided  into  small  subregions,  which  are
selected  for  processing  based  on  a  face  relevance  map.  From  these  regions,  local
directional  pattern  histograms  are  extracted  and  concatenated  into  a  single  feature
histogram, which is classified into one of seven defined emotional states using support
vector  machines.  In  our  case,  we  distinguish:  anger,  disgust,  fear,  happiness,
neutrality,  sadness  and  surprise.  In  our  experimental  study  we  demonstrate  that  the
expression  recognition  accuracy  for  Japanese  Female  Facial  Expression  database  is
one of the best compared with the results reported in the literature."
47ca2df3d657d7938d7253bed673505a6a819661,UNIVERSITY OF CALIFORNIA Santa Barbara Facial Expression Analysis on Manifolds A Dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy,"UNIVERSITY OF CALIFORNIA
Santa Barbara
Facial Expression Analysis on Manifolds
A Dissertation submitted in partial satisfaction of the
requirements for the degree Doctor of Philosophy
in Computer Science
Ya Chang
Committee in charge:
Professor Matthew Turk, Chair
Professor Yuan-Fang Wang
Professor B.S. Manjunath
Professor Andy Beall
September 2006"
47f5f740e225281c02c8a2ae809be201458a854f,Simultaneous Unsupervised Learning of Disparate Clusterings,"Simultaneous Unsupervised Learning of Disparate Clusterings
Prateek Jain*, Raghu Meka and Inderjit S. Dhillon
Department of Computer Sciences, University of Texas, Austin, TX 78712-1188, USA
Received 14 April 2008; accepted 05 May 2008
DOI:10.1002/sam.10007
Published online 3 November 2008 in Wiley InterScience (www.interscience.wiley.com)."
47a567ef9d049e5775ffd005f9d74e6aab108f82,A Survey of Automatic Event Detection in Multi-Camera Third Generation Surveillance Systems,"3:40 WSPC/INSTRUCTION
International Journal of Pattern Recognition and Artificial Intelligence
(cid:176) World Scientific Publishing Company
A survey of automatic event detection in multi-camera third
generation surveillance systems
Tiziana D’Orazio
Institute of Intelligent Systems for Automation - C.N.R.
via Amendola 122/D-I Bari, 70126,ITALY
Cataldo Guaragnella
Politecnico di Bari, Via Orabona 4,
Bari, 70126, IATLY
Third generation surveillance systems are largely requested for intelligent surveillance
of different scenarios such as public areas, urban traf‌f‌ic control, smart homes and so
on. They are based on multiple cameras and processing modules that integrate data
oming from a large surveillance space. The semantic interpretation of data from a multi
view context is a challenging task and requires the development of image processing
methodologies that could support applications in extensive and real time contexts. This
paper presents a survey of automatic event detection functionalities that have been
developed for third generation surveillance systems with a particular emphasis on open
problems that limit the application of computer vision methodologies to commercial"
47ce78c9f49248a7d1bd395befb43e45d89555ee,Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments,"Vision-and-Language Navigation: Interpreting visually-grounded
navigation instructions in real environments
Peter Anderson1
Niko S¨underhauf3
Qi Wu2
Damien Teney2
Jake Bruce3
Mark Johnson4
Ian Reid2
Stephen Gould1
Anton van den Hengel2
Australian National University 2University of Adelaide 3Queensland University of Technology 4Macquarie University"
47fdd1579f732dd6389f9342027560e385853180,Deep Sparse Subspace Clustering,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
Deep Sparse Subspace Clustering
Xi Peng, Jiashi Feng, Shijie Xiao, Jiwen Lu Senior Member, IEEE, Zhang Yi Fellow, IEEE,
Shuicheng Yan Fellow, IEEE,"
47e3029a3d4cf0a9b0e96252c3dc1f646e750b14,Facial expression recognition in still pictures and videos using active appearance models: a comparison approach,"International Conference on Computer Systems and Technologies - CompSysTech’07
Facial Expression Recognition in still pictures and videos using Active
Appearance Models. A comparison approach.
Drago(cid:1) Datcu
Léon Rothkrantz"
478261574ddc6cf297611000735aa9808f8f0030,ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes,
47541d04ec24662c0be438531527323d983e958e,Affective Information Processing,Affective Information Processing
47b34a8ad5100582aa7cbfd85df3ca7659adc392,Is this a wampimuk? Cross-modal mapping between distributional semantics and the visual world,"Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pages 1403–1414,
Baltimore, Maryland, USA, June 23-25 2014. c(cid:13)2014 Association for Computational Linguistics"
478a1ed7dc1890ca9476dcc1befe7f21c9bf9149,Learning to Learn from Noisy Labeled Data,"Learning to Learn from Noisy Labeled Data
Junnan Li, Yongkang Wong, Qi Zhao, Mohan S. Kankanhalli"
474d9967b69f9afd5404686f16c8f53c1951526b,"Real-time Detection, Tracking, and Classification of Moving and Stationary Objects using Multiple Fisheye Images","Real-time Detection, Tracking, and Classification of Moving and
Stationary Objects using Multiple Fisheye Images
Iljoo Baek∗, Albert Davies∗, Geng Yan, and Ragunathan (Raj) Rajkumar"
99302fc2bc72ff166fe41e1614df606120b2f1b7,Combining passive visual cameras and active IMU sensors to track cooperative people,"Combining Passive Visual Cameras and Active IMU
Sensors to Track Cooperative People
Wenchao Jiang
Missouri University of Science and
Technology, Mo, USA, 65401
Email:
Zhaozheng Yin
Missouri University of Science and
Technology, Mo, USA, 65401
Email:"
99ae92bae7c873432a6a60238b33d494bbae13eb,RECOGNITION OF HUMAN POSE FROM IMAGES BASED ON GRAPH SPECTRA,"RECOGNITION OF HUMAN POSE FROM IMAGES BASED ON GRAPH SPECTRA
A. A. Zakharov a *, A. E. Barinov a, A. L. Zhiznyakov a
Murom Institut Vladimir State University, CAD Department, , 602264, Orlovskaya 23, Murom, Russian Federation, aa-
Commission VI, WG VI/4
KEY WORDS: Image Recognition, Human Pose, Spectral Graph Matching"
99f565df31ef710a2d8a1b606e3b7f5f92ab657c,Geometry Score: A Method For Comparing Generative Adversarial Networks,"Geometry Score: A Method For Comparing Generative Adversarial Networks
Valentin Khrulkov 1 Ivan Oseledets 1 2"
99d7678039ad96ee29ab520ff114bb8021222a91,Political image analysis with deep neural networks,"Political image analysis with deep neural
networks
L. Jason Anastasopoulos∗
Shiry Ginosar§.
Dhruvil Badani†
Jake Ryland Williams¶
Crystal Lee‡
November 28, 2017"
998f2cfb4a3bac6b38d8a4a96a3827e06a0eaadb,Geo-Supervised Visual Depth Prediction,"Geo-Supervised Visual Depth Prediction
Xiaohan Fei
Alex Wong
Stefano Soatto"
998e829cc72080c88a780f322d6bf7ab78dbd743,Towards Real-Time Multiresolution Face/Head Detection,"´AAAAAAAAAAAAAAAAAAAAAAAA
´AAAAAAAAAAAAAAAAAAAAAAAA
ART´ICULO
Towards Real-Time Multiresolution Face/Head
Detection*
M. Castrill´on-Santana, H. Kruppa**, C. Guerra-Artal, M. Hern´andez-Tejera
Universidad de Las Palmas de Gran Canaria
Instituto Universitario de Sistemas Inteligentes
y Aplicaciones Num´ericas en Ingenier´ıa
Edificio Central del Parque Cient´ıfico-Tecnol´ogico
Campus Universitario de Tafira
5017 Las Palmas - Espa˜na"
9974a806bb69ed26fb8cf49ebdd53d7756336eec,Object Recognition Using Multiresolution Trees,"Object recognition using Multiresolution trees
Monica Bianchini, Marco Maggini, and Lorenzo Sarti
DII - Universit`a degli Studi di Siena
Via Roma, 56 - 53100 Siena - Italy
Email:"
994f7c469219ccce59c89badf93c0661aae34264,Model Based Face Recognition Across Facial Expressions,"Model Based Face Recognition Across Facial
Expressions
Zahid Riaz, Christoph Mayer, Matthias Wimmer, and Bernd Radig, Senior Member, IEEE
screens,  embedded  into  mobiles  and  installed  into  everyday
living  and  working  environments  they  become  valuable  tools
for human system interaction. A particular important aspect of
this  interaction  is  detection  and  recognition  of  faces  and
interpretation  of  facial  expressions.  These  capabilities  are
deeply  rooted  in  the  human  visual  system  and  a  crucial
uilding  block  for  social  interaction.  Consequently,  these
apabilities  are  an  important  step  towards  the  acceptance  of
many technical systems.
trees  as  a  classifier
lies  not  only"
9900be092f81547ad71e4124cd850048e1969063,3 D Face Analysis for Facial Expression Recognition,"Author manuscript, published in ""20th International Conference on Pattern Recognition (ICPR 2010), Istanbul : Turquie (2010)"""
99c20eb5433ed27e70881d026d1dbe378a12b342,Semi-Supervised and Unsupervised Data Extraction Targeting Speakers: From Speaker Roles to Fame?,"ISCA Archive
http://www.isca-speech.org/archive
First Workshop on Speech, Language
nd Audio in Multimedia
Marseille, France
August 22-23, 2013
Proceedings of the First Workshop on Speech, Language and Audio in Multimedia (SLAM), Marseille, France, August 22-23, 2013."
99ced8f36d66dce20d121f3a29f52d8b27a1da6c,Organizing Multimedia Data in Video Surveillance Systems Based on Face Verification with Convolutional Neural Networks,"Organizing Multimedia Data in Video
Surveillance Systems Based on Face Verification
with Convolutional Neural Networks
Anastasiia D. Sokolova, Angelina S. Kharchevnikova, Andrey V. Savchenko
National Research University Higher School of Economics, Nizhny Novgorod, Russian
Federation"
9993f1a7cfb5b0078f339b9a6bfa341da76a3168,"A Simple, Fast and Highly-Accurate Algorithm to Recover 3D Shape from 2D Landmarks on a Single Image","JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
A Simple, Fast and Highly-Accurate Algorithm to
Recover 3D Shape from 2D Landmarks on a Single
Image
Ruiqi Zhao, Yan Wang, Aleix M. Martinez"
9963af1199679e176f0836e6d63572b3a69fa7da,23 Generating Facial Expressions with Deep Belief Nets,"We are IntechOpen,
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9990e0b05f34b586ffccdc89de2f8b0e5d427067,Auto-Optimized Multimodal Expression Recognition Framework Using 3 D Kinect Data for ASD Therapeutic Aid,"International Journal of Modeling and Optimization, Vol. 3, No. 2, April 2013
Auto-Optimized Multimodal Expression Recognition
Framework Using 3D Kinect Data for ASD Therapeutic
Amira E. Youssef, Sherin F. Aly, Ahmed S. Ibrahim, and A. Lynn Abbott
regarding
emotion
recognize"
99e1ab1fb08af137cad6efbc0454c6e1e68dca51,3D human action recognition and motion analysis using selective representations,"D HUMAN ACTION RECOGNITION
AND MOTION ANALYSIS USING
SELECTIVE REPRESENTATIONS
D LEIGHTLEY
PhD  2015"
99582ce8439dce17d9d6f74eb54fc5c89dbe06d9,"Hough Forests for Object Detection, Tracking, and Action Recognition","Hough Forests for Object Detection, Tracking,
nd Action Recognition
Juergen Gall Member, IEEE, Angela Yao, Nima Razavi, Luc Van Gool Member, IEEE, and
Victor Lempitsky"
9941a408ae031d1254bbc0fe7a63fac5f85fe347,Neural Processes,"Neural Processes
Marta Garnelo 1 Jonathan Schwarz 1 Dan Rosenbaum 1 Fabio Viola 1 Danilo J. Rezende 1 S. M. Ali Eslami 1
Yee Whye Teh 1"
99df887213407f612c1f5df502b637709a29cd6b,Ensembles of exemplar-SVMs for video face recognition from a single sample per person,"Ensembles of Exemplar-SVMs for Video Face Recognition from a
Single Sample Per Person
Saman Bashbaghi, Eric Granger, Robert Sabourin
Guillaume-Alexandre Bilodeau
Laboratoire d’imagerie de vision et d’intelligence artificielle
LITIV Lab
École de technologie supérieure, Université du Québec, Montréal, Canada
Polytechnique Montréal, Montréal, Canada
{eric.granger,"
99a3a4151abbc2e5d33d4beec88dc55a057df299,DISCRETE SCALAR DATA,"TOPOLOGICAL ANALYSIS OF
DISCRETE SCALAR DATA
DAVID GÜNTHER
DISSERTATION ZUR ERLANGUNG DES GRADES
DES DOKTORS DER INGENIEURWISSENSCHAFTEN
DER NATURWISSENSCHAFTLICH-TECHNISCHEN FAKULTÄTEN
DER UNIVERSITÄT DES SAARLANDES
SAARBRÜCKEN, 2012"
992ebd81eb448d1eef846bfc416fc929beb7d28b,Exemplar-Based Face Parsing Supplementary Material,"Exemplar-Based Face Parsing
Supplementary Material
Brandon M. Smith Li Zhang
Jonathan Brandt Zhe Lin Jianchao Yang
University of Wisconsin–Madison
Adobe Research
http://www.cs.wisc.edu/~lizhang/projects/face-parsing/
. Additional Selected Results
Figures 1 and 2 supplement Figure 4 in our paper. In all cases, the input images come from our Helen [1] test set. We note
that our algorithm generally produces accurate results, as shown in Figures 1. However, our algorithm is not perfect and makes
mistakes on especially challenging input images, as shown in Figure 2.
In our view, the mouth is the most challenging region of the face to segment: the shape and appearance of the lips vary
widely from subject to subject, mouths deform significantly, and the overall appearance of the mouth region changes depending
on whether the inside of the mouth is visible or not. Unusual mouth expressions, like those shown in Figure 2, are not repre-
sented well in the exemplar images, which results in poor label transfer from the top exemplars to the test image. Despite these
hallenges, our algorithm generally performs well on the mouth, with large segmentation errors occurring infrequently.
. Comparisons with Liu et al. [2]
The scene parsing approach by Liu et al. [2] shares sevaral similarities with our work. Like our approach, they propose a
nonparametric system that transfers labels from exemplars in a database to annotate a test image. This begs the question, Why
not simply apply the approach from Liu et al. to face images?"
9922a2ec8dfb307bb1fcb334098fd912e23b3bab,Particle-based pedestrian path prediction using LSTM-MDL models,"Particle-based Pedestrian Path Prediction using LSTM-MDL Models
Ronny Hug∗, Stefan Becker∗, Wolfgang H¨ubner∗ and Michael Arens∗"
99227909e5733d76b0d50fc3fab975ab7a43fce3,A Cascaded Inception of Inception Network With Attention Modulated Feature Fusion for Human Pose Estimation,"A Cascaded Inception of Inception Network with Attention Modulated Feature
Fusion for Human Pose Estimation
Submission ID: 2065"
998b7c8608fb9f80177ce54230761d8c3d82b2da,SHEF-Multimodal: Grounding Machine Translation on Images,"Proceedings of the First Conference on Machine Translation, Volume 2: Shared Task Papers, pages 660–665,
Berlin, Germany, August 11-12, 2016. c(cid:13)2016 Association for Computational Linguistics"
99246f998a684879038f39583bcf75b8f218e0a3,Object Detection for Autonomous Driving Using Deep Learning,"UNIVERSITAT POLITÈCNICA DE CATALUNYA
Doctoral Programme:
AUTOMÀTICA, ROBÒTICA I VISIÓ
Research Plan:
Object Detection for Autonomous Driving
Using Deep Learning
Victor Vaquero Gomez
Advisors:
Alberto Sanfeliu Cortes, Prof.
Francesc Moreno Noguer, Dr.
December 2015"
99e1fd6a378209d48c12a70229e4f6d4d83f4417,Modular Vehicle Control for Transferring Semantic Information Between Weather Conditions Using GANs,"Modular Vehicle Control for Transferring Semantic
Information Between Weather Conditions Using
Patrick Wenzel1,2∗
, Qadeer Khan1,2∗
, Daniel Cremers1,2, and Laura Leal-Taixé1
Technical University of Munich
Artisense"
29bd7de310438c2b9d8b6e7eb7df662079934747,Semantic Scene Mapping with Spatio-temporal Deep Neural Network for Robotic Applications,"Cogn Comput
https://doi.org/10.1007/s12559-017-9526-9
Semantic Scene Mapping with Spatio-temporal Deep Neural
Network for Robotic Applications
Ruihao Li1
· Dongbing Gu1 · Qiang Liu1 · Zhiqiang Long2 · Huosheng Hu1
Received: 25 September 2017 / Accepted: 31 October 2017
© Springer Science+Business Media, LLC, part of Springer Nature 2017"
29933de38d72a0941d763b7ac5a480e733ef74a2,Set Logo Detection and Retrieval,"Open Set Logo Detection and Retrieval
Andras T¨uzk¨o1, Christian Herrmann1,2, Daniel Manger1, J¨urgen Beyerer1,2
Fraunhofer IOSB, Karlsruhe, Germany
Karlsruhe Institute of Technology KIT, Vision and Fusion Lab, Karlsruhe, Germany
Keywords:
Logo Detection, Logo Retrieval, Logo Dataset, Trademark Retrieval, Open Set Retrieval, Deep Learning."
2903630d9582172f38108dc171fd239e337654a7,Deep Face Image Retrieval: a Comparative Study with Dictionary Learning,"Deep Face Image Retrieval: a Comparative Study
with Dictionary Learning
Ahmad S. Tarawneh1, Ahmad B. A. Hassanat2, Ceyhun Celik3, Dmitry
Chetverikov1, M. Sohel Rahman4 and Chaman Verma1
Department of Algorithms and Their Applications , Eötvös Loránd
University, Budapest, Hungary
Department of Information Technology, Mutah University, Karak, Jordan
Department of Computer Engineering, Gazi University, Ankara, Turkey
Department of CSE, BUET, ECE Building, West Palasi, Dhaka 1205,
Bangladesh
December 14, 2018"
2939169aed69aa2626c5774d9b20e62c905e479b,Fast Exact HyperGraph Matching with Dynamic Programming for Spatio-Temporal Data,"Fast Exact Hyper-Graph Matching with Dynamic
Programming for Spatio-Temporal Data
Oya Celiktutan, Christian Wolf, Bülent Sankur, Eric Lombardi
To cite this version:
Oya Celiktutan, Christian Wolf, Bülent Sankur, Eric Lombardi. Fast Exact Hyper-Graph Matching
with Dynamic Programming for Spatio-Temporal Data. Journal of Mathematical Imaging and Vision,
Springer Verlag, 2015, 51, pp.1-21. <hal-01151755>
HAL Id: hal-01151755
https://hal.archives-ouvertes.fr/hal-01151755
Submitted on 13 May 2015
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
29c6b06ac98dbdaf25e4cc9a05b4ab314923cccd,Assessment of the communicative and coordination skills of children with Autism Spectrum Disorders and typically developing children using social signal processing,"Research  in  Autism  Spectrum  Disorders  7  (2013)  741–756
Contents  lists  available  at  SciVerse  ScienceDirect
Research  in  Autism  Spectrum  Disorders
J o u r  n a l  h o m e p a g e :  h t  t p : / / e e s . e l s e v i e r . c o m / R A  S D / d e f a u l t . a s p
Assessment  of  the  communicative  and  coordination  skills  of
hildren  with  Autism  Spectrum  Disorders  and  typically
developing  children  using  social  signal  processing
Emilie  Delaherche a,  Mohamed  Chetouani a,  Fabienne  Bigouret b,c,  Jean  Xavier c,
Monique  Plaza a,  David  Cohen a,c,*
Institute  of  Intelligent  Systems  and  Robotics,  University  Pierre  and  Marie  Curie,  75005  Paris,  France
University  of  Paris  8,  93526  Saint-Denis,  France
Department  of  Child  and  Adolescent  Psychiatry,  Hoˆpital  de  la  Pitie´-Salpeˆtrie`re,  University  Pierre  and  Marie  Curie,  75013  Paris,  France
A  R  T  I  C  L  E
I  N  F  O
A  B  S  T  R  A  C  T
Article  history:
Received  27  November  2012
Received  in  revised  form  5  February  2013
Accepted  8  February  2013
Keywords:"
2962f226b658b13c358695bcd1f403133afa19ed,Optical Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation,"Highly Accurate Large Displacement Optical
Flow Estimation
Christian Bailer, Bertram Taetz and Didier Stricker"
29f46586c95af2fa6326724c867aa88b55b5400e,Failure Prediction for Autonomous Driving,"Failure Prediction for Autonomous Driving
Simon Hecker1, Dengxin Dai1, and Luc Van Gool1,2"
29a46aed79df53a1984ee755bed4c8ba2ae94040,Multiple Object Tracking Using K-Shortest Paths Optimization,"Multiple Object Tracking using
K-Shortest Paths Optimization
J´erˆome Berclaz, Franc¸ois Fleuret, Engin T¨uretken, and Pascal Fua, Senior Member, IEEE"
29a705a5fa76641e0d8963f1fdd67ee4c0d92d3d,SCface – surveillance cameras face database,"Multimed Tools Appl (2011) 51:863–879
DOI 10.1007/s11042-009-0417-2
SCface – surveillance cameras face database
Mislav Grgic & Kresimir Delac & Sonja Grgic
Published online: 30 October 2009
# Springer Science + Business Media, LLC 2009"
290c8196341bbac80efc8c89af5fc60e1b8c80e6,Learning deep representations by mutual information estimation and maximization,"Learning deep representations by mutual information
estimation and maximization
R Devon Hjelm
MSR Montreal, MILA, UdeM, IVADO
Alex Fedorov
MRN, UNM
Samuel Lavoie-Marchildon
MILA, UdeM
Karan Grewal
U Toronto
Phil Bachman
MSR Montreal
Adam Trischler
MSR Montreal
Yoshua Bengio
MILA, UdeM, IVADO, CIFAR"
295d978cf47c873936ad774169cac651ea5f3c96,Monocular Depth Prediction using Generative Adversarial Networks,"018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops
Monocular Depth Prediction using Generative Adversarial Networks
Arun CS Kumar
Suchendra M. Bhandarkar
The University of Georgia
Mukta Prasad
Trinity College Dublin"
292eba47ef77495d2613373642b8372d03f7062b,Deep Secure Encoding: An Application to Face Recognition,"Deep Secure Encoding: An Application to Face Recognition
Rohit Pandey
Yingbo Zhou
Venu Govindaraju"
29d94f275b1483f575c05b90464994ecfa86e27f,A Passive Learning Sensor Architecture for Multimodal Image Labeling: An Application for Social Robots,"Article
A Passive Learning Sensor Architecture for
Multimodal Image Labeling: An Application for
Social Robots
Marco A. Gutiérrez 1,*, Luis J. Manso 1, Harit Pandya 2 and Pedro Núñez 1
Robotics and Artificial Vision Laboratory, University of Extremadura, 10003 Cáceres, Spain;
(L.J.M.); (P.N.)
Robotics Research Center, IIIT Hyderabad, 500032 Hyderabad, India;
* Correspondence: Tel.: +34-927-257-259
Academic Editor: Vittorio M. N. Passaro
Received: 21 December 2016; Accepted: 8 February 2017; Published: 11 February 2017"
291be6e3027575287c24f4363e4bf7a8b415d4c1,MSER-Based Real-Time Text Detection and Tracking,"To appear in the proceedings of the 2014 International Conference on Pattern Recognition.
MSER-based Real-Time Text Detection and Tracking
Llu´ıs G´omez and Dimosthenis Karatzas
Computer Vision Center
Universitat Aut`onoma de Barcelona
Email:"
2988f24908e912259d7a34c84b0edaf7ea50e2b3,A Model of Brightness Variations Due to Illumination Changes and Non-rigid Motion Using Spherical Harmonics,"A Model of Brightness Variations Due to
Illumination Changes and Non-rigid Motion
Using Spherical Harmonics
Jos´e M. Buenaposada
Alessio Del Bue
Dep. Ciencias de la Computaci´on,
U. Rey Juan Carlos, Spain
http://www.dia.fi.upm.es/~pcr
Inst. for Systems and Robotics
Inst. Superior T´ecnico, Portugal
http://www.isr.ist.utl.pt/~adb
Enrique Mu˜noz
Facultad de Inform´atica,
U. Complutense de Madrid, Spain
Luis Baumela
Dep. de Inteligencia Artificial,
U. Polit´ecnica de Madrid, Spain
http://www.dia.fi.upm.es/~pcr
http://www.dia.fi.upm.es/~pcr"
2965d092ed72822432c547830fa557794ae7e27b,Improving representation and classification of image and video data for surveillance applications,"Improving Representation and Classification of Image and
Video Data for Surveillance Applications
Andres Sanin
BSc(Biol), MSc(Biol), MSc(CompSc)
A thesis submitted for the degree of Doctor of Philosophy at
The University of Queensland in 2012
School of Information Technology and Electrical Engineering"
2914a20df10f3bb55c5d4764ece85101c1a3e5a8,User interest profiling using tracking-free coarse gaze estimation,"User Interest Profiling Using
Tracking-free Coarse Gaze Estimation
Federico Bartoli, Giuseppe Lisanti, Lorenzo Seidenari, Alberto Del Bimbo
Media Integration and Communication Center
Universit`a degli Studi di Firenze
Firenze, Italy"
29633712a36c3efc77ce3a9844a2e9a029daf310,AdaBoost for Parking Lot Occupation Detection,"AdaBoost for Parking Lot Occupation
Detection
Radovan Fusek1, Karel Mozdˇreˇn1, Milan ˇSurkala1 and Eduard Sojka1"
2903b8f45b3fafc26c8416eae0ba264f5b76d8ef,Interactive and life-long learning for identification and categorization tasks,"Stephan Kirstein
Interactive and life-long learning for identification
nd categorization tasks"
29d414bfde0dfb1478b2bdf67617597dd2d57fc6,Perfect histogram matching PCA for face recognition,"Multidim Syst Sign Process (2010) 21:213–229
DOI 10.1007/s11045-009-0099-y
Perfect histogram matching PCA for face recognition
Ana-Maria Sevcenco · Wu-Sheng Lu
Received: 10 August 2009 / Revised: 21 November 2009 / Accepted: 29 December 2009 /
Published online: 14 January 2010
© Springer Science+Business Media, LLC 2010"
29107badb19e7c5c89f57f81f50df08422e53304,Automatic localisation and segmentation of the Left Ventricle in Cardiac Ultrasound Images,"MASTER THESIS
Automatic localisation and
segmentation of the Left Ventricle in
Cardiac Ultrasound Images
Presented by:
Esther PUYOL
IG 3A F4B and MR 2A SISEA
013/2014
Supervisor:
Paolo PIRO
Academic supervisor:
Guy CAZUGUEL
MEDISYS - PHILIPS RESEARCH PARIS
Company:
University:
TELECOM BRETAGNE
7th March - 12th September 2014"
29230bbb447b39b7fc3de7cb34b313cc3afe0504,Face Detection and Recognition Using Maximum Likelihood Classifiers on Gabor Graphs,"SPI-J068 00721
International Journal of Pattern Recognition
nd Artificial Intelligence
Vol. 23, No. 3 (2009) 433–461
(cid:1) World Scientific Publishing Company
FACE DETECTION AND RECOGNITION USING MAXIMUM
LIKELIHOOD CLASSIFIERS ON GABOR GRAPHS
MANUEL G ¨UNTHER and ROLF P. W ¨URTZ
Institut f¨ur Neuroinformatik
Ruhr-Universit¨at Bochum
D–44780 Bochum, Germany
We present an integrated face recognition system that combines a Maximum Likelihood
(ML) estimator with Gabor graphs for face detection under varying scale and in-plane
rotation and matching as well as a Bayesian intrapersonal/extrapersonal classifier (BIC)
on graph similarities for face recognition. We have tested a variety of similarity functions
nd achieved verification rates (at FAR 0.1%) of 90.5% on expression-variation and 95.8%
on size-varying frontal images within the CAS-PEAL database. Performing Experiment 1
of FRGC ver2.0, the method achieved a verification rate of 72%.
Keywords: Face recognition; Maximum Likelihood estimators; Gabor graphs.
. Introduction"
29c7dfbbba7a74e9aafb6a6919629b0a7f576530,Automatic Facial Expression Analysis and Emotional Classification,"Automatic Facial Expression Analysis and Emotional
Classification
Robert Fischer
Submitted to the Department of Math and Natural Sciences
in partial fulfillment of the requirements for the degree of a
Diplomingenieur der Optotechnik und Bildverarbeitung (FH)
(Diplom Engineer of Photonics and Image Processing)
t the
UNIVERSITY OF APPLIED SCIENCE DARMSTADT (FHD)
Accomplished and written at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY (MIT)
October 2004
Author . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Department of Math and Natural Sciences
October 30, 2004
Certified by . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Dr. Harald Scharfenberg
Professor at FHD
Thesis Supervisor
Accepted by . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ."
2926a7b4e6f92498236817300a253e8f3a88bd49,Neural Paraphrase Generation using Transfer Learning,"Proceedings of The 10th International Natural Language Generation conference, pages 257–261,
Santiago de Compostela, Spain, September 4-7 2017. c(cid:13)2017 Association for Computational Linguistics"
299af7d4fe6da8ac0b390e3ce45c48f7a8b5bb37,"Attribute And-Or Grammar for Joint Parsing of Human Attributes, Part and Pose.","Attribute And-Or Grammar for Joint Parsing of
Human Attributes, Part and Pose
Seyoung Park, Bruce Xiaohan Nie and Song-Chun Zhu"
29756b6b16d7b06ea211f21cdaeacad94533e8b4,Thresholding Approach based on GPU for Facial Expression Recognition,"Thresholding Approach based on GPU for Facial
Expression Recognition
Jesús García-Ramírez1, J. Arturo Olvera-López1, Ivan Olmos-Pineda1, Georgina
Flores-Becerra2, Adolfo Aguilar-Rico2
Benemérita Universidad Autónoma de Puebla, Faculty of Computer Science, Puebla, México
Instituto Tecnológico de Puebla, Puebla, México"
29a6cbf089a8d916b563e02480a1844909754bcf,"The rules of implicit evaluation by race, religion, and age.","The Rules of Implicit Evaluation by Race, Religion, and Age
Axt JR, Ebersole CR, Nosek BA.
014; 25(9):1804-1815
ARTICLE IDENTIFIERS
DOI: 10.1177/0956797614543801
PMID: 25079218
PMCID: not available
JOURNAL IDENTIFIERS
LCCN: not available
pISSN: 0956-7976
eISSN: 1467-9280
OCLC ID: not available
CONS ID: not available
US National Library of Medicine ID: not available
This article was identified from a query of the SafetyLit database.
Powered by TCPDF (www.tcpdf.org)"
29c5a44e01d1126505471b2ab46163d598c871c7,Improving Landmark Localization with Semi-Supervised Learning,"Improving Landmark Localization with Semi-Supervised Learning
Sina Honari1∗, Pavlo Molchanov2, Stephen Tyree2, Pascal Vincent1,4,5, Christopher Pal1,3, Jan Kautz2
MILA-University of Montreal, 2NVIDIA, 3Ecole Polytechnique of Montreal, 4CIFAR, 5Facebook AI Research.
{honaris,
{pmolchanov, styree,"
29113ed00421953e0ddc4fa6784eaba60f05e801,Automatic Track Creation and Deletion Framework for Face Tracking,"IJCSNS International Journal of Computer Science and Network Security, VOL.15 No.2, February 2015
Automatic Track Creation and Deletion Framework for Face
Tracking
Dept. of Information and Communication, St.Xavier’s Catholic College of Engineering, Nagercoil, Tamilnadu, India.
Renimol T G, Anto Kumar R.P"
29bd9cdcb6f3c8c7475df5918c0d87283ffa254f,Evolution of Visual Odometry Techniques,"Evolution of Visual Odometry Techniques
Shashi Poddar, Rahul Kottath, Vinod Karar"
292c6b743ff50757b8230395c4a001f210283a34,Fast violence detection in video,"Fast Violence Detection in Video
O. Deniz1, I. Serrano1, G. Bueno1 and T-K. Kim2
VISILAB group, University of Castilla-La Mancha, E.T.S.I.Industriales, Avda. Camilo Jose Cela s.n, 13071 Spain
Department of Electrical and Electronic Engineering, Imperial College, South Kensington Campus, London SW7 2AZ, UK.
{oscar.deniz, ismael.serrano,
Keywords:
ction recognition, violence detection, fight detection"
299c14458e13eb290534eb4484ad910ea0e828a7,Evaluation of the most appropriate Kernel Function for the Purpose of Feature Extraction in Face Recognition in video surveillance systems,"Evaluation of the most appropriate Kernel Function for the
Purpose of Feature Extraction in Face Recognition in video
surveillance systems
Sepehr Damavandinejadmonfared1, Sina Ashooritootkaboni2, and 3Taha Bahraminezhad Jooneghani
, 2 School of Electrical and Electronic Engineering, UniversitiSains Malaysia (USM), Penang, Malaysia
School of Software Engeenering, Jaber Ebn Hayan University, Rasht, Iran"
294163a4126b3a886bf62ab896865ce3fc1147a8,Group Sparse Non-negative Matrix Factorization for Multi-Manifold Learning,BMVC 2011 http://dx.doi.org/10.5244/C.25.56
29b3f9f0fb821883a3c3bccbf0337c242c3b8a64,Transfer Learning for Video Recognition with Scarce Training Data,"Transfer Learning for Video Recognition
with Scarce Training Data
for Deep Convolutional Neural Network
Yu-Chuan Su, Tzu-Hsuan Chiu, Chun-Yen Yeh, Hsin-Fu Huang, Winston H. Hsu"
29e96ec163cb12cd5bd33bdf3d32181c136abaf9,Regularized Locality Preserving Projections with Two-Dimensional Discretized Laplacian Smoothing ∗,"Report No. UIUCDCS-R-2006-2748
UILU-ENG-2006-1788
Regularized Locality Preserving Projections with Two-Dimensional
Discretized Laplacian Smoothing
Deng Cai, Xiaofei He, and Jiawei Han
July 2006"
295266d09fde8f85e6e577b5181cbc73a1594b6b,Parallel effects of processing fluency and positive affect on familiarity-based recognition decisions for faces,"ORIGINAL RESEARCH ARTICLE
published: 22 April 2014
doi: 10.3389/fpsyg.2014.00328
Parallel effects of processing fluency and positive affect on
familiarity-based recognition decisions for faces
Devin Duke*, Chris M. Fiacconi and Stefan Köhler*
Department of Psychology, Brain and Mind Institute, Western University, London, ON, Canada
Edited by:
Kevin Bradley Clark, Veterans Affairs
Greater Los Angeles Healthcare
System, USA
Reviewed by:
Bernhard Hommel, Leiden
University, Netherlands
Sascha Topolinski, Universität
Würzburg, Germany
*Correspondence:
Devin Duke and Stefan Köhler,
Department of Psychology, Brain
nd Mind Institute, Western"
29ca8ddf79d4cd1dc20cc8160a6d3326933e943f,Pragmatic descriptions of perceptual stimuli,"Proceedings of the Student Research Workshop at the 15th Conference of the European Chapter of the Association for Computational Linguistics,
pages 1–10, Valencia, Spain, April 3-7 2017. c(cid:13)2017 Association for Computational Linguistics"
29dbb9492292b574f7bfd8629d6801d3136887b7,Towards Autonomous Situation Awareness,"Towards Autonomous Situation Awareness
Nikhil Naikal
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2014-124
http://www.eecs.berkeley.edu/Pubs/TechRpts/2014/EECS-2014-124.html
May 21, 2014"
296afa5f7e99fc16df47f961c9539347732f7b13,GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks,"GradNorm: Gradient Normalization for Adaptive
Loss Balancing in Deep Multitask Networks
Zhao Chen 1 Vijay Badrinarayanan 1 Chen-Yu Lee 1 Andrew Rabinovich 1"
293193d24d5c4d2975e836034bbb2329b71c4fe7,Building a Corpus of Facial Expressions for Learning-Centered Emotions,"Building a Corpus of Facial Expressions
for Learning-Centered Emotions
María Lucía Barrón-Estrada, Ramón Zatarain-Cabada,
Bianca Giovanna Aispuro-Medina, Elvia Minerva Valencia-Rodríguez,
Ana Cecilia Lara-Barrera
Instituto Tecnológico de Culiacán, Culiacán, Sinaloa,
Mexico
{lbarron, rzatarain, m06170904, m95170906, m15171452}"
2933da06df9e47da8e855266f5ff50e03c0ccd27,Combination of RGB-D Features for Head and Upper Body Orientation Classification,"Combination of RGB-D Features for Head and Upper
Body Orientation Classification
Laurent Fitte-Duval, Alhayat Ali Mekonnen, Frédéric Lerasle
To cite this version:
Laurent Fitte-Duval, Alhayat Ali Mekonnen, Frédéric Lerasle. Combination of RGB-D Features for
Head and Upper Body Orientation Classification. Advanced Concepts for Intelligent Vision Systems
, Oct 2016, Lecce, Italy. 2016. <hal-01763125>
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29d591806cdc6ef0d580e4a21f32e5ad9d09d148,Large scale image annotation: learning to rank with joint word-image embeddings,"Large Scale Image Annotation:
Learning to Rank with Joint Word-Image
Embeddings
Jason Weston1, Samy Bengio1, and Nicolas Usunier2
Google, USA
Universit´e Paris 6, LIP6, France"
294eef6848403520016bb2c93bfb71b3c75c73fa,Extension of Robust Principal Component Analysis for Incremental Face Recognition,"Extension of Robust Principal Component Analysis for Incremental Face
Recognition
Ha¨ıfa Nakouri and Mohamed Limam
Institut Sup´erieur de Gestion, LARODEC Laboratory
University of Tunis, Tunis, Tunisia
Keywords:
Image alignment, Robust Principal Component Analysis, Incremental RPCA."
293ca770a66313c9427dc71cf86bef7e1b94f2d9,Steerable part models,"Steerable Part Models
Hamed Pirsiavash Deva Ramanan
Department of Computer Science, University of California, Irvine"
296502c6370cabd2b7e38e71cfc757d2e5fa2199,Detection of Deep Network Generated Images Using Disparities in Color Components,"Detection of Deep Network Generated Images
Using Disparities in Color Components
Haodong Li, Bin Li, Shunquan Tan, Jiwu Huang"
29cf7937a1c1848c24b294569d50a2f7122de51b,MarioQA: Answering Questions by Watching Gameplay Videos,"MarioQA: Answering Questions by Watching Gameplay Videos
Jonghwan Mun*
Bohyung Han
Paul Hongsuck Seo*
Ilchae Jung
Department of Computer Science and Engineering, POSTECH, Korea
{choco1916, hsseo, chey0313,"
29ade322a7d4a88da0b451d8ff814193991fb4fc,Real-time reliability measure-driven multi-hypothesis tracking using 2D and 3D features,"Zúñiga et al. EURASIP Journal on Advances in Signal Processing 2011, 2011:142
http://asp.eurasipjournals.com/content/2011/1/142
RESEARCH
Real-time reliability measure-driven multi-
hypothesis tracking using 2D and 3D features
Marcos D Zúñiga1*, François Brémond2 and Monique Thonnat2
Open Access"
2921719b57544cfe5d0a1614d5ae81710ba804fa,Face Recognition Enhancement Based on Image File Formats and Wavelet,"Face Recognition Enhancement Based on Image
File Formats and Wavelet De-noising
Isra’a Abdul-Ameer Abdul-Jabbar, Jieqing Tan, and Zhengfeng Hou"
2960663d6d9f09bf52b030fb5b760cd32afdff99,CURE-OR: Challenging Unreal and Real Environments for Object Recognition,"Citation D. Temel, J. Lee, and G. AlRegib, “CURE-OR: Challenging unreal and real environments
for object recognition,” 2018 17th IEEE International Conference on Machine Learning
nd Applications (ICMLA), Orlando, Florida, USA, 2018.
Dataset
https://ghassanalregib.com/cure-or/
ICMLA,
uthor={D. Temel and J. Lee and G. AlRegib},
ooktitle={2018 17th IEEE International Conference on Machine Learning and Applications
(ICMLA)},
title={CURE-OR: Challenging unreal and real environments for object recognition},
year=2018,}
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292c4bd6fa516393e9c8c5f1dae5afe0bb0ece35,IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1 Interacting Multiview Tracker,"IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 38, NO. 5, MAY 2016
Interacting Multiview Tracker
Ju Hong Yoon, Ming-Hsuan Yang, Senior Member, IEEE, and Kuk-Jin Yoon"
29e793271370c1f9f5ac03d7b1e70d1efa10577c,Face Recognition Based on Multi-classifierWeighted Optimization and Sparse Representation,"International Journal of Signal Processing, Image Processing and Pattern Recognition
Vol.6, No.5 (2013), pp.423-436
http://dx.doi.org/10.14257/ijsip.2013.6.5.37
Face Recognition Based on Multi-classifierWeighted Optimization
nd Sparse Representation
Deng Nan1, Zhengguang Xu2 and ShengQin Bian3
,2,3Institute of control science and engineering,
University of Science and Technology Beijing
,2,330 Xueyuan Road, Haidian District, Beijing 100083 P. R.China"
294d1fa4e1315e1cf7cc50be2370d24cc6363a41,A modular non-negative matrix factorization for parts-based object recognition using subspace representation,"008 SPIE Digital Library -- Subscriber Archive Copy
Processing: Machine Vision Applications, edited by Kurt S. Niel, David Fofi, Proc. of SPIE-IS&T Electronic Imaging, SPIE Vol. 6813, 68130C, © 2008 SPIE-IS&T · 0277-786X/08/$18SPIE-IS&T/ Vol. 6813  68130C-1"
29d6176f2ac871446cfa2b8ac52e9d26a2b0838e,Fast Pedestrian Detection Using Histogram of Oriented Gradients and Principal Components Analysis,"In Seop Na : Fast Pedestrian Detection Using Histogram of Oriented Gradients and Principal Components Analysis
http://dx.doi.org/10.5392/IJoC.2013.9.3.001
Histogram of Oriented Gradients and Principal Components Analysis
Fast Pedestrian Detection Using
Trung Quy Nguyen, Soo Hyung Kim, In Seop Na
School of Electronics and Computer Engineering
Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju, 500-757, Korea"
29d291e71334392f6a04c53a4194d4ff29a460bf,Multiple human tracking in RGB-depth data: a survey,"Camplani, M., Paiement, A., Mirmehdi, M., Damen, D., Hannuna,
S., Burghardt, T. and Tao, L. (2016) Multiple human tracking in
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29ce6b54a87432dc8371f3761a9568eb3c5593b0,Age Sensitivity of Face Recognition Algorithms,"Kent Academic Repository
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Yassin, DK H. PHM and Hoque, Sanaul and Deravi, Farzin  (2013) Age Sensitivity of Face Recognition
pp. 12-15.
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2942964bc62b3d693b9bf238173fdca6e1f57875,An LSTM network for highway trajectory prediction,"An LSTM Network for Highway Trajectory Prediction
Florent Altché2,1 and Arnaud de La Fortelle1"
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Brendan Klare and Anil K. Jain"
9b3ed8190d99b107837de142324e4aa2be8b7eb2,An Efficient Multimodal 2D-3D Hybrid Approach to Automatic Face Recognition,"An Efficient Multimodal 2D-3D Hybrid
Approach to Automatic Face Recognition
Ajmal S. Mian, Mohammed Bennamoun, and Robyn Owens"
9bd973e64750a94dcf528da402b39e3a53118312,An FPGA-Accelerated Design for Deep Learning Pedestrian Detection in Self-Driving Vehicles,"An FPGA-Accelerated Design for Deep
Learning Pedestrian Detection in Self-Driving
Vehicles
Abdallah Moussawi, Kamal Haddad, and Anthony Chahine
Department of Electrical and Computer Engineering
American University of Beirut
Beirut, Lebanon
Email:"
9bf6fbccfdf013cfd076f9357a05fb00b50735ee,JAR-Aibo: A Multi-view Dataset for Evaluation of Model-Free Action Recognition Systems,"JAR-Aibo: A Multi-View Dataset for Evaluation
of Model-Free Action Recognition Systems
Marco K¨orner and Joachim Denzler
Friedrich Schiller University of Jena
Computer Vision Group
Ernst-Abbe-Platz 3, 07743 Jena, Germany
http://www.inf-cv.uni-jena.de"
9be5129fec3b6f1efc22e19dae3ae684961f5efb,Probability based Extended Direct Attribute Prediction,"Probability based Extended Direct Attribute Prediction
International Journal of Computer Applications (0975 – 8887)
Volume 155 – No 5, December 2016
Manju
Research Scholar,
Department of computer science,
Baba Mastnath University, Rohtak"
9b18cc5c938062161a4b6b0c71ee7a6c550a15f7,A Scalable Optimization Mechanism for Pairwise based Discrete Hashing.,"A Scalable Optimization Mechanism for Pairwise
ased Discrete Hashing
Xiaoshuang Shi, Fuyong Xing, Zizhao Zhang, Manish Sapkota, Zhenhua Guo, and Lin Yang"
9b318098f3660b453fbdb7a579778ab5e9118c4c,Joint Patch and Multi-label Learning for Facial Action Unit and Holistic Expression Recognition,"Joint Patch and Multi-label Learning for Facial
Action Unit and Holistic Expression Recognition
Kaili Zhao, Wen-Sheng Chu, Student Member, IEEE, Fernando De la Torre,
Jeffrey F. Cohn, and Honggang Zhang, Senior Member, IEEE
lassifiers without"
9b164cef4b4ad93e89f7c1aada81ae7af802f3a4,A Fully Automatic and Haar like Feature Extraction-Based Method for Lip Contour Detection,"Research Journal of Recent Sciences _________________________________________________ ISSN 2277-2502
Vol. 2(1), 17-20, January (2013)
Res.J.Recent Sci.
A Fully Automatic and Haar like Feature Extraction-Based Method for Lip
Contour Detection
Zahedi Morteza and Mohamadian Zahra
School of Computer Engineering, Shahrood University of Technology, Shahrood, IRAN
Received 26th September 2012, revised 27th October 2012, accepted 6th November 2012
Available online at: www.isca.in"
9b69ea8034a24db2bb1a1eef73ec11b6367d2f2e,Face Recognition System Using PCA and DCT in HMM,"International Journal of Advanced Research in Computer and Communication Engineering
Vol. 4, Issue 1, January 2015
Face Recognition System Using PCA and DCT
ISSN (Online) : 2278-1021
ISSN (Print)    : 2319-5940
in HMM
SamerKais Jameel
Lecturer, Computer Science, University of Raparin, Sulaimaniya, Iraq"
9b474d6e81e3b94e0c7881210e249689139b3e04,VG-RAM Weightless Neural Networks for Face Recognition,"VG-RAM Weightless Neural Networks for
Face Recognition
Alberto F. De Souza, Claudine Badue, Felipe Pedroni, Stiven Schwanz Dias,
Hallysson Oliveira and Soterio Ferreira de Souza
Departamento de Inform´atica
Universidade Federal do Esp´ırito Santo
Av. Fernando Ferrari, 514, 29075-910 - Vit´oria-ES
Brazil
. Introduction
Computerized human face recognition has many practical applications, such as access control,
security monitoring, and surveillance systems, and has been one of the most challenging and
ctive research areas in computer vision for many decades (Zhao et al.; 2003). Even though
urrent machine recognition systems have reached a certain level of maturity, the recognition
of faces with different facial expressions, occlusions, and changes in illumination and/or pose
is still a hard problem.
A general statement of the problem of machine recognition of faces can be formulated as fol-
lows: given an image of a scene, (i) identify or (ii) verify one or more persons in the scene
using a database of faces. In identification problems, given a face as input, the system reports
ack the identity of an individual based on a database of known individuals; whereas in veri-
fication problems, the system confirms or rejects the claimed identity of the input face. In both"
9beac041302100493681cb8ce82eb4383f48f603,Acoustic-labial Speaker Verification,"Pattern Recognition Letters 18 1997 853–858
(cid:14)
Acoustic-labial speaker verification 1
P. Jourlin a,b,), J. Luettin a, D. Genoud a, H. Wassner a
IDIAP, rue du Simplon 4, CP 592, CH-1920 Martigny, Switzerland
LIA, 339 chemin des Meinajaries, BP 1228, 84911 A˝ignon Cedex 9, France"
9b19be86280c8dbb3fdccc24297449290bd2b6aa,Robust Compressive Phase Retrieval via Deep Generative Priors,"Robust Compressive Phase Retrieval via Deep Generative
Priors
Fahad Shamshad, Ali Ahmed
Dept. of Electrical Engg., Information Technology University, Lahore, Pakistan.
{fahad.shamshad,"
9b74de11c62ce16d0b4509554556e6b6b0d4f5c0,Bayesian Probabilistic Co-Subspace Addition,"Bayesian Probabilistic Co-Subspace Addition
Lei Shi
Baidu.com, Inc"
9bcfadd22b2c84a717c56a2725971b6d49d3a804,How to Detect a Loss of Attention in a Tutoring System using Facial Expressions and Gaze Direction,"How to Detect a Loss of Attention in a Tutoring System
using Facial Expressions and Gaze Direction
Mark ter Maat"
9b678aa28facf4f90081d41c2c484c6addddb86d,Fully Convolutional Attention Networks for Fine-Grained Recognition,"Fully Convolutional Attention Networks for Fine-Grained Recognition
Xiao Liu, Tian Xia, Jiang Wang, Yi Yang, Feng Zhou and Yuanqing Lin
Baidu Research
{liuxiao12,xiatian,wangjiang03,yangyi05, zhoufeng09,"
9bddd98289ecc7a8dc5517122d21d5c6f5a9a01a,DS*: Tighter Lifting-Free Convex Relaxations for Quadratic Matching Problems,
9bac481dc4171aa2d847feac546c9f7299cc5aa0,Matrix Product State for Higher-Order Tensor Compression and Classification,"Matrix Product State for Higher-Order Tensor
Compression and Classification
Johann A. Bengua1, Ho N. Phien1, Hoang D. Tuan1 and Minh N. Do2"
9b7974d9ad19bb4ba1ea147c55e629ad7927c5d7,Faical Expression Recognition by Combining Texture and Geometrical Features,"Faical Expression Recognition by Combining
Texture and Geometrical Features
Renjie Liu, Ruofei Du, Bao-Liang Lu*"
9b6d61491120bdd579f53e8c5f7cbe1e05cbc91e,Modeling Multimodal Behaviors from Speech Prosody,"Modeling Multimodal Behaviors From Speech
Prosody
Yu Ding1, Catherine Pelachaud1, and Thierry Arti`eres2
CNRS-LTCI, Institut Mines-TELECOM, TELECOM ParisTech, Paris, France
{yu.ding,
Universit´e Pierre et Marie Curie (LIP6), Paris, France"
9b5b2fd938a9337475cb90a143cf7568f8f63709,Illumination Processing in Face Recognition,"Illumination	Processing	in	Face	Recognition187Illumination	Processing	in	Face	RecognitionYongping	Li,	Chao	Wang	and	Xinyu	AoX 	Illumination Processing in Face Recognition  Yongping Li, Chao Wang and Xinyu Ao Shanghai Institute of Applied Physics, Chinese Academy of Sciences China  1. Introduction Driven by the demanding of public security, face recognition has emerged as a viable solution and achieved comparable accuracies to fingerprint system under controlled lightning environment. In recent years, with wide installing of camera in open area, the automatic face recognition in watch-list application is facing a serious problem. Under the open environment, lightning changes is unpredictable, and the performance of face recognition degrades seriously. Illumination processing is a necessary step for face recognition to be useful in the uncontrolled environment. NIST has started a test called FRGC to boost the research in improving the performance under changing illumination. In this chapter, we will focus on the research effort made in this direction and the influence on face recognition caused by illumination. First of all, we will discuss the quest on the image formation mechanism under various illumination situations, and the corresponding mathematical modelling. The Lambertian lighting model, bilinear illuminating model and some recent model are reviewed. Secondly, under different state of face, like various head pose and different facial expression, how illumination influences the recognition result, where the different pose and illuminating will be examined carefully. Thirdly, the current methods researcher employ to counter the change of illumination to maintain good performance on face recognition are assessed briefly. The processing technique in video and how it will improve face recognition on video, where Wang’s (Wang & Li, 2009) work will be discussed to give an example on the related advancement in the fourth part. And finally, the current state-of-art of illumination processing and its future trends will be discussed.  2. The formation of camera imaging and its difference from the human visual system With the camera invented in 1814 by Joseph N, recording of human face began its new era. Since we do not need to hire a painter to draw our figures, as the nobles did in the middle age. And the machine recorded our image as it is, if the camera is in good condition.  Currently, the imaging system is mostly to be digital format. The central part is CCD (charge-coupled device) or CMOS (complimentary metal-oxide semiconductor). The CCD/CMOS operates just like the human eyes. Both CCD and CMOS image sensors operate 11www.intechopen.com"
9badcba793a54dd90383a55d7dfee1281c510f75,Local Gradients Smoothing: Defense against localized adversarial attacks,"Local Gradients Smoothing: Defense against localized adversarial attacks
Muzammal Naseer
Australian National University (ANU)
Salman H. Khan
Data61, CSIRO
Fatih Porikli
Australian National University (ANU)"
9b30771968b577ea1b71c0cfaee31f3824bfa027,Capturing Form of Non-verbal Conversational Behavior for Recreation on Synthetic Conversational Agent,"Capturing Form of Non-verbal Conversational Behavior for Recreation
on Synthetic Conversational Agent EVA
IZIDOR MLAKAR, 2MATEJ ROJC
Roboti c.s. d.o.o, 2Faculty of Electrical Engineering and Computer Science, University of Maribor
Tržaška cesta 23, 2Smetanova ulica 17
SLOVENIA"
9be0de78bb69e7b243e92ab7530f9fd5a08c62cc,Spontaneous Trait Inferences on Social Media,"Article
Spontaneous Trait Inferences
on Social Media
Ana Levordashka1 and Sonja Utz1
Social Psychological and
Personality Science
017, Vol. 8(1) 93-101
ª The Author(s) 2016
Reprints and permission:
sagepub.com/journalsPermissions.nav
DOI: 10.1177/1948550616663803
journals.sagepub.com/home/spp"
9b555d8c8f518d907fa273d8691b008d55aedd92,Reasoning with shapes: profiting cognitive susceptibilities to infer linear mapping transformations between shapes,"REASONING WITH SHAPES
Reasoning with shapes: profiting cognitive
susceptibilities to infer linear mapping
transformations between shapes
Vahid Jalili"
9b7c6ef333c6e64f2dfa97a1a3614d0775d81a8a,A New Evaluation Protocol and Benchmarking Results for Extendable Cross-media Retrieval,"A New Evaluation Protocol and Benchmarking
Results for Extendable Cross-media Retrieval
Ruoyu Liu, Yao Zhao, Liang Zheng, Shikui Wei, and Yi Yang"
9bda5f8659b3834369cbc52fe8f852c6bfd2eaf8,Efficient decentralized visual place recognition from full-image descriptors,"Efficient Decentralized Visual Place Recognition
From Full-Image Descriptors
Titus Cieslewski and Davide Scaramuzza"
9b0f2fb0faa27c4fd9d50e84c65ecd81ab26bd75,POTs: Protective Optimization Technologies,"POTs: Protective Optimization Technologies
Rebekah Overdorf
imec-COSIC KU Leuven
Bogdan Kulynych
EPFL SPRING Lab
Ero Balsa
imec-COSIC KU Leuven
Carmela Troncoso
EPFL SPRING Lab
Seda Gürses
imec-COSIC KU Leuven"
9bdd3ce1879f8fd32d2a3f2c4cedcadcf292a1a5,Geometric Active Learning via Enclosing Ball Boundary,"IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Geometric Active Learning via Enclosing Ball
Boundary
Xiaofeng Cao, Ivor W. Tsang, Jianliang Xu, Zenglin Shi, Guandong Xu"
9b6d0b3fbf7d07a7bb0d86290f97058aa6153179,"NII , Japan at the first THUMOS Workshop 2013","NII, Japan at the first THUMOS Workshop 2013
Sang Phan, Duy-Dinh Le, Shin’ichi Satoh
National Institute of Informatics
-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, Japan 101-8430"
13b8d657f0f9a0178339570bdc153bfd10a81300,Harvesting large-scale weakly-tagged image databases from the web,"Harvesting Large-Scale Weakly-Tagged Image Databases from the Web
Jianping Fan1, Yi Shen1, Ning Zhou1, Yuli Gao2
Department of Computer Science, UNC-Charlotte, NC28223, USA
Multimedia Interaction and Understanding, HP Labs, Palo Alto, CA94304, USA"
138f079382e2802f3c98c4c81218d413472c6d53,Large Scale Deep Convolutional Neural Network Features Search with Lucene,"Large Scale Deep Convolutional Neural Network
Features Search with Lucene
Claudio Gennaro
ISTI-CNR
April 4, 2016"
13aac86217231a7d118ecdff444ee07234fcff50,Classification via Incoherent Subspaces,"Classification via Incoherent Subspaces
Karin Schnass, Pierre Vandergheynst, Senior Member, IEEE"
13451899558d7217206b275ca0bb1f48fa4afdd9,Hidden Markov Models Training by a Particle Swarm Optimization Algorithm,"Journal of Mathematical Modelling and Algorithms (2007) 6: 175–193
DOI: 10.1007/s10852-005-9037-7
# Springer 2006
Hidden Markov Models Training by a Particle
Swarm Optimization Algorithm
, NICOLAS MONMARCHE´
SE´ BASTIEN AUPETIT
nd MOHAMED SLIMANE
Laboratoire d’Informatique, Polytech’Tours, Universite´ Franc¸ois-Rabelais de Tours,
64 avenue Jean Portalis, 37200 Tours, France.
e-mail: {sebastien.aupetit, nicolas.monmarche,
(Received 16 July 2005; in final form 22 December 2005; published online 28 February 2006)
In this work we consider the problem of Hidden Markov Models (HMM) training. This"
138e3f6bc164a7b26a0ff283379a325afc0fee14,Geo-Supervised Visual Depth Prediction,"Geo-Supervised Visual Depth Prediction
Xiaohan Fei
Alex Wong
Stefano Soatto"
137457bbf46009b25d7f6d853083b6da02bfd6b9,Following Eye Gaze Activates a Patch in the Posterior Temporal Cortex That Is not Part of the Human “Face Patch” System,"New Research
Cognition and Behavior
Following Eye Gaze Activates a Patch in the
Posterior Temporal Cortex That Is not Part of the
Human “Face Patch” System
Kira Marquardt,1,ⴱ Hamidreza Ramezanpour,1,2,3,ⴱ Peter W. Dicke,1 and Peter Thier1,4
DOI:http://dx.doi.org/10.1523/ENEURO.0317-16.2017
Department of Cognitive Neurology, Hertie Institute for Clinical Brain Research, 72076 Tübingen, Germany,
Graduate School of Neural and Behavioural Sciences, University of Tübingen, 72074 Tübingen, Germany,
International Max Planck Research School for Cognitive and Systems Neuroscience, University of Tübingen, 72074
Tübingen, Germany, 4Werner Reichardt Centre for Integrative Neuroscience (CIN), University of Tübingen, 72076
Tübingen, Germany"
139763a0b2e78b63b245a456f2b9dbde2d1d573c,"RPRG: Toward Real-time Robotic Perception, Reasoning and Grasping with One Multi-task Convolutional Neural Network","RPRG: Toward Real-time Robotic Perception, Reasoning and Grasping
with One Multi-task Convolutional Neural Network
Hanbo Zhang, Xuguang Lan, Lipeng Wan, Chenjie Yang, Xinwen Zhou, and Nanning Zheng"
135fcdab631ab30ae837a743040f1c8751268e41,DeepStyle: Multimodal Search Engine for Fashion and Interior Design,"SUBMITTED TO IEEE TRANSACTIONS ON MULTIMEDIA
DeepStyle: Multimodal Search Engine
for Fashion and Interior Design
Ivona Tautkute1, 3, Tomasz Trzci´nski2, 3, Aleksander Skorupa3, Lukasz Brocki1 and Krzysztof Marasek1"
13141284f1a7e1fe255f5c2b22c09e32f0a4d465,Object Tracking by Oversampling Local Features,"Object Tracking by
Oversampling Local Features
Federico Pernici and Alberto Del Bimbo"
13f8c13cfbf2a504f02745bd44da4ac40fd8f8df,Feature Sets and Dimensionality Reduction for Visual Object Detection,"Author manuscript, published in ""British Machine Vision Conference, Aberystwyth :
Royaume-Uni (2010)""
DOI : 10.5244/C.24.112"
13ec6666b8b722ad9eb68a21a302e3f2f1ab4df7,0 Biometric Human Identification of Hand Geometry Features Using Discrete Wavelet Transform,"Biometric Human Identification of Hand
Geometry Features Using Discrete
Wavelet Transform
Osslan Osiris Vergara Villegas, Humberto de Jesús Ochoa Domínguez,
Vianey Guadalupe Cruz Sánchez, Leticia Ortega Maynez
nd Hiram Madero Orozco
Universidad Autónoma de Ciudad Juárez
Instituto de Ingeniería y Tecnología
Mexico
. Introduction
Since the security factor became a basic need for civilization, a lot of systems have been
developed. Those systems, try to ensure the safety in all the things that driving a certain
degree of exclusivity. Historically, keys, cards and passwords were used as security systems;
however, these methods are vulnerable to loss and theft. As a result biometric identification
methods emerge in order to tackle the disadvantages of the non biometric classical methods.
Biometrics,
is an emerging technology that addresses the automated identification of
individuals, based on their physiological and behavioral traits. The main advantage of
iometric methods is the ability to recognize, which is made by means of a physical feature or
unique pattern (Jain et al. (2008)). With these methods and individual can hardly be victim"
135fe2a0a0e6b726e5d81299edad4b3ce39d6614,Multichannel-Kernel Canonical Correlation Analysis for Cross-View Person Reidentification,"This is a pre-print version, the final version of the manuscript with more experiments can be found at:
https://doi.org/10.1145/3038916
Multi Channel-Kernel Canonical Correlation
Analysis for Cross-View Person Re-Identification
Giuseppe Lisanti, Svebor Karaman, Iacopo Masi"
133dd0f23e52c4e7bf254e8849ac6f8b17fcd22d,Active Clustering with Model-Based Uncertainty Reduction,"This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
Active Clustering with Model-Based
Uncertainty Reduction
Caiming Xiong, David M. Johnson, and Jason J. Corso Senior Member, IEEE"
137ff9b84649683326e85df1994932c21eec3e4c,Neural Relational Inference for Interacting Systems,"Neural Relational Inference for Interacting Systems
Thomas Kipf * 1 Ethan Fetaya * 2 3 Kuan-Chieh Wang 2 3 Max Welling 1 4 Richard Zemel 2 3 4"
13631379de6487fd0571e5919f4efb65d16c1633,Accelerated Inference in Markov Random Fields via Smooth Riemannian Optimization,"Accelerated Inference in Markov Random Fields
via Smooth Riemannian Optimization
Siyi Hu and Luca Carlone"
131059ea24073d08de0bd153f9caddc123911e51,Facial emotional recognition in schizophrenia : preliminary results of the Virtual Reality Program for Facial Emotional Recognition,"Facial emotional recognition in schizophrenia: preliminary results of the Virtual
Reality Program for Facial Emotional Recognition
Reconhecimento emocional de faces na esquizofrenia: resultados preliminares do Programa de Realidade Virtual
para o Reconhecimento Emocional de Faces
Teresa souTo1,2, alexandre BapTisTa1, diana Tavares1,3, CrisTina Queirós1,2, anTónio MarQues1,3
Psychosocial Rehabilitation Laboratory of Faculty of Psychology and Educational Sciences, Porto University/School of Allied Health Sciences, Porto Polytechnic Institute (FPCEUP/ESTSPIPP), Porto,
Portugal.
FPCEUP, Porto, Portugal.
ESTSPIPP, Porto, Portugal.
Institution where the study was elaborated: Faculty of Psychology and Educational Sciences, Porto University, Portugal.
Received: 11/6/2012 – Accepted: 2/14/2013"
13ae3c8afef5a0d6f4c9e684da9fc1fa96caaeb6,Online Anomaly Detection in Crowd Scenes via Structure Analysis,"Online Anomaly Detection in Crowd Scenes
via Structure Analysis
Yuan Yuan, Senior Member, IEEE, Jianwu Fang, and Qi Wang"
13a60e75d05ef4e1ae688526bb5a4b1859a65501,Cost-effective Object Detection: Active Sample Mining with Switchable Selection Criteria,"Cost-effective Object Detection: Active Sample
Mining with Switchable Selection Criteria
Keze Wang, Liang Lin, Xiaopeng Yan, Ziliang Chen, Dongyu Zhang, and Lei Zhang, Fellow, IEEE"
13afc4f8d08f766479577db2083f9632544c7ea6,Multiple kernel learning for emotion recognition in the wild,"Multiple Kernel Learning for
Emotion Recognition in the Wild
Karan Sikka, Karmen Dykstra, Suchitra Sathyanarayana,
Gwen Littlewort and Marian S. Bartlett
Machine Perception Laboratory
EmotiW Challenge, ICMI, 2013"
132f88626f6760d769c95984212ed0915790b625,Exploring Entity Resolution for Multimedia Person Identification,"UC Irvine
UC Irvine Electronic Theses and Dissertations
Title
Exploring Entity Resolution for Multimedia Person Identification
Permalink
https://escholarship.org/uc/item/9t59f756
Author
Zhang, Liyan
Publication Date
014-01-01
Peer reviewed|Thesis/dissertation
eScholarship.org
Powered by the California Digital Library
University of California"
13de254db85eefaa9533d746eb2ad2079e8f2c74,Description and evaluation of techniques for transfer learning across sub-categories,"FP7–216529
PinView Deliverable D6.3
Deliverable D6.3
Description and evaluation of techniques for transfer
learning across sub-categories
Contract number: FP7–216529 PinView
Personal Information Navigator Adapting Through Viewing
The research leading to these results has received funding from the European Community’s
Seventh Framework Programme (FP7/2007-2013) under grant agreement n◦ 216529.
Revision: 1.0
Page 1 of 30"
13f6ab2f245b4a871720b95045c41a4204626814,Cortex commands the performance of skilled movement,"RESEARCH ARTICLE
Cortex commands the performance of
skilled movement
Jian-Zhong Guo, Austin R Graves, Wendy W Guo, Jihong Zheng, Allen Lee,
Juan Rodrı´guez-Gonza´ lez, Nuo Li, John J Macklin, James W Phillips,
Brett D Mensh, Kristin Branson, Adam W Hantman*
Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United
States"
134dd3bb637b51c61fa9d2332f11e39efc0b359a,High-level activity learning and recognition in structured environments,"High-level activity learning and recognition in
structured environments
John Patrick Greenall
Submitted in accordance with the requirements
for the degree of Doctor of Philosophy.
The University of Leeds
School of Computing
June 2012"
1306ccfec94a36b94085d4cc71fed45abd998b0e,Strategies for Exploiting Independent Cloud Implementations of Biometric Experts in Multibiometric Scenarios,"Publishing CorporationMathematical Problems in EngineeringVolume 2014, Article ID 585139, 15 pageshttp://dx.doi.org/10.1155/2014/585139"
13425bb41d326982ec6b3c6f3034aa978a1300ac,Face Recognition for Smart Environments,"Face Recognition for Smart Environments
Alex Pentland and Tanzeem Choudhury
Reprint from
February 2000
© 2000 IEEE. Personal use of this material is permitted.
However, permission to reprint/republish this material for
dvertising or promotional purposes or for creating new
ollective works for resale or redistribution to servers or
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This material is presented to ensure timely dissemination of
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therein are retained by authors or by other copyright
holders. All persons copying this information are expected
to adhere to the terms and constraints invoked by each
uthor's copyright. In most cases, these works may not be
reposted without the explicit permission of the copyright
holder."
1373195c26eab581138579f7389cdf8b7a94a4bb,Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing,"Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing
Magnus Wrenninge1,∗ Jonas Unger1,2,†
7D Labs
Link¨oping University, Sweden
Figure 1: Example image from Synscapes."
13a82da2bfa24583caf78ab1d14b5cfa4798b3b3,Robust face hallucination using quantization-adaptive dictionaries,"Robust Face Hallucination using
Quantization-Adaptive Dictionaries
Reuben Farrugia
Christine Guillemot
IEEE Int. Conf. on Image Processing, Arizona, USA
6th September 2016"
132781c1b2495ff0e792b46b94fdf33867394e4a,Autistic Traits and Symptoms of Social Anxiety are Differentially Related to Attention to Others’ Eyes in Social Anxiety Disorder,"J Autism Dev Disord (2017) 47:3814–3821
DOI 10.1007/s10803-016-2978-z
S.I. : ANXIETY IN AUTISM SPECTRUM DISORDERS
Autistic Traits and Symptoms of Social Anxiety are Differentially
Related to Attention to Others’ Eyes in Social Anxiety Disorder
Johan Lundin Kleberg1 · Jens Högström2,3 · Martina Nord2,3 · Sven Bölte4,5 ·
Eva Serlachius2,3 · Terje Falck‑Ytter1,4,5
Published online: 20 December 2016
© The Author(s) 2016. This article is published with open access at Springerlink.com"
134fe1c4f45cea3339c094fee817e7a024d73d88,Inferring door locations from a teammate's trajectory in stealth human-robot team operations,"Inferring door locations from a teammate’s trajectory in stealth
human-robot team operations
Jean Oh, Luis Navarro-Serment, Arne Supp´e, Anthony Stentz and Martial Hebert1"
13841d54c55bd74964d877b4b517fa94650d9b65,Generalised ambient reflection models for Lambertian and Phong surfaces,"Generalised Ambient Reflection Models for Lambertian and
Phong Surfaces
Author
Zhang, Paul, Gao, Yongsheng
Published
Conference Title
Proceedings of the 2009 IEEE International Conference on Image Processing (ICIP 2009)
https://doi.org/10.1109/ICIP.2009.5413812
Copyright Statement
© 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/
republish this material for advertising or promotional purposes or for creating new collective
works for resale or redistribution to servers or lists, or to reuse any copyrighted component of
this work in other works must be obtained from the IEEE.
Downloaded from
http://hdl.handle.net/10072/30001
Griffith Research Online
https://research-repository.griffith.edu.au"
133900a0e7450979c9491951a5f1c2a403a180f0,Social Grouping for Multi-Target Tracking and Head Pose Estimation in Video,"JOURNAL OF LATEX CLASS FILES
Social Grouping for Multi-target Tracking and
Head Pose Estimation in Video
Zhen Qin and Christian R. Shelton"
13d9da779138af990d761ef84556e3e5c1e0eb94,Learning to Locate Informative Features for Visual Identification,"Int J Comput Vis (2008) 77: 3–24
DOI 10.1007/s11263-007-0093-5
Learning to Locate Informative Features for Visual Identification
Andras Ferencz · Erik G. Learned-Miller ·
Jitendra Malik
Received: 18 August 2005 / Accepted: 11 September 2007 / Published online: 9 November 2007
© Springer Science+Business Media, LLC 2007"
13ab059e6b592ca7bcb14337316ec1ac14aa5c5a,Constrained planar cuts - Object partitioning for point clouds,"Constrained Planar Cuts - Object Partitioning for Point Clouds
Markus Schoeler, Jeremie Papon and Florentin W¨org¨otter
Bernstein Center for Computational Neuroscience (BCCN)
III Physikalisches Institut - Biophysik, Georg-August University of G¨ottingen"
139bb2a4034a0498934185e8c6d515d8f9330e2a,One-Shot Segmentation in Clutter,"One-Shot Segmentation in Clutter
Claudio Michaelis 1 2 Matthias Bethge 1 2 3 4 Alexander S. Ecker 1 2 4"
13c4a4359e9d7f5b2abe1b9542c0950946b0565a,Learning sparse tag patterns for social image classification,"This document is downloaded from DR-NTU, Nanyang Technological
University Library, Singapore.
Title
Learning sparse tag patterns for social image
lassification
Author(s)
Lin, Jie; Duan, Ling-Yu; Yuan, Junsong; Li, Qingyong;
Luo, Siwei
Citation
http://hdl.handle.net/10220/12960
Rights"
1394ca71fc52db972366602a6643dc3e65ee8726,EmoReact: a multimodal approach and dataset for recognizing emotional responses in children,"See	discussions,	stats,	and	author	profiles	for	this	publication	at:	https://www.researchgate.net/publication/308407783
EmoReact:	A	Multimodal	Approach	and	Dataset
for	Recognizing	Emotional	Responses	in	Children
Conference	Paper	·	November	2016
DOI:	10.1145/2993148.2993168
CITATIONS
READS
authors,	including:
Behnaz	Nojavanasghari
University	of	Central	Florida
PUBLICATIONS			20	CITATIONS
Tadas	Baltrusaitis
Carnegie	Mellon	University
0	PUBLICATIONS			247	CITATIONS
SEE	PROFILE
SEE	PROFILE
Charles	E.	Hughes
University	of	Central	Florida
85	PUBLICATIONS			1,248	CITATIONS
SEE	PROFILE"
135fc59c8adb8d97a0a8dacf615f1b18a2102372,Language-Based Image Editing with Recurrent Attentive Models,"Language-Based Image Editing with Recurrent Attentive Models
Jianbo Chen∗, Yelong Shen†, Jianfeng Gao†, Jingjing Liu†, Xiaodong Liu†
University of California, Berkeley∗ and Microsoft Research†
yeshen, jfgao, jingjl,"
13b2e01030ae41983003e3ae53b5bb3ed3e764f0,Detection-Tracking for Efficient Person Analysis: The DetTA Pipeline,"Detection-Tracking for Efficient Person Analysis: The DetTA Pipeline
Stefan Breuers1, Lucas Beyer1, Umer Rafi1, Bastian Leibe1"
13f03aab62fc29748114a0219426613cf3ba76ae,MORPH-II: Feature Vector Documentation,"MORPH-II: Feature Vector Documentation
Troy P. Kling
NSF-REU Site at UNC Wilmington, Summer 2017
MORPH-II Subsets
Four different subsets of the MORPH-II database were selected for a wide range of purposes, including age
estimate, gender and race classification, and facial recognition.
• The “Full” data set contains all 55,134 mugshots [1].
• The “Partial” data set contains 1,000 mugshots randomly selected from the full data set.
• The “Partial (Even)” data set contains 1,000 mugshots selected from the full data set according to very
strict rules and is intended mainly for age estimation tasks. The subjects range in age from 21 to 45,
with exactly 40 subjects in each age category (thus the term “even” in the name of the data set). Of
these 40 subjects in each age group, exactly 30 are male and 10 are female, giving rise to a 3:1 gender
ratio. Additionally, half of the males in each age group are black, and the same goes for the females,
so there is a precise 1:1 ratio of black to white individuals. No subject is represented more than once
in this data set, so it should not be used for face recognition tasks.
• The “Recognition” data set contains 1,660 mugshots selected from the full data set according to certain
rules and is intended to be used for facial recognition tasks. There are 166 subjects present in the data
set – 83 males and 83 females – each of whom has exactly 10 images, usually taken over the span of
multiple years. No restrictions on age or race were placed on this data set.
Image Preprocessing"
133da0d8c7719a219537f4a11c915bf74c320da7,A Novel Method for 3D Image Segmentation with Fusion of Two Images using Color K-means Algorithm,"International Journal of Computer Applications (0975 – 8887)
Volume 123 – No.4, August 2015
A Novel Method for 3D Image Segmentation with Fusion
of Two Images using Color K-means Algorithm
Neelam Kushwah
Dept. of CSE
ITM Universe
Gwalior
Priusha Narwariya
Dept. of CSE
ITM Universe
Gwalior"
13f9922632ff5311046229b849615fcd2f5d0c06,On Multi-scale differential features for face recognition,"On Multi-scale differential features for face recognition
Center for Intelligent Information Retrieval
S. Ravela
Allen R. Hanson
Vision Laboratory
Dept. of Computer Science, University of Massachusetts at Amherst, MA, 01002"
1369e9f174760ea592a94177dbcab9ed29be1649,Geometrical facial modeling for emotion recognition,"Geometrical Facial Modeling for Emotion Recognition
Giampaolo L. Libralon and Roseli A. F. Romero"
138778d75fc4e2fd490897ac064b9ac84b6b9f04,Generation and visualization of emotional states in virtual characters,"COMPUTER ANIMATION AND VIRTUAL WORLDS
Comp. Anim. Virtual Worlds 2008; 19: 259–270
Published online 25 July 2008 in Wiley InterScience
(www.interscience.wiley.com) DOI: 10.1002/cav.234
...........................................................................................
Generation and visualization of
emotional states in virtual characters
By Diana Arellano*, Javier Varona and Francisco J. Perales
..........................................................................
This paper presents an affective model that determines the emotional state of a character
ccording to the personality traits and the experienced emotions. We consider an emotional
state as the layer between personality and emotion. The proposed affective model offers a
mapping between emotions and emotional states. To evidence emotional states of a virtual
haracter, we can attribute them facial expressions based on their associated emotions.
Facial expressions for intermediate emotions are generated automatically from expressions
for universal emotions. The experiments show coherent emotional states produced by a
simulated story. They also present how the corresponding emotions were represented
through dynamic and static facial expressions. Finally, the obtained results demonstrate the
satisfactory recognition by a group of people unfamiliar with the work described. Copyright
© 2008 John Wiley & Sons, Ltd."
13347c0790a5f6a8739d293bfaf8e135a10c2c88,Facial Pose Interpretation for Human-Robot Symbiosis,"Daffodil International University
Institutional Repository
Proceedings of NCCIS
February 2009
009-02-14
Facial Pose Interpretation for
Human-Robot Symbiosis
Bhuiyan, Md. Al-Amin
Daffodil International University
http://hdl.handle.net/20.500.11948/772
Downloaded from  http://dspace.library.daffodilvarsity.edu.bd, Copyright Daffodil International University Library"
132d5724f8531aef54cadb79748929808ba685c0,Handling Occlusions with Franken-Classifiers,
13e348264fe1077caa44e1b59c71e67a8e4b5ad9,EFFECT OF EYES DETECTION AND POSITION ESTIMATION METHODS ON THE ACCURACY OF COMPARATIVE TESTING OF FACE DETECTION ALGORITHMS,"EFFECT OF EYES DETECTION AND POSITION ESTIMATION METHODS
ON THE ACCURACY OF COMPARATIVE TESTING OF FACE
DETECTION ALGORITHMS1
N. Degtyarev, O. Seredin
Tula State University, 92 Lenin Ave., Tula 300600, Russian Federation;
Phone: +7(4872)353637; E-mail:
Many  published  comparisons  of  face  detection  algorithms  used  different  evaluation
procedures for each algorithm or even contain only a summary of the originally reported
performance among several face detection algorithms on the pair of small datasets. Deg-
tyarev et al. have proposed the FD algorithm evaluation procedure containing model of
face  representation  conversion  unifying  the  FD  algorithms  comparison  procedures,
which  makes  such  evaluation  more  reliable.  However,  there  is  no  evidence  that  such
""conversion"" does not diminish the localization accuracy. The aim of this work is to ex-
mined the effects of two different face representation conversion techniques - eyes es-
timation  model proposed by Degtyarev et  al.  and highly scored eyes detection method
proposed by Bolme et al. and based on ASE filters - via routine testing.
Introduction
Face  detection  (FD)  algorithms  are  getting
widely used in the modern world: security sys-
tems, interactive user interfaces, advertisement"
60a33bcfe4b40cf46772e6aa1ead10489e924847,Bayesian representation learning with oracle constraints,"When crowds hold privileges: Bayesian unsupervised
representation learning with oracle constraints
Theofanis Karaletsos
Computational Biology Program, Sloan Kettering Institute
275 York Avenue, New York, USA
Serge Belongie
Cornell Tech
11 Eighth Avenue #302, New York, USA
Gunnar R¨atsch
Computational Biology Program, Sloan Kettering Institute
275 York Avenue, New York, USA"
60ffc8db53b02e95d852f5a06f97686486f72195,Video matching using DC-image and local features,"Video Matching Using DC-image and Local
Features
Saddam Bekhet, Amr Ahmed and Andrew Hunter"
60cdcf75e97e88638ec973f468598ae7f75c59b4,Face Annotation Using Transductive Kernel Fisher Discriminant,"Face Annotation Using Transductive
Kernel Fisher Discriminant
Jianke Zhu, Steven C.H. Hoi, and Michael R. Lyu"
60d75d32d345c519fa5c0d8d6b6eb62e633a8d13,Person reidentification by semisupervised dictionary rectification learning with retraining module,"Downloaded From: https://www.spiedigitallibrary.org/journals/Journal-of-Electronic-Imaging on 9/13/2018
Terms of Use: https://www.spiedigitallibrary.org/terms-of-use
PersonreidentificationbysemisuperviseddictionaryrectificationlearningwithretrainingmoduleHongyuanWangZongyuanDingJiZhangSuolanLiuTongguangNiFuhuaChenHongyuanWang,ZongyuanDing,JiZhang,SuolanLiu,TongguangNi,FuhuaChen,“Personreidentificationbysemisuperviseddictionaryrectificationlearningwithretrainingmodule,”J.Electron.Imaging27(4),043043(2018),doi:10.1117/1.JEI.27.4.043043."
60978f66eac568ae65d3acdc6559273fc30bc8c4,GReTA-A Novel Global and Recursive Tracking Algorithm in Three Dimensions,"GReTA – a novel Global and Recursive
Tracking Algorithm in three dimensions
Alessandro Attanasi, Andrea Cavagna, Lorenzo Del Castello, Irene Giardina, Asja Jeli´c,
Stefania Melillo, Leonardo Parisi, Fabio Pellacini, Edward Shen, Edmondo Silvestri, Massimiliano Viale"
608de8217aeb7851d0425ef412e7e65da804f682,Real-Time Face Recognition from Surveillance Video,"Real-Time Face Recognition from Surveillance Video
Davis, M., Popa, S., & Surlea, C. (2010). Real-Time Face Recognition from Surveillance Video. In Intelligent
Video Event Analysis and Understanding (1st ed., pp. 155-194). (Studies in Computational Intelligence; Vol.
32). Berlin Heidelberg: Springer. DOI: 10.1007/978-3-642-17554-1_8
Published in:
Intelligent Video Event Analysis and Understanding
Document Version:
Peer reviewed version
Queen's University Belfast - Research Portal:
Link to publication record in Queen's University Belfast Research Portal
Publisher rights
This is the author's version of the publication, with figures in colour. The original publication is available at www.springerlink.com.
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Copyright for the publications made accessible via the Queen's University Belfast Research Portal is retained by the author(s) and / or other
opyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated
with these rights.
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The Research Portal is Queen's institutional repository that provides access to Queen's research output. Every effort has been made to
ensure that content in the Research Portal does not infringe any person's rights, or applicable UK laws. If you discover content in the
Research Portal that you believe breaches copyright or violates any law, please contact"
60ce4a9602c27ad17a1366165033fe5e0cf68078,Combination of Face Regions in Forensic Scenarios.,"TECHNICAL NOTE
DIGITAL & MULTIMEDIA SCIENCES
J Forensic Sci, 2015
doi: 10.1111/1556-4029.12800
Available online at: onlinelibrary.wiley.com
Pedro Tome,1 Ph.D.; Julian Fierrez,1 Ph.D.; Ruben Vera-Rodriguez,1 Ph.D.; and Javier Ortega-Garcia,1
Ph.D.
Combination of Face Regions in Forensic
Scenarios*"
6092110d67c8082a1fa16e721aaa0421ec3161d7,Target container: A target-centric parallel programming abstraction for video-based surveillance,Target Container: A Target-Centric Parallel
6017b19c4c0e6c0e5b32e54efda6eff78b69d1dd,An Efficient 3D Geometrical Consistency Criterion for Detection of a Set of Facial Feature Points,"MVA2007 IAPR Conference on Machine Vision Applications, May 16-18, 2007, Tokyo, JAPAN
An Ef‌f‌icient 3D Geometrical Consistency Criterion for Detection of a
Set of Facial Feature Points
Mayumi Yuasa, Tatsuo Koazkaya and Osamu Yamaguchi
Corporate Research & Development Center, Toshiba Corporation
, Komukai-Toshiba-cho, Saiwai-ku, Kawasaki 212–8582, Japan"
60c06e5884a672e0ba3bf1d3488307489583b7e5,Audiovisual speech perception and eye gaze behavior of adults with asperger syndrome.,"J Autism Dev Disord
DOI 10.1007/s10803-011-1400-0
O R I G I N A L P A P E R
Audiovisual Speech Perception and Eye Gaze Behavior of Adults
with Asperger Syndrome
Satu Saalasti • Jari Ka¨tsyri • Kaisa Tiippana •
Mari Laine-Hernandez • Lennart von Wendt •
Mikko Sams
Ó Springer Science+Business Media, LLC 2011"
60e065dbb795cc0d76ec187116eb87d1f42b5485,A General Framework for Density Based Time Series Clustering Exploiting a Novel Admissible Pruning Strategy,"IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, MANUSCRIPT ID
A General Framework for Density Based
Time Series Clustering Exploiting a Novel
Admissible Pruning Strategy
Nurjahan Begum1, Liudmila Ulanova1, Hoang Anh Dau1, Jun Wang2, and Eamonn Keogh1"
60bffecd79193d05742e5ab8550a5f89accd8488,Proposal Classification using sparse representation and applications to skin lesion diagnosis,"PhD Thesis Proposal
Classification using sparse representation and applications to skin
lesion diagnosis
I. Description
In only a few decades, sparse representation modeling has undergone a tremendous expansion with
successful applications in many fields including signal and image processing, computer science,
machine  learning,  statistics.  Mathematically,  it  can  be  considered  as  the  problem  of  finding  the
sparsest solution (the one with the fewest non-zeros entries) to an underdetermined linear system
of equations [1]. Based on the observation for natural images (or images rich in textures) that small
scale  structures  tend  to  repeat  themselves  in  an  image  or  in  a  group  of  similar  images,  a  signal
source can be sparsely represented over some well-chosen redundant basis (a dictionary). In other
words, it can be approximately representable by a linear combination of a few elements (also called
toms or basis vectors) of a redundant/over-complete dictionary.
Such models have been proven successful in many tasks including denoising [2]-[5], compression
[6],[7], super-resolution [8],[9], classification and pattern recognition [10]-[16]. In the context of
lassification, the objective is to find the class to which a test signal belongs, given training data
from multiple classes. Sparse representation has become a powerful technique in classification and
pplications, including texture classification [16], face recognition [12], object detection [10], and
segmentation of medical images [17], [18]. In conventional Sparse Representation Classification
(SRC) schemes, learned dictionaries and sparse representation are involved to classify image pixels"
60161c712a491764b6f227d72e9d01e956caa873,"Wrong Today, Right Tomorrow: Experience-Based Classification for Robot Perception","Wrong Today, Right Tomorrow:
Experience-Based Classification for
Robot Perception
Jeffrey Hawke†, Corina Gur˘au†, Chi Hay Tong and Ingmar Posner"
6025f0761024006e0ea5782a7cea29ed69231fbf,Neural Mechanisms of Qigong Sensory Training Massage for Children With Autism Spectrum Disorder: A Feasibility Study,"Original Article
Neural Mechanisms of Qigong Sensory
Training Massage for Children With Autism
Spectrum Disorder: A Feasibility Study
Global Advances in Health and Medicine
Volume 7: 1–10
! The Author(s) 2018
Reprints and permissions:
sagepub.com/journalsPermissions.nav
DOI: 10.1177/2164956118769006
journals.sagepub.com/home/gam
Kristin K Jerger, MD, LMBT1, Laura Lundegard2, Aaron Piepmeier, PhD1,
Keturah Faurot, PA, MPH, PhD1, Amanda Ruffino, BA1,
Margaret A Jerger, PhD, CCC-SLP1, and Aysenil Belger, PhD3"
6097c33a382c62a44379926ee96b23b51dba49c4,From Depth Data to Head Pose Estimation: a Siamese approach,"From Depth Data to Head Pose Estimation: a Siamese approach
Marco Venturelli, Guido Borghi, Roberto Vezzani, Rita Cucchiara
University of Modena and Reggio Emilia, DIEF
{marco.venturelli, guido.borghi, roberto.vezzani,
Via Vivarelli 10, Modena, Italy
Keywords:
Head Pose Estimation, Deep Learning, Depth Maps, Automotive"
604a4f7c0958c5cac017b853a7d0f5f5b4a4c509,Can We Teach Empathy ? Techniques Using Standardized Patients to Assist Learners with Empathy ( Submission # 1039 ),
60cdd2ae71d39f2a8a3c6d4c22284a602428b347,Image of face captured Face Detection and localization Feature extraction Learning Classification Decision,"Complete Architecture of a Robust System of Face
International Journal of Computer Applications (0975 – 8887)
Volume 122 – No.1, July 2015
Abdellatif Hajraoui
Faculty of Science and
Technology, University Sultan
Moulay Slimane, Beni Mellal
3000, Morocco
Recognition
Mohamed Sabri
Faculty of Science and
Technology, University Sultan
Moulay Slimane, Beni Mellal
3000, Morocco
Mohamed Fakir
Faculty of Science and
Technology, University Sultan
Moulay Slimane, Beni Mellal
3000, Morocco"
60fb007eef153fdf9c3d6620c419bef1c657c555,A soft-biometrics dataset for person tracking and re-identification,"A Soft-Biometrics Dataset for Person Tracking and Re-Identification
Arne Schumann, Eduardo Monari
Fraunhofer Institute for Optronics, System Technologies and Image Exploitation
{arne.schumann,"
60bc358296ae11ac8f11286bba0a49ac7e797d26,Diverse Image-to-Image Translation via Disentangled Representations,"Diverse Image-to-Image Translation via
Disentangled Representations
Hsin-Ying Lee(cid:63)1, Hung-Yu Tseng(cid:63)1, Jia-Bin Huang2, Maneesh Singh3,
Ming-Hsuan Yang1,4
University of California, Merced 2Virginia Tech 3Verisk Analytics 4Google Cloud
Photo to van Gogh
Content
Attribute Generated
Winter to summer
Photograph to portrait
Input
Output
Input
Output
Fig. 1: Unpaired diverse image-to-image translation. (Lef t) Our model
learns to perform diverse translation between two collections of images without
ligned training pairs. (Right) Example-guided translation."
608c5103ad8e745b98dfe92ef33b66b93f01b051,Amélioration de performance de la navigation basée vision pour la robotique autonome : une approche par couplage vision/commande. (Performance improvment of vision-based navigation for autonomous robotics : a vision and control coupling approach),"Amélioration de performance de la navigation basée
vision pour la robotique autonome : une approche par
ouplage vision/commande
Helène Roggeman
To cite this version:
Helène Roggeman. Amélioration de performance de la navigation basée vision pour la robotique
utonome : une approche par couplage vision/commande. Robotique [cs.RO]. Université Paris-Saclay,
017. Français. <NNT : 2017SACLS497>. <tel-01695641>
HAL Id: tel-01695641
https://tel.archives-ouvertes.fr/tel-01695641
Submitted on 29 Jan 2018
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,"
604d7533bdcfb06f4ae217a2cd9fd2e1467192f8,Gender Recognition using Hog with Maximized Inter-Class Difference,
6084cac63fe6fcc1436610f1db4a3764ec2e3692,TST / BTD : An End-to-End Visual Recognition System,"TST/BTD: An End-to-End Visual Recognition System
Taehee Lee
Stefano Soatto
Technical Report UCLA-CSD100008
February 8, 2010, Revised March 18, 2010"
60189e2b592056d43a28b6ffa491867f793ebe1e,Bağlamın Hiyerarşik Doğası,"Ba˘glamın Hiyerar¸sik Do˘gası
Fethiye Irmak Do˘gan, Sinan Kalkan
Bilgisayar Mühendisli˘gi Bölümü
Orta Do˘gu Teknik Üniversitesi
Ankara, Türkiye
Email:
Özetçe —Ba˘glam, insan bili¸si için oldukça elzemdir ve du-
ru¸s, davranı¸s, konu¸sma biçimi gibi gündelik insan hayatı için
önemli pek çok sürece etki etmektedir. Yakın zamanda hay-
tımızda yer edinmesini bekledi˘gimiz robotların da i¸slevlerini
yerine do˘gru ve verimli bir biçimde getirebilmesi için, ba˘glamı
lgılama ve kullanma yetene˘gine sahip olması beklenmektedir.
Ancak ba˘glam, yapay veya do˘gal bili¸s için ne kadar elzem
olsa da, ba˘glamın yapısı yeterince çalı¸sılmı¸s ve çözümlenebilmi¸s
de˘gildir. Bu çalı¸smada, ba˘glamın çözümlenememi¸s ö˘gelerinden
ir tanesine, ba˘glamın yapısının hiyerar¸sik olup olmadı˘gına
odaklanılmaktadır. Yaptı˘gımız irdelemeye göre, ba˘glama ait
muhtelif sosyal, uzamsal ve zamansal özellikler ve olgular,
a˘glamın hiyerar¸sik bir yapıya sahip oldu˘gunu önermektedir. Bu
konudaki sinirbilim, psikoloji bulguları ve bili¸simsel modelleme"
60b3601d70f5cdcfef9934b24bcb3cc4dde663e7,Binary Gradient Correlation Patterns for Robust Face Recognition,"SUBMITTED TO IEEE TRANS. ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Binary Gradient Correlation Patterns
for Robust Face Recognition
Weilin Huang, Student Member, IEEE, and Hujun Yin, Senior Member, IEEE"
603ffcfad879aaf559cac118894cd38666158f2f,Learning from scratch a confidence measure,"M. POGGI, S. MATTOCCIA: LEARNING FROM SCRATCH A CONFIDENCE MEASURE
Learning from scratch a confidence measure
Matteo Poggi
http://vision.disi.unibo.it/~mpoggi
Stefano Mattoccia
http://vision.disi.unibo.it/~smatt
University of Bologna
Department of Computer Science and
Engineering (DISI)
Viale del Risorgimento 2
Bologna, Italy"
60ab5c64375c4f5f8949a184fd9bfb68778ae6ea,Understanding and Verifying Kin Relationships in a Photo 1,"N. S. Syed et al Int. Journal of Engineering Research and Applications                          www.ijera.com
ISSN : 2248-9622, Vol. 3, Issue 6, Nov-Dec 2013, pp.1225-1229
RESEARCH ARTICLE                                                                            OPEN ACCESS
Understanding and Verifying Kin Relationships in a Photo
Ms.N.S.Syed, 2mr.B.K.Patil, 3mr.Zafar Ul Hasan
(Department of Computer Science, Everest College of Engg. & Tech., Aurangabad, M.S., India )
(Department of Computer Science, Everest College of Engg. & Tech., Aurangabad, M.S., India )
(Department of Computer Science, Sandip Institute of Technology and Research Centre, Nashik, M.S,India)"
60ea05df719973ac4d9d70d3141e671131a55db5,A Practical Subspace Approach To Landmarking,"A Practical Subspace Approach To Landmarking
Signals and systems group, Faculty of Electrical Engineering, Mathematics and Computer Science, University of
G. M. Beumer, and R.N.J. Veldhuis
Twente, Enschede, The Netherlands
Email:"
602ff4fd0f5bd10c9fb971ecd2317e542f070883,Object Detection from the Satellite Images using Divide and Conquer Model,"SSRG International Journal of Computer Science and Engineering (SSRG-IJCSE) – volume1 issue10 Dec 2014
Object Detection from the Satellite Images
using Divide and Conquer Model
Lakhwinder Kaur, Guru Kashi University
Er.Vinod Kumar Sharma (Assistant professor), Guru Kashi University"
60c36bfa7881435e2111fe3e522a36880dee6d09,Study of the Changing Trends in Facial Expression Recognition,"Study of the Changing Trends in Facial Expression Recognition
{tag}                                                                   {/tag}
International Journal of Computer Applications
© 2011 by IJCA Journal
Number 5 - Article 3
Year of Publication: 2011
Authors:
Dr. S. Ravi
Mahima S
10.5120/2509-3397"
60c12b3a1bfd547f5a165c95774a1a17d18a5941,People recognition by mobile robots,"People Recognition by Mobile Robots
Grzegorz Cielniak and Tom Duckett
Centre for Applied Autonomous Sensor Systems
Dept. of Technology, ¨Orebro University
SE-70182 ¨Orebro, Sweden
Phone: +46 19 30 11 13, +46 19 30 34 83
Email:
Telefax: +46 19 30 34 63"
60529952f6346ebe26a3d4e5fdf79a925d68621f,Towards a Generalized Eigenspace-Based Face Recognition Framework,"Towards a Generalized Eigenspace-based
Face Recognition Framework
Javier Ruiz del Solar and Pablo Navarrete
Department of Electrical Engineering, Universidad de Chile.
Email: {jruizd,"
60824ee635777b4ee30fcc2485ef1e103b8e7af9,Cascaded Collaborative Regression for Robust Facial Landmark Detection Trained Using a Mixture of Synthetic and Real Images With Dynamic Weighting,"Cascaded Collaborative Regression for Robust Facial
Landmark Detection Trained using a Mixture of Synthetic and
Real Images with Dynamic Weighting
Zhen-Hua Feng, Student Member, IEEE, Guosheng Hu, Student Member, IEEE, Josef Kittler,
Life Member, IEEE, William Christmas, and Xiao-Jun Wu"
60bd1d33d74619f08baf0d7477b3f8cb8fc711e5,AMYGDALA CONNECTIVITY DURING INVOLUNTARY ATTENTION TO EMOTIONAL FACES IN TYPICAL DEVELOPMENT AND AUTISM SPECTRUM DISORDERS,"AMYGDALA CONNECTIVITY DURING INVOLUNTARY ATTENTION TO EMOTIONAL FACES
IN TYPICAL DEVELOPMENT AND AUTISM SPECTRUM DISORDERS
A Dissertation
Submitted to the Faculty of the
Graduate School of Arts and Sciences
of Georgetown University
in partial fulfillment of the requirement for the
degree of
Doctor of Philosophy
in Psychology
Eric R. Murphy, M.A.
Washington, DC
August 27th, 2013"
60c7711bf9a00f697fff61474433da01f8550bf4,A Hybrid Approach of Facial Emotion Detection using Genetic Algorithm along with Artificial Neural Network,"A Hybrid Approach of Facial Emotion Detection using Genetic Algorithm along with Artificial Neural Network
{tag}                                                 {/tag}
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 175
Number 4
Year of Publication: 2017
Authors:
Amrendra Sharan, Sunil Kumar Chhillar
10.5120/ijca2017915494
{bibtex}2017915494.bib{/bibtex}"
608dfcdbb393f44d4ae1520f6c6fdd736cee337c,Empirical Performance Analysis of Linear Discriminant Classifiers,"EmpiricalPerformanceAnalysisofLinearDiscriminantClassi(cid:12)ers
W.Zhao
N.Nandhakumar
R.Chellappa
CenterforAutomationResearch
Image&VideoProcessing
UniversityofMaryland
LGERCA,Inc.
CollegePark,MD-
PrincetonJct,NJ
energyforavoidingsigni(cid:12)cantperformancedegrada-"
60464c4bd94a14b63898e322f9ea651830e54ae0,Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers,"Published as a conference paper at ICLR 2018
RETHINKING THE SMALLER-NORM-LESS-
INFORMATIVE ASSUMPTION IN CHANNEL PRUNING
OF CONVOLUTION LAYERS
Jianbo Ye∗
College of Information Sciences and Technology
The Pennsylvania State University
James Z. Wang
College of Information Sciences and Technology
The Pennsylvania State University
Xin Lu, Zhe Lin
Adobe Research"
60040e4eae81ab6974ce12f1c789e0c05be00303,Graphical Facial Expression Analysis and Design Method: An Approach to Determine Humanoid Skin Deformation,"Yonas Tadesse1,2
e-mail:
Shashank Priya
e-mail:
Center for Energy Harvesting
Materials and Systems (CEHMS),
Bio-Inspired Materials and
Devices Laboratory (BMDL),
Center for Intelligent Material
Systems and Structure (CIMSS),
Department of Mechanical Engineering,
Virginia Tech,
Blacksburg, VA 24061
Graphical Facial Expression
Analysis and Design Method:
An Approach to Determine
Humanoid Skin Deformation
The architecture of human face is complex consisting of 268 voluntary muscles that perform
oordinated action to create real-time facial expression. In order to replicate facial expres-
sion on humanoid face by utilizing discrete actuators, the first and foremost step is the identi-"
609ff585468ad0faba704dde1a69edb9f847c201,LogDet Rank Minimization with Application to Subspace Clustering,"Hindawi Publishing Corporation
Computational Intelligence and Neuroscience
Volume 2015, Article ID 824289, 10 pages
http://dx.doi.org/10.1155/2015/824289
Research Article
LogDet Rank Minimization with Application to
Subspace Clustering
Zhao Kang,1 Chong Peng,1 Jie Cheng,2 and Qiang Cheng1
Computer Science Department, Southern Illinois University, Carbondale, IL 62901, USA
Department of Computer Science and Engineering, University of Hawaii at Hilo, Hilo, HI 96720, USA
Correspondence should be addressed to Qiang Cheng;
Received 25 March 2015; Revised 15 June 2015; Accepted 18 June 2015
Academic Editor: Jos´e Alfredo Hernandez
Copyright © 2015 Zhao Kang et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Low-rank matrix is desired in many machine learning and computer vision problems. Most of the recent studies use the nuclear
norm as a convex surrogate of the rank operator. However, all singular values are simply added together by the nuclear norm, and
thus the rank may not be well approximated in practical problems. In this paper, we propose using a log-determinant (LogDet)
function as a smooth and closer, though nonconvex, approximation to rank for obtaining a low-rank representation in subspace
lustering. Augmented Lagrange multipliers strategy is applied to iteratively optimize the LogDet-based nonconvex objective"
60efdb2e204b2be6701a8e168983fa666feac1be,Transferring Deep Object and Scene Representations for Event Recognition in Still Images,"Int J Comput Vis
DOI 10.1007/s11263-017-1043-5
Transferring Deep Object and Scene Representations for Event
Recognition in Still Images
Limin Wang1
· Zhe Wang2 · Yu Qiao3 · Luc Van Gool1
Received: 31 March 2016 / Accepted: 1 September 2017
© Springer Science+Business Media, LLC 2017"
77db171a523fc3d08c91cea94c9562f3edce56e1,Gauss-Laguerre wavelet textural feature fusion with geometrical information for facial expression identification,"Poursaberi et al. EURASIP Journal on Image and Video Processing 2012, 2012:17
http://jivp.eurasipjournals.com/content/2012/1/17
R ES EAR CH
Open Access
Gauss–Laguerre wavelet textural feature fusion
with geometrical information for facial expression
identification
Ahmad Poursaberi1*, Hossein Ahmadi Noubari2, Marina Gavrilova1 and Svetlana N Yanushkevich1"
7711330fb88e2522a5779a09c1622b75557f9254,Real-time detection and tracking of pedestrians in CCTV images using a deep convolutional neural network,"Real-time detection and tracking of pedestrians in
CCTV images using a deep convolutional neural network
Debaditya Acharya
Kourosh Khoshelham
Stephan Winter
Infrastructure Engineering, The University of Melbourne"
77ad2727065cb3dc5c91975604af01c82ec5c9f6,Convolutional Neural Networks for Disaster Images Retrieval,"Convolutional Neural Networks for Disaster Images Retrieval
Sheharyar Ahmad1,Kashif Ahmad2, Nasir Ahmad1, Nicola Conci2
DCSE, UET Peshawar, Pakistan
DISI-University of Trento, Trento"
771b7d76df1ed476dea859034a276f14ad1e49f1,Multi-scale elastic graph matching for face detection,"Sato and Kuriya EURASIP Journal on Advances in Signal Processing 2013, 2013:175
http://asp.eurasipjournals.com/content/2013/1/175
REVIEW
Open Access
Multi-scale elastic graph matching for face
detection
Yasuomi D Sato1,2,3* and Yasutaka Kuriya1"
771a9e7dc747fa2282815a4863502183f4e887c8,Efficient Bootsrapping and Query Adaptive Ranking for Image Search 1,"The International Journal Of Science & Technoledge      (ISSN  2321 – 919X)
www.theijst.com
THE INTERNATIONAL JOURNAL OF
SCIENCE & TECHNOLEDGE
Efficient Bootsrapping and Query Adaptive Ranking for Image Search
A. A. R. Senthilkumar
Head of the Department, Department of Master of Computer Application
PGP College of Engineering and Technology, Namakkal
P. Mayuri
Department of Computer Science and Engineering
PGP College of Engineering and Technology, Namakkal"
77d31d2ec25df44781d999d6ff980183093fb3de,The Multiverse Loss for Robust Transfer Learning,"The Multiverse Loss for Robust Transfer Learning
Supplementary
. Omitted proofs
for which the joint loss:
m(cid:88)
L(F r, br, D, y)
J(F 1, b1...F m, bm, D, y) =
is bounded by:
mL∗(D, y) ≤ J(F 1, b1...F m, bm, D, y)
m−1(cid:88)
≤ mL∗(D, y) +
Alλd−j+1
where [A1 . . . Am−1] are bounded parameters.
We provide proofs that were omitted from the paper for
lack of space. We follow the same theorem numbering as in
the paper.
Lemma 1. The minimizers F ∗, b∗ of L are not unique, and
it holds that for any vector v ∈ Rc and scalar s, the solu-
tions F ∗ + v1(cid:62)
Proof. denoting V = v1(cid:62)"
7714a5aa27ab5ad4d06a81fbb3e973d3b1002ac1,SSD-Sface : Single shot multibox detector for small faces,"SSD-Sface: Single shot multibox detector for small faces
C. Thuis"
77052654a37b88719c014c5afd3db89cb2288aeb,Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features,"Hindawi Publishing Corporation
e Scientific World Journal
Volume 2015, Article ID 786013, 17 pages
http://dx.doi.org/10.1155/2015/786013
Research Article
Lung Cancer Prediction Using Neural Network Ensemble with
Histogram of Oriented Gradient Genomic Features
Emmanuel Adetiba and Oludayo O. Olugbara
ICT and Society Research Group, Durban University of Technology, P.O. Box 1334, Durban 4000, South Africa
Correspondence should be addressed to Oludayo O. Olugbara;
Received 12 December 2014; Accepted 29 January 2015
Academic Editor: Alexander Schonhuth
Copyright © 2015 E. Adetiba and O. O. Olugbara. This is an open access article distributed under the Creative Commons
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is
properly cited.
This paper reports an experimental comparison of artificial neural network (ANN) and support vector machine (SVM) ensembles
nd their “nonensemble” variants for lung cancer prediction. These machine learning classifiers were trained to predict lung cancer
using samples of patient nucleotides with mutations in the epidermal growth factor receptor, Kirsten rat sarcoma viral oncogene,
nd tumor suppressor p53 genomes collected as biomarkers from the IGDB.NSCLC corpus. The Voss DNA encoding was used to
map the nucleotide sequences of mutated and normal genomes to obtain the equivalent numerical genomic sequences for training"
77851ca35105ebe007d99e5d78ceb3473491071c,Spatiotemporal Stacked Sequential Learning for Pedestrian Detection,"Spatiotemporal Stacked Sequential Learning for Pedestrian Detection
Alejandro Gonz´alez1
Sebastian Ramos1
David V´azquez1
Antonio M. L´opez1
Jaume Amores1
Computer Vision Center, Barcelona
Universitat Aut`onoma de Barcelona
United Technologies Research Center"
770b3855cdd15b49c89e4053b6cedafe53cecd6f,Improved Face Recognition Using Pseudo 2-D Hidden Markov Models,"ImprovedFaceRecognitionUsingPseudo-D
HiddenMarkovModels
StefanEickeler,StefanM(cid:127)uller,GerhardRigoll
Gerhard-Mercator-UniversityDuisburg
DepartmentofComputerScience
FacultyofElectricalEngineering
Duisburg-Germany
-ti.uni-duisburg.de"
77d4843a177031b2b5721824280033e2e601334c,Comparative Evaluation of 3 D versus 2 D Modality for Automatic Detection of Facial Action Units,"Author’s Accepted Manuscript
Comparative Evaluation of 3D versus 2D Modality
for Automatic Detection of Facial Action Units
Arman Savran, Bülent Sankur, M. Taha Bilge
Reference:
S0031-3203(11)00310-4
doi:10.1016/j.patcog.2011.07.022
PR 4228
To appear in:
Pattern Recognition
Received date:
Revised date:
Accepted date:
3 November 2010
5 July 2011
9 July 2011
www.elsevier.com/locate/pr
Cite this article as: Arman Savran, Bülent Sankur and M. Taha Bilge, Comparative Eval-
uation of 3D versus 2D Modality for Automatic Detection of Facial Action Units, Pattern
Recognition, doi:10.1016/j.patcog.2011.07.022"
77351eaeb65e374a4d1e54acc28fea426670e364,COMPRESSION BASED FACE RECOGNITION USING TRANSFORM DOMAIN FEATURES FUSED AT MATCHING LEVEL,"Signal & Image Processing : An International Journal (SIPIJ) Vol.8, No.4, August 2017
COMPRESSION BASED FACE RECOGNITION
USING TRANSFORM DOMAIN FEATURES
FUSED AT MATCHING LEVEL
Srinivas Halvia, Nayina Ramapurb , K B Rajac and Shanti Prasadd
Dayananda Sagar College of Engineering, Bangalore, India.
Sai-Tektronix Pvt. Ltd., Bangalore, India.
University Visvesvaraya College of Engineering, Bangalore, India.
dK.S. Institute of Technology, Bangalore, India."
77c7f5c5852c189b59c34ebbbbec03e5e4060428,Talking to Robots : Learning to Ground Human Language in Perception and Execution,"(cid:13)Copyright 2014
Cynthia Matuszek"
77acb8847a76bfcc925f45387fb7abd4f2bd38ac,A novel polar-based human face recognition computational model.,"Novel polar-based human face recognition computational model
Brazilian Journal of Medical and Biological Research (2009) 42: 637-646
ISSN 0100-879X
A novel polar-based human face
recognition computational model
Y. Zana1, J.P. Mena-Chalco2 and R.M. Cesar Jr.2
Núcleo de Cognição e Sistemas Complexos, Centro de Matemática, Computação e Cognição,
Universidade Federal do ABC, Santo André, SP, Brasil
Departamento de Ciências da Computação, Instituto de Matemática e Estatística, Universidade de São
Paulo, São Paulo, SP, Brasil
Correspondence to: Y. Zana, Núcleo de Cognição e Sistemas Complexos, Centro de Matemática,
Computação e Cognição, UFABC, Rua Catequese, 242, 09090-400 Santo André, SP, Brasil
Fax: +55-11-4437-8403. E-mail:
Motivated by a recently proposed biologically inspired face recognition approach, we investigated the relation between human
ehavior  and  a  computational  model  based  on  Fourier-Bessel  (FB)  spatial  patterns.  We  measured  human  recognition
performance of FB filtered face images using an 8-alternative forced-choice method. Test stimuli were generated by converting
the images from the spatial to the FB domain, filtering the resulting coefficients with a band-pass filter, and finally taking the
inverse  FB  transformation  of  the  filtered  coefficients.  The  performance  of  the  computational  models  was  tested  using  a
simulation of the psychophysical experiment. In the FB model, face images were first filtered by simulated V1- type neurons and
later analyzed globally for their content of FB components. In general, there was a higher human contrast sensitivity to radially"
7766ab86a7bea8809129e4af769b4595578e63fc,Improvements to Tracking Pedestrians in Video Streams Using a Pre-trained Convolutional Neural Network,"Western University
Electronic Thesis and Dissertation Repository
August 2016
Improvements to Tracking Pedestrians in Video
Streams Using a Pre-trained Convolutional Neural
Network
Marjan Ramin
The University of Western Ontario
Supervisor
Dr. Jagath Samarabandu
The University of Western Ontario
Graduate Program in Electrical and Computer Engineering
A thesis submitted in partial fulfillment of the requirements for the degree in Master of Engineering Science
© Marjan Ramin 2016
Follow this and additional works at: https://ir.lib.uwo.ca/etd
Part of the Computer Engineering Commons
Recommended Citation
Ramin, Marjan, ""Improvements to Tracking Pedestrians in Video Streams Using a Pre-trained Convolutional Neural Network"" (2016).
Electronic Thesis and Dissertation Repository. 3886.
https://ir.lib.uwo.ca/etd/3886"
77037a22c9b8169930d74d2ce6f50f1a999c1221,Robust Face Recognition With Kernelized Locality-Sensitive Group Sparsity Representation,"Robust Face Recognition With Kernelized
Locality-Sensitive Group Sparsity  Representation
Shoubiao Tan, Xi Sun, Wentao Chan, Lei Qu, and Ling    Shao"
7793c7431f3ddce74fe2d444df614d8d8fd9af4a,A Review of Neural Network based Semantic Segmentation for Scene Understanding in Context of the self driving Car,"A Review of Neural Network based Semantic Segmentation for
Scene Understanding in Context of the self driving Car
J. Niemeijer1, P. Pekezou Fouopi2, S. Knake-Langhorst2, and E. Barth3
Medizinische Informatik, Universität zu Lübeck,
German Aerospace Center, Braunschweig,
Institute of Neuro- and Bioinformatics, Universität zu Lübeck,"
778bff335ae1b77fd7ec67404f71a1446624331b,Hough forest-based facial expression recognition from video sequences,"Hough Forest-based Facial Expression Recognition from
Video Sequences
Gabriele Fanelli, Angela Yao, Pierre-Luc Noel, Juergen Gall, and Luc Van Gool
BIWI, ETH Zurich http://www.vision.ee.ethz.ch
VISICS, K.U. Leuven http://www.esat.kuleuven.be/psi/visics"
775c15a5dfca426d53c634668e58dd5d3314ea89,Image Quality-aware Deep Networks Ensemble for Efficient Gender Recognition in the Wild,
77fb0266b354d33f3725629c2ddce3d2342b318a,Is Attribute-Based Zero-Shot Learning an Ill-Posed Strategy?,"Is Attribute-Based Zero-Shot Learning
n Ill-Posed Strategy?
Ibrahim Alabdulmohsin1, Moustapha Cisse2, and Xiangliang Zhang1(B)
Computer, Electrical and Mathematical Sciences and Engineering Division,
King Abdullah University of Science and Technology (KAUST),
Thuwal 23955-6900, Saudi Arabia
Facebook Artificial Intelligence Research (FAIR), Menlo Park, USA
http://mine.kaust.edu.sa"
77c81c13a110a341c140995bedb98101b9e84f7f,WILDTRACK : A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection,"WILDTRACK: A Multi-camera HD Dataset for
Dense Unscripted Pedestrian Detection
Tatjana Chavdarova1, Pierre Baqu´e2, St´ephane Bouquet2,
Andrii Maksai2, Cijo Jose1, Timur Bagautdinov2, Louis Lettry3,
Pascal Fua2, Luc Van Gool3, and Franc¸ois Fleuret1
Machine Learning group, Idiap Research Institute & ´Ecole Polytechnique F´ed´erale de Lausanne
CVLab, ´Ecole Polytechnique F´ed´erale de Lausanne
Computer Vision Lab, ETH Zurich"
77882930692d41db107430a5a524ff5e4bb2ee5c,Hyperbolic Attention Networks,"Hyperbolic Attention Networks
Caglar Gulcehre Misha Denil Mateusz Malinowski Ali Razavi
Razvan Pascanu Karl Moritz Hermann
Peter Battaglia Victor Bapst
David Raposo Adam Santoro Nando de Freitas
Deepmind"
7726a6ab26a1654d34ec04c0b7b3dd80c5f84e0d,Content-aware compression using saliency-driven image retargeting,"CONTENT-AWARE COMPRESSION USING SALIENCY-DRIVEN IMAGE RETARGETING
Fabio Z¨und*†, Yael Pritch*, Alexander Sorkine-Hornung*, Stefan Mangold*, Thomas Gross†
*Disney Research Zurich
ETH Zurich"
7796f01b9b128fa09093e6170088c70f20091fcc,Face Recognition: Demystification of Multifarious Aspect in Evaluation Metrics,"We are IntechOpen,
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778c9f88839eb26129427e1b8633caa4bd4d275e,Pose pooling kernels for sub-category recognition,"Pose Pooling Kernels for Sub-category Recognition
Ning Zhang
ICSI & UC Berkeley
Ryan Farrell
ICSI & UC Berkeley
Trever Darrell
ICSI & UC Berkeley"
779f67f2fe406828bbe7a19e8736cb5fd309e321,Fine-Grained Recognition in the Wild: A Multi-task Domain Adaptation Approach,"Fine-grained Recognition in the Wild:
A Multi-Task Domain Adaptation Approach
Timnit Gebru
Judy Hoffman
Li Fei-Fei
CS Department Stanford University
{tgebru, jhoffman,"
77d0c4ed1c2d971b3e81fe7919be7a5a19309f40,Deep Generative Modeling of LiDAR Data,"Deep Generative Modeling of LiDAR Data
Lucas Caccia1,2, Herke van Hoof1,4, Aaron Courville2,3, Joelle Pineau1,2,3"
7789252476073a77e80fb0668eecf94a99b81d8d,Fast Landmark Localization With 3D Component Reconstruction and CNN for Cross-Pose Recognition,"Fast Landmark Localization
with 3D Component Reconstruction and CNN for
Cross-Pose Recognition
Gee-Sern (Jison) Hsu, Hung-Cheng Shie, Cheng-Hua Hsieh"
77b11260154e13e33c84599feba4cdc4f781bf71,Building User Profiles from Shared Photos,Building User Profiles from Shared Photos
774c8945ccf0f5315482abb8cf84ac5d37c60aa0,A Comparative Study of Feature Extraction Methods in Images Classification,"I.J. Image, Graphics and Signal Processing, 2015, 3, 16-23
Published Online February 2015 in MECS (http://www.mecs-press.org/)
DOI: 10.5815/ijigsp.2015.03.03
A Comparative Study of Feature Extraction
Methods in Images Classification
University of Sciences and Technology Mohamed Boudiaf USTO-MB, Faculty of Mathematics and Computer Science,
Seyyid Ahmed Medjahed
Oran, 31000, Algeria
Email:"
77205cedeb36ef1e6aadd1927c7b269871571ab9,Robust Pallet Detection for Automated Logistics Operations,
77cb6ea4feff6f44e9977cc7572185d24e48ce40,On the Complementarity of Face Parts for Gender Recognition,"On the Complementarity of Face Parts for
Gender Recognition
Yasmina Andreu and Ram´on A. Mollineda
Dept. Llenguatges i Sistemes Inform`atics
Universitat Jaume I. Castell´o de la Plana, Spain"
77b4fad7e1e16b8628289a1fe5c09c55bf83d85b,Image normalization for face recognition using 3D model,"Image Normalization for Face Recognition using 3D Model
Zahid Riaz, Michael Beetz and Bernd Radig"
77e69753fc7cf007a136b12f102e1e11a93f87f5,Head and Body Orientation Estimation Using Convolutional Random Projection Forests.,"This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TPAMI.2017.2784424, IEEE
Transactions on Pattern Analysis and Machine Intelligence
Head and Body Orientation Estimation Using
Convolutional Random Projection Forests
Donghoon Lee, Ming-Hsuan Yang, and Songhwai Oh∗"
7730fd15ff14dd84d71f965bfeab8e4d790d91d8,SpaRTA - Tracking across occlusions via global partitioning of 3D clouds of points,"SpaRTA
Tracking across occlusions via global
partitioning of 3D clouds of points
Andrea Cavagna, Stefania Melillo, Leonardo Parisi, Federico Ricci-Tersenghi"
77dc158a979731d2ed01145b1d3ead34a6c33487,Preference for geometric patterns early in life as a risk factor for autism.,"ORIGINAL ARTICLE
ONLINE FIRST
Preference for Geometric Patterns Early in Life
s a Risk Factor for Autism
Karen Pierce, PhD; David Conant; Roxana Hazin, BS; Richard Stoner, PhD; Jamie Desmond, MPH
Context: Early identification efforts are essential for the
early treatment of the symptoms of autism but can only oc-
ur if robust risk factors are found. Children with autism
often engage in repetitive behaviors and anecdotally pre-
fertovisuallyexaminegeometricrepetition,suchasthemov-
ing blade of a fan or the spinning of a car wheel. The ex-
tent to which a preference for looking at geometric repeti-
tion is an early risk factor for autism has yet to be examined.
Objectives: To determine if toddlers with an autism spec-
trum disorder (ASD) aged 14 to 42 months prefer to vi-
sually examine dynamic geometric images more than so-
ial images and to determine if visual fixation patterns
an correctly classify a toddler as having an ASD.
Design: Toddlers were presented with a 1-minute movie
depicting moving geometric patterns on 1 side of a video"
776c5e37eecd26049ae31f56b3249c390e25e4e9,Angry and Beautiful : The Interactive Effect of Facial Expression and Attractiveness on Time Perception,"Psihologijske teme, 25, 2016 (2), 299-315
Izvorni znanstveni rad – UDK –159.925.072
59.937.072:115
Angry and Beautiful: The Interactive Effect of Facial
Expression and Attractiveness on Time Perception
Jasmina Tomas
Department of Psychology, Faculty of Humanities and Social Sciences,
University of Zagreb, Croatia
Ana Marija Španić
Child Protection Center of Zagreb, Zagreb, Croatia"
776b77306bdb852c89a22ba142fb57c8e8bb7bb5,Efficient On-Board Stereo Vision Pose Estimation,"Ef‌f‌icient On-Board Stereo Vision
Pose Estimation(cid:2)
Angel D. Sappa1, Fadi Dornaika2, David Ger´onimo1, and Antonio L´opez1
Computer Vision Center, Edifici O Campus UAB
08193 Bellaterra, Barcelona, Spain
{asappa, dgeronimo,
Institut G´eographique National
94165 Saint Mand´e, France"
9aad8e52aff12bd822f0011e6ef85dfc22fe8466,Temporal-Spatial Mapping for Action Recognition,"Temporal-Spatial Mapping for Action Recognition
Xiaolin Song, Cuiling Lan, Wenjun Zeng, Junliang Xing, Jingyu Yang, and Xiaoyan Sun"
9a9af8a5b6939a1da9936608fbf071f852eca7e1,Deep Part Features Learning by a Normalised Double-Margin-Based Contrastive Loss Function for Person Re-Identification,
9a9a888bcce37e582b8a5b5f12f662e487443e5c,Cascaded Pyramid Network for Multi-Person Pose Estimation,"Cascaded Pyramid Network for Multi-Person Pose Estimation
Yilun Chen∗ Zhicheng Wang∗ Yuxiang Peng1
Zhiqiang Zhang2 Gang Yu
Jian Sun
Megvii Inc. (Face++), {chenyilun, wangzhicheng, pyx, zhangzhiqiang, yugang,
Tsinghua University 2HuaZhong University of Science and Technology"
9a03b7b71a82fc2c86b3b4cbec802dfc16978486,One-Shot Observation Learning,"One-Shot Observation Learning
Leo Pauly, Wisdom C. Agboh, Mohamed Abdellatif, David C. Hogg, Raul Fuentes"
9a1a9dd3c471bba17e5ce80a53e52fcaaad4373e,Automatic Recognition of Spontaneous Facial Actions,"Automatic Recognition of Spontaneous Facial
Actions
Marian Stewart Bartlett1, Gwen C. Littlewort1, Mark G. Frank2, Claudia Lainscsek1,
Ian R. Fasel1, Javier R. Movellan1
Institute for Neural Computation, University of California, San Diego.
Department of Communication, University at Buffalo, State University of New York."
9ad27106b8e0cf14e8e2814dc318142138d5527b,Camera Style Adaptation for Person Re-identification,"Camera 6Style Transfer(a)  Example images under two cameras from Market-1501(b)  Examples of camera-aware style transfer between two camerasrealtransferredrealtransferredFigure1.(a)ExampleimagesfromMarket-1501[42].(b)Exam-plesofcamera-awarestyletransferbetweentwocamerasusingourmethod.Imagesinthesamecolumnrepresentthesameperson.ancepropertyunderdifferentcameras.Examplesintradi-tionalapproachesincludeKISSME[16],XQDA[20],DNS[39],etc.Examplesindeeprepresentationlearningmeth-odsincludeIDE[43],SVDNet[29],TripletNet[11],etc.Comparingtopreviousmethods,thispaperresortstoanexplicitstrategyfromtheviewofcamerastyleadapta-tion.Wearemostlymotivatedbytheneedforlargedatavolumeindeeplearningbasedpersonre-ID.Tolearnrichfeatureswhicharerobusttocameravariations,annotatinglarge-scaledatasetsisusefulbutprohibitivelyexpensive.Nevertheless,ifwecanaddmoresamplestothetrainingsetthatareawareofthestyledifferencesbetweencameras,weareableto1)addressthedatascarcityprobleminpersonre-IDand2)learninvariantfeaturesacrossdifferentcameras.Preferably,thisprocessshouldnotcostanymorehumanla-beling,sothatthebudgetiskeptlow.Basedontheabovediscussions,weproposeacam-erastyle(CamStyle)adaptationmethodtoregularizeCNNtrainingforpersonre-ID.Initsvanillaversion,welearnimage-imagetranslationmodelsforeachcamerapairwithCycleGAN[51].WiththelearnedCycleGANmodel,foratrainingimagecapturedbyacertaincamera,wecangener-"
9a3b38cec29e78163a135faf953edb5ad30c8d18,Face authentication with sparse grid Gabor information,"FACE AUTHENTICATION WITH SPARSE GRID GABOR INFORMATION
Beno
t Duc Stefan Fischer and Josef Bigun
Signal Processing Laboratory Swiss Federal Institute of Technology
CH

 Lausanne Switzerland"
9ac82909d76b4c902e5dde5838130de6ce838c16,Recognizing Facial Expressions Automatically from Video,"Recognizing Facial Expressions Automatically
from Video
Caifeng Shan and Ralph Braspenning
Introduction
Facial expressions, resulting from movements of the facial muscles, are the face
hanges in response to a person’s internal emotional states, intentions, or social
ommunications. There is a considerable history associated with the study on fa-
ial expressions. Darwin (1872) was the first to describe in details the specific fa-
ial expressions associated with emotions in animals and humans, who argued that
ll mammals show emotions reliably in their faces. Since that, facial expression
nalysis has been a area of great research interest for behavioral scientists (Ekman,
Friesen, and Hager, 2002). Psychological studies (Mehrabian, 1968; Ambady and
Rosenthal, 1992) suggest that facial expressions, as the main mode for non-verbal
ommunication, play a vital role in human face-to-face communication. For illus-
tration, we show some examples of facial expressions in Fig. 1.
Computer recognition of facial expressions has many important applications in
intelligent human-computer interaction, computer animation, surveillance and se-
urity, medical diagnosis, law enforcement, and awareness systems (Shan, 2007).
Therefore, it has been an active research topic in multiple disciplines such as psy-
hology, cognitive science, human-computer interaction, and pattern recognition."
9a276c72acdb83660557489114a494b86a39f6ff,Emotion Classification through Lower Facial Expressions using Adaptive Support Vector Machines,"Emotion Classification through Lower Facial Expressions using Adaptive
Support Vector Machines
Porawat Visutsak
Department of Information Technology, Faculty of Industrial Technology and Management,
King Mongkut’s University of Technology North Bangkok,"
9a08459b0cb133f0f4352c58225446f9dc95ecc4,Metadata of the chapter that will be visualized in SpringerLink,"Metadata of the chapter that will be visualized in
SpringerLink
Book Title
Series Title
Chapter Title
Copyright Year
Copyright HolderName
Author
Corresponding Author
Author
Author
Instituto de Investigación en Informática de Albacete
Universidad de Castilla-La Mancha
02071, Albacete, Spain
Ambient Assisted Living. ICT-based Solutions in Real Life Situations
Sokolova
Marina V.
Fernández-Caballero
Experimentation on Emotion Regulation with Single-Colored Images
Springer International Publishing Switzerland"
9a4db91c5af7866af67f5b043cfb448170d13090,An Investigation of Face and Fingerprint Feature-Fusion Guidelines,"An Investigation of Face and Fingerprint
Feature-Fusion Guidelines
Dane Brown1,2 and Karen Bradshaw1
Rhodes University,
Department of Computer Science, Grahamstown, South Africa
Council for Scientific and Industrial Research,
Modelling and Digital Sciences, Pretoria, South Africa"
9ad65c5c5a2b22ef0343831fe0dabc2055d72497,EYEDIAP Database: Data Description and Gaze Tracking Evaluation Benchmarks,"EYEDIAP DATABASE: DATA DESCRIPTION
AND GAZE TRACKING EVALUATION
BENCHMARKS
Kenneth Alberto Funes Mora        Florent Monay
Jean-Marc Odobez
Idiap-RR-08-2014
Version of SEPTEMBER 18, 2014
Centre du Parc, Rue Marconi 19, P.O. Box 592, CH - 1920 Martigny
T +41 27 721 77 11  F +41 27 721 77 12   www.idiap.ch"
9ab463d117219ed51f602ff0ddbd3414217e3166,Weighted Transmedia Relevance Feedback for Image Retrieval and Auto-annotation,"Weighted Transmedia
Relevance Feedback for
Image Retrieval and
Auto-annotation
Thomas Mensink, Jakob Verbeek, Gabriela Csurka
TECHNICAL
REPORT
N° 0415
December 2011
Project-Teams LEAR - INRIA
nd TVPA - XRCE"
9a42c519f0aaa68debbe9df00b090ca446d25bc4,Face Recognition via Centralized Coordinate Learning,"Face Recognition via Centralized Coordinate
Learning
Xianbiao Qi, Lei Zhang"
9a6b80f8ea7e5f24e3da05a5151ba8b42494962f,Leveraging multiple tasks to regularize fine-grained classification,"Cancún Center, Cancún, México, December 4-8, 2016
978-1-5090-4847-2/16/$31.00 ©2016 IEEE
KingfisherRingedKingfisherWhite Breasted KingfisherMegaceryleCeryleChloroceryleHalcyonAlcedinidaeHalcyonidaeFig.1.Leveragingthetaxonomicontologyofbirdsforfinegrainedrecogni-tion.Fromtoptobottom,wehavefamily,orderandspeciesforfiveclassesofkingfishersintheCUB-200-2011dataset[6].Observehowidentifyingthefamilyorordercanhelpidentifyingtheclass,e.g.incaseofringedkingfisherandgreenkingfisher.Bestviewedenlarged,incolor.differencesandstrikinginter-classsimilarities.Mostmodernmethodsforfinegrainedrecognitionrelyonacombinationoflocalizingdiscriminativeregionsandlearningcorrespondingdiscriminativefeatures.Thisinturnrequiresstrongsuper-visionsuchaskeypointorattributeannotations,whichareexpensiveanddifficulttoobtainatscale.Ontheotherhand,sincefinegrainedrecognitiondealswithsubordinate-levelclassification,thereexistsanimpliedrelationshipsamonglabels.Theserelationshipsmaybetaxonomical(suchassuperclasses)orsemantic(suchasattributes)innature.Theontol-ogyobtainedinthismannercontainsrichlatentknowledgeaboutfinerdifferencesbetweenclassesthatcanbeexploitedforvisualclassification.Themodelweproposeconsistsofasingledeepconvolutionalneuralnetwork,witheachleveloftheontologygivingrisetoanadditionalsetoflabelsfortheinputimages.Theseadditionallabelsareusedasauxiliarytasksforamulti-tasknetwork,whichcanbetrainedend-to-endusingasimpleweightedobjectivefunction.Wealsoproposeanovelmethodtodynamicallyupdatethelearningrates(hereforthreferredtoasthetaskcoefficients)foreachtaskinthemulti-tasknetwork,basedonitsrelatednesstotheprimarytask.Inthiswork,weanalyzetheutilityofjointlylearningmultiplerelated/auxiliarytasksthatcouldregularizeeachothertopreventover-fitting,whileensuringthatthenetworkretainsitsdiscriminativecapability.Muchlikedropoutisbaggingtakentotheextreme,multi-tasklearningisanalogoustoboosting,ifeachtaskisconsideredaweaklearner.Wenotethatourmodelcanbepluggedintoorusedinconjunctionwithmorecomplexmulti-stagepipelinemethodssuchas[7]–[10]"
9af1cf562377b307580ca214ecd2c556e20df000,Video-Based Facial Expression Recognition Using Local Directional Binary Pattern,"Feb. 28
International Journal of Advanced Studies in Computer Science and Engineering
IJASCSE, Volume 4, Issue 2, 2015
Video-Based Facial Expression Recognition
Using Local Directional Binary Pattern
Sahar Hooshmand, Ali Jamali Avilaq, Amir Hossein Rezaie
Electrical Engineering Dept., AmirKabir Univarsity of Technology
Tehran, Iran"
9a7784eea6bfa62bf2834ee0b87a3cdda46006f2,Digital Comics Image Indexing Based on Deep Learning,"Article
Digital Comics Image Indexing Based on
Deep Learning
Nhu-Van Nguyen * ID , Christophe Rigaud ID and Jean-Christophe Burie ID
Lab L3I, University of La Rochelle, 17000 La Rochelle, France; (C.R.);
(J.-C.B.)
* Correspondence:
Received: 30 April 2018; Accepted: 27 June 2018; Published: 2 July 2018"
9a7858eda9b40b16002c6003b6db19828f94a6c6,Mooney face classification and prediction by learning across tone,"MOONEY FACE CLASSIFICATION AND PREDICTION BY LEARNING ACROSS TONE
Tsung-Wei Ke(cid:63)†
Stella X. Yu(cid:63)†
David Whitney(cid:63)
(cid:63) UC Berkeley / †ICSI"
9a88d23234ee41965ac17fc5774348563448a94d,3021977 GI P_212 Cover.indd,"Gesellschaft für Informatik e.V. (GI)
publishes  this  series  in  order  to  make  available  to  a  broad  public
recent findings in informatics (i.e. computer science and informa-
tion  systems),  to  document  conferences  that  are  organized  in  co-
operation with GI and to publish the annual GI Award dissertation.
Broken down into
• seminars
• proceedings
• dissertations
• thematics
urrent topics are dealt with from the vantage point of research and
development, teaching and further training in theory and practice.
The Editorial Committee uses an intensive review process in order
to ensure high quality contributions.
The volumes are published in German or English.
Information: http://www.gi.de/service/publikationen/lni/
ISSN 1617-5468
ISBN 978-3-88579-606-0
The proceedings of the BIOSIG 2013 include scientific contributions of the annual
onference of the Biometrics Special Interest Group (BIOSIG) of the Gesellschaft"
9a23a0402ae68cc6ea2fe0092b6ec2d40f667adb,High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs,"High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
Ting-Chun Wang1 Ming-Yu Liu1
Jun-Yan Zhu2 Andrew Tao1
Jan Kautz1 Bryan Catanzaro1
NVIDIA Corporation
UC Berkeley
Figure 1: We propose a generative adversarial framework for synthesizing 2048 × 1024 images from semantic label maps
(lower left corner in (a)). Compared to previous work [5], our results express more natural textures and details. (b) We can
hange labels in the original label map to create new scenes, like replacing trees with buildings. (c) Our framework also
llows a user to edit the appearance of individual objects in the scene, e.g. changing the color of a car or the texture of a road.
Please visit our website for more side-by-side comparisons as well as interactive editing demos."
9a10845115794117485fc84f9b9e6ada2a7d2b00,Eye In-painting with Exemplar Generative Adversarial Networks,"Eye In-Painting with Exemplar Generative Adversarial Networks
Brian Dolhansky, Cristian Canton Ferrer
Facebook Inc.
Hacker Way, Menlo Park (CA), USA
{bdol,"
9af9fa7727df11b86301a252db8a916c3a516a8d,VIBIKNet: Visual Bidirectional Kernelized Network for Visual Question Answering,"VIBIKNet: Visual Bidirectional Kernelized
Network for Visual Question Answering
Marc Bola˜nos1,2, ´Alvaro Peris3, Francisco Casacuberta3, Petia Radeva1,2
Universitat de Barcelona, Barcelona, Spain,
Computer Vision Center, Bellaterra, Spain,
PRHLT Research Center, Universitat Polit`ecnica de Val`encia, Val`encia, Spain,"
9a7843f19b7e1e089db9ba875fbea9773d739f71,A Review of Benchmarking Content Based Image Retrieval,"A Review of Benchmarking Content Based Image Retrieval
Gareth Loy∗ and Jan-Olof Eklundh
Royal Institute of Technology (KTH)
Stockholm, Sweden
tel: +46 8 790 6353
e-mail:
fax: +46 8 723 0302"
9a9019972dece591f502a2f794e81648b9e064fe,Combination of facial landmarks for robust eye localization using the Discriminative Generalized Hough Transform,"Combination of Facial Landmarks
for Robust Eye Localization
Using the Discriminative Generalized Hough Transform
Ferdinand Hahmann, Gordon B¨oer, Hauke Schramm
Institute of Applied Computer Science
University of Applied Sciences Kiel
Grenzstraße 3, 24149 Kiel"
0cfca73806f443188632266513bac6aaf6923fa8,Predictive Uncertainty in Large Scale Classification using Dropout - Stochastic Gradient Hamiltonian Monte Carlo,"Predictive Uncertainty in Large Scale Classification
using Dropout - Stochastic Gradient Hamiltonian
Monte Carlo.
Vergara, Diego∗1, Hern´andez, Sergio∗2, Valdenegro-Toro, Mat´ıas∗∗3 and Jorquera, Felipe∗4.
Laboratorio de Procesamiento de Informaci´on Geoespacial, Universidad Cat´olica del Maule, Chile.
German Research Centre for Artificial Intelligence, Bremen, Germany.
Email:"
0cd98be65a1a645f3c9618d9920be3a3dfc77574,Just-in-Time Reconstruction: Inpainting Sparse Maps Using Single View Depth Predictors as Priors,"Inpainting Sparse Maps using Single View Depth Predictors as Priors
Just-in-Time Reconstruction:
Chamara Saroj Weerasekera1, Thanuja Dharmasiri2, Ravi Garg1, Tom Drummond2 and Ian Reid1"
0c769c19d894e0dbd6eb314781dc1db3c626df57,Joint Detection and Identification Feature Learning for Person Search,"Joint Detection and Identification Feature Learning for Person Search
Tong Xiao1∗ Shuang Li1∗ Bochao Wang2 Liang Lin2 Xiaogang Wang1
The Chinese University of Hong Kong 2Sun Yat-Sen University"
0ccd410b6ae977a945a84bad1c2785cef4c73214,Pseudo two-dimensional Hidden Markov Models for face detection in colour images,"Pseudo two-dimensional Hidden Markov Models
for face detection in colour images
ephane Marchand-Maillet
Bernard M
erialdo
Department of Multimedia Communications
EURECOM Institute
	 Sophia-Antipolis, France
http:www.eurecom.fr~marchand
To be presented in the
nd Int. Conf. on Audio- and Video-based Biometric Person Authentication"
0c95ff762bdf6a20609f49f1eb5248de3f748866,Fine-Grained Walking Activity Recognition via Driving Recorder Dataset,"Fine-grained Walking Activity Recognition
via Driving Recorder Dataset
Hirokatsu Kataoka (AIST), Yoshimitsu Aoki (Keio Univ.), Yutaka Satoh (AIST)
Shoko Oikawa (NTSEL), Yasuhiro Matsui (NTSEL)
Email:
http://hirokatsukataoka.net/"
0c5ddfa02982dcad47704888b271997c4de0674b,Model-driven and Data-driven Approaches for some Object Recognition Problems,
0c3f7272a68c8e0aa6b92d132d1bf8541c062141,Kruskal-Wallis-Based Computationally Efficient Feature Selection for Face Recognition,"Hindawi Publishing Corporation
e Scientific World Journal
Volume 2014, Article ID 672630, 6 pages
http://dx.doi.org/10.1155/2014/672630
Research Article
Kruskal-Wallis-Based Computationally Efficient Feature
Selection for Face Recognition
Sajid Ali Khan,1,2 Ayyaz Hussain,3 Abdul Basit,1 and Sheeraz Akram1
Department of Software Engineering, Foundation University, Rawalpindi 46000, Pakistan
Department of Computer Science, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology Islamabad,
Islamabad 44000, Pakistan
Department of Computer Science and Software Engineering, International Islamic University, Islamabad 44000, Pakistan
Correspondence should be addressed to Sajid Ali Khan;
Received 5 December 2013; Accepted 10 February 2014; Published 21 May 2014
Academic Editors: S. Balochian, V. Bhatnagar, and Y. Zhang
Copyright © 2014 Sajid Ali Khan et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Face recognition in today’s technological world, and face recognition applications attain much more importance. Most of the
existing work used frontal face images to classify face image. However these techniques fail when applied on real world face images.
The proposed technique effectively extracts the prominent facial features. Most of the features are redundant and do not contribute"
0cc2fc148eef46c1141edd276d903853052fc19d,Estado del arte en reconocimiento facial,"Estado del arte en reconocimiento facial
Martín Adrián Garduño Santana, L. E. Díaz-Sánchez, Israel Tabarez Paz,
Marcelo Romero Huertas
Universidad Autónoma del Estado de México, Toluca, México
Resumen.  En  este  trabajo  se  resumen  los  métodos  más  utilizados  para  el
reconocimiento  facial,  incluyendo  las  ventajas  y  desventajas  de  los  sistemas
desarrollados  hasta  ahora.    También  se  describen  las  futuras  líneas  de
investigación  y  se  discute  el  rumbo del  reconocimiento  facial  en  los  próximos
ños. Esta revisión es relevante pues se busca la implementación de un novedoso
sistema de reconocimiento facial.
Palabras  clave:  reconocimiento  facial,  sistemas  biométricos,  ciudades
inteligentes, imágenes 2D y 3D.
Face Recognition: a Survey"
0c3c469e46668ea2c38a6de610d675975f337522,Self-tuned Visual Subclass Learning with Shared Samples An Incremental Approach,"Self-tuned Visual Subclass Learning with Shared Samples
An Incremental Approach
Updated ICCV 2013 Submission
Hossein Azizpour
Royal Insitute of Technology(KTH)
Stefan Carlsson
Royal Insitute of Technology(KTH)"
0cff123a31dcc115377ecca6ba137bebca909ff8,Anxiety dissociates the adaptive functions of sensory and motor response enhancements to social threats,"RESEARCH ARTICLE
Anxiety dissociates the adaptive functions
of sensory and motor response
enhancements to social threats
Marwa El Zein1,2*, Valentin Wyart1†, Julie Gre` zes1†
Laboratoire de Neurosciences Cognitives, De´ partement d’Etudes Cognitives, Ecole
Normale Supe´ rieure, PSL Research University, Paris, France; 2Universite´ Pierre et
Marie Curie, Paris, France"
0ced7b814ec3bb9aebe0fcf0cac3d78f36361eae,Central Local Directional Pattern Value Flooding Co-occurrence Matrix based Features for Face Recognition,"Dr. P Chandra Sekhar Reddy, International Journal of Computer Science and Mobile Computing, Vol.6 Issue.1, January- 2017, pg. 221-227
Available Online at www.ijcsmc.com
International Journal of Computer Science and Mobile Computing
A Monthly Journal of Computer Science and Information Technology
ISSN 2320–088X
IMPACT FACTOR: 6.017
IJCSMC, Vol. 6, Issue. 1, January 2017, pg.221 – 227
Central Local Directional Pattern Value
Flooding Co-occurrence Matrix based
Features for Face Recognition
Dr. P Chandra Sekhar Reddy
Professor, CSE Department, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad"
0cfcc1cd8bae5f5899cef0995debd7b38c46e817,Discrete texture traces: Topological representation of geometric context,"Discrete Texture Traces: Topological Representation of Geometric Context
Jan Ernst∗ and Maneesh K. Singh
Siemens Corporation, Corporate Research and Technology, Princeton, NJ, USA
Department of Computer Science and Mathematics, Goethe University, Frankfurt am Main, Germany
Visvanathan Ramesh†"
0ceda9dae8b9f322df65ca2ef02caca9758aec6f,Context-Aware CNNs for Person Head Detection,"Context-aware CNNs for person head detection
Tuan-Hung Vu∗
Anton Osokin†
INRIA/ENS
Ivan Laptev∗"
0c79485f64733bd128ef8c395034b6bc77abf94d,Fully automatic expression-invariant face correspondence,"Fully Automatic Expression-Invariant Face Correspondence
Augusto Salazar∗†
Stefanie Wuhrer†‡
Chang Shu‡
Flavio Prieto §
February 1, 2013"
0c91808994a250d7be332400a534a9291ca3b60e,Weak Hypotheses and Boosting for Generic Object Detection and Recognition,"Weak Hypotheses and Boosting for Generic
Object Detection and Recognition
A. Opelt1,2, M. Fussenegger1,2, A. Pinz2, and P. Auer1
Institute of Computer Science,
8700 Leoben, Austria
Institute of Electrical Measurement and Measurement Signal Processing,
8010 Graz, Austria"
0c5a2bb5d1a1e9bb332207be61e13d0afb8f278c,A Supervised Learning Methodology for Real-Time Disguised Face Recognition in the Wild,"A Supervised Learning Methodology for Real-Time Disguised Face
Recognition in the Wild
Saumya Kumaar3, Abhinandan Dogra4, Abrar Majeedi4, Hanan Gani4, Ravi M. Vishwanath2 and S N Omkar1"
0cd8895b4a8f16618686f622522726991ca2a324,Discrete Choice Models for Static Facial Expression Recognition,"Discrete Choice Models for Static Facial Expression
Recognition
Gianluca Antonini1, Matteo Sorci1, Michel Bierlaire2, and Jean-Philippe Thiran1
Ecole Polytechnique Federale de Lausanne, Signal Processing Institute
Ecole Polytechnique Federale de Lausanne, Operation Research Group
Ecublens, 1015 Lausanne, Switzerland
Ecublens, 1015 Lausanne, Switzerland"
0c8d675bcd4489e886f35bee2a347c948ffee270,Semantic bottleneck for computer vision tasks,"Semantic bottleneck for computer vision tasks
Maxime Bucher1,2, St´ephane Herbin1, and Fr´ed´eric Jurie2
ONERA, Universit´e Paris-Saclay, FR-91123 Palaiseau, France
Normandie Univ, UNICAEN, ENSICAEN, CNRS"
0c24ccc6d6c386a8d555a81166eaf6e8d4dfccc3,Person count localization in videos from noisy foreground and detections,"Person Count Localization in Videos from Noisy Foreground and Detections
Sheng Chen1, Alan Fern1, Sinisa Todorovic1
Oregon State University.
In this paper, we introduce a new problem, person count localization from
noisy foreground and person detections. Our formulation strikes a middle-
ground between person detection and frame-level counting. Given a video,
our goal is to output for each frame a set of:
. Detections optimally covering both isolated individuals and crowds
of people in the video; and
. Counts assigned to each detection indicating the number of people
inside.
The problem of detecting people in videos of crowded scenes, where
people frequently appear under severe occlusion by other people in the
rowd is an important line of research, since detecting people in video frames
has become the standard initial step of many approaches to activity recogni-
tion [1, 3, 4], and multi-object tracking by detection [6, 8, 9]. They typically
use as input human appearance, pose, and orientation, and thus critically
depend on robust person detections. In many domains, however, such as
videos of American football or public spaces crowded with pedestrians, de-
tecting every individual person is highly unreliable, and remains an open"
0cbe059c181278a373292a6af1667c54911e7925,"""Owl"" and ""Lizard"": Patterns of Head Pose and Eye Pose in Driver Gaze Classification","Owl and Lizard: Patterns of Head Pose and Eye
Pose in Driver Gaze Classification
Lex Fridman1, Joonbum Lee1, Bryan Reimer1, and Trent Victor2
Massachusetts Institute of Technology (MIT)
Chalmers University of Technology, SAFER"
0c049cc7320f9b92f91210ab6961aa6644c867cd,Delving Deep Into Coarse-to-Fine Framework for Facial Landmark Localization,"Delving Deep into Coarse-to-fine Framework
for Facial Landmark Localization
Xi Chen, Erjin Zhou, Yuchen Mo, Jiancheng Liu, Zhimin Cao
Megvii Research
{chenxi, zej, moyuchen, liujiancheng,"
0ca96dc1557032ff9259562a5b8fc026334997a6,Spectral Graph-Based Method of Multimodal Word Embedding,"Proceedings of TextGraphs-11: the Workshop on Graph-based Methods for Natural Language Processing, ACL 2017, pages 39–44,
Vancouver, Canada, August 3, 2017. c(cid:13)2017 Association for Computational Linguistics"
0c286be42e734c2469563e189d7a8b11155386d5,ABSTRACT Title of Dissertation: GAIT AS A BIOMETRIC FOR PERSON IDENTIFICATION IN VIDEO,
0c17c42d71eacd2244e43fa55a8ed96607337cca,Automatic Face Reenactment,"Automatic Face Reenactment
Pablo Garrido1
Thorsten Thorm¨ahlen2
Levi Valgaerts1
Patrick P´erez3
Ole Rehmsen1
Christian Theobalt1
Philipps-Universit¨at Marburg
Technicolor
MPI for Informatics"
0cb2dd5f178e3a297a0c33068961018659d0f443,IARPA Janus Benchmark-B Face Dataset,"© 2017 Noblis, Inc.  IARPA Janus Benchmark-B Face Dataset Cameron Whitelam, Emma Taborsky*, Austin Blanton, Brianna Maze*, Jocelyn Adams*, Tim Miller*, Nathan Kalka*, Anil K. Jain**, James A. Duncan*, Kristen Allen, Jordan Cheney*, Patrick Grother***  Noblis* Michigan State University** NIST*** 21 July 2017"
0ca475433d74abb3c0f38fbe9d212058dc771570,Learning pairwise feature dissimilarities for person re-identification,"Learning Pairwise Feature Dissimilarities
for Person Re-Identification
Niki Martinel
University of Udine
Udine, Italy
Christian Micheloni
University of Udine
Udine, Italy
Claudio Piciarelli
University of Udine
Udine, Italy"
0cd032a93890d61b9bd187119abee0d6aeb899f7,Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval,"IEEE TRANSACTION ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Iterative Quantization:
A Procrustean Approach to Learning Binary
Codes for Large-scale Image Retrieval
Yunchao Gong, Svetlana Lazebnik, Albert Gordo, Florent Perronnin"
0c87f5a6deba422c0db261c4497b9b013b4ef5b8,Robust Face Detection using Convolutional Neural Network,"International Journal of Computer Applications (0975 – 8887)
Volume 170 – No.6, July 2017
Robust Face Detection using Convolutional
Robert Yao Aaronson
Sch. of Comp. Sci.& Tech
Jiangsu Univ. of Sci. & Tech.
No. 2 Mengxi Road Jingkou
District Zhenjiang Prov. 212003
Neural Network
Wu Chen
Sch. of Comp. Sci. & Tech
Jiangsu Univ. of Sci. & Tech.
No. 2 Mengxi Road Jingkou
District Zhenjiang Prov. 212003
Ben-Bright Benuwa
Sch. of Comp. Sci. & Comm.
Eng. Jiangsu Univ. Xuefu Road
01 Jingkou District Zhenjiang
Prov. 212003
supported  by"
0cf7da0df64557a4774100f6fde898bc4a3c4840,Shape matching and object recognition using low distortion correspondences,"Shape Matching and Object Recognition using Low Distortion Correspondences
Alexander C. Berg Tamara L. Berg
Jitendra Malik
Department of Electrical Engineering and Computer Science
U.C. Berkeley"
0c0b33baf60c787b3361a2671ae9aa077545b845,A meta-analysis of face recognition covariates,"A Meta-Analysis of Face Recognition Covariates
Yui Man Lui, David Bolme, Bruce A. Draper, J. Ross Beveridge, Geoff Givens, P. Jonathon Phillips"
0cec42a1593a02ce3f4a44d375e3b95f5797aa21,Recognizing Scene Categories of Historical Postcards,"Recognizing Scene Categories of Historical
Postcards
Rene Grzeszick, Gernot A. Fink
{rene.grzeszick,
Department of Computer Science, TU Dortmund"
0c98defb5a83ea5dc5d90538d1cc8c4b6267a1cb,Perception of Dynamic Facial Expressions of Emotion: Electrophysiological Evidence,"Humboldt-Universität zu Berlin
Dissertation
Perception of Dynamic Facial Expressions
of Emotion: Electrophysiological Evidence
zur Erlangung des akademischen Grades Doctor rerum naturalium im Fach Psychologie
Mathematisch-Naturwisseschafttlichen Fakultät II
Guillermo Recio
Dekan: Prof. Dr. Dr. Elmar Kulke
Gutachter/in:  1. Prof. Dr. Werner Sommer
2. Prof. Dr. Annekathrin Schacht
3. Prof. Dr. Birgit Stürmer
Datum der Einreichung:
7.09.2012
Datum der Promotion:
07.03.2013"
0c1fc3636dd9f8c9fd651b78ff65b03277a3aa47,Smart surveillance framework: A versatile tool for video analysis,"Smart Surveillance Framework: A Versatile Tool for Video Analysis
Antonio C. Nazare Jr., Cassio E. dos Santos Jr., Renato Ferreira, William Robson Schwartz
Department of Computer Science,Universidade Federal de Minas Gerais, Belo Horizonte, Brazil"
0c03bb741972c99b71d8d733b92e5fa9430cbede,Learning rank reduced interpolation with principal component analysis,"Learning Rank Reduced Interpolation
with Principal Component Analysis
Matthias Ochs1, Henry Bradler1 and Rudolf Mester1,2"
0c25a4636ebde18e229f7e459f1adaab1e9a2db9,Multi-class Classification and Clustering based Multi-object Tracking,"Multi-class Classification and Clustering based
Multi-object Tracking
Nii Longdon Sowah, Qingbo Wu, Fanman Meng"
0c5f9f5083b9fca4dcdbc4b122099ac1f630728b,Visual Semantic Role Labeling,"Visual Semantic Role Labeling
Saurabh Gupta
UC Berkeley
Jitendra Malik
UC Berkeley"
0c30850067c296a01b72cf4803c9712926ae5a95,Text-Dependent Audiovisual Synchrony Detection for Spoofing Detection in Mobile Person Recognition,"INTERSPEECH 2016
September 8–12, 2016, San Francisco, USA
Text-Dependent Audiovisual Synchrony Detection for Spoofing Detection in
Mobile Person Recognition
Amit Aides1,2, Hagai Aronowitz1
Dept of Electrical Engineering,Technion - Israel Institute of Technology, Haifa, Israel
IBM Research - Haifa, Israel
{amitaid,"
0cbefba0f41982bdff091d0e5f0d5ef93185a55c,"Challenges in Monocular Visual Odometry: Photometric Calibration, Motion Bias, and Rolling Shutter Effect","Challenges in Monocular Visual Odometry:
Photometric Calibration, Motion Bias and
Rolling Shutter Effect
Nan Yang1,2,∗, Rui Wang1,2,∗, Xiang Gao1 and Daniel Cremers1,2"
0c53ef79bb8e5ba4e6a8ebad6d453ecf3672926d,Weakly Supervised PatchNets: Describing and Aggregating Local Patches for Scene Recognition,"SUBMITTED TO JOURNAL
Weakly Supervised PatchNets: Describing and
Aggregating Local Patches for Scene Recognition
Zhe Wang, Limin Wang, Yali Wang, Bowen Zhang, and Yu Qiao, Senior Member, IEEE"
0cb7e4c2f6355c73bfc8e6d5cdfad26f3fde0baf,XPRESSION R ECOGNITION BASED ON WAPA AND OEPA F AST ICA,"International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 5, No. 3, May 2014
FACIAL EXPRESSION RECOGNITION BASED ON
WAPA AND OEPA FASTICA
Humayra Binte Ali1 and David M W Powers2
Computer Science, Engineering and Mathematics School, Flinders University, Australia
Computer Science, Engineering and Mathematics School, Flinders University, Australia"
0c069a870367b54dd06d0da63b1e3a900a257298,Weakly Supervised Learning of Foreground-Background Segmentation using Masked RBMs,"Author manuscript, published in ""ICANN 2011 - International Conference on Artificial Neural Networks (2011)"""
0cca85ee872ae6f6a4d305880b4461f152b1d808,Automatic framework for tracking honeybee's antennae and mouthparts from low framerate video,"AUTOMATIC FRAMEWORK FOR TRACKING HONEYBEE’S ANTENNAE AND
MOUTHPARTS FROM LOW FRAMERATE VIDEO
Minmin Shen⋆
Paul Szyszka†
C. Giovanni Galizia†
Dorit Merhof⋆
⋆ INCIDE Center, University of Konstanz
Institute of Neurobiology, University of Konstanz"
0ce4110d4c3d8b19ca0f7f75bc680aa9ba8d239a,Genetic algorithms for classifiers' training sets optimisation applied to human face recognition,"JOURNAL OF MEDICAL INFORMATICS & TECHNOLOGIES Vol. 11/2007, ISSN 1642-6037
Michał KAWULOK*
GENETIC ALGORITHMS FOR CLASSIFIERS’ TRAINING SETS
OPTIMISATION APPLIED TO HUMAN FACE RECOGNITION
support vector machines,
genetic algorithms,
human face recognition
Human  face  recognition  is  a  multi-stage  process  within  which  many  classification  problems  must  be
solved. This is performed by learning machines which elaborate classification rules based on a given training set.
Therefore, one of the most important issues is selection of a training set which would properly represent the data
that will be further classified. This paper presents an approach which utilizes genetic algorithms for selecting
lassifiers’ training sets. This approach was implemented for the Support Vector Machines which is applied in
two areas of automatic human face recognition: face verification and feature vectors comparison. Effectiveness
of the presented concept was confirmed with appropriate experiments which results are described in this paper.
.  INTRODUCTION
Face recognition [7, 13, 14] is among the most popular biometric techniques which are
eing developed nowadays and it is worth noticing that this is the method which is the most
frequently used naturally by humans. Automatic face recognition is characterized by a low
level of required interaction with a person who is being recognized, but offers relatively low
effectiveness  comparing  to  other  biometric  methods  [4,  9].  A  face  recognition  system"
0c990e779067c563a79ae17c9d36094a745d7ed8,Model interpolation for eye localization using the Discriminative Generalized Hough Transform,"Model Interpolation for Eye Localization Using the
Discriminative Generalized Hough Transform
Ferdinand Hahmann, Heike Ruppertshofen, Gordon B¨oer, Hauke Schramm
Institute of Applied Computer Science
University of Applied Sciences Kiel
Grenzstraße 3
4149 Kiel"
0cdf238fd44684b49302c22b062772e7c66ea182,U NSUPERVISED ROBOTIC SORTING : T OWARDS AUTONOMOUS DECISION MAKING ROBOTS,"International Journal of Artificial Intelligence and Applications (IJAIA), Vol.9, No.2, March 2018
UNSUPERVISED ROBOTIC SORTING: TOWARDS
AUTONOMOUS DECISION MAKING ROBOTS
Joris Gu´Erin, St´Ephane Thiery, Eric Nyiri And Olivier Gibaru
Arts et M´etiers ParisTech, Lille, FRANCE"
0cdac46ec42be2d81f64ec4ee53d88be43290d52,Temporal Poselets for Collective Activity Detection and Recognition,"Temporal Poselets for Collective Activity Detection and Recognition
Moin Nabi
Alessio Del Bue
Vittorio Murino
Pattern Analysis and Computer Vision (PAVIS)
Istituto Italiano di Tecnologia (IIT)
Via Morego 30, Genova, Italy"
0c53b45321131e61d1266cb960fc47c401f856f1,Space-Time Body Pose Estimation in Uncontrolled Environments,"Space-time Body Pose Estimation in Uncontrolled Environments
Marcel Germann
ETH Zurich
Switzerland
Tiberiu Popa
ETH Zurich
Switzerland
Remo Ziegler
LiberoVision AG
Switzerland
Richard Keiser
LiberoVision AG
Switzerland
Markus Gross
ETH Zurich
Switzerland"
71766bf224d5c74a0be6996b38d8885c2eed5a2c,Fooling Vision and Language Models Despite Localization and Attention Mechanism,
7135bed472d9a307d0612634d690c6306f5bce26,A Unified Framework for Concurrent Pedestrian and Cyclist Detection,"This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.
A Unified Framework for Concurrent Pedestrian
nd Cyclist Detection
Xiaofei Li, Lingxi Li, Fabian Flohr, Jianqiang Wang, Hui Xiong, Morys Bernhard, Shuyue Pan,
Dariu M. Gavrila, and Keqiang Li"
71529e3e51f2967e338124652e93a3d34eb6c5e1,Deep triplet-group network by exploiting symmetric and asymmetric information for person reidentification,"Downloaded From: https://www.spiedigitallibrary.org/journals/Journal-of-Electronic-Imaging on 9/6/2018
Terms of Use: https://www.spiedigitallibrary.org/terms-of-use
Deeptriplet-groupnetworkbyexploitingsymmetricandasymmetricinformationforpersonreidentificationBenzhiYuNingXuBenzhiYu,NingXu,“Deeptriplet-groupnetworkbyexploitingsymmetricandasymmetricinformationforpersonreidentification,”J.Electron.Imaging27(3),033033(2018),doi:10.1117/1.JEI.27.3.033033."
71c966967fe77132a6c87999bde17a80e76b1202,Object Detection Using Deep Learning - Learning where to search using visual attention,"Eberhard Karls Universit¨at T¨ubingen
Mathematisch-Naturwissenschaftliche Fakult¨at
Wilhelm-Schickard-Institut f¨ur Informatik
Master Thesis Computer Science
Object Detection Using Deep Learning
Learning where to search using visual attention
Alina Kloss
May 26, 2015
Reviewers
Prof. Hendrik Lensch
Computer Graphics
Wilhelm-Schickard-Institute for
Computer Science
University of T¨ubingen
Prof. Martin Butz
Cognitive Modeling
Wilhelm-Schickard-Institute for
Computer Science
University of T¨ubingen
Supervisors"
71d8fae870ea78a89e231247afb3259267e09799,Probabilistic multi-class segmentation for the Amazon Picking Challenge,"Probabilistic Multi-Class Segmentation
for the Amazon Picking Challenge
Rico Jonschkowski
Clemens Eppner∗
Sebastian H¨ofer∗
Roberto Mart´ın-Mart´ın∗ Oliver Brock"
714794c74941e45798d9c405a4fec1138cff2df3,Iris Segmentation: State of the Art and Innovative Methods,"Iris segmentation: state of the art and innovative
methods
Ruggero Donida Labati, Angelo Genovese, Vincenzo Piuri, and Fabio Scotti"
7173871866fc7e555e9123d1d7133d20577054e8,Simultaneous Adversarial Training - Learn from Others Mistakes,"Simultaneous Adversarial Training - Learn from
Others’ Mistakes
Zukang Liao
Lite-On Singapore Pte. Ltd, 2Imperial College London"
714947e4d7f79f753c5c44eac701185e37086276,An Exponential Representation in the API Algorithm for Hidden Markov Models Training,"An Exponential Representation in the API
Algorithm for Hidden Markov Models Training
S´ebastien Aupetit1, Nicolas Monmarch´e1, Mohamed Slimane1, and
Pierre Liardet2
Universit´e Fran¸cois-Rabelais de Tours, Laboratoire d’Informatique
Polytech’Tours, 64, Av Jean Portalis, 37200 Tours, France
Universit´e de Provence, CMI
Laboratoire ATP, UMR-CNRS 6632
9 rue F. Joliot-Curie, 13453 Marseille cedex 13, France"
714d487571ca0d676bad75c8fa622d6f50df953b,eBear: An expressive Bear-Like robot,"eBear: An Expressive Bear-Like Robot
Xiao Zhang, Ali Mollahosseini, Amir H. Kargar B., Evan Boucher,
Richard M. Voyles, Rodney Nielsen and Mohammd H. Mahoor"
719fd645c4da6575ab0e774891ba30d7dfcc53aa,LOAM: Lidar Odometry and Mapping in Real-time,"LOAM: Lidar Odometry and Mapping in Real-time
Ji Zhang and Sanjiv Singh"
7174e77f8e26aef3105996512b787b336320d46f,People Counting in High Density Crowds from Still Images,"People Counting in High Density Crowds from Still
Images
Ankan Bansal, and K S Venkatesh"
7189d5584416ef2a39d6ab16929dfecdddc10081,A Review of Face Sketch Recognition Systems,"Journal of Theoretical and Applied Information Technology
20th November 2015. Vol.81. No.2
© 2005 - 2015 JATIT & LLS. All rights reserved.
ISSN: 1992-8645                                                       www.jatit.org                                                          E-ISSN: 1817-3195
A REVIEW OF FACE SKETCH RECOGNITION SYSTEMS
SALAH EDDINE LAHLALI,  2ABDELALIM SADIQ, 3 SAMIR MBARKI
23Department of Computing, Faculty of sciences, IbnTofail University, Kenitra, Morocco
E-mail:"
71286a2b3d564daf171cdef54ff8972159152729,Combinatorial Resampling Particle Filter: An Effective and Efficient Method for Articulated Object Tracking,"Noname manuscript No.
(will be inserted by the editor)
Combinatorial Resampling Particle Filter: an Effective and Efficient
Method for Articulated Object Tracking
Christophe Gonzales · S´everine Dubuisson
Received: date / Accepted: date"
710ce8cf25f31df8547b888519b414187e989257,Amygdala activation predicts gaze toward fearful eyes.,"The Journal of Neuroscience, July 15, 2009 • 29(28):9123–9126 • 9123
Brief Communications
Amygdala Activation Predicts Gaze toward Fearful Eyes
Matthias Gamer and Christian Bu¨chel
Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, D-20246 Hamburg, Germany
The human amygdala can be robustly activated by presenting fearful faces, and it has been speculated that this activation has functional
relevance for redirecting the gaze toward the eye region. To clarify this relationship between amygdala activation and gaze-orienting behavior,
functional magnetic resonance imaging data and eye movements were simultaneously acquired in the current study during the evaluation of
facial expressions. Fearful, angry, happy, and neutral faces were briefly presented to healthy volunteers in an event-related manner. We con-
trolled for the initial fixation by unpredictably shifting the faces downward or upward on each trial, such that the eyes or the mouth were
presentedatfixation.Acrossemotionalexpressions,participantsshowedabiastoshifttheirgazetowardtheeyes,butthemagnitudeofthiseffect
followed the distribution of diagnostically relevant regions in the face. Amygdala activity was specifically enhanced for fearful faces with the
mouth aligned to fixation, and this differential activation predicted gazing behavior preferentially targeting the eye region. These results reveal
direct role of the amygdala in reflexive gaze initiation toward fearfully widened eyes. They mirror deficits observed in patients with amygdala
lesions and open a window for future studies on patients with autism spectrum disorder, in which deficits in emotion recognition, probably
related to atypical gaze patterns and abnormal amygdala activation, have been observed.
Introduction
The human amygdala is known to be robustly activated by the
presentation of fearful faces (Morris et al., 1996; Hariri et al.,
002; Gla¨scher et al., 2004; Reinders et al., 2005), which seems to"
71edcfe5e3a4e1678698a0659a7e51555291d242,Who's that Actor? Automatic Labelling of Actors in TV Series Starting from IMDB Images,"Who’s that Actor? Automatic Labelling of
Actors in TV series starting from IMDB Images
Rahaf Aljundi(cid:63), Punarjay Chakravarty(cid:63) and Tinne Tuytelaars
KU Leuven, ESAT-PSI, iMinds, Belgium"
71f969fdc6990b21536c5662c52110d7fdb29028,Driver Gaze Tracking and Eyes Off the Road Detection System Using a Depth Camera,"X Encontro de Alunos e Docentes do DCA/FEEC/UNICAMP (EADCA)
X DCA/FEEC/University of Campinas (UNICAMP) Workshop (EADCA)
Campinas, 26 e 27 de outubro de 2017
Campinas, Brazil, October 26-27, 2017
Driver Gaze Tracking and Eyes Off the Road Detection System
Using a Depth Camera
Ribeiro, Rafael F. , Costa, P. D. P (Orientador)
Dept. of Computer Engineering and Industrial Automation (DCA)
School of Electrical and Computer Engineering (FEEC)
University of Campinas (Unicamp)
Postal Code 6101, 13083-970 – Campinas, SP, Brazil"
71f1e72670e676b6902cce0d6fc0b4f63b46ca28,Survey paper : Face Detection and Face Recognition,"Survey paper:
Face Detection and Face Recognition
By Hyun Hoi James Kim
. Introduction
Face recognition is one of biometric methods identifying individuals by the features of face. Research in this
rea has been conducted for more than 30 years; as a result, the current status of face recognition technology
is  well  advanced.  Many  commercial  applications  of  face  recognition  are  also  available  such  as  criminal
identification, security system, image and film processing.
From the sequence of images captured by camera, the goal is to find best match with given image. Using a
pre-stored  image  database,  the  face  recognition  system  should  be  able  to  identify  or  verify  one  or  more
persons in the scene. Before face recognition is performed, the system should determine whether or not there
is a face in a given image or given video, a sequence of images. This process is called face detection. Once a
face is detected, face region should be isolated from the scene for the face recognition. The face detection and
face extraction are often performed simultaneously. The overall process is depicted in Fig 1.
Identification
or Verification
Feature Extraction
Face Detection
Face Recognition
Input"
71dcbca34d71bda0bc41c33c04d2c1a740274feb,An Innovative Mean Approach for Plastic Surgery Face Recognition,"International Journal of Science and Research (IJSR)
ISSN (Online): 2319-7064
Index Copernicus Value (2014): 6.14 | Impact Factor (2014): 4.438
An Innovative Mean Approach for Plastic Surgery
Face Recognition
Mahendra P. Randive1, Umesh W. Hore2
Student of M.E., Department of Electronics & Telecommunication Engineering,
P. R. Patil College of Engineering, Amravati Maharashtra – India
Assistant Professor, Department of Electronics & Telecommunication Engineering,
P. R. Patil College of Engineering, Amravati Maharashtra – India"
71f7be73a575f3689b0137446289d02462e1c5b0,Adaptive Multi-Scale Information Flow for Object Detection.,"CHEN ET AL.: ADAPTIVE MULTI-SCALE INFORMATION FLOW FOR DETECTION
Adaptive Multi-Scale Information Flow for
Object Detection
Xiaoyu Chen
Wei Li
Qingbo Wu
Fanman Meng
School of Information and
Communication Engineering
University of Electronic Science and
Technology of China"
713f7659eba67f12c0a3ce44518a11d9b748e225,Depth superresolution using motion adaptive regularization,"Depth Superresolution using Motion Adaptive Regularization
Ulugbek S. Kamilov∗ and Petros T. Boufounos
September 11, 2018"
712237121aa189179ac216bee7ecd5eaa79aff56,Prevention of Problem Gambling : A Comprehensive Review of the Evidence,"University of Lethbridge Research Repository
Faculty Research and Publications
http://opus.uleth.ca
Williams, Robert
008-12-01
Prevention of Problem Gambling: A
Comprehensive Review of the Evidence
Williams, Robert J.
Prepared for the Ontario Problem Gambling Research Centre
Williams, R. J., West, B. L., & Simpson, R. I. (2008). Prevention of problem gambling: A
omprehensive review of the evidence. Report prepared for the Ontario Problem Gambling
Research Centre, Guelph, Ontario, CANADA. Dec 1, 2007 [Revised December 1, 2008].
http://hdl.handle.net/10133/414
Downloaded from University of Lethbridge Research Repository, OPUS"
71dcf25a3ea3801f09d6cc446dbf78e22481d609,Face recognition with the continuous n-tuple classifier.,"FaceRecognitionwiththecontinuous
n-tupleclassi(cid:12)er
S.M.Lucas
DepartmentofElectronicSystemsEngineering
UniversityofEssex
ColchesterCOSQ,UK"
712609494dd049b44ebfd82698b9305ef07f027b,Biometric bits extraction through phase quantization based on feature level fusion,"Telecommun Syst (2011) 47:255–273
DOI 10.1007/s11235-010-9317-z
Biometric bits extraction through phase quantization based
on feature level fusion
Hyunggu Lee · Andrew Beng Jin Teoh · Jaihie Kim
Published online: 4 June 2010
© Springer Science+Business Media, LLC 2010"
710011644006c18291ad512456b7580095d628a2,Learning Residual Images for Face Attribute Manipulation,"Learning Residual Images for Face Attribute Manipulation
Wei Shen
Rujie Liu
Fujitsu Research & Development Center, Beijing, China.
{shenwei,"
713345804a00c6c0083e4155b904956bb95949da,Scalable Normalized Cut with Improved Spectral Rotation,Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
71424a706a2e4b9bc5fd049aefe83d73873c0145,How Unlabeled Web Videos Help Complex Event Detection?,"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
positive videosunlabeled web videosObjectfish, human……Actionpull, stand……Scenelake, river……helpful videosharmful videos concepts in positive videosFigure1:Anexampleshowingtheinfluenceofunlabeledwebvideosw.r.tthedetectionoftheeventlandingafish.detection,traditionalapproachesrelyonintroducingsomeexternalsourcesthatarerelevanttothetargetevents.Someresearchesassignedsoftlabelstorelatedexemplarsbyassess-ingtheir“relatedness”withrespecttothepositiveones.Withvariousconceptselectionstrategies,Yeetal.andMaetal.madeuseofhigh-levelconceptsourcessuchasSINdatasettofacilitatethecomplexeventdetection[Yeetal.,2015].In[Duanetal.,2012],event-relatedwebvideoswerefilteredouttohelpdetectcomplexevents.Generally,suchexternalsourcesusedbyextantmethodsareartificiallypickedandla-beled.However,buildingtheseexternaldatausuallyrequiresprofessionalhumanannotators,andtheprocedureistootime-consumingandcostlytoscale.Onthecontrary,thereareplentyoflow-costunlabeledvideosontheweb,whichmighthavesignificantinformationforthecomplexeventdetection.Asaresult,itwouldbebeneficialtoexploitamoreflexibleapproachwhichisabletoutilizetheeasy-to-getunlabeledwebvideostogetherwiththelimitedlabeleddata.Opensourcevideos,i.e.,unlabeledwebvideoswithout"
71406b7358812400d0626e8d62e7eb38cea99bbe,PAID I ON IMPROVING FACE DETECTION PERFORMANCE BY MODELLING CONTEXTUAL INFORMATION,"ON IMPROVING FACE DETECTION
PERFORMANCE BY MODELLING
CONTEXTUAL INFORMATION
Cosmin Atanasoaei        Chris McCool
Sébastien Marcel
Idiap-RR-43-2010
DECEMBER 2010
Centre du Parc, Rue Marconi 19, P.O. Box 592, CH - 1920 Martigny
T +41 27 721 77 11  F +41 27 721 77 12   www.idiap.ch"
71f98c3f7a5b02ab193110d5ae9f9d48a1c5ec38,Deep Human Parsing with Active Template Regression,"Deep Human Parsing with Active Template
Regression
Xiaodan Liang, Si Liu, Xiaohui Shen, Jianchao Yang, Luoqi Liu, Jian Dong, Liang Lin, Shuicheng
Yan, Senior Member, IEEE"
71b376dbfa43a62d19ae614c87dd0b5f1312c966,The temporal connection between smiles and blinks,"The Temporal Connection Between Smiles and Blinks
Laura C. Trutoiu, Jessica K. Hodgins, and Jeffrey F. Cohn"
7128f1239cbd1007ef19d8fd8cdab083d33a6984,"Aligned to the Object, not to the Image: A Unified Pose-aligned Representation for Fine-grained Recognition","Aligned to the Object, not to the Image:
A Unified Pose-aligned Representation for Fine-grained Recognition
Pei Guo, Ryan Farrell
Computer Science Department
Brigham Young University"
71ab53b0b3635411d5985f71cc56bb1784023834,RoboCupRescue 2012 - Robot League Team,"RoboCupRescue 2012 - Robot League Team
Hector Darmstadt (Germany)
Thorsten Graber2, Stefan Kohlbrecher1, Johannes Meyer2, Karen Petersen1,
Oskar von Stryk1, Uwe Klingauf2(cid:63)
Department of Computer Science (1) and Department of Mechanical Engineering (2),
Technische Universit¨at Darmstadt,
Karolinenplatz 5, D-64289 Darmstadt, Germany
E-Mail:
Web: www.gkmm.tu-darmstadt.de/rescue"
711801297f23df9ac8ca1c2d3c9d7dfa2ed12043,Enhancing Energy Efficiency of Multimedia Applications in Heterogeneous Mobile Multi-Core Processors,"Contention-Aware Fair Scheduling for
Asymmetric Single-ISA Multicore Systems
Adrian Garcia-Garcia , Juan Carlos Saez , and Manuel Prieto-Matias"
71403805e67eeb6ec336e0cb83646fdb7c819757,Visual Strategies for Sparse Spike Coding,"Visual Strategies for Sparse Spike Coding
Laurent Perrinet
Manuel Samuelides
ONERA/DTIM,
, av. Belin,
1055 Toulouse, France"
71fd29c2ae9cc9e4f959268674b6b563c06d9480,End-to-end 3D shape inverse rendering of different classes of objects from a single input image,"End-to-end 3D shape inverse rendering of different classes
of objects from a single input image
Shima Kamyab1 and S. Zohreh Azimifar1
Computer Science and Engineering and Information Technology, Shiraz
university, Shiraz, Iran
November 17, 2017"
70bfe8dfd9c9b05c8854a5d4aca9c3ee3a3b7eff,3 D Object Reconstruction using Multiple Views,"!, >A?J 4A?IJHK?JE KIEC KJEFA 8EAMI
,CD E
,AF=HJAJ B +FKJAH 5?EA?A 5J=JEIJE?I
7ELAHIEJO B ,K>E 6HEEJO +ACA
) JDAIEI J JDA 7ELAHIEJO B ,K>E 6HEEJO +ACA E BK
AJ B
JDA HAGKEHAAJI BH JDA B
,?JH B 2DEIFDO
5AFJA>AH"
706236308e1c8d8b8ba7749869c6b9c25fa9f957,Crowdsourced data collection of facial responses,"Crowdsourced Data Collection of Facial Responses
Daniel McDuff
MIT Media Lab
Cambridge
02139, USA
Rosalind Picard
MIT Media Lab
Cambridge
02139, USA
Rana el Kaliouby
MIT Media Lab
Cambridge
02139, USA"
70ce1a17f257320fc718d61964b21e7aeabd8cd5,Person re-identification with fusion of hand-crafted and deep pose-based body region features,"Person re-identification with fusion of hand-crafted and deep pose-based body
region features
Jubin Johnson1
Shunsuke Yasugi2
Yoichi Sugino2
Sugiri Pranata1
Panasonic R&D Center
Singapore
Shengmei Shen1
Panasonic Corporation
Core Element Technology Development Center
Japan
http://www.prdcsg.panasonic.com.sg/"
70c58700eb89368e66a8f0d3fc54f32f69d423e1,In Unsupervised Spatio-temporal Feature Learning,"INCORPORATING SCALABILITY IN UNSUPERVISED SPATIO-TEMPORAL FEATURE
LEARNING
Sujoy Paul, Sourya Roy and Amit K. Roy-Chowdhury
Dept. of Electrical and Computer Engineering, University of California, Riverside, CA 92521"
70f0636b14b9e3916a780d70a5c712e8fea739da,"On Artefact Reduction , Segmentation and Classification of 3 D Computed Tomography Imagery in Baggage Security Screening","CRANFIELD UNIVERSITY
SCHOOL OF ENGINEERING
PhD THESIS
Academic Year 2013-2014
ANDRE MOUTON
On Artefact Reduction, Segmentation and Classification of
D Computed Tomography Imagery in Baggage Security
Screening
Supervised by: Dr Toby Breckon and Dr Carol Armitage
March 2014
This thesis is submitted in partial fulfilment of the requirements for
the Degree of Doctor of Philosophy
©Cranfield University, 2014. All rights reserved. No part of this
publication may be reproduced without the written permission of
the copyright holder."
70480ee0e636a77f6289be98ae39300a584808f6,Iterative Robust Registration Approach based on Feature Descriptors Correspondence - Application to 3D Faces Description,"Iterative Robust Registration Approach based on Feature Descriptors
Correspondence
Application to 3D Faces Description
Cristal lab.Grift research group, National School of Computer Science, La Mannouba, Tunisia
Wieme Gadacha and Faouzi Ghorbel
Keywords:
D Rigid Registration, Hausdorff Distance in Shape Space, 3D Parametrisation, Matching, Face Description,
Shannon Theorem."
70f3d3d9a7402a0f62a5646a16583c6c58e3b07a,"An Architecture for Deep, Hierarchical Generative Models","An Architecture for Deep, Hierarchical Generative
Models
Philip Bachman
Maluuba Research"
70b42bbd76e6312d39ea06b8a0c24beb4a93e022,Solving Multiple People Tracking in a Minimum Cost Arborescence,"Solving Multiple People Tracking In A Minimum Cost Arborescence
Institut f¨ur Informationsverarbeitung
Institute of Geodesy and Photogrammetry
Laura Leal-Taix´e
ETH Z¨urich
Roberto Henschel
Universit¨at Hannover
Bodo Rosenhahn
Institut f¨ur Informationsverarbeitung
Universit¨at Hannover
. Introduction
For many applications of computer vision, it is neces-
sary to localize and track humans that appear in a video
sequence. Multiple people tracking has thus evolved as an
ongoing research topic in the computer vision domain.
A commonly used approach to solve the data associa-
tion problem within the tracking task is to apply a hierarchi-
al tracklet framework [5]. Although there has been great
progress in such a model, mainly due to its good bootstrap-
ping capabilities, so far little attention has been drawn to"
70bf1769d2d5737fc82de72c24adbb7882d2effd,Face detection in intelligent ambiences with colored illumination,"Face detection in intelligent ambiences with colored illumination
Christina Katsimerou, Judith A. Redi, Ingrid Heynderickx
Department of Intelligent Systems
TU Delft
Delft, The Netherlands"
70c38203d9bf5ff3a6f4639a1fb13dcaab233a61,Occlusion-robust Detector Trained with Occluded Pedestrians,
708355d319a88485fdbbea3524104982b8cf37c2,2D/3D Sensor Exploitation and Fusion for Enhanced Object Detection,"D/3D Sensor Exploitation and Fusion for Enhanced Object Detection
Jiejun Xu
HRL Laboratories LLC
Kyungnam Kim
HRL Laboratories LLC
Zhiqi Zhang
HRL Laboratories LLC
Hai-wen Chen
HRL Laboratories LLC
Yuri Owechko
HRL Laboratories LLC"
70f0f0ad8fcf45538bbb49dd339e16ba3a0033e0,Mobile Biometrics ( MoBio ) : Joint Face and Voice Verification for a Mobile Platform,"Mobile Biometrics (MoBio): Joint Face and Voice
Verification for a Mobile Platform
P. A. Tresadern, C. McCool, N. Poh, P. Matejka,
A. Hadid, C. Levy, T. F. Cootes and S. Marcel"
70109c670471db2e0ede3842cbb58ba6be804561,Zero-Shot Visual Recognition via Bidirectional Latent Embedding,"Noname manuscript No.
(will be inserted by the editor)
Zero-Shot Visual Recognition via Bidirectional Latent Embedding
Qian Wang · Ke Chen
Received: date / Accepted: date"
70e79d7b64f5540d309465620b0dab19d9520df1,Facial Expression Recognition System Using Extreme Learning Machine,"International Journal of Scientific & Engineering Research, Volume 8, Issue 3, March-2017
ISSN 2229-5518
Facial Expression Recognition System
Using Extreme Learning Machine
Firoz Mahmud, Dr. Md. Al Mamun"
7026aa20d83800aff72f2e13d02770d1e42acd2d,A Tale of Two Losses : Discriminative Deep Feature Learning for Person Re-Identification,"A Tale of Two Losses: Discriminative Deep Feature Learning for
Person Re-Identification
Borgia, A., Hua, Y., & Robertson, N. (2017). A Tale of Two Losses: Discriminative Deep Feature Learning for
Person Re-Identification. In Irish Machine Vision and Image Processing Conference 2017: Proceedings
Published in:
Irish Machine Vision and Image Processing Conference 2017: Proceedings
Document Version:
Peer reviewed version
Queen's University Belfast - Research Portal:
Link to publication record in Queen's University Belfast Research Portal
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Copyright for the publications made accessible via the Queen's University Belfast Research Portal is retained by the author(s) and / or other
opyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated
with these rights.
Take down policy
The Research Portal is Queen's institutional repository that provides access to Queen's research output. Every effort has been made to
ensure that content in the Research Portal does not infringe any person's rights, or applicable UK laws. If you discover content in the"
70b0538af40672e3be4b72f97cec486693d5204f,Mixture component identification and learning for visual recognition,"Mixture Component Identification and Learning
for Visual Recognition
Omid Aghazadeh, Hossein Azizpour, Josephine Sullivan, and Stefan Carlsson
Computer Vision and Active Perception laboratory (CVAP), KTH, Sweden"
7099e4a8b01b5cf22f9f9ecbfbed16bb44d3d76d,A Regularized Framework for Feature Selection in Face Detection and Authentication,"A Regularized Framework for Feature
Selection in Face Detection
nd Authentication
Augusto Destrero, Christine De Mol, Francesca Odone,
Alessandro Verri, 2008
Present By
Mr. Apichon Witayangkurn, CSIM
ID: 106800
AT70.9011: Machine Vision for Robotics and HCI, Summer 2009"
70f189798c8b9f2b31c8b5566a5cf3107050b349,The challenge of face recognition from digital point-and-shoot cameras,"The Challenge of Face Recognition from Digital Point-and-Shoot Cameras
J. Ross Beveridge∗
Geof H. Givens§
W. Todd Scruggs¶
P. Jonathon Phillips†
Yui Man Lui∗
Kevin W. Bowyer(cid:107)
David Bolme‡
Mohammad Nayeem Teli∗
Patrick J. Flynn(cid:107)
Bruce A. Draper∗,
Hao Zhang∗
Su Cheng†"
7056a051e0589ab6aa299c7d2a31588800b8c93e,Facial expression recognition and histograms of oriented gradients: a comprehensive study,"Carcagnì et al. SpringerPlus  (2015) 4:645
DOI 10.1186/s40064-015-1427-3
RESEARCH
Facial expression recognition
nd histograms of oriented gradients: a
omprehensive study
Pierluigi Carcagnì*†, Marco Del Coco†, Marco Leo† and Cosimo Distante†
Open Access
*Correspondence:
Pierluigi Carcagnì, Marco Del
Coco, Marco Leo and Cosimo
Distante contributed equally
to this work
National Research Council
of Italy, Institute of Applied
Sciences and Intelligent
Systems, Via della Libertà, 3,
73010 Arnesano , LE, Italy"
70bb5c2570673eae86a3f9ced55c7ef00e0be8b5,Combinaison de Descripteurs Hétérogènes pour la Reconnaissance de Micro-Mouvements Faciaux,"Combinaison de Descripteurs Hétérogènes pour la Reconnaissance de
Micro-Mouvements Faciaux.
Vincent Rapp1, Thibaud Senechal1, Hanan Salam2, Lionel Prevost3, Renaud Seguier2, Kevin Bailly1
ISIR - CNRS UMR 7222
Université Pierre et Marie Curie, Paris
{rapp, senechal,
Supelec - ETR (UMR 6164)
Avenue de la Boulaie, 35511,
Cesson-Sevigne
{salam,
LAMIA - EA 4540
Université des Antilles et de la Guyanne
Résumé
Dans cet article, nous présentons notre réponse au premier
hallenge international sur la reconnaissance et l’analyse
d’émotions faciales (Facial Emotion Recognition and Ana-
lysis Challenge). Nous proposons une combinaison de dif-
férents types de descripteurs dans le but de détecter de ma-
nière automatique, les micro-mouvements faciaux d’un vi-
sage. Ce système utilise une Machine à Vecteurs Supports"
70e90b9df5b8617ef6636c5492db727f9d48d0ec,People Search with Textual Queries About Clothing Appearance Attributes,"People search with textual queries about
lothing appearance attributes
Riccardo Satta, Federico Pala, Giorgio Fumera, and Fabio Roli"
706b9767a444de4fe153b2f3bff29df7674c3161,Fast Metric Learning For Deep Neural Networks,"Fast Metric Learning For Deep Neural Networks
Henry Gouk1, Bernhard Pfahringer1, and Michael Cree2
Department of Computer Science, University of Waikato, Hamilton, New Zealand
School of Engineering, University of Waikato, Hamilton, New Zealand"
70af8e4ff3c029aea788bc28b45c56932b50c056,Robust Facial Landmark Detection Using a Mixture of Synthetic and Real Images with Dynamic Weighting : A Survey,"Om Prakash Gupta et al. 2016, Volume 4 Issue 1
ISSN (Online): 2348-4098
ISSN (Print): 2395-4752"
703d4f376eb2379ccce814c729d15f1165312167,Location Prediction on Trajectory Data : A Review,"BIG DATA MINING AND ANALYTICS
ISSN 2096-0654
02/06 pp108–127
Volume 1, Number 2, June 2018
DOI: 10.26599/BDMA.2018.9020010
Location Prediction on Trajectory Data: A Review
Ruizhi Wu, Guangchun Luo(cid:3), Junming Shao, Ling Tian, and Chengzong Peng"
70671018d4597b6d2d0c99b38b1f1a3f1271eaec,Learning Representations Specialized in Spatial Knowledge: Leveraging Language and Vision,"Transactions of the Association for Computational Linguistics, vol. 6, pp. 133–144, 2018. Action Editor: Stefan Riezler.
Submission batch: 6/2017; Revision batch: 9/2017; Published 2/2018.
(cid:13)2018 Association for Computational Linguistics. Distributed under a CC-BY 4.0 license."
70920447b8300fd65745c0a884523e4d52d000ef,1 AUTOMATED CROWD DETECTION IN STADIUM ARENAS,"AUTOMATED CROWD DETECTION IN STADIUM ARENAS
Loris Nanni, 1 Sheryl Brahnam, 2 Stefano Ghidoni, 1 Emanuele Menegatti1
DIE, University of Padua, Via Gradenigo, 6 - 35131- Padova – Italy e-mail: {loris.nanni, ghidoni,
CIS, Missouri State University, 901 S. National, Springfield, MO 65804, USA e-mail:"
70990e1b13cec2b3e4831a00c6ac901dae76b27a,"Mareckova , Klara ( 2013 ) Sex differences and the role of sex hormones in face development and face processing","Mareckova, Klara (2013) Sex differences and the role of
sex hormones in face development and face processing.
PhD thesis, University of Nottingham.
Access from the University of Nottingham repository:
http://eprints.nottingham.ac.uk/13333/1/KlaraMareckova_PhDThesis_finalversion1.pdf
Copyright and reuse:
The Nottingham ePrints service makes this work by researchers of the University of
Nottingham available open access under the following conditions.
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Please see our full end user licence at:
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A note on versions:"
7020df589ef9bf220d5289b0092a07b191534972,"An Automatic Feature Based Face Authentication System,","An Automatic Feature Based Face
Authentication System
Stefano Arca, Paola Campadelli, Elena Casiraghi, Ra(cid:11)aella Lanzarotti
Dipartimento di Scienze dell’Informazione
Universit(cid:18)a degli Studi di Milano
Via Comelico, 39/41 20135 Milano, Italy
farca, campadelli, casiraghi,"
70560383cbf7c0dc5e9be1f2fd9efba905377095,Accelerating Online CP Decompositions for Higher Order Tensors,"Accelerating Online CP Decompositions for
Higher Order Tensors
Shuo Zhou1, Nguyen Xuan Vinh1, James Bailey1, Yunzhe Jia1, Ian Davidson2
Dept. of Computing and Information Systems, The University of Melbourne, Australia
Dept. of Computer Science, University of California, Davis, USA"
708a55d65568faf8158417ddfb79e728b2b28f86,3D Body Model Construction and Matching for Real Time People Re-Identification,"Eurographics Italian Chapter Conference (2010)
E. Puppo, A. Brogni, and L. De Floriani (Editors)
D Body Model Construction and Matching for Real Time
People Re-Identification
D. Baltieri, R. Vezzani and R. Cucchiara
Dipartimento di Ingegneria dell’Informazione
University of Modena and Reggio Emilia
Via Vignolese, 905 - 41100 Modena - Italy"
70e3c02575e4041519434e0dacb291bbb8791380,Generative 2D and 3D Human Pose Estimation with Vote Distributions,"Generative 2D and 3D
Human Pose Estimation
with Vote Distributions
J¨urgen Brauer, Wolfgang H¨ubner, Michael Arens
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation
{juergen.brauer, wolfgang.huebner,
Gutleuthausstr. 1, 76275 Ettlingen, Germany"
70ec156f7e6de0275c7e4e95e35f1bc1e92e29b3,Deep Learning Ensembles for Melanoma Recognition in Dermoscopy Images,"Deep learning ensembles for melanoma recognition in dermoscopy images1
N. C. F. Codella, Q. B. Nguyen, S. Pankanti, D. Gutman, B. Helba, A. Halpern, J. R. Smith"
ff398e7b6584d9a692e70c2170b4eecaddd78357,Face Recognition and Verification in Unconstrained Environments by Huimin Guo,
ff7470805588ba1ea63bcd8992e48ac4e9ef9771,Séquences de maillages : classification et méthodes de segmentation. (Mesh sequences : classification and segmentation),"Séquences de maillages : classification et méthodes de
segmentation
Romain Arcila
To cite this version:
Romain Arcila. Séquences de maillages : classification et méthodes de segmentation. Ordinateur
et société [cs.CY]. Université Claude Bernard - Lyon I, 2011. Français. <NNT : 2011LYO10233>.
<tel-00653542v3>
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ff01bc3f49130d436fca24b987b7e3beedfa404d,Fuzzy System-Based Face Detection Robust to In-Plane Rotation Based on Symmetrical Characteristics of a Face,"Article
Fuzzy System-Based Face Detection Robust to
In-Plane Rotation Based on Symmetrical
Characteristics of a Face
Hyung Gil Hong, Won Oh Lee, Yeong Gon Kim, Ki Wan Kim, Dat Tien Nguyen and
Kang Ryoung Park *
Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu,
Seoul 100-715, Korea; (H.G.H.); (W.O.L.); (Y.G.K.);
(K.W.K.); (D.T.N.)
* Correspondence: Tel.: +82-10-3111-7022; Fax: +82-2-2277-8735
Academic Editor: Angel Garrido
Received: 15 June 2016; Accepted: 29 July 2016; Published: 3 August 2016"
ffe3779c1102394ffc3c93846a8b5ee51340afeb,Project AutoVision: Localization and 3D Scene Perception for an Autonomous Vehicle with a Multi-Camera System,"Project AutoVision: Localization and 3D Scene Perception for an
Autonomous Vehicle with a Multi-Camera System
Lionel Heng1, Benjamin Choi1, Zhaopeng Cui2, Marcel Geppert2, Sixing Hu4, Benson Kuan1, Peidong Liu2,
Rang Nguyen4, Ye Chuan Yeo1, Andreas Geiger3, Gim Hee Lee4, Marc Pollefeys2, and Torsten Sattler2"
ffcbedb92e76fbab083bb2c57d846a2a96b5ae30,Sparse Dictionary Learning and Domain Adaptation for Face and Action Recognition,
ff4dec12d0ba0bb1d2c6bbc194545819bc9c1e5a,Face Recognition at a Distance : System Issues,"Chapter 6
Face Recognition at a Distance:
System Issues
Meng Ao, Dong Yi, Zhen Lei, and Stan Z. Li"
ff9195f99a1a28ced431362f5363c9a5da47a37b,Serial dependence in the perception of attractiveness,"Journal of Vision (2016) 16(15):28, 1–8
Serial dependence in the perception of attractiveness
Ye Xia
Department of Psychology, University of California,
Berkeley, CA, USA
Allison Yamanashi Leib
Department of Psychology, University of California,
Berkeley, CA, USA
David Whitney
Department of Psychology, University of California,
Berkeley, CA, USA
Helen Wills Neuroscience Institute, University of
California, Berkeley, CA, USA
Vision Science Group, University of California,
Berkeley, CA, USA
The perception of attractiveness is essential for choices
of food, object, and mate preference. Like perception of
other visual features, perception of attractiveness is
stable despite constant changes of image properties due
to factors like occlusion, visual noise, and eye"
ffdaa12d37c720561f74d23fc3b5d47afa268000,Pose Proposal Networks,"Pose Proposal Networks
Taiki Sekii[0000−0002−1895−3075]
Konica Minolta, Inc."
ff83aade985b981fbf2233efbbd749600e97454c,Towards Understanding Adversarial Learning for Joint Distribution Matching,"ALICE: Towards Understanding Adversarial
Learning for Joint Distribution Matching
Chunyuan Li1, Hao Liu2, Changyou Chen3, Yunchen Pu1, Liqun Chen1,
Ricardo Henao1 and Lawrence Carin1
Duke University 2Nanjing University 3University at Buffalo"
ff3fa31882bb9c7573a38c7d0883503a464522a6,Imcube @ MediaEval 2015 Placing Task: Hierarchical Approach for Geo-referencing Large-Scale Datasets,"Imcube MediaEval 2015 Placing Task: A Hierarchical
Approach for Geo-referencing Large-Scale Datasets
Pascal Kelm, Sebastian Schmiedeke, and Lutz Goldmann
{kelm, schmiedeke,
Imcube Labs GmbH
Berlin, Germany"
ff46c41e9ea139d499dd349e78d7cc8be19f936c,A Novel Method for Movie Character Identification and its Facial Expression Recognition,"International Journal of Modern Engineering Research (IJMER)
www.ijmer.com              Vol.3, Issue.3, May-June. 2013 pp-1339-1342             ISSN: 2249-6645
A Novel Method for Movie Character Identification and its
Facial Expression Recognition
M. Dharmateja Purna, 1 N. Praveen2
M.Tech, Sri Sunflower College of Engineering & Technology, Lankapalli
Asst. Professor, Dept. of ECE, Sri Sunflower College of Engineering & Technology, Lankapalli"
ff70cfaf3e085a6c32bfa7ebedb98adfb7658210,TABULA RASA Trusted Biometrics under Spoofing Attacks,"TABULA RASA
Trusted Biometrics under Spoofing Attacks
http://www.tabularasa-euproject.org/
Funded under the 7th FP (Seventh Framework Programme)
[Trustworthy Information and Communication Technologies]
Theme ICT-2009.1.4
D3.2: Evaluation of baseline non-ICAO
iometric systems
Due date: 30/09/2011
Project start date: 01/11/2010 Duration: 42 months
WP Manager: Abdenour Hadid Revision: 0
Submission date: 30/09/2011
Author(s): Federico Alegre, Xuran Zhao, Nick Evans (EURECOM);
John Bustard, Mark Nixon (USOU); Abdenour Hadid (UOULU); William
Ketchantang, Sylvaine Picard, St´ephane Revelin (MORPHO); Ale-
jandro Riera, Aureli Soria-Frisch (STARLAB); Gian Luca Marcialis
(UNICA)
Project funded by the European Commission
in the 7th Framework Programme (2008-2010)
Dissemination Level"
ff1b253636c878f5b464a7623f36242327e6b485,Visual Place Recognition with Probabilistic Vertex Voting,"Visual Place Recognition with Probabilistic Vertex Voting
Mathias Gehrig, Elena Stumm, Timo Hinzmann and Roland Siegwart
Autonomous Systems Lab, ETH Zurich"
ffeff854e7fcf5af663497be00c86537f7d9ed11,Face recognition in JPEG compressed domain: a novel coefficient selection approach,"SIViP (2015) 9:651–663
DOI 10.1007/s11760-013-0492-8
ORIGINAL PAPER
Face recognition in JPEG compressed domain: a novel coefficient
selection approach
Mohammad-Shahram Moin ·
Alireza Sepas-Moghaddam
Received: 14 October 2012 / Revised: 30 April 2013 / Accepted: 1 May 2013 / Published online: 4 June 2013
© Springer-Verlag London 2013"
fff854b3d8f8e916162dc5451cf6f46caf50002b,Multi-task Learning for Universal Sentence Embeddings: A Thorough Evaluation using Transfer and Auxiliary Tasks,"Multi-task Learning for Universal Sentence Embeddings: A Thorough
Evaluation using Transfer and Auxiliary Tasks
Wasi Uddin Ahmad†, Xueying Bai∗, Zhechao Huang§, Chao Jiang∗, Nanyun Peng(cid:63), Kai-Wei Chang†
§Fudan University, ∗University of Virginia
(cid:63)University of Southern California, †University of California, Los Angeles"
ffd81d784549ee51a9b0b7b8aaf20d5581031b74,Performance Analysis of Retina and DoG Filtering Applied to Face Images for Training Correlation Filters,"Performance Analysis of Retina and DoG
Filtering Applied to Face Images for Training
Correlation Filters
Everardo Santiago Ram(cid:19)(cid:16)rez1, Jos(cid:19)e (cid:19)Angel Gonz(cid:19)alez Fraga1, Omar (cid:19)Alvarez
Xochihua1, Everardo Gutierrez L(cid:19)opez1, and Sergio Omar Infante Prieto2
Facultad de Ciencias, Universidad Aut(cid:19)onoma de Baja California,
Carretera Transpeninsular Tijuana-Ensenada, N(cid:19)um. 3917, Colonia Playitas,
Ensenada, Baja California, C.P. 22860
{everardo.santiagoramirez,angel_fraga,
Facultad de Ingenier(cid:19)(cid:16)a, Arquitectura y Dise~no, Universidad Aut(cid:19)onoma de Baja
California, Carretera Transpeninsular Tijuana-Ensenada, N(cid:19)um. 3917, Colonia
Playitas, Ensenada, Baja California, C.P. 22860"
ff44d8938c52cfdca48c80f8e1618bbcbf91cb2a,Towards Video Captioning with Naming: A Novel Dataset and a Multi-modal Approach,"Towards Video Captioning with Naming: a
Novel Dataset and a Multi-Modal Approach
Stefano Pini, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
Dipartimento di Ingegneria “Enzo Ferrari”
Universit`a degli Studi di Modena e Reggio Emilia"
ff2e25cb67209de8ae922abdfc31f922b130276e,Chapter 25 Information Granulation and Pattern Recognition,"Chapter 25
Information Granulation and Pattern Recognition
Andrzej Skowron,1 Roman W. Swiniarski2
Institute of Mathematics, Warsaw University, Banacha 2, 02-097 Warsaw, Poland
San Diego State University, Department of Mathematical and Computer Sciences, 5500
Campanile Drive, San Diego, CA 92182, USA
Summary. We discuss information granulation applications in pattern recognition. The chap-
ter consists of two parts. In the first part, we present applications of rough set methods for
feature selection in pattern recognition. We emphasize the role of different forms of reducts
that are the basic constructs of the rough set approach in feature selection. In the overview
of methods for feature selection, we discuss feature selection criteria based on the rough set
pproach and the relationships between them and other existing criteria. Our algorithm for
feature selection used in the application reported is based on an application of the rough set
method to the result of principal component analysis used for feature projection and reduc-
tion. Finally, the first part presents numerical results of face recognition experiments using a
neural network, with feature selection based on proposed principal component analysis and
rough set methods. The second part consists of an outline of an approach to pattern recog-
nition with the application of background knowledge specified in natural language. The ap-
proach is based on constructing approximations of reasoning schemes. Such approximations
re called approximate reasoning schemes and rough neural networks."
ffd73d1956163a4160ec2c96b3ab256f79fc92e8,Attributes as Semantic Units between Natural Language and Visual Recognition,"Attributes as Semantic Units between
Natural Language and Visual Recognition
Marcus Rohrbach"
ffe8a4cef9dec30ddd2c956c2f63b128a4568f84,Intensity Video Guided 4D Fusion for Improved Highly Dynamic 3D Reconstruction,"Intensity Video Guided 4D Fusion for
Improved Highly Dynamic 3D Reconstruction
Jie Zhang, Christos Maniatis, Luis Horna and Robert B. Fisher"
ffc8f9fe66a14aa0657e59e219364b5a852ecb8f,On the Utility of Context (or the Lack Thereof) for Object Detection,"On the Utility of Context (or the Lack Thereof) for Object Detection
Ehud Barnea and Ohad Ben-Shahar
Dept. of Computer Science, Ben-Gurion University
Beer-Sheva, Israel
{barneaeh,"
ffc9d6a5f353e5aec3116a10cf685294979c63d9,Eigenphase-based face recognition : a comparison of phase-information extraction methods,"Eigenphase-based face recognition: a comparison of phase-
information extraction methods
Slobodan Ribarić, Marijo Maračić
Faculty of Electrical Engineering and Computing,
University of Zagreb, Unska 3, 10 000 Zagreb
E-mail:"
ff269353b4e49274ff85dfb98b531888c98da365,Master : a Mobile Autonomous Scientist for Terretrial and Extra-terrestrial Research,"MASTER: A MOBILE AUTONOMOUS SCIENTIST FOR TERRETRIAL AND EXTRA-
TERRESTRIAL RESEARCH
Iain Wallace (1), Mark Woods  (2)
(1) SCISYS, 23 Clothier Road, Bristol, BS4 5SS, UK, Email:
(2) SCISYS, 23 Clothier Road, Bristol, BS4 5SS, UK, Email:
paper
includes
utonomy.  The
INTRODUCTION"
ff18125a8f549135e6320fed91d0002bd2dae635,Colour Terms: a Categorisation Model Inspired by Visual Cortex Neurons,"Colour Terms: a Categorisation Model Inspired by Visual Cortex Neurons
Arash Akbarinia
Centre de Visi´o per Computador
Universitat Aut`onoma de Barcelona
C. Alejandro Parraga
Centre de Visi´o per Computador
Universitat Aut`onoma de Barcelona"
ffae2fe85d3c93610ac6270db2ddf1f2f6779ea8,Learning pullback HMM distances for action recognition,"#****
ICCV 2011 Submission #****. CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE.
Learning pullback HMM distances for action recognition
Anonymous ICCV submission
Paper ID ****"
ff3ec3607b77a1dbb685cf90dd23a273d622dda5,Visual Attribute Extraction Using Human Pose Estimation,"Visual Attribute Extraction using Human Pose
Estimation
Angelo Nodari, Marco Vanetti, and Ignazio Gallo
Universit`a dell’Insubria, Dipartimento di Scienze Teoriche e Applicate
via Mazzini 5, 21100 Varese, Italy"
ffb2d596c22be7b0ed8f809fdfbeaa95bd4db835,"The BDD-Nexar Collective : A Large-Scale , Crowsourced , Dataset of Driving Scenes Vashisht Madhavan","The BDD-Nexar Collective: A Large-Scale, Crowsourced,
Dataset of Driving Scenes
Vashisht Madhavan
Trevor Darrell
Fisher Yu, Ed.
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2017-113
http://www2.eecs.berkeley.edu/Pubs/TechRpts/2017/EECS-2017-113.html
May 29, 2017"
fff12919cf912347776b70aa76af7635280dc401,Are object detection assessment criteria ready for maritime computer vision?,"Are object detection assessment criteria ready
for maritime computer vision?
Dilip K. Prasad1,∗, Deepu Rajan2, and Chai Quek2"
ffcb92719dcd993dda292ca82d4585950ea22ac9,Handwritten Digit Recognition Using Convolutional Neural Networks,"ISSN(Online): 2320-9801
ISSN (Print):  2320-9798
International Journal of Innovative Research in Computer
nd Communication Engineering
(An ISO 3297: 2007 Certified Organization)
Vol. 4, Issue 2, February 2016
Handwritten Digit Recognition Using
Convolutional Neural Networks
Haider A. Alwzwazy1, Hayder M. Albehadili2, Younes S. Alwan3, Naz E. Islam4
M.E Student, Dept. of Electrical and Computer Eng. University of Missouri-Columbia, MO, USA1,2,3
Professor, Dept. of Electrical and Computer Eng. University of Missouri-Columbia, MO, USA4"
ff7de2ea4d21e7d32d7f07e07fd278bebf6b5d66,Comparative survey of visual object classifiers,"Comparative survey of visual object classifiers
Laboratory Le2i, Universite Bourgogne - Franche-Comte,
Hiliwi Leake Kidane
1000 Dijon, France,"
ff25c6602305ac46e9c35ffa4e30b14d679a5413,Face Templates Creation for Surveillance Face Recognition System,"Face Templates Creation for Surveillance Face Recognition System
Department of Radio Electronics, Brno University of Technology, Brno, Czech Republic
Department of Telecommunications, Brno University of Technology, Brno, Czech Republic
Tobias Malach1,2 and Jiri Prinosil3
EBIS, spol. s r.o., Brno, Czech Republic
Keywords:
Face  Templates,  Template  Database  Creation,  Face  Recognition  System  Application,  Real-world
Conditons."
ff8ef43168b9c8dd467208a0b1b02e223b731254,BreakingNews: Article Annotation by Image and Text Processing,"BreakingNews: Article Annotation by
Image and Text Processing
Arnau Ramisa*, Fei Yan*, Francesc Moreno-Noguer,
nd Krystian Mikolajczyk"
ab98abfbdfd700c27bee31ca1f8850db72120c5d,Video Event Detection by Exploiting Word Dependencies from Image Captions,"Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers,
pages 3318–3327, Osaka, Japan, December 11-17 2016."
ab58a7db32683aea9281c188c756ddf969b4cdbd,Efficient Solvers for Sparse Subspace Clustering,"Efficient Solvers for Sparse Subspace Clustering
Farhad Pourkamali-Anaraki and Stephen Becker"
ab03a1656d9e45c80379512161f6c90dfbb0b6b3,Active Learning for Regression Tasks with Expected Model Output Changes,"KÄDING ET AL.: ACTIVE LEARNING FOR REGRESSION TASKS WITH EMOC
Active Learning for Regression Tasks
with Expected Model Output Changes
Computer Vision Group
Friedrich Schiller University Jena
Jena, Germany
Carl Zeiss AG
Jena, Germany
Christoph Käding1
Erik Rodner2
Alexander Freytag2
Oliver Mothes1
Björn Barz1
Joachim Denzler1"
abfcafaa765433b8f5b8be7eae392a8daec54b8e,Facial EMG Responses to Emotional Expressions Are Related to Emotion Perception Ability,"Facial EMG Responses to Emotional Expressions Are
Related to Emotion Perception Ability
Janina Ku¨ necke1*, Andrea Hildebrandt1, Guillermo Recio1,2, Werner Sommer1, Oliver Wilhelm2
Department of Psychology, Humboldt Universita¨t zu Berlin, Berlin, Germany, 2 Department of Psychology, University Ulm, Ulm, Germany"
aba1669b44f853d94f45baf8f28b37b48f469ed7,Image Classification by Sparse Representation-based Transduction,"Sparse Graph-based Transduction for Image Classification
Sheng Huanga, Dan Yanga,∗, Jia Zhoua, Luwen Huangfub, Xiaohong Zhangc,d
College of Computer Science at Chongqing University, Chongqing, 400044, P.R.C
Eller College of Management at University of Arizona, Tucson, AZ, 85712, USA
School of Software Engineering at Chongqing University, Chongqing, 400044, P.R.C
dMinistry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society, Chongqing, 400044, P.R.C"
abeda55a7be0bbe25a25139fb9a3d823215d7536,Understanding Human-Centric Images: From Geometry to Fashion,"UNIVERSITATPOLITÈCNICADECATALUNYAProgramadeDoctorat:AUTOMÀTICA,ROBÒTICAIVISIÓTesiDoctoralUnderstandingHuman-CentricImages:FromGeometrytoFashionEdgarSimoSerraDirectors:FrancescMorenoNoguerCarmeTorrasMay2015"
ab969cfae95f62d68c61830128b35786eb6c84a9,Contents 1 Introduction 2,"Contents1Introduction22Tracking:FundamentalNotions22.1Trackingbydetection........................................22.2TrackingusingFlow........................................22.3Flowmodelsfromkinematicmodels................................22.4TrackingwithProbability......................................23Tracking:Relationsbetween3Dand2D23.1KinematicInferencewithMultipleViews.............................23.2Liftingto3D............................................33.3MultipleModes,RandomizedSearchandHumanTracking....................34Tracking:DataAssociationforHumanTracking54.1DetectingHumans.........................................54.2TrackingbyMatchingRevisited..................................64.3Evaluation..............................................75MotionSynthesisandAnimation95.1Motioncapture...........................................95.2Footskate..............................................95.3ResolvingKinematicAmbiguitieswithExamples.........................95.4MotionSignalProcessing......................................95.5MotionGraphs...........................................95.6MotionPrimitives..........................................105.7EnrichingaMotionCollection...................................105.8MotionfromPhysicalConsiderations...............................105.8.1SimplifiedCharacters....................................105.8.2ModifiedPhysics......................................115.8.3ReducedDimensions....................................115.8.4ModifyingExistingMotions................................116DescribingActivities126.1WhatshouldanActivityRepresentationdo?............................126.1.1NecessaryPropertiesofanActivityRepresentation....................136.1.2WhatDataisAvailable?..................................136.2MiscellaneousMethods.......................................146.2.1ActivityRepresentationMethodsbasedaroundTemporalLogics.............146.2.2ActivityRepresentationMethodsbasedonTemplates...................146.3ActivityRepresentationusingHiddenMarkovModelsandFiniteStateRepresentations.....146.4TheSpeechAnalogy........................................146.4.1FiniteStateTransducers..................................156.4.2WhyshouldweCare?...................................156.5ActivityRecognitionMethodsbasedaroundHMM’s.......................166.6SignLanguageRecognition.....................................176.7Morerecentmaterial........................................171"
abba22ed4713a5ee5fa91fcf7b8dde58a9b621db,Acquisition of a 3D Audio-Visual Corpus of Affective Speech,"BIWI Technical Report n. 270
Acquisition of a 3D Audio-Visual Corpus of
Affective Speech
Gabriele Fanelli, Juergen Gall, Harald Romsdorfer, Thibaut Weise,
nd Luc Van Gool"
aba770a7c45e82b2f9de6ea2a12738722566a149,Face Recognition in the Scrambled Domain via Salience-Aware Ensembles of Many Kernels,"Face Recognition in the Scrambled Domain via Salience-Aware
Ensembles of Many Kernels
Jiang, R., Al-Maadeed, S., Bouridane, A., Crookes, D., & Celebi, M. E. (2016). Face Recognition in the
Scrambled Domain via Salience-Aware Ensembles of Many Kernels. IEEE Transactions on Information
Forensics and Security, 11(8), 1807-1817. DOI: 10.1109/TIFS.2016.2555792
Published in:
Document Version:
Peer reviewed version
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with these rights.
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ab1719f573a6c121d7d7da5053fe5f12de0182e7,Combining visual recognition and computational linguistics : linguistic knowledge for visual recognition and natural language descriptions of visual content,"Combining Visual Recognition
nd Computational Linguistics
Linguistic Knowledge for Visual Recognition
nd Natural Language Descriptions
of Visual Content
Thesis for obtaining the title of
Doctor of Engineering Science
(Dr.-Ing.)
of the Faculty of Natural Science and Technology I
of Saarland University
Marcus Rohrbach, M.Sc.
Saarbrücken
March 2014"
ab450a7968555532d9ea79f81189c0d52f9c5f11,RGB-D Face Recognition in Surveillance Videos,"RGB-D Face Recognition in Surveillance Videos
Anurag Chowdhury
IIIT-D-MTech-CS-GEN-14-002
June 23, 2016
Indraprastha Institute of Information Technology Delhi
New Delhi
Thesis Advisors
Dr. Richa Singh
Dr. Mayank Vatsa
Submitted in partial fulfillment of the requirements
for the Degree of M.Tech. in Computer Science
(cid:13) Chowdhury, 2016
Keywords : RGB-D, Kinect, Face Detection, Face Recognition, Deep Learning"
ab8fb278db4405f7db08fa59404d9dd22d38bc83,Implicit and automated emotional tagging of videos,"UNIVERSITÉ DE GENÈVE
Département d'Informatique
FACULTÉ DES SCIENCES
Professeur Thierry Pun
Implicit and Automated Emotional
Tagging of Videos
THÈSE
présenté à la Faculté des sciences de l'Université de Genève
pour obtenir le grade de Docteur ès sciences, mention informatique
Mohammad SOLEYMANI
Téhéran (IRAN)
Thèse No 4368
GENÈVE
Repro-Mail - Université de Genève"
ab69f49fedb6936ce04b2e9d1f161772b2f24b7d,Architecture-aware optimization of an HEVC decoder on asymmetric multicore processors,"(will be inserted by the editor)
Architecture-Aware Optimization of an HEVC decoder on
Asymmetric Multicore Processors
Rafael Rodr´ıguez-S´anchez · Enrique S. Quintana-Ort´ı
Received: date / Revised: date"
ab559473a01836e72b9fb9393d6e07c5745528f3,cGANs with Projection Discriminator,"Published as a conference paper at ICLR 2018
CGANS WITH PROJECTION DISCRIMINATOR
Takeru Miyato1, Masanori Koyama2
Preferred Networks, Inc. 2Ritsumeikan University"
abc4d51d510cd8222484f7f4f11a739e8bce42ff,On Fast Non-metric Similarity Search by Metric Access Methods,"On Fast Non-metric Similarity Search
y Metric Access Methods
Tom´aˇs Skopal
Charles University in Prague, FMP, Department of Software Engineering,
Malostransk´e n´am. 25, 118 00 Prague 1, Czech Republic"
ab87ab1cf522995510561cd9f494223704f1de91,Human Centric Facial Expression Recognition,"Human Centric Facial Expression Recognition
K. Clawson 1*, L. S. Delicato, 2** and C. Bowerman, 1***
Faculty of Computer Science, University of Sunderland, Sunderland, SR1 3SD, UK
. Faculty of Health, Sciences and Wellbeing, University of Sunderland, SR1 3QR, UK
Facial expression recognition (FER) is an area of active research, both in computer science and in
ehavioural science. Across these domains there is evidence to suggest that humans and machines
find it easier to recognise certain emotions, for example happiness, in comparison to others. Recent
ehavioural studies have explored human perceptions of emotion further, by evaluating the relative
ontribution  of  features  in  the  face  when  evaluating  human  sensitivity  to  emotion.  It  has  been
identified that certain facial regions have more salient features for certain expressions of emotion,
especially when emotions are subtle in nature. For example, it is easier to detect fearful expressions
when the eyes are expressive. Using this observation as a starting point for analysis, we similarly
examine  the  effectiveness with which knowledge  of  facial  feature  saliency  may  be  integrated  into
urrent approaches to automated FER. Specifically, we compare and evaluate the accuracy of ‘full-
face’ versus upper and lower facial area convolutional neural network (CNN) modelling for emotion
recognition in static images, and propose a human centric CNN hierarchy which uses regional image
inputs  to  leverage  current  understanding  of  how  humans  recognise  emotions  across  the  face.
Evaluations  using  the  CK+  dataset  demonstrate  that  our  hierarchy  can  enhance  classification
ccuracy
individual  CNN  architectures,  achieving  overall  true  positive"
ab8778793b0f2f06d9e97b6277f3b1125f31432c,Stochastic Models for Face Image Analysis,"Stochastic Models for Face Image Analysis
St(cid:19)ephane Marchand-Maillet and Bernard M(cid:19)erialdo
Department of Multimedia Communications
Institut EURECOM { B.P. 
	
	 Sophia-Antipolis { France"
ab989225a55a2ddcd3b60a99672e78e4373c0df1,"Sample, computation vs storage tradeoffs for classification using tensor subspace models","Sample, Computation vs Storage Tradeoffs for
Classification Using Tensor Subspace Models
Mohammadhossein Chaghazardi and Shuchin Aeron, Senior Member, IEEE"
ab2b09b65fdc91a711e424524e666fc75aae7a51,Multi-modal Biomarkers to Discriminate Cognitive State *,"Multi-modal Biomarkers to Discriminate Cognitive State*
Thomas F. Quatieri 1, James R. Williamson1, Christopher J. Smalt1,
Joey Perricone, Tejash Patel, Laura Brattain, Brian S. Helfer, Daryush D. Mehta, Jeffrey Palmer
Kristin Heaton2, Marianna Eddy3, Joseph Moran3
MIT Lincoln Laboratory, Lexington, Massachusetts, USA
USARIEM, 3NSRDEC
. Introduction
Multimodal biomarkers based on behavorial, neurophysiolgical, and cognitive measurements have
recently obtained increasing popularity in the detection of cognitive stress- and neurological-based
disorders. Such conditions are significantly and adversely affecting human performance and quality
of life for a large fraction of the world’s population. Example modalities used in detection of these
onditions  include  voice,  facial  expression,  physiology,  eye  tracking,  gait,  and  EEG  analysis.
Toward  the  goal  of  finding  simple,  noninvasive  means  to  detect,  predict  and  monitor  cognitive
stress and neurological conditions, MIT Lincoln Laboratory is developing biomarkers that satisfy
three  criteria.  First,  we  seek  biomarkers  that  reflect  core  components  of  cognitive  status  such  as
working memory capacity, processing speed, attention, and arousal. Second, and as importantly, we
seek  biomarkers  that  reflect  timing  and  coordination  relations  both  within  components  of  each
modality and across different modalities. This is based on the hypothesis that neural coordination
cross different parts of the brain is essential in cognition (Figure 1). An example of timing and
oordination  within  a  modality  is  the  set  of  finely  timed  and  synchronized  physiological"
ab02c78c9cc4ab80da45def34f0cf2c1b54fd8ed,Multi-agent path topology in support of socially competent navigation planning,"Article
Multi-agent path topology in support of
socially competent navigation planning
The International Journal of
Robotics Research
© The Author(s) 2018
Reprints and permissions:
sagepub.co.uk/journalsPermissions.nav
DOI: 10.1177/0278364918781016
journals.sagepub.com/home/ijr
Christoforos I Mavrogiannis1
nd Ross A Knepper2"
abe9f3b91fd26fa1b50cd685c0d20debfb372f73,The Pascal Visual Object Classes Challenge: A Retrospective,"(will be inserted by the editor)
The Pascal Visual Object Classes Challenge – a Retrospective
Mark Everingham, S. M. Ali Eslami, Luc Van Gool,
Christopher K. I. Williams, John Winn, Andrew Zisserman
Received: date / Accepted: date"
abb3df5b61dc7550db96fc112f98fb99a9db8c93,End-to-End Learning of Deep Visual Representations for Image Retrieval,"Noname manuscript No.
(will be inserted by the editor)
End-to-end Learning of Deep Visual Representations
for Image Retrieval
Albert Gordo · Jon Almaz´an · Jerome Revaud · Diane Larlus
Received: date / Accepted: date"
ab1f98b59fa98216f052ae19adce6fd94ebb800d,"Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos","Preprint submitted to International Journal of Computer Vision manuscript No.
(will be inserted by the editor)
Explaining First Impressions: Modeling,
Recognizing, and Explaining Apparent Personality
from Videos
Hugo Jair Escalante∗ · Heysem Kaya∗ ·
Albert Ali Salah∗ · Sergio Escalera ·
Ya˘gmur G¨u¸cl¨ut¨urk · Umut G¨u¸cl¨u ·
Xavier Bar´o · Isabelle Guyon · Julio
Jacques Junior · Meysam Madadi ·
Stephane Ayache · Evelyne Viegas ·
Furkan G¨urpınar · Achmadnoer Sukma
Wicaksana · Cynthia C. S. Liem ·
Marcel A. J. van Gerven · Rob van Lier
Received: date / Accepted: date
Means equal contribution by the authors.
Hugo Jair Escalante
INAOE, Mexico and ChaLearn, USA E-mail:
Heysem Kaya
Namık Kemal University, Department of Computer Engineering, Turkey"
ab3baf12ce8316ae495fc7679ba34c5f704934cc,A Convex Max-Flow Approach to Distribution-Based Figure-Ground Separation,"Vol. 5, No. 4, pp. 1333–1354
(cid:2) 2012 Society for Industrial and Applied Mathematics
A Convex Max-Flow Approach to Distribution-Based Figure-Ground Separation
, Jing Yuan
, Ismail Ben Ayed
, Shuo Li
, and Yuri Boykov
Kumaradevan Punithakumar"
abc66504927075c5878dc6b0d70f2d4b009a9c9a,Damaged Building Detection in the crisis areas using Image Processing Tools,
ab8af4cb5243544e38852bb670aafe5a2fd9b3ec,Real-Time Human Detection Using Relational Depth Similarity Features,"Real-Time Human Detection using Relational
Depth Similarity Features
Sho Ikemura, Hironobu Fujiyoshi
Dept. of Computer Science, Chubu University.
Matsumoto 1200, Kasugai, Aichi, 487-8501 Japan.
http://www.vision.cs.chubu.ac.jp"
ab6776f500ed1ab23b7789599f3a6153cdac84f7,A Survey on Various Facial Expression Techniques,"International Journal of Scientific & Engineering Research, Volume 6, Issue 4, April-2015                                                                                                   1212
ISSN 2229-5518
A Survey on Various Facial Expression
Techniques
Md. Sarfaraz Jalil, Joy Bhattacharya"
ab0f9bc35b777eaefff735cb0dd0663f0c34ad31,Semi-supervised Learning of Geospatial Objects through Multi-modal Data Integration,"Semi-Supervised Learning of Geospatial Objects
Through Multi-Modal Data Integration
Yi Yang and Shawn Newsam
Electrical Engineering and Computer Science
University of California, Merced, CA, 95343
Email:"
abddbb57258d85b1f3d9789128fd284d30a91e23,A research and education initiative at the MIT Sloan School of Management Network Structure & Information Advantage Paper 235,"A research and education initiative at the MIT
Sloan School of Management
Network Structure & Information Advantage
Paper 235
Sinan Aral
Marshall Van Alstyne
July 2007
For more information,
please visit our website at  http://digital.mit.edu
or contact the Center directly at
or 617-253-7054"
abc1ef570bb2d7ea92cbe69e101eefa9a53e1d72,Raisonnement abductif en logique de description exploitant les domaines concrets spatiaux pour l'interprétation d'images,"Raisonnement abductif en logique de
description exploitant les domaines concrets
spatiaux pour l’interprétation d’images
Yifan Yang 1, Jamal Atif 2, Isabelle Bloch 1
. LTCI, Télécom ParisTech, Université Paris-Saclay, Paris, France
. Université Paris-Dauphine, PSL Research University, CNRS, UMR 7243,
LAMSADE, 75016 Paris, France
RÉSUMÉ. L’interprétation d’images a pour objectif non seulement de détecter et reconnaître des
objets dans une scène mais aussi de fournir une description sémantique tenant compte des in-
formations contextuelles dans toute la scène. Le problème de l’interprétation d’images peut être
formalisé comme un problème de raisonnement abductif, c’est-à-dire comme la recherche de la
meilleure explication en utilisant une base de connaissances. Dans ce travail, nous présentons
une nouvelle approche utilisant une méthode par tableau pour la génération et la sélection
d’explications possibles d’une image donnée lorsque les connaissances, exprimées dans une
logique de description, comportent des concepts décrivant les objets mais aussi les relations
spatiales entre ces objets. La meilleure explication est sélectionnée en exploitant les domaines
oncrets pour évaluer le degré de satisfaction des relations spatiales entre les objets."
abb4b8f9df14f7b15aa43920d0329eccada33b97,LBP Yardımıyla Görüntüdeki Kişinin Yaşının Bulunması,"C¸ ankaya University Journal of Science and Engineering
Volume 8 (2011), No. 1, 27–41
LBP Yardımıyla G¨or¨unt¨udeki Ki¸sinin Ya¸sının
Bulunması
Vasif V. Nabiyev1,∗ ve Asuman G¨unay1
Karadeniz Teknik ¨Universitesi, Bilgisayar M¨uhendisli˘gi B¨ol¨um¨u, 61080 Trabzon, T¨urkiye
Corresponding author:
¨Ozet. Y¨uz g¨or¨unt¨us¨unden ya¸sın do˘gru ¸sekilde tahmin edilmesi ve daha sonra ki¸sinin
ge¸cmi¸s ve gelecekteki g¨or¨unt¨ulerinin ¨uretilmesi, g¨uvenlik sistemlerinin tasarımında b¨uy¨uk
¨onem ta¸sımaktadır. Bu ¸calı¸smada y¨uz g¨or¨unt¨us¨unden ya¸sın sınıflandırılmasında yerel
ikili ¨or¨unt¨u (local binary pattern-LBP) histogramlarından faydalanılmaktadır. LBP opera-
t¨or¨u performansı y¨uksek bir doku tanımlayıcısı olup doku sınıflandırma, segmentasyon, y¨uz
tespiti, ki¸si tanıma ve cinsiyet tahmini gibi alanlarda kullanılmaktadır. G¨or¨unt¨u ¨uzerindeki
d¨uzg¨un yerel ikili ¨or¨unt¨uler, yerel g¨or¨unt¨u dokusunun ¨onemli ¨ozelliklerindendir. Bunların
meydana gelme sıklı˘gını veren histogram ise g¨u¸cl¨u bir doku ¨ozniteli˘gidir. C¸ alı¸smada y¨uz
g¨or¨unt¨us¨u k¨u¸c¨uk b¨olgelere ayrılmı¸stır. Her bir b¨olge i¸cin ¨uretilen d¨uzg¨un LBP histogram-
larının birle¸stirilmesiyle, y¨uz i¸cin verimli bir vekt¨orel g¨osterim ¸sekli olu¸sturulmu¸stur. Sis-
teme sunulan her yeni y¨uz i¸cin b¨olgesel LBP histogramı ¨uretilmekte ve ya¸s sınıflarına
it LBP histogramlarıyla kar¸sıla¸stırılarak sınıflandırılmaktadır. Sınıflandırmada minimum
uzaklık (minimum distance), en yakın kom¸suluk (nearest neighbor) ve k-en yakın kom¸suluk"
abb1289cfdc4c23d72d0680c3ec100eae74d4fdb,PatchMatch : A Fast Randomized Matching Algorithm with Application to Image and Video,"PatchMatch: A Fast Randomized Matching
Algorithm with Application to Image and Video
Connelly Barnes
A Dissertation
Presented to the Faculty
of Princeton University
in Candidacy for the Degree
of Doctor of Philosophy
Recommended for Acceptance
y the Department of
Computer Science
Adviser: Adam Finkelstein
May 2011"
ab036048cf90296171ad2bb7265c5a5b7f3252f7,Multimodal Recurrent Neural Networks With Information Transfer Layers for Indoor Scene Labeling,"Multimodal Recurrent Neural Networks with
Information Transfer Layers for Indoor Scene
Labeling
Abrar H. Abdulnabi, Student Member, IEEE, Bing Shuai, Student Member, IEEE,
Zhen Zuo, Student Member, IEEE, Lap-Pui Chau, Fellow, IEEE, and Gang Wang, Senior Member, IEEE"
abd4152773ebb97b90163b9a6bbdf2075e825481,Procedural Text Generation from an Execution Video,"Proceedings of the The 8th International Joint Conference on Natural Language Processing, pages 326–335,
Taipei, Taiwan, November 27 – December 1, 2017 c(cid:13)2017 AFNLP"
ab8f9a6bd8f582501c6b41c0e7179546e21c5e91,Nonparametric Face Verification Using a Novel Face Representation,"Nonparametric Face Verification Using a Novel
Face Representation
Hae Jong Seo, Student Member, IEEE, Peyman Milanfar, Fellow, IEEE,"
ae8ed3b0b8043c5af76390751938edfd100fa9cd,An Overview of MultiTask Learning in Deep Neural Networks,"of 21
9 May 2017
An Overview of Multi-Task Learning in Deep
Neural Networks 
Table of contents:
Introduction
Motivation
Two MTL methods for Deep Learning
Hard parameter sharing
Soft parameter sharing
Why does MTL work?
Implicit data augmentation
Attention focusing
Eavesdropping
Representation bias
Regularization
MTL in non-neural models
Block-sparse regularization
http://sebastianruder.com/multi-task/index.html
5/31/17, 9:38 AM"
ae8cc8db9e05c79adad03da64a4a9ba0b00f4eb5,Large Scale Local Online Similarity/Distance Learning Framework based on Passive/Aggressive,"International Journal of Machine Learning and Cybernetics
DOI –x
ORI GI NAL    ARTI CLE
Large Scale Local Online Similarity/Distance Learning Framework based on
Passive/Aggressive
Baida Hamdan1, Davood Zabihzadeh*1, Monsefi Reza1
Computer Department, Engineering Faculty, Ferdowsi University of Mashhad (FUM), Mashhad, IRAN"
aeabcbdff7ab810b961a9f7e4399b6c0421d00cd,TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents,"TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents
Yuexin Ma1,2, Xinge Zhu3, Sibo Zhang1, Ruigang Yang1, Wenping Wang2, Dinesh Manocha4
Baidu Research, Baidu Inc.1, The University of Hong Kong2,
The Chinese University of Hong Kong3, University of Maryland at College Park4"
aef3ecc926ed79478f9d1f38c0fec2a29bae9c3b,Counting in High Density Crowd Videos,"Counting in High Density Crowd Videos
Edgar Lopez
University of Texas at El Paso"
ae9257f3be9f815db8d72819332372ac59c1316b,Deciphering the enigmatic face: the importance of facial dynamics in interpreting subtle facial expressions.,"P SY CH O L O GIC AL SC I E NC E
Research Article
Deciphering the Enigmatic Face
The Importance of Facial Dynamics in Interpreting Subtle
Facial Expressions
Zara Ambadar,1 Jonathan W. Schooler,2 and Jeffrey F. Cohn1
University of Pittsburgh and 2University of British Columbia, Vancouver, British Columbia, Canada"
ae818858a88299090748446b8662e68628612c65,Analysis of Expressiveness of Portuguese Sign Language Speakers,"FACULDADE DE ENGENHARIA DA UNIVERSIDADE DO PORTO
Analysis of Expressiveness of
Portuguese Sign Language Speakers
Maria Inês Coutinho Vigário Rodrigues
MASTER THESIS
Integrated Master in Bioengineering
Supervisor: Luis Filipe Pinto de Almeida Teixeira (PhD)
Co-supervisor: Eduardo José Marques Pereira (Eng.)
June 2014"
ae2b2493f35cecf1673eb3913fdce37e037b53a2,Optimal Transport Maps for Distribution Pre- Serving Operations on Latent Spaces of Gener-,"OPTIMAL TRANSPORT MAPS FOR DISTRIBUTION PRE-
SERVING OPERATIONS ON LATENT SPACES OF GENER-
ATIVE MODELS
Eirikur Agustsson
D-ITET, ETH Zurich
Switzerland
Alexander Sage
D-ITET, ETH Zurich
Switzerland
Radu Timofte
D-ITET, ETH Zurich
Merantix GmbH
Luc Van Gool
D-ITET, ETH Zurich
ESAT, KU Leuven"
aeff403079022683b233decda556a6aee3225065,DeepFace: Face Generation using Deep Learning,"DeepFace: Face Generation using Deep Learning
Hardie Cate
Fahim Dalvi
Zeshan Hussain"
ae2cf545565c157813798910401e1da5dc8a6199,Cascade of Boolean detector combinations,"Mahkonen et al. EURASIP Journal on Image and Video
Processing  (2018) 2018:61
https://doi.org/10.1186/s13640-018-0303-9
EURASIP Journal on Image
nd Video Processing
RESEARCH
Open Access
Cascade of Boolean detector
ombinations
Katariina Mahkonen*
, Tuomas Virtanen and Joni Kämäräinen"
ae419d28ab936cbbc420dcfd1decb16a45afc8a9,Real-time face verification using multiple feature combination and a support vector machine supervisor,
ae0514be12d200bd9fecf0d834bdcb30288c7a1e,Automatic Opinion Question Generation,"Automatic Opinion Question Generation
Yllias Chali
University of Lethbridge
401 University Drive
Lethbridge, Alberta, T1K 3M4
Tina Baghaee
University of Lethbridge
401 University Drive
Lethbridge, Alberta, T1K 3M4"
ae32279ce2828286a40800045b2d9f3b53bebb8c,Traffic Signs Recognition and Classification based on Deep Feature Learning,
ae0a0ee1c6e2adcddffebf9b0e429a25b7d9c0e1,"A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms","A Review and Analysis of Eye-Gaze Estimation
Systems, Algorithms and Performance
Evaluation Methods in Consumer Platforms
Anuradha Kar, Student Member, IEEE, Peter Corcoran Fellow, IEEE"
aeeea6eec2f063c006c13be865cec0c350244e5b,"Induced Disgust , Happiness and Surprise : an Addition to the MMI Facial Expression Database","Induced Disgust, Happiness and Surprise: an Addition to the MMI Facial
Expression Database
Michel F. Valstar, Maja Pantic
Imperial College London / Twente University
Department of Computing / EEMCS
80 Queen’s Gate / Drienerlolaan 5
London / Twente"
ae2e46a10d6dd83883809aa5df766050f83aaa91,Drivable Road Detection with 3D Point Clouds Based on the MRF for Intelligent Vehicle,"Drivable road detection with 3D Point Clouds
ased on the MRF for Intelligent Vehicle
Jaemin Byun,Ki-in Na,Beom-su Seo and Myungchan Roh"
aeee98c90799cd44dde4046754cff27c8ed28d44,Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review,"Deep convolutional neural networks for brain image analysis on magnetic
resonance imaging: a review
Jose Bernal∗, Kaisar Kushibar, Daniel S. Asfaw, Sergi Valverde, Arnau Oliver, Robert Mart´ı, Xavier Llad´o
Computer Vision and Robotics Institute
Dept. of Computer Architecture and Technology
University of Girona
Ed. P-IV, Av. Lluis Santal´o s/n, 17003 Girona (Spain)"
ae87896c38f1871457d811a0588487db0155a833,Attentional allocation of ASD individuals : Searching for a Face-inthe-Crowd,"Attentional allocation of ASD individuals: Searching for a Face-in-the-Crowd
David J. Moore, John Reidy and Lisa Heavey
Department of Psychology, Sociology and Politics,
Sheffield Hallam University
Running Header: Attentional allocation of ASD individuals"
ae753fd46a744725424690d22d0d00fb05e53350,Describing clothing by semantic attributes,"Describing Clothing by Semantic Attributes
Anonymous ECCV submission
Paper ID 727"
ae4d06143a6b46ffaf5d228b1fa464dc322ddc18,R 4-A . 3 : Human Detection & Re-Identification for Mass Transit Environments,"R4-A.3: Human Detection & Re-Identification for
Mass Transit Environments
PARTICIPANTS
Rich Radke
Title
Faculty/Staff
Institution
Graduate, Undergraduate and REU Students
Srikrishna Karanam
Eric Lam
Degree Pursued
Institution
Email
Month/Year of Graduation
5/2017
5/2017
PROJECT DESCRIPTION
Project Overview
The computer vision research problem of human re-identification or “re-id” is generally posed as follows:
Given a cropped rectangle of pixels representing a human in one view, a re-id algorithm produces a similarity"
aed5b3b976077ecdcf3f88ffc511f63d9f9e8697,"A Qualitative Comparison of CoQA, SQuAD 2.0 and QuAC","A Qualitative Comparison of CoQA, SQuAD 2.0 and QuAC
Mark Yatskar
Allen Institute for Artificial Intelligence"
aef55af11d8ecaeaf4c13ed765e74a3471ce9b7c,Probabilistic Video Generation Using Holistic Attribute Control,
ae3a81e69ef3ffc22017cec5bb2c5ea26114ce2b,A weighted voting model of associative memory: theoretical analysis,"A Weighted Voting Model of Associative Memory: Theoretical Analysis
Xiaoyan Mu
Department of Electrical and Computer Engineering
Rose-Hullman Institute of Technology
Terre Haute, IN 47803
Paul Watta
Dept. Electrical and Computer Engineering
University of Michigan-Dearborn
Dearborn, MI 48128"
ae21fdc2dae83c951c3cc7e5b8a1c0455470909d,Jadisha Yarif Ramírez Cornejo Emotion Recognition based on Facial Expressions Robust to Occlusions Reconhecimento de Emoções Baseado em Expressões Faciais Robusto a Oclusões,"Jadisha Yarif Ramírez Cornejo
Emotion Recognition based on Facial Expressions
Robust to Occlusions
Reconhecimento de Emoções Baseado em
Expressões Faciais Robusto a Oclusões
CAMPINAS"
aeae58c5e326c162fa5b0019e84f867c58dd20a2,Investigation into DCT Feature Selection for Visual Lip-Based Biometric Authentication,"Investigation into DCT Feature Selection for Visual Lip-Based
Biometric Authentication
Wright, C., Stewart, D., Miller, P., & Campbell-West, F. (2015). Investigation into DCT Feature Selection for
Visual Lip-Based Biometric Authentication. In R. Dahyot, G. Lacey, K. Dawson-Howe, F. Pitié, & D. Moloney
(Eds.), Irish Machine Vision & Image Processing Conference Proceedings 2015 (pp. 11-18). Irish Pattern
Recognition & Classification Society.
Published in:
Irish Machine Vision &amp; Image Processing Conference Proceedings 2015
Document Version:
Publisher's PDF, also known as Version of record
Queen's University Belfast - Research Portal:
Link to publication record in Queen's University Belfast Research Portal
Publisher rights
© 2015 The Authors
This is an open access article published under a Creative Commons Attribution-NonCommercial-ShareAlike License
(https://creativecommons.org/licenses/by-nc-sa/4.0/), which permits use, distribution and reproduction for non-commercial purposes,
provided the author and source are cited and new creations are licensed under the identical terms.
General rights
Copyright for the publications made accessible via the Queen's University Belfast Research Portal is retained by the author(s) and / or other
opyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated"
ae9ab89c51d264fb7b6b57d37399a7c629836e35,Transfer Learning Based on AdaBoost for Feature Selection from Multiple ConvNet Layer Features,"Obtaining Better Image Representations by
Combining Complementary Activation Features of
Multiple ConvNet Layers for Transfer Learning
Jumabek Alikhanov
School of Computer and
Information Engineering
Seunghyun Ko
School of Computer and
Information Engineering
Jo Geun Sik
School of Computer and
Information Engineering
Inha University Incheon, South Korea
Inha University Incheon, South Korea
Inha University Incheon, South Korea
Email:
Email:
Email:"
aeec61ef41d55b5c1becfdc00c2e4dbca0e379c0,Automatic Recognition by Gait,"I N V I T E D
P A P E R
Automatic Recognition by Gait
Recognizing people by the way they walk promises to be useful for identifying
individuals from a distance; improved techniques are under development.
By Mark S. Nixon, Member IEEE, and John N. Carter, Member IEEE"
ae5195c44ef7bff090bb5a17a9fe5f86a8c3b316,Web Scale Image Annotation : Learning to Rank with Joint Word-Image Embeddings,"Web Scale Image Annotation: Learning to Rank with Joint
Word-Image Embeddings"
aeee02b8c8bb749a1203fa634407319dd6874667,VIDEO-SURVEILLANCE IN CLOUD Platform and software aaS for people detection and soft-biometry,"VIDEO-SURVEILLANCE IN CLOUD
Platform and software aaS for people detection and soft-
iometry
R. Cucchiara°,*, A. Prati°,+, R. Vezzani°,*, S. Calderara°,*, C. Grana°,*
°SOFTECH-ICT, *Università di Modena e Reggio Emilia, +Università IUAV di Venezia"
aef3bcb3b09f708edad335cfc0caf8ad208d4741,Learning robotic perception through prior knowledge,"Learning Robotic Perception
Through Prior Knowledge
vorgelegt von
M.Sc.
Rico Jonschkowski
geb. in Havelberg
von der Fakultät IV – Elektrotechnik und Informatik
der Technischen Universität Berlin
zur Erlangung des akademischen Grades
Doktor der Naturwissenschaften
— Dr. rer. nat. —
genehmigte Dissertation
Promotionsausschuss:
Vorsitzender: Prof. Dr. Manfred Opper
Gutachter:
Gutachter:
Gutachter:
Tag der wissenschaftlichen Aussprache: 02. Mai 2018
Prof. Dr. Oliver Brock
Prof. Dr. George Konidaris"
aebb9649bc38e878baef082b518fa68f5cda23a5,A Multi-scale TVQI-based Illumination Normalization Model,
aed5aecd3f0a07036e570c84c06cd37ab8904acc,The Resiliency of Memorability: A Predictor of Memory Separate from Attention and Priming,"The Resiliency of Memorability: A Predictor of Memory
Separate from Attention and Priming
Wilma A. Bainbridge
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology. Cambridge, MA. USA.
Keywords: Memorability, top-down attention, bottom-up attention, priming, visual search,
spatial cueing, directed forgetting, depth of encoding"
aef49f85368baf186d2a95308d14793b9347703c,Automated Fingertip Detection,"Brigham Young University
BYU ScholarsArchive
All Theses and Dissertations
012-04-10
Automated Fingertip Detection
Joseph G. Butler
Brigham Young University - Provo
Follow this and additional works at: https://scholarsarchive.byu.edu/etd
Part of the Computer Sciences Commons
BYU ScholarsArchive Citation
Butler, Joseph G., ""Automated Fingertip Detection"" (2012). All Theses and Dissertations. 3164.
https://scholarsarchive.byu.edu/etd/3164
This Thesis is brought to you for free and open access by BYU ScholarsArchive. It has been accepted for inclusion in All Theses and Dissertations by an
uthorized administrator of BYU ScholarsArchive. For more information, please contact"
ae89b7748d25878c4dc17bdaa39dd63e9d442a0d,On evaluating face tracks in movies,"On evaluating face tracks in movies
Alexey Ozerov, Jean-Ronan Vigouroux, Louis Chevallier, Patrick Pérez
To cite this version:
Alexey Ozerov, Jean-Ronan Vigouroux, Louis Chevallier, Patrick Pérez. On evaluating face tracks
in movies. IEEE International Conference on Image Processing (ICIP 2013), Sep 2013, Melbourne,
Australia. 2013. <hal-00870059>
HAL Id: hal-00870059
https://hal.inria.fr/hal-00870059
Submitted on 4 Oct 2013
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de
recherche français ou étrangers, des laboratoires"
ae0322119c8d70ba98f32aeb205393dad8dd287b,OxIOD: The Dataset for Deep Inertial Odometry,"OxIOD: The Dataset for Deep Inertial Odometry
Changhao Chen∗, Peijun Zhao∗, Chris Xiaoxuan Lu, Wei Wang, Andrew Markham, Niki Trigoni"
ae8d5be3caea59a21221f02ef04d49a86cb80191,Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks,"Published as a conference paper at ICLR 2018
SKIP RNN: LEARNING TO SKIP STATE UPDATES IN
RECURRENT NEURAL NETWORKS
V´ıctor Campos∗†, Brendan Jou‡, Xavier Gir´o-i-Nieto§, Jordi Torres†, Shih-Fu ChangΓ
Barcelona Supercomputing Center, ‡Google Inc,
§Universitat Polit`ecnica de Catalunya, ΓColumbia University
{victor.campos,"
ae13485e75f5e7fc9a9659ce960c8b299c7b889b,SPARSE MODELING FOR HIGH-DIMENSIONAL MULTI-MANIFOLD DATA ANALYSIS by,"SPARSE MODELING FOR HIGH-DIMENSIONAL
MULTI-MANIFOLD DATA ANALYSIS
Ehsan Elhamifar
A dissertation submitted to The Johns Hopkins University in conformity with the
requirements for the degree of Doctor of Philosophy.
Baltimore, Maryland
October, 2012
(cid:13) Ehsan Elhamifar 2012
All rights reserved"
070ab604c3ced2c23cce2259043446c5ee342fd6,An Active Illumination and Appearance (AIA) Model for Face Alignment,"AnActiveIlluminationandAppearance(AIA)ModelforFaceAlignment
FatihKahraman,MuhittinGokmen
IstanbulTechnicalUniversity,
ComputerScienceDept.,Turkey
{fkahraman,
InformaticsandMathematicalModelling,Denmark
SuneDarkner,RasmusLarsen
TechnicalUniversityofDenmark"
070199a5087590f96c4422b82e4803911bb0652e,What Are We Tracking: A Unified Approach of Tracking and Recognition,"What Are We Tracking: A Unified Approach of
Tracking and Recognition
Jialue Fan, Xiaohui Shen, Student Member, IEEE, and Ying Wu, Senior Member, IEEE"
073c9ec4ff069218f358b9dd8451a040cf1a4a82,Object Classification and Detection in High Dimensional Feature Space,"Object Classification and Detection
in High Dimensional Feature Space
THIS IS A TEMPORARY TITLE PAGE
It will be replaced for the final print by a version
provided by the service académique.
Thèse n. 6043
présentée le 17 Décembre 2013
à la Faculté Sciences et Techniques de l’Ingénieur
Laboratoire de l’Idiap
Programme doctoral en Informatique, Communications et Infor-
mation
École Polytechnique Fédérale de Lausanne
pour l’obtention du grade de Docteur ès Sciences
Charles Dubout
cceptée sur proposition du jury:
Prof Mark Pauly, président du jury
Dr François Fleuret, directeur de thèse
Prof Pascal Fua, rapporteur
Prof Gilles Blanchard, rapporteur
Prof Frédéric Jurie, rapporteur"
07c6744e25ed01967e448a397f5d7e9d540345c3,Effective Multi-Query Expansions: Collaborative Deep Networks for Robust Landmark Retrieval,"Effective Multi-Query Expansions: Collaborative Deep Networks for Robust
Landmark Retrieval
Yang Wang, Xuemin Lin, Lin Wu, Wenjie Zhang"
074061bc12af98e3c9fd7650452d896b2c03b3ac,A FPGA Implementation of Neural / Wavelet Face Detection System,"Australian  Journal  of  Basic  and  Applied  Sciences,  4(3):  379-388,  2010
ISSN  1991-8178
©  2010,  INSInet  Publication
A FPGA Implementation of Neural/Wavelet
Face Detection System
Hossein Sahoolizadeh,  Ahmad Keshavarz
Islamic Azad Universitiy, Dashtestan branch, Iran
Behin Tadbir Intelligent Systems Co, Kermanshah, Iran
Persian Golg University, Bushehr 75169, Iran"
07892741feb277639b8a7d4c1dcc0054077cb7ce,Sparse Representation for 3D Shape Estimation: A Convex Relaxation Approach,"Sparse Representation for 3D Shape
Estimation: A Convex Relaxation Approach
Xiaowei Zhou, Menglong Zhu, Spyridon Leonardos, and Kostas Daniilidis, Fellow, IEEE"
0726a45eb129eed88915aa5a86df2af16a09bcc1,Introspective perception: Learning to predict failures in vision systems,"Introspective Perception: Learning to Predict Failures in Vision Systems
Shreyansh Daftry, Sam Zeng, J. Andrew Bagnell and Martial Hebert"
0750a816858b601c0dbf4cfb68066ae7e788f05d,CosFace: Large Margin Cosine Loss for Deep Face Recognition,"CosFace: Large Margin Cosine Loss for Deep Face Recognition
Hao Wang, Yitong Wang, Zheng Zhou, Xing Ji, Dihong Gong, Jingchao Zhou,
Zhifeng Li∗, and Wei Liu∗
Tencent AI Lab"
07ad6bb9b21c065cd92ab2f24a22c1d4a8f205a7,Realtime facial animation with on-the-fly correctives,"Realtime Facial Animation with On-the-fly Correctives
Hao Li⇤
Jihun Yu†
Yuting Ye‡
Chris Bregler§
Industrial Light & Magic
input depth map & 2D features
data-driven tracking
our tracking
data-driven retargeting
our retargeting
Figure 1: Our adaptive tracking model conforms to the input expressions on-the-fly, producing a better fit to the user than state-of-the-art
data driven techniques [Weise et al. 2011] which are confined to learned motion priors and generate plausible but not accurate tracking.
Links:
Introduction
The essence of high quality performance-driven facial animation is
to capture every trait and characteristic of an actor’s facial and ver-
al expression and to reproduce those on a digital double or crea-
ture. Even with the latest 3D scanning and motion capture tech-
nology, the creation of realistic digital faces in film and game pro-"
07ea3dd22d1ecc013b6649c9846d67f2bf697008,HUMAN-CENTRIC VIDEO UNDERSTANDING WITH WEAK SUPERVISION A DISSERTATION SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE AND THE COMMITTEE ON GRADUATE STUDIES OF STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY,"HUMAN-CENTRIC VIDEO UNDERSTANDING WITH WEAK
SUPERVISION
A DISSERTATION
SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE
AND THE COMMITTEE ON GRADUATE STUDIES
OF STANFORD UNIVERSITY
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
Vignesh Ramanathan
June 2016"
0717b47ab84b848de37dbefd81cf8bf512b544ac,Robust Face Recognition and Tagging in Visual Surveillance System,"International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622
International Conference on Humming Bird ( 01st March 2014)
RESEARCH ARTICLE
OPEN ACCESS
Robust Face Recognition and Tagging in Visual Surveillance
Kavitha MS 1, Siva Pradeepa S2
System
Kavitha MS Author is currently pursuing M.E(CSE)in VINS Christian college of Engineering,Nagercoil.
Siva pradeepa,Assistant Lecturer in VINS Christian college of Engineering"
07e5bd5d3830e55dc85677821eea8256e252f966,Hierarchical Representation Learning for Kinship Verification,"IEEE TRANSACTIONS ON IMAGE PROCESSING
Hierarchical Representation Learning for Kinship
Verification
Naman Kohli, Student Member, IEEE, Mayank Vatsa, Senior Member, IEEE, Richa Singh, Senior Member, IEEE,
Afzel Noore, Senior Member, IEEE, and Angshul Majumdar, Senior Member, IEEE"
078d507703fc0ac4bf8ca758be101e75ea286c80,Large-Scale Content Based Face Image Retrieval using Attribute Enhanced Sparse,"ISSN: 2321-8169
International Journal on Recent and Innovation Trends in Computing and Communication
Volume: 3 Issue: 8
5287 - 5296
________________________________________________________________________________________________________________________________
Large- Scale Content Based Face Image Retrieval using Attribute Enhanced
Sparse Codewords.
Chaitra R,
Mtech Digital Coomunication Engineering
Acharya Institute Of Technology
Bangalore"
074faec2e546f292800c0c028912ced147b25218,Chapter 6 Face Recognition in the Thermal Infrared ?,"Chapter 6
Face Recognition in the Thermal Infrared (cid:63)
Lawrence B. Wolff, Diego A. Socolinsky, and Christopher K. Eveland
Equinox Corporation, 9 West 57th Street, New York, New York 10019
Summary. Recent research has demonstrated distinct advantages of using thermal
infrared imaging for improving face recognition performance. While conventional
video cameras sense reflected light, thermal infrared cameras primarily measure
emitted radiation from objects such as faces. Visible and thermal infrared image
data collections of frontal faces have been on-going at NIST for over two years, pro-
ducing the most comprehensive face database known to involve thermal infrared im-
gery. Rigorous experimentation with this database has revealed consistently supe-
rior recognition performance of algorithms when applied to thermal infrared, partic-
ularly under variable illumination conditions. Physical phenomenology responsible
for this observation is analyzed. An end-to-end face recognition system incorporat-
ing simultaneous coregistered thermal infrared and visible has been developed and
tested indoors with good performance.
6.1 Introduction
Accelerated developments in camera technology over the last decade have
given computer vision researchers a whole new diversity of imaging options,
particularly in the infrared spectrum. Conventional video cameras use pho-"
07c83f544d0604e6bab5d741b0bf9a3621d133da,Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognition,"Learning Spatio-Temporal Features with 3D Residual Networks
for Action Recognition
Kensho Hara, Hirokatsu Kataoka, Yutaka Satoh
National Institute of Advanced Industrial Science and Technology (AIST)
Tsukuba, Ibaraki, Japan
{kensho.hara, hirokatsu.kataoka,"
07faa38d4d0e9d14d72bd049362efa83fae78ee3,Quick Identification of Child Pornography in Digital Videos,"IJoFCS (2012) 2, 21-32
DOI: 10.5769/J201202002 or http://dx.doi.org/10.5769/J201202002
Quick Identification of Child Pornography
in Digital Videos
Mateus de Castro Polastro and Pedro Monteiro da Silva Eleuterio
Brazilian Federal Police
Campo Grande/MS
E-mails:"
071135dfb342bff884ddb9a4d8af0e70055c22a1,Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification,"New Architecture and Transfer Learning for Video Classification
Temporal 3D ConvNets:
Ali Diba1,4,(cid:63), Mohsen Fayyaz2,(cid:63), Vivek Sharma3, Amir Hossein Karami4, Mohammad Mahdi Arzani4,
Rahman Yousefzadeh4, Luc Van Gool1,4
ESAT-PSI, KU Leuven, 2University of Bonn, 3CV:HCI, KIT, Karlsruhe, 4Sensifai"
0779875eff440365184dd8bf44e9f85f78267c5f,An Intelligent Extraversion Analysis Scheme from Crowd Trajectories for Surveillance,"JOURNAL OF LATEX CLASS FILES, VOL. XX, NO. YY, JULY 2017
An Intelligent Extraversion Analysis Scheme from
Crowd Trajectories for Surveillance
Wenxi Liu, Yuanlong Yu, Chun-Yang Zhang, Genggeng Liu, Naixue Xiong"
07dbf04089b015db773fe95e664fa73aef874b36,Fishy Faces: Crafting Adversarial Images to Poison Face Authentication,"Fishy Faces: Crafting Adversarial Images to Poison Face Authentication
Giuseppe Garofalo
Vera Rimmer
Tim Van hamme
imec-DistriNet, KU Leuven
imec-DistriNet, KU Leuven
imec-DistriNet, KU Leuven
Davy Preuveneers
Wouter Joosen
imec-DistriNet, KU Leuven
imec-DistriNet, KU Leuven"
0744af11a025e9c072ef6ad102af208e79cc6f44,Learning Smooth Pattern Transformation Manifolds,"Learning Smooth Pattern Transformation Manifolds
Elif Vural and Pascal Frossard"
079a0a3bf5200994e1f972b1b9197bf2f90e87d4,Component-Based Face Recognition with 3D Morphable Models,"Component-based Face Recognition with 3D
Morphable Models
Jennifer Huang1, Bernd Heisele1;2, and Volker Blanz3
Center for Biological and Computational Learning, M.I.T., Cambridge, MA, USA
Honda Research Institute US, Boston, MA, USA
Computer Graphics Group, Max-Planck-Institut, Saarbr˜ucken, Germany"
077492a77812a68c86b970557e97a452a6689427,Automatic 3D face reconstruction from single images or video,"Automatic 3D Face Reconstruction from Single Images or Video
Pia Breuer†, Kwang-In Kim‡, Wolf Kienzle‡, Bernhard Sch¨olkopf‡, Volker Blanz†
University of Siegen, ‡Max Planck Institute for Biological Cybernetics
{pbreuer,
{kwangin.kim, kienzle,"
07a8a4b8f207b2db2a19e519027f70cd1c276294,Pixel Recursive Super Resolution,"Pixel Recursive Super Resolution
Ryan Dahl ∗
Jonathon Shlens
Mohammad Norouzi
Google Brain"
07d6238d8f8edbfe0fd2887fa0a7939735f21e13,Learning Human Optical Flow,"RANJAN, ROMERO, BLACK: LEARNING HUMAN OPTICAL FLOW
Learning Human Optical Flow
MPI for Intelligent Systems
Tübingen, Germany
Amazon Inc.
Anurag Ranjan1
Javier Romero∗,2
Michael J. Black1"
07f5c01b1e272fca1fc575ab84ac1710cbe58518,A Deep Structure of Person Re-Identification Using Multi-Level Gaussian Models,"> REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) <
A Deep Structure of Person Re-Identification
using Multi-Level Gaussian Models
Dinesh Kumar Vishwakarma, IEEE Member, Sakshi Upadhyay"
0716e1ad868f5f446b1c367721418ffadfcf0519,Interactively Guiding Semi-Supervised Clustering via Attribute-Based Explanations,"Interactively Guiding Semi-Supervised
Clustering via Attribute-Based Explanations
Shrenik Lad and Devi Parikh
Virginia Tech, Blacksburg, VA, USA"
07eaf19eecf4ccdd5f8e3367c1675d9f4addd2df,Learning pullback manifolds of dynamical models,"IEEE TRANSACTIONS ON PAMI, VOL. XX, NO. Y, MONTH 2010
SubmittedtoIEEETrans.onPatternAnalysisandMachineIntelligence;October27,2010
Learning pullback manifolds of dynamical
models
Fabio Cuzzolin"
0725b950792ddbe4edf812a7ee8cef14447236ed,Efficient Large-Scale Multi-Modal Classification,"Efficient Large-Scale Multi-Modal Classification
Douwe Kiela, Edouard Grave, Armand Joulin and Tomas Mikolov
Facebook AI Research"
0742d051caebf8a5d452c03c5d55dfb02f84baab,Real-time geometric motion blur for a deforming polygonal mesh,"Real-Time Geometric Motion Blur for a Deforming Polygonal Mesh
Nathan Jones
Formerly: Texas A&M University
Currently: The Software Group"
0726152a1c1a5723ac34d54abec0dc8d4659598e,Realtime Image Matching for Vision Based Car Navigation with Built-in Sensory Data,"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-3/W2, 2013
ISA13 - The ISPRS Workshop on Image Sequence Analysis 2013, 11 November 2013, Antalya, Turkey"
073c5c27f26c9d174e2ee060b27a090457e3f774,Backprop KF: Learning Discriminative Deterministic State Estimators,"Backprop KF: Learning Discriminative Deterministic
State Estimators
Tuomas Haarnoja, Anurag Ajay, Sergey Levine, Pieter Abbeel
{haarnoja, anuragajay, svlevine,
Department of Computer Science, University of California, Berkeley"
07ca211bde38009697c964702a29d0fe3260bf97,Resource Aware Person Re-identification across Multiple Resolutions,"Resource Aware Person Re-identification across Multiple Resolutions
Yan Wang∗ †, Lequn Wang∗ †, Yurong You∗ ‡, Xu Zou§, Vincent Chen†
Serena Li†, Gao Huang†, Bharath Hariharan†, Kilian Q. Weinberger†"
074a12f9187beafe40386f19aa2544df30fa5703,Product Characterisation towards Personalisation: Learning Attributes from Unstructured Data to Recommend Fashion Products,"Product Characterisation towards Personalisation
Learning Attributes from Unstructured Data to Recommend Fashion Products
Ângelo Cardoso∗
ISR, IST, Universidade de Lisboa
Lisbon, Portugal
Fabio Daolio
ASOS.com
London, UK
Saúl Vargas
ASOS.com
London, UK"
073bbe41f2fdfb2c87b75dbc154adc7020baab0a,Face Recognition by Cortical Multi-scale Line and Edge Representations,"Face recognition by cortical multi-scale
line and edge representations
Jo˜ao Rodrigues1 and J.M.Hans du Buf 2
Escola Superior de Tecnologia, Campus da Penha
Vision Laboratory, Campus de Gambelas – FCT,
University of Algarve, 8000-117 Faro, Portugal"
0735e0b0266d94b670fa6e1b974d3676ef4e3e24,Face Recognition by Elastic Bunch Graph Matching,"IEEE Transactions on Pattern Analysis and Machine Intelligence, 
	:-	 
		.
Face Recognition by Elastic Bunch Graph Matching
Laurenz Wiskott, Jean-Marc Fellous,
Norbert Kruger, and Christoph von der Malsburg"
07adc7429fb22352946b675023df7db11c905701,Active Multitask Learning Using Both Latent and Supervised Shared Topics,"Active Multitask Learning Using Both Latent and Supervised Shared Topics
Ayan Acharya∗
Raymond J. Mooney∗
Joydeep Ghosh∗"
0760b9375db1505e9b9c182e98bb9579dd9197af,Robust Subspace Discovery through Supervised Low-Rank Constraints,"Robust Subspace Discovery through Supervised Low-Rank Constraints
Sheng Li∗
Yun Fu∗"
071099a4c3eed464388c8d1bff7b0538c7322422,Facial expression recognition in the wild using rich deep features,"FACIAL EXPRESSION RECOGNITION IN THE WILD USING RICH DEEP FEATURES
Abubakrelsedik Karali, Ahmad Bassiouny and Motaz El-Saban
Microsoft Advanced Technology labs, Microsoft Technology and Research, Cairo, Egypt"
079b6800e3130ca2ef1815a35632ab6998848ef3,Fine-grained Apparel Classification and Retrieval without rich annotations,"Fine-grained Apparel Classification and Retrieval
without rich annotations
Aniket Bhatnagar · Sanchit Aggarwal"
0770f0f8f168c284a63e46b394150a8c429549da,Project-Team Pulsar Perception Understanding Learning Systems for Activity Recognition,"INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET EN AUTOMATIQUE
Project-Team Pulsar
Perception Understanding Learning
Systems for Activity Recognition
Sophia Antipolis - Méditerranée
THEME COG
tivitytepor2008"
0728f788107122d76dfafa4fb0c45c20dcf523ca,The Best of BothWorlds: Combining Data-Independent and Data-Driven Approaches for Action Recognition,"The Best of Both Worlds: Combining Data-independent and Data-driven
Approaches for Action Recognition
Zhenzhong Lan, Dezhong Yao, Ming Lin, Shoou-I Yu, Alexander Hauptmann
{lanzhzh, minglin, iyu,"
07d49098ada2d8e1ca0608c70e559dd517ca3432,Modélisation de contextes pour l'annotation sémantique de vidéos. (Context based modeling for video semantic annotation),"Modélisation de contextes pour l’annotation sémantique
de vidéos
Nicolas Ballas
To cite this version:
Nicolas Ballas. Modélisation de contextes pour l’annotation sémantique de vidéos. Autre [cs.OH].
Ecole Nationale Supérieure des Mines de Paris, 2013. Français. <NNT : 2013ENMP0051>. <pastel-
00958135>
HAL Id: pastel-00958135
https://pastel.archives-ouvertes.fr/pastel-00958135
Submitted on 11 Mar 2014
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
07ca470ed3be3a476b6fc1917bbbf4182846d1db,Transforming sensor data to the image domain for deep learning — An application to footstep detection,"Transforming Sensor Data to the Image Domain for
Deep Learning - an Application to Footstep
Detection
Monit Shah Singh1∗, Vinaychandran Pondenkandath1†§, Bo Zhou‡,
Paul Lukowicz∗‡ and Marcus Liwicki†§
MindGarage, TU Kaiserslautern, Germany
§DIVA, University of Fribourg, Switzerland
TU Kaiserslautern, Germany
DFKI, Kaiserslautern, Germany
M onit ki.de, r.ch
ki.de, P ki.de,"
072fd0b8d471f183da0ca9880379b3bb29031b6a,Image-to-Image Translation with Conditional Adversarial Networks,"Image-to-Image Translation with Conditional Adversarial Networks
Phillip Isola
Jun-Yan Zhu
Tinghui Zhou
Alexei A. Efros
Berkeley AI Research (BAIR) Laboratory, UC Berkeley
Figure 1: Many problems in image processing, graphics, and vision involve translating an input image into a corresponding output image.
These problems are often treated with application-specific algorithms, even though the setting is always the same: map pixels to pixels.
Conditional adversarial nets are a general-purpose solution that appears to work well on a wide variety of these problems. Here we show
results of the method on several. In each case we use the same architecture and objective, and simply train on different data."
f0418d8029323e37e14ccf2e2a7143e197fb36e4,Robust tracking via weighted online extreme learning machine,"Noname manuscript No.
(will be inserted by the editor)
Robust Tracking via Weighted Online Extreme
Learning Machine
Jing Zhang · Huibing Wang · Yonggong
Received: date / Accepted: date"
f0d18a5d205c23d1309387dfbd4ecfbcf3b1687e,Atypical neural modulation in the right prefrontal cortex during an inhibitory task with eye gaze in autism spectrum disorder as revealed by functional near-infrared spectroscopy.,"Terms of Use: https://journals.spiedigitallibrary.org/terms-of-use
Atypicalneuralmodulationintherightprefrontalcortexduringaninhibitorytaskwitheyegazeinautismspectrumdisorderasrevealedbyfunctionalnear-infraredspectroscopyTakahiroIkedaMasahiroHiraiTakeshiSakuradaYukifumiMondenTatsuyaTokudaMasakoNagashimaHideoShimoizumiIppeitaDanTakanoriYamagataTakahiroIkeda,MasahiroHirai,TakeshiSakurada,YukifumiMonden,TatsuyaTokuda,MasakoNagashima,HideoShimoizumi,IppeitaDan,TakanoriYamagata,“Atypicalneuralmodulationintherightprefrontalcortexduringaninhibitorytaskwitheyegazeinautismspectrumdisorderasrevealedbyfunctionalnear-infraredspectroscopy,”Neurophoton.5(3),035008(2018),doi:10.1117/1.NPh.5.3.035008."
f050b9f46711e48c5ebe6e79944a54e363bc2939,CoSy Cognitive Systems for Cognitive Assistants Integrated Project Information Society Technologies,"FP6-004250CoSyCognitiveSystemsforCognitiveAssistantsIntegratedProjectInformationSocietyTechnologiesDR.7.5MechanismsforrobustandscalablerecognitionandcategorizationofobjectsandplacesDuedateofdeliverable:30/09/2006Actualsubmissiondate:30/09/2006Startdateofproject:September1st,2004Duration:48monthsOrganisationnameofleadcontractorforthisdeliverable:TUDRevision:draftDisseminationLevel:PU"
f0558f6f80e1a8229ad241b3de000f744074a030,Incorporating Computation Time Measures During Heterogeneous Features Selection in a Boosted Cascade People Detector,"Incorporating Computation Time Measures during
Heterogeneous Features Selection in a Boosted Cascade
People Detector
Alhayat Ali Mekonnen, Frédéric Lerasle, Ariane Herbulot, Cyril Briand
To cite this version:
Alhayat Ali Mekonnen, Frédéric Lerasle, Ariane Herbulot, Cyril Briand. Incorporating Computation
Time Measures during Heterogeneous Features Selection in a Boosted Cascade People Detector. Inter-
national Journal of Pattern Recognition and Artificial Intelligence, World Scientific Publishing, 2016,
0 (8), pp.1655022. <10.1142/S0218001416550223>. <hal-01300472>
HAL Id: hal-01300472
https://hal.archives-ouvertes.fr/hal-01300472
Submitted on 11 Apr 2016
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents"
f06f3e1cef2d04af915a932e83b22e46a45f3b73,Action understanding and social learning in Autism : a developmental perspective,"Life Span and Disability / XIV, 1 (2011), 7-29
Action understanding and social learning in Autism:
developmental perspective
Giacomo Vivanti1 & Sally J. Rogers2"
f0b30a9bb9740c2886d96fc44d6f35b8eacab4f3,Are You Sure You Want To Do That ? Classification with Interpretable Queries,"Are You Sure You Want To Do That?
Classification with Interpretable Queries
Anonymous Author(s)
Affiliation
Address
email"
f05358fb80283f0f242215d459367bddce810cd0,An Efficient Video to Video Face Recognition using Neural Networks,"An Efficient Video to Video Face Recognition using Neural Networks
{tag}                                                 {/tag}
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 170
Number 8
Year of Publication: 2017
Authors:
Wilson S., Lenin Fred
10.5120/ijca2017914924
{bibtex}2017914924.bib{/bibtex}"
f0e17f27f029db4ad650ff278fe3c10ecb6cb0c4,The EuroCity Persons Dataset: A Novel Benchmark for Object Detection,"The EuroCity Persons Dataset:
A Novel Benchmark for Object Detection
Markus Braun, Sebastian Krebs, Fabian Flohr, and Dariu M. Gavrila"
f04cffcd0cc68e28cf05827ab998cf84b1ab0f3d,Crowdsourced Data Preprocessing with R and Amazon Mechanical Turk,"CONTRIBUTED RESEARCH ARTICLES
Crowdsourced Data Preprocessing with R
nd Amazon Mechanical Turk
y Thomas J. Leeper"
f09432b7f470268c28d3d4ebd17a44773b678900,Structured Attentions for Visual Question Answering,"Structured Attentions for Visual Question Answering
Chen Zhu, Yanpeng Zhao, Shuaiyi Huang, Kewei Tu, Yi Ma
{zhuchen, zhaoyp1, huangsy, tukw,
ShanghaiTech University"
f0f876b5bf3d442ef9eb017a6fa873bc5d5830c8,"LOH and behold: Web-scale visual search, recommendation and clustering using Locally Optimized Hashing","LOH and Behold: Web-scale visual search,
recommendation and clustering using Locally
Optimized Hashing
Yannis Kalantidis:, Lyndon Kennedy;‹, Huy Nguyen:,
Clayton Mellina: and David A. Shamma§‹
:Computer Vision and Machine Learning Group, Flickr, Yahoo
;Futurewei Technologies Inc.
§CWI: Centrum Wiskunde & Informatica, Amsterdam"
f07956d0031ff046c5c719296f7916d7897fdd21,A Flexible Real-Time Control System for Autonomous Vehicles,"A Flexible Real-Time Control System for Autonomous Vehicles.
Johannes Meyer, Armin Strobel
Institute of Flight Systems and Automatic Control, Technische Universität Darmstadt, Germany 1"
f040e4fcedca0c07788ecb6e92ad246b9c1697a9,REAL-TIME MULTIPLE HEAD TRACKING USING TEXTURE AND COLOUR CUES,"REAL-TIME MULTIPLE HEAD TRACKING
USING TEXTURE AND COLOUR CUES
Vasil Khalidov        Jean-Marc Odobez
Idiap-RR-02-2017
FEBRUARY 2017
Centre du Parc, Rue Marconi 19, P.O. Box 592, CH - 1920 Martigny
T +41 27 721 77 11  F +41 27 721 77 12   www.idiap.ch"
f006161327d3ea3484064c1a86e4c87c729fd7b8,ROUGH SETS METHODS IN FEATURE REDUCTION AND CLASSIFICATION,"Int. J. Appl. Math. Comput. Sci., 2001, Vol.11, No.3, 565{582
ROUGH SETS METHODS IN FEATURE REDUCTION
AND CLASSIFICATION
Roman W. (cid:145)WINIARSKI(cid:3)
The paper presents an application of rough sets and statistical methods to fea-
ture reduction and pattern recognition. The presented description of rough sets
theory emphasizes the role of rough sets reducts in feature selection and data
reduction in pattern recognition. The overview of methods of feature selection
emphasizes feature selection criteria, including rough set-based methods. The
paper also contains a description of the algorithm for feature selection and re-
duction based on the rough sets method proposed jointly with Principal Compo-
nent Analysis. Finally, the paper presents numerical results of face recognition
experiments using the learning vector quantization neural network, with feature
selection based on the proposed principal components analysis and rough sets
methods.
Keywords: rough sets, feature selection, classi(cid:12)cation
. Introduction
One of the fundamental steps in classi(cid:12)er design is reduction of pattern dimensional-
ity through feature extraction and feature selection (Cios et al., 1998; Kittler, 1986;
Langley and Sage, 1994; Liu and Motoda, 1999). Feature selection is often isolated as"
f0ae807627f81acb63eb5837c75a1e895a92c376,Facial Landmark Detection using Ensemble of Cascaded Regressions,"International Journal of Emerging Engineering Research and Technology
Volume 3, Issue 12, December 2015, PP 128-133
ISSN 2349-4395 (Print) & ISSN 2349-4409 (Online)
Facial  Landmark  Detection  using  Ensemble  of  Cascaded
Regressions
Martin Penev1*, Ognian Boumbarov2
Faculty of Telecommunications, Technical University, Sofia, Bulgaria
Faculty of Telecommunications, Technical University, Sofia, Bulgaria"
f0f4f16d5b5f9efe304369120651fa688a03d495,Temporal Generative Adversarial Nets,"Temporal Generative Adversarial Nets
Masaki Saito∗
Eiichi Matsumoto∗
Preferred Networks inc., Japan
{msaito,"
f0cc615b14c97482faa9c47eb855303c71ff03a7,Tracklet clustering for robust multiple object tracking using distance dependent Chinese restaurant processes,"SIViP
DOI 10.1007/s11760-015-0817-x
ORIGINAL PAPER
Tracklet clustering for robust multiple object tracking
using distance dependent Chinese restaurant processes
Ibrahim Saygin Topkaya1 · Hakan Erdogan1 · Fatih Porikli2,3
Received: 4 June 2015 / Revised: 19 August 2015 / Accepted: 10 September 2015
© Springer-Verlag London 2015"
f0b77702c8f2249ee1f48e51ff9b86faffe177c9,Reformulating Level Sets as Deep Recurrent Neural Network Approach to Semantic Segmentation,"Reformulating Level Sets as Deep Recurrent Neural Network Approach
to Semantic Segmentation
Ngan Le 1 Kha Gia Quach 1 2 Khoa Luu 1 Marios Savvides 1 Chenchen Zhu 1"
f0e609a2e11a093273c0d8e3321e7b20eaca46e9,Closing the gap towards end-to-end autonomous vehicle system,"Closing the gap towards
end-to-end autonomous vehicle system
Yonatan Glassner∗, Liran Gispan∗, Ariel Ayash∗, and Tal Furman Shohet∗
AV AI Solutions - General Motors Israel
January 7, 2019"
f0a0e963f1ddd8a0b3269392e3d67043d2ace7d0,Roweis-Saul Classifier for Machine Learning,"MAEI5=K ?=IIE
AH BH =?DEA A=HEC
 4====H? /KHKKHJDE 8 4===??
)7*+ 4AIA=H?D +AJHA
16 +=FKI B )= 7ELAHIEJO
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)>IJH=?J 1   5=K 4MAEI ?=O EA=H
=I = J BH EA=H   1 JDEI F=FAH MA
JDA - =CHEJD BHK=JA EJ =I = ?=IIE
AH E = =AH
HAEEI?AJ B 0A AJ = ! =A EJ =BJAH 4MAEI 5=K KH AN
FAHEAJI MEJD JDA 4 ;)- .-4-6 B=?A 156
IDM JD=J KH ?=IIE
AH D=I HA?CEJE H=JAI B
'#"" '### '#"" ' # HAIFA?JELAO ?A=HO KJFAHBHEC
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=F=?E=B=?AI 9A FHFIA = J JDA JH=EEC FD=IA B JDA
?=IIE
AH >O FAHJKH>EC JDA MEJDE ?=II AJHEAI B JDA HA?IJHK?JE =
JHEN JDA JH=EEC FD=IA 6DEI FAHJKH>=JE J
= E?HA=IA E JDA IK??AII H=JAI BH IA 9A FEJ KJ JDA HA=
JEIDEF >AJMAA JDA 4MAEI5=K ?=IIE
AH 2+) ,) 8=HEKI
DOFJDAIEI JAIJI D=LA >AA BH ?F=HEC ?=IIE
AHI ""# 9A
=FFO IA B JDA DOFJDAIEI JAIJI >O ,EAJJAHE?D"
f0ca04fe6de04a46f44dabd8744b4163e8e0b4d3,Low-Resolution and Low-Quality Face Super-Resolution in Monitoring Scene via Support-Driven Sparse Coding,"J Sign Process Syst (2014) 75:245–256
DOI 10.1007/s11265-013-0804-9
Low-Resolution and Low-Quality Face Super-Resolution
in Monitoring Scene via Support-Driven Sparse Coding
Junjun Jiang & Ruimin Hu & Zhen Han & Zhongyuan Wang
Received: 25 April 2013 / Revised: 2 June 2013 / Accepted: 4 June 2013 / Published online: 26 June 2013
# Springer Science+Business Media New York 2013"
f0483ebab9da2ba4ae6549b681cf31aef2bb6562,3 C-GAN : A N CONDITION-CONTEXT-COMPOSITE GENERATIVE ADVERSARIAL NETWORKS FOR GENERATING IMAGES SEPARATELY,"Under review as a conference paper at ICLR 2018
C-GAN: AN
CONDITION-CONTEXT-COMPOSITE
GENERATIVE ADVERSARIAL NETWORKS FOR GEN-
ERATING IMAGES SEPARATELY
Anonymous authors
Paper under double-blind review"
f0865d11131a84ef1d91e1c8b5718692f153267d,AUTISM SPECTRUM DISORDERS 1 2 Explaining autism spectrum disorders : central coherence versus predictive coding theories,"Articles in PresS. J Neurophysiol (May 28, 2014). doi:10.1152/jn.00242.2014
EXPLAINING AUTISM SPECTRUM DISORDERS
Explaining autism spectrum disorders: central coherence versus predictive coding theories.
Target Article: Stevenson, R. A., Siemann, J. K., Schneider, B. C., Eberly, H. E., Woynaroski, T. G.,
Camarata, S. M., & Wallace, M. T. (2014). Multisensory Temporal Integration in Autism Spectrum
Disorders. The Journal of Neuroscience, 34(3), 691-697. doi: 10.1523/jneurosci.3615-13.2014
Jason S. Chan* & Marcus J. Naumer
Institute of Medical Psychology
Goethe-University, Frankfurt
KEYWORDS: Autism Spectrum Disorder, Multisensory Integration, Temporal Binding Window
Acknowledgements: This was funded by the Hessian initiative for the development of scientific and
economic excellence (LOEWE) Neuronal Coordination Research Focus Frankfurt (NeFF).
*Corresponding author:
Jason Chan
Copyright © 2014 by the American Physiological Society."
f0dd265dfbe9ffe86ca56ba053335626720059a3,CNN Fixations: An unraveling approach to visualize the discriminative image regions,"CNN Fixations: An unraveling approach to
visualize the discriminative image regions
Konda Reddy Mopuri*, Utsav Garg*, R. Venkatesh Babu, Senior Member, IEEE"
f074f216e6eecd1be64398fdf1e06927b94c8df8,Classifying Faces with Non-negative Matrix Factorization,"Classifying Faces with Non-negative Matrix
Factorization
David Guillamet , Jordi Vitri`a
Computer Vision Center, Dept. Inform`atica
Universitat Aut`onoma de Barcelona
08193 Bellaterra, Barcelona, Spain"
f00e51ec0e3894bdb2977a01824f37b15bb82c6e,A Gaussian Approximation of Feature Space for Fast Image Similarity,"Computer Science and ArtificialIntelligence LaboratoryTechnical Reportmassachusetts institute of technology, cambridge, ma 02139 usa — www.csail.mit.eduMIT-CSAIL-TR-2012-032October 1, 2012A Gaussian Approximation of Feature Space for Fast Image Similarity Michael Gharbi, Tomasz Malisiewicz, Sylvain Paris, and FrØdo Durand"
cfffae38fe34e29d47e6deccfd259788176dc213,Training bookcowgrass flower ? ? water sky doggrass water boat water chair road ? cow grass chair grass dog building ?,"TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. X, NO. X, DECEMBER 2012
Matrix Completion for Weakly-supervised
Multi-label Image Classification
Ricardo Cabral, Fernando De la Torre, João P. Costeira, Alexandre Bernardino"
cfcf66e4b22dc7671a5941e94e9d4afae75ba2f8,The Cramer Distance as a Solution to Biased Wasserstein Gradients,"The Cramer Distance as a Solution to Biased
Wasserstein Gradients
Marc G. Bellemare1, Ivo Danihelka1,3, Will Dabney1, Shakir Mohamed1
Balaji Lakshminarayanan1, Stephan Hoyer2, Rémi Munos1
Google DeepMind, London UK, 2Google
CoMPLEX, Computer Science, UCL"
cf77d2e7411814b30aca203376709b12a0eb3e08,Obtaining Better Image Representations by Combining Complementary Activation Features of Multiple ConvNet Layers for Transfer Learning,"Obtaining Better Image Representations by
Combining Complementary Activation Features of
Multiple ConvNet Layers for Transfer Learning
Jumabek Alikhanov
School of Computer and
Information Engineering
Seunghyun Ko
School of Computer and
Information Engineering
Jo Geun Sik
School of Computer and
Information Engineering
Inha University Incheon, South Korea
Inha University Incheon, South Korea
Inha University Incheon, South Korea
Email:
Email:
Email:"
cfd4004054399f3a5f536df71f9b9987f060f434,Person Recognition in Social Media Photos,"Person Recognition in Personal Photo Collections
Seong Joon Oh,Rodrigo Benenson, Mario Fritz, and Bernt Schiele, Fellow, IEEE"
cfa92e17809e8d20ebc73b4e531a1b106d02b38c,Parametric classification with soft labels using the evidential EM algorithm: linear discriminant analysis versus logistic regression,"Advances in Data Analysis and Classification manuscript No.
(will be inserted by the editor)
Parametric Classification with Soft Labels using the
Evidential EM Algorithm
Linear Discriminant Analysis vs. Logistic Regression
Benjamin Quost · Thierry Denœux ·
Shoumei Li
Received: date / Accepted: date"
cfbfcf538c1c9bbf170a524995098fe4aacde374,Symmetric generalized low rank approximations of matrices,"978-1-4673-0046-9/12/$26.00 ©2012 IEEE
ICASSP 2012"
cfbb2d32586b58f5681e459afd236380acd86e28,Improving alignment of faces for recognition,"Improving Alignment of Faces for Recognition
Md. Kamrul Hasan
Christopher J. Pal
D´epartement de g´enie informatique et g´enie logiciel
´Ecole Polytechnique de Montr´eal,
D´epartement de g´enie informatique et g´enie logiciel
´Ecole Polytechnique de Montr´eal,
Qu´ebec, Canada
Qu´ebec, Canada"
cf40951840bfa9b8721d722e9422c73e3a6fbf59,Real-time Appearance-based Person Re-identification Over Multiple KinectTM Cameras,"Real-time appearance-based person re-identification
over multiple KinectTMcameras
Riccardo Satta, Federico Pala, Giorgio Fumera and Fabio Roli
Department of Electrical and Electronic Engineering, University of Cagliari, Italy
{riccardo.satta, fumera,
Keywords:
Video surveillance, Person Re-identification, Kinect"
cf98c333c8d7d5870c1ce5538bb0c3de3de16657,Panoptic Segmentation,"Panoptic Segmentation
Alexander Kirillov1,2 Kaiming He1 Ross Girshick1 Carsten Rother2
Piotr Doll´ar1
Facebook AI Research (FAIR)
HCI/IWR, Heidelberg University, Germany"
cfdc632adcb799dba14af6a8339ca761725abf0a,Probabilistic Formulations of Regression with Mixed Guidance,"Probabilistic Formulations of Regression with Mixed
Guidance
Aubrey Gress, Ian Davidson University of California, Davis"
cf74dceae075bde213d2aafad115d2afc893c21b,Master's Thesis : Deep Learning for Visual Recognition,"Master’s Thesis
Deep Learning for Visual Recognition
Supervised by Nicolas Thome and Matthieu Cord
Remi Cadene
Wednesday 7th September, 2016"
cf65c5cfa2a2b0370407810479f179f5fbe88fb1,Multi-Modal Biometrics : An Overview,"Multi-Modal Biometrics: An Overview
Kevin W. Bowyer,1 K. I. Chang,1 P. Yan,1 P. J. Flynn,1 E. Hansley,2 S. Sarkar2
. Computer Science and Engineering / University of Notre Dame / Notre Dame, IN 46556 USA
. Computer Science and Engineering / University of South Florida / Tampa, FL 33620 USA"
cfd8c66e71e98410f564babeb1c5fd6f77182c55,Vector Quantization Segmentation for Head Pose Estimation,"Comparative Study of Coarse Head Pose Estimation
Lisa M. Brown and Ying-Li Tian
IBM T.J. Watson Research Center
Hawthorne, NY 10532"
cf384eda31030a45238ebd8356ace7600da5076b,Cross-Domain CNN for Hyperspectral Image Classification,"CROSS-DOMAIN CNN FOR HYPERSPECTRAL IMAGE CLASSIFICATION
Hyungtae Lee†‡, Sungmin Eum†‡, Heesung Kwon‡
Booz Allen Hamilton Inc., McLean, Virginia, U.S.A.
U.S. Army Research Laboratory, Adelphi, Maryland, U.S.A.
lee eum"
cf009a6b02fbef514a4bac9695a928080ceac764,COLUMBUS : Feature Selection on Data Analytics Systems,"COLUMBUS: Feature Selection on Data Analytics Systems
Arun Kumar
Pradap Konda
Christopher R´e
February 28, 2013"
cf6eef2ac9ca1a76d0e70006ca9bc3cb3fb80b0b,Recognizing Open-Vocabulary Relations between Objects in Images,"Recognizing Open-Vocabulary Relations between Objects in Images
Masayasu Muraoka∗
Kota Yamaguchi‡
Masaki Saito‡
Naoaki Okazaki† Takayuki Okatani‡ Kentaro Inui†
Sumit Maharjan†
IBM Research – Tokyo∗
Tohoku University†‡"
cf34bda14a2f6148f330213e0ec0dbfe3062c959,Concatenative Resynthesis Using Twin Networks,"INTERSPEECH 2017
August 20–24, 2017, Stockholm, Sweden
Concatenative Resynthesis using twin networks
Soumi Maiti1, Michael I Mandel1,2
The Graduate Center, CUNY, USA
Brooklyn College, CUNY, USA"
cf86616b5a35d5ee777585196736dfafbb9853b5,Learning Multiscale Active Facial Patches for Expression Analysis,"This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.
Learning Multiscale Active Facial Patches for
Expression Analysis
Lin Zhong, Qingshan Liu, Peng Yang, Junzhou Huang, and Dimitris N. Metaxas, Senior Member, IEEE"
cf968e26add377a7edeae338629f2f4edb050b0d,"Project-Team PRIMA Perception , recognition and integration for interactive environments Rhône-Alpes","INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET EN AUTOMATIQUE
Project-Team PRIMA
Perception, recognition and integration for
interactive environments
Rhône-Alpes
THEME COG
tivitytepor2005"
cf103f2fe5595a55f918ecbd9119800f4747fc8e,Human recognition based on ear shape images using PCA-Wavelets and different classification methods,"Human recognition based on ear shape images using
PCA-Wavelets and different classification methods
Ali Mahmoud Mayya1* and Mariam Mohammad Saii
PhD student, Computer Engineering, Tishreen University, Syria"
cf5bdf52cf269c9f3ebf548756146763f55e42d6,Title of Thesis: Contour Based 3d Face Modeling from Monocular Video Contour Based 3d Face Modeling from Monocular Video,
cf280435c471ee099148c4eb9eb2e106ccb2b218,HoME: a Household Multimodal Environment,"HoME: a Household Multimodal Environment
Simon Brodeur1, Ethan Perez2,3∗, Ankesh Anand2∗, Florian Golemo2,4∗,
Luca Celotti1, Florian Strub2,5, Jean Rouat1, Hugo Larochelle6,7, Aaron Courville2,7
Université de Sherbrooke, 2MILA, Université de Montréal, 3Rice University, 4INRIA Bordeaux,
5Univ. Lille, Inria, UMR 9189 - CRIStAL, 6Google Brain, 7CIFAR Fellow
{simon.brodeur, luca.celotti,
{florian.golemo,
{ankesh.anand,"
cf6527d8d42a9958eea7d8d1f90ea4c86d591408,Convolutional Neural Network-Based Classification of Driver’s Emotion during Aggressive and Smooth Driving Using Multi-Modal Camera Sensors,"Article
Convolutional Neural Network-Based Classification
of Driver’s Emotion during Aggressive and Smooth
Driving Using Multi-Modal Camera Sensors
Kwan Woo Lee, Hyo Sik Yoon, Jong Min Song and Kang Ryoung Park *
Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu,
Seoul 100-715, Korea; (K.W.L.); (H.S.Y.);
(J.M.S.)
* Correspondence: Tel.: +82-10-3111-7022; Fax: +82-2-2277-8735
Received: 20 February 2018; Accepted: 21 March 2018; Published: 23 March 2018"
cf8f5cad6aa87a6364f6b5dd985116b902050acf,Slack and Margin Rescaling as Convex Extensions of Supermodular Functions,"Slack and Margin Rescaling as Convex Extensions of
Supermodular Functions
Matthew B. Blaschko
Center for Processing Speech & Images
Departement Elektrotechniek, KU Leuven
Kasteelpark Arenberg 10
001 Leuven, Belgium"
cf7b4fa0a8b58473b94496f353f3c8d0f9531b71,Recognition of 3 D Frontal Face Images Using Local Ternary Patterns and MLDA Algorithm,"International Journal of Science and Research (IJSR)
ISSN (Online): 2319-7064
Impact Factor (2012): 3.358
Recognition of 3D Frontal Face Images Using Local
Ternary Patterns and MLDA Algorithm
Dr. T. Karthikeyan1, T. K. Sumathi2
Associate Professor, PSG College of Arts & Science, Coimbatore
Research Scholar, Karpagam University, Coimbatore
identification"
cf805d478aeb53520c0ab4fcdc9307d093c21e52,Finding Tiny Faces in the Wild with Generative Adversarial Network,"Finding Tiny Faces in the Wild with Generative Adversarial Network
Yancheng Bai1
Yongqiang Zhang1
Mingli Ding2
Bernard Ghanem1
Visual Computing Center, King Abdullah University of Science and Technology (KAUST)
School of Electrical Engineering and Automation, Harbin Institute of Technology (HIT)
Institute of Software, Chinese Academy of Sciences (CAS)
{zhangyongqiang,
Figure1. The detection results of tiny faces in the wild. (a) is the original low-resolution blurry face, (b) is the result of
re-sizing directly by a bi-linear kernel, (c) is the generated image by the super-resolution method, and our result (d) is learned
y the super-resolution (×4 upscaling) and refinement network simultaneously. Best viewed in color and zoomed in."
cffc94574c8796cbd8234422a979e57e67eca7b5,Multiracial Children's and Adults' Categorizations of Multiracial Individuals.,"Journal of Cognition and Development
ISSN: 1524-8372 (Print) 1532-7647 (Online) Journal homepage: http://www.tandfonline.com/loi/hjcd20
Multiracial Children’s and Adults’ Categorizations
of Multiracial Individuals
Steven O. Roberts & Susan A. Gelman
To cite this article: Steven O. Roberts & Susan A. Gelman (2017) Multiracial Children’s and
Adults’ Categorizations of Multiracial Individuals, Journal of Cognition and Development, 18:1,
-15, DOI: 10.1080/15248372.2015.1086772
To link to this article:  http://dx.doi.org/10.1080/15248372.2015.1086772
Accepted author version posted online: 23
Feb 2016.
Published online: 23 Feb 2016.
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http://www.tandfonline.com/action/journalInformation?journalCode=hjcd20
Download by: [University of Michigan]"
cf216fcd4cf537e53b9ed4f46e59c445e845cfc5,Nonnegative Restricted Boltzmann Machines for Parts-based Representations Discovery and Predictive Model Stabilization,"Noname manuscript No.
(will be inserted by the editor)
Nonnegative Restricted Boltzmann Machines for
Parts-based Representations Discovery and
Predictive Model Stabilization
Tu Dinh Nguyen, Truyen Tran, Dinh
Phung, Svetha Venkatesh
the date of receipt and acceptance should be inserted later"
cfc30ce53bfc204b8764ebb764a029a8d0ad01f4,Regularizing Deep Neural Networks by Noise: Its Interpretation and Optimization,"Regularizing Deep Neural Networks by Noise:
Its Interpretation and Optimization
Hyeonwoo Noh
Tackgeun You
Dept. of Computer Science and Engineering, POSTECH, Korea
Jonghwan Mun
Bohyung Han"
cf875336d5a196ce0981e2e2ae9602580f3f6243,"7 What 1 s It Mean for a Computer to "" Have "" Emotions ?","7  What 1
Rosalind W. Picard
It Mean for a Computer to  ""Have""  Emotions?
There  is a  lot  of  talk  about  giving machines  emotions,  some  of
it fluff. Recently at a large technical meeting, a researcher stood up
nd talked of how a Bamey stuffed animal [the purple dinosaur for
kids) ""has  emotions.""  He did not define what he meant by this, but
fter  repeating  it several  times,  it became  apparent  that  children
ttributed  emotions  to  Barney,  and that  Barney  had  deliberately
expressive behaviors that would  encourage the  kids to think. Bar-
ney had emotions. But kids have  attributed  emotions to  dolls and
stuffed animals for as long a s  we  know; and most of  my technical
olleagues would agree that such toys have never had and still do
not have emotions. What is different now that prompts a researcher
to make such a claim? Is the computational plush an example of  a
omputer that really does have emotions?
If  not Barney, then what would  be  an example  of  a  computa-
tional system that has emotions? I am not a philosopher, and this
paper will not be  a  discussion  of  the meaning  of  this question in
ny philosophical sense. However, as an engineer I am interested"
cfc22c35ad191cf9d70f4a3655840748b0e1322c,Real-Time Dense Mapping for Self-driving Vehicles using Fisheye Cameras,"Real-Time Dense Mapping
for Self-Driving Vehicles using Fisheye Cameras
Zhaopeng Cui1, Lionel Heng2, Ye Chuan Yeo2, Andreas Geiger3, Marc Pollefeys1,4, and Torsten Sattler1"
cf185d0d8fcad2c7f0a28b7906353d4eca5a098b,Improved gradient local ternary patterns for facial expression recognition,"Holder and Tapamo EURASIP Journal on Image and Video
Processing  (2017) 2017:42
DOI 10.1186/s13640-017-0190-5
EURASIP Journal on Image
nd Video Processing
RESEARCH
Open Access
Improved gradient local ternary patterns
for facial expression recognition
Ross P. Holder and Jules R. Tapamo*"
cf2a313b039b8adfee2a14ca5e81f2f5da52b0f2,Learning Fashion Traits with Label Uncertainty,"Learning Fashion Traits with Label Uncertainty
Gal Levi
Eli Alshan
Assaf Neuberger
Amazon Lab 126
Herzliya, Israel 4672560
Amazon Lab 126
Herzliya, Israel 4672560
Amazon Lab 126
Herzliya, Israel 4672560
Sharon Alpert
Amazon Lab 126
Herzliya, Israel 4672560
Eduard Oks
Amazon Lab 126
Herzliya, Israel 4672560"
75522dfc1610c8765185c4344d97db33e1af5047,3D Human Body-Part Tracking and Action Classification Using A Hierarchical Body Model,"RASKIN, RUDZSKY, RIVLIN: BODY-PART TRACKING AND ACTION CLASSIFICATION
D Human Body-Part Tracking and Action
Classification Using a Hierarchical Body
Model
Leonid Raskin
Michael Rudzsky
Ehud Rivlin
Computer Science Department
Technion -Israel Institute of Technology
Haifa, Israel, 3200"
758d7e1be64cc668c59ef33ba8882c8597406e53,"AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild","IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
AffectNet: A Database for Facial Expression,
Valence, and Arousal Computing in the Wild
Ali Mollahosseini, Student Member, IEEE, Behzad Hasani, Student Member, IEEE,
nd Mohammad H. Mahoor, Senior Member, IEEE"
758572c5779a47e898caff7232af76eda253163b,Csr: Medium: Collaborative Research: Architecture and System Support for Power-agile Computing,"CSR: MEDIUM: COLLABORATIVE RESEARCH: ARCHITECTURE AND
SYSTEM SUPPORT FOR POWER-AGILE COMPUTING
Co-PI: Geoffrey Challen (University at Buffalo), Co-PI: Mark Hempstead (Drexel University)
NSF PROPOSAL
5 OCT 2013
As energy management on energy-constrained devices continues to challenge researchers and frustrate
users, device designs are addressing the problem by integrating more hardware components that can trade
off energy and performance. Dynamic voltage-and-frequency scaling (DVFS) allows CPUs and memory
to trade off speed and energy, buffering and polling rates allow radios to trade off latency and energy,
nd screen refresh rates allow displays to trade off quality and energy. And as the Dark Silicon utilization
wall forces systems to choose what parts of the CPU to operate, the already-large configuration space will
explode. This proposal refers to the emerging class of devices integrating multiple energy-proportional
omponents as power-agile, reflecting their potential ability to adaptively reallocate energy usage between
omponents to improve performance and save energy. But as energy-management features proliferate,
new interfaces enabling coordination between applications, the operating system (OS), and hardware are
urgently needed to realize the potential energy and performance benefits.
INTELLECTUAL MERIT: Our proposal describes a new architecture for power-agile systems with both
novel interfaces that cleanly separate energy management responsibilities and a new approach to energy
llocation driven by differences in hardware energy efficiency. Applications use resource requests to allo-
ate energy between hardware components, making their resource needs explicit. The OS manages energy"
75073faadb967823db48794e9cd54b681bb0729b,Thermal-Aware Task Allocation and Scheduling for Heterogeneous Multi-core Cyber-Physical Systems,"Thermal-Aware Task Allocation and Scheduling for
Heterogeneous Multi-core Cyber-Physical Systems
Department of Electrical and Computer Engineering University of Massachusetts Amherst, Amherst, MA, 01003
Shikang Xu, Israel Koren and C. M. Krishna"
75c3ba0c7e5b0d4a11e9d2e073ccd02ee688c0c9,"A Multimodal LDA Model integrating Textual, Cognitive and Visual Modalities","Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 1146–1157,
Seattle, Washington, USA, 18-21 October 2013. c(cid:13)2013 Association for Computational Linguistics"
750e567370fd8c37bab657207195517405727a71,Time Aware Task Delegation in Agent Interactions for Video-Surveillance,"Time aware task delegation in agent interactions
for video-surveillance
Paolo Sernani1, Matteo Biagiola2,3, Nicola Falcionelli1,
Dagmawi Neway Mekuria1, Stefano Cremonini4, Aldo Franco Dragoni1
Dipartimento di Ingegneria dell’Informazione,
Universit`a Politecnica delle Marche,
Ancona, Italy
{p.sernani,
{n.falcionelli,
Fondazione Bruno Kessler,
Trento, Italy
Universit`a degli Studi di Genova,
Genova, Italy
Site Spa, Bologna, Italy"
759a3b3821d9f0e08e0b0a62c8b693230afc3f8d,Attribute and simile classifiers for face verification,"Attribute and Simile Classifiers for Face Verification
Neeraj Kumar
Alexander C. Berg
Peter N. Belhumeur
Columbia University∗
Shree K. Nayar"
7538ad235caf4dbc64a8b94a6146e1212d4de1ff,Amygdala dysfunction in men with the fragile X premutation.,"doi:10.1093/brain/awl338
Brain (2007), 130, 404–416
Amygdala dysfunction in men with the fragile
X premutation
David Hessl,1,2 Susan Rivera,1,5 Kami Koldewyn,1,6 Lisa Cordeiro,1 John Adams,1 Flora Tassone,1,4
Paul J. Hagerman1,4 and Randi J. Hagerman1,3
Medical Investigation of Neurodevelopmental Disorders (MIND) Institute and Departments of 2Psychiatry and Behavioral
Sciences, 3Pediatrics, University of California-Davis, Medical Center, Sacramento, 4Department of Biochemistry and
Molecular Medicine, University of California-Davis, School of Medicine, 5Department of Psychology and 6Center for
Neuroscience, University of California-Davis, Davis, CA, USA
Correspondence to: David Hessl, PhD, Assistant Clinical Professor, MIND Institute, University of California, Davis Medical
Center, 2825 50th Street, Sacramento, CA 95817, USA.
E-mail:
Premutation alleles (55–200 CGG repeats) of the fragile X mental retardation 1 (FMR1) gene are associated
with autism spectrum disorder in childhood, premature ovarian failure, and the neurodegenerative disorder,
fragile X-associated tremor/ataxia syndrome (FXTAS). FXTAS, and perhaps the other clinical presentations
mong carriers, are thought to be due to toxic gain-of-function of elevated levels of the expanded-repeat
FMR1 mRNA. Previous structural MRI studies have implicated the amygdala as a potential site of dysfunction
underlying social deficits and/or risk for FXTAS. As a preliminary investigation of this possible association, adult
males with the premutation, and male controls matched for IQ, age and education, completed three protocols"
7515dc37fd1e62a3c1e9bbe175c093c0c5cc7bed,Multi-Pose Face Recognition Using Fuzzy Ant Algorithm and Center of Gravity Search,"IJCSNS International Journal of Computer Science and Network Security, VOL.11 No.3, March 2011
Multi-Pose Face Recognition Using Fuzzy Ant Algorithm and
Center of Gravity Search
Supawee Makdee1, Chom Kimpan2 and Seri Pansang3
Faculty of Information Technology, Rangsit University, Bangkok, THAILAND
Summary
In  this  paper,  we  present  the  novel  technique  to  solve  the
recognition errors and minimize memory size of invariant range
image  multi-pose  face  recognition.  Range  image  face  data
(RIFD) was obtained from a laser range finder and was used in
the  model  to  generate  multi-pose.  The  fuzzy  ant  clustering
lgorithm is used to classify and find the number of clusters for
reduced  recognition  time.  RIFD  will  be  transformed  by  the
gradient transformation into significant feature and matching by
using  Membership  Matching  Score  (MMS)  and  Center  of
Gravity  (CG)  search.  The  proposed  method  was  tested  using
facial  range  images  from  130  persons  with  normal  facial
expressions.  The  processing  time  of  the  recognition  system  is
etter  than  3LMS.  Moreover,  it  is  6  times  faster  without  any
hange of recognition rate. Memory size of this experimental was"
75827a2021ac2ad2256144b2a2fe301948d39b51,AI Benchmark: Running Deep Neural Networks on Android Smartphones,"AI Benchmark: Running Deep Neural Networks
on Android Smartphones
Andrey Ignatov
ETH Zurich
Radu Timofte
ETH Zurich
William Chou
Qualcomm, Inc.
Ke Wang
Huawei, Inc.
Max Wu
MediaTek, Inc.
Tim Hartley
Arm, Inc.
Luc Van Gool ∗
ETH Zurich"
75d59ae0ed3ce51e37b383985cfff310251f591a,Cost-Sensitive Robustness against Adversarial Examples,"Cost-Sensitive Robustness against Adversarial Examples
Xiao Zhang∗
nd David Evans†"
75249ebb85b74e8932496272f38af274fbcfd696,Face Identification in Large Galleries,"Face Identification in Large Galleries
Rafael H. Vareto, Filipe Costa, William Robson Schwartz
Smart Surveillance Interest Group, Department of Computer Science
Universidade Federal de Minas Gerais, Belo Horizonte, Brazil"
75e9401e70c05c4d080e2d17f83ed2b61b44b3af,A distributed algorithm for partitioned robust submodular maximization,"A Distributed Algorithm for Partitioned
Robust Submodular Maximization
Ilija Bogunovic, Slobodan Mitrovi´c, Jonathan Scarlett, and Volkan Cevher
École Polytechnique Fédérale de Lausanne (EPFL)
{ilija.bogunovic, slobodan.mitrovic, jonathan.scarlett,"
75503aff70a61ff4810e85838a214be484a674ba,Improved facial expression recognition via uni-hyperplane classification,"Improved Facial Expression Recognition via Uni-Hyperplane Classification
S.W. Chew∗, S. Lucey†, P. Lucey‡, S. Sridharan∗, and J.F. Cohn‡"
75650bfc20036d99314f7ddae8f2baecde3d57e2,Concave Losses for Robust Dictionary Learning,"CONCAVE LOSSES FOR ROBUST DICTIONARY LEARNING
Rafael Will M. de Araujo, R. Hirata Jr ∗
Alain Rakotomamonjy †
University of S˜ao Paulo
Institute of Mathematics and Statistics
Rua do Mat˜ao, 1010 – 05508-090 – S˜ao Paulo-SP, Brazil
Universit´e de Rouen Normandie
LITIS EA 4108
76800 Saint- ´Etienne-du-Rouvray, France"
75df6bc2dd7225446f2a421d99e9db80b9c50837,Multithread Face Recognition in Cloud,"Publishing CorporationJournal of SensorsVolume 2016, Article ID 2575904, 21 pageshttp://dx.doi.org/10.1155/2016/2575904"
75a9d9ea6c1a5ee55fc0ccb347b263785b15ac0a,An Image Search Reranking Model based on attribute assisted hypergraph Miss,"International Research Journal of Engineering and Technology (IRJET)      e-ISSN: 2395 -0056
Volume: 03 Issue: 05 | May-2016                       www.irjet.net                                                               p-ISSN: 2395-0072
An Image Search Reranking Model based on
ttribute assisted hypergraph
Miss. Madhuri J.Mhaske1, Prof. Sachin P.Patil2
PG Scholar Computer Engineering , G. H. Raisoni College of Engineering and Management,
Savitribai Phule Pune University , Chas, Ahmednagar.414001,Maharashtra, India.
Assistant professor, computer engineering, G.H. Raisoni College of engineering and Management,
Savitribai Phule Pune University, Wagholi, Pune 411015, Maharashtra, India.
---------------------------------------------------------------------***---------------------------------------------------------------------
user wants to search for a red image, the images cannot be"
75e02f14e9871a405d2d367dd7c77c730499fed6,Perceptual Generative Adversarial Networks for Small Object Detection,"Perceptual Generative Adversarial Networks for Small Object Detection
Jianan Li Xiaodan Liang Yunchao Wei
Tingfa Xu
Jiashi Feng
Shuicheng Yan"
75879ab7a77318bbe506cb9df309d99205862f6c,Analysis of emotion recognition from facial expressions using spatial and transform domain methods,"Analysis Of Emotion Recognition From Facial
Expressions Using Spatial And Transform Domain
Methods
Ms. P. Suja* and Dr. Shikha Tripathi"
7543cf85a3fb56470b0020c0fc6db45e64f7ae5e,Object Proposals Estimation in Depth Image Using Compact 3D Shape Manifolds,"Object Proposal Estimation in Depth Images using
Compact 3D Shape Manifolds
Shuai Zheng1, Victor Adrian Prisacariu1, Melinos Averkiou2, Ming-Ming Cheng1,5,
Niloy J. Mitra2, Jamie Shotton3, Philip H. S. Torr1, Carsten Rother4
University of Oxford†, 2University College London‡, 3Microsoft Research, 4TU Dresden,
5Naikai University §"
75308067ddd3c53721430d7984295838c81d4106,Rapid Facial Reactions in Response to Facial Expressions of Emotion Displayed by Real Versus Virtual Faces,"Article
Rapid Facial Reactions
in Response to Facial
Expressions of Emotion
Displayed by Real Versus
Virtual Faces
i-Perception
018 Vol. 9(4), 1–18
! The Author(s) 2018
DOI: 10.1177/2041669518786527
journals.sagepub.com/home/ipe
Leonor Philip, Jean-Claude Martin and Ce´ line Clavel
LIMSI, CNRS, University of Paris-Sud, Orsay, France"
751e11880b54536a89bfcc4fd904b0989345a601,Hierarchical Adversarially Learned Inference,"HIERARCHICAL ADVERSARIALLY LEARNED
INFERENCE
Mohamed Ishmael Belghazi1, Sai Rajeswar1, Olivier Mastropietro1,
Negar Rostamzadeh2, Jovana Mitrovic2 and Aaron Courville1†
MILA, Université de Montréal,
Element AI,
DeepMind,
CIFAR Fellow."
75d5e67e31cefa09ae46044fa1f9f7696e058c99,MRI based Techniques for Detection of Alzheimer: A Survey,"MRI based Techniques for Detection of Alzheimer: A Survey
{tag}                                                 {/tag}
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 159
Number 5
Year of Publication: 2017
Authors:
Ruaa Adeeb Abdulmunem Al-falluji
10.5120/ijca2017912929
{bibtex}2017912929.bib{/bibtex}"
75976b1d3fae34f975374357eb9632ebb6c3a5f0,Binarising SIFT-Descriptors to Reduce the Curse of Dimensionality in Histogram-Based Object Recognition,"International Journal of Signal Processing, Image Processing and Pattern Recognition
Vol. 3, No. 1, March, 2010
Binarising SIFT-Descriptors to Reduce the Curse of Dimensionality
in Histogram-Based Object Recognition
TZI Center for Computing and Communication Technologies
University Bremen, Am Fallturm 1, 28359 Bremen, Germany
Martin Stommel"
754ffd18b106e6ef3644e3670faf28d798b841cd,Momo: Monocular motion estimation on manifolds,"Momo: Monocular Motion Estimation on Manifolds
Johannes Graeter1, Tobias Strauss1, and Martin Lauer1
Institute of Measurement and Control (MRT) , Karlsruhe Institute
of Technology (KIT), Email:
August 2, 2017"
75d8f2da0e9d80eef141c765254d7752445afb53,Violent video detection based on MoSIFT feature and sparse coding,"014 IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP)
978-1-4799-2893-4/14/$31.00 ©2014 IEEE
Long Xu1, Chen Gong1, Jie Yang1(cid:3), Qiang Wu2, Lixiu Yao1
. INTRODUCTION"
75e4efae6de6d1ac787a6ca381fb49381fcb062b,Hierarchical Representation Learning for Kinship Verification,"IEEE TRANSACTIONS ON IMAGE PROCESSING
Hierarchical Representation Learning for Kinship
Verification
Naman Kohli, Student Member, IEEE, Mayank Vatsa, Senior Member, IEEE, Richa Singh, Senior Member, IEEE,
Afzel Noore, Senior Member, IEEE, and Angshul Majumdar, Senior Member, IEEE"
75cb21fa931e957941c0237a1030aa36209bae36,GAUSSIAN PROCESS FOR ACTIVITY MODELING AND ANOMALY DETECTION,"GAUSSIAN PROCESS FOR ACTIVITY MODELING AND ANOMALY DETECTION
Wentong Liaoa, Bodo Rosenhahna, Michael Ying Yangb
Institute for Information Processing, Leibniz University Hannover, Germany
Computer Vision Lab, TU Dresden, Germany
KEY WORDS: Gaussian Process regression, activity modeling, anomaly detection
Commission WG III/3"
75b987f86af2bc7f68edc45be240dd30e1ef2699,Sampling Algorithms to Handle Nuisances in Large-Scale Recognition,"UNIVERSITY OF CALIFORNIA
Los Angeles
Sampling Algorithms to Handle Nuisances in Large-Scale Recognition
A dissertation submitted in partial satisfaction
of the requirements for the degree
Doctor of Philosophy in Computer Science
Nikolaos Karianakis"
cc5f4d5aa9c3ffa75a335f3305a1caf9cbdeb71f,LEARNING HIERARCHICAL REPRESENTATIONS FOR VIDEO ANALYSIS USING DEEP LEARNING,"LEARNING HIERARCHICAL REPRESENTATIONS FOR VIDEO ANALYSIS USING DEEP
LEARNING
YANG YANG
B.S. Beijing University of Technology, 2008
A dissertation submitted in partial fulfilment of the requirements
for the degree of Doctor of Philosophy
in the Department of Electrical Engineering and Computer Science
in the College of Engineering and Computer Science
t the University of Central Florida
Orlando, Florida
Summer Term
Major Professor: Mubarak Shah"
ccbd7e417158e7ae0f9f61c3b6d1e5a3317cce34,Object Proposals in Computer Vision,"Object Proposals in Computer Vision
Neelima Chavali
Thesis submitted to the faculty of the Virginia Polytechnic Institute and State University
in partial fulfillment of the requirements for the degree of
Master of Science
Electrical Engineering
Dhruv Batra, Chair
Devi Parikh
Lynn Abbott
2nd July, 2015
Blacksburg, Virginia
Keywords: Object proposals, evaluation, computer vision
Copyright 2015, Neelima Chavali"
cc4a2cab31ed06d0d8723df0bdf8cd0ece71bbe9,Analysis of Using Metric Access Methods for Visual Search of Objects in Video Databases,"Analysis of Using Metric Access Methods for Visual Search
of Objects in Video Databases
Henrique Batista da Silva 1
Zenilton Kleber Gonçalves do Patrocínio Júnior 2
Silvio Jamil Ferzoli Guimarães 2"
cc5a62bd7c45a9ca479506acb572566331354fa3,Eye localization through multiscale sparse dictionaries,"Eye Localization through Multiscale Sparse Dictionaries
Fei Yang, Junzhou Huang, Peng Yang and Dimitris Metaxas"
cc09cf5831fcae802ed2905a61ab502956655bbe,Shape-based instance detection under arbitrary viewpoint,"Shape-based instance detection under arbitrary
viewpoint
Edward Hsiao and Martial Hebert"
cc3c273bb213240515147e8be68c50f7ea22777c,Gaining Insight Into Films Via Topic Modeling & Visualization,"Gaining Insight Into Films
Via Topic Modeling & Visualization
MISHA RABINOVICH, MFA
YOGESH GIRDHAR, PHD
KEYWORDS Collaboration, computer vision, cultural
nalytics, economy of abundance, interactive data
visualization
We moved beyond misuse when the software actually
ecame useful for film analysis with the addition of audio
nalysis, subtitle analysis, facial recognition, and topic
modeling. Using multiple types of visualizations and
back-and-fourth workflow between people and AI
we arrived at an approach for cultural analytics that
an be used to review and develop film criticism. Finally,
we present ways to apply these techniques to Database
Cinema and other aspects of film and video creation.
PROJECT DATE 2014
URL http://misharabinovich.com/soyummy.html"
cc91001f9d299ad70deb6453d55b2c0b967f8c0d,Performance Enhancement of Face Recognition in Smart TV Using Symmetrical Fuzzy-Based Quality Assessment,"OPEN ACCESS
ISSN 2073-8994
Article
Performance Enhancement of Face Recognition in Smart TV
Using Symmetrical Fuzzy-Based Quality Assessment
Yeong Gon Kim, Won Oh Lee, Ki Wan Kim, Hyung Gil Hong and Kang Ryoung Park *
Division of Electronics and Electrical Engineering, Dongguk University, 26 Pil-dong 3-ga, Jung-gu,
Seoul 100-715, Korea; E-Mails: (Y.G.K.); (W.O.L.);
(K.W.K.); (H.G.H.)
*  Author to whom correspondence should be addressed; E-Mail:
Tel.: +82-10-3111-7022; Fax: +82-2-2277-8735.
Academic Editor: Christopher Tyler
Received: 31 March 2015 / Accepted: 21 August 2015 / Published: 25 August 2015"
cc2bb4318191a04e3fc82c008c649f5b90151e4d,Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering,"Published as a conference paper at ICLR 2018
BEYOND SHARED HIERARCHIES: DEEP MULTITASK
LEARNING THROUGH SOFT LAYER ORDERING
Elliot Meyerson & Risto Miikkulainen
The University of Texas at Austin and Sentient Technologies, Inc.
{ekm,"
cc7dd285ee25174f184c0f23a02bc23fb80ad573,Images Similarity Detection Based on Directional Gradient Angular Histogram,"Images Similarity Detection Based on Directional Gradient Angular Histogram
Jinye Peng1,2, Bianzhang Yu2, Dakai Wang1
. Department of Electronics, Northwest University, Xi’an, 710069, P.R.China;
. Department of Electrical Engineering, Northwestern Polytechnic University,
Xi’an, 710072, P.R.China
E-mail:"
cce261b47bbeec42cf4036e89e2413e25f66ce61,Gender recognition from facial images : 2 D or 3 D ?,"Zhang, W., Smith, M., Smith, L. and Farooq, A. (2016) Gender recog-
nition from facial images: 2D or 3D? Journal of the Optical Society
of America A, 33 (3). pp. 333-344. ISSN 1084-7529 Available from:
http://eprints.uwe.ac.uk/28147
We recommend you cite the published version.
The publisher’s URL is:
http://dx.doi.org/10.1364/JOSAA.33.000333
Refereed: Yes
(no note)
Disclaimer
UWE has obtained warranties from all depositors as to their title in the material
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UWE accepts no liability for any infringement of intellectual property rights
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ing investigation in the event of an allegation of any such infringement."
cca228b47a603a9b9e2a1e3a1b278b35612d078d,Randomized Face Recognition on Partially Occluded Images,"Randomized Face Recognition on Partially
Occluded Images
Ariel Morelli Andres, Sebastian Padovani, Mariano Tepper, Marta Mejail, and
Julio Jacobo
Departamento de Computación, Facultad de Ciencias Exactas y Naturales,
Universidad de Buenos Aires, Argentina."
cc9f473584c1a7f224b42d4a3a3ea2864173cc28,Hephaestus: Data Reuse for Accelerating Scientific Discovery,"Hephaestus: Data Reuse for
Accelerating Scientific Discovery
Jennie Duggan
Northwestern EECS"
cca198ae698e7956992f2fb326c04965b2964a18,Learning Pain from Emotion: Transferred HoT Data Representation for Pain Intensity Estimation,"Learning Pain from Emotion: Transferred HoT
Data Representation for Pain Intensity
Estimation
Corneliu Florea1, Laura Florea1, and Constantin Vertan1
Image Processing and Applications Laboratory,
{corneliu.florea; laura.florea; constantin.vertan}
University “Politehnica” of Bucharest,"
cc622a0ac114821be935ca9c66cc177b93e18876,Anomaly Detection Based on Trajectory Analysis Using Kernel Density Estimation and Information Bottleneck Techniques,"Anomaly Detection Based on Trajectory Analysis
Using Kernel Density Estimation and Information
Bottleneck Techniques
Yuejun Guo, Qing Xu(cid:3), Yu Yang, Sheng Liang, Yu Liu, Mateu Sbert"
cc96eab1e55e771e417b758119ce5d7ef1722b43,An Empirical Study of Recent Face Alignment Methods,"An Empirical Study of Recent
Face Alignment Methods
Heng Yang, Xuhui Jia, Chen Change Loy and Peter Robinson"
cc3e1a6376928138dff5582b7a56d40cfb3b7367,Cost-Effective Features for Reidentification in Camera Networks,"Cost-effective features for
re-identification in camera networks
Syed Fahad Tahir and Andrea Cavallaro"
cc246025ec8e1d32ecfbeefaba0727fdf73cd9cb,Vehicle Tracking by Simultaneous Detection and Viewpoint Estimation,"Vehicle Tracking by Simultaneous Detection and
Viewpoint Estimation
Ricardo Guerrero-G´omez-Olmedo1, Roberto L´opez-Sastre1, Saturnino
Maldonado-Basc´on1, and Antonio Fern´andez-Caballero2
GRAM, Department of Signal Theory and Communications, UAH, Alcal´a de Henares, Spain.
Department of Computing Systems, UCLM, Albacete, Spain."
ccd5bd5ce40640ebc6665b97a86ba3d28e457d11,Contributions to a fast and robust object recognition in images. (Contributions à une reconnaissance d'objet rapide et robuste en images),"Contributions to a fast and robust object recognition in
images
J´erˆome Revaud
To cite this version:
J´erˆome Revaud. Contributions to a fast and robust object recognition in images. Other [cs.OH].
INSA de Lyon, 2011. English. <NNT : 2011ISAL0042>. <tel-00694442>
HAL Id: tel-00694442
https://tel.archives-ouvertes.fr/tel-00694442
Submitted on 4 May 2012
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
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destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,
´emanant des ´etablissements d’enseignement et de
recherche fran¸cais ou ´etrangers, des laboratoires"
cca2bee0973e347efd7b3d145b6d23286c801bd1,VoCMex: a voice corpus in Mexican Spanish for research in speaker recognition,"Int J Speech Technol
DOI 10.1007/s10772-012-9183-z
VoCMex: a voice corpus in Mexican Spanish for research
in speaker recognition
José-Martín Olguín-Espinoza · Pedro Mayorga-Ortiz ·
Hugo Hidalgo-Silva · Luis Vizcarra-Corral ·
Mónica-Livier Mendiola-Cárdenas
Received: 17 July 2012 / Accepted: 14 November 2012
© Springer Science+Business Media New York 2012"
cc34b0ab84e82a6d8ebce08eff1b7556026b5352,Face Recognition using Gaussian Hermite Moments,"Special Issue of International Journal of Computer Applications (0975 – 8887)
on Software Engineering, Databases and Expert Systems – SEDEXS, September 2012
D Face Recognition using Gaussian Hermite Moments
Naouar Belghini
Faculty of Technical Sciences
B.P. 2202 – Road of Imouzzer
Fez – Morocco
Arsalane Zarghili
Faculty of Technical Sciences
B.P. 2202 – Road of Imouzzer
Fez – Morocco
Jamal Kharroubi
Faculty of Technical Sciences
B.P. 2202 – Road of Imouzzer
Fez – Morocco"
ccd99008d942b890cecd308a31ba61240eac9e54,Learning to Segment Every Thing,"Learning to Segment Every Thing
Ronghang Hu1,2,∗
Piotr Doll´ar2 Kaiming He2
Trevor Darrell1 Ross Girshick2
BAIR, UC Berkeley
Facebook AI Research (FAIR)"
cc3bda396bb41face52cd1c7fd132b43a9ec426c,Evaluating Merging Strategies for Sampling-based Uncertainty Techniques in Object Detection,"Evaluating Merging Strategies for Sampling-based Uncertainty
Techniques in Object Detection
Dimity Miller, Feras Dayoub, Michael Milford, and Niko S¨underhauf"
ccd2152c77ae65e4d3d0988990f6e243133a5efc,Learning Human Activities and Poses with Interconnected Data Sources,"Copyright
Chao-Yeh Chen"
95e83661648ba6bf2f0fbbf436bc8304c3cf016f,The impact of empathy and reappraisal on emotional intensity recognition.,"Cognition and Emotion
ISSN: 0269-9931 (Print) 1464-0600 (Online) Journal homepage: http://www.tandfonline.com/loi/pcem20
The impact of empathy and reappraisal on
emotional intensity recognition
Navot Naor, Simone G. Shamay-Tsoory, Gal Sheppes & Hadas Okon-Singer
To cite this article: Navot Naor, Simone G. Shamay-Tsoory, Gal Sheppes & Hadas Okon-Singer
(2017): The impact of empathy and reappraisal on emotional intensity recognition, Cognition and
Emotion, DOI: 10.1080/02699931.2017.1372366
To link to this article:  http://dx.doi.org/10.1080/02699931.2017.1372366
Published online: 11 Sep 2017.
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Download by: [Tel Aviv University]
Date: 11 September 2017, At: 04:46"
95a9e256c8f8bbce0d86199cacea92b15004dd45,Using semantic similarity for multi-label zero-shot classification of text documents,"Using Semantic Similarity for Multi-Label Zero-Shot
Classification of Text Documents
Jinseok Nam2,3
Sappadla Prateek Veeranna1
Johannes F¨urnkranz2 ∗
Eneldo Loza Menc´ıa2
- Birla Institute of Technology and Science - Pilani - India
- Knowledge Engineering Group - TU Darmstadt - Germany
- Knowledge Discovery in Scientific Literature - DIPF - Germany"
9547a7bce2b85ef159b2d7c1b73dea82827a449f,Facial expression recognition using Gabor motion energy filters,"Facial Expression Recognition Using Gabor Motion Energy Filters
Tingfan Wu
Dept. Computer Science Engineering
UC San Diego
Marian S. Bartlett
Javier R. Movellan
Institute for Neural Computation
UC San Diego"
95ed2269c4a13771cc8dfe0ff2d4d6a7f4d73033,Deep Learning for Domain Adaption: Engagement Recognition,"Engagement Recognition using Deep Learning and Facial Expression
Omid Mohamad Nezami , Len Hamey , Deborah Richards , and Mark Dras
Macquarie University, Sydney, NSW, Australia"
9501db000474dbd182579d311dfb1b1ab8fa871f,Supplementary of Multi-scale Deep Learning Architectures for Person Re-identification,"Supplementary of Multi-scale Deep Learning Architectures for Person
Re-identification
Xuelin Qian1 Yanwei Fu2,5,* Yu-Gang Jiang1,3 Tao Xiang4 Xiangyang Xue1,2
Shanghai Key Lab of Intelligent Info. Processing, School of Computer Science, Fudan University;
School of Data Science, Fudan University; 3Tencent AI Lab;
Queen Mary University of London; 5University of Technology Sydney;
. Multi-scale stream layers
Multi-scale-A layer (Fig. 1), analyses the data stream with
the size 1 × 1, 3 × 3 and 5 × 5 of receptive field. Further-
more, in order to increase both depth and width of this layer,
we split the filter size of 5 × 5 into two 3 × 3 streams cas-
aded (i.e. stream-4 and stream-3 in Tab 1 and Fig. 1). The
weights of each stream are also tied with the corresponding
stream in another branch. Such a design art is, in general,
inspired by, and yet different from the inception architec-
tures [11, 12, 10]. The key difference lies in the weights
which are not tied between any two streams from the same
ranch, but are tied between the two corresponding streams
of different branches.
Reduction layer (Fig. 2) further passes the data stream"
95a835cdb5dc46e4de071865f9dccdaf9ec944ad,Euclidean and geodesic distance between a facial feature points in two-dimensional face recognition system,"The International Arab Journal of Information Technology, Vol. 14, No. 4A, Special Issue 2017                                            565
Euclidean and Geodesic Distance between a Facial
Feature Points in Two-Dimensional Face
Recognition System
Rachid Ahdid1,2, Said Safi1, and Bouzid Manaut2
Department of Mathematics and Informatics, Sultan Moulay Slimane University, Morocco
Poladisciplinary Faculty, Sultan Moulay Slimane University, Morocco"
9595fa763c4f1d92c604a131cfc624b9edbd8b02,Integral Histogram Image Computing For Parallel Hardware Architecture,"Journal of Recent Research in Engineering and Technology, 4(6), June 2017, PP.31-39
ISSN (Online): 2349 –2252, ISSN (Print):2349 –2260 © Bonfay Publications, 2017
Integral Histogram Image Computing For Parallel Hardware Architecture
J.Nandhini1, M. Vasanthakumar 2
(M.E, Department of Electronics and Communications Engineering, AVS Engineering College, Salem)
(Assistant Professor, Department of Electronics and Communications Engineering, AVS Engineering
Mail id:
College, Salem) Mail id:
Received 25 May 2017; Accepted 04 June 2017"
95deb62b82ede5c6732c5c498d3f9452866eaba7,Unsupervised Video Understanding by Reconciliation of Posture Similarities,"Unsupervised Video Understanding by Reconciliation of Posture Similarities
Timo Milbich, Miguel Bautista, Ekaterina Sutter, Bj¨orn Ommer
Heidelberg Collaboratory for Image Processing
IWR, Heidelberg University, Germany
{timo.milbich, miguel.bautista, ekaterina.sutter,"
95de749dd1c3451d0842ecf33101244a1fa9d4af,Temporal Dynamics Underlying the Modulation of Social Status on Social Attention,"Temporal Dynamics Underlying the Modulation of Social
Status on Social Attention
Mario Dalmaso*, Giovanni Galfano, Carol Coricelli, Luigi Castelli
Dipartimento di Psicologia dello Sviluppo e della Socializzazione, Universita` di Padova, Padova, Italy"
950cfcbaafad1e2aaae43728fe499d8a4c90f6ec,Object Instance Detection and Dynamics Modeling in a Long-Term Mobile Robot Context,"Object Instance Detection and Dynamics Modeling in
Long-Term Mobile Robot Context
NILS BORE
Doctoral Thesis
Stockholm, Sweden 2017"
95aef5184b89daebd0c820c8102f331ea7cae1ad,Recognising facial expressions in video sequences,"Recognising facial expressions in video sequences
Jos´e M. Buenaposada1, Enrique Mu˜noz2⋆, Luis Baumela2
ESCET, Universidad Rey Juan Carlos
C/Tulip´an s/n, 28933 M´ostoles, Spain
Facultad Inform´atica, Universidad Polit´ecnica de Madrid
Campus Montegancedo s/n, 28660 Boadilla del Monte, Spain
http://www.dia.fi.upm.es/~pcr
Received: 7 Jan 2007 / Accepted: 10 July 2007/ Online: 18 Oct 2007"
9590b09c34fffda08c8f54faffa379e478f84b04,Efficient Dual Approach to Distance Metric Learning,"An Efficient Dual Approach to
Distance Metric Learning
Chunhua Shen, Junae Kim, Fayao Liu, Lei Wang, Anton van den Hengel
Experimental results
Distance metric learning . . . . .
UCI benchmark test . .
IV-A1
Unconstrained face recognition
IV-A2
IV-A3
Metric learning for action
recognition . .
. . . . .
Maximum variance unfolding . . .
IV-B1
Quantitative Assessment .
Conclusion
References"
95be490aef44da67ca1cef76b16df14b6e40c421,Learning Cross-View Binary Identities for Fast Person Re-Identification,"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16)
Learning Cross-View Binary Identities
for Fast Person Re-Identification
Feng Zheng1, Ling Shao2
Department of Electronic and Electrical Engineering, The University of Sheffield.
Department of Computer Science and Digital Technologies, Northumbria University."
9561c7ef4f89019eb7fb779a7b18ef810964b491,Real-Time Object Segmentation Using a Bag of Features Approach,"Real-Time Object Segmentation Using a
Bag of Features Approach
David ALDAVERT a,1, Arnau RAMISA c,b, Ramon LOPEZ DE MANTARAS b and
Ricardo TOLEDO a
Computer Vision Center, Dept. Ciencies de la Computació, Universitat Autonòma de
Barcelona, Catalunya, Spain
Institut d’Investigació d’Inteligencia Artificial (IIIA-CSIC), Campus UAB, Catalunya,
Spain
INRIA-Grenoble, LEAR Team, France"
958b761f1ebacbc38d4edc4b1bd5b42204fd91a1,Pattern Recognition for Ecological Science and Environmental Monitoring : An Initial Report,"Pattern Recognition for Ecological Science and
Environmental Monitoring: An Initial Report
Eric N. Mortensen, Enrique L. Delgado, Hongli Deng, David Lytle, Andrew
Moldenke, Robert Paasch, Linda Shapiro, Pengcheng Wu, Wei Zhang,
Thomas G. Dietterich"
95296302a7fc82edf782cece082d7319cfa584b7,Detection-free Bayesian Multi-object Tracking via Spatio-Temporal Video Bundles Grouping,"Detection-free Bayesian Multi-object Tracking
via Spatio-Temporal Video Bundles Grouping
Technical Report, November 2013
Yongyi Lu, Liang Lin, Yuanlu Xu, Zefeng Lai"
95029b1041a169e5b4e1ad79f60bfedb7a6844d0,Learning Superpixels with Segmentation-Aware Affinity Loss,"Learning Superpixels with Segmentation-Aware Affinity Loss
Wei-Chih Tu1 Ming-Yu Liu2 Varun Jampani2 Deqing Sun2 Shao-Yi Chien1 Ming-Hsuan Yang2
Jan Kautz2
National Taiwan University 2NVIDIA 3UC Merced"
9513503867b29b10223f17c86e47034371b6eb4f,Comparison of Optimisation Algorithms for Deformable Template Matching,"Comparison of optimisation algorithms for
deformable template matching
Vasileios Zografos
Link¨oping University, Computer Vision Laboratory
ISY, SE-581 83 Link¨oping, SWEDEN"
95f990600abb9c8879e4f5f7cd03f3d696fcdec4,An Online Algorithm for Constrained Face Clustering in Videos,"Manuscript version: Author’s Accepted Manuscript
The version presented in WRAP is the author’s accepted manuscript and may differ from the
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ibliographic details are credited, a hyperlink and/or URL is given for the original metadata
page and the content is not changed in any way."
951af0494e8812fdb7d578b68c342ab876acb27e,DOCTEUR DE L’ECOLE NORMALE SUPERIEURE DE CACHAN,"THÈSEDEDOCTORATDEL’ÉCOLENORMALESUPÉRIEUREDECACHANprésentéeparJULIENMAIRALpourobtenirlegradedeDOCTEURDEL’ÉCOLENORMALESUPÉRIEUREDECACHANDomaine:MATHÉMATIQUESAPPLIQUÉESSujetdelathèse:Représentationsparcimonieusesenapprentissagestatistique,traitementd’imageetvisionparordinateur—Sparsecodingformachinelearning,imageprocessingandcomputervisionThèseprésentéeetsoutenueàCachanle30novembre2010devantlejurycomposéde:FrancisBACHDirecteurderecherche,INRIAParis-RocquencourtDirecteurdethèseStéphaneMALLATProfesseur,EcolePolytechnique,New-YorkUniversityRapporteurEricMOULINESProfesseur,Télécom-ParisTechExaminateurBrunoOLSHAUSENProfesseur,UniversityofCalifornia,BerkeleyRapporteurJeanPONCEProfesseur,EcoleNormaleSupérieure,ParisDirecteurdethèseGuillermoSAPIROProfesseur,UniversityofMinnesotaExaminateurJean-PhilippeVERTDirecteurderecherche,EcolesdesMines-ParisTechExaminateurThèsepréparéeauseindel’équipeWillowdulaboratored’informatiquedel’ÉcoleNormaleSupérieure,Paris.(INRIA/ENS/CNRSUMR8548).23avenued’Italie,75214Paris."
95225bab187483e37823daab5c503f6b327fb008,Improved MinMax Cut Graph Clustering with Nonnegative Relaxation,"Improved MinMax Cut Graph Clustering with
Nonnegative Relaxation
Feiping Nie, Chris Ding, Dijun Luo, and Heng Huang
Department of Computer Science and Engineering,
University of Texas, Arlington, America"
95977c279c3c7c0a4368fb4f097e5002bbdce259,Application à la création d ’ un atlas probabiliste de perfusion cérébrale en imagerie médicale,"No d’ordre: 4616
TH(cid:18)ESE
pr(cid:19)esent(cid:19)ee pour obtenir le grade de
Docteur de l’Universit(cid:19)e Louis Pasteur - Strasbourg I
(cid:19)Ecole doctorale : Sciences pour l’ing(cid:19)enieur
Discipline
Sp(cid:19)ecialit(cid:19)e
(cid:19)Electronique, (cid:19)electrotechnique, automatique
: Traitement d’images et vision par ordinateur
Mod(cid:18)eles statistiques d’apparence non gaussiens.
Application (cid:18)a la cr(cid:19)eation d’un atlas probabiliste de
perfusion c(cid:19)er(cid:19)ebrale en imagerie m(cid:19)edicale
English title: \Non-Gaussian Statistical Appearance Models.
Application to the Creation of a Probabilistic Atlas of Brain Perfusion in Medical Imaging.""
Soutenue publiquement
le 21 septembre 2004
Torbj(cid:28)rn VIK
Membres du jury:
BLOCH
Isabelle"
95aa80cf672771730393e1d7d263ab6f6d6e535d,Learning articulated body models for people re-identification,"Learning Articulated Body Models
for People Re-identification
Davide Baltieri, Roberto Vezzani, Rita Cucchiara
University of Modena and Reggio Emilia
Via Vignolese 905, 41125 Modena - Italy
{davide.baltieri, roberto.vezzani,"
95593fb20df8ce1273cebe0690cf2cdab054b9b5,Robust Multi-Image HDR Reconstruction for the Modulo Camera,
95ea564bd983129ddb5535a6741e72bb1162c779,Multi-Task Learning by Deep Collaboration and Application in Facial Landmark Detection,"Multi-Task Learning by Deep Collaboration and
Application in Facial Landmark Detection
Ludovic Trottier
Philippe Giguère
Brahim Chaib-draa
Laval University, Québec, Canada"
955dc25def91eff6bfa5698249bb189ccfa83367,Geometric Model for Human Body Orientation Classification,"CommIT (Communication and Information Technology) Journal, Vol. 9 No. 1, pp. 29–33
GEOMETRIC MODEL FOR HUMAN
BODY ORIENTATION CLASSIFICATION
Igi Ardiyanto
Department of Electrical Engineering and Information Technology,
Faculty of Engineering, Gadjah Mada University
Yogyakarta 55281, Indonesia
Email:"
9588a42bff63fb36015e10fac9f3121154c3ab1d,Explaining Potential Risks in Traffic Scenes by Combining Logical Inference and Physical Simulation,"International Journal of Machine Learning and Computing, Vol. 6, No. 5, October 2016
Explaining Potential Risks in Traffic Scenes by Combining
Logical Inference and Physical Simulation
Ryo Takahashi, Naoya Inoue, Yasutaka Kuriya, Sosuke Kobayashi, and Kentaro Inui
from  observation  and"
c81b303005459285a5864ea4de71f77025cd5be5,Norm-Induced Entropies for Decision Forests,"Norm-induced entropies for decision forests
Christoph Lassner
Rainer Lienhart
Multimedia Computing and Computer Vision Lab, University of Augsburg"
c8ebe4c7d884c468d572a1ccf8583ac912215088,Emotion Dysregulation and Anxiety in Adults with ASD: Does Social Motivation Play a Role?,"J Autism Dev Disord
DOI 10.1007/s10803-015-2567-6
S . I . : A S D I N A D U L T H O O D : C O M O R B I D I T Y A N D I N T E R V E N T I O N
Emotion Dysregulation and Anxiety in Adults with ASD: Does
Social Motivation Play a Role?
Deanna Swain1
• Angela Scarpa1
• Susan White1
• Elizabeth Laugeson2
Ó Springer Science+Business Media New York 2015"
c8e32484bbbc63908080284790edafc4b66008d2,Suivi par ré-identification dans un réseau de caméras à champs disjoints,"Suivi par r´e-identification dans un r´eseau de cam´eras `a
hamps disjoints
Boris Meden, Patrick Sayd, Fr´ed´eric Lerasle
To cite this version:
Boris Meden, Patrick Sayd, Fr´ed´eric Lerasle. Suivi par r´e-identification dans un r´eseau de
am´eras `a champs disjoints. RFIA 2012 (Reconnaissance des Formes et Intelligence Artificielle),
Jan 2012, Lyon, France. pp.978-2-9539515-2-3, 2012.
HAL Id: hal-00656507
https://hal.archives-ouvertes.fr/hal-00656507
Submitted on 17 Jan 2012
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,
´emanant des ´etablissements d’enseignement et de"
c8f216dbd43dda14783677f44bb336c92211cd46,Synthesis from 3 D Mesh Sequences Driven by Combined Speech Features,"VISUAL SPEECH SYNTHESIS FROM 3D MESH SEQUENCES DRIVEN BY COMBINED
SPEECH FEATURES
Felix Kuhnke and J¨orn Ostermann
Institut f¨ur Informationsverarbeitung, Leibniz Universit¨at Hannover, Germany"
c88a1d52d92a47704a797fce2202970bb1f2008c,RECOGNIZING FACE SKETCHES BY HUMAN VOLUNTEERS,"RECOGNIZING FACE SKETCHES BY HUMAN VOLUNTEERS
Priyanka Reddy Gangam
Submitted in Partial Fulfillment of the Requirements
for the Degree of
Master of Computing and Information Systems
YOUNGSTOWN STATE UNIVERSITY
August, 2010"
c8ee4812c32b0ad4e26d53b99e1514514bbcaf14,A NEaT Design for Reliable and Scalable Network Stacks,"A NEaT Design for Reliable and Scalable
Network Stacks
Tomas Hruby
Cristiano Giuffrida
Lionel Sambuc
Herbert Bos
Andrew S. Tanenbaum
Vrije Universiteit Amsterdam"
c88b2a351e207698dae6a2b314b5f4fd25f2e9c1,A study in facial regions saliency: a fuzzy measure approach,"Soft Comput
DOI 10.1007/s00500-013-1064-0
M E T H O D O L O G I E S A N D A P P L I C A T I O N
A study in facial regions saliency: a fuzzy measure approach
Paweł Karczmarek • Witold Pedrycz •
Marek Reformat • Elaheh Akhoundi
Ó The Author(s) 2013. This article is published with open access at Springerlink.com"
c896502edcdec38466e7d66f38fb53a57c8e05db,Image Companding and Inverse Halftoning using Deep Convolutional Neural Networks,"Image Companding and Inverse Halftoning using
Deep Convolutional Neural Networks
Xianxu Hou and Guoping Qiu
low-level"
c81326a1ecb7e71ae38a665779b8d959d3938d1a,A Novel Neural Network Model Specified for Representing Logical Relations,"A Novel Neural Network Model Specified for Representing Logical
Relations
Gang Wang
With computers to handle more and more complicated things in variable environments, it becomes an urgent requirement that
the artificial intelligence has the ability of automatic judging and deciding according to numerous specific conditions so as to deal
with the complicated and variable cases. ANNs inspired by brain is a good candidate. However, most of current numeric ANNs are
not good at representing logical relations because these models still try to represent logical relations in the form of ratio based on
functional approximation. On the other hand, researchers have been trying to design novel neural network models to make neural
network model represent logical relations. In this work, a novel neural network model specified for representing logical relations is
proposed and applied. New neurons and multiple kinds of links are defined. Inhibitory links are introduced besides exciting links.
Different from current numeric ANNs, one end of an inhibitory link connects an exciting link rather than a neuron. Inhibitory
model can simulate the operations of Boolean logic gates, and construct complex logical relations with the advantages of simpler
neural network structures than recent works in this area. This work provides some ideas to make neural networks represent logical
relations more directly and efficiently, and the model could be used as the complement to current numeric ANN to deal with logical
issues and expand the application areas of ANN.
Index Terms—Brain-inspired computing, logical representation, neural network structure, inhibitory link.
I. INTRODUCTION
With computers to handle more and more complicated
things in variable environments like driverless car and ad-
vanced medical diagnosis expert system, higher artificial intel-"
c8855bebdaa985dfc4c1a07e5f74a0e29787e47e,Multi-label Object Attribute Classification using a Convolutional Neural Network,"Multi-label Object Attribute Classification using
Convolutional Neural Network
Soubarna Banik, Mikko Lauri, Simone Frintrop
Department of Informatics, Universit¨at Hamburg"
c8bcd8e0b2ab6cc00a565efbcf904235c33ac2dc,Unsupervised Person Image Synthesis in Arbitrary Poses,"Unsupervised Person Image Synthesis in Arbitrary Poses
Albert Pumarola
Antonio Agudo
Alberto Sanfeliu
Francesc Moreno-Noguer
Institut de Rob`otica i Inform`atica Industrial (CSIC-UPC)
08028, Barcelona, Spain
Figure 1: Given an original image of a person (left) and a desired body pose defined by a 2D skeleton (bottom-row), our
model generates new photo-realistic images of the person under that pose (top-row). The main contribution of our work is to
train this generative model with unlabeled data."
c8e2582948d60d6363aa20208000f07c002c21cb,A STATE OF THE ART COMPARISON OF DATABASES FOR FACIAL OCCLUSION,"Jurnal
Teknologi
A  STATE  OF  THE  ART  COMPARISON  OF
DATABASES FOR FACIAL OCCLUSION
Abdulganiyu  Abdu  Yusufa,b*,  Fatma  Susilawati  Mohamada,
Zahraddeen Sufyanua
Faculty  of  Informatics  and  Computing,  21300  Gong  Badak
Campus,  Universiti  Sultan  Zainal  Abidin  (UniSZA),  Terengganu,
Malaysia
National  Biotechnology  Development  Agency  (NABDA),  Abuja,
Nigeria
Full Paper
Article history
Received
5 April 2015
Received in revised form
9 September 2015
Accepted
2 November 2015
*Corresponding author"
c8b592fcf2ed2f75799b94c428d2ccdf1e82c5f7,"RUC-Tencent at ImageCLEF 2015: Concept Detection, Localization and Sentence Generation","RUC-Tencent at ImageCLEF 2015:
Concept Detection, Localization and Sentence
Generation
Xirong Li(cid:63)1, Qin Jin(cid:63)1, Shuai Liao1, Junwei Liang1, Xixi He1, Yujia Huo1,
Weiyu Lan1, Bin Xiao2, Yanxiong Lu2, Jieping Xu1
Multimedia Computing Lab, School of Information, Renmin University of China
Pattern Recognition Center, WeChat Technical Architecture Department, Tencent"
c840d85f6dce0fb69fb6113923f17e1e314c6134,Disparity Sliding Window: Object Proposals From Disparity Images,"Disparity Sliding Window: Object Proposals From Disparity Images
Julian M¨uller1, Andreas Fregin2 and Klaus Dietmayer1"
c8db8764f9d8f5d44e739bbcb663fbfc0a40fb3d,Modeling for part-based visual object detection based on local features,"Modeling for part-based visual object
detection based on local features
Von der Fakult¨at f¨ur Elektrotechnik und Informationstechnik
der Rheinisch-Westf¨alischen Technischen Hochschule Aachen
zur Erlangung des akademischen Grades eines Doktors
der Ingenieurwissenschaften genehmigte Dissertation
vorgelegt von
Diplom-Ingenieur
Mark Asbach
us Neuss
Berichter:
Univ.-Prof. Dr.-Ing. Jens-Rainer Ohm
Univ.-Prof. Dr.-Ing. Til Aach
Tag der m¨undlichen Pr¨ufung: 28. September 2011
Diese Dissertation ist auf den Internetseiten der
Hochschulbibliothek online verf¨ugbar."
c85adcc3cc2f3ab27def7e1c615b52ac182dde80,Improving face gender classification by adding deliberately misaligned faces to the training data,"Improving Face Gender Classification By Adding
Deliberately Misaligned Faces To The Training Data
M. Mayo, E. Zhang
Dept. of Computer Science, University of Waikato
Hamilton, New Zealand.
Email:"
c83dba889132f0d2b909474e5e187f254bd09e29,Fourier Power Spectrum Characteristics of Face Photographs: Attractiveness Perception Depends on Low-Level Image Properties,"RESEARCH ARTICLE
Fourier Power Spectrum Characteristics of
Face Photographs: Attractiveness Perception
Depends on Low-Level Image Properties
Claudia Menzel1,2☯, Gregor U. Hayn-Leichsenring1,2☯*, Oliver Langner1,3, Holger Wiese1,4,
Christoph Redies1,2
Person Perception Research Unit, Friedrich-Schiller-University Jena, Jena, Germany, 2 Experimental
Aesthetics Group, Institute of Anatomy I, Jena University Hospital, Friedrich-Schiller-University Jena, Jena,
Germany, 3 Department of Neurology, University of Lübeck, Lübeck, Germany, 4 Department of
Psychology, Durham University, Durham, United Kingdom
☯ These authors contributed equally to this work."
c866a2afc871910e3282fd9498dce4ab20f6a332,Surveillance Face Recognition Challenge,"Noname manuscript No.
(will be inserted by the editor)
Surveillance Face Recognition Challenge
Zhiyi Cheng · Xiatian Zhu · Shaogang Gong
Received: date / Accepted: date"
c8a4b4fe5ff2ace9ab9171a9a24064b5a91207a3,Locating facial landmarks with binary map cross-correlations,"LOCATING FACIAL LANDMARKS WITH BINARY MAP CROSS-CORRELATIONS
J´er´emie Nicolle
K´evin Bailly
Vincent Rapp
Mohamed Chetouani
Univ. Pierre & Marie Curie, ISIR - CNRS UMR 7222, F-75005, Paris - France
{nicolle, bailly, rapp,"
c85aa12331bdeaba06d4c3e44b969e6060c3310c,Ensemble of Part Detectors for Simultaneous Classification and Localization,"Ensemble of Part Detectors for Simultaneous
Classification and Localization
Xiaopeng Zhang, Hongkai Xiong, Senior Member, IEEE, Weiyao Lin, Qi Tian, Fellow, IEEE"
c82840923eeded245a8dab2dd102d8b0cf96758a,KDGAN: Knowledge Distillation with Generative Adversarial Networks,"KDGAN: Knowledge Distillation with
Generative Adversarial Networks
Xiaojie Wang
University of Melbourne
Yu Sun
Twitter Inc.
Rui Zhang∗
University of Melbourne
Jianzhong Qi
University of Melbourne"
c8ddeeea803e50cab2d82f6d3c7f9e08b5f51f4b,Sparse-to-Continuous: Enhancing Monocular Depth Estimation using Occupancy Maps,"Sparse-to-Continuous: Enhancing Monocular Depth
Estimation using Occupancy Maps
N´ıcolas Rosa1, Vitor Guizilini2, and Valdir Grassi Jr1"
c8dcb7b3c5ed43e61b90b50fedc76568d8e30675,GUARDING AGAINST ADVERSARIAL DOMAIN SHIFTS,"Under review as a conference paper at ICLR 2018
GUARDING AGAINST ADVERSARIAL DOMAIN SHIFTS
WITH COUNTERFACTUAL REGULARIZATION
Anonymous authors
Paper under double-blind review"
c8adbe00b5661ab9b3726d01c6842c0d72c8d997,Deep Architectures for Face Attributes,"Deep Architectures for Face Attributes
Tobi Baumgartner, Jack Culpepper
Computer Vision and Machine Learning Group, Flickr, Yahoo,
{tobi,"
c8c035ff19fdea2b8053d781b999356110a43ff5,A Hierarchical Approach for Multi-task Logistic Regression,"A Hierarchical Approach for Multi-Task Logistic
Regression
(cid:18)Agata Lapedriza1, David Masip2 and Jordi Vitri(cid:18)a1
Computer Vision Center-Dept. Inform(cid:18)atica
Universitat Aut(cid:18)onoma de Barcelona, 08193 Bellaterra, Spain
fagata,
Universitat de Barcelona (UB), 08007 Barcelona , Spain"
c813413fc84be33d7c4ccdd4a1f025ccc73a77bd,Discriminative Bayesian Active Shape Models,"Discriminative Bayesian Active Shape Models
Pedro Martins, Rui Caseiro, Jo˜ao F. Henriques, Jorge Batista
Institute of Systems and Robotics - University of Coimbra, Portugal"
c8f035510b72b84c21430a887ed03c8836eeddc2,Optical-inertial Synchronization of MoCap Suit with Single Camera Setup for Reliable Position Tracking,
c84ca95638893700d8f806e844984a5b2c50b5e3,Automatic Facial Expression Recognition Using 3 D Faces,"Paper 071, ENG 101
Automatic Facial Expression Recognition Using 3D Faces
Chao Li, Antonio Soares
Florida A&M University
hao.li,"
c867caf3f29abb2f3fd5c4c7e98e5f551a70be25,DeLS-3D: Deep Localization and Segmentation with a 3D Semantic Map,"DeLS-3D: Deep Localization and Segmentation with a 3D Semantic Map
Peng Wang, Ruigang Yang, Binbin Cao, Wei Xu, Yuanqing Lin
Baidu Research
National Engineering Laboratory for Deep Learning Technology and Applications
{wangpeng54, yangruigang, caobinbin, wei.xu,"
c8fc65c83473c633e2bf1c13031ccd10617cc8a2,Every Object Tells a Story,"Every Object Tells a Story
James Pustejovsky
Computer Science Department
Brandeis University
Waltham, MA 02453
Nikhil Krishnaswamy
Computer Science Department
Brandeis University
Waltham, MA 02453"
694dda2a9f6d86c4bf3f57d85dfd376e2067ec62,CA : HOW MUCH FACE INFORMATION IS NEEDED ?,"HOW MUCH FACE INFORMATION IS NEEDED?
P2CA:
Davide Onofrio*, Antonio Rama+, Francesc Tarres+, Stefano Tubaro*
*Dipartimento di Elettronica e Informazione - Politecnico di Milano
+Department Teoria del Senyal i Comunicacions de la Universitat Politècnica de Catalunya"
6953911c6756ca70de1555df14a06f13305e1926,Author Profiling based on Text and Images: Notebook for PAN at CLEF 2018,"Author Profiling based on Text and Images
Notebook for PAN at CLEF 2018
Luka Stout, Robert Musters, and Chris Pool
Anchormen, The Netherlands"
6900bb437679dd0b0c5cea0acdaa9429d0127d38,Self-Erasing Network for Integral Object Attention,"Self-Erasing Network for Integral Object Attention
Qibin Hou
Peng-Tao Jiang
Colledge of Computer Science, Nankai University
Yunchao Wei
Urbana-Champaign, IL, USA
Colledge of Computer Science, Nankai University
Ming-Ming Cheng ∗"
69a605b2ef38c59e0c8da284d6f27d33e3573620,AUTOMATED MULTI-MODAL SEARCH AND RESCUE USING BOOSTED HISTOGRAM OF ORIENTED GRADIENTS,"AUTOMATED MULTI-MODAL SEARCH AND RESCUE USING BOOSTED
HISTOGRAM OF ORIENTED GRADIENTS
A Thesis
presented to
the Faculty of California Polytechnic State University,
San Luis Obispo
In Partial Fulfillment
of the Requirements for the Degree
Master of Science in Electrical Engineering
Matthew Lienemann
December 2015"
698812f7d37e148c0a99e768f0a7d24e7b9605ab,Image Classification and Retrieval from User-Supplied Tags,"Image Classification and Retrieval from User-Supplied Tags
Hamid Izadinia
Univ. of Washington
Ali Farhadi
Univ. of Washington
Aaron Hertzmann
Adobe Research
Matthew D. Hoffman
Adobe Research"
693905c29feb7f9be3517308c8a9c2dc68aa8682,Self-supervised CNN for Unconstrained 3D Facial Performance Capture from an RGB-D Camera,"Self-supervised CNN for Unconstrained 3D Facial
Performance Capture from an RGB-D Camera
Yudong Guo, Juyong Zhang†, Lin Cai, Jianfei Cai and Jianmin Zheng"
699b6cbd72ee0274699b939863813499c377ea00,Enlightening Deep Neural Networks with Knowledge of Confounding Factors,"Enlightening Deep Neural Networks
with Knowledge of Confounding Factors
Yu Zhong
Gil Ettinger
{yu.zhong,
Systems & Technology Research"
6957baa0db5576997aef9de43b93fe8fd4d07632,Identifica\c{c}\~ao autom\'atica de picha\c{c}\~ao a partir de imagens urbanas,"Identificac¸˜ao autom´atica de pichac¸˜ao a partir de
imagens urbanas
Eric K. Tokuda and Roberto M. Cesar-Jr.
Institute of Mathematics and Statistics
University of S˜ao Paulo (USP)
Brazil
Claudio Silva
Tandon School of Engineering
New York University (NYU)"
69dc87575b56ba7f60fa24bdd4fceabeeaf39a80,Decoding of nonverbal language in alcoholism: A perception or a labeling problem?,"tapraid5/ze6-adb/ze6-adb/ze600216/ze62965d15z
xppws S⫽1
/8/16
6:36 Art: 2015-0668
APA NLM
Psychology of Addictive Behaviors
016, Vol. 30, No. 2, 175–183
0893-164X/16/$12.00
© 2016 American Psychological Association
http://dx.doi.org/10.1037/adb0000147
Decoding of Nonverbal Language in Alcoholism:
A Perception or a Labeling Problem?
Université Libre de Bruxelles and Centre Hospitalier
Charles Kornreich
Universitaire Brugmann
Géraldine Petit and Heidi Rolin
Université Libre de Bruxelles
Elsa Ermer
University of Maryland Baltimore
Salvatore Campanella and Paul Verbanck"
695f56d6b1b294d1691c93d86a23e77016a42720,A Multimodal User Authentication System Using Faces and Gestures,"Hindawi Publishing Corporation
BioMed Research International
Volume 2015, Article ID 343475, 8 pages
http://dx.doi.org/10.1155/2015/343475
Research Article
A Multimodal User Authentication System Using
Faces and Gestures
Hyunsoek Choi1 and Hyeyoung Park2
School of Electrical Engineering and Computer Science, Kyungpook National University, Deagu 702-701, Republic of Korea
School of Computer Science and Engineering, Kyungpook National University, Deagu 702-701, Republic of Korea
Correspondence should be addressed to Hyeyoung Park;
Received 26 September 2014; Accepted 19 November 2014
Academic Editor: Sabah Mohammed
Copyright © 2015 H. Choi and H. Park. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
As a novel approach to perform user authentication, we propose a multimodal biometric system that uses faces and gestures
obtained from a single vision sensor. Unlike typical multimodal biometric systems using physical information, the proposed system
utilizes gesture video signals combined with facial images. Whereas physical information such as face, fingerprints, and iris is fixed
nd not changeable, behavioral information such as gestures and signatures can be freely changed by the user, similar to a password.
Therefore, it can be a countermeasure when the physical information is exposed. We aim to investigate the potential possibility of"
69c03f69ddf77586f83bf13d473abf53a70e6793,"EigenFaces Image normalization : rotation , scale and intensity Eye coordinates + input frame Voting method Person identity DCT HMM Face extraction Recognition Modules","Lemieux & Parizeau, Vision Interface 2003.
Flexible multi-classifier architecture for
face recognition systems
Alexandre Lemieux and Marc Parizeau
Laboratoire de vision et syst`emes num´eriques (LVSN),
D´epartement de g´enie ´electrique et de g´enie informatique,
Universit´e Laval, Ste-Foy (Qc), Canada, G1K 7P4."
694c7c7bf7ffad6caedb97aca425acfc08bd90ee,Aerial Detection in Maritime Scenarios Using Convolutional Neural Networks,"Aerial Detection in Maritime Scenarios
Using Convolutional Neural Networks
Gon¸calo Cruz1(B) and Alexandre Bernardino2
Portuguese Air Force, Sintra, Portugal
Computer and Robot Vision Laboratory, Instituto de Sistemas e Rob´otica,
Instituto Superior T´ecnico, Lisboa, Portugal"
69f49bae5b1c15adc644b47e6c3b6c3f7aa84171,Variational Bayesian Inference for Audio-Visual Tracking of Multiple Speakers,"Variational Bayesian Inference for Audio-Visual
Tracking of Multiple Speakers
Yutong Ban, Xavier Alameda-Pineda, Laurent Girin and Radu Horaud"
69ee78388e0f40941496ab92efe3e0fa065ad22e,Person Re-Identification with RGB-D Camera in Top-View Configuration through Multiple Nearest Neighbor Classifiers and Neighborhood Component Features Selection,"Article
Person Re-Identification with RGB-D Camera in
Top-View Configuration through Multiple Nearest
Neighbor Classifiers and Neighborhood Component
Features Selection
Marina Paolanti *
Emanuele Frontoni
, Luca Romeo, Daniele Liciotti
, Rocco Pietrini, Annalisa Cenci,
nd Primo Zingaretti
Department of Information Engineering, Universitá Politecnica delle Marche, I-60131 Ancona, Italy;
(L.R.); (D.L.); (R.P.);
(A.C.); (E.F.); (P.Z.)
* Correspondence:
Received: 30 August 2018 ; Accepted: 11 October 2018 ; Published: 15 October 2018"
699a7c88a6d226f59c7a5619b3cfad714415c31a,"Incorporating Luminance, Depth and Color Information by Fusion-based Networks for Semantic Segmentation","Incorporating Luminance, Depth and Color Information by
Fusion-based Networks for Semantic Segmentation
Shao-Yuan Lo
Shang-Wei Hung
National Chiao Ting University, UC San Diego
National Chiao Ting University
Figure 1: Flowchart of the proposed semantic segmentation
system. Y: luminance information.
omplexity. Lately, DenseNet [11] designs the invention of
dding  dense  connections  between  each  layer,  which
enhances  the  information  flow  in  networks,  and  thus  it
previously
outperforms  many
network
rchitectures including ResNet [12].
proposed
With  the  help  of  depth  sensors  such  as  Kinect,  depth
maps can be obtained along with RGB images. Since the
depth channel provides complementary information to the
RGB channels, containing the depth information is believed"
6997039127d9b262d4a9aa9467c4f4fa3d596085,Classification of Vehicle Types in Car Parks using Computer Vision Techniques,"Classification of Vehicle Types in Car Parks using
Computer Vision Techniques
Chadly Marouane
Research & Development
VIRALITY GmbH
Rauchstraße 7
81679 Munich, Germany
Lorenz Schauer
Ludwig-Maximilians-
Universität
München
Philipp Bauer
Ludwig-Maximilians-
Universität
München
Oettingenstraße 67
80538 München, Germany
Oettingenstraße 67
80538 München, Germany"
69f638309fe692f7d57a72d2df8fe2bf1d81dff4,A Study of Artificial Personality from the Perspective of the Observer,"A Study of Artificial Personality from the Perspective of the Observer
Sheryl Brahnam
Computer Information Systems Department
Southwest Missouri State University
Springfield, MO 65804"
690d669115ad6fabd53e0562de95e35f1078dfbb,"Progressive versus Random Projections for Compressive Capture of Images, Lightfields and Higher Dimensional Visual Signals","Progressive versus Random Projections for Compressive Capture of Images,
Lightfields and Higher Dimensional Visual Signals
Rohit Pandharkar
MIT Media Lab
75 Amherst St, Cambridge, MA
Ashok Veeraraghavan
01 Broadway, Cambridge MA
Ramesh Raskar
MIT Media Lab
75 Amherst St, Cambridge, MA"
695f6dc7165aa3fca15d1b1deb4c496fc093ac19,Learning Discriminative Visual N-grams from Mid-level Image Features.,"GUPTA, PANDEY, CHIA: VISUAL N-GRAMS
Learning Discriminative Visual N-grams
from Mid-level Image Features
Raj Kumar Gupta
Megha Pandey
Alex YS Chia
Institute of High Performance
Computing (A*STAR)
Singapore
Institute of Infocomm Research
(A*STAR)
Singapore
Rakuten Institute of Technology
Singapore"
69f27ca2f1280587004c8fae6b3b0021305e52eb,Title of dissertation : Scene and Video Understanding,
695b040a9550a46b5ffe31e4a6abbadfac02c1ad,Face recognition with illumination distinction description,"1st International Conference on Pattern Recognition (ICPR 2012)
November 11-15, 2012. Tsukuba, Japan
978-4-9906441-1-6 ©2012 IAPR"
6937fe93e6238ee21904c172809bea0086da4570,Contour Grouping Based on Contour-Skeleton Duality,"Int J Comput Vis (2009) 83: 12–29
DOI 10.1007/s11263-009-0208-2
Contour Grouping Based on Contour-Skeleton Duality
Nagesh Adluru · Longin Jan Latecki
Received: 30 May 2008 / Accepted: 6 January 2009 / Published online: 27 January 2009
© Springer Science+Business Media, LLC 2009"
69ff40fd5ce7c3e6db95a2b63d763edd8db3a102,HUMAN AGE ESTIMATION VIA GEOMETRIC AND TEXTURAL FEATURES,"HUMAN AGE ESTIMATION VIA GEOMETRIC AND TEXTURAL
FEATURES
Merve KILINC1 and Yusuf Sinan AKGUL2
TUBITAK BILGEM UEKAE, Anibal Street, 41470, Gebze, Kocaeli, Turkey
GIT Vision Lab, http://vision.gyte.edu.tr/, Department of Computer Engineering, Gebze Institute of Technology, 41400,
Kocaeli, Turkey
Keywords:
Age estimation:age classification:geometric features:LBP:Gabor:LGBP:cross ratio:FGNET:MORPH"
695e44e9582f2bef78726e3c44f46b45ef12eab1,Using rapid visually evoked EEG activity for person identification,"1st Annual International Conference of the IEEE EMBS
Minneapolis, Minnesota, USA, September 2-6, 2009
Using Rapid Visually Evoked EEG Activity for Person Identification
Koel Das, Sheng Zhang, Barry Giesbrecht and Miguel P. Eckstein"
6911686f00c99c51c21f057c45d561c88027f676,Articulated pose estimation with parts connectivity using discriminative local oriented contours,"Articulated Pose Estimation with Parts Connectivity
using Discriminative Local Oriented Contours
Norimichi Ukita
Nara Institute of Science and Technology"
69e1ccc3f9ac8410135cdd694135460440503d91,Recognition of quantized still face images,"Recognition of Quantized Still Face Images
Tao Wu and Rama Chellappa"
6946acb595095407871992da62298254658f8d84,An Efficient Method for Face Recognition System In Various Assorted Conditions,"An Efficient Method for Face Recognition System
In Various Assorted Conditions
V.Karthikeyan
K.Vijayalakshmi
P.Jeyakumar
finding"
692aecba13add2b8c1d82db303f5b2ec743ceb44,FaceForensics: A Large-scale Video Dataset for Forgery Detection in Human Faces.,"FaceForensics: A Large-scale Video Dataset for Forgery
Detection in Human Faces
Andreas R¨ossler1 Davide Cozzolino2 Luisa Verdoliva2 Christian Riess3
Justus Thies1
Matthias Nießner1
Technical University of Munich
University Federico II of Naples
University of Erlangen-Nuremberg"
6971bdac5119c4cc1b6d92adac605e13f1bcd80f,Limiting the reconstruction capability of generative neural network using negative learning,"LIMITING THE RECONSTRUCTION CAPABILITY OF GENERATIVE NEURAL NETWORK
USING NEGATIVE LEARNING
Asim Munawar, Phongtharin Vinayavekhin and Giovanni De Magistris
IBM Research - Tokyo"
69aef3ce50967a00c568849fed630c573f6cd1eb,3-D Face Analysis and Identification Based on Statistical Shape Modelling,"-D Face Analysis and Identification Based on Statistical Shape
Modelling
Wei Quan*, Charlie Frowd †
*School of Computing, Engineering and Physical Sciences
University of Central Lancashire, Preston PR1 2HE, UK.
Department of Psychology
University of Winchester, Winchester SO22 4NR, UK.
Keywords: shape modelling, face analysis, identification."
69d9b79757d76b73ed940754f4d05288b76eb8c3,Preschool Externalizing Behavior Predicts Gender-Specific Variation in Adolescent Neural Structure,"RESEARCH ARTICLE
Preschool Externalizing Behavior Predicts
Gender-Specific Variation in Adolescent
Neural Structure
Jessica Z. K. Caldwell1*¤, Jeffrey M. Armstrong2, Jamie L. Hanson1, Matthew J. Sutterer1,
Diane E. Stodola1, Michael Koenigs2, Ned H. Kalin2, Marilyn J. Essex2☯, Richard
J. Davidson1,2,3☯
Department of Psychology, University of Wisconsin–Madison, Madison, Wisconsin, United States of
America, 2 Department of Psychiatry, University of Wisconsin–Madison, Madison, Wisconsin, United States
of America, 3 Center for Investigating Healthy Minds, University of Wisconsin–Madison, Madison,
Wisconsin, United States of America
☯ These authors contributed equally to this work.
¤. Current address: Marquette General Hospital/Michigan State University, Marquette, MI, United States of
America"
699aa8b9b05f746b913bf86efdfa3bcab372f3e1,On Matching Forensic Sketches to Mugshot Photos,"On Matching Forensic Sketches to Mugshot Photos
Under review TPAMI
Brendan Klare, Zhifeng Li, and Anil K. Jain"
69526cdf6abbfc4bcd39616acde544568326d856,Face Verification Using Template Matching,"[17] B. Moghaddam, T. Jebara, and A. Pentland, “Bayesian face recogni-
tion,” Pattern Recognit., vol. 33, no. 11, pp. 1771–1782, Nov. 2000.
[18] A. Nefian, “A hidden Markov model-based approach for face detection
nd recognition,” Ph.D. dissertation, Dept. Elect. Comput. Eng. Elect.
Eng., Georgia Inst. Technol., Atlanta, 1999.
[19] P. J. Phillips et al., “Overview of the face recognition grand challenge,”
presented at the IEEE CVPR, San Diego, CA, Jun. 2005.
[20] H. T. Tanaka, M. Ikeda, and H. Chiaki, “Curvature-based face surface
recognition using spherical correlation-principal direction for curved
object recognition,” in Proc. Int. Conf. Automatic Face and Gesture
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[21] M. Turk and A. Pentland, “Eigenfaces for recognition,” J. Cognit. Sci.,
pp. 71–86, 1991.
[22] V. N. Vapnik, Statistical Learning Theory. New York: Wiley, 1998.
[23] W. Zhao, R. Chellappa, A. Rosenfeld, and P. Phillips, “Face recogni-
tion: A literature survey,” ACM Comput. Surveys, vol. 35, no. 44, pp.
99–458, 2003.
[24] W. Zhao, R. Chellappa, and P. J. Phillips, “Subspace linear discrimi-
nant analysis for face recognition,” UMD TR4009, 1999.
Face Verification Using Template Matching"
69d1b055807ef35a8f9490775348cce899421841,An improved ABC algorithm approach using SURF for face identification,"An Improved ABC Algorithm Approach Using
SURF for Face Identification
Chidambaram Chidambaram1,2, Marlon Subtil Mar¸cal2, Leyza Baldo Dorini2,
Hugo Vieira Neto2, and Heitor Silv´erio Lopes2
State University of Santa Catarina-UDESC, Brazil
Federal University of Technology - Paran´a - UTFPR, Brazil
http://www.sbs.udesc.br
http://www.utfpr.edu.br"
d5fe9c84710b71a754676b2ee67cec63e8cd184b,FPGA Implementation of a HOG-based Pedestrian Recognition System,"Sebastian Bauer, Ulrich Brunsmann, Stefan Schlotterbeck-Macht
Aschaffenburg University of Applied Sciences, Aschaffenburg, Germany
Faculty of Engineering
FPGA Implementation of a HOG-based
Pedestrian Recognition System
FPGA Implementation of a HOG-based
Pedestrian Recognition System
{sebastian.bauer, ulrich.brunsmann, stefan.schlotterbeck-macht}
terms  of
With  respect  to  road  crash  statistics,  on-board
pedestrian  detection  is  a  key  task  for  future
dvanced  driver  assistance  systems.
In  this
paper,  we  describe  the  implementation  of  a  real-
time pedestrian recognition system that combines
FPGA-based  extraction  of  image  features  with  a
CPU-based  object  localization  and  classification
framework.
features,  we  have
implemented"
d54703c366bce363130f1e633e033a0116c8a0da,Review on Emotion Recognition Databases,"We are IntechOpen,
the world’s leading publisher of
Open Access books
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d55d6ccefe797317996805ebf58a74587b158950,Distribution-based Label Space Transformation for Multi-label Learning,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
Distribution-based Label Space Transformation for
Multi-label Learning
Zongting Lyu, Yan Yan, and Fei Wu"
d55cce6ecbad2c6ecccbaa1cb0d14ae3a46b1454,Multimodal representation learning with neural networks,"Multimodal representation learning with
neural networks
John Edilson Arevalo Ovalle
National University of Colombia
Engineering School, Systems and Industrial Engineering Departament
Bogot´a, Colombia"
d5de42d37ee84c86b8f9a054f90ddb4566990ec0,Asynchronous Temporal Fields for Action Recognition,"Asynchronous Temporal Fields for Action Recognition
Gunnar A. Sigurdsson1∗ Santosh Divvala2,3 Ali Farhadi2,3 Abhinav Gupta1,3
Carnegie Mellon University 2University of Washington 3Allen Institute for Artificial Intelligence
github.com/gsig/temporal-fields/"
d5444f9475253bbcfef85c351ea9dab56793b9ea,BoxCars: Improving Fine-Grained Recognition of Vehicles using 3-D Bounding Boxes in Traffic Surveillance,"IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
BoxCars: Improving Fine-Grained Recognition
of Vehicles using 3D Bounding Boxes
in Traffic Surveillance
Jakub Sochor, Jakub ˇSpaˇnhel, Adam Herout
in contrast"
d5ab6aa15dad26a6ace5ab83ce62b7467a18a88e,Optimized Structure for Facial Action Unit Relationship Using Bayesian Network,"World Journal of Computer Application and Technology 2(7): 133-138, 2014
DOI: 10.13189/wjcat.2014.020701
http://www.hrpub.org
Optimized Structure for Facial Action Unit Relationship
Using Bayesian Network
Yee Koon Loh*, Shahrel A. Suandi
Intelligent Biometric Group, School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Pulau
*Corresponding Author:
Pinang, Malaysia
Copyright © 2014 Horizon Research Publishing All rights reserved."
d56fe69cbfd08525f20679ffc50707b738b88031,Training of multiple classifier systems utilizing partially labeled sequential data sets,"Training of multiple classifier systems utilizing
partially labelled sequences
Martin Schels, Patrick Schillinger, and Friedhelm Schwenker
Ulm University - Department of Neural Information Processing
89069 Ulm - Germany"
d5de20cca347d6c5e6f662292e4d52e765ff5cee,Learning Tensors in Reproducing Kernel Hilbert Spaces with Multilinear Spectral Penalties,
d50c6d22449cc9170ab868b42f8c72f8d31f9b6c,Dynamic MultiTask Learning with Convolutional Neural Network,Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
d5bef023a7d1032a5c717109a9c1b600ee1e8a71,Autism Spectrum Disorder (ASD) and Fragile X Syndrome (FXS): Two Overlapping Disorders Reviewed through Electroencephalography—What Can be Interpreted from the Available Information?,"Brain Sci. 2015, 5, 92-117; doi:10.3390/brainsci5020092
OPEN ACCESS
rain sciences
ISSN 2076-3425
www.mdpi.com/journal/brainsci/
Review
Autism Spectrum Disorder (ASD) and Fragile X Syndrome
(FXS): Two Overlapping Disorders Reviewed through
Electroencephalography—What Can be Interpreted
from the Available Information?
Niamh Mc Devitt 1,2,*, Louise Gallagher 1,3,4,5,6 and Richard B. Reilly 1,2,3,7
School of Medicine, Trinity College, the University of Dublin, Dublin, Ireland;
E-Mails: (L.G.); (R.B.R.)
Trinity Centre for Bioengineering, Trinity College Dublin, the University of Dublin, Dublin, Ireland
Trinity College Institute for Neuroscience, Trinity College Dublin, the University of Dublin,
Dublin, Ireland
Department of Psychiatry, Trinity College Dublin, the University of Dublin, Dublin, Ireland
5  Institute of Molecular Medicine, Trinity Centre for Health Sciences, St James’ Hospital,
Dublin, Ireland
6  Linn Dara Child and Adolescent Mental Health Services, Cherry Orchard Hospital Dublin 10,"
d5b5c63c5611d7b911bc1f7e161a0863a34d44ea,Extracting Scene-Dependent Discriminant Features for Enhancing Face Recognition under Severe Conditions,"Extracting Scene-dependent Discriminant
Features for Enhancing Face Recognition
under Severe Conditions
Rui Ishiyama and Nobuyuki Yasukawa
Information and Media Processing Research Laboratories, NEC Corporation
753, Shimonumabe, Nakahara-Ku, Kawasaki 211-8666 Japan"
d5e5dc9bb068841ce2b0923d8250489426dc7ffe,Les modèles génératifs en classification supervisée et applications à la catégorisation d'images et à la fiabilité industrielle. (Generative models in supervised statistical learning with applications to digital image categorization and structural reliability),"Les modèles génératifs en classification supervisée et
pplications à la catégorisation d’images et à la fiabilité
industrielle
Guillaume Bouchard
To cite this version:
Guillaume Bouchard. Les modèles génératifs en classification supervisée et applications à la catégorisa-
tion d’images et à la fiabilité industrielle. Interface homme-machine [cs.HC]. Université Joseph-Fourier
- Grenoble I, 2005. Français. <tel-00541059>
HAL Id: tel-00541059
https://tel.archives-ouvertes.fr/tel-00541059
Submitted on 29 Nov 2010
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,"
d50e51d0a349dd904b85734083e59643ba99bd2c,A Robust Face Recognition method,"International Journal of Scientific & Engineering Research, Volume 5, Issue 5, May-2014                                                                                                      805
ISSN 2229-5518
A Robust Face Recognition method
G.Seshikala,U.P.Kulakrni,M.N.GiriPrasad"
d5579b2708a1c713e1b2feb8646533ce26085a3a,Effective Use of Dilated Convolutions for Segmenting Small Object Instances in Remote Sensing Imagery,"Effective Use of Dilated Convolutions for Segmenting Small Object Instances in
Remote Sensing Imagery
Ryuhei Hamaguchi  Aito Fujita  Keisuke Nemoto
Tomoyuki Imaizumi  Shuhei Hikosaka
PASCO CORPORATION, Japan
{riyhuc2734, aaitti6875, koetio8807, tiommu4352,"
d50a40f2d24363809a9ac57cf7fbb630644af0e5,End-to-End Trained CNN Encoder-Decoder Networks for Image Steganography,"END-TO-END TRAINED CNN ENCODER-DECODER NETWORKS FOR IMAGE
STEGANOGRAPHY
Atique ur Rehman, Rafia Rahim, Shahroz Nadeem, Sibt ul Hussain
National University of Computer & Emerging Sciences (NUCES-FAST), Islamabad, Pakistan.
Reveal.ai (Recognition, Vision & Learning) Lab"
d510ed87dff0ac430974a44ccd4ef7cf265b0c56,Face Databases and Evaluation,"Face Databases and Evaluation?
Dmitry O. Gorodnichy
Laboratory and Scientific Services Directorate, Canada Border Services Agency, 79 Bentley Ave., Ottawa, ON, K1A 0L5, Canada
Email:
Synonyms
Face Datasets; Face Recognition Performance Evaluation
Definition
Face Databases are imagery data that are used for testing face processing algorithms. In the contents of biometrics, face
databases are collected and used to evaluate the performance of face recognition biometric systems.
Face recognition evaluation is the procedure that is used to access the recognition quality of a face recognition system.
It involves testing the system on a set of face databases and/or in a specific setup for the purpose of obtaining measurable
statistics that can be used to compare systems to one another.
Introduction: factors affecting face recognition performance
While for humans recognizing a face in a photograph or in video is natural and easy, computerized face recognition is very
hallenging. In fact, automated recognition of faces is known to be more difficult than recognition of other imagery data such
s iris, vein, or fingerprint images due to the fact that the human face is a non-rigid 3D object which can be observed at
different angles and which may also be partially occluded. Specifically, face recognition systems have to be evaluated with
respect to the following factors [19]:
. face image resolution – face images can be captured at different resolutions: face images scanned from documents may
have very high resolution, while face images captured with a video camera will mostly be of very low resolution,"
d5d3c1b299e81b4ab96d052f8a37013305b731d9,Performance Evaluation of Human Detection Systems for Robot Safety,"J Intell Robot Syst
DOI 10.1007/s10846-016-0334-3
Performance Evaluation of Human Detection Systems
for Robot Safety
William Shackleford · Geraldine Cheok ·
Tsai Hong · Kamel Saidi · Michael Shneier
Received: 9 April 2015 / Accepted: 11 January 2016
© Springer Science+Business Media Dordrecht (outside the USA) 2016"
d590ca357910532cc62eeacc56af8f86b9fe642b,Metric Spaces Library,"Metric Spaces Library
www.sisap.org
Karina Figueroa1,2, Gonzalo Navarro2, Edgar Ch´avez1
Escuela de Ciencias F´ısico-Matem´aticas, Universidad Michoacana, Mexico
Center for Web Research, Department of Computer Science, University of Chile
Bug reports, comments, and contributions to
November 14, 2008
We describe a library to support similarity searching in metric spaces. It
ontains various metric space and index implementations, as well as some
tools to evaluate their performance for similarity searching. The library is
is an integral part of the new conference Similarity Search and Applications
(SISAP) created in 2008.
It was defined and initially populated by the
uthors, but we expect it to grow with other contributions over time.
The goal of similarity searching is, given a finite set of objects U (called a
database) drawn from a (possibly infinite) universe X, and a distance function
d(·,·) defined among objects of X, preprocess U and build a data structure
(called an index) so that similarity searches can be carried out on that set.
The objects are seen as black boxes, on which the only operation one can
perform is to compute distances, and nothing else should be assumed on"
d5856f47fe117c114e8bcfbf2abc4e80691a512c,Interpreting Complex Scenes using a Hierarchy of Prototypical Scene Models,"Interpreting Complex Scenes using a
Hierarchy of Prototypical Scene
Models
Dissertation
zur Erlangung des akademischen Grades
Doktor der Ingenieurwissenschaften (Dr.-Ing.)
vorgelegt an
der Technischen Fakult¨at der Universit¨at Bielefeld
Sarah Bonnin
4.10.2014"
d5d6b3959958adb1333fa1a72227378ad3f7c16d,Collaborative Contributions for Better Annotations,
d5c6c0fb51947a2df1389f1aab7a635bf687ac1d,A Multiview Approach to Learning Articulated Motion Models,"A Multiview Approach to Learning
Articulated Motion Models
Andrea F. Daniele, Thomas M. Howard, and Matthew R. Walter"
d51e4b2425c07cd26813b3af646762ff45682ef9,Image Features in Space - Evaluation of Feature Algorithms for Motion Estimation in Space Scenarios,
d59a9d80e7d8c875d2b73241a8b02078ea6ad0a7,A Deep Learning Perspective on the Origin of Facial Expressions,"BREUER, KIMMEL: A DEEP LEARNING PERSPECTIVE ON FACIAL EXPRESSIONS
A Deep Learning Perspective on the Origin
of Facial Expressions
Ran Breuer
Ron Kimmel
Department of Computer Science
Technion - Israel Institute of Technology
Technion City, Haifa, Israel
Figure 1: Demonstration of the filter visualization process."
d588dd4f305cdea37add2e9bb3d769df98efe880,Audio-Visual Authentication System over the Internet Protocol,"Audio-Visual Authentication System over the
Internet Protocol
Yee Wan Wong, Kah Phooi Seng, and Li-Minn Ang
bandoned.
illumination  based
is  developed  with  the  objective  to"
d5cf6a02f8308e948e3bcd1fd1ca660ea8ea8921,G ENERATIVE NETWORKS AS INVERSE PROBLEMS WITH SCATTERING TRANSFORMS,"Under review as a conference paper at ICLR 2018
GENERATIVE NETWORKS AS INVERSE PROBLEMS
WITH SCATTERING TRANSFORMS
Anonymous authors
Paper under double-blind review"
d56407072eb9847fa44d49969129b5a4d1ef9ceb,Gaussian Process Prior Variational Autoencoders,"Gaussian Process Prior Variational Autoencoders
Francesco Paolo Casale†∗, Adrian V Dalca‡§, Luca Saglietti†¶,
Jennifer Listgarten(cid:93), Nicolo Fusi†
Microsoft Research New England, Cambridge (MA), USA
Computer Science and Artificial Intelligence Lab, MIT, Cambridge (MA), USA
§ Martinos Center for Biomedical Imaging, MGH, HMS, Boston (MA), USA;
¶ Italian Institute for Genomic Medicine, Torino, Italy
(cid:93) EECS Department, University of California, Berkeley (CA), USA."
d53994f28deb2800120fab8a42852813b3b8c081,Does the Left Hair Part Look Better ( or Worse ) Than the Right ?,"Article
Does the Left Hair Part Look Better
(or Worse) Than the Right?
Social Psychological and
Personality Science
ª The Author(s) 2018
Reprints and permission:
sagepub.com/journalsPermissions.nav
DOI: 10.1177/1948550618762500
journals.sagepub.com/home/spp
Jeremy A. Frimer1"
d53c5a974f9fccf18f3c8f7d73522d6ca7162115,X-GAN : Improving Generative Adversarial Networks with ConveX Combinations,"X-GAN: Improving Generative Adversarial
Networks with ConveX Combinations
Oliver Blum, Biagio Brattoli, and Bj¨orn Ommer
Heidelberg University, HCI / IWR, Germany"
d5813a4a0cca115b05e03d8d8c1ac8bf07176e96,Supplementary Material : Reinforced Video Captioning with Entailment Rewards,"Supplementary Material: Reinforced Video Captioning with Entailment
Rewards
Ramakanth Pasunuru and Mohit Bansal
UNC Chapel Hill
{ram,
Attention-based Baseline Model
(Cross-Entropy)
Reinforcement Learning (Policy
Gradient)
Our attention baseline model is similar to the Bah-
danau et al. (2015) architecture, where we encode
input frame level video features to a bi-directional
LSTM-RNN and then generate the caption using a
single layer LSTM-RNN, with an attention mech-
nism. Let {f1, f2, ..., fn} be the frame-level fea-
tures of a video clip and {w1, w2, ..., wm} be the
sequence of words forming a caption. The distri-
ution of words at time step t given the previously
generated words and input video frame-level fea-
tures is given as follows:"
d59404354f84ad98fa809fd1295608bf3d658bdc,Face Synthesis from Visual Attributes via Sketch using Conditional VAEs and GANs.,"International Journal of Computer Vision manuscript No.
(will be inserted by the editor)
Face Synthesis from Visual Attributes via Sketch using
Conditional VAEs and GANs
Xing Di · Vishal M. Patel
Received: date / Accepted: date"
d5b83d6b4a3c1093edc9138ab9dfe4e965a80261,Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates,"Skeleton-Based Action Recognition Using
Spatio-Temporal LSTM Network with Trust Gates
Jun Liu, Amir Shahroudy, Dong Xu, Alex C. Kot, and Gang Wang"
d58516957d376e1e682130825efd74a8d34e81d6,Pedestrian Detection Using Thermal Imaging for Night Driving Assistance,"International Journal of Multimed ia Technology                                                                                                                     IJMT
Pedestrian Detection Using Thermal Imaging for
Night Driving Assistance
Ali Mahmoud *1, Ahmed EL-Barkouky 2, James Graham 3, Aly Farag 4
,2,3,4Electrica l and Co mputer Engineering  Depart ment, Un iversity Of Louisville,
Kentucky, USA
*1ali.mah isville.edu  ; 3ja"
d522c162bd03e935b1417f2e564d1357e98826d2,Weakly supervised object extraction with iterative contour prior for remote sensing images,"He et al. EURASIP Journal on Advances in Signal Processing 2013, 2013:19
http://asp.eurasipjournals.com/content/2013/1/19
RESEARCH
Open Access
Weakly supervised object extraction with
iterative contour prior for remote sensing
images
Chu He1,2*, Yu Zhang1, Bo Shi1, Xin Su3, Xin Xu1 and Mingsheng Liao2"
f23d4ed760a35fbfaeab47efde3d876c1818d3d1,Dynamicity and Durability in Scalable Visual Instance Search,"Dynamicity and Durability in Scalable Visual Instance Search
Herwig Lejsek∗
Videntifier Technologies, Iceland
Björn Þór Jónsson†
Reykjavík University, Iceland
ITU Copenhagen, Denmark
Laurent Amsaleg
IRISA–CNRS, France
Friðrik Heiðar Ásmundsson∗
Videntifier Technologies, Iceland"
f2e9494d0dca9fb6b274107032781d435a508de6,UNCONSTRAINED FACE RECOGNITION,
f2d2dd3db244dcbc6fb32ff9c01ed0cdeb3fd437,Unsupervised Feature Learning Based on Deep Models for Environmental Audio Tagging,"Unsupervised Feature Learning Based on Deep
Models for Environmental Audio Tagging
Yong Xu, Qiang Huang, Wenwu Wang, Senior Member, IEEE,
Peter Foster, Siddharth Sigtia, Philip J. B. Jackson, and Mark D. Plumbley, Fellow, IEEE"
f2b2d50d6ca72666bab34e0f101ae1b18b434925,High-Fidelity Monocular Face Reconstruction based on an Unsupervised Model-based Face Autoencoder.,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
High-Fidelity Monocular Face Reconstruction based on an
Unsupervised Model-based Face Autoencoder
Ayush Tewari, Michael Zollh¨ofer, Florian Bernard, Pablo Garrido,
Hyeongwoo Kim, Patrick P´erez, and Christian Theobalt
(Invited Paper)"
f20e0eefd007bc310d2a753ba526d33a8aba812c,Accurate and robust face recognition from RGB-D images with a deep learning approach,"Lee et al.:  RGB-D FACE RECOGNITION WITH A DEEP LEARNING APPROACH
Accurate and robust face recognition from
RGB-D images with a deep learning
pproach
Yuancheng Lee
http://cv.cs.nthu.edu.tw/php/people/profile.php?uid=150
Jiancong Chen
http://cv.cs.nthu.edu.tw/php/people/profile.php?uid=153
Ching-Wei Tseng
http://cv.cs.nthu.edu.tw/php/people/profile.php?uid=156
Computer Vision Lab,
Department of
Computer Science,
National Tsing Hua
University,
Hsinchu, Taiwan
Shang-Hong Lai
http://www.cs.nthu.edu.tw/~lai/"
f2b95f135b95c3df4f6ebe6015098a2e1667711d,Weakly Supervised Object Localization Using Things and Stuff Transfer,"Weakly Supervised Object Localization Using Things and Stuff Transfer
Miaojing Shi1,2
Holger Caesar1
University of Edinburgh 2Tencent Youtu Lab
Vittorio Ferrari1"
f22058a3003cee6b17c6c25c8a635a653e78614c,Multimodal Attention in Recurrent Neural Networks for Visual Question Answering,"Global Journal of Computer Science and Technology: D
Neural & Artificial Intelligence
Volume 17   Issue 1 Version 1.0 Year 2017
Type: Double Blind Peer Reviewed International Research Journal
Publisher: Global Journals Inc. (USA)
Online ISSN: 0975-4172 & Print ISSN: 0975-4350
Multimodal  Attention  in  Recurrent  Neural  Networks  for  Visual
Question Answering
By Lorena Kodra & Elinda Kajo Meçe
Polytechnic University of Tirana"
f26a8dcfbaf9f46c021c41a3545fcfa845660c47,Human Pose Regression by Combining Indirect Part Detection and Contextual Information,"Human Pose Regression by Combining Indirect Part Detection and Contextual
Information
Diogo C. Luvizon
Hedi Tabia
ETIS Lab., UMR 8051, Universit´e Paris Seine,
Universit´e Cergy-Pontoise, ENSEA, CNRS.
{diogo.luvizon, hedi.tabia,
David Picard"
f2bccfb12c1546bdf73b11904ac44b1cfa130072,RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement,"RoarNet: A Robust 3D Object Detection based on
RegiOn Approximation Refinement
Kiwoo Shin∗†, Youngwook Paul Kwon∗‡ and Masayoshi Tomizuka†"
f2d95a5b29986a6a28746b30adfa43497b27ff02,Global Self-Similarity and Saliency Measures Based on Sparse Representations for Classification of Objects and Spatio-temporal Sequences,"Global Self-Similarity and Saliency Measures Based on
Sparse Representations for Classification of Objects and
Spatio-temporal Sequences.
A DISSERTATION
SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL
OF THE UNIVERSITY OF MINNESOTA
Guruprasad Somasundaram
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
Doctor of Philosophy
Nikolaos Papanikolopoulos
November, 2012"
f2efc85f9e20840c591b4590fd9ed202f727546a,Distributed signature fusion for person re-identification,"Distributed Signature Fusion for
Person Re-Identification
Niki Martinel
University of Udine
Udine, Italy
Christian Micheloni
University of Udine
Udine, Italy
Claudio Piciarelli
University of Udine
Udine, Italy"
f2d215d25630b15122f8f773b00ac33bdf597c05,Real-Time Face Recognition Using Local Ternary Patterns with Collaborative Representation-Based Classification for Mobile Robots,"Real time Face Recognition using Local Ternary
Patterns with Collaborative Representation based
Classification for Mobile Robots
Duc My Vo and Andreas Zell
Chair of Cognitive Systems, Computer Science Department, University of T¨ubingen,
Sand 1, D-72076 T¨ubingen, Germany
http://www.cogsys.cs.uni-tuebingen.de"
f218df397afb1f070ee093bb9a19616f61b562c4,A Neural Network Model of Face Detection for Active Vision Implementation,"International Journal of Modern Engineering Research (IJMER)
www.ijmer.com              Vol. 2, Issue. 5, Sept.-Oct. 2012 pp-2969-2974               ISSN: 2249-6645
A Neural Network Model of Face Detection for Active Vision
Implementation
Yasuomi D. Sato*, ** Yasutaka Kuriya*
* Department of Brain Science and Engineering, Graduate School for Life Science and Systems Engineering, Kyushu
** Frankfurt Institute for Advanced Studies (FIAS), Goethe University Frankfurt, Germany
Institute of Technology, Japan
impaired"
f20f93a5b2291283c0e40bd0418927efb06acb6a,A Tale of Two Encodings : Comparing Bag-of-Words and Word 2 vec for VQA,"A Tale of Two Encodings: Comparing Bag-of-Words and Word2vec for VQA
Berthy Feng
Princeton University ’19
Divya Thuremella
Princeton University ’18"
f2828ae81327276407504caab558c13362439476,"Modeling , Representing and Learning of Visual Categories","Modeling, Representing and
Learning of Visual Categories
A dissertation submitted to the
TECHNISCHE UNIVERSIT¨AT DARMSTADT
Fachbereich 20
for the degree of
Dr. ing.
presented by
MARIO FRITZ
Dipl.–Inf.
orn 16th of January, 1978
in Adenau, Germany
Prof. Dr. Bernt Schiele, examiner
Prof. Dr. Pietro Perona, co-examiner
Date of Submission: 12th of June, 2008
Date of Defense: 8th of August, 2008"
f2889f3ab8e330e1ba6b23d493f8d727f49a9bc8,Recent Advances in Neural Program Synthesis,"Recent Advances in Neural Program Synthesis
Neel Kant
Machine Learning at Berkeley
UC Berkeley"
f21a8e372fa12d87fec77d3297afe1e566e229a7,"Nonnegative tensor factorization as an alternative Csiszar–Tusnady procedure: algorithms, convergence, probabilistic interpretations and novel probabilistic tensor latent variable analysis algorithms","Data Min Knowl Disc (2011) 22:419–466
DOI 10.1007/s10618-010-0196-4
Nonnegative tensor factorization as an alternative
Csiszar–Tusnady procedure: algorithms, convergence,
probabilistic interpretations and novel probabilistic
tensor latent variable analysis algorithms
Stefanos Zafeiriou · Maria Petrou
Received: 1 May 2009 / Accepted: 8 July 2010 / Published online: 1 August 2010
© The Author(s) 2010"
f29aae30c2cb4c73a3c814408ee5692e22176329,Pairwise Relational Networks using Local Appearance Features for Face Recognition,"Pairwise Relational Networks using Local
Appearance Features for Face Recognition
Bong-Nam Kang
Yonghyun Kim, Daijin Kim
Department of Creative IT Engineering
Department of Computer Science and Engineering
POSTECH, Korea
POSTECH, Korea"
f2b79ae191fc03a93ed50eea773279f67c8351e1,Annotating images with suggestions: user study of a tagging system,"Annotating images with suggestions — user
study of a tagging system
Michal Hradiˇs, Martin Kol´aˇr, Aleˇs L´an´ık, Jiˇr´ı Kr´al, Pavel Zemˇc´ık and Pavel
Smrˇz
Faculty of Information Technology
VUT — Brno University of Technology
Brno Czech Republic"
f2fafa5d2c49034ba8f6318f869822b462b33a42,iQIYI-VID: A Large Dataset for Multi-modal Person Identification,"iQIYI-VID: A Large Dataset for Multi-modal Person Identification
Yuanliu Liu, Peipei Shi, Bo Peng, He Yan, Yong Zhou, Bing Han, Yi Zheng, Chao Lin,
Jianbin Jiang,Yin Fan, Tingwei Gao, Ganwen Wang, Jian Liu, Xiangju Lu, Danming Xie
iQIYI, Inc."
f2b547b0bbda1478cbecbd5c184c3c42c3db7e3c,Semi-parametric Image Synthesis,
f26d34d8a8d082ce2c81937f61c28f3769c38372,Probability of Seeing Increases Saccadic Readiness,"Probability of Seeing Increases Saccadic Readiness
The´ re` se Collins*
Laboratoire Psychologie de la Perception, Universite´ Paris Descartes & CNRS, Paris, France"
f2a7f9bd040aa8ea87672d38606a84c31163e171,Human Action Recognition without Human,"Human Action Recognition without Human
Yun He, Soma Shirakabe, Yutaka Satoh, Hirokatsu Kataoka
National Institute of Advanced Industrial Science and Technology (AIST)
Tsukuba, Ibaraki, Japan
{yun.he, shirakabe-s, yu.satou,"
f2877cdbffb0c9a4de1f562099d2f0597bcfec0b,"COGNIMUSE: a multimodal video database annotated with saliency, events, semantics and emotion with application to summarization","Zlatintsi et al. EURASIP Journal on Image and Video Processing  (2017) 2017:54
DOI 10.1186/s13640-017-0194-1
EURASIP Journal on Image
nd Video Processing
RESEARCH
Open Access
COGNIMUSE: a multimodal video
database annotated with saliency, events,
semantics and emotion with application to
summarization
Athanasia Zlatintsi1*
Niki Efthymiou1, Katerina Pastra4, Alexandros Potamianos1 and Petros Maragos1
, Petros Koutras1, Georgios Evangelopoulos2, Nikolaos Malandrakis3,"
c3bd3b9782dc504ee4f2b8a12bd9c562a5c0d7ad,Learning Complexity-Aware Cascades for Deep Pedestrian Detection,"Learning Complexity-Aware Cascades for Deep Pedestrian Detection
Zhaowei Cai
Mohammad Saberian
Nuno Vasconcelos
Yahoo Labs"
c3293ef751d3fb041bd3016fbc3fa5cc16f962fa,Inferencing based on unsupervised learning of disentangled representations,"Accepted as a conference paper at the European Symposium on Artificial Neural
Networks, Computational Intelligence and Machine Learning (ESANN) 2018
Inferencing Based on Unsupervised Learning
of Disentangled Representations
Tobias Hinz and Stefan Wermter ∗
Universit¨at Hamburg, Department of Informatics, Knowledge Technology
Vogt-Koelln-Str. 30, 22527 Hamburg, Germany
http://www.informatik.uni-hamburg.de/WTM/"
c37a971f7a57f7345fdc479fa329d9b425ee02be,A Novice Guide towards Human Motion Analysis and Understanding,"A Novice Guide towards Human Motion Analysis and Understanding
Dr. Ahmed Nabil Mohamed"
c3599c91d0e3473178c1578b731b03e4be5d3ff1,Improving Resource Efficiency in Cloud Computing a Dissertation Submitted to the Department of Electrical Engineering and the Committee on Graduate Studies of Stanford University in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy,"IMPROVING RESOURCE EFFICIENCY IN CLOUD COMPUTING
A DISSERTATION
SUBMITTED TO THE DEPARTMENT OF ELECTRICAL
ENGINEERING
AND THE COMMITTEE ON GRADUATE STUDIES
OF STANFORD UNIVERSITY
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
Christina Delimitrou
August 2015"
c3beae515f38daf4bd8053a7d72f6d2ed3b05d88,ACL 2014 52nd Annual Meeting of the Association for Computational Linguistics TACL Papers,"ACL201452ndAnnualMeetingoftheAssociationforComputationalLinguisticsTACLPapersJune23-25,2014Baltimore,Maryland,USA"
c398684270543e97e3194674d9cce20acaef3db3,Chapter 2 Comparative Face Soft Biometrics for Human Identification,"Chapter 2
Comparative Face Soft Biometrics for
Human Identification
Nawaf Yousef Almudhahka, Mark S. Nixon and Jonathon S. Hare"
c32f798663bd00268e3ceb8a855222dae877a58a,Anomaly detection from videos under sparse data and partial observations ∗,"Anomaly detection from videos
under sparse data and partial observations∗
Sangmin Oh, Anthony Hoogs
{sangmin.oh,
Kitware Inc. 28 Corporate Dr., Clifton Park, NY
Anomaly detection from videos is an important computer vision problem where the goal is to identify rare
ctivity examples which significantly deviate from normal behaviors observed in scenes. For example, Fig 1 shows
scene (a) and tracking results (b), where vehicles (blue) and people (green) are tracked over an extended period
of time. An example anomaly is shown in Fig. 1(d) where a vehicle (red circle) is turning at a traf‌f‌ic intersection
onfronting oncoming traf‌f‌ic.
(a) Original Scene 1
(b) Tracking results 1
(c) Tracking results 2
(d) Abnormal vehicle turning
(e) Functional scene elements 1
(f) Functional scene elements 2
(g) Activity words of vehicles
(h) Fragmented tracks
(i) Linked tracklets
Figure 1: (a) Scene 1. (b) Tracking results in scene 1 with vehicles (blue) and people (green). (c) Tracking results"
c33289788ca69a55c7eefe6e672c82a0cac5a299,Semantic Video CNNs Through Representation Warping,"Semantic Video CNNs through Representation Warping
Raghudeep Gadde1,3, Varun Jampani1,4 and Peter V. Gehler1,2,3
MPI for Intelligent Systems,
University of W¨urzburg
Bernstein Center for Computational Neuroscience,
NVIDIA"
c390fb954a07ecee473e0704ac065875121f6137,Heterogeneous Tensor Decomposition for Clustering via Manifold Optimization,"IEEE TRANSACTIONS ON XXXX, VOL. XX, NO. X, APRIL 2015
Heterogeneous Tensor Decomposition for
Clustering via Manifold Optimization
Yanfeng Sun, Junbin Gao, Xia Hong, Bamdev Mishra and Baocai Yin"
c33522fc5d2cf92c5a10f32ba9416365944cdb85,Scaling the Scattering Transform: Deep Hybrid Networks,"Scaling the Scattering Transform: Deep Hybrid Networks
Edouard Oyallon
D´epartement Informatique
Ecole Normale Sup´erieure
Eugene Belilovsky
University of Paris-Saclay
INRIA and KU Leuven
Paris, France
Sergey Zagoruyko
Universit´e Paris-Est
´Ecole des Ponts ParisTech
Paris, France"
c3955d74f2a084a8ddcbd7e73952c326e81804b2,Mutual Information Neural Estimation,"Mutual Information Neural Estimation
Mohamed Ishmael Belghazi 1 Aristide Baratin 1 2 Sai Rajeswar 1 Sherjil Ozair 1 Yoshua Bengio 1 3 4
Aaron Courville 1 3 R Devon Hjelm 1 4"
c3dc704790e1a170919087baab0ad10d7df6c24e,Oxytocin in the socioemotional brain: implications for psychiatric disorders,"C l i n i c a l   r e s e a r c h
Oxytocin in the socioemotional brain:
implications for psychiatric disorders
Peter Kirsch, PhD
Introduction
During  recent  years,  the  neuropeptide  oxytocin
(OXT) has attracted enormous interest in neuroscien-
tific research on social and emotional processes. Given
the generally increased interest in social cognition in
the area of psychiatric research, the number of publi-
ations focusing on OXT in the context of mental dis-
orders has also increased markedly in recent years. The
role of OXT in the context of childbirth and lactation
has long been studied; however, two lines of research
have motivated investigation into the role of OXT in
social behavior. First, animal research initiated by In-
sel  and Young1  on  the  role  of  OXT  in  maternal  be-
havior and bonding revealed that OXT in the central
nervous  system  modulates  social  behavior.  Second,
in human research, a startling paper by Kosfeld et al2"
c317181fa1de2260e956f05cd655642607520a4f,Objective Classes for Micro-Facial Expression Recognition,"Research Article
Research
Article for submission to journal
Subject Areas:
omputer vision, pattern recognition,
feature descriptor
Keywords:
micro-facial expression, expression
recognition, action unit
Moi Hoon Yap
e-mail:
Objective Classes for
Micro-Facial Expression
Recognition
Adrian K. Davison1, Walied Merghani2 and
Moi Hoon Yap3
Centre for Imaging Sciences, University of
Manchester, Manchester, United Kingdom
Sudan University of Science and Technology,
Khartoum, Sudan"
c391029d67e5a0c352f9f328b838cb19528336fe,Responding to Other People’s Direct Gaze: Alterations in Gaze Behavior in Infants at Risk for Autism Occur on Very Short Timescales,"J Autism Dev Disord (2017) 47:3498–3509
DOI 10.1007/s10803-017-3253-7
ORIGINAL PAPER
Responding to Other People’s Direct Gaze: Alterations in Gaze
Behavior in Infants at Risk for Autism Occur on Very Short
Timescales
Pär Nyström1
· Sven Bölte2,3 · Terje Falck‑Ytter1,2 · The EASE Team
Published online: 4 September 2017
© The Author(s) 2017. This article is an open access publication"
c3a1a3d13bf1cb2b9c054857b857c3fb9d7176f6,Détection de marqueurs affectifs et attentionnels de personnes âgées en interaction avec un robot. (Audio-visual detection of emotional (laugh and smile) and attentional markers for elderly people in social interaction with a robot),"Détection de marqueurs affectifs et attentionnels de
personnes âgées en interaction avec un robot
Fan Yang
To cite this version:
Fan Yang. Détection de marqueurs affectifs et attentionnels de personnes âgées en interaction
vec un robot.
Intelligence artificielle [cs.AI]. Université Paris-Saclay, 2015. Français. <NNT :
015SACLS081>. <tel-01280505>
HAL Id: tel-01280505
https://tel.archives-ouvertes.fr/tel-01280505
Submitted on 29 Feb 2016
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
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broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,"
c380aa240ebcdb8bf2cad4f30bcef2390fada091,Empty Cities: Image Inpainting for a Dynamic-Object-Invariant Space,"Empty Cities: Image Inpainting for a Dynamic-Object-Invariant Space
Berta Bescos1, Jos´e Neira1, Roland Siegwart2 and Cesar Cadena2"
c3b5ec36a29b320a576f6b9e58188b505becb4aa,Practical Gauss-Newton Optimisation for Deep Learning,"Practical Gauss-Newton Optimisation for Deep Learning
Aleksandar Botev 1 Hippolyt Ritter 1 David Barber 1 2"
c3dc4f414f5233df96a9661609557e341b71670d,Utterance independent bimodal emotion recognition in spontaneous communication,"Tao et al. EURASIP Journal on Advances in Signal Processing 2011, 2011:4
http://asp.eurasipjournals.com/content/2011/1/4
RESEARCH
Utterance independent bimodal emotion
recognition in spontaneous communication
Jianhua Tao*, Shifeng Pan, Minghao Yang, Ya Li, Kaihui Mu and Jianfeng Che
Open Access"
c34911e9fefd987470edf8f620d9ce8f0030339d,"
Autism, Emotion Recognition and the Mirror
Neuron System: The Case of Music
				","Copyright © 2009 by MJM
MJM 2009 12(2): 87-98
FoCuS rEViEW
Autism, Emotion Recognition and the Mirror
Neuron System: The Case of Music
Istvan Molnar-Szakacs*, Martha J. Wang, Elizabeth A. Laugeson,
Katie Overy, Wai-Ling Wu, Judith Piggot"
c399c0089fb134d1476fadf5f0426e0e8b70eebd,The Lovász Hinge: A Novel Convex Surrogate for Submodular Losses.,1EXPERIMENTALRESULTS1.1JaccardlossarXiv:1512.07797v2  [stat.ML]  15 May 2017
c338045f80ab3465bdc381f2b1791744b060fbb3,A Diffusion and Clustering-Based Approach for Finding Coherent Motions and Understanding Crowd Scenes,"A Diffusion and Clustering-based Approach for
Finding Coherent Motions and Understanding
Crowd Scenes
Weiyao Lin, Yang Mi, Weiyue Wang, Jianxin Wu, Jingdong Wang, and Tao Mei"
c3124b0491d479e8a869c61f059ffa08dad49362,A Generative Model for Zero Shot Learning Using Conditional Variational Autoencoders,"A Generative Model For Zero Shot Learning
Using Conditional Variational Autoencoders
Ashish Mishra1 , Shiva Krishna Reddy1, Anurag Mittal, and Hema A Murthy
Indian Institute of Technology Madras"
c30e4e4994b76605dcb2071954eaaea471307d80,Feature Selection for Emotion Recognition based on Random Forest,
c32b5f8d400cdfd4459b0dfdeccf011744df0b4b,Object Tracking Using Local Multiple Features and a Posterior Probability Measure,"Article
Object Tracking Using Local Multiple Features and a
Posterior Probability Measure
Wenhua Guo *, Zuren Feng and Xiaodong Ren
Systems Engineering Institute, State Key Laboratory for Manufacturing Systems Engineering,
Xi’an Jiaotong University, Xi’an 710049, China; (Z.F.); (X.R.)
* Correspondence: Tel.: +86-29-8266-7771
Academic Editors: Xue-Bo Jin, Shuli Sun, Hong Wei and Feng-Bao Yang
Received: 20 February 2017; Accepted: 28 March 2017; Published: 31 March 2017"
c37de914c6e9b743d90e2566723d0062bedc9e6a,Joint and Discriminative Dictionary Learning for Facial Expression Recognition,"©2016 Society for Imaging Science and Technology
DOI: 10.2352/ISSN.2470-1173.2016.11.IMAWM-455
Joint  and  Discriminative  Dictionary  Learning
Expression Recognition
for  Facial
Sriram Kumar, Behnaz Ghoraani, Andreas Savakis"
c35724d227eb1e3d680333469fb9b94c677e871f,Multi-view Generative Adversarial Networks,"Under review as a conference paper at ICLR 2017
MULTI-VIEW GENERATIVE ADVERSARIAL NET-
WORKS
Mickaël Chen
Sorbonne Universités, UPMC Univ Paris 06, UMR 7606, LIP6, F-75005, Paris, France
Ludovic Denoyer
Sorbonne Universités, UPMC Univ Paris 06, UMR 7606, LIP6, F-75005, Paris, France"
c3ea346826467f04779e55679679c7c7e549c8a2,Learning Short-Cut Connections for Object Counting,"OÑORO-RUBIO, NIEPERT, LÓPEZ-SASTRE: LEARNING SHORT-CUT CONNECTIONS. . .
Learning Short-Cut Connections for Object
Counting
Daniel Oñoro-Rubio1
Mathias Niepert1
Roberto J. López-Sastre2
SysML,
NEC Lab Europe,
Heidelberg, Germany
GRAM,
University of Alcalá,
Alcalá de Henares, Spain"
c36f933a46e1d1c51785295bb97154df9ceada36,"Optimizing Program Performance via Similarity, Using a Feature-Agnostic Approach","Optimizing Program Performance via Similarity,
Using a Feature-agnostic Approach
Rosario Cammarota, Laleh Aghababaie Beni
Alexandru Nicolau, and Alexander V. Veidenbaum
Department of Computer Science, University of California Irvine, Irvine, USA"
c32b09f20badd9ce04309d7c5ebea88336a3345a,Token-level and sequence-level loss smoothing for RNN language models,"Token-level and sequence-level loss smoothing for RNN language models
Maha Elbayad1,2
Laurent Besacier1
Jakob Verbeek2
Univ. Grenoble Alpes, CNRS, Grenoble INP, Inria, LIG, LJK, F-38000 Grenoble France"
c3b037fd6fb4542f7ed18c194a03ae328bcca423,Random binary mappings for kernel learning and efficient SVM,"Random Decision Stumps for
Kernel Learning and Efficient SVM
Gemma Roig *
Xavier Boix *
Luc Van Gool
Computer Vision Lab, ETH Zurich, Switzerland
* Both first authors contributed equally."
c3780c0fd7f6ee82339a974045eebbc9e5b61c75,User-specific Score Normalization and Fusion for Biometric Person Recognition,"User-specific Score Normalization and Fusion
for Biometric Person Recognition
Norman Poh"
c34ec5dd51880acf72336e85e4e45da5fcfc75f4,LEGO: Learning Edge with Geometry all at Once by Watching Videos,"LEGO: Learning Edge with Geometry all at Once by Watching Videos
Zhenheng Yang1 Peng Wang2 Yang Wang2 Wei Xu3 Ram Nevatia1
University of Southern California 2Baidu Research
National Engineering Laboratory for Deep Learning Technology and Applications"
c32f04ccde4f11f8717189f056209eb091075254,Analysis and Synthesis of Behavioural Specific Facial Motion,"Analysis and Synthesis of Behavioural Specific
Facial Motion
Lisa Nanette Gralewski
A dissertation submitted to the University of Bristol in accordance with the requirements
for the degree of Doctor of Philosophy in the Faculty of Engineering, Department of
Computer Science.
February 2007
71657 words"
c3c73bb626efec988aadbac519c61810710282fe,Saccadic movements using eye-tracking technology in individuals with autism spectrum disorders: pilot study.,"Arq Neuropsiquiatr 2006;64(3-A):559-562
SACCADIC MOVEMENTS USING EYE-TRACKING
TECHNOLOGY IN INDIVIDUALS WITH AUTISM
SPECTRUM DISORDERS
Pilot study
Marcos T. Mercadante, Elizeu C. Macedo, Patrícia M. Baptista,
Cristiane S. Paula, José S. Schwartzman"
c3b3636080b9931ac802e2dd28b7b684d6cf4f8b,Face Recognition via Local Directional Pattern,"International Journal of Security and Its Applications
Vol. 7, No. 2, March, 2013
Face Recognition via Local Directional Pattern
Dong-Ju Kim*, Sang-Heon Lee and Myoung-Kyu Sohn
Division of IT Convergence, Daegu Gyeongbuk Institute of Science & Technology
50-1, Sang-ri, Hyeonpung-myeon, Dalseong-gun, Daegu, Korea."
c324986c8599fee2f6da7b59751e89ed9624afa3,Dual Quaternions as Constraints in 4D-DPM Models for Pose Estimation,"Article
Dual Quaternions as Constraints in 4D-DPM Models
for Pose Estimation
Enrique Martinez-Berti *, Antonio-José Sánchez-Salmerón and Carlos Ricolfe-Viala
Departamento de Ingeniería de Sistemas y Automática, Instituto de Automática e informática Industrial,
Universitat Politècnica de València, València, 46022, Spain ; (A.-J.S.-S.);
(C.R.-V.)
* Correspondence:
Received: 1 June 2017; Accepted: 13 August 2017; Published: 19 August 2017"
c3fb2399eb4bcec22723715556e31c44d086e054,Face recognition based on SIGMA sets of image features,"014 IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP)
978-1-4799-2893-4/14/$31.00 ©2014 IEEE
. INTRODUCTION"
c32fb755856c21a238857b77d7548f18e05f482d,Multimodal Emotion Recognition for Human-Computer Interaction : A Survey,"Multimodal Emotion Recognition for Human-
Computer Interaction: A Survey
School of Computer and Communication Engineering, University of Science and Technology Beijing, 100083 Beijing, China.
Michele Mukeshimana, Xiaojuan Ban, Nelson Karani, Ruoyi Liu"
c3de7c38493cfe67654411d77f47069cfa7b077b,Multiple context mere exposure: Examining the limits of liking.,"ISSN: 1747-0218 (Print) 1747-0226 (Online) Journal homepage: http://www.tandfonline.com/loi/pqje20
Multiple context mere exposure: Examining the
limits of liking
Daniel de Zilva, Ben R. Newell & Chris J. Mitchell
To cite this article: Daniel de Zilva, Ben R. Newell & Chris J. Mitchell (2015): Multiple context
mere exposure: Examining the limits of liking, The Quarterly Journal of Experimental
Psychology, DOI: 10.1080/17470218.2015.1057188
To link to this article:  http://dx.doi.org/10.1080/17470218.2015.1057188
Accepted online: 29 Jun 2015.Published
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c348118690d2e6544ec1e68f904dbf9e5b6397bd,Video-to-Video Synthesis,"Video-to-Video Synthesis
Ting-Chun Wang1, Ming-Yu Liu1, Jun-Yan Zhu2, Guilin Liu1,
Andrew Tao1, Jan Kautz1, Bryan Catanzaro1
NVIDIA, 2MIT CSAIL"
c3285a1d6ec6972156fea9e6dc9a8d88cd001617,Extreme 3D Face Reconstruction: Seeing Through Occlusions,
c141b7c4ccb4a0e5d2a327aa260b318c7c3bcfbb,Real-Time Adaptive Image Compression,"Real-Time Adaptive Image Compression
Oren Rippel * 1 Lubomir Bourdev * 1"
c1d98fca75c63fd5975fc2fcd3fe07ac02de4a5b,An End-to-End Traffic Vision and Counting System Using Computer Vision and Machine Learning: The Challenges in Real-Time Processing,"SIGNAL 2018 : The Third International Conference on Advances in Signal, Image and Video Processing
An End-to-End Traffic Vision and Counting System Using Computer Vision and
Machine Learning: The Challenges in Real-Time Processing
Haiyan Wang, Mehran Mazari, Mohammad Pourhomayoun
Computer Science Department
California State University Los Angeles
Los Angeles, USA
Email:
Janna Smith
Department of Transportation
City of Los Angeles
Los Angeles, USA
Email:
Hunter Owens
Data Science Federation
City of Los Angeles
Los Angeles, USA
Email:
William Chernicoff
Toyota Mobility Foundation"
c18e11d0578dc67e46160afa527e1a9e73b8fa15,Predicting Motion of Vulnerable Road Users using High-Definition Maps and Efficient ConvNets,"Predicting Motion of Vulnerable Road Users
using High-Definition Maps and Efficient ConvNets
Fang-Chieh Chou, Tsung-Han Lin, Henggang Cui, Vladan Radosavljevic,
Thi Nguyen, Tzu-Kuo Huang, Matthew Niedoba, Jeff Schneider, Nemanja Djuric
Uber Advanced Technologies Group
{fchou, hanklin, hcui2, vradosavljevic, thi,
tkhuang, mniedoba, jschneider,"
c1bd99083098cf8dbfed8d25514755bc5356bc06,Fly Page (This sheet is left blank and not counted) GENERALIZED DISCRIMINANT ANALYSIS IN CONTENT-BASED IMAGE RETRIEVAL APPROVED BY SUPERVISING COMMITTEE:,"Fly Page
(This sheet is left blank and not counted)"
c1bbcdf3b5901e3378a89808b07e53a502c295f0,Allostasis and the human brain: Integrating models of stress from the social and life sciences.,"Psychol Rev. Author manuscript; available in PMC 2011 January 1.
Published in final edited form as:
Psychol Rev. 2010 January; 117(1): 134–174.
doi:  10.1037/a0017773
Allostasis and the human brain: Integrating models of stress from the social and life sciences
Barbara L. Ganzel, Pamela A. Morris, and Elaine Wethington
Author information ► Copyright and License information ►
The publisher's final edited version of this article is available at Psychol Rev
See other articles in PMC that cite the published article."
c1100efda7c00d3181a6a065ab1474c2f864e267,Video visual analytics,"Video Visual Analytics
Von der Fakultät Informatik, Elektrotechnik und
Informationstechnik der Universität Stuttgart
genehmigte Abhandlung
zur Erlangung der Würde eines
Doktors der Naturwissenschaften (Dr. rer. nat.)
Vorgelegt von
Markus Johannes Höferlin
us Herrenberg
Hauptberichter: Prof. Dr. Daniel Weiskopf
Mitberichter:
Prof. Dr. Gunther Heidemann
Prof. Min Chen, BSc, PhD, FBCS, FEG, FLSW
Tag der mündlichen Prüfung: 27. Mai 2013
Visualisierungsinstitut
der Universität Stuttgart"
c1ff88493721af1940df0d00bcfeefaa14f1711f,Subspace Regression: Predicting a Subspace from one Sample,"#1369
CVPR 2010 Submission #1369. CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE.
#1369
Subspace Regression: Predicting a Subspace from one Sample
Anonymous CVPR submission
Paper ID 1369"
c1130d5c7bb1311e04cffbaf2bf6cbe734adc2ac,DFNet: Semantic Segmentation on Panoramic Images with Dynamic Loss Weights and Residual Fusion Block,"DFNet: Semantic Segmentation on Panoramic Images with Dynamic Loss
Weights and Residual Fusion Block
Wei Jiang, Yan Wu∗
technique, moreover,"
c1b971cd7263e788e114cf8c4aa076a2e170990f,Establishing the fundamentals for an elephant early warning and monitoring system,"Establishing the fundamentals for an elephant
early warning and monitoring system
Zeppelzauer and Stoeger
Zeppelzauer and Stoeger  BMC Res Notes  (2015) 8:409
DOI 10.1186/s13104-015-1370-y"
c1dd69df9dfbd7b526cc89a5749f7f7fabc1e290,Unconstrained face identification with multi-scale block-based correlation,"Unconstrained face identification with multi-scale block-based
orrelation
Gaston, J., MIng, J., & Crookes, D. (2016). Unconstrained face identification with multi-scale block-based
orrelation. In Proceedings of the 2017 IEEE International Conference on Acoustics, Speech and Signal
Processing (pp. 1477-1481). [978-1-5090-4117-6/17] Institute of Electrical and Electronics Engineers (IEEE).
Published in:
Proceedings of the 2017 IEEE International Conference on Acoustics, Speech and Signal Processing
Document Version:
Peer reviewed version
Queen's University Belfast - Research Portal:
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Copyright for the publications made accessible via the Queen's University Belfast Research Portal is retained by the author(s) and / or other
opyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated
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Take down policy"
c126f053855396f9e1ac1a408201d50d5280f79f,FPGA implementation of an embedded face detection system based on LEON 3,"FPGA implementation of an embedded face detection
system based on LEON3
L. Acasandrei1 and A. Barriga2
IMSE-CNM-CSIC, Seville, Spain
IMSE-CNM-CSIC/University of Seville, Seville, Spain"
c175f1666f3444e407660c5935a05b2a53f346f0,Modifying the Memorability of Face,"Modifying the Memorability of Face Photographs
The MIT Faculty has made this article openly available. Please share
how this access benefits you.  Your story matters.
Citation
As Published
Publisher
Version
Accessed
Citable Link
Terms of Use
Detailed Terms
Khosla, Aditya, Wilma A. Bainbridge, Antonio Torralba, and Aude
Oliva. “Modifying the Memorability of Face Photographs.” 2013
IEEE International Conference on Computer Vision (December
013).
http://dx.doi.org/10.1109/ICCV.2013.397
Institute of Electrical and Electronics Engineers (IEEE)
Author's final manuscript
Mon Nov 05 02:44:57 EST 2018
http://hdl.handle.net/1721.1/90986"
c100cb3ee6326d4f420c3be27b1232de6cd3bc8d,Methods for vehicle detection and vehicle presence analysis for traffic applications,"Methods for vehicle detection and
vehicle presence analysis for
traffic applications
Oliver Sidla*, Yuriy Lipetski
SLR Engineering GmbH, Gartengasse 19, A-8010 Graz, Austria"
c1c3e32ecf6da8e1372fab7d504cb8cd2c86fd93,ΓΨ Face recognition based on artificial immune networks and principal component analysis with single training image per person,"Face recognition based on artificial immune networks and principal
omponent analysis with single training image per person
, Department of Mechanical Engineering, Tatung University, Taiwan, ROC,
Guan-Chun Luh"
c1c8ea4b2118095bea55cf6b51c36dbf95cc7f2c,Learning 3D Segment Descriptors for Place Recognition,"Learning 3D Segment Descriptors for Place Recognition
Andrei Cramariuc
Renaud Dubé
Hannes Sommer
Roland Siegwart
Igor Gilitschenski∗"
c19845c84abc9e3afe17003fdcd545ed020d0624,A face biometric benchmarking review and characterisation,"A Face Biometric
Benchmarking Review and
Characterisation
Sandra Mau
Senior Research Engineer
NICTA Advanced Surveillance
BeFIT workshop – ICCV 2011"
c1df441663f18ce608ad396b6bb77f6c2e585678,FACE CLASSIFICATION USING WIDROW-HOFF LEARNING PARALLEL LINEAR COLLABORATIVE DISCRIMINANT REGRESSION ( WH-PLCDRC ),"Journal of Theoretical and Applied Information Technology
31st July 2016. Vol.89. No.2
© 2005 - 2016 JATIT & LLS. All rights reserved.
ISSN: 1992-8645                                                       www.jatit.org                                                          E-ISSN: 1817-3195
FACE CLASSIFICATION USING WIDROW-HOFF
LEARNING PARALLEL LINEAR COLLABORATIVE
DISCRIMINANT REGRESSION (WH-PLCDRC)
VISHWANATH P., 2Dr. V M VISWANATHA
NIMS University, Jaipur(Rajasthan), Electronics and Communication Engineering
S L N College of Engineering, Raichur, Electronics and Communication Engineering
E-mail:"
c1dfabe36a4db26bf378417985a6aacb0f769735,Describing Visual Scene through EigenMaps,"Journal of Computer Vision and Image Processing, NWPJ-201109-50
Describing Visual Scene through EigenMaps
Shizhi Chen, Student Member, IEEE, and YingLi Tian, Senior Member, IEEE"
c1643ad49ab52b7a38e416430583ca6adaaf5a9d,Face Recognition for Smart Environments Means of Identification Achieving Face Recognition,"C O V E R   F E A T U R E
Face Recognition
for Smart
Environments
Computers of the future will interact with us more like humans. A key
element of that interaction will be their ability to recognize our faces and
even understand our expressions.
Alex (Sandy)
Pentland
Tanzeem
Choudhury
MIT Media
Laboratory
Smart environments, wearable computers, and
ubiquitous computing in general are the com-
ing “fourth generation” of computing and
information technology.1-3 These devices will
e  everywhere—clothes,  home,  car,  and
office—and their economic impact and cultural sig-
nificance will dwarf those of the first three generations."
c160bcbc8f0517a97e46042c84343bf3f0477478,A Dynamic Approach and a New Dataset for Hand-detection in First Person Vision,"A Dynamic Approach and a New Dataset for
Hand-Detection in First Person Vision.
Alejandro Betancourt1,2, Pietro Morerio1, Emilia I. Barakova2, Lucio Marcenaro1,
Matthias Rauterberg2, Carlo S. Regazzoni1
Department of Naval, Electric, Electronic and Telecommunications Engineering - University
Designed Intelligence Group, Department of Industrial Design - Eindhoven University of
Technology, The Netherlands.
of Genoa, Italy."
c19ed5102ecd953d5c78d5a0b87eaa51658e07d8,Recovering Accurate 3D Human Pose in the Wild Using IMUs and a Moving Camera,"Recovering Accurate 3D Human Pose in The
Wild Using IMUs and a Moving Camera
Timo von Marcard1, Roberto Henschel1, Michael J. Black2, Bodo Rosenhahn1,
nd Gerard Pons-Moll3
Leibniz Universit¨at Hannover, Germany
MPI for Intelligent Systems, T¨ubingen, Germany
MPI for Informatics, Saarland Informatics Campus, Germany"
c11a2501204e9e7c4a53d8a3c87055b2b11c73df,Adaptive Learning Algorithms for Transferable Visual Recognition,"Adaptive Learning Algorithms for Transferable Visual
Recognition
Judy Hoffman
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2016-139
http://www.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-139.html
August 8, 2016"
c132a6e869cd171e403784c172961471733dce31,IN-VEHICLE PEDESTRIAN DETECTION USING STEREO VISION TECHNOLOGY,"IN-VEHICLE PEDESTRIAN DETECTION USING STEREO VISION
TECHNOLOGY
Wei Zhang, Ph.D., P.E.
Highway Research Engineer, Office of Safety Research & Development, HRDS-10
Federal Highway Administration
6300 Georgetown Pike, McLean, VA 22101, USA, e-mail:
Submitted to the 3rd International Conference on Road Safety and Simulation, September 14-16,
011, Indianapolis, USA"
c1087c588960dd7c00a2b5feed57fbdb70d066f1,Quantifying cortical surface asymmetry via logistic discriminant analysis,"Quantifying Cortical Surface Asymmetry
via Logistic Discriminant Analysis
Moo K. Chung1,2, Daniel J. Kelley2, Kim M. Dalton2, Richard J. Davidon2,3
Department of Biostatistics and Medical Informatics
Waisman Laboratory for Brain Imaging and Behavior
Department of Psychology and Psychiatry
University of Wisconsin, Madison, WI 53706, USA"
c149626b93c8949fb8b181c6220d8e8e8a558f98,Robot Sequential Decision Making using LSTM-based Learning and Logical-probabilistic Reasoning,"Robot Sequential Decision Making
using LSTM-based Learning and Logical-probabilistic Reasoning
Saeid Amiri1 , Mohammad Shokrolah Shirazi2 , Shiqi Zhang1
SUNY Binghamton
Cleveland State University"
c1974d59d55b6ea57d4acb8fcdc4a10f19d3cfca,Dynamic Local Feature Analysis for Face Recognition,"Dynamic Local Feature Analysis for Face Recognition
Johnny NG and Humphrey CHEUNG
Titanium Technology Research Centre
0/F, Tianjin Building, 167 Connaught Road West, Hong Kong, PR China
{Johnny.ng,"
c158009b33989c6677f1daa3f5926887c9471c5e,Controlling Complex Systems and Developing Dynamic Technology,"Electronic Thesis and Dissertations
Peer Reviewed
Title:
Controlling Complex Systems and Developing Dynamic Technology
Author:
Avizienis, Audrius Victor
Acceptance Date:
Series:
UCLA Electronic Theses and Dissertations
Degree:
Ph.D., Chemistry 0153UCLA
Advisor(s):
Gimzewski, James K
Committee:
Kodambaka, Suneel, Baugh, Delroy A
Permalink:
https://escholarship.org/uc/item/35c10822"
c18d80d00f2a7107bfe780eeec21b51a634ea925,Computational perspectives on the other-race effect,"This article was downloaded by: [The University of Texas at Dallas], [Alice
O'Toole]
On: 25 July 2013, At: 12:46
Publisher: Routledge
Informa Ltd Registered in England and Wales Registered Number: 1072954
Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH,
Visual Cognition
Publication details, including instructions for authors
nd subscription information:
http://www.tandfonline.com/loi/pvis20
Computational perspectives on
the other-race effect
Alice J. O'Toole a & Vaidehi Natu a
School of Behavioural and Brain Sciences , University
of Texas at Dallas , Richardson , TX , USA
Published online: 14 Jun 2013.
To cite this article: Visual Cognition (2013): Computational perspectives on the other-
race effect, Visual Cognition, DOI: 10.1080/13506285.2013.803505
To link to this article:  http://dx.doi.org/10.1080/13506285.2013.803505
PLEASE SCROLL DOWN FOR ARTICLE"
c1b2668186fcd01b3c0e93a9a0a68e3eb88a09ab,Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360 ^\circ ∘ Panoramic Imagery,"Eliminating the Blind Spot: Adapting 3D Object
Detection and Monocular Depth Estimation to
60◦ Panoramic Imagery
Gr´egoire Payen de La Garanderie, Amir Atapour Abarghouei,
nd Toby P. Breckon
Department of Computer Science
Durham University"
c1c34a3ab7815af1b9bcaf2822e4b9da8505f915,Image transmorphing with JPEG,"IMAGE TRANSMORPHING WITH JPEG
Lin Yuan and Touradj Ebrahimi
Multimedia Signal Processing Group, EPFL, Lausanne, Switzerland"
c1059a702f53c44bb26d3313964e811adf01d9b4,Low and mid-level features for target detection in satellite images,"ISSN: 2278 – 1323
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 2, Issue 2, February 2013
Low and mid-level features for target detection in satellite images
Rajani.D.C"
c165003060eeb01e05800a5ee4cd327f1e0bf5e3,SDC-Net: Video Prediction Using Spatially-Displaced Convolution,"SDC-Net: Video prediction using
spatially-displaced convolution
Fitsum A. Reda, Guilin Liu, Kevin J. Shih, Robert Kirby, Jon Barker,
David Tarjan, Andrew Tao, and Bryan Catanzaro
Nvidia Corporation, Santa Clara CA 95051, USA
Fig. 1. Frame prediction on a YouTube video frame featuring a panning camera. Left
to right: Ground-truth, MCNet [34] result, and our SDC-Net result. The SDC-Net
predicted frame is sharper and preserves fine image details, while color distortion and
lurriness is seen in the tree and text in MCNet’s predicted frame."
c1b90cf91837628c430a796e7b6be6d8c010cc43,Local Steerable Pyramid Binary Pattern Sequence LSPBPS for face recognition method,"World Academy of Science, Engineering and Technology
International Journal of Electronics and Communication Engineering
Vol:3, No:11, 2009
Local Steerable Pyramid Binary Pattern Sequence
LSPBPS for face recognition method
Mohamed El Aroussi, Mohammed El Hassouni, Sanaa Ghouzali, Mohammed Rziza, and Driss Aboutajdine"
1eb4ea011a3122dc7ef3447e10c1dad5b69b0642,Contextual Visual Recognition from Images and Videos,"Contextual Visual Recognition from Images and Videos
Georgia Gkioxari
Jitendra Malik
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2016-132
http://www.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-132.html
July 19, 2016"
1e1334f76177ddf3ddc35f7359a1e04b65438dc4,What Is the Most Efficient Way to Select Nearest Neighbor Candidates for Fast Approximate Nearest Neighbor Search ?,"013 IEEE International Conference on Computer Vision
013 IEEE International Conference on Computer Vision
What Is the Most Efficient Way to Select Nearest Neighbor Candidates for Fast
Approximate Nearest Neighbor Search?
Masakazu Iwamura, Tomokazu Sato and Koichi Kise
Graduate School of Engineering, Osaka Prefecture University
{masa,"
1ecb56e7c06a380b3ce582af3a629f6ef0104457,View-invariant face detection method based on local PCA cells,"List of Contents Vol.8
Contents of
Journal of Advanced Computational
Intelligence and Intelligent Informatics
Volume 8
Vol.8 No.1, January 2004
Editorial:
o Special Issue on Selected Papers from Humanoid,
Papers:
o Dynamic Color Object Recognition Using Fuzzy
Nano-technology, Information Technology,
Communication and Control, Environment, and
Management (HNICEM’03).
Elmer P. Dadios
Papers:
o A New Way of Discovery of Belief, Desire and
Intention  in the BDI Agent-Based Software
Modeling .
Chang-Hyun Jo
o Integration of Distributed Robotic Systems"
1e1a67a78badc619b2f9938e4a03922dcbee0fb6,Food/Non-food Image Classification and Food Categorization using Pre-Trained GoogLeNet Model,"Food/Non-food Image Classification and Food
Categorization using Pre-Trained GoogLeNet Model
Ashutosh Singla
Lin Yuan
Touradj Ebrahimi
Multimedia Signal Processing Group
Ecole Polytechnique Fédérale de Lausanne
Station 11, 1015 Lausanne, Switzerland"
1eda03469d860ac725122bd27faaae6b2cb47d0d,Image Question Answering Using Convolutional Neural Network with Dynamic Parameter Prediction,"Image Question Answering using Convolutional Neural Network
with Dynamic Parameter Prediction
Hyeonwoo Noh
Paul Hongsuck Seo
Bohyung Han
{shgusdngogo, hsseo,
Department of Computer Science and Engineering, POSTECH, Korea"
1ef46f7bb7463ead4369a796435106da63578733,Shamann: Shared Memory Augmented Neural Networks,"Under review as a conference paper at ICLR 2019
SHAMANN: SHARED MEMORY AUGMENTED
NEURAL NETWORKS
Anonymous authors
Paper under double-blind review"
1e8394cc9fe7c2392aa36fb4878faf7e78bbf2de,Zero-Shot Object Recognition System Based on Topic Model,"TO APPEAR IN IEEE THMS
Zero-Shot Object Recognition System
ased on Topic Model
Wai Lam Hoo and Chee Seng Chan"
1e1a3ee9626c740be78f9c5f75f9c4d7edc45666,Estimating the Natural Illumination Conditions from a Single Outdoor Image,E-mail:
1e6a26deea0a38310368d9c2a6dadc317b50bdf8,Joint Attention in Autonomous Driving (JAAD),"Joint Attention in Autonomous Driving (JAAD)
Iuliia Kotseruba, Amir Rasouli and John K. Tsotsos
{yulia_k, aras,"
1e82a8965f08e8d38b16f39412e6e3c456f6f22e,Social force model aided robust particle PHD filter for multiple human tracking,"978-1-4799-9988-0/16/$31.00 ©2016 IEEE
ICASSP 2016"
1e2087908e6ce34032c821c7fb6629f2d0733086,Affective Embodied Conversational Agents for Natural Interaction,"Affective Embodied Conversational Agents for
Natural Interaction
Eva Cerezo, Sandra Baldassarri, Isabelle Hupont and Francisco J. Seron
Advanced Computer Graphics Group (GIGA)
Computer Science Department, Engineering Research Institute of Aragon(I3A),
University of Zaragoza,
Spain
. Introduction
Human  computer  intelligent  interaction  is  an  emerging  field  aimed  at  providing  natural
ways for humans to use computers as aids. It is argued that for a computer to be able to
interact  with  humans  it  needs  to  have  the  communication  skills  of  humans.  One  of  these
skills is the affective aspect of communication, which is recognized to be a crucial part of
human intelligence and has been argued to be more fundamental in human behaviour and
success in social life than intellect (Vesterinen, 2001; Pantic, 2005).
Embodied conversational agents, ECAs (Casell et al., 2000), are graphical interfaces capable
of using verbal and non-verbal modes of communication to interact with users in computer-
ased environments. These agents are sometimes just as an animated talking face, may be
displaying simple facial expressions and, when using speech synthesis, with some kind of
lip  synchronization,  and  sometimes  they  have  sophisticated  3D  graphical  representation,
with complex body movements and facial expressions."
1e058b3af90d475bf53b3f977bab6f4d9269e6e8,Manifold Relevance Determination,"Manifold Relevance Determination
Andreas C. Damianou
Dept. of Computer Science & Sheffield Institute for Translational Neuroscience, University of Sheffield, UK
Carl Henrik Ek
KTH – Royal Institute of Technology, CVAP Lab, Stockholm, Sweden
Michalis K. Titsias
Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford OX3 7BN, UK
Neil D. Lawrence
Dept. of Computer Science & Sheffield Institute for Translational Neuroscience, University of Sheffield, UK"
1ed6a05a226cb0d09afd76ff9b7560c404d8eb49,D4g: Pre-completion report on exemplar,"D4g: Pre-completion report on exemplar
Workpackage 4 Deliverable
Date: 31th August 2007"
1e2b8778cfe44de4bbe4a099ee7cdff5c2ca5f38,Attention to Scale: Scale-Aware Semantic Image Segmentation,"Attention to Scale: Scale-aware Semantic Image Segmentation
Liang-Chieh Chen∗
{yangyi05, wangjiang03,
Yi Yang, Jiang Wang, Wei Xu
Alan L. Yuille"
1efaa128378f988965841eb3f49d1319a102dc36,Hierarchical binary CNNs for landmark localization with limited resources,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
Hierarchical binary CNNs for landmark
localization with limited resources
Adrian Bulat and Georgios Tzimiropoulos"
1eeca84f33079c6d7a95daf8994370b2d7a93443,Fingertip Detection and Tracking for Recognition of Air-Writing in Videos,"Fingertip Detection and Tracking for Recognition of
Air-Writing in Videos
Sohom Mukherjeea, Sk. Arif Ahmedb, Debi Prosad Dograc, Samarjit Kard and
Partha Pratim Roye
Department of Electrical Engineering,
National Institute of Technology Durgapur, Durgapur-713209, Indiaa
National Institute of Technology Durgapur, Durgapur-713209, Indiab,d
Department of Mathematics,
School of Electrical Science,
Indian Institute of Technology Bhubaneswar, Bhubaneswar-751013, Indiac
Department of Computer Science and Engineering,
Indian Institute of Technology Roorkee, Roorkee-247667, Indiae
Email addresses:"
1e5edbd39b4c61f785515e117a74e2d280aefbe7,The urrent tate and TRL ssessment of eople racking echnology for ideo urveillance pplications,"The Furrent Vtate and TRL Dssessment of Seople
Wracking Wechnology for Yideo Vurveillance
Dpplications
Prepared by:
Diego MacriniVafa Khoshaein, Ghazal Moradian,
Chris Whitten,Dmitry O. Gorodnichy, Robert Laganiere
Canada Border Services Agency
%HQWOH\$YHQXH
Ottawa ON Canada
K1A 0L8
URMHFW3673%,20
Scientific Authority:
Pierre Meunier
DRDC Centre for Security Science
613-992-0753
The scientific or technical validity of this Contract Report is entirely the responsibility of the
Contractor and the contents do not necessarily have the approval or endorsement of the
Department of National Defence of Canada.
Defence Research and Development Canada 
&RQWUDFW5HSRUW"
1e7ae86a78a9b4860aa720fb0fd0bdc199b092c3,A Brief Review of Facial Emotion Recognition Based on Visual Information,"Article
A Brief Review of Facial Emotion Recognition Based
on Visual Information
Byoung Chul Ko ID
Department of Computer Engineering, Keimyung University, Daegu 42601, Korea;
Tel.: +82-10-3559-4564
Received: 6 December 2017; Accepted: 25 January 2018; Published: 30 January 2018"
1e1dc91c2ac3ad0ae44941e711aed193231c3335,Universal Adversarial Perturbations Against Semantic Image Segmentation,"Universal Adversarial Perturbations Against Semantic Image Segmentation
Bosch Center for Artificial Intelligence, Robert Bosch GmbH
Jan Hendrik Metzen
Mummadi Chaithanya Kumar
University of Freiburg
Thomas Brox
University of Freiburg
Bosch Center for Artificial Intelligence, Robert Bosch GmbH
Volker Fischer"
1e02dfeb93e8fd8753d2e69baf705baf8996cb81,"Online Object Tracking, Learning and Parsing with And-Or Graphs","ARXIV VERSION
Online Object Tracking, Learning and Parsing
with And-Or Graphs
Tianfu Wu, Yang Lu and Song-Chun Zhu"
1e64b2d2f0a8a608d0d9d913c4baee6973995952,Dominant and Complementary Multi-Emotional Facial Expression Recognition Using C-Support Vector Classification,"DOMINANT AND
COMPLEMENTARY MULTI-
EMOTIONAL FACIAL
EXPRESSION RECOGNITION
USING C-SUPPORT VECTOR
CLASSIFICATION
Christer Loob, Pejman Rasti, Iiris Lusi, Julio C. S. Jacques
Junior, Xavier Baro, Sergio Escalera, Tomasz Sapinski,
Dorota Kaminska and Gholamreza Anbarjafari"
1e2d9ea6fe9c50a5c26a629b94446250e1be4e7d,The Freiburg Groceries Dataset,"The Freiburg Groceries Dataset
Philipp Jund, Nichola Abdo, Andreas Eitel, Wolfram Burgard"
1ea2a53a6cb9c08312276a2f0646935d5fab5ed3,Real-time Crowd Tracking using Parameter Optimized Mixture of Motion Models,"Noname manuscript No.
(will be inserted by the editor)
Real-time Crowd Tracking using Parameter Optimized
Mixture of Motion Models
Aniket Bera · David Wolinski · Julien Pettr´e · Dinesh Manocha
Received: date / Accepted: date"
1ee9598f88f40dabb70965a74eed87aedb276171,Face recognition using Histogram of co-occurrence Gabor phase patterns,"978-1-4799-2341-0/13/$31.00 ©2013 IEEE
ICIP 2013"
1ee27c66fabde8ffe90bd2f4ccee5835f8dedbb9,9 Entropy Regularization,"Entropy Regularization
Yves Grandvalet
Yoshua Bengio
The problem of semi-supervised induction consists in learning a decision rule from
labeled and unlabeled data. This task can be undertaken by discriminative methods,
provided that learning criteria are adapted consequently. In this chapter, we moti-
vate the use of entropy regularization as a means to bene(cid:12)t from unlabeled data in
the framework of maximum a posteriori estimation. The learning criterion is derived
from clearly stated assumptions and can be applied to any smoothly parametrized
model of posterior probabilities. The regularization scheme favors low density sep-
ration, without any modeling of the density of input features. The contribution
of unlabeled data to the learning criterion induces local optima, but this problem
an be alleviated by deterministic annealing. For well-behaved models of posterior
probabilities, deterministic annealing EM provides a decomposition of the learning
problem in a series of concave subproblems. Other approaches to the semi-supervised
problem are shown to be close relatives or limiting cases of entropy regularization.
A series of experiments illustrates the good behavior of the algorithm in terms of
performance and robustness with respect to the violation of the postulated low den-
sity separation assumption. The minimum entropy solution bene(cid:12)ts from unlabeled
data and is able to challenge mixture models and manifold learning in a number of"
1ebcf5dbb37fcd369530b0ee4df5d4a60f756f3e,Unsupervised High-level Feature Learning by Ensemble Projection for Semi-supervised Image Classification and Image Clustering,"High-level Feature Learning by Ensemble Projection for Image
Classification with Limited Annotations $
Dengxin Dai∗, Luc Van Gool
Computer Vision Lab, ETH Z¨urich, CH-8092, Switzerland"
1e15c5cba95cbb475ddb67157fdd480f5253502e,Face Recognition under Varying Lighting Conditions : A Combination of Weber-face and Local Directional Pattern for Feature Extraction and Support Vector Machines for Classification,"Journal of Information Hiding and Multimedia Signal Processing
Ubiquitous International
©2017 ISSN 2073-4212
Volume 8, Number 5, September 2017
Face Recognition under Varying Lighting Conditions:
A Combination of Weber-face and Local Directional
Pattern for Feature Extraction and Support Vector
Machines for Classification
Chin-Shiuh Shieh1,5, Liyun Chang4,∗, and Tsair-Fwu Lee1,3,5,∗
Chi-Kien Tran1,2, Chin-Dar Tseng1, Pei-Ju Chao1,3
Medical Physics and Informatics Laboratory of Electronics Engineering,
National Kaohsiung University of Applied Sciences, Kaohsiung 80778, Taiwan, ROC
Center for Information Technology, Hanoi University of Industry, Hanoi, Vietnam
Department of Radiation Oncology, Kaohsiung Chang Gung Memorial Hospital,
Department of Medical Imaging and Radiological Sciences, I-Shou University,
Kaohsiung 83305,Taiwan, ROC
Kaohsiung 82445,Taiwan, ROC
5 Graduate Institute of Clinical Medicine, Kaohsiung Medical University,
Corresponding authors:
Kaohsiung 807,Taiwan, ROC"
1e9c3d0d87e09ea359ce1e31114b677d627bf9e7,Rapid Stress System Drives Chemical Transfer of Fear from Sender to Receiver,"RESEARCH ARTICLE
Rapid Stress System Drives Chemical Transfer
of Fear from Sender to Receiver
Jasper H. B. de Groot1*, Monique A. M. Smeets1, Gün R. Semin1,2,3
Department of Social and Organizational Psychology, Faculty of Social and Behavioral Sciences, Utrecht
University, Utrecht, the Netherlands, 2 Department of Psychology, Koç University, Istanbul, Turkey,
Instituto Superior de Psicologia Aplicada (ISPA), Instituto Universitário, Lisbon, Portugal
11111"
1e1e35284591b6a69569c48b3677b6f4409c5edc,Matrix Product State for Feature Extraction of Higher-Order Tensors,"Matrix Product State for Feature Extraction of
Higher-Order Tensors
Johann A. Bengua1, Ho N. Phien1, Hoang D. Tuan1 and Minh N. Do2
een applied in neuroscience, pattern analysis, image classifi-
ation and signal processing [7], [8], [9]. The central concept
of using the TD is to decompose a large multidimensional
tensor into a set of common factor matrices and a single core
tensor which is considered as reduced features of the original
tensor in spite of its lower dimension [7]. In practice, the
TD is often performed in conjunction with some constraints,
e.g. nonnegativity, orthogonality, etc., imposed on the common
factors in order to obtain a better feature core tensor [7].
However, constraints like orthogonality often leads to an NP-
hard computational problem [10]. Practical application of the
TD is normally limited to small-order tensors. This is due
to the fact
the TD core tensor preserves the higher-
order structure of the original tensor, with its dimensionality
remaining fairly large in order to capture relevant interactions
etween components of the tensor [2]."
1e17202d6de18d5e1965edce5fee79744b717d0b,MIML-FCN+: Multi-Instance Multi-Label Learning via Fully Convolutional Networks with Privileged Information,"MIML-FCN+: Multi-instance Multi-label Learning via Fully Convolutional
Networks with Privileged Information
Hao Yang*, Joey Tianyi Zhou**, Jianfei Cai*, and Yew Soon Ong*
*School of Computer Science and Engineering, NTU, Singapore."
1e21b925b65303ef0299af65e018ec1e1b9b8d60,Unsupervised Cross-Domain Image Generation,"Under review as a conference paper at ICLR 2017
UNSUPERVISED CROSS-DOMAIN IMAGE GENERATION
Yaniv Taigman, Adam Polyak & Lior Wolf
Facebook AI Research
Tel-Aviv, Israel"
1e5c6c9fa9ba089931cfb2bc81e4368a4db5dd2d,Multi- View Fusion for Action Recognition in Child-Robot Interaction,"978-1-4799-7061-2/18/$31.00 ©2018 IEEE
ICIP 2018
#2Kinect #1Kinect #3Multi-view action recognition systemSenseActDecisionSpeakRec.ActionActFig.1:Multi-viewactionrecognitionsystemforchild-robotinteraction.presentspontaneousbehaviorandaninformalwayofcommunica-tion.Inaddition,thesameactionscanbeperformedinavarietyofwaysandawidespectrum,furthercomplicatingtherecognitionofactions.Althoughhumanactionrecognitionisapopularproblemwithmanyproposedmethods[8–13],therequirementsofmulti-viewac-tionrecognitiondiffersignificantlyasithastotakeintoaccountbothactionrecognitionthatresultsfromsingleviewsandalsothefusionamongtheresultinginformationfromthedifferentstreams[14,15].Incross-viewactionrecognitionworksitisattemptedtoshareknowledgefortheactionamongthedifferentsetupviews.Forexample,in[16]aspecificviewistreatedasthetargetdomainandtheotherviewsassourcedomainsinordertoformulateacross-viewlearningframework.Inotherapproaches,theknowledgeofactionsistransferredfromthedifferentviewsinasinglecanoni-calview[17].In[18]itisproposedtolearnview-invariantfeaturesrobusttoviewvariationsusingdeepmodels.Inthefieldofmulti-viewactionrecognition,anewglobalrepresentationthatiscalledmulti-viewsupervectorhasalsobeenproposedinordertoenhancerecognitionperformance[19].Finally,anotherinterestingapproachispresentedin[20]whereitisattemptedtotransferthelow-levelfeaturesintoahigh-levelsemanticspaceandamulti-tasklearningapproachforjointactionmodelingisexamined.Inthispaperwedevelopamulti-viewactionrecognitionsystemsuitableforCRI.Themaincontributionsofthispapercanbesum-marizedasfollows:1)Single-viewmethodsareexploredinordertocreaterobustactionrecognitionmodelsforparticularusers,i.e.children,underdifficulttaskswithfewtrainingdata.2)Methodsforthefusionofinformationfromdifferentstreamsinamulti-viewsys-temareproposedtoenhanceactionrecognitionduringCRI.3)Themulti-viewactionrecognitionsystemisintegratedinroboticplat-"
1e146982a7b088e7a3790d2683484944c3b9dcf7,Video Person Re-Identification for Wide Area Tracking based on Recurrent Neural Networks,"Video Person Re-Identification for Wide Area Tracking based on
Recurrent Neural Networks
McLaughlin, N., Martinez del Rincon, J., & Miller, P. (2017). Video Person Re-Identification for Wide Area
Tracking based on Recurrent Neural Networks. IEEE Transactions on Circuits and Systems for Video
Technology. https://doi.org/10.1109/TCSVT.2017.2736599
Published in:
IEEE Transactions on Circuits and Systems for Video Technology
Document Version:
Peer reviewed version
Queen's University Belfast - Research Portal:
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Take down policy
The Research Portal is Queen's institutional repository that provides access to Queen's research output. Every effort has been made to"
1ee3b4ba04e54bfbacba94d54bf8d05fd202931d,Celebrity Face Recognition using Deep Learning,"Indonesian Journal of Electrical Engineering and Computer Science
Vol. 12, No. 2, November 2018, pp. 476~481
ISSN: 2502-4752, DOI: 10.11591/ijeecs.v12.i2.pp476-481
      476
Celebrity Face Recognition using Deep Learning
Nur Ateqah Binti Mat Kasim1, Nur Hidayah Binti Abd Rahman2, Zaidah Ibrahim3,
Nur Nabilah Abu Mangshor4
,2,3Faculty of Computer and Mathematical Sciences, UniversitiTeknologi MARA (UiTM),
Faculty of Computer and Mathematical Sciences, UniversitiTeknologi MARA (UiTM),
Shah Alam, Selangor, Malaysia
Campus Jasin, Melaka, Malaysia
Article Info
Article history:
Received May 29, 2018
Revised Jul 30, 2018
Accepted Aug 3, 2018
Keywords:
AlexNet
Convolutional neural network
Deep learning"
1e2d965df330a72b3426279f9327f77330c2ee64,Simultaneous Detection and Segmentation of Pedestrians using Top-down and Bottom-up Processing,"Simultaneous Detection and Segmentation of Pedestrians
using Top-down and Bottom-up Processing ∗
Vinay Sharma
James W. Davis
Dept. of Computer Science and Engineering
Ohio State University
Columbus OH 43210 USA"
1e8a265ec741584e851b83b5efc00351048bbe3f,Real Time Human Detection and Localization Using Consumer Grade Camera and Commercial UAV,"Preprints (www.preprints.org)  |  NOT PEER-REVIEWED  |  Posted: 7 November 2018                   doi:10.20944/preprints201811.0156.v1
Article
Real Time Human Detection and Localization Using
Consumer Grade Camera and Commercial UAV
Nemi Bhattarai 1,*, Tai Nakamura 1 and Chitrini Mozumder 1,*
Remote Sensing and Geographic Information Systems, School of Engineering and Technology, Asian
Institute of Technology, Thailand; (T.N.)
*  Correspondence: (N.B); (C.M); Tel.: +66-099-421-7492"
1eec03527703114d15e98ef9e55bee5d6eeba736,Automatic identification of persons in TV series S UBMITTED,"UNIVERSITÄT KARLSRUHE (TH)
FAKULTÄT FÜR INFORMATIK
INTERACTIVE SYSTEMS LABS
Prof. Dr. A. Waibel
DIPLOMA THESIS
Automatic identification
of persons in TV series
SUBMITTED BY
Mika Fischer
MAY 2008
ADVISORS
M.Sc. Hazım Kemal Ekenel
Dr.-Ing. Rainer Stiefelhagen"
1e4c717a8a5eed5c3385b77641ebe3d8c4ceb3ac,An efficient algorithm for maximal margin clustering,"J Glob Optim
DOI 10.1007/s10898-011-9691-4
An efficient algorithm for maximal margin clustering
Jiming Peng · Lopamudra Mukherjee · Vikas Singh ·
Dale Schuurmans · Linli Xu
Received: 29 April 2009 / Accepted: 5 February 2011
© Springer Science+Business Media, LLC. 2011"
1ef1f33c48bc159881c5c8536cbbd533d31b0e9a,Identity-based Adversarial Training of Deep CNNs for Facial Action Unit Recognition,"Z. ZHANG ET AL.: ADVERSARIAL TRAINING FOR ACTION UNIT RECOGNITION
Identity-based Adversarial Training of Deep
CNNs for Facial Action Unit Recognition
Zheng Zhang
Shuangfei Zhai
Lijun Yin
Department of Computer Science
State University of New York at
Binghamton
NY, USA."
1e1e66783f51a206509b0a427e68b3f6e40a27c8,Semi-supervised estimation of perceived age from face images,"SEMI-SUPERVISED ESTIMATION OF PERCEIVED AGE
FROM FACE IMAGES
VALWAY Technology Center, NEC Soft, Ltd., Tokyo, Japan
Kazuya Ueki
Masashi Sugiyama
Keywords:"
1ebf201b34d9687fa17e336a608ab43e466ca13f,Detecting Parts for Action Localization.,"Nicolas Chesneau
Grégory Rogez
Karteek Alahari
Cordelia Schmid
CHESNEAU ET AL.: DETECTING PARTS FOR ACTION LOCALIZATION
Detecting Parts for Action Localization
Inria∗"
1eadafc27372b33a73eca062438a58d4280fd3a1,DeepSkeleton: Learning Multi-Task Scale-Associated Deep Side Outputs for Object Skeleton Extraction in Natural Images,"DeepSkeleton: Learning Multi-task Scale-associated
Deep Side Outputs for Object Skeleton Extraction
in Natural Images
Wei Shen, Kai Zhao, Yuan Jiang, Yan Wang, Xiang Bai and Alan Yuille"
09dbdc05f0f093ed71f6f29abbc516c58c75ad2a,Zero Shot Hashing,"Zero Shot Hashing
Shubham Pachori
Electrical Engineering
Shanmuganathan Raman
Electrical Engineering &
Indian Institute of Technology Gandhinagar
Computer Science and Engineering
Gandhinagar, Gujarat 382355
Indian Institute of Technology Gandhinagar
Email: shubham
Gandhinagar, Gujarat 382355
Email:"
096e68f8d632f4363056d54a7de9c59d66b806d8,Impaired visuocortical discrimination learning of socially conditioned stimuli in social anxiety.,"Impaired Visuocortical Discrimination Learning of Socially
Conditioned Stimuli in Social Anxiety
Lea M. Ahrens1, Andreas Mühlberger2, Paul Pauli1, & Matthias J. Wieser1
Department of Psychology I, University of Würzburg, Germany
Department of Clinical Psychology and Psychotherapy, University of Regensburg, Germany
Address for correspondence:
Lea M. Ahrens, University of Würzburg, Department of Psychology, Biological Psychology, Clinical
Psychology, and Psychotherapy, Marcusstr. 9-11, D-97070 Würzburg, Phone.: +49 931 31-81929,
Fax: +49 931 31-82733,
Running title:
Social Conditioning in Social Anxiety
Words: 4995 (+ 8 place marker)
© The Author (2014). Published by Oxford University Press. For Permissions, please email:"
09750c9bbb074bbc4eb66586b20822d1812cdb20,Estimation of the neutral face shape using Gaussian Mixture Models,"978-1-4673-0046-9/12/$26.00 ©2012 IEEE
ICASSP 2012"
09a6261c3334471bb0bc1a173aff672afe963ae3,Key-Pose Prediction in Cyclic Human Motion,"Key-Pose Prediction in Cyclic Human Motion
Multimedia Computing and Computer Vision Lab, University of Augsburg
Dan Zecha
Rainer Lienhart"
09251a324dc4865732e2ead50334bfb906f8ffb4,Beyond Text based sentiment analysis : Towards multi-modal systems,"Springer Cognitive Computation manuscript No.
(will be inserted by the editor)
Beyond Text based sentiment analysis: Towards multi-modal
systems
Soujanya Poria · Amir Hussain · Erik Cambria
the date of receipt and acceptance should be inserted later"
09137e3c267a3414314d1e7e4b0e3a4cae801f45,Two Birds with One Stone: Transforming and Generating Facial Images with Iterative GAN,"Noname manuscript No.
(will be inserted by the editor)
Two Birds with One Stone: Transforming and Generating
Facial Images with Iterative GAN
Dan Ma · Bin Liu · Zhao Kang · Jiayu Zhou · Jianke Zhu · Zenglin Xu
Received: date / Accepted: date"
09ba6b87736fa29aae88c5b4cf30f25188e4c6ef,Gaze Estimation in the 3 D Space Using RGB-D sensors Towards Head-Pose And User Invariance,"International Journal of Computer Vision (Accepted Manuscript)
The final publication is available at Springer via http://dx.doi.org/10.1007/s11263-015-0863-4
Gaze Estimation in the 3D Space Using RGB-D sensors
Towards Head-Pose And User Invariance
Kenneth A. Funes-Mora · Jean-Marc Odobez
Received: 19 November 2014 / Accepted: 23 September 2015"
09a99ca583b0eff0a34de32b4eed23d6d8ff14c2,Domain-Independent Captioning of Domain-Specific Images,"Proceedings of the NAACL HLT 2013 Student Research Workshop, pages 69–76,
Atlanta, Georgia, 13 June 2013. c(cid:13)2013 Association for Computational Linguistics"
0965a62c9c354d2c7175e313ade9e38120f1bd4e,Efficient Face Detection Method using Modified Hausdorff Distance Method with C 4 . 5 Classifier and Canny Edge Detection,"International Journal of Computer Applications (0975 – 8887)
Volume 123 – No.10, August 2015
Efficient Face Detection Method using Modified
Hausdorff Distance Method with C4.5 Classifier and
Canny Edge Detection
Neelima Singh
Research Scholar
Computer Science and
Engineering Department
Samrat Ashok Technological
Institute, Vidisha, M. P.
Satish Pawar
Assistant Professor
Computer Science and
Engineering Department
Samrat Ashok Technological
Institute, Vidisha, M. P.
Yogendra Kumar Jain
Head of Department
Computer Science and"
09d78009687bec46e70efcf39d4612822e61cb8c,Consistent Re-identification in a Camera Network,"Consistent Re-identification in a Camera
Network
Abir Das(cid:2), Anirban Chakraborty(cid:2), and Amit K. Roy-Chowdhury(cid:2)(cid:2)
Dept. of Electrical Engineering, University of California, Riverside, CA 92521, USA"
0994916f67fd15687dd5d7e414becb1cd77129ac,Multi Class Different Problem Solving Using Intelligent Algorithm,"SIVAKUMAR R, Dr.M.SRIDHAR / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622   www.ijera.com
Vol. 2, Issue4, July-August 2012, pp.1782-1785
Multi Class Different Problem Solving Using Intelligent
Algorithm
SIVAKUMAR R, 2Dr.M.SRIDHAR
Research Scholar Dept of ECE BHARATH UNIVERSITY India
Dept of ECE BHARATH UNIVERSITY India"
09d9d9d153119558e83643f0097ffb87e1037649,Face Recognition and Verification Using Artificial Neural Network,"©2010 International Journal of Computer Applications (0975 – 8887)
Volume 1 – No. 14
Face Recognition and Verification
Using Artificial Neural Network
Ms. S. S.Ranawade
Maharashtra Institute Technology, Pune 05
/  nonface
images.  We  solve"
09e15bb266da86d0a9525d2a94ac0b38f0b53b88,Detect What You Can: Detecting and Representing Objects Using Holistic Models and Body Parts,"Detect What You Can: Detecting and Representing Objects using Holistic
Models and Body Parts
Xianjie Chen1, Roozbeh Mottaghi2, Xiaobai Liu1, Sanja Fidler3, Raquel Urtasun3, Alan Yuille1
University of California, Los Angeles 2Stanford University 3University of Toronto"
09222c50d8ffcc74bbb7462400bd021772850bba,Incorporating Network Built-in Priors in Weakly-Supervised Semantic Segmentation,"Incorporating Network Built-in Priors in
Weakly-supervised Semantic Segmentation
Fatemeh Sadat Saleh, Mohammad Sadegh Aliakbarian, Mathieu Salzmann, Lars Petersson,
Jose M. Alvarez, and Stephen Gould"
09eaa332ddcd036b0f0950bbdb3624072f105a3b,When appearance does not match accent: neural correlates of ethnicity-related expectancy violations,"Social Cognitive and Affective Neuroscience, 2017, 507–515
doi: 10.1093/scan/nsw148
Advance Access Publication Date: 19 October 2016
Original article
When appearance does not match accent: neural
orrelates of ethnicity-related expectancy violations
Karolina Hansen,1 Melanie C. Steffens,2 Tamara Rakic,3 and Holger Wiese4
University of Warsaw, Warsaw, Poland, 2University of Koblenz-Landau, Landau, Germany, 3Lancaster
University, Lancaster, UK, and 4Durham University, Durham, UK
Correspondence should be addressed to Karolina Hansen, Faculty of Psychology, University of Warsaw, Stawki 5/7, 00-183 Warszawa, Poland.
E-mail:"
0917de8a3be50f2a813e7b77fc53b81125a58acb,Video based head detection and tracking surveillance system,978-1-4673-0024-7/10/$26.00 ©2012 IEEE                                 2832
09d08e543a9b2fc350cb37e47eb087935c12be16,"A Multimodal, Full-Surround Vehicular Testbed for Naturalistic Studies and Benchmarking: Design, Calibration and Deployment.","A Multimodal, Full-Surround Vehicular Testbed for Naturalistic Studies
nd Benchmarking: Design, Calibration and Deployment
Akshay Rangesh1, Kevan Yuen1, Ravi Kumar Satzoda1, Rakesh Nattoji Rajaram1,
Pujitha Gunaratne2, and Mohan M. Trivedi1
Laboratory for Intelligent and Safe Automobiles (LISA), UC San Diego
Toyota Collaborative Safety Research Center (CSRC)
in autonomous"
092597b8e0f31be1671025cea1b9fd28a48e04bc,Supervised Person Re-ID based on Deep Hand-crafted and CNN Features,
09e3967a34cca8dc0f00c9ee7a476a96812a55e0,1 Machine Learning Methods for Social Signal Processing,"Machine Learning Methods for
Social Signal Processing
Ognjen Rudovic, Mihalis A. Nicolaou and Vladimir Pavlovic
Introduction
In this chapter we focus on systematization, analysis, and discussion of recent
trends in machine learning methods for Social signal processing (SSP)(Pentland
007). Because social signaling is often of central importance to subconscious de-
ision making that affects everyday tasks (e.g., decisions about risks and rewards,
resource utilization, or interpersonal relationships) the need for automated un-
derstanding of social signals by computers is a task of paramount importance.
Machine learning has played a prominent role in the advancement of SSP over
the past decade. This is, in part, due to the exponential increase of data avail-
bility that served as a catalyst for the adoption of a new data-driven direction in
ffective computing. With the dif‌f‌iculty of exact modeling of latent and complex
physical processes that underpin social signals, the data has long emerged as the
means to circumvent or supplement expert- or physics-based models, such as the
deformable musculo-sceletal models of the human body, face or hands and its
movement, neuro-dynamical models of cognitive perception, or the models of the
human vocal production. This trend parallels the role and success of machine
learning in related areas, such as computer vision, c.f., (Poppe 2010, Wright"
093b6af0e5f00f9578088a49822d8d500283cab0,Human visual behaviour for collaborative human-machine interaction,"Human Visual Behaviour for
Collaborative Human-Machine
Interaction
Andreas Bulling
Perceptual User Interfaces
Group
Max Planck Institute for
Informatics
Saarbr¨ucken, Germany
Permission to make digital or hard copies of all or part of this work for
personal or classroom use is granted without fee provided that copies are not
made or distributed for profit or commercial advantage and that copies bear
this notice and the full citation on the first page. Copyrights for components"
09f853ce12f7361c4b50c494df7ce3b9fad1d221,Random Forests for Real Time 3D Face Analysis,"myjournal manuscript No.
(will be inserted by the editor)
Random forests for real time 3D face analysis
Gabriele Fanelli · Matthias Dantone · Juergen Gall · Andrea Fossati ·
Luc Van Gool
Received: date / Accepted: date"
09da5ae17cf1bf382f69036a96ec953c18f676d4,Person Re-Identification using Deep Foreground Appearance Modelling,"Original citation:
Watson, Gregory and Bhalerao, Abhir (2018) Person reidentification using deep foreground
ppearance modeling.Journal of Electronic Imaging, 27 (05).
051215. doi:10.1117/1.jei.27.5.051215
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Watson, Gregory and Bhalerao, Abhir Person reidentification using deep foreground"
09e64d9e0654fe5d472ef73b479364d31ab362f6,Automated Classification of Female Facial Beauty Using Learning Algorithms,"Automated Classification of Female Facial Beauty Using Learning Algorithms
Hatice Gunes, Massimo Piccardi, Tony Jan
Computer Vision Group -Faculty of Information Technology
University of Technology, Sydney (UTS)
PO Box 123 Broadway 2007 NSW - Australia
e-mail:"
09f4e1064afffd8464e9fd558fc8ef7be5e33170,Spatial and Temporal Organization of the Individual Human Cerebellum,"Article
Spatial and Temporal Organization of the Individual
Human Cerebellum"
095ccb4e2e0f3934dc1aa51c685b2f54c8a6e588,Derivate-based Component-Trees for Multi-Channel Image Segmentation,"Derivate-based Component-Trees for
Multi-Channel Image Segmentation
Dominik Gutermuth+∗
Tobias B¨ottger+∗
+MVTec Software GmbH, Munich, Germany
Technical University of Munich (TUM)
April 20, 2018"
09df62fd17d3d833ea6b5a52a232fc052d4da3f5,Mejora de Contraste y Compensación en Cambios de la Iluminación,"ISSN: 1405-5546
Instituto Politécnico Nacional
México
Rivas Araiza, Edgar A.; Mendiola Santibañez, Jorge D.; Herrera Ruiz, Gilberto; González Gutiérrez,
Carlos A.; Trejo Perea, Mario; Ríos Moreno, G. J.
Mejora de Contraste y Compensación en Cambios de la Iluminación
Instituto Politécnico Nacional
Distrito Federal, México
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Sistema de Información Científica
Red de Revistas Científicas de América Latina, el Caribe, España y Portugal
Proyecto académico sin fines de lucro, desarrollado bajo la iniciativa de acceso abierto"
09ac8added26307b358b83884b55af29de8b5bf9,Learning to grasp objects with multiple contact points,"Learning to grasp objects with multiple contact points
Quoc V. Le, David Kamm, Arda F. Kara, Andrew Y. Ng"
092f955f701b31f3e58adb57c57e39a4dcab9fcd,Weighted Additive Criterion for Linear Dimension Reduction,"Seventh IEEE International Conference on Data Mining
Seventh IEEE International Conference on Data Mining
Seventh IEEE International Conference on Data Mining
Seventh IEEE International Conference on Data Mining
Seventh IEEE International Conference on Data Mining
Weighted Additive Criterion for Linear Dimension Reduction
Jing Peng & Stefan Robila
Computer Science Department, Montclair State University
Montclair, NJ 07043"
09879f7956dddc2a9328f5c1472feeb8402bcbcf,Density estimation using Real NVP,"Published as a conference paper at ICLR 2017
DENSITY ESTIMATION USING REAL NVP
Laurent Dinh∗
Montreal Institute for Learning Algorithms
University of Montreal
Montreal, QC H3T1J4
Jascha Sohl-Dickstein
Google Brain
Samy Bengio
Google Brain"
092b64ce89a7ec652da935758f5c6d59499cde6e,Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments,"Human3.6M:
Large Scale Datasets and Predictive Methods
for 3D Human Sensing in Natural Environments
Catalin Ionescu∗†‡, Dragos Papava∗‡, Vlad Olaru∗, Cristian Sminchisescu§∗"
098388c08ef7d23ab583819b793b0057c0396dc8,Low Rank Approximation using Error Correcting Coding Matrices,"Low Rank Approximation using Error Correcting Coding Matrices
Shashanka Ubaru
Arya Mazumdar
Yousef Saad
University of Minnesota-Twin Cities, MN USA"
09b0040ad09d61f3403c57c437c03271f8614add,HUMAN ACTIVITY RECOGNITION AND GYMNASTICS ANALYSIS THROUGH DEPTH IMAGERY by,"HUMAN ACTIVITY RECOGNITION AND
GYMNASTICS ANALYSIS THROUGH
DEPTH IMAGERY
Brian J. Reily"
09fbfb566a8f2af9df4d3a1bf5df00d0693a22eb,Conformal Prediction for Automatic Face Recognition,"Proceedings of Machine Learning Research 60:1–20, 2017 Conformal and Probabilistic Prediction and Applications
Conformal Prediction for Automatic Face Recognition
Charalambos Eliades
Harris Papadopoulos
Computer Science and Engineering Department, Frederick University,
7 Y. Frederickou St., Palouriotisa, Nicosia 1036, Cyprus
Editor: Alex Gammerman, Vladimir Vovk, Zhiyuan Luo, and Harris Papadopoulos"
09d954e980133014cb4dfe3f6b1444edaa099c97,Real-time self-adaptive deep stereo,"Real-time self-adaptive deep stereo
Alessio Tonioni, Fabio Tosi, Matteo Poggi, Stefano Mattoccia and Luigi Di Stefano
Department of Computer Science and Engineering (DISI)
University of Bologna, Bologna, Italy"
09c019141b209401b76a35184c86bab6cd1fe6b9,3D Deformable Shape Reconstruction with Diffusion Maps,"TAO, MATUSZEWSKI: 3D RECONSTRUCTION WITH DIFFUSION MAPS
D Deformable Shape Reconstruction with
Diffusion Maps
Lili Tao
Bogdan J. Matuszewski
Applied Digital Signal and Image
Processing Research Centre
University of Central Lancashire, UK"
091b4ad74ac5bec206604673506b19838d6a0c52,Person Re-identification by Saliency Learning,"|| Volume 2 ||Issue 10 ||MAY 2017||ISSN (Online) 2456-0774
INTERNATIONAL JOURNAL OF ADVANCE SCIENTIFIC RESEARCH
AND ENGINEERING TRENDS
Person Re-Identification By Saliency Learning
Shaihenila
P.G. Student, Computer Science & Engineering, Everest Educational Society's Group of Institutions, Aurangabad, India."
097f674aa9e91135151c480734dda54af5bc4240,Face Recognition Based on Multiple Region Features,"Proc. VIIth Digital Image Computing: Techniques and Applications, Sun C., Talbot H., Ourselin S. and Adriaansen T. (Eds.), 10-12 Dec. 2003, Sydney
Face Recognition Based on Multiple Region Features
Jiaming Li, Geoff  Poulton, Ying Guo,  Rong-Yu Qiao
CSIRO Telecommunications & Industrial Physics
Australia
Tel: 612 9372 4104, Fax: 612 9372 4411, Email:"
09749e7b0ae6bd9ab37671fcc4f0e7a7bcf9ff2e,Perceptual enhancement of emotional mocap head motion: An experimental study,"Perceptual Enhancement of Emotional Mocap Head Motion: An Experimental
Study
Yu Ding
Univeristy of Houston
Houston, TX, USA
Lei Shi
Univeristy of Houston
Houston, TX, USA
Zhigang Deng
Univeristy of Houston
Houston, TX, USA"
0949f46d5db3169813ae23acafa345c6b8a37f08,When Slower Is Faster: On Heterogeneous Multicores for Reliable Systems,"When Slower is Faster: On Heterogeneous Multicores for Reliable Systems
Tomas Hruby
The Network Institute, VU University Amsterdam
Herbert Bos
Andrew S. Tanenbaum"
0956a3c628959afcf870f5d7ec581160a4aa5221,LIFEisGAME Prototype: A Serious Game about Emotions for Children with Autism Spectrum Disorders,"Volume 11, Number 3, 191 – 211
LIFEisGAME Prototype: A Serious Game about Emotions
for Children with Autism Spectrum Disorders
Samanta Alves1, António Marques2, Cristina Queirós∗1 and Verónica Orvalho3
Psychosocial
Rehabilitation
Laboratory, Faculty of
Psychology and
Educational Sciences,
Porto University
(Portugal)
Psychosocial
Rehabilitation
Laboratory, School of
Allied Health Sciences,
Porto Polytechnic
Institute
(Portugal)
Porto
Interactive"
0971a5e835f365b6008177a867cfe4bae76841a5,Supervised Dictionary Learning by a Variational Bayesian Group Sparse Nonnegative Matrix Factorization,"Supervised Dictionary Learning by a
Variational Bayesian Group Sparse
Nonnegative Matrix Factorization
Ivan Ivek"
0910a4c470a410fac446f4026f7c8ef512ae7427,Hierarchical Question-Image Co-Attention for Visual Question Answering,"Hierarchical Question-Image Co-Attention
for Visual Question Answering
Jiasen Lu∗, Jianwei Yang∗, Dhruv Batra∗† , Devi Parikh∗†
Virginia Tech, † Georgia Institute of Technology
{jiasenlu, jw2yang, dbatra,"
09926ed62511c340f4540b5bc53cf2480e8063f8,Tubelet Detector for Spatio-Temporal Action Localization,"Action Tubelet Detector for Spatio-Temporal Action Localization
Vicky Kalogeiton1,2
Philippe Weinzaepfel3
Vittorio Ferrari2
Cordelia Schmid1"
092d5bc60a21933abf98aa85ace8a9c85df16958,Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments,"Implementing Randomized Matrix Algorithms in Parallel and
Distributed Environments
Jiyan Yang ∗
Xiangrui Meng †
Michael W. Mahoney ‡"
092a02ed126f8151c03e15716b8c27d73533358b,MAD-Bayes: MAP-based Asymptotic Derivations from Bayes,"MAD-Bayes: MAP-based Asymptotic Derivations from Bayes
Tamara Broderick
UC Berkeley, Statistics Department
Brian Kulis
Ohio State University, CSE Department
Michael I. Jordan
UC Berkeley, Statistics Department and EECS Department"
09edf114f8764c82713f8dd35b1b32ad83ecaa17,Large-Margin Learning of Compact Binary Image Encodings,"MANUSCRIPT
Large-margin Learning of Compact Binary Image
Encodings
Sakrapee Paisitkriangkrai, Chunhua Shen, Anton van den Hengel"
094f5e36dae2602e179f2c1d95a616df3dbe967f,Bilinear classifiers for visual recognition,"Bilinear classifiers for visual recognition
Hamed Pirsiavash
Deva Ramanan
Charless Fowlkes
Department of Computer Science
University of California at Irvine"
09cf3d036f84b6b3ac5244a2ecf8ac74f69de5d3,Integrating Graph Partitioning and Matching for Trajectory Analysis in Video Surveillance,"Integrating Graph Partitioning and Matching for
Trajectory Analysis in Video Surveillance
Liang Lin, Yongyi Lu, Yan Pan, Xiaowu Chen"
09e5f2f819a21162d833f356670a140cd555a740,Adaptive Algorithm and Platform Selection for Visual Detection and Tracking,"Adaptive Algorithm and Platform Selection for
Visual Detection and Tracking
Shu Zhang, Qi Zhu, and Amit K. Roy-Chowdhury"
09556420a7441bb5259fd6dc68af340f6ac15ade,Gradient boosting for distributional regression: faster tuning and improved variable selection via noncyclical updates,"Stat Comput
DOI 10.1007/s11222-017-9754-6
Gradient boosting for distributional regression: faster tuning and
improved variable selection via noncyclical updates
Janek Thomas1
Adam Smith4 · Benjamin Hofner5
· Andreas Mayr2,3 · Bernd Bischl1 · Matthias Schmid3 ·
Received: 3 November 2016 / Accepted: 5 May 2017
© Springer Science+Business Media New York 2017"
096eb8b4b977aaf274c271058feff14c99d46af3,Multi-observation visual recognition via joint dynamic sparse representation,"REPORT DOCUMENTATION PAGE
Form Approved OMB NO. 0704-0188
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instructions,
The  public  reporting  burden
Send  comments
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Headquarters  Services,  Directorate"
0930af7c2d6f02b5e81c2003f76451aaff957a65,A Comprehensive Analysis of Deep Regression,"DRAFT
A Comprehensive Analysis of Deep Regression
St´ephane Lathuili`ere, Pablo Mesejo, Xavier Alameda-Pineda, Member IEEE, and Radu Horaud"
17d84ca10607442a405f3c4c8b4572bdd79801c2,Expression robust 3D face recognition via mesh-based histograms of multiple order surface differential quantities,"EXPRESSION ROBUST 3D FACE RECOGNITION VIA MESH-BASED HISTOGRAMS OF
MULTIPLE ORDER SURFACE DIFFERENTIAL QUANTITIES
Huibin Li1,2, Di Huang1,2, Pierre Lemaire1,2, Jean-Marie Morvan1,3,4, Liming Chen1,2
Universit´e de Lyon, CNRS
Ecole Centrale de Lyon, LIRIS UMR5205, F-69134, Lyon, France
Universit´e Lyon 1, Institut Camille Jordan,
3 blvd du 11 Novembre 1918, F-69622 Villeurbanne - Cedex, France
King Abdullah University of Science and Technology, GMSV Research Center,
Bldg 1, Thuwal 23955-6900, Saudi Arabia"
171d7762137725839fe5292901fe90d91b74811d,SLAM Algorithm by using Global Appearance of Omnidirectional Images,
176bc3f528d3d87a7543e377f9b7e4c04b5a9408,Towards Estimating the Upper Bound of Visual-Speech Recognition: The Visual Lip-Reading Feasibility Database,"Towards Estimating the Upper Bound of Visual-Speech Recognition:
The Visual Lip-Reading Feasibility Database
Adriana Fernandez-Lopez, Oriol Martinez and Federico M. Sukno
Department of Information and Communication Technologies, Pompeu Fabra University, Barcelona, Spain"
173657da03e3249f4e47457d360ab83b3cefbe63,HKU-Face : A Large Scale Dataset for Deep Face Recognition Final Report,"HKU-Face: A Large Scale Dataset for
Deep Face Recognition
Final Report
Haicheng Wang
035140108
COMP4801 Final Year Project
Project Code: 17007"
177cbeb83c3a0868b9a5c75cd74edf4b972cba80,Exact Primitives for Time Series Data Mining,"UNIVERSITY OF CALIFORNIA
RIVERSIDE
Exact Primitives for Time Series Data Mining
A Dissertation submitted in partial satisfaction
of the requirements for the degree of
Doctor of Philosophy
Computer Science
Abdullah Al Mueen
March 2012
Dissertation Committee:
Dr. Eamonn Keogh, Chairperson
Dr. Vassilis Tsotras
Dr. Stefano Lonardi"
17e769ef3d86e74c21f2616c7f7a6f20a4e2fbaa,Bag of Machine Learning Concepts for Visual Concept Recognition in Images,"Bag of Machine Learning Concepts for
Visual Concept Recognition in Images
vorgelegt vom
Diplom-Mathematiker
Alexander Binder
us Berlin
von der Fakult¨at IV – Elektrotechnik und Informatik
der Technischen Universit¨at Berlin
zur Erlangung des akademischen Grades
Doktor der Naturwissenschaften
– Dr. rer. nat. –
genehmigte Dissertation
Promotionsausschuss:
Vorsitzender:
. Gutachter:
. Gutachter:
. Gutachter:
Prof. Dr. Olaf Hellwich
Prof. Dr. Klaus-Robert M¨uller
Prof. Dr. Volker Tresp"
174930cac7174257515a189cd3ecfdd80ee7dd54,Multi-view Face Detection Using Deep Convolutional Neural Networks,"Multi-view Face Detection Using Deep Convolutional
Neural Networks
Sachin Sudhakar Farfade
Yahoo
Mohammad Saberian
inc.com
Yahoo
Li-Jia Li
Yahoo"
17c5db2190e25a66ab7f4067f7f7b72893d01d3d,From the Lab to the Real World: Re-identification in an Airport Camera Network,"Real-World Re-Identification in an Airport Camera Network
Yang Li
Rensselaer Polytechnic
Institute, Troy, NY
Ziyan Wu
Rensselaer Polytechnic
Institute, Troy, NY
Srikrishna Karanam
Rensselaer Polytechnic
Institute, Troy, NY
Richard J. Radke
Rensselaer Polytechnic
Institute, Troy, NY"
1701ee9e9518a055e82e79f6425645c4797c19de,Supervised Hashing Using Graph Cuts and Boosted Decision Trees,"APPEARING IN IEEE TRANS. PATTERN ANALYSIS AND MACHINE INTELLIGENCE, FEB. 2015
Supervised Hashing Using Graph Cuts and
Boosted Decision Trees
Guosheng Lin, Chunhua Shen, Anton van den Hengel"
174b6d661b96840e27cd9435c2dbb8e538b2c8a6,Progressive Representation Adaptation for Weakly Supervised Object Localization,"Progressive Representation Adaptation for
Weakly Supervised Object Localization
Dong Li, Jia-Bin Huang, Yali Li, Shengjin Wang(cid:63) and Ming-Hsuan Yang"
17a995680482183f3463d2e01dd4c113ebb31608,Structured Label Inference for Visual Understanding,"IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. X, NO. Y, MONTH Z
Structured Label Inference for
Visual Understanding
Nelson Nauata, Hexiang Hu, Guang-Tong Zhou, Zhiwei Deng,
Zicheng Liao and Greg Mori"
17dea513763c57dcd0e62085045fb5be6770c600,"Dynamic thread mapping for high-performance, power-efficient heterogeneous many-core systems","Summary: Dynamic Thread Mapping for High-Performance, Power-Efficient
Heterogeneous Many-core Systems
Guangshuo Liu, Jinpyo Park, Diana Marculescu
I. OVERVIEW
throughput
for  maximizing
This  paper  investigates  about  the  problem  of  dynamic  thread
mapping  in  heterogeneous  many-core  systems  via  an  efficient
lgorithm that maximizes performance under power constraints.
The  approach  is  to  formulate  the  mapping  problem  as  a  0-1
integer  linear  program  (ILP),  given  any  numbers  of  threads,
ores  and  type  of  cores.  An  iterative  O(n2/m)  heuristic-based
lgorithm  for  solving  the  0-1  ILP  thread  mapping  is  proposed,
thereby providing, a novel scalable approach for effective thread
mapping
on  many-core
heterogeneous systems.
The paper considers multi-threaded workloads and assumes that
each  core  runs  at  most  one  thread  at  a  time  thereby  supporting
single  threaded  execution,  without  simultaneous  multithreading"
177c48590469c62d430cf74fee7b5bd28bfbbc1d,Articulated Motion Learning via Visual and Lingual Signals,"Learning Articulated Motion Models from Visual and Lingual Signals
Zhengyang Wu
Georgia Tech
Atlanta, GA 30332
Mohit Bansal
TTI-Chicago
Chicago, IL 60637
Matthew R. Walter
TTI-Chicago
Chicago, IL 60637"
17dc9ca88bb8f36d167af1fe672278c8edd09713,Learning to Update for Object Tracking.,"Learning to Update for Object Tracking
Bi Li, Wenxuan Xie, Wenjun Zeng, Fellow, IEEE, and Wenyu Liu, Senior Member, IEEE"
174cd8e98f17b3f5bda1c8e16cb39e3dec800f74,Multi-scale Context Intertwining for Semantic Segmentation,"Multi-Scale Context Intertwining
for Semantic Segmentation
Di Lin1, Yuanfeng Ji1, Dani Lischinski2, Daniel Cohen-Or1,3, and Hui Huang1(cid:63)
Shenzhen University 2The Hebrew University of Jerusalem 3Tel Aviv University"
17ff59bb388b155f613f7566ba7cd71ec780cdec,Asymmetric Sparse Kernel Approximations for Large-Scale Visual Search,"Asymmetric sparse kernel approximations
for large-scale visual search
Damek Davis
University of California
Los Angeles, CA 90095
Jonathan Balzer
University of California
Los Angeles, CA 90095
Stefano Soatto
University of California
Los Angeles, CA 90095"
1742e6c347037d5d4ccbdf5c7a27dfbf0afedb91,A Unified Framework for Representation-Based Subspace Clustering of Out-of-Sample and Large-Scale Data,"JOURNAL OF LATEX CLASS FILES, VOL. 13, NO. 9, SEPTEMBER 2014
A Unified Framework for Representation-Based
Subspace Clustering of Out-of-Sample and
Large-Scale Data
Xi Peng, Huajin Tang, Member IEEE, Lei Zhang, Member IEEE, Zhang Yi, Senior Member IEEE, Shijie
Xiao Student Member IEEE,"
179ca4195c90e032aa1e052bf6dfea62095e1006,A Review on a Person Cross Domain Re Identification Based Adaptive Ranking Support Vector Machines ( AdaRSVMs ),"International Journal of Engineering Research in Electronic and Communication
Engineering (IJERECE) Vol 3, Issue 3, March 2016
A Review on a Person Cross Domain
Re Identification Based Adaptive Ranking Support
Vector Machines (AdaRSVMs)
[1] V .Hemanth Kumar, [2] Y.Penchalaiah, [3] Pushpalatha
[1] M.Tech Student [DSCE], [2][3] Assistant Professor
Annamacharya Institute of Technology & Sciences (AITS)
[1] [2] [3]"
17a85799c59c13f07d4b4d7cf9d7c7986475d01c,Extending Procrustes Analysis: Building Multi-view 2-D Models from 3-D Human Shape Samples,"ADVERTIMENT.  La  consulta  d’aquesta  tesi  queda  condicionada  a  l’acceptació  de  les  següents
ondicions  d'ús:  La  difusió  d’aquesta  tesi  per  mitjà  del  servei  TDX  (www.tesisenxarxa.net)  ha
estat  autoritzada  pels  titulars  dels  drets  de  propietat  intel·lectual  únicament  per  a  usos  privats
emmarcats en activitats d’investigació i docència. No s’autoritza la seva reproducció amb finalitats
de lucre ni la seva difusió i posada a disposició des d’un lloc aliè al servei TDX. No s’autoritza la
presentació  del  seu  contingut  en  una  finestra  o  marc  aliè  a  TDX  (framing).  Aquesta  reserva  de
drets afecta tant al resum de presentació de la tesi com als seus continguts. En la utilització o cita
de parts de la tesi és obligat indicar el nom de la persona autora.
ADVERTENCIA. La consulta de esta tesis queda condicionada a la aceptación de las siguientes
ondiciones de uso: La difusión de esta tesis por medio del servicio TDR (www.tesisenred.net) ha
sido autorizada por los titulares de los derechos de propiedad intelectual únicamente para usos
privados enmarcados en actividades de investigación y docencia. No se autoriza su reproducción
on finalidades de lucro ni su difusión y puesta a disposición desde un sitio ajeno al servicio TDR.
No se autoriza la presentación de su contenido en una ventana o marco ajeno a TDR (framing).
Esta  reserva  de  derechos  afecta  tanto  al  resumen  de  presentación  de  la  tesis  como  a  sus
ontenidos.  En  la  utilización  o  cita  de  partes  de  la  tesis  es  obligado  indicar  el  nombre  de  la
persona autora.
WARNING.  On  having  consulted  this  thesis  you’re  accepting  the  following  use  conditions:
Spreading  this  thesis  by  the  TDX  (www.tesisenxarxa.net)  service  has  been  authorized  by  the
titular of the intellectual property rights only for private uses placed in investigation and teaching"
17cf838720f7892dbe567129dcf3f7a982e0b56e,Global-Local Face Upsampling Network,"Global-Local Face Upsampling Network
Oncel Tuzel
Yuichi Taguchi
John R. Hershey
Mitsubishi Electric Research Labs (MERL), Cambridge, MA, USA"
17c62bff70eb0919864f111df4930062aded729a,Encoding Spatial Context in Local Image Descriptors,"Universit¨at des Saarlandes
Max-Planck-Institut f¨ur Informatik
Encoding Spatial Context in
Local Image Descriptors
Masterarbeit im Fach Informatik
Master’s Thesis in Computer Science
von / by
Dushyant Mehta
ngefertigt unter der Leitung von / supervised by
Dr. Roland Angst
etreut von / advised by
Dr. Roland Angst
egutachtet von / reviewers
Dr. Roland Angst
Prof. Dr. Joachim Weickert
Saarbr¨ucken, February 28, 2016"
179cab3d5a800f846225117e708e8d7c49754828,Toward Seamless Multiview Scene Analysis From Satellite to Street Level,"Towards seamless multi-view scene analysis
from satellite to street-level
S´ebastien Lef`evre, Devis Tuia, Senior Member, IEEE, Jan D. Wegner, Timoth´ee
Produit, Ahmed Samy Nassar"
17127e17c00dda8d5cbad6ad126a5509ead8b284,Bayesian face recognition using Gabor features,"Bayesian Face Recognition Using Gabor Features
Xiaogang Wang
Department of Information Engineering
The Chinese University if Hong Kong
Shatin, Hong Kong
Xiaoou Tang
Department of Information Engineering
The Chinese University if Hong Kong
Shatin, Hong Kong
(Email:
(Email:"
174ddb6379b91a0e799e9988d0e522a5af18f91d,ChatPainter: Improving Text to Image Generation using Dialogue,"ChatPainter: Improving Text to Image Generation using Dialogue
Shikhar Sharma 1 Dendi Suhubdy 2 3 Vincent Michalski 2 3 1 Samira Ebrahimi Kahou 1 Yoshua Bengio 2 3"
17605bec1ea7a55d87eb07a872858d86b703e9b3,Smile at Me ! Dogs Activate the Temporal Cortex Towards Smiling Human Faces,"ioRxiv preprint first posted online May. 4, 2017;
http://dx.doi.org/10.1101/134080
The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a
CC-BY-NC-ND 4.0 International license
Smile at Me! Dogs Activate the Temporal Cortex
Towards Smiling Human Faces
Laura V. Cuaya1,*, Ra ´ul Hern´andez-P´erez1, and Luis Concha1
Instituto de Neurobiolog´ıa, Universidad Nacional Aut´onoma de M´exico, Quer´etaro, M´exico."
178a82e3a0541fa75c6a11350be5bded133a59fd,BioHDD: a dataset for studying biometric identification on heavily degraded data,"Techset Composition Ltd, Salisbury
{IEE}BMT/Articles/Pagination/BMT20140045.3d
www.ietdl.org
Received on 15th July 2014
Revised on 17th September 2014
Accepted on 23rd September 2014
doi: 10.1049/iet-bmt.2014.0045
ISSN 2047-4938
BioHDD: a dataset for studying biometric
identification on heavily degraded data
Gil Santos1, Paulo T. Fiadeiro2, Hugo Proença1
Department of Computer Science, IT – Instituto de Telecomunicações, University of Beira Interior, Covilhã, Portugal
Department of Physics, Remote Sensing Unit – Optics, Optometry and Vision Sciences Group, University of Beira Interior,
Covilhã, Portugal
E-mail:"
1750db78b7394b8fb6f6f949d68f7c24d28d934f,Detecting Facial Retouching Using Supervised Deep Learning,"Detecting Facial Retouching Using Supervised
Deep Learning
Aparna Bharati, Richa Singh, Senior Member, IEEE, Mayank Vatsa, Senior Member, IEEE, Kevin W.
Bowyer, Fellow, IEEE"
17daa9ddaf524de914e7440157fc0314db171884,Data driven analysis of faces from images,"Data Driven Analysis
of Faces from Images
Dissertation zur Erlangung des Grades „Doktor der Ingenieurwissenschaften (Dr.-Ing.)”
der Naturwissenschaftlich-Technischen Fakultäten der Universität des Saarlandes
Kristina Scherbaum
8.05.2013
Universität des Saarlandes | Max-Planck-Institut für Informatik
Saarbrücken – Germany"
17a9db524ddbeb5577a94924c2a7cca048dd19f9,Object Recognition with Multi-Scale Pyramidal Pooling Networks,"Object Recognition with Multi-Scale Pyramidal
Pooling Networks
Jonathan Masci1, Ueli Meier1, Gabriel Fricout2, and J¨urgen Schmidhuber1
IDSIA – USI – SUPSI, Manno – Lugano, Switzerland,
http://idsia.ch/~masci/
ArcelorMittal, Maizi`eres Research, Measurement and Control Dept., France"
17635e22a73da3ff60a72715b7dd8837de6fee89,The ABBA study – approach bias modification in bulimia nervosa and binge eating disorder: study protocol for a randomised controlled trial,"Brockmeyer et al. Trials  (2016) 17:466
DOI 10.1186/s13063-016-1596-6
ST UD Y P R O T O C O L
Open Access
The ABBA study – approach bias
modification in bulimia nervosa and binge
eating disorder: study protocol for a
randomised controlled trial
Timo Brockmeyer1,2*, Ulrike Schmidt2 and Hans-Christoph Friederich1,3"
17c0094c68d6efd19b80287c51d228fa50750f46,An efficient partial face detection method using AlexNet CNN,"SSRG International Journal of Electronics and Communication Engineering - (ICRTECITA-2017) - Special Issue - March 2017
An efficient partial face detection method using
AlexNet CNN
Prof Mr.Sivalingam.T, S.Kabilan ,
Dhanabal.M ,Arun.R ,Chandrabhagavan.K
V.S.B Engineering College,Karur"
1748867e04ba16673ec5231f6a2ca0ae03835658,Fast Exact Search in Hamming Space With Multi-Index Hashing,"Fast Exact Search in Hamming Space
with Multi-Index Hashing
Mohammad Norouzi, Ali Punjani, David J. Fleet,
{norouzi, alipunjani,"
17db741725b9f8406f69b27a117e99bee1a9a323,Person Re-identification with a Body Orientation-Specific Convolutional Neural Network,"Person Re-identification with a Body
Orientation-Specific Convolutional Neural Network
Yiqiang Chen, Stefan Duffner, Andrei Stoian, Jean-Yves Dufour, Atilla
Baskurt
To cite this version:
Yiqiang Chen, Stefan Duffner, Andrei Stoian, Jean-Yves Dufour, Atilla Baskurt. Person Re-
identification with a Body Orientation-Specific Convolutional Neural Network. Advanced Concepts
for Intelligent Vision systems, Sep 2018, Poitiers, France. <hal-01895374>
HAL Id: hal-01895374
https://hal.archives-ouvertes.fr/hal-01895374
Submitted on 15 Oct 2018
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
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L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,"
17dd242e6d7afb5d7fafcf9f8e8b201573ce4b89,An Extensive Review on Spectral Imaging in Biometric Systems: Challenges and Advancements,"An Extensive Review on Spectral Imaging in Biometric Systems: Challenges &
Advancements
Rumaisah Munira,∗, Rizwan Ahmed Khana,b,∗∗
Faculty of IT, Barrett Hodgson University, Karachi, Pakistan.
LIRIS, Universite Claude Bernard Lyon1, France."
11be33019f591214c8f79dbcb24a50d8f7fa5c95,Salgan 360 : Visual Saliency Prediction on 360 Degree Images with Generative Adversarial Networks,"SALGAN360: VISUAL SALIENCY PREDICTION ON 360 DEGREE IMAGES WITH
GENERATIVE ADVERSARIAL NETWORKS
Fang-Yi Chao, Lu Zhang, Wassim Hamidouche, Olivier Deforges
Univ Rennes, INSA Rennes, CNRS, IETR - UMR 6164, F-35000 Rennes, France
{fang-yi.chao, lu.ge, wassim.hamidouche,"
112cc60d3c6268b1563df4e716904d29a7feb466,Machine Learning for Spatiotemporal Sequence Forecasting: A Survey,"Machine Learning for Spatiotemporal
Sequence Forecasting: A Survey
Xingjian Shi, Dit-Yan Yeung, Senior Member, IEEE"
11691f1e7c9dbcbd6dfd256ba7ac710581552baa,SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos,"SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos
Silvio Giancola, Mohieddine Amine, Tarek Dghaily, Bernard Ghanem
King Abdullah University of Science and Technology (KAUST), Saudi Arabia"
1174b869c325222c3446d616975842e8d2989cf2,CosFace: Large Margin Cosine Loss for Deep Face Recognition,"CosFace: Large Margin Cosine Loss for Deep Face Recognition
Hao Wang, Yitong Wang, Zheng Zhou, Xing Ji, Dihong Gong, Jingchao Zhou,
Zhifeng Li∗, and Wei Liu∗
Tencent AI Lab"
1172ce24f6e9242b9c26c84c6aa89a72ed8203d0,Find your own way: Weakly-supervised segmentation of path proposals for urban autonomy,"Find Your Own Way: Weakly-Supervised Segmentation of Path
Proposals for Urban Autonomy
Dan Barnes, Will Maddern and Ingmar Posner"
1114c2aba97a5782a48341817811df2438d0fdbf,Robust Visual Tracking using Multi-Frame Multi-Feature Joint Modeling,"Robust Visual Tracking using Multi-Frame
Multi-Feature Joint Modeling
Peng Zhang∗, Shujian Yu∗, Student Member, IEEE, Jiamiao Xu, Xinge You†, Senior Member, IEEE,
Xiubao Jiang, Xiao-Yuan Jing, and Dacheng Tao, Fellow, IEEE.
etc. On the other hand,
pplications impedes the usage of overcomplicated models.
the real-time requirement
in real"
11f7f939b6fcce51bdd8f3e5ecbcf5b59a0108f5,Rolling Riemannian Manifolds to Solve the Multi-class Classification Problem,"Rolling Riemannian Manifolds to Solve the Multi-class Classification Problem
Rui Caseiro1, Pedro Martins1, João F. Henriques1, Fátima Silva Leite1,2, and Jorge Batista1
Institute of Systems and Robotics - University of Coimbra, Portugal
Department of Mathematics - University of Coimbra, Portugal ,
{ruicaseiro, pedromartins, henriques,"
11467733103a3e58ae88cb238f620cf6cafd4420,Learning of Graphical Models and Efficient Inference for Object Class Recognition,"Learning of Graphical Models and Ef‌f‌icient
Inference for Object Class Recognition
Martin Bergtholdt, J¨org Kappes, and Christoph Schn¨orr
Computer Vision, Graphics, and Pattern Recognition Group
Department of Mathematics and Computer Science
University of Mannheim, 68131 Mannheim, Germany"
11d36e1687fc2fc3e3cc9d06fedee7b0f8fb79bf,A Deep Structure of Person Re-Identification using Multi-Level Gaussian Models,"> REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) <
A Deep Structure of Person Re-Identification
using Multi-Level Gaussian Models
Dinesh Kumar Vishwakarma, IEEE Member, Sakshi Upadhyay"
11ed823555aabf7e32df5b09a04111a686f8ebb6,Learning visual dictionaries and decision lists for object recognition,"CONFIDENTIAL. Limited circulation. For review only.
Preprint submitted to 19th International Conference on Pattern Recognition.
Received April 10, 2008."
11da2d589485685f792a8ac79d4c2e589e5f77bd,Show and tell: A neural image caption generator,"Show and Tell: A Neural Image Caption Generator
Oriol Vinyals
Google
Alexander Toshev
Google
Samy Bengio
Google
Dumitru Erhan
Google"
1149c6ac37ae2310fe6be1feb6e7e18336552d95,"Classification of Face Images for Gender, Age, Facial Expression, and Identity","Proc. Int. Conf. on Artificial Neural Networks (ICANN’05), Warsaw, LNCS 3696, vol. I, pp. 569-574, Springer Verlag 2005
Classification of Face Images for Gender, Age,
Facial Expression, and Identity1
Torsten Wilhelm, Hans-Joachim B¨ohme, and Horst-Michael Gross
Department of Neuroinformatics and Cognitive Robotics
Ilmenau Technical University, P.O.Box 100565, 98684 Ilmenau, Germany"
110556d073a4d930877edc597a92995f0ff9d294,Application of Faster R-CNN model on Human Running Pattern Recognition,"Application of Faster R-CNN Model on Human Running Pattern
Recognitions
Kairan Yang, Feng Geng
Lexington High School, KTByte Computer Science Academy"
11f17191bf74c80ad0b16b9f404df6d03f7c8814,Characteristics of Visual Categorization of Long-Concatenated and Object-Directed Human Actions by a Multiple Spatio-Temporal Scales Recurrent Neural Network Model,"Recognition of Visually Perceived Compositional
Human Actions by Multiple Spatio-Temporal Scales
Recurrent Neural Networks
Haanvid Lee, Minju Jung, and Jun Tani"
11b00a4be68e9622d7b4698aca84da85aca3e288,Modeling Social Interactions in Real Work Environments,"Modeling Social Interactions in Real Work Environments
Salvatore Vanini
SUPSI-DTI
via Cantonale
6928 Manno, Switzerland
Silvia Giordano
SUPSI-DTI
via Cantonale
6928 Manno, Switzerland
Dario Gallucci
SUPSI-DTI
via Cantonale
6928 Manno, Switzerland
Kamini Garg
SUPSI-DTI
via Cantonale
6928 Manno, Switzerland
Victoria Mirata
FFHS-IFeL
Überlandstrasse 12"
116261c74ad54646f7d1d6be38cb9930f1bf44f6,3D Twins and Expression Challenge,"D Twins and
Expression Challenge
Vipin Vijayan, Kevin W. Bowyer, and Patrick J. Flynn."
1169f3386a49daccbe199cccb518238a0130a537,"Analyzing Complex Events and Human Actions in ""in-the-wild"" Videos",
115724ce1ce9422dad095b301c7d096498ad50d3,The E2E Dataset: New Challenges For End-to-End Generation,"Saarbr¨ucken, Germany, 15-17 August 2017. c(cid:13)2017 Association for Computational Linguistics
Proceedings of the SIGDIAL 2017 Conference, pages 201–206,"
1151a81118368e7596843b8db2508e4974fd7435,A Testbed for Cross-Dataset Analysis,"A Testbed for Cross-Dataset Analysis
Tatiana Tommasi and Tinne Tuytelaars
ESAT-PSI/VISICS - iMinds, KU Leuven, Belgium"
1120e88663a38ed05120af378f57ecf557660160,Generic Object Crowd Tracking by Multi-Task Learning,"LUOETAL.:GENERICOBJECTCROWDTRACKINGBYMULTI-TASKLEARNING
Generic Object Crowd Tracking by
Multi-Task Learning
Wenhan Luo
http://www.iis.ee.ic.ac.uk/~whluo
Tae-Kyun Kim
http://www.iis.ee.ic.ac.uk/~tkkim
Department of Electrical and Electronic
Engineering, Imperial College,
London, UK"
11155ee686bfb675816a2acdf5a8ddf06e67b65f,EmoDetect – Smart Emotion Detection from Facial Expressions,"EmoDetect – Smart Emotion Detection from Facial Expressions
Rishabh Animesh
Skand Hurkat
Abhinandan Majumdar
Aayush Saxena
ra523
sh953
m2352
s2825"
1198572784788a6d2c44c149886d4e42858d49e4,Learning Discriminative Features using Encoder-Decoder type Deep Neural Nets,"Learning Discriminative Features using Encoder/Decoder type Deep
Neural Nets
Vishwajeet Singh1, Killamsetti Ravi Kumar2, K Eswaran3
ALPES, Bolarum, Hyderabad 500010,
ALPES, Bolarum, Hyderabad 500010,
SNIST, Ghatkesar, Hyderabad 501301,"
111f2f1255fa9e5a82753bf5b3f2f0974e87f86d,Development of an Efficient Face Recognition System based on Linear and Nonlinear Algorithms,"Development of an Efficient Face Recognition System based on Linear and Nonlinear Algorithms
{tag}                                                 {/tag}
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 134
Number 7
Year of Publication: 2016
Authors:
Filani Araoluwa S., Adetunmbi Adebayo O.
10.5120/ijca2016907932
{bibtex}2016907932.bib{/bibtex}"
111ff5420111751454a2f4f55b7bb75d837ed5f4,Automatic Annotation of Structured Facts in Images,"Proceedings of the 5th Workshop on Vision and Language, pages 1–9,
Berlin, Germany, August 12 2016. c(cid:13)2016 Association for Computational Linguistics"
11a7c4aadb47753c8d30cbda4ab347c361e4c66a,How to collect high quality segmentations : use human or computer drawn object boundaries ?,"Boston University Computer Science Technical Report No. BUCS-TR-2013-20
How to Collect High Quality Segmentations: Use Human or Computer Drawn
Object Boundaries?
Danna Gurari, Zheng Wu, Brett Isenberg, Chentian Zhang, Alberto Purwada, Joyce Y. Wong, Margrit Betke"
1178beb48d666d7fc41b2d476f6a92450c0726c0,Challenges in multimodal gesture recognition,"Submitted 11/14; Revised 1/16; Published 4/16
Challenges in multimodal gesture recognition
Sergio Escalera
Computer Vision Center UAB and University of Barcelona
Vassilis Athitsos
University of Texas
Isabelle Guyon
ChaLearn, Berkeley, California
Editors: Zhuowen Tu"
11feb48d2c4c8f8a5ed9054d49e7a13b0f75f2af,Chapter 1 Feature Representation and Extraction for Image Search and Video Retrieval,"Chapter 1
Feature Representation and Extraction for
Image Search and Video Retrieval
Qingfeng Liu, Yukhe Lavinia, Abhishek Verma, Joyoung Lee, Lazar Spasovic, and
Chengjun Liu"
11f2e65a0cd4e27505f1306d39b5717b5c5c92a5,Gradient Feature Selection for Online Boosting,"Gradient Feature Selection for Online Boosting
Visualization and Computer Vision Lab
General Electric Global Research, Niskayuna, NY, 12309, USA
Xiaoming Liu
Ting Yu
{liux,yut} AT research.ge.com"
1152b88194214d4ea0f85b727f4b120915ad8056,Exploiting feature dynamics for active object recognition,"Exploiting Feature Dynamics for Active
Object Recognition
Philipp Robbel and Deb Roy
MIT Media Laboratory
Cambridge, MA 02139, USA"
11f73583ba373487967225ae4797d723ff367c1c,"End-to-end, sequence-to-sequence probabilistic visual odometry through deep neural networks","Article
End-to-end, sequence-to-sequence
probabilistic visual odometry through
deep neural networks
The International Journal of
Robotics Research
© The Author(s) 2017
Reprints and permissions:
sagepub.co.uk/journalsPermissions.nav
DOI: 10.1177/0278364917734298
journals.sagepub.com/home/ijr
Sen Wang1,2, Ronald Clark3, Hongkai Wen4 and Niki Trigoni2"
11bfc54a64ca69786323551bbf88b85b216ae486,Exploring the Facial Expression Perception-Production Link Using Real-Time Automated Facial Expression Recognition,"Exploring the Facial Expression
Perception-Production Link Using Real-Time
Automated Facial Expression Recognition
David M. Deriso1, Josh Susskind1, Jim Tanaka2, Piotr Winkielman3,
John Herrington4, Robert Schultz4, and Marian Bartlett1
Machine Perception Laboratory, University of California, San Diego
Department of Psychology, University of Victoria
Department of Psychology, University of California, San Diego
Center for Autism Research, Children’s Hospital of Philadelphia"
111d0b588f3abbbea85d50a28c0506f74161e091,Facial Expression Recognition from Visual Information using Curvelet Transform,"International Journal of Computer Applications (0975 – 8887)
Volume 134 – No.10, January 2016
Facial Expression Recognition from Visual Information
using Curvelet Transform
Pratiksha Singh
Surabhi Group of Institution Bhopal
systems.  Further  applications"
11a3084768f035c824662a85a348f02466693d2a,Lifting Object Detection Datasets into 3D,"Lifting Object Detection Datasets into 3D
Jo˜ao Carreira*, Sara Vicente*, Lourdes Agapito and Jorge Batista"
1171ec9250743c349e5218d4a01c4fdad94c7707,Low-Cost Transfer Learning of Face Tasks,"Low-Cost Transfer Learning of Face Tasks
Thrupthi Ann John1, Isha Dua1, Vineeth N Balasubramanian2, and C. V. Jawahar1
IIIT Hyderabad
IIT Hyderabad"
117b78d154309d87c4d84ffb64057c1dfcdb1dd7,Gender Classification with Jointing Multiple Models for Occlusion Images,"GENDER CLASSIFICATION WITH JOINTING MULTIPLE MODELS FOR
OCCLUSION IMAGES
CHIAO-WEN KAO1, HUI-HUI CHEN2, BOR-JIUNN HWANG2, YU-JU HUANG1,
KUO-CHIN FAN1
Department of Computer Science and Information Engineering, National Central
Department of Computer Communication and Engineering, Ming Chuan University,
University, Taoyuan, Taiwan
Taoyuan, Taiwan
E-MAIL:"
11e79b96cc87794592c0aa8f573d440a70a9a941,Systematic Testing of Convolutional Neural Networks for Autonomous Driving,"Systematic Testing of Convolutional Neural Networks for Autonomous Driving
Tommaso Dreossi 1 Shromona Ghosh 1 Alberto Sangiovanni-Vincentelli 1 Sanjit A. Seshia 1"
111a9645ad0108ad472b2f3b243ed3d942e7ff16,Facial Expression Classification Using Combined Neural Networks,"Facial Expression Classification Using
Combined Neural Networks
Rafael V. Santos, Marley M.B.R. Vellasco, Raul Q. Feitosa, Ricardo Tanscheit
DEE/PUC-Rio, Marquês de São Vicente 225, Rio de Janeiro – RJ - Brazil"
1131088237aacddcc078547b4455e8572c61766b,Object Referring in Videos with Language and Human Gaze,"Object Referring in Videos with Language and Human Gaze
Arun Balajee Vasudevan1, Dengxin Dai1, Luc Van Gool1,2
ETH Zurich1
KU Leuven 2"
116a7ac8891cd22f97df508e696e8280658c858c,"Discriminant Analysis via Joint Euler Transform and ℓ2, 1-Norm","IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. *, NO. *, * *
Discriminant Analysis via Joint Euler Transform
nd (cid:96)2,1-norm
Shuangli Liao, Quanxue Gao, Zhaohua Yang, Fang Chen, Feiping Nie, and Jungong Han"
11f732fe8f127c393cc8404ee8db2b3e85dd3d59,Disentangling Latent Factors with Whitening,"DISENTANGLING LATENT FACTORS WITH WHITENING
Sangchul Hahn, Heeyoul Choi
School of Information Technology
{schahn21,
Handong Global University
Pohang, South Korea"
11a34bda2daecad5f7c1caa309897cc9cc334480,Person re-identification using view-dependent score-level fusion of gait and color features,"1st International Conference on Pattern Recognition (ICPR 2012)
November 11-15, 2012. Tsukuba, Japan
978-4-9906441-1-6 ©2012 IAPR"
2c883977e4292806739041cf8409b2f6df171aee,Are Haar-Like Rectangular Features for Biometric Recognition Reducible?,"Aalborg Universitet
Are Haar-like Rectangular Features for Biometric Recognition Reducible?
Nasrollahi, Kamal; Moeslund, Thomas B.
Published in:
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
DOI (link to publication from Publisher):
0.1007/978-3-642-41827-3_42
Publication date:
Document Version
Early version, also known as pre-print
Link to publication from Aalborg University
Citation for published version (APA):
Nasrollahi, K., & Moeslund, T. B. (2013). Are Haar-like Rectangular Features for Biometric Recognition
Reducible? In J. Ruiz-Shulcloper, & G. Sanniti di Baja (Eds.), Progress in Pattern Recognition, Image Analysis,
Computer Vision, and Applications (Vol. 8259, pp. 334-341). Springer Berlin Heidelberg: Springer Publishing
Company.  Lecture Notes in Computer Science, DOI: 10.1007/978-3-642-41827-3_42
General rights
Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners
nd it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.
? Users may download and print one copy of any publication from the public portal for the purpose of private study or research."
2cac8ab4088e2bdd32dcb276b86459427355085c,A Face-to-Face Neural Conversation Model,"A Face-to-Face Neural Conversation Model
Hang Chu1
Daiqing Li1 Sanja Fidler1
University of Toronto 2Vector Institute
{chuhang1122, daiqing,"
2c564f5241b0905baafc3677e7ca15c27fd2c6e7,An Integrated Approach to Contextual Face Detection,"AN INTEGRATED APPROACH TO CONTEXTUAL FACE
DETECTION.
Santi Segu´ı1, Michal Drozdzal1,2, Petia Radeva1,2 and Jordi Vitri`a1,2
Computer Vision Center, Universitat Aut`onoma de Barcelona, Bellaterra, Spain
Dept. Matem`atica Aplicada i An`alisi, Universitat de Barcelona, Barcelona, Spain
{ssegui, michal, petia,
Keywords:
face detection, object detection."
2cad358676854505517307314728e8920fe53d77,Mixture of Ridge Regressors for Human Pose Estimation,"#1754
CVPR 2012 Submission #1754. CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE.
#1754
Mixture of Ridge Regressors
for Human Pose Estimation
Anonymous CVPR submission
Paper ID 1754"
2c48683c9663ea82fcbbd5891d892a884f1a32d0,Explore semantic pixel sets based local patterns with information entropy for face recognition,"Chai et al. EURASIP Journal on Image and Video Processing 2014, 2014:26
http://jivp.eurasipjournals.com/content/2014/1/26
RESEARCH
Open Access
Explore semantic pixel sets based local
patterns with information entropy for face
recognition
Zhenhua Chai1*, Heydi Mendez-Vazquez2, Ran He1, Zhenan Sun1 and Tieniu Tan1"
2c9c597ab660815e07980e9655c3c5989402205b,Vision-Based Reacquisition for Task-Level Control,"Vision-based Reacquisition for Task-level
Control
Matthew R. Walter, Yuli Friedman, Matthew Antone, and Seth Teller"
2c8743089d9c7df04883405a31b5fbe494f175b4,Real-time full-body human gender recognition in (RGB)-D data,"Washington State Convention Center
Seattle, Washington, May 26-30, 2015
978-1-4799-6922-7/15/$31.00 ©2015 IEEE"
2cf7383e238fe37516e2607c4741f79a230834bf,A new Sparse Coding Approach for Human Face and Action Recognition,"A new Sparse Coding Approach for Human Face and Action
Recognition
Mohsen Nikpour*
Department of Electrical and Computer Engineering, Babol Noushirvani University of Technology, Babol, Iran
Mohammad Reza Karami Molaei
Department of Electrical and Computer Engineering, Babol Noushirvani University of Technology, Babol, Iran
Reza Ghaderi
Department of nuclear Engineering, Shahid Beheshti University of Tehran, Tehran, Iran
Received: 27/Jul/2016            Revised: 07/Jan/2017            Accepted: 14/Jan/2017"
2c786b32a621a52fc7d00499e4b056f149a4fba7,Face Recognition with Decision Tree-Based Local Binary Patterns,"Face Recognition with Decision Tree-based Local
Binary Patterns
Daniel Maturana, Domingo Mery and ´Alvaro Soto
Department of Computer Science, Pontificia Universidad Cat´olica de Chile"
2cdde47c27a8ecd391cbb6b2dea64b73282c7491,Order-aware Convolutional Pooling for Video Based Action Recognition,"ORDER-AWARE CONVOLUTIONAL POOLING FOR VIDEO BASED ACTION RECOGNITION
Order-aware Convolutional Pooling for Video Based
Action Recognition
Peng Wang, Lingqiao Liu, Chunhua Shen, and Heng Tao Shen"
2cfdf540840a983907e957aacf68b405214c721c,Can We Predict the Scenic Beauty of Locations from Geo-tagged Flickr Images?,"Can We Predict the Scenic Beauty of Locations from
Geo-tagged Flickr Images?
Ch. Md. Rakin Haider
Bangladesh University of Engineering and
Technology, Dhaka, Bangladesh
Mohammed Eunus Ali
Bangladesh University of Engineering and
Technology, Dhaka, Bangladesh"
2c92839418a64728438c351a42f6dc5ad0c6e686,Pose-Aware Face Recognition in the Wild,"Pose-Aware Face Recognition in the Wild
Iacopo Masi1
Prem Natarajan2
USC Institute for Robotics and Intelligent Systems (IRIS), Los Angeles, CA
G´erard Medioni1
Stephen Rawls2
USC Information Sciences Institute (ISI), Marina Del Rey, CA"
2cbb8de53759e75411bc528518947a3094fbce3a,Billion-scale similarity search with GPUs,"Billion-scale similarity search with GPUs
Jeff Johnson
Facebook AI Research
New York
Matthijs Douze
Facebook AI Research
Paris
Herv´e J´egou
Facebook AI Research
Paris"
2c47ef6a9979cb51f7cdfe3bafdbaa2aec28da8b,A Lazy Man's Approach to Benchmarking: Semisupervised Classifier Evaluation and Recalibration,"UvA-DARE (Digital Academic Repository)
A Lazy Man's Approach to Benchmarking: Semisupervised Classifier Evaluation and
Recalibration
Welinder, P.; Welling, M.; Perona, P.
Published in:
013, Portland, Oregon, USA
0.1109/CVPR.2013.419
Link to publication
Citation for published version (APA):
Welinder, P., Welling, M., & Perona, P. (2013). A Lazy Man's Approach to Benchmarking: Semisupervised
Classifier Evaluation and Recalibration. In Proceedings: 2013 IEEE Conference on Computer Vision and Pattern
Recognition: CVPR 2013: 23-28 June 2013, Portland, Oregon, USA (pp. 3262-3269). Los Alamitos, CA: IEEE
Computer Society Conference Publishing Services. DOI: 10.1109/CVPR.2013.419
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the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam,"
2cdd5b50a67e4615cb0892beaac12664ec53b81f,Mirror mirror: crowdsourcing better portraits,"To appear in ACM TOG 33(6).
Mirror Mirror: Crowdsourcing Better Portraits
Jun-Yan Zhu1
Aseem Agarwala2
Alexei A. Efros1
Eli Shechtman2
Jue Wang2
University of California, Berkeley1 Adobe2
Figure 1: We collect thousands of portraits by capturing video of a subject while they watch movie clips designed to elicit a range of positive
emotions. We use crowdsourcing and machine learning to train models that can predict attractiveness scores of different expressions. These
models can be used to select a subject’s best expressions across a range of emotions, from more serious professional portraits to big smiles."
2c72096bbecd70000f919b1cec3f31a649c94fd5,Neural Network Interpretation via Fine Grained Textual Summarization,"Neural Network Interpretation via Fine-Grained Textual Summarization
Pei Guo, Connor Anderson, Kolton Pearson, Ryan Farrell
Brigham Young University"
2c2bf22e2f0a1817475aefb37e0c4e0404e8d479,Structured Prediction of 3D Human Pose with Deep Neural Networks,"TEKIN ET AL.: STRUCTURED PREDICTION OF 3D HUMAN POSE
Structured Prediction of 3D Human Pose
with Deep Neural Networks
Bugra Tekin∗1
Isinsu Katircioglu∗1
Mathieu Salzmann1
Vincent Lepetit2
Pascal Fua1
CVLab
EPFL,
Lausanne, Switzerland
CVARLab
TU Graz,
Graz, Austria"
2cd03c6e78d09bb98872bb34bb70e08c32dc5f7e,Pedestrian Alignment Network for Large-scale Person Re-identification,"Noname manuscript No.
(will be inserted by the editor)
Pedestrian Alignment Network for
Large-scale Person Re-identification
Zhedong Zheng · Liang Zheng · Yi Yang
Received: date / Accepted: date"
2cf5f2091f9c2d9ab97086756c47cd11522a6ef3,MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation,"MPIIGaze: Real-World Dataset and Deep
Appearance-Based Gaze Estimation
Xucong Zhang, Yusuke Sugano∗, Mario Fritz, Andreas Bulling"
2c0a71b5e111d2c7d99c3f23989d317a0d845adc,N-best maximal decoders for part models,"N-best maximal decoders for part models
Dennis Park Deva Ramanan
UC Irvine"
2c2786ea6386f2d611fc9dbf209362699b104f83,Title of Dissertation Local Feature Representations for Facial Expression Recognition Based on Differences of Gray Color Values of Neighboring Pixels,1)LOCAL FEATURE REPRESENTATIONS FOR FACIAL EXPRESSION RECOGNITION BASED ON DIFFERENCES OF GRAY COLOR VALUES OF NEIGHBORING PIXELS        Mohammad Shahidul Islam          A Dissertation Submitted in Partial  Fulfillment of the Requirement for the Degree of  Doctor of Philosophy (Computer Science and Information Systems) School of Applied Statistics National Institute of Development Administration 2013
2c93c8da5dfe5c50119949881f90ac5a0a4f39fe,Advanced local motion patterns for macro and micro facial expression recognition,"Advanced local motion patterns for macro and micro facial
expression recognition
B. Allaerta,∗, IM. Bilascoa, C. Djerabaa
Univ. Lille, CNRS, Centrale Lille, UMR 9189 - CRIStAL -
Centre de Recherche en Informatique Signal et Automatique de Lille, F-59000 Lille, France"
2cdd9e445e7259117b995516025fcfc02fa7eebb,Temporal Exemplar-Based Bayesian Networks for Facial Expression Recognition,"Title
Temporal Exemplar-based Bayesian Networks for facial
expression recognition
Author(s)
Shang, L; Chan, KP
Citation
Proceedings - 7Th International Conference On Machine
Learning And Applications, Icmla 2008, 2008, p. 16-22
Issued Date
http://hdl.handle.net/10722/61208
Rights
This work is licensed under a Creative Commons Attribution-
NonCommercial-NoDerivatives 4.0 International License.;
International Conference on Machine Learning and Applications
Proceedings. Copyright © IEEE.; ©2008 IEEE. Personal use of
this material is permitted. However, permission to
reprint/republish this material for advertising or promotional
purposes or for creating new collective works for resale or
redistribution to servers or lists, or to reuse any copyrighted
omponent of this work in other works must be obtained from"
2ce2560cf59db59ce313bbeb004e8ce55c5ce928,Anthropometric 3D Face Recognition,"Int J Comput Vis
DOI 10.1007/s11263-010-0360-8
Anthropometric 3D Face Recognition
Shalini Gupta · Mia K. Markey · Alan C. Bovik
Received: 3 July 2009 / Accepted: 20 May 2010
© Springer Science+Business Media, LLC 2010"
2cc8371c483f76fff65a5fb1c9cc89e974ce83ea,Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks,"Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural
Networks
Michael Gygli
gifs.com
Zurich, Switzerland"
2cc0e431d7cc0bcb926b9a19e7be8a3592d670d4,NovaMedSearch: a multimodal search engine for medical case-based retrieval,"NovaMedSearch: A multimodal search engine for medical
ase-based retrieval
André Mourão
Flávio Martins
Faculdade de Ciências e Tecnologia
Universidade Nova de Lisboa
Departamento de Informática
Caparica, Portugal"
2c1cd58790cf07d5fa7f1f1f096023a993a6ead3,"Improving 3 D perception for Object Detection , Classification and Localization using Fused Multi-modal Sensors","Improving 3D perception for Object Detection,
Classification and Localization using Fused Multi-modal
Sensors
Saif Imran, Aaron Gonzalez, Mehmet Akif Alper
February 21, 2017
Introduction
Object perception in 3-D is a highly challenging problem in computer vision. The major concern in
these tasks involves object occlusion, different object poses, appearance and limited perception of
the environment by individual sensors in terms of range measurements. In this particular project,
our goal is improving 3D perception of the environment by using fusion from lidars and cameras
with focus to autonomous driving. The main reason for using lidars and cameras are to combine
the complementary information from each of the modalities for ef‌f‌icient feature set extraction that
leads to improved perception.
Motivation/Related Work
The main focus of this work involves autonomous driving of cars using improved 3D perception of
the environment. The real challenge is how to fuse the information from both the modalities in order
to learn useful patterns/features of objects. There are some real advances in semantic labelling of
objects in 2D images through deep networks. But using fused data for object classification and
recognition is still at premature stage.
We would like to use lidar and camera for this purpose. Lidars are good in the sense that"
2c5ff99e7e9769677df3eeab9f198e3ead016c35,Registration of 3D facial surfaces using covariance matrix pyramids,"Anchorage Convention District
May 3-8, 2010, Anchorage, Alaska, USA
978-1-4244-5040-4/10/$26.00 ©2010 IEEE"
2c98165dd72bac574ed463b00f1dd4c276808cb4,Efficient Object Pixel-Level Categorization Using Bag of Features,"Ef‌f‌icient Object Pixel-Level Categorization using
Bag of Features
David Aldavert1, Arnau Ramisa2, Ricardo Toledo1, and Ramon Lopez de
Mantaras2
Computer Vision Center (CVC)
Dept. Ci`encies de la Computaci´o
Universitat Aut`onoma de Barcelona (UAB), 08193, Bellaterra, Spain
Artificial Intelligence Research Institute (IIIA-CSIC)
Campus de la UAB, 08193, Bellaterra, Spain"
2c848cc514293414d916c0e5931baf1e8583eabc,An automatic facial expression recognition system evaluated by different classifiers,"An automatic facial expression recognition system
evaluated by different classifiers
Caroline Silva∗, Andrews Sobral∗ and Raissa Tavares Vieira†
Programa de P´os-Graduac¸˜ao em Mecatrˆonica
Universidade Federal da Bahia,
Email:
Email:
Department of Electrical Engineering - EESC/USP
Email:"
2c7932c2096669113328a75d1ad1d1bfb8f86ad0,Multi30K: Multilingual English-German Image Descriptions,"Proceedings of the 5th Workshop on Vision and Language, pages 70–74,
Berlin, Germany, August 12 2016. c(cid:13)2016 Association for Computational Linguistics"
2c7b72d0b66074bff2f0b3493673a51f6f094d5f,A biometric verification system based on the fusion of palmprint and face features,"A Biometric Verification System Based on the Fusion of Palmprint and Face
Features
Slobodan Ribaric, Ivan Fratric and Kristina Kis
Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia"
2cc17e1ccb5f1f67f8ce882e683d8c66475330be,Multitarget tracking with the von Mises-Fisher filter and probabilistic data association,"JOURNAL OF ADVANCES IN INFORMATION FUSION
Multitarget tracking with the von Mises-Fisher filter
nd probabilistic data association
Ivan Markovi´c, Mario Bukal, Josip ´Cesi´c and Ivan Petrovi´c"
2ce1b73cee6ddf4dbff391e29b73b35e3fb67685,TSDF Manifolds: A Scalable and Consistent Dense Mapping Approach,"TSDF Manifolds: A Scalable and Consistent Dense
Mapping Approach
Alexander Millane, Zachary Taylor, Helen Oleynikova, Juan Nieto, Roland Siegwart, C´esar Cadena"
2c4b96f6c1a520e75eb37c6ee8b844332bc0435c,Automatic Emotion Recognition in Robot-Children Interaction for ASD Treatment,"Automatic Emotion Recognition in Robot-Children Interaction for ASD
Treatment
Marco Leo, Marco Del Coco, Pierluigi Carcagn`ı, Cosimo Distante
ISASI UOS Lecce
Campus Universitario via Monteroni sn, 73100 Lecce Italy
Massimo Bernava, Giovanni Pioggia
ISASI UOS Messina
Giuseppe Palestra
Univerisita’ di Bari
Marine Institute, via Torre Bianca, 98164 Messina Italy
Via Orabona 4, 70126 Bari, Italy"
2c5c89103605c6f0ed8924778526133dfa064a16,Blurred face recognition algorithm guided by a no-reference blur metric,"Blurred face recognition algorithm guided by a
no-reference blur metric
Cécile Fiche, Patricia Ladret, Ngoc-Son Vu
To cite this version:
Cécile Fiche, Patricia Ladret, Ngoc-Son Vu. Blurred face recognition algorithm guided by a no-
Image Processing: Machine Vision Applications
reference blur metric.
III, Jan 2010, San Jose, Californie, United States.
7538 (75380U), pp.75380U-75380U-9, 2010,
<10.1117/12.840245>. <hal-00522115>
SPIE Digital Library.
HAL Id: hal-00522115
https://hal.archives-ouvertes.fr/hal-00522115
Submitted on 29 Oct 2010
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers."
2c9179fec33f69a5c1a453034dc7d3d3302839d3,Exploiting Hierarchical Dense Structures on Hypergraphs for Multi-Object Tracking,"Exploiting Hierarchical Dense Structures
on Hypergraphs for Multi-Object Tracking
Longyin Wen, Zhen Lei, Siwei Lyu, Stan Z. Li, Fellow, IEEE, and Ming-Hsuan Yang"
83b700f0777a408eb36eef4b1660beb3f6dc1982,Violent behaviour detection using local trajectory response Conference,"See	discussions,	stats,	and	author	profiles	for	this	publication	at:	https://www.researchgate.net/publication/317628106
Violent	behaviour	detection	using	local
trajectory	response
Conference	Paper	·	January	2016
DOI:	10.1049/ic.2016.0082
CITATIONS
authors,	including:
Paul	L.	Rosin
Cardiff	University
READS
David	Marshall
Cardiff	University
31	PUBLICATIONS			7,739	CITATIONS
98	PUBLICATIONS			2,855	CITATIONS
SEE	PROFILE
SEE	PROFILE
Simon	Christopher	Moore
University	of	Wales
08	PUBLICATIONS			1,069	CITATIONS
SEE	PROFILE"
8377ac1b2dffb11cf48f456be2531c95d14aa6e5,Improving the Annotation of DeepFashion Images for Fine-grained Attribute Recognition,"Improving the Annotation of DeepFashion
Images for Fine-grained Attribute Recognition
Roshanak Zakizadeh, Michele Sasdelli, Yu Qian and Eduard Vazquez
Cortexica Vision Systems, London, UK"
838a4bcfeb36dc7bdb4a38f776fc0a70ce8ae9f0,Face Presentation Attack Detection using Biologically-inspired Features,
837e99301e00c2244023a8a48ff98d7b521c93ac,Local Feature Evaluation for a Constrained Local Model Framework,"Local Feature Evaluation for a Constrained
Local Model Framework
Maiya Hori(B), Shogo Kawai, Hiroki Yoshimura, and Yoshio Iwai
Graduate School of Engineering, Tottori University,
01 Minami 4-chome, Koyama-cho, Tottori 680-8550, Japan"
832aae00e16c647716f1be38de233c9c15af9a28,Author ' s Accepted Manuscript Feature fusion for facial landmark detection,"Author's Accepted Manuscript
Feature fusion for facial landmark detection
Panagiotis Perakis, Theoharis Theoharis, Ioan-
nis A. Kakadiaris
Reference:
S0031-3203(14)00105-8
http://dx.doi.org/10.1016/j.patcog.2014.03.007
PR5053
www.elsevier.com/locate/pr
To appear in:
Pattern Recognition
Received date: 10 March 2013
Revised date: 18 September 2013
Accepted date: 8 March 2014
Cite this article as: Panagiotis Perakis, Theoharis Theoharis,
Kakadiaris, Feature fusion for facial landmark detection, Pattern Recognition,
http://dx.doi.org/10.1016/j.patcog.2014.03.007
Ioannis A.
This is a PDF file of an unedited manuscript that has been accepted for
publication. As a service to our customers we are providing this early version of"
8397956c7ad3bd24c6c6c0b38866e165367327c0,Social Relation Trait Discovery from Visual LifeLog Data with Facial Multi-Attribute Framework,
83c00537e0c3e226d999a5abf02464e138867e96,Pedestrians and their phones - detecting phone-based activities of pedestrians for autonomous vehicles,"Windsor Oceanico Hotel, Rio de Janeiro, Brazil, November 1-4, 2016
978-1-5090-1889-5/16/$31.00 ©2016 IEEE"
830b48f210f3905117b335e305166df4ec092b8b,Pixel-Level Encoding and Depth Layering for Instance-Level Semantic Labeling,"Pixel-level Encoding and Depth Layering for
Instance-level Semantic Labeling
Jonas Uhrig1,2, Marius Cordts1,3, Uwe Franke1, Thomas Brox2
Daimler AG R&D, 2University of Freiburg, 3TU Darmstadt"
83e093a07efcf795db5e3aa3576531d61557dd0d,Facial Landmark Localization Using Robust Relationship Priors and Approximative Gibbs Sampling,"Facial Landmark Localization using Robust
Relationship Priors and Approximative Gibbs
Sampling
Karsten Vogt, Oliver M¨uller and J¨orn Ostermann
Institut f¨ur Informationsverarbeitung (tnt)
Leibniz Universit¨at Hannover, Germany
{vogt, omueller,"
83e7c51c4d6f04049f5a3dbf4ac9e129ed96caee,Spatio-temporal Pain Recognition in CNN-Based Super-Resolved Facial Images,"Aalborg Universitet
Spatio-Temporal Pain Recognition in CNN-based Super-Resolved Facial Images
Bellantonio, Marco; Haque, Mohammad Ahsanul; Rodriguez, Pau; Nasrollahi, Kamal; Telve,
Taisi; Guerrero, Sergio Escalera; Gonzàlez, Jordi; Moeslund, Thomas B.; Rasti, Pejman;
Anbarjafari, Gholamreza
Published in:
Video Analytics
DOI (link to publication from Publisher):
0.1007/978-3-319-56687-0_13
Publication date:
Document Version
Accepted author manuscript, peer reviewed version
Link to publication from Aalborg University
Citation for published version (APA):
Bellantonio, M., Haque, M. A., Rodriguez, P., Nasrollahi, K., Telve, T., Guerrero, S. E., ... Anbarjafari, G. (2017).
Spatio-Temporal Pain Recognition in CNN-based Super-Resolved Facial Images. In Video Analytics: Face and
Facial Expression Recognition and Audience Measurement Springer.  Lecture Notes in Computer Science, Vol..
0165 https://doi.org/10.1007/978-3-319-56687-0_13
General rights
Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners"
833ada09759039b7c620b8930a50a0521d70b2c7,Attend in Groups: A Weakly-Supervised Deep Learning Framework for Learning from Web Data,"Attend in groups: a weakly-supervised deep learning framework for learning
from web data
Bohan Zhuang∗, Lingqiao Liu∗, Yao Li, Chunhua Shen,
Ian Reid
The University of Adelaide; and Australian Centre for Robotic Vision
. Experiments
. . . . .
.1. Datasets . . . .
. . . . . . . . . .
.2. Implementation details . . . . . . . . . . .
.3. Evaluation on the WebCars . . . . . . . . .
. . . . . . . . . .
.4. Analysis of group size .
.5. Web Images re-ranking .
. . . . . . . . . .
.6. Evaluation on Web Images + ImageNet
5. Conclusion"
8309e8f27f3fb6f2ac1b4343a4ad7db09fb8f0ff,Generic versus Salient Region-Based Partitioning for Local Appearance Face Recognition,"Generic versus Salient Region-based Partitioning
for Local Appearance Face Recognition
Hazım Kemal Ekenel and Rainer Stiefelhagen
Computer Science Depatment, Universit¨at Karlsruhe (TH)
Am Fasanengarten 5, Karlsruhe 76131, Germany
http://isl.ira.uka.de/cvhci"
831cbffbfe39a059b1212d49e8fdfd458d1d01c5,"Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Applications to Face and Palm Biometrics","Globally Maximizing, Locally Minimizing:
Unsupervised Discriminant Projection with
Applications to Face and Palm Biometrics
Jian Yang, David Zhang, Senior Member, IEEE, Jing-yu Yang, and Ben Niu"
83ca4cca9b28ae58f461b5a192e08dffdc1c76f3,Detecting emotional stress from facial expressions for driving safety,"DETECTING EMOTIONAL STRESS FROM FACIAL EXPRESSIONS FOR DRIVING SAFETY
Hua Gao, Anil Y¨uce, Jean-Philippe Thiran
Signal Processing Laboratory (LTS5),
´Ecole Polytechnique F´ed´erale de Lausanne, Switzerland"
83b7578e2d9fa60d33d9336be334f6f2cc4f218f,The S-HOCK dataset: Analyzing crowds at the stadium,"The S-HOCK Dataset: Analyzing Crowds at the Stadium
Davide Conigliaro1,3, Paolo Rota2, Francesco Setti3, Chiara Bassetti3, Nicola Conci4, Nicu Sebe4, Marco Cristani1,
University of Verona. 2Vienna Institute of Technology. 3ISTC–CNR (Trento). 4University of Trento.
The topic of crowd modeling in computer vision usually assumes a sin-
gle generic typology of crowd, which is very simplistic. In this paper we
dopt a taxonomy that is widely accepted in sociology, focusing on a partic-
ular category, the spectator crowd, which is formed by people “interested in
watching something specific that they came to see” [1]. This can be found
t the stadiums, amphitheaters, cinema, etc.
In particular, we propose a
novel dataset, the Spectators Hockey (S-HOCK), which deals with 4 hockey
matches during an international tournament.
The dataset is unique in the crowd literature, and in general in the
surveillance realm. The dataset analyzes the crowd at different levels of
detail. At the highest level, it models the network of social connections
mong the public (who knows whom in the neighborhood), what is the sup-
ported team and what has been the best action in the match; all of this has
een obtained by interviews at the stadium. At a medium level, spectators
re localized, and information regarding the pose of their heads and body is
given. Finally, at a lowest level, a fine grained specification of all the actions"
83cd39c3a171dbf3e684899c79bb596652d32d91,TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents,"TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents
Yuexin Ma1,2, Xinge Zhu3, Sibo Zhang1, Ruigang Yang1, Wenping Wang2, Dinesh Manocha4
Baidu Research, Baidu Inc.1, The University of Hong Kong2,
The Chinese University of Hong Kong3, University of Maryland at College Park4
http://gamma.cs.unc.edu/TPredict/TrafficPredict.html"
8395cf3535a6628c3bdc9b8d0171568d551f5ff0,Entropy Non-increasing Games for the Improvement of Dataflow Programming,"Entropy Non-increasing Games for the
Improvement of Dataflow Programming
Norbert B´atfai, Ren´at´o Besenczi, Gerg˝o Bogacsovics,
Fanny Monori∗
February 16, 2017"
831fbef657cc5e1bbf298ce6aad6b62f00a5b5d9,Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning,
833f6ab858f26b848f0d747de502127406f06417,Learning weighted similarity measurements for unconstrained face recognition,"978-1-4244-5654-3/09/$26.00 ©2009 IEEE
ICIP 2009"
83a4b9c9ae3f75bf7e4a3222c46d99be7b7998ab,A random forest approach to segmenting and classifying gestures,"A Random Forest Approach to Segmenting and Classifying Gestures
Ajjen Joshi1, Camille Monnier2, Margrit Betke1 and Stan Sclaroff1
Department of Computer Science, Boston Univeristy, Boston, MA 02215 USA
Charles River Analytics, Cambridge, MA 02138 USA"
83e71455ee2070617ea35c02f03b7451187985d1,Faces Recognition with Image Feature Weights and Least Mean Square Learning Approach,"Faces Recognition with Image Feature Weights and Least Mean Square
Learning Approach
Dept. of Electrical Engineering, National Taiwan Uni. of Sci. & Technology, Taipei, Taiwan
Wei-Li Fang, Ying-Kuei Yang and Jung-Kuei Pan
Email:"
834b15762f97b4da11a2d851840123dbeee51d33,Landmark-free smile intensity estimation,"Landmark-free smile intensity estimation
J´ulio C´esar Batista, Olga R. P. Bellon and Luciano Silva
IMAGO Research Group - Universidade Federal do Paran´a
Fig. 1. Overview of our method for smile intensity estimation"
839e7491cd6032162ee4bb6d73b7122cc4af12f1,Improved Person Detection on Omnidirectional Images with Non-maxima Supression,"Improved Person Detection on Omnidirectional Images with
Non-maxima Supression
Roman Seidel, André Apitzsch, Gangolf Hirtz
Department of Information Technology, Chemnitz University of Technology, Chemnitz, Germany
{roman.seidel, andre.apitzsch,
Keywords:
Ambient Assisted Living, Convolutional Neural Networks, Object Detection, Non-maxima Supression,
Omnidirectional Images"
833da007d1cb183287728b720a03237bee072cd7,A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection,"A Unified Multi-scale Deep Convolutional
Neural Network for Fast Object Detection
Zhaowei Cai1, Quanfu Fan2, Rogerio S. Feris2, and Nuno Vasconcelos1
SVCL, UC San Diego
IBM T. J. Watson Research"
8399c71abc9a820bacd9c4e21c85c461c0b830b3,"Adaboost with ""Keypoint Presence Features"" for Real-Time Vehicle Visual Detection","Author manuscript, published in ""16th World Congress on Intelligent Transport Systems (ITSwc'2009), Sweden (2009)"""
83b20fdd3eafd21a6971dacc73d85c484a093bfc,Interleaved Structured Sparse Convolutional Neural Networks,"Interleaved Structured Sparse Convolutional Neural Networks
Guotian Xie1,2,∗ Jingdong Wang3† Ting Zhang3
Jianhuang Lai1,2 Richang Hong4 Guo-Jun Qi5
Sun Yat-Sen University 2Guangdong Key Laboratory of Information Security Technology
Microsoft Research 4Hefei University of Technology 5University of Central Florida"
839a359df925e6b159c8402bc81c39790a26febb,Automatic Person Identification using Multiple Cues,"ICCAS2005                                                                                     June 2-5, KINTEX, Gyeonggi-Do, Korea
Automatic Person Identification using Multiple Cues
Danuwat Swangpol and Thanarat Chalidabhongse
Faculty of Information Technology, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand
(Tel: +66-2-737-2551; E-mail: ,"
838420cebfdad4e93221f8fe203c09155983141a,Subspace Alignment Based Domain Adaptation for RCNN Detector,"RAJ, NAMBOODIRI, TUYTELAARS: ADAPTING RCNN DETECTOR
Subspace Alignment Based Domain
Adaptation for RCNN Detector
Anant Raj
Vinay P. Namboodiri
Department of Electrical Engineering,
IIT Kanpur,
Kanpur, India.
Department of Computer Science and
Engineering,
IIT Kanpur,
Kanpur, India.
ESAT, PSI-VISICS
KU Leuven,
Heverlee, Belgium"
83cc0768927dfdac32f2d5753cf70ac23b7cddeb,BLACKFACE SURVEILLANCE CAMERA DATABASE FOR EVALUATING FACE RECOGNITION IN LOW QUALITY SCENARIOS,"ISSN:
Print    -  2277 - 0593
Online -  2315 - 7461
© FUNAAB 2016
BLACKFACE SURVEILLANCE CAMERA DATABASE FOR
EVALUATING FACE RECOGNITION IN LOW QUALITY
Journal of Natural
Science, Engineering
nd Technology
SCENARIOS
Y. AKINGBOYE
*1A. ABAYOMI-ALLI, 2E. O. OMIDIORA, 3S. O. OLABIYISI, 4J. A. OJO AND 5A.
Department of Computer Science, Federal University of Agriculture, Abeokuta, Nigeria.
,3Department of Computer Science and Engineering, Ladoke Akintola University of
Technology, Ogbomoso, Nigeria.
Department of Electrical and Electronic Engineering, Ladoke Akintola University of
Technology, Ogbomoso, Nigeria.
5Department of Electrical and Computer Engineering, Igbinedion University Okada,
Nigeria.
*Corresponding author:   Tel: +2347030672420"
833bdee366f1e6250dea59bdebdcad271c7cfddd,Bayesian non-parametrics for multi-modal segmentation,"Bayesian Non-Parametrics for
Multi-Modal Segmentation
Thesis for obtaining the title of
Doctor of Engineering Science
(Dr.-Ing.)
of the Faculty of Natural Science and Technology I
of Saarland University
Wei-Chen Chiu, M.Sc.
Saarbrücken
September 2016"
833fbf0e4be3ba82e7a1efdbc16813ee849d9942,Restricted Deformable Convolution based Road Scene Semantic Segmentation Using Surround View Cameras,"SUBMITTED TO IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Restricted Deformable Convolution based
Road Scene Semantic Segmentation
Using Surround View Cameras
Liuyuan Deng, Ming Yang, Hao Li, Tianyi Li, Bing Hu, Chunxiang Wang"
8373c162ae0574eac1239f075fafeda02de56e6a,Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose,"Real-time 2D Multi-Person Pose Estimation on CPU:
Lightweight OpenPose
Daniil Osokin
Intel"
833cd4265bd8162d3cfb483ce8f31eaef28e7a2e,TOWARDS EFFECTIVE GANS,"Under review as a conference paper at ICLR 2018
TOWARDS EFFECTIVE GANS
FOR DATA DISTRIBUTIONS WITH DIVERSE MODES
Anonymous authors
Paper under double-blind review"
83ce2c969ea323784b9098b9b170e015d559a1df,Detecting domestic objects with ensembles of view-tuned support vector machine cascades trained on Web images,"Detecting Domestic Objects with Ensembles of
View-tuned Support Vector Machine Cascades Trained
on Web Images
Marco Kortkamp"
8306e384e7ca48445843bc025b08236cd181d7c6,Histogram of Oriented Gradients with Cell Average Brightness for Human Detection,"Metrol. Meas. Syst., Vol. XXIII (2016), No. 1, pp. 27–36.
METROLOGY AND MEASUREMENT SYSTEMS
Index 330930, ISSN 0860-8229
www.metrology.pg.gda.pl
HISTOGRAM OF ORIENTED GRADIENTS WITH CELL AVERAGE
BRIGHTNESS FOR HUMAN DETECTION
Marek Wójcikowski
Gdańsk University of Technology, Faculty of Electronics, Telecommunications and Informatics, G. Narutowicza 11/12, 80-233 Gdańsk, Poland
((cid:1) +48 58 347 1974)"
832e1d128059dd5ed5fa5a0b0f021a025903f9d5,Pairwise Conditional Random Forests for Facial Expression Recognition,"Pairwise Conditional Random Forests for Facial Expression Recognition
Arnaud Dapogny1
Kevin Bailly1
S´everine Dubuisson1
Sorbonne Universit´es, UPMC Univ Paris 06, CNRS, ISIR UMR 7222, 4 place Jussieu 75005 Paris"
83c332971c4534907afc4865179c2de30f2792c4,Sparse and Dense Hybrid Representation via Dictionary Decomposition for Face Recognition,"Sparse And Dense Hybrid Representation
via Dictionary Decomposition
for Face Recognition
Xudong Jiang, Senior Member, IEEE, and Jian Lai, Student Member, IEEE"
83686b88e989bc6c9b66302e16546bde23ee34da,Coarse to fine non-rigid registration: a chain of scale-specific neural networks for multimodal image alignment with application to remote sensing,"chain of scale-specific neural networks for multimodal image alignment
Coarse to fine non-rigid registration:
with application to remote sensing
Armand Zampieri1, Guillaume Charpiat2, Yuliya Tarabalka1
Titane team, INRIA Sophia-Antipolis
TAU team, LRI, INRIA Saclay, Universit´e Paris-Sud
This project started early 2017 and this paper was sub-
mitted for publication in November 2017.
Introduction"
83e7254431486d24715d4170680c6cbc8bdb2328,Image retrieval using visual attention,"IMAGE RETRIEVAL USING VISUAL ATTENTION
Liam M. Mayron
A Dissertation Submitted to the Faculty of
The College of Engineering and Computer Science
in Partial Fulfillment of the Requirements for the Degree of
Doctor of Philosophy
Florida Atlantic University
Boca Raton, Florida
May 2008"
8387c58a5a3fd847f9b03760842dd49fec7cbb0e,Two-year-olds with autism orient to nonsocial contingencies rather than biological motion,"Vol 459 | 14 May 2009 | doi:10.1038/nature07868
LETTERS
Two-year-olds with autism orient to non-social
ontingencies rather than biological motion
Ami Klin1, David J. Lin1{, Phillip Gorrindo1{, Gordon Ramsay1,2 & Warren Jones1,3
Typically developing human infants preferentially attend to bio-
logical motion within the first days of life1. This ability is highly
onserved across species2,3 and is believed to be critical for filial
ttachment and for detection of predators4. The neural under-
pinnings of biological motion perception are overlapping with
rain regions involved in perception of basic social signals such
s facial expression and gaze direction5, and preferential attention
to biological motion is seen as a precursor to the capacity for
ttributing intentions to others6. However, in a serendipitous
observation7, we recently found that an infant with autism failed
to recognize point-light displays of biological motion, but was
instead highly sensitive to the presence of a non-social, physical
ontingency that occurred within the stimuli by chance. This
observation raised the possibility that perception of biological
motion may be altered in children with autism from a very early"
8322ed1a3db7c63af40280a782e39fb01bfe96dd,Class label autoencoder for zero-shot learning,"Class label autoencoder for zero-shot learning
Guangfeng Lina,∗, Caixia Fana, Wanjun Chena, Yajun Chena, Fan Zhaoa
Information Science Department, Xian University of Technology,
5 South Jinhua Road, Xi’an, Shaanxi Province 710048, PR China"
8331fb280f083767fe85ba476862e519e0275233,OMNIA Faster R-CNN: Detection in the wild through dataset merging and soft distillation,"Detection in the wild through dataset merging and soft distillation
OMNIA Faster R-CNN:
Alexandre Rame ∗ 1, Emilien Garreau † 1, Hedi Ben-Younes ‡ 1,2, and Charles Ollion § 1
Heuritech 2LIP6"
83d0b7100ddce32e37af72585f9aa4181e6447e3,Online Social Behavior Modeling for Multi-target Tracking,"Online Social Behavior Modeling for Multi-Target Tracking
Shu Zhang1 Abir Das1 Chong Ding2 Amit K. Roy-Chowdhury1
University of California, Riverside, CA 92521 USA"
83acbf0bee402b0472ff101cee5942f4137d91c3,Semi-automatic Annotation on Image Segmentation Hierarchies,"Semi-automatic Annotation on
Image Segmentation Hierarchies
DIPLOMARBEIT
zur Erlangung des akademischen Grades
Diplom-Ingenieur
im Rahmen des Studiums
Visual Computing
eingereicht von
Georg M. Zankl
Matrikelnummer 0625388
n der
Fakultät für Informatik der Technischen Universität Wien
Betreuung: Univ.Ass. Dipl.-Ing. Dr.techn. Yll Haxhimusa
Mitwirkung: Ing. Dr. techn. Adrian Ion
Wien, 12.10.2012
(Unterschrift Verfasser)
(Unterschrift Betreuung)
A-1040 Wien (cid:5) Karlsplatz 13 (cid:5) Tel. +43-1-58801-0 (cid:5) www.tuwien.ac.at
Technische Universität Wien"
8323af714efe9a3cadb31b309fcc2c36c8acba8f,Automatic Real-Time Facial Expression Recognition for Signed Language Translation,"Automatic Real-Time
Facial Expression Recognition
for Signed Language Translation
Jacob Richard Whitehill
A thesis submitted in partial fulfillment of the requirements for the de-
gree of Magister Scientiae in the Department of Computer Science,
University of the Western Cape.
May 2006"
834f5ab0cb374b13a6e19198d550e7a32901a4b2,Face Translation between Images and Videos using Identity-aware CycleGAN,"Face Translation between Images and Videos using Identity-aware CycleGAN
Zhiwu Huang†, Bernhard Kratzwald†, Danda Pani Paudel†, Jiqing Wu†, Luc Van Gool†‡
Computer Vision Lab, ETH Zurich, Switzerland
VISICS, KU Leuven, Belgium
{zhiwu.huang, paudel, jwu,"
83963d1454e66d9cc82e28ff4efc562f5fe6b7d3,"Automated detection of feeding strikes by larval fish using continuous high-speed digital video: a novel method to extract quantitative data from fast, sparse kinematic events.","© 2016. Published by The Company of Biologists Ltd | Journal of Experimental Biology (2016) 219, 1608-1617 doi:10.1242/jeb.133751
METHODS & TECHNIQUES
Automated detection of feeding strikes by larval fish using
ontinuous high-speed digital video: a novel method to extract
quantitative data from fast, sparse kinematic events
Eyal Shamur1,‡, Miri Zilka2,*,‡, Tal Hassner1, Victor China3,4, Alex Liberzon5 and Roi Holzman3,4,§
the observer and subject"
83ef7de2669bb2827208fd3a64ac910e276fbdb4,Fully Convolutional Networks for Dense Semantic Labelling of High-Resolution Aerial Imagery,"Fully Convolutional Networks for Dense Semantic Labelling of
High-Resolution Aerial Imagery
Jamie Sherrah
Defence Science & Technology Group
Edinburgh, South Australia
email:
https://au.linkedin.com/jsherrah
June 9, 2016"
8380b8f4e36c993eef23af42ccb382ae60aceabf,"URBAN-i: From urban scenes to mapping slums, transport modes, and pedestrians in cities using deep learning and computer vision","URBAN-i: From urban scenes to mapping slums, transport modes, and pedestrians
in cities using deep learning and computer vision
Mohamed R. Ibrahim1, James Haworth2 and Tao Cheng3
Department of Civil, Environmental and Geomatic Engineering, University College London (UCL)"
83fd5c23204147844a0528c21e645b757edd7af9,USDOT number localization and recognition from vehicle side-view NIR images,"USDOT Number Localization and Recognition From Vehicle Side-View NIR
Images
Orhan Bulan, Safwan Wshah, Ramesh Palghat, Vladimir Kozitsky and Aaron Burry
Palo Alto Research Center (PARC)
800 Phillips Rd. Webster NY 14580"
8326d3e57796dad294ab1c14a0688221550098b6,ABC-GAN: Adaptive Blur and Control for improved training stability of Generative Adversarial Networks,"Adaptive Blur and Control for improved training stability of
Generative Adversarial Networks
ABC-GAN:
Igor Susmelj 3 Eirikur Agustsson 3 Radu Timofte 3"
832a9584e85af1675d49ee35fd13283b21ce3a3f,Generating Photo-Realistic Training Data to Improve Face Recognition Accuracy,"Generating Photo-Realistic Training Data to Improve
Face Recognition Accuracy
Daniel S´aez Trigueros, Li Meng
School of Engineering and Technology
University of Hertfordshire
Hatfield AL10 9AB, UK
Margaret Hartnett
GBG plc
London E14 9QD, UK"
bb2944569a2b3d3b8340b36d4903c8cddf20047f,Improving Regression Performance with Distributional Losses,"Improving Regression Performance with Distributional Losses
Ehsan Imani 1 Martha White 1"
bb79bb04e569f9319fbc9d8e1f275bbb2cf8d32e,NMT-Keras: a Very Flexible Toolkit with a Focus on Interactive NMT and Online Learning,"NMT-Keras: a Very Flexible Toolkit with a Focus
on Interactive NMT and Online Learning
Álvaro Peris, Francisco Casacuberta
Pattern Recognition and Human Language Technology Research Center, Universitat Politècnica de València, Spain"
bb131650627cf2d1da570589f6c540041df1ae92,Improving the Intra Class Distance using RBSQI Technique for Facial Images with Illumination Variations,"Volume 2 Special Issue                                                                                                                     ISSN 2079-8407
Journal of Emerging Trends in Computing and Information Sciences
©2010-11 CIS Journal. All rights reserved.
http://www.cisjournal.org
Improving the Intra Class Distance using RBSQI Technique for Facial
Images with Illumination Variations
K. R. Singh1, M. A. Zaveri2, M.M. Raghuwanshi3
,2Computer Engineering Department, S.V.National Institute of Technology, Surat, 329507, India.
NYSS College of Engineering and Research, Nagpur, 441 110, India."
bbf534b8ee9455b8e492a252bef26f9293d4f91a,Effects of cannabis use and subclinical depression on the P3 event-related potential in an emotion processing task,"Observational Study
Medicine®
Effects of cannabis use and subclinical
depression on the P3 event-related potential
in an emotion processing task
Lucy J. Troup, PhD
, Robert D. Torrence, MS, Jeremy A. Andrzejewski, BSc, Jacob T. Braunwalder, BSc"
bb35ef89addbbc28d960bc0cab70d8a29fdf6eee,A Survey on Multi-Task Learning,"A Survey on Multi-Task Learning
Yu Zhang and Qiang Yang"
bb667cbbf050040fa39cd9e756cd5bf485fccf32,Effective Deterministic Initialization for $k$-Means-Like Methods via Local Density Peaks Searching,"Effective Deterministic Initialization for
k-Means-Like Methods via Local Density Peaks
Searching
Fengfu Li, Hong Qiao, and Bo Zhang"
bb021f58f8822d12f5747d583a46005ade4a0b10,Breaking Microsoft ’ s CAPTCHA,"Breaking Microsoft’s CAPTCHA
Colin Hong Bokil Lopez-Pineda Karthik Rajendran Adri`a Recasens
May 2015"
bbc4bbf7aa80a8108d62644fea24e6f70a805df9,Inducing Wavelets into Random Fields via Generative Boosting,"Inducing Wavelets into Random Fields via Generative
Boosting
Jianwen Xie, Yang Lu, Song-Chun Zhu, and Ying Nian Wu∗
Department of Statistics, University of California, Los Angeles, USA"
bb7c5a521607a02e7a291dca7fc33b595c3b7bff,Texture Classification using Local Binary Patterns and Modular PCA,"ISSN: 2278 – 1323
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 5, Issue 5, May 2016
Texture Classification using Local Binary
Patterns and Modular PCA
Sayanshree Ghosh, Srimanta Kundu and Sayantari Ghosh
www.ijarcet.org"
bbf5575f0d20b79b61c8c0d8b7c2a57224c359de,Emotion Recognition from Decision Level Fusion of Visual and Acoustic Features using Hausdorff Classifier,"Emotion Recognition from Decision Level Fusion
of Visual and Acoustic Features using Hausdorff
Classifier
H.D.Vankayallapati1, K.R.Anne2, and K. Kyamakya1
Institute of Smart System Technologies, Transportation Informatics Group
University of Klagenfurt, Klagenfurt, Austria.
Department of Information Technology, TIFAC-CORE in Telematics
VR Siddhartha Engineering College, Vijayawada, India."
bb893fac40eb901229567abb507a8cb82553d198,Will the Pedestrian Cross? Probabilistic Path Prediction Based on Learned Motion Features,"Will the Pedestrian Cross?
Probabilistic Path Prediction Based on Learned Motion Features
Christoph G. Keller1, Christoph Hermes2, and Dariu M. Gavrila3,4
Image & Pattern Analysis Group, Univ. of Heidelberg, Germany
Applied Informatics Group, Univ. of Bielefeld, Germany
Environment Perception, Group Research, Daimler AG, Ulm, Germany
Intelligent Systems Lab, Fac. of Science, Univ. of Amsterdam, The Netherlands"
bb980dd94463b03c6584513bcccf780e43f089b2,Prediction Error Meta Classification in Semantic Segmentation: Detection via Aggregated Dispersion Measures of Softmax Probabilities,"Prediction Error Meta Classification in Semantic
Segmentation: Detection via Aggregated Dispersion
Measures of Softmax Probabilities
Matthias Rottmann∗, Pascal Colling∗, Thomas Paul Hack†,
Fabian H¨uger‡, Peter Schlicht‡ and Hanno Gottschalk∗"
bb4650130c460f413e97b0328624a485bf094967,Dynamic Lexicon Generation for Natural Scene Images,"Dynamic Lexicon Generation for Natural Scene
Images
Yash Patel1,2, Lluis Gomez2, Mar¸cal Rusi˜nol2, and Dimosthenis Karatzas2
Computer Vision Center, Universitat Aut`onoma de Barcelona.
CVIT, IIIT Hyderabad, India."
bb1dc1e9e9c20d99b55f37b9e635457af86a065f,Neural Ctrl-F: Segmentation-Free Query-by-String Word Spotting in Handwritten Manuscript Collections,"Neural Ctrl-F: Segmentation-free Query-by-String Word Spotting in
Handwritten Manuscript Collections
Tomas Wilkinson
Department of Information Technology
Uppsala University
Jonas Lindstr¨om
Department of History
Uppsala University
Anders Brun
Department of Information Technology
Uppsala University"
bb06c12e83255b2c3afca1e3e115e721c53b46b3,Beyond Local Appearance: Category Recognition from Pairwise Interactions of Simple Features,"Beyond Local Appearance: Category Recognition from Pairwise Interactions of
Simple Features
Marius Leordeanu1
Martial Hebert1
Rahul Sukthankar2,1
Carnegie Mellon University 2Intel Research Pittsburgh"
bb127015474fdc51d4cd6b4dda7176a8c778ea49,Examining the Impact of Blur on Recognition by Convolutional Networks.,"Examining the Impact of Blur on Recognition by Convolutional Networks
Igor Vasiljevic
University of Chicago
Ayan Chakrabarti
TTI-Chicago
Gregory Shakhnarovich
TTI-Chicago"
bb288a5c653659411f95ef5db8e1ad652e0a8173,Learning Depth From Single Images With Deep Neural Network Embedding Focal Length,"Learning Depth from Single Images with Deep
Neural Network Embedding Focal Length
Lei He, Guanghui Wang (Senior Member, IEEE) and Zhanyi Hu"
bba22e04fbe124bf58330e5d911d873a80afa0eb,Probabilistic Global Scale Estimation for MonoSLAM Based on Generic Object Detection,"Probabilistic Global Scale Estimation for MonoSLAM
Based on Generic Object Detection
Centro de Investigaci´on en Matem´aticas - Universidad de Guanajuato
Jalisco S/N, Col. Valenciana CP: 36023 Guanajuato, Gto, Mxico
Edgar Sucar, Jean-Bernard Hayet"
bbab2c3d0ebc0957c5e962298ffd8c6d4bc25c5a,Have we met before? Neural correlates of emotional learning in women with social phobia.,"Research Paper
Have we met before? Neural correlates of emotional
learning in women with social phobia
Inga Laeger, MA; Kati Keuper, MA; Carina Heitmann, MA; Harald Kugel, PhD;
Christian Dobel, PhD; Annuschka Eden, MA; Volker Arolt, MD; Pienie Zwitserlood, PhD;
Udo Dannlowski, MD, PhD*; Peter Zwanzger, MD*
Laeger,  Heitmann,  Arolt,  Dannlowski,  Zwanzger  —  Department  of  Psychiatry,  University  of  Muenster,  Germany;  Keuper,
Dobel, Eden — Institute for Biomagnetism and Biosignalanalysis, University of Muenster, Germany; Kugel — Department of
Clinical  Radiology,  University  of  Muenster,  Germany;  Zwitserlood  —  Institute  for  Psychology,  University  of  Muenster,  Ger-
many; Dannlowski — Department of Psychiatry, University of Marburg, Germany
Background: Altered memory processes are thought to be a key mechanism in the etiology of anxiety disorders, but little is known about
the neural correlates of fear learning and memory biases in patients with social phobia. The present study therefore examined whether pa-
tients with social phobia exhibit different patterns of neural activation when confronted with recently acquired emotional stimuli. Methods:
Patients with social phobia and a group of healthy controls learned to associate pseudonames with pictures of persons displaying either a
fearful or a neutral expression. The next day, participants read the pseudonames in the magnetic resonance imaging scanner. Afterwards,
memory tests were carried out.  Results: We enrolled 21 patients and 21 controls in our study. There were no group differences for
learning performance, and results of the memory tests were mixed. On a neural level, patients showed weaker amygdala activation than
ontrols for the contrast of names previously associated with fearful versus neutral faces. Social phobia severity was negatively related to
mygdala  activation.  Moreover,  a  detailed  psychophysiological  interaction  analysis  revealed  an  inverse  correlation  between  disorder
severity and frontolimbic connectivity for the emotional > neutral pseudonames contrast. Limitations: Our sample included only women."
bb1f4c8e4f310047e50b7dc41d87292025d42eb7,Intersubject Differences in False Nonmatch Rates for a Fingerprint-Based Authentication System,"Hindawi Publishing Corporation
EURASIP Journal on Advances in Signal Processing
Volume 2009, Article ID 896383, 9 pages
doi:10.1155/2009/896383
Research Article
Intersubject Differences in False Nonmatch Rates for
Fingerprint-Based Authentication System
Jeroen Breebaart, Ton Akkermans, and Emile Kelkboom
Philips Research, HTC 34 MS61, 5656 AE Eindhoven, The Netherlands
Correspondence should be addressed to Jeroen Breebaart,
Received 4 September 2008; Accepted 7 July 2009
Recommended by Jonathon Phillips
The intersubject dependencies of false nonmatch rates were investigated for a minutiae-based biometric authentication process
using single enrollment and verification measurements. A large number of genuine comparison scores were subjected to statistical
inference tests that indicated that the number of false nonmatches depends on the subject and finger under test. This result was also
observed if subjects associated with failures to enroll were excluded from the test set. The majority of the population (about 90%)
showed a false nonmatch rate that was considerably smaller than the average false nonmatch rate of the complete population.
The remaining 10% could be characterized as “goats” due to their relatively high probability for a false nonmatch. The image
quality reported by the template extraction module only weakly correlated with the genuine comparison scores. When multiple
verification attempts were investigated, only a limited benefit was observed for “goats,” since the conditional probability for a false"
bb489e4de6f9b835d70ab46217f11e32887931a2,Everything You Wanted to Know about Deep Learning for Computer Vision but Were Afraid to Ask,"Everything you wanted to know about Deep Learning for Computer Vision but were
fraid to ask
Moacir A. Ponti, Leonardo S. F. Ribeiro, Tiago S. Nazare
ICMC – University of S˜ao Paulo
S˜ao Carlos/SP, 13566-590, Brazil
Tu Bui, John Collomosse
CVSSP – University of Surrey
Guildford, GU2 7XH, UK
Email: [ponti, leonardo.sampaio.ribeiro,
Email: [t.bui,
tools,"
bb22104d2128e323051fb58a6fe1b3d24a9e9a46,Analyzing Facial Expression by Fusing Manifolds,")=OEC .=?E= -NFHAIIE >O .KIEC
9A;= +D=C1,2 +DK5C +DA1,3 ;E2EC 0KC1,2,3
1IJEJKJA B 1BH=JE 5?EA?A 5EE?= 6=EM=
,AFJ B +FKJAH 5?EA?A 1BH=JE -CEAAHEC =JE= 6=EM= 7ELAHIEJO
IJEJKJA B AJMHEC =JE= 6=EM= 7ELAHIEJO
{wychang,
)>IJH=?J .A=JKHA HAFHAIAJ=JE ?=IIE?=JE =HA JM =H EIIKAI E B=?E=
ANFHAIIE ==OIEI 1 JDA F=IJ IJ AEJDAH DEIJE? H ?= HAFHA
IAJ=JE BH ==OIEI 1 AIIA?A ?= EBH=JE =EO B?KIAI  JDA IK>JA
L=HE=JEI B ANFHAIIEI DEIJE? HAFHAIAJ=JE IJHAIIAI  C>=
JEAI 6 J=A JDA B >JD = HAFHAIAJ=JE EI E JDEI
F=FAH A=HEC EI J ?D=H=?JAHEA C>= ?= EBH=
JE 7EA IA KIEC A=H
EC =FFH=?DAI B JDA HAFHAIAJ=JE =HA >O
= A=HEC JA?DEGKA 6 EJACH=JA JDAIA
ABBA?JELAO = BKIE ?=IIEAH EI MDE?D ?= DAF J AFO IKEJ=>A
?>E=JE MAECDJI B B=?E= ?FAJI J = ANFHAIIE +FHADA
IELA ?F=HEII  B=?E= ANFHAIIE HA?CEJE =HA J JDA
ABBA?JELAAII B KH =CHEJD
A=EEC DK= AJEI F=OI = EFHJ=J HA E DK= ?KE?=JE 6"
bb08d1570685a20861e9b8c15c57aae9c01d3eac,Modeling cooperative navigation in dense human crowds,"Modeling Cooperative Navigation in Dense Human Crowds
Anirudh Vemula1, Katharina Muelling1 and Jean Oh1"
bbd1eb87c0686fddb838421050007e934b2d74ab,Look at Boundary: A Boundary-Aware Face Alignment Algorithm,"(68	points)	COFW	(29	points)	AFLW	(19	points)	Figure1:Thefirstcolumnshowsthefaceimagesfromdifferentdatasetswithdifferentnumberoflandmarks.Thesecondcolumnillustratestheuniversallydefinedfacialboundariesestimatedbyourmethods.Withthehelpofboundaryinformation,ourapproachachieveshighaccuracylocalisationresultsacrossmultipledatasetsandannotationprotocols,asshowninthethirdcolumn.Differenttofacedetection[45]andrecognition[75],facealignmentidentifiesgeometrystructureofhumanfacewhichcanbeviewedasmodelinghighlystructuredout-put.Eachfaciallandmarkisstronglyassociatedwithawell-definedfacialboundary,e.g.,eyelidandnosebridge.However,comparedtoboundaries,faciallandmarksarenotsowell-defined.Faciallandmarksotherthancornerscanhardlyremainthesamesemanticallocationswithlargeposevariationandocclusion.Besides,differentannotationschemesofexistingdatasetsleadtoadifferentnumberoflandmarks[28,5,66,30](19/29/68/194points)andanno-tationschemeoffuturefacealignmentdatasetscanhardlybedetermined.Webelievethereasoningofauniquefacial"
bbc8254b170918d619574496c138dac101dee61f,Context-aware robot navigation using interactively built semantic maps,"Research Article
Akansel Cosgun* and Henrik I. Christensen
Context-aware robot navigation using
interactively built semantic maps
Open Access"
bba281fe9c309afe4e5cc7d61d7cff1413b29558,An unpleasant emotional state reduces working memory capacity: electrophysiological evidence,"Social Cognitive and Affective Neuroscience, 2017, 984–992
doi: 10.1093/scan/nsx030
Advance Access Publication Date: 11 April 2017
Original article
An unpleasant emotional state reduces working
memory capacity: electrophysiological evidence
Jessica S. B. Figueira,1 Leticia Oliveira,1 Mirtes G. Pereira,1 Luiza B. Pacheco,1
Isabela Lobo,1,2 Gabriel C. Motta-Ribeiro,3 and Isabel A. David1
Laboratorio de Neurofisiologia do Comportamento, Departamento de Fisiologia e Farmacologia, Instituto
Biome´dico, Universidade Federal Fluminense, Niteroi, Brazil, 2MograbiLab, Departamento de Psicologia,
Pontifıcia Universidade Catolica do Rio de Janeiro, Rio de Janeiro, Brazil, and 3Laboratorio de Engenharia
Pulmonar, Programa de Engenharia Biome´dica, COPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
Correspondence should be addressed to Isabel A. David, Departamento de Fisiologia e Farmacologia, Instituto Biome´dico, Universidade Federal
Fluminense, Rua Hernani Pires de Mello, 101, Niteroi, RJ 24210-130, Brazil. E-mail:"
bb6ac4e26499dea5bdedb05b269f40f56247b4c6,An Action Unit based Hierarchical Random Forest Model to Facial Expression Recognition,
bb7f2c5d84797742f1d819ea34d1f4b4f8d7c197,From Images to 3D Shape Attributes.,"TO APPEAR IN TPAMI
From Images to 3D Shape Attributes
David F. Fouhey, Abhinav Gupta, Andrew Zisserman"
587f81ae87b42c18c565694c694439c65557d6d5,DeepFace : Face Generation using Deep Learning,"DeepFace: Face Generation using Deep Learning
Hardie Cate
Fahim Dalvi
Zeshan Hussain"
581e920ddb6ecfc2a313a3aa6fed3d933b917ab0,Automatic Mapping of Remote Crowd Gaze to Stimuli in the Classroom,"Automatic Mapping of Remote Crowd Gaze to
Stimuli in the Classroom
Thiago Santini1, Thomas K¨ubler1, Lucas Draghetti1, Peter Gerjets2, Wolfgang
Wagner3, Ulrich Trautwein3, and Enkelejda Kasneci1
University of T¨ubingen, T¨ubingen, Germany
Leibniz-Institut f¨ur Wissensmedien, T¨ubingen, Germany
Hector Research Institute of Education Sciences and Psychology, T¨ubingen,
Germany"
58d16e23e1192be4acaf6a29c1f5995817146554,Bringing back simplicity and lightliness into neural image captioning,"Bringing back simplicity and lightliness into neural image captioning
Jean-Benoit Delbrouck and St´ephane Dupont
{jean-benoit.delbrouck,
TCTS Lab, University of Mons, Belgium"
580054294ca761500ada71f7d5a78acb0e622f19,A Subspace Model-Based Approach to Face Relighting Under Unknown Lighting and Poses,"A Subspace Model-Based Approach to Face
Relighting Under Unknown Lighting and Poses
Hyunjung Shim, Student Member, IEEE, Jiebo Luo, Senior Member, IEEE, and Tsuhan Chen, Fellow, IEEE"
5865e824e3d8560e07840dd5f75cfe9bf68f9d96,Embodied conversational agents for multimodal automated social skills training in people with autism spectrum disorders,"RESEARCH ARTICLE
Embodied conversational agents for
multimodal automated social skills training in
people with autism spectrum disorders
Hiroki Tanaka1*, Hideki Negoro2, Hidemi Iwasaka3, Satoshi Nakamura1
Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma-shi, Nara, 630-
0101, Japan, 2 Center for Special Needs Education, Nara University of Education, Nara-shi, Nara, 630-8538,
Japan, 3 Developmental Center for Child and Adult, Shigisan Hospital, Ikoma-gun, Nara, 636-0815, Japan"
58cb6677b77d5a79fc5b8058829693ca30b36ac5,Under Review as a Conference Paper at Iclr 2016 Learning Metrics by Learning Constrained Em- Beddings of Objects to R N,"Learning Similarity Metrics by Factorising Adjacency Matrices
Henry Gouk†
Bernhard Pfahringer†
Michael Cree‡
Department of Computer Science, University of Waikato, Hamilton, New Zealand
School of Engineering, University of Waikato, Hamilton, New Zealand"
581fb0f0405c7f0e60610d88ceaceb9af44d8569,Final Report : Smart Trash Net : Waste Localization and Classification,"Final Report: Smart Trash Net: Waste Localization and
Classification
Oluwasanya Awe
Robel Mengistu
Vikram Sreedhar
December 15, 2017"
58081cb20d397ce80f638d38ed80b3384af76869,Embedded Real-Time Fall Detection Using Deep Learning For Elderly Care,"Embedded Real-Time Fall Detection Using Deep
Learning For Elderly Care
Hyunwoo Lee∗
Jooyoung Kim
Dojun Yang
Joon-Ho Kim
Samsung Research, Samsung Electronics
{hyun0772.lee, joody.kim, dojun.yang,"
58fa85ed57e661df93ca4cdb27d210afe5d2cdcd,Facial expression recognition by re-ranking with global and local generic features,"Cancún Center, Cancún, México, December 4-8, 2016
978-1-5090-4847-2/16/$31.00 ©2016 IEEE"
587b607a176588b4646bcdb3d60b6204e98806fe,Extraction in Volumetric Bioimages A Dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Electrical Engineering,"UNIVERSITY OF CALIFORNIA
RIVERSIDE
Feature Extraction in Volumetric Bioimages
A Dissertation submitted in partial satisfaction
of the requirements for the degree of
Doctor of Philosophy
Electrical Engineering
Min Liu
June 2012
Dissertation Committee:
Professor Amit K. Roy-Chowdhury, Chairperson
Professor Venugopala Gonehal Reddy
Professor Ertem Tuncel"
58b80f0e484d32c9fe5b57648848e048270d435b,Development of an Efficient Face Recognition System based on Linear and Nonlinear Algorithms,"Development of an Efficient Face Recognition System based on Linear and Nonlinear Algorithms
{tag}                                                 {/tag}
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 134
Number 7
Year of Publication: 2016
Authors:
Filani Araoluwa S., Adetunmbi Adebayo O.
10.5120/ijca2016907932
{bibtex}2016907932.bib{/bibtex}"
58888b30e9123c1b1709be1efa92898e090d7bd2,Person Re-Identification by Discriminative Selection in Video Ranking,"Person Re-Identification by Discriminative
Selection in Video Ranking
Taiqing Wang, Shaogang Gong, Xiatian Zhu, and Shengjin Wang"
5884eaf9f7c20d6d65892a9eb91020448262c5d8,Perceptual expectation evokes category-selective cortical activity.,"doi:10.1093/cercor/bhp188
Advance Access publication September 16, 2009
Perceptual Expectation Evokes Category-
Selective Cortical Activity
Michael Esterman and Steven Yantis
Department of Psychological and Brain Sciences, Johns
Hopkins University, Baltimore, MD 21218-2686, USA
Selective visual attention directed to a location (even in the
bsence of a stimulus) increases activity in the corresponding
regions of visual cortex and enhances the speed and accuracy of
target perception. We further explored top-down influences on
perceptual representations by manipulating observers’ expectations
bout
the category of an upcoming target. Observers viewed
display in which an object (either a face or a house) gradually
emerged from a state of phase-scrambled noise; a cue established
expectation about the object category. Observers were faster to
ategorize faces (gender discrimination) or houses (structural
discrimination) when the category of the partially scrambled object
matched their expectation. Functional magnetic resonance imaging"
585efe3c8efd1a4fa2ed8221c278997521668bc1,Recognizing Face Images with Disguise Variations,
5801690199c1917fa58c35c3dead177c0b8f9f2d,Application of Object Based Classification and High Resolution Satellite Imagery for Savanna Ecosystem Analysis,"Remote Sens. 2010, 2, 2748-2772; doi:10.3390/rs2122748
OPEN ACCESS
Article
Application of Object Based Classification and High Resolution
Satellite Imagery for Savanna Ecosystem Analysis
ISSN 2072-4292
www.mdpi.com/journal/remotesensing
Cerian Gibbes *, Sanchayeeta Adhikari, Luke Rostant, Jane Southworth, and Youliang Qiu
Department of Geography & Land Use and Environmental Change Institute (LUECI), University of
Florida, 3141 Turlington Hall, P. O. Box 117315, Gainesville, FL 32611, USA;
E-Mails: (S.A.); (L.R.); (J.S.);
(Y.Q.)
*  Author to whom correspondence should be addressed; E-Mail:
Tel.: +1-352-392-0494; Fax: +1-352-392-8855.
Received: 16 October 2010; in revised form: 7 December 2010 / Accepted: 8 December 2010 /
Published: 10 December 2010"
589b30ebdb76659ce5d3a19cd9fa0e7a3466d85d,Very Low Resolution Face Recognition Problem,"Very Low Resolution Face Recognition Problem
Wilman ZOU
Pong C. Yuen"
58cbd5a31e92cff29e29e8b25ee79f30ff4e6d4b,Culture shapes spatial frequency tuning for face identification.,"Journal of Experimental Psychology:
Human Perception and Performance
017, Vol. 43, No. 2, 294 –306
0096-1523/17/$12.00
© 2016 American Psychological Association
http://dx.doi.org/10.1037/xhp0000288
Culture Shapes Spatial Frequency Tuning for Face Identification
Université de Montréal and Université du Québec en Outaouais
Jessica Tardif
Daniel Fiset
Université du Québec en Outaouais
Ye Zhang
Hangzhou Normal University
Amanda Estéphan
Université du Québec en Outaouais
Qiuju Cai, Canhuang Luo, and Dan Sun
Hangzhou Normal University
Frédéric Gosselin
Université de Montréal
Caroline Blais"
5860cf0f24f2ec3f8cbc39292976eed52ba2eafd,COMPUTATION EvaBio : A TOOL FOR PERFORMANCE EVALUATION IN BIOMETRICS,"International Journal of Automated Identification Technology, 3(2), July-December 2011, pp. 51-60
COMPUTATION EvaBio: A TOOL FOR PERFORMANCE
EVALUATION IN BIOMETRICS
Julien Mahier, Baptiste Hemery, Mohamad El-Abed*, Mohamed T. El-Allam, Mohamed Y.
Bouhaddaoui and Christophe Rosenberger
GREYC Laboratory, ENSICAEN - University of Caen Basse Normandie - CNRS,
6 Boulevard Maréchal Juin, 14000 Caen Cedex - France"
58bf72750a8f5100e0c01e55fd1b959b31e7dbce,PyramidBox: A Context-assisted Single Shot Face Detector,"PyramidBox: A Context-assisted Single Shot
Face Detector.
Xu Tang∗, Daniel K. Du∗, Zeqiang He, and Jingtuo Liu†
Baidu Inc."
58f7b9ebdb9b380cdfbef12b8abefceee0160a58,Public Document Document Evolution Executive Summary,"Project N° IST-2002-507634 - BioSecure
D7.2.2 – Revision: b3
Contract Number:
Project Acronym:
Project Title:
Instrument:
Start Date of Project:
Duration:
Deliverable Number:
Title of Deliverable:
8 April 2005
IST-2002-507634
BioSecure
Biometrics for Secure Authentication
Network of Excellence
01 June, 2004
6 months
D7.2.2
Report on the face state of the art
Contractual Due Date:"
580a39100e0d1466c914915a2a30ec0a57a94bcc,Voxblox: Building 3D Signed Distance Fields for Planning,"Voxblox: Building 3D Signed Distance Fields for Planning
Helen Oleynikova, Zachary Taylor, Marius Fehr, Juan Nieto, and Roland Siegwart
Autonomous Systems Lab, ETH Z¨urich"
58823377757e7dc92f3b70a973be697651089756,Automatic facial expression analysis,"Technical Report
UCAM-CL-TR-861
ISSN 1476-2986
Number 861
Computer Laboratory
Automatic facial expression analysis
Tadas Baltrusaitis
October 2014
5 JJ Thomson Avenue
Cambridge CB3 0FD
United Kingdom
phone +44 1223 763500
http://www.cl.cam.ac.uk/"
5892f8367639e9c1e3cf27fdf6c09bb3247651ed,CASC Multimedia Information Retrieval Extract “ faces ” and,"Estimating Missing Features to Improve Multimedia Information Retrieval
Abraham Bagherjeiran
Nicole S. Love
Chandrika Kamath (cid:3)"
5834555d239c27369e7a4167bb0c0fed725d761e,Improved illumination invariant homomorphic filtering using the dual tree complex wavelet transform,"978-1-4799-9988-0/16/$31.00 ©2016 IEEE
ICASSP 2016"
5808f5285bc60a73bc240621ad0fce606867ebc1,VAIN: Attentional Multi-agent Predictive Modeling,"VAIN: Attentional Multi-agent Predictive Modeling
Yedid Hoshen
Facebook AI Research, NYC"
587c48ec417be8b0334fa39075b3bfd66cc29dbe,Serial Dependence in the perception of attractiveness.,"Serial dependence in the perception of attractiveness
Ye Xia
Department of Psychology, University of California,
Berkeley, CA, USA
Allison Yamanashi Leib
Department of Psychology, University of California,
Berkeley, CA, USA
David Whitney
Department of Psychology, University of California,
Berkeley, CA, USA
Helen Wills Neuroscience Institute, University of
California, Berkeley, CA, USA
Vision Science Group, University of California,
Berkeley, CA, USA
The perception of attractiveness is essential for choices
of food, object, and mate preference. Like perception of
other visual features, perception of attractiveness is
stable despite constant changes of image properties due
to factors like occlusion, visual noise, and eye
movements. Recent results demonstrate that perception"
58bb77dff5f6ee0fb5ab7f5079a5e788276184cc,Facial expression recognition with PCA and LBP features extracting from active facial patches,"Facial Expression Recognition with PCA and LBP
Features Extracting from Active Facial Patches
Yanpeng Liua, Yuwen Caoa, Yibin Lia, Ming Liu, Rui Songa
Yafang Wang, Zhigang Xu , Xin Maa†"
58abb5001087f51dd2e9ab17b9fb8fb3567988e8,Array of Multilayer Perceptrons with No-class Resampling Training for Face Recognition,"Inteligencia Artificial 44(2009), 5-13
doi: 10.4114/ia.v13i44.1041
INTELIGENCIA ARTIFICIAL
http://erevista.aepia.org/
Array of Multilayer Perceptrons with No-class
Resampling Training for Face Recognition
D. Capello1, C. Mart´ınez2,3, D. Milone2 and G. Stegmayer1
CIDISI-UTN-FRSF, CONICET, Lavaise 610 - Santa Fe (Argentina)
Sinc(i)-FICH-UNL, CONICET, Ciudad Universitaria UNL - Santa Fe (Argentina)
Laboratorio de Cibern´etica-FI-UNER, C.C. 47 Suc. 3-3100, Entre R´ıos (Argentina)"
5882e62866fe1fcf7f8458e0bd0bcb39057afce3,Attention to Head Locations for Crowd Counting,"Attention to Head Locations for Crowd Counting
Youmei Zhang, Chunluan Zhou, Faliang Chang, and Alex C. Kot, Fellow Member, IEEE"
58a6eb3584b2f5df2f25d39a218904d510cae516,The UAVid Dataset for Video Semantic Segmentation,"The UAVid Dataset for Video Semantic Segmentation
Ye Lyu1, George Vosselman1, Guisong Xia2, Alper Yilmaz3, Michael Ying Yang1∗"
0dccc881cb9b474186a01fd60eb3a3e061fa6546,Effective face frontalization in unconstrained images,"Effective Face Frontalization in Unconstrained Images
Tal Hassner1, Shai Harel1 †, Eran Paz1 † and Roee Enbar2
The open University of Israel. 2Adience.
Figure 1: Frontalized faces. Top: Input photos; bottom: our frontalizations,
obtained without estimating 3D facial shapes.
“Frontalization” is the process of synthesizing frontal facing views of faces
ppearing in single unconstrained photos. Recent reports have suggested
that this process may substantially boost the performance of face recogni-
tion systems. This, by transforming the challenging problem of recognizing
faces viewed from unconstrained viewpoints to the easier problem of rec-
ognizing faces in constrained, forward facing poses. Previous frontalization
methods did this by attempting to approximate 3D facial shapes for each
query image. We observe that 3D face shape estimation from unconstrained
photos may be a harder problem than frontalization and can potentially in-
troduce facial misalignments. Instead, we explore the simpler approach of
using a single, unmodified, 3D surface as an approximation to the shape of
ll input faces. We show that this leads to a straightforward, efficient and
easy to implement method for frontalization. More importantly, it produces
esthetic new frontal views and is surprisingly effective when used for face
recognition and gender estimation."
0d7cd7256784d29296065cce07e432142fd4cad2,Machine Learning Approach for Object Recognition,"Machine Learning Approach for Object Recognition
V. N. Pawar and S. N. Talbar
learning  technique.  The"
0d0041aefb16c5f7b1e593b440bb3df7b05b411c,Secure JPEG scrambling enabling privacy in photo sharing,"Secure JPEG Scrambling Enabling
Privacy in Photo Sharing
Lin Yuan, Pavel Korshunov, Touradj Ebrahimi
Multimedia Signal Processing Group, EPFL
De-ID workshop, Ljubljana, Slovenia
8/14/2015
Workshop on De-identification for Privacy Protection in Multimedia"
0dcc768631d9ede8a3679e980b37204b782781b2,Stating the Obvious: Extracting Visual Common Sense Knowledge,"San Diego, California, June 12-17, 2016. c(cid:13)2016 Association for Computational Linguistics
Proceedings of NAACL-HLT 2016, pages 193–198,"
0d0199e48d22ff4b80c983e3b28532f908467da7,Linear regression motion analysis for unsupervised temporal segmentation of human actions,"Linear Regression Motion Analysis for Unsupervised Temporal
Segmentation of Human Actions
Simon Jones, Ling Shao
Department of Electronic and Electrical Engineering
The University of Shef‌f‌ield, Mappin St, Shef‌f‌ield, S1 3JD, UK"
0d30066576c029cd888d7c759349379bdb0e88c2,"How Much Information Kinect Facial Depth Data Can Reveal About Identity, Gender and Ethnicity?","How Much Information Kinect Facial Depth
Data Can Reveal about Identity, Gender and
Ethnicity?
Elhocine Boutellaaa;b, Messaoud Bengherabia, Samy Ait-Aoudiab, Abdenour
Hadidc
Centre de D(cid:19)eveloppement des Technologies Avanc(cid:19)ees (DZ),
Ecole Nationale Sup(cid:18)erieure d’Informatique (DZ),
University of Oulu (FI)"
0d96c9d14f079b7b8b6b56b4fa86f611a4ff237f,Semi-supervised low-rank mapping learning for multi-label classification,"Semi-supervised Low-Rank Mapping Learning for Multi-label Classification
Liping Jing1, Liu Yang1, Jian Yu1, Michael K. Ng2
Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University. 2Department of Mathematics, Hong Kong Baptist University.
With the rapid growth of online content such as images, videos, web pages,
it is crucial to design a scalable and effective classification system to au-
tomatically organize, store, and search the content. In conventional clas-
sification, each instance is assumed to belong to exactly one class among
finite number of candidate classes. However, in modern applications, an
instance can have multiple labels. For example, an image can be annotated
y many conceptual tags in semantic scene classification. Multi-label data
have ubiquitously occurred in many application domains: multimedia infor-
mation retrieval, tag recommendation, query categorization, gene function
prediction, medical diagnosis, drug discovery and marketing. An important
nd challenging research problem [1, 4] in multi-label learning is how to
exploit and make use of label correlations.
In this paper, we develop a novel method for multi-label learning when
there is only a small number of labeled data. Our main idea is to design
Semi-supervised Low-Rank Mapping (SLRM) from a feature space to a
label space based on given multi-label data. More specifically, the SLRM
model can be formularized as"
0da4c3d898ca2fff9e549d18f513f4898e960aca,The Headscarf Effect Revisited: Further Evidence for a Culture-Based Internal Face Processing Advantage.,"Wang, Y., Thomas, J., Weissgerber, S. C., Kazemini, S., Ul-Haq, I., &
Quadflieg, S. (2015). The Headscarf Effect Revisited: Further Evidence for a
36. 10.1068/p7940
Peer reviewed version
Link to published version (if available):
0.1068/p7940
Link to publication record in Explore Bristol Research
PDF-document
University of Bristol - Explore Bristol Research
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This document is made available in accordance with publisher policies. Please cite only the published
version using the reference above. Full terms of use are available:
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nd include the following information in your message:
• Your contact details
• Bibliographic details for the item, including a URL
• An outline of the nature of the complaint"
0d760e7d762fa449737ad51431f3ff938d6803fe,LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems,"LCDet: Low-Complexity Fully-Convolutional Neural Networks for
Object Detection in Embedded Systems
Subarna Tripathi
UC San Diego ∗
Gokce Dane
Qualcomm Inc.
Byeongkeun Kang
UC San Diego
Vasudev Bhaskaran
Qualcomm Inc.
Truong Nguyen
UC San Diego"
0dd86ec40c90c436a1ec566501dc8429d85b9d88,Driver Gaze Zone Estimation Using Convolutional Neural Networks: A General Framework and Ablative Analysis,"Driver Gaze Zone Estimation using Convolutional Neural Networks:
A General Framework and Ablative Analysis
Sourabh Vora, Akshay Rangesh, and Mohan M. Trivedi"
0d4dbd59e42e615ccf6cd4f71203be97afac48fb,End-to-End Joint Semantic Segmentation of Actors and Actions in Video,"End-to-End Joint Semantic Segmentation of
Actors and Actions in Video
Jingwei Ji1, Shyamal Buch1, Alvaro Soto2, and Juan Carlos Niebles1
Stanford Vision and Learning Lab,2Pontificia Universidad Catlica de Chile
{jingweij, shyamal,"
0d8a2034bbdefa214d8debecc704cadc5b9ec6e8,SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY AT THE UNIVERSITY OF SUSSEX,"A University of Sussex DPhil thesis
Available online via Sussex Research Online:
http://sro.sussex.ac.uk/
This thesis is protected by copyright which belongs to the author.
This thesis cannot be reproduced or quoted extensively from without first
obtaining permission in writing from the Author
The content must not be changed in any way or sold commercially in any
format or medium without the formal permission of the Author
When referring to this work, full bibliographic details including the
uthor, title, awarding institution and date of the thesis must be given
Please visit Sussex Research Online for more information and further details"
0d6008f2b2e198e9eac44e8ad68e590cf6b41c57,Human and chimpanzee face recognition in chimpanzees (Pan troglodytes): role of exposure and impact on categorical perception.,"007, Vol. 121, No. 6, 1145–1155
Copyright 2007 by the American Psychological Association
0735-7044/07/$12.00 DOI: 10.1037/0735-7044.121.6.1145
Human and Chimpanzee Face Recognition in Chimpanzees (Pan
troglodytes): Role of Exposure and Impact on Categorical Perception
Emory University and Yerkes National Primate Research Center
Julie Martin-Malivel
Kazunori Okada
San Francisco State University
The respective influences of exposure and inborn neural networks on conspecific and nonconspecific face
processing remain unclear. Although the importance of exposure in the development of object and face
recognition in general is well documented, studies explicitly comparing face recognition across species
showed a species-specific effect. For instance, laboratory monkeys exposed daily to human faces were
etter at discriminating monkeys than humans, suggesting that the role of exposure may not be the only
factor affecting cross-species recognition. In the present study, the authors investigated conspecific and
nonconspecific face recognition in chimpanzees (Pan troglodytes) from 2 primate centers that provided
different exposure to chimpanzee and human faces. The authors showed that the chimpanzees from the
enter providing more exposure to human faces than to chimpanzee faces were better at discriminating
human faces than they were at discriminating chimpanzee faces. The chimpanzees from the other center
did not show the same effect. A computational simulation was developed to evaluate the average"
0d82ac80275283c3dd26aca9e629ee6a9ca8a07a,An object-based semantic world model for long-term change detection and semantic querying,"An Object-Based Semantic World Model for
Long-Term Change Detection and Semantic Querying
Julian Mason and Bhaskara Marthi"
0dab1ab19a44b73ce0fdd15014b635eb7362af3c,Reinforcement Cutting-Agent Learning for Video Object Segmentation,"Reinforcement Cutting-Agent Learning for Video Object Segmentation
Junwei Han1, Le Yang1, Dingwen Zhang1
, Xiaojun Chang3, Xiaodan Liang3
Northwestern Polytechincal University, 2Xidian University, 3Carnegie Mellon University"
0dcdef6b8d97483f4d4dab461e1cb5b3c4d1fe1a,Probabilistic Semantic Inpainting with Pixel Constrained CNNs,"Probabilistic Semantic Inpainting with Pixel Constrained CNNs
Emilien Dupont
Suhas Suresha
Schlumberger Software Technology Innovation Center"
0df347f5e3118fac7c351917e3a497899b071d1e,Datasheets for Datasets,"Datasheets for Datasets
Timnit Gebru 1 Jamie Morgenstern 2 Briana Vecchione 3 Jennifer Wortman Vaughan 1 Hanna Wallach 1
Hal Daumé III 1 4 Kate Crawford 1 5"
0d82013cbe9f65ddb34e5d99eab730fce4f0effe,A system based on sequence learning for event detection in surveillance video,"978-1-4799-2341-0/13/$31.00 ©2013 IEEE
ICIP 2013"
0d90d046db16d3d5ce70590e6dab32cdd58928f6,A robust feature extraction algorithm based on class-Modular Image Principal Component Analysis for face verification,"978-1-4577-0539-7/11/$26.00 ©2011 IEEE
ICASSP 2011"
0d130b5536bb1b909ff9a62737d768d4b4fab2f6,Semantic Segmentation with Scarce Data,"Semantic Segmentation with Scarce Data
Isay Katsman * 1 Rohun Tripathi * 1 Andreas Veit 1 Serge Belongie 1"
0d0cee830772c3b2b274bfb5c3ad0ee42d8a0a57,Multimodal Convolutional Neural Networks for Matching Image and Sentence,"Multimodal Convolutional Neural Networks for Matching Image and Sentence
Lin Ma
Zhengdong Lu
Lifeng Shang
Hang Li
{Lu.Zhengdong, Shang.Lifeng,
Noah’s Ark Lab, Huawei Technologies"
0d6017c54d1f08d60d1423a3b84b01c387276387,Geometry meets semantics for semi-supervised monocular depth estimation,"Geometry meets semantics for semi-supervised
monocular depth estimation
Pierluigi Zama Ramirez, Matteo Poggi, Fabio Tosi,
Stefano Mattoccia, and Luigi Di Stefano
University of Bologna,
Viale del Risorgimento 2, Bologna, Italy"
0d185e6de595bd3844909d3606e9218a498a9bd8,Trace optimization and eigenproblems in dimension reduction methods,"TRACE OPTIMIZATION AND EIGENPROBLEMS IN DIMENSION
REDUCTION METHODS
E. KOKIOPOULOU∗, J. CHEN†, AND Y. SAAD†"
0d1a87dad1e4538cc7bd3c923767c8bf1a9b779f,The Riemannian Geometry of Deep Generative Models,"The Riemannian Geometry of Deep Generative Models
Hang Shao
University of Utah
Salt Lake City, UT
Abhishek Kumar
IBM Research AI
Yorktown Heights, NY
P. Thomas Fletcher
University of Utah
Salt Lake City, UT"
0dd151d003ac9b7f3d6936ccdd5ff38fce76c29f,A Review and Comparison of Measures for Automatic Video Surveillance Systems,"Hindawi Publishing Corporation
EURASIP Journal on Image and Video Processing
Volume 2008, Article ID 824726, 30 pages
doi:10.1155/2008/824726
Research Article
A Review and Comparison of Measures for
Automatic Video Surveillance Systems
Axel Baumann, Marco Boltz, Julia Ebling, Matthias Koenig, Hartmut S. Loos, Marcel Merkel,
Wolfgang Niem, Jan Karl Warzelhan, and Jie Yu
Corporate Research, Robert Bosch GmbH, D-70049 Stuttgart, Germany
Correspondence should be addressed to Julia Ebling,
Received 30 October 2007; Revised 28 February 2008; Accepted 12 June 2008
Recommended by Andrea Cavallaro
Today’s video surveillance systems are increasingly equipped with video content analysis for a great variety of applications.
However, reliability and robustness of video content analysis algorithms remain an issue. They have to be measured against
ground truth data in order to quantify the performance and advancements of new algorithms. Therefore, a variety of measures
have been proposed in the literature, but there has neither been a systematic overview nor an evaluation of measures for
specific video analysis tasks yet. This paper provides a systematic review of measures and compares their effectiveness for specific
spects, such as segmentation, tracking, and event detection. Focus is drawn on details like normalization issues, robustness, and
representativeness. A software framework is introduced for continuously evaluating and documenting the performance of video"
0dca6aa1c0143aa190973fb2256c16d700992473,"An introduction to the good, the bad, & the ugly face recognition challenge problem","An Introduction to the Good, the Bad, & the Ugly Face Recognition
Challenge Problem
P. Jonathon Phillips, J. Ross Beveridge, Bruce A. Draper, Geof Givens, Alice J. O’Toole,
David S. Bolme, Joseph Dunlop, Yui Man Lui, Hassan Sahibzada, and Samuel Weimer"
0dc34e186e8680336e88c3b5e73cde911a8774b8,Image Classification Using Naive Bayes Classifier With Pairwise Local Observations,"JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 32, XXXX-XXXX (2017)
Image Classification Using Naive Bayes Classifier With
Pairwise Local Observations
SHIH-CHUNG HSU1, I-CHIEH CHEN1 AND CHUNG-LIN HUANG2
Department of Electrical Engineering, National Tsing-Hua University, Hsin-Chu, Taiwan
Department of M-Commerce and Multimedia Applications, Asia Univ., Tai-Chung, Taiwan
E-mail:
We propose a pairwise local observation-based Naive Bayes (NBPLO) classifier for
image classification. First, we find the salient regions (SRs) and the Keypoints (KPs) as
the local observations. Second, we describe the discriminative pairwise local observations
using Bag-of-features (BoF) histogram. Third, we train the object class models by using
random forest to develop the NBPLO classifier for image classification. The two major
ontributions in this paper are multiple pairwise local observations and regression object
lass model training for NBPLO classifier. In the experiments, we test our method using
Scene-15 and Caltech-101 database and compare the results with the other methods.
Keywords: Local observation-based Naive Bayes classifier (NBPLO), Salient Region(SR),
Keypoint(KP), Bag-of-feature(BoF).
. INTRODUCTION
Image classification has been a challenging unsolved problem due to the complexity of
image  contents.  It  has  been  a  popular  research  subject  of  many  recently  published  re-"
0d8e7cda7d8a2ff737c0ad72f31dfd4d80d3a09a,Network Structure & Information Advantage,"A research and education initiative at the MIT
Sloan School of Management
Network Structure & Information Advantage
Paper 235
Sinan Aral
Marshall Van Alstyne
July 2007
For more information,
please visit our website at  http://digital.mit.edu
or contact the Center directly at
or 617-253-7054"
0d07db3510c7f9c2ceab65444cb8fc8ec49197b2,Learning-based Composite Metrics for Improved Caption Evaluation,"Learning-based Composite Metrics for Improved Caption Evaluation
Naeha Sharif, Lyndon White, Mohammed Bennamoun and Syed Afaq Ali Shah,
{naeha.sharif,
nd {mohammed.bennamoun,
The University of Western Australia.
5 Stirling Highway, Crawley, Western Australia"
0d3404530399eaa1f657d925a9a49c9e88a2e23b,Detection Evolution with Multi-order Contextual Co-occurrence,"Detection Evolution with Multi-Order Contextual Co-occurrence
Guang Chen∗
Yuanyuan Ding†
Epson Research and Development, Inc.
San Jose, CA, USA
Jing Xiao†
Tony X. Han∗
Dept. of ECE, Univ. of Missouri
Columbia, MO, USA"
0db787317ba0d63ec8f9918905e7db181a489026,Automatic Eye Localization in Color Images,"Automatic Eye Localization in Color Images
José Gilvan Rodrigues Maia1, Fernando de Carvalho Gomes1, Osvaldo de Souza2
Departamento de Computação – Universidade Federal do Ceará (UFC)
Depto de Engenharia de Teleinformática – Universidade Federal do Ceará (UFC)
60455-760 – Fortaleza – CE – Brasil
{gilvan,"
0d52f1ae438a395fadebf04990d0d1750cdd0218,Face Recognition in Various Illuminations,"Saurabh D. Parmar et al Int. Journal of Engineering Research and Applications          www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 5( Version 5), May 2014, pp.98-102
RESEARCH ARTICLE
Face Recognition in Various Illuminations
Saurabh D. Parmar, Vaishali J. Kalariya
Research Scholar, CE/IT Department-School of Engineering, R.K. University, Rajkot
Professor, CE/IT Department-School of Engineering, R.K. University, Rajkot
OPEN ACCESS"
0dd72a3522b99aedea83b47c5d7b33a1df058fd0,A Set of Selected SIFT Features for 3D Facial Expression Recognition,"A Set of Selected SIFT Features for 3D Facial
Expression Recognition
Stefano Berretti, Alberto Del Bimbo, Pietro Pala, Boulbaba Ben Amor,
Daoudi Mohamed
To cite this version:
Stefano Berretti, Alberto Del Bimbo, Pietro Pala, Boulbaba Ben Amor, Daoudi Mohamed. A Set
of Selected SIFT Features for 3D Facial Expression Recognition. 20th International Conference on
Pattern Recognition, Aug 2010, Istanbul, Turkey. pp.4125 - 4128, 2010. <hal-00829354>
HAL Id: hal-00829354
https://hal.archives-ouvertes.fr/hal-00829354
Submitted on 3 Jun 2013
HAL is a multi-disciplinary open access
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entific research documents, whether they are pub-
lished or not. The documents may come from
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broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,"
0d371fcd99e321257a1b7f87a436c6cc5b8b7557,Stability Based Filter Pruning for Accelerating Deep CNNs,"Stability Based Filter Pruning for Accelerating Deep CNNs
Pravendra Singh
IIT Kanpur
Vinay Sameer Raja Kadi
Nikhil Verma
Samsung R&D Institute, Delhi
{k.raja,
Vinay P. Namboodiri
IIT Kanpur"
0d48c282737793b234c56382053cc69cdddeccb0,A Poodle or a Dog? Evaluating Automatic Image Annotation Using Human Descriptions at Different Levels of Granularity,"Proceedings of the 25th International Conference on Computational Linguistics, pages 38–45,
Dublin, Ireland, August 23-29 2014."
0d7ddcf97b1341d8d4bbc4718f4ca3094e994a1f,Homographic Active Shape Models for View-Independent Facial Analysis,"Homographic Active Shape Models for View-Independent
Facial Analysis
Federico M. Sukno12 and Jos´e J. Guerrero32 and Alejandro F. Frangi1
Department of Technology, Pompeu Fabra University, Barcelona, Spain;
Aragon Institute of Engineering Research, University of Zaragoza, Spain;
Computer Science and System Engineering Department, University of Zaragoza, Spain"
0d3882b22da23497e5de8b7750b71f3a4b0aac6b,Context is routinely encoded during emotion perception.,"Research Article
Context Is Routinely Encoded
During Emotion Perception
1(4) 595 –599
© The Author(s) 2010
Reprints and permission:
sagepub.com/journalsPermissions.nav
DOI: 10.1177/0956797610363547
http://pss.sagepub.com
Lisa Feldman Barrett1,2,3 and Elizabeth A. Kensinger1,3
Boston College; 2Psychiatric Neuroimaging Program, Massachusetts General Hospital, Harvard Medical School; and 3Athinoula A. Martinos
Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School"
0d2a9f3357717e0a44eb82d5eabfc047cc4d46f1,Classifier Ensembles with Trajectory Under-Sampling for Face Re-Identification,"Classifier Ensembles with Trajectory Under-Sampling
for Face Re-Identification
Roghayeh Soleymani1, Eric Granger1 and Giorgio Fumera2
Laboratoire d’imagerie, de vision et d’intelligence artificielle, École de technologie supérieure,
Pattern Recognition and Applications Group, Dept. of Electrical and Electronic Engineering, University of
Université du Québec, Montreal, Canada
Cagliari,Cagliari, Italy
Keywords:
Person Re-Identification, Class Imbalance, Ensemble Methods."
0d30a662061a495e4b5aeb92a2edfac868b225ea,Chapter 7 Quantification of Emotions for Facial Expression : Generation of Emotional Feature Space Using Self-Mapping,"Chapter 7
Quantification of Emotions for Facial Expression:
Generation of Emotional Feature Space Using Self-
Mapping
Masaki Ishii, Toshio Shimodate, Yoichi Kageyama,
Tsuyoshi Takahashi and Makoto Nishida
Additional information is available at the end of the chapter
http://dx.doi.org/10.5772/51136
. Introduction
Facial expression recognition for the purpose of emotional communication between humans
nd machines has been investigated in recent studies [1-7].
The shape (static diversity) and motion (dynamic diversity) of facial components, such as
the eyebrows, eyes, nose, and mouth, manifest expression. From the viewpoint of static di‐
versity, owing to the individual variation in facial configurations, it is presumed that a facial
expression pattern due to the manifestation of a facial expression includes subject-specific
features.  In  addition,  from  the  viewpoint  of  dynamic  diversity,  because  the  dynamic
hanges in facial expressions originate from subject-specific facial expression patterns, it is
presumed that the displacement vector of facial components has subject-specific features.
On the other hand, although an emotionally generated facial expression pattern of an indi‐
vidual is unique, internal emotions expressed and recognized by humans via facial expres‐"
0dc2fdf1b97c76de1e7380e8126f8acc7d87e23a,Robust PCA Via Nonconvex Rank Approximation,"Robust PCA via Nonconvex Rank Approximation
Department of Computer Science, Southern Illinois University, Carbondale, IL 62901, USA
Zhao Kang, Chong Peng, Qiang Cheng
{zhao.kang, pchong,"
0d076edd62e258316bc310fafcec88db3ab85434,Automatic detection and tracking of pedestrians from a moving stereo rig,"Automatic detection and tracking of pedestrians from a
moving stereo rig
Konrad Schindlera, Andreas Essb, Bastian Leibec, Luc Van Goolb,d
Photogrammetry and Remote Sensing, ETH Z¨urich, Switzerland
Computer Vision Lab, ETH Z¨urich, Switzerland
UMIC research centre, RWTH Aachen, Germany
dESAT/PSI–VISICS, IBBT, KU Leuven, Belgium"
0d538084f664b4b7c0e11899d08da31aead87c32,Deformable Part Descriptors for Fine-Grained Recognition and Attribute Prediction,"Deformable Part Descriptors for
Fine-grained Recognition and Attribute Prediction
Ning Zhang1
Ryan Farrell1,2
Forrest Iandola1
ICSI / UC Berkeley 2Brigham Young University
Trevor Darrell1"
0da611ca979327840161df87564fd07299c268b5,Bodyprint: Biometric User Identification on Mobile Devices Using the Capacitive Touchscreen to Scan Body Parts,"Bodyprint
Biometric User Identification on Mobile Devices
Using the Capacitive Touchscreen to Scan Body Parts
Christian Holz
Senaka Buthpitiya
Marius Knaust"
0d2a1a3897d50ba490a2ffaaecc3135f573a7023,Discriminative training for object recognition using image patches,"Discriminative Training for Object Recognition Using Image Patches
Thomas Deselaers, Daniel Keysers, and Hermann Ney
Lehrstuhl f¨ur Informatik VI – Computer Science Department
RWTH Aachen University – 52056 Aachen, Germany"
b62486261104d5136aea782ee8596425b5f228da,Modelling perceptions of criminality and remorse from faces using a data-driven computational approach.,"Cognition and Emotion
ISSN: 0269-9931 (Print) 1464-0600 (Online) Journal homepage: http://www.tandfonline.com/loi/pcem20
Modelling perceptions of criminality and remorse
from faces using a data-driven computational
pproach
Friederike Funk, Mirella Walker & Alexander Todorov
To cite this article: Friederike Funk, Mirella Walker & Alexander Todorov (2017) Modelling
perceptions of criminality and remorse from faces using a data-driven computational approach,
Cognition and Emotion, 31:7, 1431-1443, DOI: 10.1080/02699931.2016.1227305
To link to this article:  http://dx.doi.org/10.1080/02699931.2016.1227305
View supplementary material
Published online: 07 Sep 2016.
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Full Terms & Conditions of access and use can be found at
http://www.tandfonline.com/action/journalInformation?journalCode=pcem20
Download by: [Princeton University]"
b6b1b0632eb9d4ab1427278f5e5c46f97753c73d,Generalização cartográfica automatizada para um banco de dados cadastral,"UNIVERSIDADE FEDERAL DE SANTA CATARINA -UFSC
DEPARTAMENTO DE ENGENHARIA CIVIL
PROGRAMA DE PÓS-GRADUAÇÃO EM
ENGENHARIA CIVIL - PPGEC
AREA DE CONCENTRAÇÃO: CADASTRO TÉCNICO E
GESTÃO TERRITORIAL
GENERALIZAÇÃO CARTOGRÁFICA AUTOMATIZADA
PARA UM BANCO DE DADOS CADASTRAL
Tese  submetida  à  Universidade  Federal  de
Santa  Catarina  como  requisito  exigido  pelo
Programa  de  Pós-Graduação  em  Engenharia
Civil  -  PPGEC,  para  a  obtenção  do  Título  de
DOUTOR em Engenharia Civil.
Mariane Alves Dal Santo
Orientador: Prof. Dr. Carlos Loch
Florianópolis, dezembro de 2007"
b66a93884f80a243f50da97e33211693a317dc45,Deep Learning for Generic Object Detection: A Survey,"Deep Learning for Generic Object Detection: A Survey
Li Liu 1,2 · Wanli Ouyang 3 · Xiaogang Wang 4 ·
Paul Fieguth 5 · Jie Chen 2 · Xinwang Liu 1 · Matti Pietik¨ainen 2
Received: 12 September 2018"
b651814360e3899cd9206bfd23621aca6551e69c,Improving Feature Level Likelihoods using Cloud Features,"IMPROVING FEATURE LEVEL LIKELIHOODS USING CLOUD
FEATURES
Heydar Maboudi Afkham1, Stefan Carlsson1, Josephine Sullivan1
Computer Vision and Active Perception Lab., KTH, Stockholm, Sweden
Keywords:
Feature inference, Latent models, Clustering"
b69badabc3fddc9710faa44c530473397303b0b9,Unsupervised Image-to-Image Translation Networks,"Unsupervised Image-to-Image Translation Networks
Ming-Yu Liu, Thomas Breuel,
Jan Kautz
NVIDIA"
b63411ed70ba315b87a716e1809faea48e70a982,"A Survey on Object Detect , Track and Identify Using Video Surveillance","IOSR Journal of Engineering (IOSRJEN)
e-ISSN: 2250-3021, p-ISSN: 2278-8719, www.iosrjen.org
Volume 2, Issue 10 (October 2012), PP 71-76
A  Survey on Object Detect, Track and Identify Using Video
Surveillance
Chandrashekhar D.Badgujar1, Dipali P.Sapkal2
1,2(Computer Science and Engineering G.H.R.E.M, Jalgoan)"
b67e2ccd0f05df5358464b9b38da3bcb9feda1ab,FaceID@home: cycle-sharing for facial recognition,"ycle-sharing for facial recognition
FaceID-BOINC: adapta¸c˜ao de algoritmos de reconhecimento facial (eigenfaces) para execu¸c˜ao
em m´aquinas multicore e GPUs integrado num cliente para plataforma BOINC
Nuno Miguel Abreu Teixeira - 55397
Instituto Superior T´ecnico"
b6aaef3be3e93a5429511011b3fcf1c768521efc,Car Detection and Tracking from a Vehicle Driving on a Roundabout,"Bachelor thesis
Czech
Technical
University
in Prague
Faculty of Electrical Engineering
Department of Cybernetics
Car Detection and Tracking from
Vehicle Driving on a
Roundabout
Libor Novák
May 2014
Supervisor: prof. Ing. Jiří Matas, Ph.D."
b6e3b42ea84bbb84658d34e7af49bff139616084,Low Cost and Usable Multimodal Biometric System Based on Keystroke Dynamics and 2 D Face Recognition,"Low Cost and Usable Multimodal Biometric System
Based on Keystroke Dynamicsand 2D Face Recognition
Romain Giot, Baptiste Hemery, Christophe Rosenberger
To cite this version:
Romain Giot, Baptiste Hemery, Christophe Rosenberger. Low Cost and Usable Multimodal
Biometric System Based on Keystroke Dynamicsand 2D Face Recognition. The 20th Interna-
tional Conference on Pattern Recognition, Aug 2010, Istanbul, Turkey. pp.4, 2010, .
HAL Id: hal-00503103
https://hal.archives-ouvertes.fr/hal-00503103
Submitted on 16 Aug 2010
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
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broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,
´emanant des ´etablissements d’enseignement et de"
b61b4eb2e28b9cf35578498e1bbcc35ec0a07651,Backtracking ScSPM Image Classifier for Weakly Supervised Top-Down Saliency,"Backtracking ScSPM Image Classifier for Weakly Supervised Top-down Saliency
Hisham Cholakkal
Jubin Johnson
Deepu Rajan
Multimedia Lab, School of Computer Science and Engineering
Nanyang Technological University Singapore
{hisham002, jubin001,"
b648d73edd1a533decd22eec2e7722b96746ceae,weedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming,"weedNet: Dense Semantic Weed Classification Using Multispectral
Images and MAV for Smart Farming
Inkyu Sa1, Zetao Chen2, Marija Popovi´c1, Raghav Khanna1, Frank Liebisch3, Juan Nieto1, Roland Siegwart1"
b6ecc8d34ebc8895378abe2b8f35e3a0691f5d26,Annotation Methodologies for Vision and Language Dataset Creation,"Annotation Methodologies for Vision and Language Dataset Creation
Gitit Kehat
Computer Science Department
Brandeis University
Waltham, MA. 02453 USA
James Pustejovsky
Computer Science Department
Brandeis University
Waltham, MA. 02453 USA"
b691463de5e30e7efd18b9d02cbf83c805834fe7,EVALUATION OF PENALTY FUNCTIONS FOR SEMI-GLOBAL MATCHING COST AGGREGATION,"EVALUATION OF PENALTY FUNCTIONS FOR SEMI-GLOBAL MATCHING
COST AGGREGATION
Christian Banz, Peter Pirsch, and Holger Blume
Institute of Microelectronic Systems
Leibniz Universität Hannover, Hannover, Germany
KEY WORDS: Stereoscopic, Quality, Matching, Vision, Reconstruction, Camera, Disparity Estimation, Semi-Global Matching"
b6dc1cd3cabdfea7363d41773a315a0d241dc836,Local Context Priors for Object Proposal Generation,"Local Context Priors for Object Proposal
Generation
Marko Ristin1, Juergen Gall2, and Luc Van Gool1,3
ETH Zurich
MPI for Intelligent Systems
KU Leuven"
b632d47eb7421a3d622b0f1ceb009e4415ccc84d,Deep Perceptual Mapping for Cross-Modal Face Recognition,"(will be inserted by the editor)
Deep Perceptual Mapping for Cross-Modal Face
Recognition
M. Saquib Sarfraz · Rainer Stiefelhagen
the date of receipt and acceptance should be inserted later"
b68dfa8723be662e6af76bef159ed929bf8a1a2f,Distance weighted discrimination of face images for gender classification,"Distance weighted discrimination of face images for
gender classification
Mónica Benito1, Eduardo García-Portugués1,4, J. S. Marron2, and Daniel Peña1,3"
b6fd905efd5da32bd32047896074a821477cb564,An Human Perceptive Model for Person Re-identification,"An Human Perceptive Model for Person Re-identification
Angelo Cardellicchio1, Tiziana D’Orazio1, Tiziano Politi2 and Vito Ren`o1
National Research Council, Institute of Intelligent Systems for Automation, Bari, Italia
Politecnico di Bari, Bari, Italia
Keywords:
Color Analysis, Feature Extraction, Histograms."
b66418ecc37ea0c79da5425e9ceac939ca9075ae,EFFICIENT GAIT-BASED GENDER CLASSIFICATION THROUGH FEATURE SELECTION,"EFFICIENT GAIT-BASED GENDER CLASSIFICATION
THROUGH FEATURE SELECTION∗
Ra´ul Mart´ın-F´elez, Javier Ortells, Ram´on A. Mollineda and J. Salvador S´anchez
Institute of New Imaging Technologies and Dept. Llenguatges i Sistemes Inform`atics
Universitat Jaume I. Av. Sos Baynat s/n, 12071, Castell´o de la Plana, Spain
{martinr, jortells, mollined,
Keywords:
Gender classification, Gait, ANOVA, Feature selection."
b6ef46621d8660eb53836202fa58f04fa20adfd7,Disgust and Anger Relate to Different Aggressive Responses to Moral Violations,"692000 PSSXXX10.1177/0956797617692000Molho et al.Moral Emotions and Aggressive Tactics
research-article2017
Research Article
Disgust and Anger Relate to Different
Aggressive Responses to Moral Violations
Catherine Molho1, Joshua M. Tybur1, Ezgi Güler2,
Daniel Balliet1, and Wilhelm Hofmann3
Department of Experimental and Applied Psychology, Vrije Universiteit Amsterdam;
Department of Political and Social Sciences, European University Institute; and 3Social Cognition
Center Cologne, University of Cologne
Psychological Science
017, Vol. 28(5) 609 –619
© The Author(s) 2017
Reprints and permissions:
sagepub.com/journalsPermissions.nav
DOI: 10.1177/0956797617692000
https://doi.org/10.1177/0956797617692000
www.psychologicalscience.org/PS"
b6aa94b81b2165e492cc2900e05dd997619bfe7a,Automatic temporal segment detection via bilateral long short-term memory recurrent neural networks,"Automatic temporal segment
detection via bilateral long short-
term memory recurrent neural
networks
Bo Sun
Siming Cao
Jun He
Lejun Yu
Liandong Li
Bo Sun, Siming Cao, Jun He, Lejun Yu, Liandong Li, “Automatic temporal segment
detection via bilateral long short-term memory recurrent neural networks,” J.
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Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 03/03/2017 Terms of Use: http://spiedigitallibrary.org/ss/termsofuse.aspx"
b64cc1f0772e9620ecf916019de85b7adb357b7a,Fast Face-Swap Using Convolutional Neural Networks,"Fast Face-swap Using Convolutional Neural Networks
Iryna Korshunova1,2
Wenzhe Shi1
{iryna.korshunova,
Twitter
Joni Dambre2
Lucas Theis1
IDLab, Ghent University
{wshi,"
b69a7be6ae438090b7ed458662abcbde5871c4ff,Vehicle Motion Detection using CNN,"Vehicle Motion Detection using CNN
Yaqi Zhang∗
Billy Wan∗
Wenshun Liu∗"
b6dd4057bdeef69148ee22e9ca31b44434c0e89f,FIDA : Face Recognition using Descriptive Input Semantics,"FIDA: Face Recognition using Descriptive Input
Semantics
Nipun Bhatia, Rakshit Kumar, Samir Menon
Department of Computer Science, Stanford University.
December 14, 2007"
b67e0ae9d64ec06b3e1c25c7f7e8b86020612d33,VOCABULARY-INFORMED VISUAL FEATURE AUGMEN-,"Under review as a conference paper at ICLR 2018
VOCABULARY-INFORMED VISUAL FEATURE AUGMEN-
TATION FOR ONE-SHOT LEARNING"
b6b9d29d25de42d78f09217c9cc457247d90fc70,Semantic Part Detection via Matching: Learning to Generalize to Novel Viewpoints from Limited Training Data,"Semantic Part Detection via Matching:
Learning to Generalize to Novel Viewpoints from Limited Training Data
Yutong Bai1∗, Qing Liu2∗, Lingxi Xie2, Yan Zheng3, Weichao Qiu2, Alan Yuille2
Northwestern Polytechnical University 2Johns Hopkins University 3Beihang University
{198808xc, yan.zheng.mat,"
b63041d05b78a66724fbcb2803508999bf885d6b,Deep Sets,"Deep Sets
Manzil Zaheer 1 2 Satwik Kottur 2 Siamak Ravanbhakhsh 2 Barnabas Poczos 2 Ruslan Ssalakhutdinov 2
Alexander Smola 1 2"
b6f682648418422e992e3ef78a6965773550d36b,"CBMM Memo No . 061 February 8 , 2017 Full interpretation of minimal images","February 8, 2017"
b62628ac06bbac998a3ab825324a41a11bc3a988,Xm2vtsdb: the Extended M2vts Database,"SecondInternationalConferenceonAudioandVideo-basedBiometricPersonAuthentication(AVBPA'		),WashingtonD.C,

XMVTSDB:TheExtendedMVTSDatabase
K.Messer,J.Matas,J.Kittler
J.Luettin,G.Maitre
UniversityofSurrey
IDIAP
Guildford,Surrey,GUXH,UK.
CP	,
	Martigny,Switzerland.
ofvideoandaudiosignalsisintheorderofTBytes
(
GBytes);technologyallowingmanipulationand"
b6ff7ead669e67ddf46bd2955b7cc0af0fa24ad7,Anticipation in Human-Robot Cooperation: A Recurrent Neural Network Approach for Multiple Action Sequences Prediction,"Anticipation in Human-Robot Cooperation: A Recurrent Neural
Network Approach for Multiple Action Sequences Prediction
Paul Schydlo1, Mirko Rakovic1,2, Lorenzo Jamone3 and Jos´e Santos-Victor1"
b640c36acc0e748553f78280fce7a840965c5cec,Detection from Natural Image using MSER and BOW 1,"International Journal of Emerging Engineering Research and Technology
Volume 3, Issue 11, November 2015, PP 152-156
ISSN 2349-4395 (Print) & ISSN 2349-4409 (Online)
Text Detection from Natural Image using MSER and BOW
K.Sowndarya Lahari, 2M.Haritha, 3P.Prasanna Murali Krishna
(M.Tech), DECS, DR.Sgit, Markapur, India.
Associate Professor, Department of ECE, DR.Sgit, Markapur, India.
.3H.O.D Department of ECE, DR.Sgit, Markapur, India."
b610e52b0a8fa11af3d01944c0383f015cade9c0,Multimodal 2 D-3 D Face Recognition,"International Journal of Future Computer and Communication, Vol. 2, No. 6, December 2013
Multimodal 2D-3D Face Recognition
Gawed M. Nagi, Rahmita Rahmat, Muhamad Taufik, and Fatimah Khalid
technology"
b61ae8216a7c3a5a3202478cd6f18bf3014e2342,Robust Pedestrian Detection by Combining Visible and Thermal Infrared Cameras,"Sensors 2015, 15, 10580-10615; doi:10.3390/s150510580
OPEN ACCESS
sensors
ISSN 1424-8220
www.mdpi.com/journal/sensors
Article
Robust Pedestrian Detection by Combining Visible and Thermal
Infrared Cameras
Ji Hoon Lee, Jong-Suk Choi, Eun Som Jeon, Yeong Gon Kim, Toan Thanh Le, Kwang Yong Shin,
Hyeon Chang Lee and Kang Ryoung Park *
Department of Electronics and Electrical Engineering, Dongguk University, 26 Pil-dong 3-ga, Jung-gu,
Seoul 100-715, Korea; E-Mails: (J.H.L.); (J.-S.C.);
(E.S.J.); (Y.G.K.); (T.T.L.);
(K.Y.S.); (H.C.L.)
*  Author to whom correspondence should be addressed; E-Mail:
Tel.: +82-10-3111-7022; Fax: +82-2-2277-8735.
Academic Editor: Vittorio M.N. Passaro
Received: 12 February 2015 / Accepted: 27 April 2015 / Published: 5 May 2015"
2e8b08c8df95d2ef8c0d03820094608e9cf456ab,License Plate Detection and Recognition in Unconstrained Scenarios,"License Plate Detection and Recognition in
Unconstrained Scenarios
S´ergio Montazzolli Silva[0000−0003−2444−3175] and Cl´audio Rosito
Jung[0000−0002−4711−5783]
Institute of Informatics - Federal University of Rio Grande do Sul
Porto Alegre, Brazil"
2e56209ed179be641e6df5efd11be8b3d54a62e9,Combining Deep and Handcrafted Image Features for Presentation Attack Detection in Face Recognition Systems Using Visible-Light Camera Sensors,"Article
Combining Deep and Handcrafted Image Features for
Presentation Attack Detection in Face Recognition
Systems Using Visible-Light Camera Sensors
Dat Tien Nguyen, Tuyen Danh Pham, Na Rae Baek and Kang Ryoung Park *
Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu,
Seoul 100-715, Korea; (D.T.N.); (T.D.P.);
(N.R.B.)
* Correspondence: Tel.: +82-10-3111-7022; Fax: +82-2-2277-8735
Received: 30 January 2018; Accepted: 24 February 2018; Published: 26 February 2018"
2e0481def73dbd3e6dfb447c1c3c8afdfaf9b7ec,UPC System for the 2015 MediaEval Multimodal Person Discovery in Broadcast TV task,"UPC System for the 2015 MediaEval Multimodal Person
Discovery in Broadcast TV task
M. India, D. Varas, V. Vilaplana, J.R. Morros, J. Hernando
Universitat Politecnica de Catalunya, Spain"
2e091b311ac48c18aaedbb5117e94213f1dbb529,Collaborative Facial Landmark Localization for Transferring Annotations Across Datasets,"Collaborative Facial Landmark Localization
for Transferring Annotations Across Datasets
Brandon M. Smith and Li Zhang
University of Wisconsin – Madison
http://www.cs.wisc.edu/~lizhang/projects/collab-face-landmarks/"
2e708431df3e7a9585a338e1571f078ddbe93a71,Deep Pain: Exploiting Long Short-Term Memory Networks for Facial Expression Classification.,"Aalborg Universitet
Deep Pain
Rodriguez, Pau; Cucurull, Guillem; Gonzàlez, Jordi; M. Gonfaus, Josep ; Nasrollahi, Kamal;
Moeslund, Thomas B.; Xavier Roca, F.
Published in:
I E E E Transactions on Cybernetics
DOI (link to publication from Publisher):
0.1109/TCYB.2017.2662199
Publication date:
Document Version
Accepted author manuscript, peer reviewed version
Link to publication from Aalborg University
Citation for published version (APA):
Rodriguez, P., Cucurull, G., Gonzàlez, J., M. Gonfaus, J., Nasrollahi, K., Moeslund, T. B., & Xavier Roca, F.
(2017). Deep Pain: Exploiting Long Short-Term Memory Networks for Facial Expression Classification. I E E E
Transactions on Cybernetics, 1-11. DOI: 10.1109/TCYB.2017.2662199
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2ea8029283e6bbb03c023070d042cb19647f06af,Neurobiological mechanisms associated with facial affect recognition deficits after traumatic brain injury,"Neurobiological mechanisms associated with facial affect recognition deficits after
traumatic brain injury
Dawn Neumann, PhD
Indiana University School of Medicine
Department of Physical Medicine and Rehabilitation
Rehabilitation Hospital of Indiana
141 Shore Drive
Indianapolis, IN 46254
Email:
Phone: 317-329-2188
Brenna C. McDonald, PsyD, MBA
Indiana University School of Medicine
Department of Radiology and Imaging Sciences
Indiana University Center for Neuroimaging
55 W. 16th St., GH Suite 4100
Indianapolis, IN 46202
Email:
John West, MS
Indiana University School of Medicine
Department of Radiology and Imaging Sciences"
2e80ce889fa47bae8583f89d501a41e283c1551b,FlowNet: Learning Optical Flow with Convolutional Networks,"FlowNet: Learning Optical Flow with Convolutional Networks
Philipp Fischer∗‡, Alexey Dosovitskiy‡, Eddy Ilg‡, Philip H¨ausser, Caner Hazırbas¸, Vladimir Golkov∗
University of Freiburg
Technical University of Munich
Patrick van der Smagt
Daniel Cremers
Thomas Brox
Technical University of Munich
Technical University of Munich
University of Freiburg"
2eefaa9c278346b9e0eb51085cff490b0a43688f,TEMPO: Feature-Endowed Teichmüller Extremal Mappings of Point Clouds,"Vol. 9, No. 4, pp. 1922–1962
(cid:13) 2016 Society for Industrial and Applied Mathematics
TEMPO: Feature-Endowed Teichm¨uller Extremal Mappings of Point Clouds∗
Ting Wei Meng† , Gary Pui-Tung Choi‡ , and Lok Ming Lui†"
2ebc35d196cd975e1ccbc8e98694f20d7f52faf3,Towards Wide-angle Micro Vision Sensors,"This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Towards Wide-angle Micro Vision Sensors
Sanjeev J. Koppal*
Ioannis Gkioulekas* Travis Young+ Hyunsung Park*
Kenneth B. Crozier* Geoffrey L. Barrows+ Todd Zickler*"
2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e,3DPeS: 3D people dataset for surveillance and forensics,"DPeS: 3D People Dataset for Surveillance and Forensics
Davide Baltieri, Roberto Vezzani, Rita Cucchiara
{davide.baltieri, roberto.vezzani, rita.cucchiara}
University of Modena and Reggio Emilia, Italy  (Dipartimento di Ingegneria dell’Informazione)
A new Dataset for People
Tracking and Reidentification
600 videos, 200 people, 8 cameras
Calibration and 3D scene reconstruction
taken
The  dataset  contains  hundreds  of  video  sequences  of
from  a  multi-camera  distributed
00  people
surveillance system over several days, with different light
onditions;  each  person  is  detected  multiple  times  and
from different points of view.
The dataset
The  starting  point  of  our  dataset  is  a  real
surveillance  setup,  composed  by  8  different
surveillance cameras, monitoring a section of the
ampus  of  the  University  of  Modena  and  Reggio"
2ed9a69ee6509c0b3fe5a51d1116dccc877653ba,Reconstruction and Analysis of Shapes from 3D Scans,"Reconstruction and Analysis
of Shapes from 3D Scans"
2e1add06cc82d139348056fe43282f1ca1832e5b,Local 3 D Shape Analysis for Facial Expression Recognition,"Local 3D Shape Analysis for Facial Expression
Recognition
Ahmed Maalej, Boulbaba Ben Amor, Mohamed Daoudi, Anuj Srivastava,
Stefano Berretti
To cite this version:
Ahmed Maalej, Boulbaba Ben Amor, Mohamed Daoudi, Anuj Srivastava, Stefano Berretti.
Local 3D Shape Analysis for Facial Expression Recognition. 20th International Conference on
Pattern Recognition (ICPR 2010), Aug 2010, Istanbul, Turkey. pp.4129 - 4132, 2010.
HAL Id: hal-00662321
https://hal.archives-ouvertes.fr/hal-00662321
Submitted on 23 Jan 2012
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,"
2e0f5e72ad893b049f971bc99b67ebf254e194f7,Apparel classification with style,"Apparel Classification with Style
Lukas Bossard1, Matthias Dantone1, Christian Leistner1,2,
Christian Wengert1,3, Till Quack3, Luc Van Gool1,4
ETH Z¨urich, Switzerland 2Microsoft, Austria 3Kooaba AG, Switzerland
KU Leuven, Belgium"
2eb9f1dbea71bdc57821dedbb587ff04f3a25f07,Face for ambient interface,"Face for Ambient Interface
Maja Pantic
Imperial College, Computing Department, 180 Queens Gate,
London SW7 2AZ, U.K."
2e082232eb37c98052e62eec76e674a491082544,Virtual Scenarios : Achievements and Current Work,"Virtual Scenarios: Achievements and Current Work
Javier Mar´ın, David V´azquez and Antonio M. L´opez
ADAS, Computer Vision Center, Universitat Autonoma de Barcelona, Spain
e-mail:{ jmarin, dvazquez, antonio"
2ef51b57c4a3743ac33e47e0dc6a40b0afcdd522,Leveraging Billions of Faces to Overcome Performance Barriers in Unconstrained Face Recognition,"Leveraging Billions of Faces to Overcome
Performance Barriers in Unconstrained Face
Recognition
Yaniv Taigman and Lior Wolf
face.com
{yaniv,"
2e2935a7489ae55fe36af6980523f8d587c18935,On testing methods for biometric authentication,
2e6c3557cb90f472e6798fcaa8ecc9dff3557f11,Towards Perspective-Free Object Counting with Deep Learning,"Towards perspective-free object counting with
deep learning
Daniel O˜noro-Rubio and Roberto J. L´opez-Sastre
GRAM, University of Alcal´a, Alcal´a de Henares, Spain"
2efc6f98720b804345c030e22aef6c9f4a53023e,Soft-biometrics evaluation for people re-identification in uncontrolled multi-camera environments,"Moctezuma et al. EURASIP Journal on Image and Video Processing  (2015) 2015:28
DOI 10.1186/s13640-015-0078-1
RESEARCH
Open Access
Soft-biometrics evaluation for people
re-identification in uncontrolled multi-camera
environments
Daniela Moctezuma1*, Cristina Conde2, Isaac Martín De Diego2 and Enrique Cabello2"
2e1415a814ae9abace5550e4893e13bd988c7ba1,Dictionary Based Face Recognition in Video Using Fuzzy Clustering and,"International Journal of Engineering Trends and Technology (IJETT) – Volume 21 Number 3 – March 2015
Dictionary Based Face Recognition in Video Using
Fuzzy Clustering and Fusion
Neeraja K.C.#1, RameshMarivendan E.#2,
#1IInd year M.E. Student, #2Assistant Professor
#1#2ECE Department, Dhanalakshmi Srinivasan College of Engineering,
Coimbatore,Tamilnadu,India.
Anna University."
2e68190ebda2db8fb690e378fa213319ca915cf8,Generating Videos with Scene Dynamics,"Generating Videos with Scene Dynamics
Carl Vondrick
Hamed Pirsiavash
Antonio Torralba"
2e53a5dbadfd30b834feea80c365ffff3925eb76,The role of alexithymia in reduced eye-fixation in Autism Spectrum Conditions.,"23Journal of Autism andDevelopmental Disorders ISSN 0162-3257Volume 41Number 11 J Autism Dev Disord (2011)41:1556-1564DOI 10.1007/s10803-011-1183-3The Role of Alexithymia in Reduced Eye-Fixation in Autism Spectrum ConditionsGeoffrey Bird, Clare Press & DanielC. Richardson"
2e475f1d496456831599ce86d8bbbdada8ee57ed,Groupsourcing: Team Competition Designs for Crowdsourcing,"Groupsourcing: Team Competition Designs for
Crowdsourcing
Markus Rokicki, Sergej Zerr, Stefan Siersdorfer
L3S Research Center, Hannover, Germany"
2eef20a11324686099ee6f9b1a7613444b0d2112,Dual-Path Convolutional Image-Text Embedding with Instance Loss,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
Dual-Path Convolutional Image-Text Embeddings
with Instance Loss
Zhedong Zheng, Liang Zheng, Michael Garrett, Yi Yang, Yi-Dong Shen"
2e927d0a2dc4b69fc03124ad876329b22a61f1b0,Temporal Reasoning in Videos using Convolutional Gated Recurrent Units,"Temporal Reasoning in Videos using Convolutional Gated Recurrent Units
Debidatta Dwibedi∗
Pierre Sermanet
Jonathan Tompson
Google Brain
{debidatta, sermanet,"
2ea78e128bec30fb1a623c55ad5d55bb99190bd2,Residual vs. Inception vs. Classical Networks for Low-Resolution Face Recognition,"Residual vs. Inception vs. Classical Networks for
Low-Resolution Face Recognition
Christian Herrmann1,2, Dieter Willersinn2, and J¨urgen Beyerer1,2
Vision and Fusion Lab, Karlsruhe Institute of Technology KIT, Karlsruhe, Germany
Fraunhofer IOSB, Karlsruhe, Germany
{christian.herrmann,dieter.willersinn,"
2e10560579f2bdeae0143141f26bd9f0a195b4b7,Mixed Precision Training,"Published as a conference paper at ICLR 2018
MIXED PRECISION TRAINING
Sharan Narang∗, Gregory Diamos, Erich Elsen†
Baidu Research
{sharan,
Paulius Micikevicius∗, Jonah Alben, David Garcia, Boris Ginsburg, Michael Houston,
Oleksii Kuchaiev, Ganesh Venkatesh, Hao Wu
NVIDIA
{pauliusm, alben, dagarcia, bginsburg, mhouston,
okuchaiev, gavenkatesh,"
2e55fd3f5138e55250aed84a7dc17adfc34970d3,The implications of social neuroscience for social disability.,"J Autism Dev Disord (2012) 42:1256–1262
DOI 10.1007/s10803-012-1514-z
O R I G I N A L P A P E R
The Implications of Social Neuroscience for Social Disability
James C. McPartland • Kevin A. Pelphrey
Published online: 29 March 2012
Ó Springer Science+Business Media, LLC 2012"
2e62b4f2f5a8e6c1bf6a21ebb860c40463d72917,Adversarial background augmentation improves object localisation using convolutional neural networks,"Master Thesis
Computing Science
Adversarial background
ugmentation improves object
localisation using convolutional
neural networks
Author:
Ing. Harm Berntsen
Supervisor Radboud University:
Prof. Dr. Tom Heskes
Supervisors Nedap:
Daan van Beek, MSc.
Dr. Wouter Kuijper
August 2015"
2ea46531f7d837c1e4b9e6a8d8fc084c6e526545,Just Look at the Image: Viewpoint-Specific Surface Normal Prediction for Improved Multi-View Reconstruction,"Just look at the image: viewpoint-specific surface normal prediction
for improved multi-view reconstruction
Silvano Galliani
Konrad Schindler
Photogrammetry and Remote Sensing, ETH Zurich"
2eae02d59a3f455f3714ce674d85d3f073c9d7a2,All in the first glance: first fixation predicts individual differences in valence bias.,"Cognition and Emotion
ISSN: 0269-9931 (Print) 1464-0600 (Online) Journal homepage: http://www.tandfonline.com/loi/pcem20
All in the first glance: first fixation predicts
individual differences in valence bias
Maital Neta, Tien T. Tong, Monica L. Rosen, Alex Enersen, M. Justin Kim &
Michael D. Dodd
To cite this article: Maital Neta, Tien T. Tong, Monica L. Rosen, Alex Enersen, M. Justin Kim &
Michael D. Dodd (2016): All in the first glance: first fixation predicts individual differences in
valence bias, Cognition and Emotion, DOI: 10.1080/02699931.2016.1152231
To link to this article:  http://dx.doi.org/10.1080/02699931.2016.1152231
View supplementary material
Published online: 10 Mar 2016.
Submit your article to this journal
View related articles
View Crossmark data
Full Terms & Conditions of access and use can be found at
http://www.tandfonline.com/action/journalInformation?journalCode=pcem20
Download by: [University of Nebraska, Lincoln]
Date: 10 March 2016, At: 09:04"
2e1a1deb7dccff41fca7447364d6748bf362fb70,A topographical nonnegative matrix factorization algorithm,"A Topographical Nonnegative Matrix Factorization
lgorithm
Rogovschi Nicoleta
Lazhar Labiod
Mohamed Nadif
LIPADE, Paris Descartes University
LIPADE, Paris Descartes University
LIPADE, Paris Descartes University
5, rue des Saints P`eres
75006 Paris, France
5, rue des Saints P`eres
75006 Paris, France
5, rue des Saints P`eres
75006 Paris, France"
2e832d5657bf9e5678fd45b118fc74db07dac9da,"Recognition of Facial Expressions of Emotion: The Effects of Anxiety, Depression, and Fear of Negative Evaluation","Running head: RECOGNITION OF FACIAL EXPRESSIONS OF EMOTION
Recognition of Facial Expressions of Emotion: The Effects of Anxiety, Depression, and Fear of Negative
Evaluation
Rachel Merchak
Wittenberg University
Rachel Merchak, Psychology Department, Wittenberg University.
Author Note
This research was conducted in collaboration with Dr. Stephanie Little, Psychology Department,
Wittenberg University, and Dr. Michael Anes, Psychology Department, Wittenberg University.
Correspondence concerning this article should be addressed to Rachel Merchak, 10063 Fox
Chase Drive, Loveland, OH 45140.
E‐mail:"
2e1822bf06d80f5ad07a79a4bfff98c1c18fb573,Knowing who to listen to: Prioritizing experts from a diverse ensemble for attribute personalization,"KNOWING WHO TO LISTEN TO: PRIORITIZING EXPERTS FROM A DIVERSE
ENSEMBLE FOR ATTRIBUTE PERSONALIZATION
Shrenik Lad1, Bernardino Romera Paredes2, Julien Valentin2, Philip Torr2, Devi Parikh1
. Virginia Tech 2. University of Oxford"
2efc4eee3953f6b52e23989bbcc2598a91e18ba0,External Cameras and a Mobile Robot for Enhanced Multi-person Tracking,"RFAntennas2D SICKLaserFirewire Cameraon PTU        LaptopCamera 1Flea RGB Camera 2Flea RGBHubFirewireFigure1:Perceptualplatform;staticcameras(withroughpositionsandfieldsofview)andthemobilerobotRackham.Thispaperisstructuredasfollows:architectureofthecooperativesystemispresentedinsection2.Sec-tion3describesthedifferentdetectionmodalitiesthatdrivethemulti-persontracker(presentedinsection4).Evaluationsandresultsarepresentedinsection5fol-lowedbyconcludingremarksinsection6.2ARCHITECTUREOurcooperativeframeworkismadeupofamobilerobotandtwofixedviewwall-mountedRGBflea2cameras(figure1).Thecamerashaveamaximumres-olutionof640x480pixelsandareconnectedtoadual-coreIntelCentrinoLaptopviaafire-wirecable.Therobot,calledRackham,isaniRobotB21rmobileplat-form.Ithasvarioussensors,ofwhichitsSICKLaserRangeFinder(LRF)isutilizedinthiswork.Commu-nicationbetweenthemobilerobotandthecomputer"
2e64682caf77309db573ee439d988233f71e88ad,Establishing Good Benchmarks and Baselines for Face Recognition,"Author manuscript, published in ""Workshop on Faces in 'Real-Life' Images: Detection, Alignment, and Recognition, Marseille : France
(2008)"""
2edf55ebc88e89c4caff0c49c6b8e79f46407d19,Pruning Deep Neural Networks using Partial Least Squares,"Pruning Deep Neural Networks using Partial Least Squares
Artur Jordao, Ricardo Kloss∗, Fernando Yamada and William Robson Schwartz
Smart Sense Laboratory, Computer Science Department
Universidade Federal de Minas Gerais, Brazil
Email: {arturjordao, rbk, fernandoakio,"
2e491c8e3d1d3314ea5e50943c0bdf2aa57b99b7,Weighted joint sparse representation-based classification method for robust alignment-free face recognition,"Downloaded From: https://www.spiedigitallibrary.org/journals/Journal-of-Electronic-Imaging on 12/17/2017 Terms of Use: https://www.spiedigitallibrary.org/terms-of-use
Weightedjointsparserepresentation-basedclassificationmethodforrobustalignment-freefacerecognitionBoSunFengXuGuoyanZhouJunHeFengxiangGe"
2e5cfa97f3ecc10ae8f54c1862433285281e6a7c,Generative Adversarial Networks for Improving Face Classification,"Generative Adversarial Networks for Improving Face Classification  JONAS NATTEN SUPERVISOR Morten Goodwin, PhD University of Agder, 2017 Faculty of Engineering and Science Department of ICT"
2e0d56794379c436b2d1be63e71a215dd67eb2ca,Improving precision and recall of face recognition in SIPP with combination of modified mean search and LSH,"Improving precision and recall of face recognition in SIPP with combination of
modified mean search and LSH
Xihua.Li"
2e7874ec37df91db1934d61d9e1181de5e4efb36,COCO-Stuff: Thing and Stuff Classes in Context,"COCO-Stuff: Thing and Stuff Classes in Context
Holger Caesar1
Jasper Uijlings2 Vittorio Ferrari1 2
University of Edinburgh1 Google AI Perception2"
2ec393b4fa5739c54ac9f61e583f5e41cfb2687c,Face Recognition using Spherical Wavelets,"Face Recognition using Spherical Wavelets
Christian Lessig∗"
2ed4973984b254be5cba3129371506275fe8a8eb,Victoria Ovsyannikova THE EFFECTS OF MOOD ON EMOTION RECOGNITION AND ITS RELATIONSHIP WITH THE GLOBAL VS LOCAL INFORMATION PROCESSING,"Victoria Ovsyannikova
THE EFFECTS OF MOOD ON
EMOTION RECOGNITION AND
ITS RELATIONSHIP WITH THE
GLOBAL VS LOCAL
INFORMATION PROCESSING
STYLES
BASIC RESEARCH PROGRAM
WORKING PAPERS
SERIES: PSYCHOLOGY
WP BRP 60/PSY/2016
This Working Paper is an output of a research project implemented at the National Research
University Higher School of Economics (HSE). Any opinions or claims contained in this
Working Paper do not necessarily reflect the views of HSE"
e27301701e4d2d7da4171e6c560c4fb3f974bf2d,Comparative Evaluations of Selected Tracking-by-Detection Approaches,"Comparative Evaluations of Selected
Tracking-by-Detection Approaches
Alhayat Ali Mekonnen, Frédéric Lerasle
To cite this version:
Alhayat Ali Mekonnen, Frédéric Lerasle. Comparative Evaluations of Selected Tracking-by-Detection
Approaches. IEEE Transactions on Circuits and Systems for Video Technology, Institute of Electrical
nd Electronics Engineers, 2018, <10.1109/TCSVT.2018.2817609>. <hal-01815850>
HAL Id: hal-01815850
https://hal.laas.fr/hal-01815850
Submitted on 14 Jun 2018
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
e20daf69526c5da9cffb252d043fdc765f37a89e,Relating images and 3D models with convolutional neural networks. (Mise en relation d'images et de modèles 3D avec des réseaux de neurones convolutifs),"Relating images and 3D models with convolutional
neural networks
Francisco Vitor Suzano Massa
To cite this version:
Francisco Vitor Suzano Massa. Relating images and 3D models with convolutional neural networks.
Signal and Image Processing. Université Paris-Est, 2017. English. <NNT : 2017PESC1198>. <tel-
01762533>
HAL Id: tel-01762533
https://pastel.archives-ouvertes.fr/tel-01762533
Submitted on 10 Apr 2018
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
e27ef52c641c2b5100a1b34fd0b819e84a31b4df,SARC3D: A New 3D Body Model for People Tracking and Re-identification,"SARC3D: a new 3D body model for People
Tracking and Re-identification
Davide Baltieri, Roberto Vezzani, and Rita Cucchiara
Dipartimento di Ingegneria dell’Informazione - University of Modena and Reggio
Emilia, Via Vignolese, 905 - 41125 Modena - Italy"
e21b1c10bee6a984971dcba414c22078dcfd21c2,Recent progress in semantic image segmentation,"Artificial Intelligence Review
https://doi.org/10.1007/s10462-018-9641-3
Recent progress in semantic image segmentation
Xiaolong Liu1 · Zhidong Deng1 · Yuhan Yang2
© The Author(s) 2018"
e282bf5a679ca4e8b7d9a2ed56d3b40dc440ab53,Referenceless Quality Estimation for Natural Language Generation,"Referenceless Quality Estimation for Natural Language Generation
Ondˇrej Duˇsek 1 Jekaterina Novikova 1 Verena Rieser 1"
e21cdb56c23e2a834a611d51abce545d2e8d01a2,Gender and Identity Classification for a Naive and Evolving System,"Gender and Identity Classification for a Naive and Evolving System
M. Castrill´on-Santana, O. D´eniz-Su´arez, J. Lorenzo-Navarro and M. Hern´andez-Tejera
IUSIANI - Edif. Ctral. del Parque Cient´ıfico Tecnol´ogico
Universidad de Las Palmas de Gran Canaria, Spain"
e2edc7e7a2832e2f6014945afce4f76643cab02c,Augsburg An annotated data set for pose estimation of swimmers,"Universit¨at Augsburg
An annotated data set for pose
estimation of swimmers
Thomas Greif and Rainer Lienhart
Report 2009-18
Januar 2010
Institut f¨ur Informatik
D-86135 Augsburg"
e2c122bea06dfa067712cdb58ce474144f93af07,Phrase-based Image Captioning with Hierarchical LSTM Model,"ACCV2016 EXTENSION
Phrase-based Image Captioning with
Hierarchical LSTM Model
Ying Hua Tan and Chee Seng Chan"
e2baf990bc60ef0d24b7556d238e40566ad23d2f,Modified Gabor Filter based Vehicle Verification,"International Journal of Computer Applications® (IJCA) (0975 – 8887)
National Conference cum Workshop on Bioinformatics and Computational Biology, NCWBCB- 2014
Modified Gabor Filter based Vehicle Verification
Amrutha Ramachandran
Mtech,AE&C,
Dept. of EC,
NCERC,Kerala.
towards
ollision
voidance
ccess,potential"
e2fc290a245d9f5c545e2e92ee8fcaff4908b97f,Picture-to-Identity linking of social network accounts based on Sensor Pattern Noise,"Picture-to-Identity linking of social network accounts based on
Sensor Pattern Noise
Riccardo Satta∗ and Pasquale Stirparo∗+
Institute for the Protection and Security of the Citizen,
Joint Research Centre (JRC), European Commission, Ispra (VA), Italy
+Royal Institute of Technology (KTH), Stockholm, Sweden
{riccardo.satta,
Keywords:
linking, digital image forensics
social network, Sensor Pattern Noise, identity,"
e2afea1a84a5bdbcb64d5ceadaa2249195e1fd82,DOOM Level Generation Using Generative Adversarial Networks,"DOOM Level Generation using Generative
Adversarial Networks
Edoardo Giacomello
Dipartimento di Elettronica,
Informazione e Bioinformatica
Politecnico di Milano
Pier Luca Lanzi
Dipartimento di Elettronica,
Informazione e Bioinformatica
Politecnico di Milano
Daniele Loiacono
Dipartimento di Elettronica,
Informazione e Bioinformatica
Politecnico di Milano"
e21c45b14d75545d40ed07896f26ec6f766f6a4b,Fisher GAN,"Fisher GAN
Youssef Mroueh∗, Tom Sercu∗
Equal Contribution
AI Foundations, IBM Research AI
IBM T.J Watson Research Center"
e260847323b48a79bd88dd95a1499cd3053d3645,Reconstructing perceived faces from brain activations with deep adversarial neural decoding,"PDF hosted at the Radboud Repository of the Radboud University
Nijmegen
The following full text is a publisher's version.
For additional information about this publication click this link.
http://hdl.handle.net/2066/179505
Please be advised that this information was generated on 2018-07-04 and may be subject to
hange."
e295f31df11ec700851c2413b9bba644a91b0629,3D face reconstruction in a binocular passive stereoscopic system using face properties,"D FACE RECONSTRUCTION IN A BINOCULAR PASSIVE STEREOSCOPIC SYSTEM
USING FACE PROPERTIES
Amel AISSAOUI, Jean MARTINET and Chaabane DJERABA
LIFL UMR Lille1-CNRS n 8022, IRCICA, 50 avenue Halley, 59658 Villeneuve d’Ascq, France"
e24294adfcdb0334c310823c591f15e8829dc224,Deep Neural Networks and Regression Models for Object Detection and Pose estimation,
e2059946b69e0854f21919c1cf13c3f618f48d12,Deep Architectures and Ensembles for Semantic Video Classification,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2018
Deep Architectures and Ensembles for Semantic
Video Classification
Eng-Jon Ong, Sameed Husain, Mikel Bober-Irizar, Miroslaw Bober∗"
e2e8db754b1ab4cd8aa07f5c5940f6921a1b7187,Interpretable visual models for human perception-based object retrieval,"Interpretable Visual Models for Human
Perception-Based Object Retrieval
Ahmed Rebai, Alexis Joly, Nozha Boujemaa
To cite this version:
Ahmed Rebai, Alexis Joly, Nozha Boujemaa.
Based Object Retrieval.
trieval, Apr 2011, Trento,
<10.1145/1991996.1992017>. <hal-00642232>
Italy.
Interpretable Visual Models for Human Perception-
ICMR’11 - First ACM International Conference on Multimedia Re-
ACM, pp.21:1–21:8, 2011, <http://www.icmr2011.org/>.
HAL Id: hal-00642232
https://hal.inria.fr/hal-00642232
Submitted on 17 Nov 2011
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or"
e2279676b01e477b5e7333bab276678f4ad34753,SEARCHING IMAGE WITH HASH CODE GENERATIONS 1,"International Research Journal of Engineering and Technology (IRJET)      e-ISSN: 2395 -0056
Volume: 02 Issue: 05 | Aug-2015                      www.irjet.net                                                               p-ISSN: 2395-0072
SEARCHING IMAGE WITH HASH CODE GENERATIONS
R.Lawanya,*2Mrs.G.Sangeetha Lakshmi, 3Ms.A.Sivasankari
,*2,3Department of Computer Science,DKM College for Women, Vellore,
Tamil Nadu, India.
----------------------------------------------------------------------------------------------------------------------"
e2945f1b10a52dd5336015363af892ad97cdeb83,Learning to Segment Moving Objects,"Noname manuscript No.
(will be inserted by the editor)
Learning to Segment Moving Objects
Pavel Tokmakov · Cordelia Schmid · Karteek Alahari
Received: date / Accepted: date"
e23ed8642a719ff1ab08799257d9566ed3bba403,Unsupervised Visual Attribute Transfer with Reconfigurable Generative Adversarial Networks,"Unsupervised Visual Attribute Transfer with
Reconfigurable Generative Adversarial Networks
Taeksoo Kim, Byoungjip Kim, Moonsu Cha, Jiwon Kim
SK T-Brain"
e27acf161f569aa876e46ffae2058bb275f12a60,Interactive learning of heterogeneous visual concepts with local features,"Interactive Learning of Heterogeneous Visual Concepts
with Local Features
Wajih Ouertani
INRIA − IMEDIA project
nd INRA, France
Michel Crucianu
INRIA − IMEDIA project
nd CEDRIC − CNAM, France
Nozha Boujemaa
INRIA − IMEDIA project
78153 Le Chesnay, France"
e2d265f606cd25f1fd72e5ee8b8f4c5127b764df,Real-Time End-to-End Action Detection with Two-Stream Networks,"Real-Time End-to-End Action Detection
with Two-Stream Networks
Alaaeldin El-Nouby∗†, Graham W. Taylor∗†‡
School of Engineering, University of Guelph
Vector Institute for Artificial Intelligence
Canadian Institute for Advanced Research"
e278218ba1ff1b85d06680e99b08e817d0962dab,Tracking Persons-of-Interest via Unsupervised Representation Adaptation,"Tracking Persons-of-Interest via
Unsupervised Representation Adaptation
Shun Zhang, Jia-Bin Huang, Jongwoo Lim, Yihong Gong, Jinjun Wang,
Narendra Ahuja, and Ming-Hsuan Yang"
e20e06ea1aa6e94721638ffc8e3bdcc0ef574b64,Secure Face Authentication Framework in Open Networks,"Secure Face Authentication Framework in
Open Networks
Yongjin Lee, Yongki Lee, Yunsu Chung, and Kiyoung Moon
knowledge
In response to increased security concerns, biometrics is
ecoming more focused on overcoming or complementing
onventional
possession-based
uthentication. However, biometric authentication requires
special care since the loss of biometric data is irrecoverable.
In  this  paper,  we  present  a  biometric  authentication
framework, where several novel techniques are applied to
provide security and privacy. First, a biometric template is
saved in a transformed form. This makes it possible for a
template  to  be  canceled  upon  its  loss  while  the  original
iometric information is not revealed. Second, when a user
is registered with a server, a biometric template is stored in
special  form,  named  a  ‘soft  vault’.  This  technique
prevents impersonation attacks even if data in a server is
disclosed  to  an  attacker.  Finally,  a  one-time  template"
e22cf1ca10c11991c2a43007e37ca652d8f0d814,A Biologically Inspired Visual Working Memory,"Under review as a conference paper at ICLR 2019
A BIOLOGICALLY INSPIRED VISUAL WORKING
MEMORY FOR DEEP NETWORKS
Anonymous authors
Paper under double-blind review"
e2af85dc41269bc7c50fcf2fb35bfeb75e3d6ee4,xytocin Improves “ Mind-Reading ” in Humans,"PRIORITY COMMUNICATION
Oxytocin Improves “Mind-Reading” in Humans
Gregor Domes, Markus Heinrichs, Andre Michel, Christoph Berger, and Sabine C. Herpertz
Background: The ability to “read the mind” of other individuals, that is, to infer their mental state by interpreting subtle social cues, is
indispensable in human social interaction. The neuropeptide oxytocin plays a central role in social approach behavior in nonhuman
mammals.
Methods: In a double-blind, placebo-controlled, within-subject design, 30 healthy male volunteers were tested for their ability to infer
the affective mental state of others using the Reading the Mind in the Eyes Test (RMET) after intranasal administration of 24 IU oxytocin.
Results: Oxytocin improved performance on the RMET compared with placebo. This effect was pronounced for difficult compared with
easy items.
Conclusions: Our data suggest that oxytocin improves the ability to infer the mental state of others from social cues of the eye region.
Oxytocin might play a role in the pathogenesis of autism spectrum disorder, which is characterized by severe social impairment.
Key Words: Emotion, oxytocin, peptide, social cognition, theory of
T he ability to infer the internal state of another person to
dapt one’s own behavior is a cornerstone of all human
social interactions. Humans have to infer internal states
from external cues such as facial expressions in order to make
sense of or predict another person’s behavior, an ability that is
referred  to  as  “mind-reading”  (Siegal  and  Varley  2002;  Stone  et  al
998).  In  particular,  individuals  with  autism  have  distinct  diffi-"
e25e07cfd0818a499033caf9d7aa8ef4feec981b,Semantic Segmentation for Real-World Data by Jointly Exploiting Supervised and Transferrable Knowledge,"Pages 84.1-84.12
DOI: https://dx.doi.org/10.5244/C.30.84"
e2b8ba13586bb9a96e4813472d1f763d37ead47d,Media Content Access : Image-Based Filtering,"(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 9, No. 3, 2018
Media Content Access: Image-Based Filtering
Rehan Ullah Khan1, Ali Alkhalifah2
Information Technology Department
Qassim University, Al-Qassim, KSA"
7e25544be9ba701c8cf02c841e0bbadb36fa0e29,Zero-Shot Visual Recognition using Semantics-Preserving Adversarial Embedding Network,"Zero-Shot Visual Recognition using Semantics-Preserving
Adversarial Embedding Networks
Long Chen1 Hanwang Zhang2
Jun Xiao1∗ Wei Liu3
Shih-Fu Chang4
Zhejiang University 2Nanyang Technological University 3Tencent AI Lab 4Columbia University
{longc, {wliu,
Figure 1: (a) Attribute variance heat maps of the 312 attributes in CUB birds [60] and the 102 attributes in SUN scenes [47]
(lighter color indicates lower variance, i.e., lower discriminability) and the t-SNE [35] visualizations of the test images
represented by all attributes (left) and only the high-variance ones (right). Some of the low-variance attributes (the lighter
part to the left of the cut-off line) discarded at training are still needed in discriminating unseen test classes. (b) Comparison
of reconstructed images using SAE [25] and our proposed SP-AEN method, which is shown to retain sufficient semantics for
photo-realistic reconstruction."
7e7e4af2a79288fd2e391020edff8552ea1ece9a,Trimming Prototypes of Handwritten Digit Images with Subset Infinite Relational Model,"Trimming Prototypes of Handwritten Digit
Images with Subset Infinite Relational Model
Tomonari Masada1 and Atsuhiro Takasu2
Nagasaki University, 1-14 Bunkyo-machi, Nagasaki-shi, Nagasaki, 852-8521 Japan,
National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, 101-8430
Japan,"
7e7b4b4a84c2aa0ee69b5cea3a4da7f62a0a37d5,GraSp: Combining Spatially-aware Mobile Devices and a Display Wall for Graph Visualization and Interaction,"Eurographics Conference on Visualization (EuroVis) 2017
J. Heer, T. Ropinski and J. van Wijk
(Guest Editors)
Volume 36 (2017), Number 3
GRASP: Combining Spatially-aware Mobile Devices
nd a Display Wall for Graph Visualization and Interaction
U. Kister1, K. Klamka1, C. Tominski2 and R. Dachselt1
Interactive Media Lab Dresden, Technische Universität Dresden, Germany
Institute for Computer Science, University of Rostock, Germany
Figure 1: Mobile devices support graph visualization and interaction on wall-sized displays close to the display wall and further away (A).
The GRASP system provides a mobile toolbox with selections, alternative representations, lenses, and filtering close to the user (B)."
7e654380bd0d1f4c00e85da71a3081d3ada432ef,MGAN: TRAINING GENERATIVE ADVERSARIAL NETS,"Under review as a conference paper at ICLR 2018
MGAN: TRAINING GENERATIVE ADVERSARIAL NETS WITH
MULTIPLE GENERATORS
Anonymous authors
Paper under double-blind review"
7e3367b9b97f291835cfd0385f45c75ff84f4dc5,Improved local binary pattern based action unit detection using morphological and bilateral filters,"Improved Local Binary Pattern Based Action Unit Detection Using
Morphological and Bilateral Filters
Anıl Y¨uce1, Matteo Sorci2 and Jean-Philippe Thiran1
Signal Processing Laboratory (LTS5)
´Ecole Polytechnique F´ed´erale de Lausanne,
Switzerland
nViso SA
Lausanne, Switzerland"
7e8edc45fa80cb0f7bc2c20e8eb893dcadde2c8c,COMBINING SPEEDED-UP ROBUST FEATURES WITH PRINCIPAL COMPONENT ANALYSIS IN FACE RECOGNITION SYSTEM,"International Journal of Innovative
Computing, Information and Control
Volume 8, Number 12, December 2012
ICIC International c(cid:13)2012 ISSN 1349-4198
pp. 8545{8556
COMBINING SPEEDED-UP ROBUST FEATURES WITH PRINCIPAL
COMPONENT ANALYSIS IN FACE RECOGNITION SYSTEM
Shinfeng D. Lin(cid:3), Bo-Feng Liu and Jia-Hong Lin
Department of Computer Science and Information Engineering
National Dong Hwa University
No. 1, Sec. 2, Da Hsueh Rd., Shoufeng, Hualien 97401, Taiwan
Corresponding author:
(cid:3)
Received October 2011; revised March 2012"
7e463877264e70d53c844cf4b1bf3b15baec8cfb,ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks,"ReNet: A Recurrent Neural Network Based
Alternative to Convolutional Networks
Francesco Visin(cid:63)
Politecnico di Milano
Kyle Kastner(cid:63)
University of Montreal
Kyunghyun Cho(cid:63)
University of Montreal
Matteo Matteucci
Politecnico di Milano
Aaron Courville
University of Montreal
Yoshua Bengio
University of Montreal
CIFAR Senior Fellow"
7eba8590558148759b0aeebb0772e19ae50edb3c,Facial Recognition and Its Applications in Distance Learning Environment,Weidong Liao & Chad Vanorsdale1
7ed9913de03dd2990b68751842306c2636852647,VQABQ: Visual Question Answering by Basic Questions,"VQABQ: Visual Question Answering by Basic Questions
Jia-Hong Huang
King Abdullah University of Science and Technology
{jiahong.huang, modar.alfadly,
Modar Alfadly
Bernard Ghanem"
7e55dab6f23f8e0f9587f76ec1dd66e2dbba436a,Pilgrims Face Recognition Dataset -- HUFRD,"Pilgrims Face Recognition Dataset – HUFRD
Salah A. Aly
Center of Research Excellence in Hajj and Umrah (HajjCore),
College of Computers and Information Systems, Umm Al-Qura University, Makkah, KSA
Email:"
7ed6ff077422f156932fde320e6b3bd66f8ffbcb,State of 3 D Face Biometrics for Homeland Security Applications,"State of 3D Face Biometrics for Homeland Security Applications
Anshuman Razdan1, Gerald Farin2, Myung Soo-Bae3 and Mahesh
Chaudhari4"
7ebd323ddfe3b6de8368c4682db6d0db7b70df62,Location-based Face Recognition Using Smart Mobile Device Sensors,"Proceedings of the International Conference on Computer and Information Science and Technology
Ottawa, Ontario, Canada, May 11 – 12, 2015
Paper No. 111
Location-based Face Recognition Using Smart Mobile Device
Sensors
Nina Taherimakhsousi, Hausi A. Müller
Department of Computer Science
University of Victoria, Victoria, Canada"
7ea7c073d13e80ec5015f41f1d57f0674502cc5e,An Implementation of Face Emotion Identification System using Active Contour Model and PCA,"IJSRD - International Journal for Scientific Research & Development| Vol. 3, Issue 04, 2015 | ISSN (online): 2321-0613
An Implementation of Face Emotion Identification System using Active
Contour Model and PCA
Namita Rathore1 Mr.Rohit Miri2
P.G. Student 2Assistant Professor
,2Department of Computer Science and Engineering
,2DR C V Raman Institute of Science and Technology Kota, bilaspur
systems,
surveillance"
7e7430b5b6ffc470284b8fa94840797610d450ad,Free-space detection using online disparity-supervised color modeling,"Free-space detection using online disparity-supervised
olor modeling
Sanberg, W.P.; Dubbelman, G.; de With, P.H.N.
Published in:
Proceedings of the 7th Workshop on Planning, Perception and Navigation for Intelligent Vehicles (PPNIV)
Published: 28/09/2015
Document Version
Publisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)
Please check the document version of this publication:
• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences
etween the submitted version and the official published version of record. People interested in the research are advised to contact the
uthor for the final version of the publication, or visit the DOI to the publisher's website.
• The final author version and the galley proof are versions of the publication after peer review.
• The final published version features the final layout of the paper including the volume, issue and page numbers.
Link to publication
Citation for published version (APA):
Sanberg, W. P., Dubbelman, G., & de With, P. H. N. (2015). Free-space detection using online disparity-
supervised color modeling. In Proceedings of the 7th Workshop on Planning, Perception and Navigation for
Intelligent Vehicles (PPNIV): at IEEE/RSJ IROS (pp. 105-110)
General rights"
7e507370124a2ac66fb7a228d75be032ddd083cc,Dynamic Pose-Robust Facial Expression Recognition by Multi-View Pairwise Conditional Random Forests,"This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TAFFC.2017.2708106, IEEE
Transactions on Affective Computing
Dynamic Pose-Robust Facial Expression
Recognition by Multi-View Pairwise Conditional
Random Forests
Arnaud Dapogny1 and Kevin Bailly1 and S´everine Dubuisson1
Sorbonne Universit´es, UPMC Univ Paris 06
CNRS, UMR 7222, F-75005, Paris, France"
7e157fb05614a158397bc2a3bf7b7962b1a123ce,Deep Network Embedding for Graph Representation Learning in Signed Networks,"This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.
Deep Network Embedding for Graph
Representation Learning in Signed Networks
Xiao Shen
nd Fu-Lai Chung"
7ebc96b4b7886b263808c2cd62b21158ebf6297c,"Crowd Motion Analysis: Segmentation, Anomaly Detection, and Behavior Classification","CROWD MOTION ANALYSIS:
SEGMENTATION, ANOMALY
DETECTION, AND BEHAVIOR
CLASSIFICATION
Habib Ullah
Advisor: Nicola Conci, PhD
February 2015"
7e5414277148c8fdf9903068b001887225b69868,Perceptive Parallel Processes Coordinating Geometry and Texture,"Perceptive Parallel Processes Coordinating Geometry and Texture
Marco A. Gutierrez1, Rafael E. Banchs2 and Luis F. D'Haro2"
7e3693fffef8d83ac109309a77f2545d32c10fc3,The effect of Ramadan fasting on spatial attention through emotional stimuli,"Psychology Research and Behavior Management
Open access Full Text article
Dovepress
open access to scientific and medical research
O Ri g i n a l   R e s e aRc h
The effect of Ramadan fasting on spatial attention
through emotional stimuli
Maziyar Molavi
Jasmy Yunus
nugraha P Utama
Department of clinical  sciences,
Faculty of Biosciences and Medical
engineering (FBMe), Universiti
Teknologi Malaysia (UTM), Johor
Bahru, Johor, Malaysia
orrespondence: nugraha P Utama
Department of clinical sciences, Faculty
of Biosciences and Medical engineering,
Universiti Teknologi Malaysia (UTM),
81310 Johor Bahru, Johor, Malaysia"
7e53ab07d0ce28484830329036a1fc018b9644dd,Online multiple people tracking-by-detection in crowded scenes,"Journal of Advances in Computer Engineering and Technology, 1(2) 2015
Online multiple people tracking-by-detection in
rowded scenes
Sahar Rahmatian1, Reza Safabakhsh2
Received (2015-01-23)
Accepted (2015-03-19)"
7e59d2d3416537dd958ff71b7a0bff87e639dad9,Feature-Based Pose Estimation,"Feature-based Pose Estimation
Cristian Sminchisescu1,2, Liefeng Bo3, Catalin Ionescu4, Atul Kanaujia5"
7ee53d931668fbed1021839db4210a06e4f33190,What If We Do Not have Multiple Videos of the Same Action? — Video Action Localization Using Web Images,"What if we do not have multiple videos of the same action? —
Video Action Localization Using Web Images
Center for Research in Computer Vision (CRCV), University of Central Florida (UCF)
Waqas Sultani, Mubarak Shah"
7ed241bcf77552889850640ca9782993fa78c1a9,The HCI Benchmark Suite: Stereo and Flow Ground Truth with Uncertainties for Urban Autonomous Driving,"The HCI Benchmark Suite: Stereo And Flow Ground Truth With Uncertainties
for Urban Autonomous Driving
Daniel Kondermann∗ Rahul Nair∗ Katrin Honauer∗ Karsten Krispin∗ Jonas Andrulis†
Alexander Brock∗ Burkhard G¨ussefeld∗ Mohsen Rahimimoghaddam∗ Sabine Hofmann‡
Claus Brenner‡ Bernd J¨ahne∗
Heidelberg Collaboratory for Image Processing,
Pallas Ludens GmbH,
Institute of Cartography and Geoinformatics,
Ruprecht-Karls Universit¨at Heidelberg, Germany
Heidelberg, Germany
Leibniz Universit¨at Hannover, Germany"
7ea07b7b27d59300840df17e5881dbe3a4769872,Detection driven adaptive multi-cue integration for multiple human tracking,"Detection Driven Adaptive Multi-cue Integration for Multiple Human Tracking
Ming Yang, Fengjun Lv, Wei Xu, Yihong Gong
NEC Laboratories America, Inc.
0080 North Wolfe Road, SW-350, Cupertino, CA 95014"
7e2602f7572add68636863504bfe9ff271f3796a,Asymmetric Bilateral Phase Correlation for Optical Flow Estimation in the Frequency Domain,"Flow Estimation in the Frequency Domain
Vasileios Argyriou
Kingston University London
London, UK"
7e3b5d30b83a20c7cffdacf53b3ffbaf81002b54,People Transitioning Across Places : A Multimethod Investigation of How People Go to Football Games,"12589 EABXXX10.1177/0013916511412589
© The Author(s) 2011
Reprints and permission: http://www.
sagepub.com/journalsPermissions.nav
Environment and Behavior
XX(X) 1 –28
© 2011 SAGE Publications
Reprints and permission: http://www.
sagepub.com/journalsPermissions.nav
DOI: 10.1177/0013916511412589
http://eab.sagepub.com
People Transitioning
Across Places:  A
Multimethod
Investigation of
How People Go to
Football Games
R. Barry Ruback1, Robert T. Collins1,
Sarah Koon-Magnin1, Weina Ge2,
Luke Bonkiewicz1, and Clifford E. Lutz1"
7ef0cc4f3f7566f96f168123bac1e07053a939b2,Triangular Similarity Metric Learning: a Siamese Architecture Approach. ( L'apprentissage de similarité triangulaire en utilisant des réseaux siamois),"Triangular Similarity Metric Learning: a Siamese
Architecture Approach
Lilei Zheng
To cite this version:
Lilei Zheng. Triangular Similarity Metric Learning: a Siamese Architecture Approach. Com-
puter Science [cs]. UNIVERSITE DE LYON, 2016. English. <NNT : 2016LYSEI045>. <tel-
01314392>
HAL Id: tel-01314392
https://hal.archives-ouvertes.fr/tel-01314392
Submitted on 11 May 2016
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,
´emanant des ´etablissements d’enseignement et de"
fd9286f0e465deffad59123f46fa4f66cb15c3e4,Learning Answer Embeddings for Visual Question Answering,"Learning Answer Embeddings for Visual Question Answering
Hexiang Hu∗
U. of Southern California
Los Angeles, CA
Wei-Lun Chao∗
Los Angeles, CA
U. of Southern California
U. of Southern California
Fei Sha
Los Angeles, CA"
fd0a1a2ecf69a6c1a6efcb18b8f23e4d5402f601,"ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events","ExtremeWeather: A large-scale climate dataset for
semi-supervised detection, localization, and
understanding of extreme weather events
Evan Racah1,2, Christopher Beckham1,3, Tegan Maharaj1,3,
Samira Ebrahimi Kahou4, Prabhat2, Christopher Pal1,3
MILA, Université de Montréal,
Lawrence Berkeley National Lab, Berkeley, CA,
École Polytechnique de Montréal,
Microsoft Maluuba,"
fdebde7926e87dbfb6e73dd4f8324ad2ec45d7a6,Image Segmentation for Biometric Identification Systems,"Image Segmentation for Biometric Identification
Systems
Eyad Haj Said
Dissertation submitted to the
College of Engineering and Mineral Resources
t West Virginia University
in partial fulfillment of the requirements
for the degree of
Doctor of Philosophy
Computer Engineering
Hany H. Ammar Ph.D, Committee Chairperson
Arun Ross,  Ph.D
Xin Li, Ph.D
Sam Mukdadi , Ph.D
Mohamed Abdel-Mottaleb, Ph.D
Lane Department of Computer Science and Electrical Engineering
Morgantown, West Virginia
Keywords:  Biometrics, Image Segmentation, Automated Segmentation
Evaluation, ADIS, AEIS
Copyright 2007 Eyad Haj Said"
fdf533eeb1306ba418b09210387833bdf27bb756,Exploiting Unrelated Tasks in Multi-Task Learning,
fdf31db5aa8cf8a7f9ac84fcc7b0949e8e000a41,MODELING FASHION Anonymous ICME submission,"MODELING FASHION
Anonymous ICME submission"
fd8b1715ad34858bf8650ac549c4249d86edbb7c,A survey of techniques for human segmentation from static images,"International Association of Scientific Innovation and Research (IASIR)
(An Association Unifying the Sciences, Engineering, and Applied Research)
ISSN (Print): 2279-0063
ISSN (Online): 2279-0071
International Journal of Software and Web Sciences (IJSWS)
www.iasir.net
A survey of techniques for human segmentation from static images
Ms.Ashwini T. Magar, Prof.J.V.Shinde
Late G.N.Sapkal College of Engineering,
Computer Engineering Department, Nashik,
University of Pune, India.
__________________________________________________________________________________________"
fde0180735699ea31f6c001c71eae507848b190f,Face Detection and Sex Identification from Color Images using AdaBoost with SVM based Component Classifier,"International Journal of Computer Applications (0975 – 8887)
Volume 76– No.3, August 2013
Face Detection and Sex Identification from Color Images
using AdaBoost with SVM based Component Classifier
Tonmoy Das
Lecturer, Department of EEE
University of Information
Technology and Sciences
(UITS)
Dhaka, Bangladesh
Manamatha Sarnaker
B.Sc. in EEE
International University of
Business Agriculture and
Technology (IUBAT)
Dhaka-1230, Bangladesh
Md. Hafizur Rahman
Lecturer, Department of EEE
International University of
Business Agriculture and"
fd4ac1da699885f71970588f84316589b7d8317b,Supervised Descent Method for Solving Nonlinear Least Squares Problems in Computer Vision,"JOURNAL OF LATEX CLASS FILES, VOL. 6, NO. 1, JANUARY 2007
Supervised Descent Method
for Solving Nonlinear Least Squares
Problems in Computer Vision
Xuehan Xiong, and Fernando De la Torre"
fd4c46bfd3bb00ed93b0bb5b28ef0336f59f0c15,Expressing emotions through vibration for perception and control,"Expressing Emotions through Vibration
for Perception and Control
Shafiq ur Réhman
Doctoral Thesis, April 2010
Department of Applied Physics and Electronics
Umeå University, Sweden
UNIVERSITETSSERVICEProfil & CopyshopÖppettider:Måndag - fredag 10-16Tel. 786 52 00 alt 070-640 52 01Universumhuset"
fdb956c7705b7f57f56f944a0f3f4ede1d6f77fa,Does Fast Fashion Increase the Demand for Premium Brands ?,"Does Fast Fashion Increase the Demand for Premium Brands?
A Structural Analysis
Zijun	(June)	Shi1,	Param	Vir	Singh,	Dokyun	Lee,	Kannan	Srinivasan
(Preliminary	draft.	Please	do	not	cite	without	the	authors’	permission.)"
fdb0472af94a726897c20b6b181c6d71ee293e71,Quality assessment of image-based biometric information,"El-Abed et al. EURASIP Journal on Image and Video Processing  (2015) 2015:3
DOI 10.1186/s13640-015-0055-8
RESEARCH
Open Access
Quality assessment of image-based biometric
information
Mohamad El-Abed1*, Christophe Charrier2,3,4 and Christophe Rosenberger2,3,4"
fd23502287ae4ca8db63e4e5080c359610398be5,Real-Time Pedestrian Detection with Deep Network Cascades,"ANGELOVA ET AL.: REAL-TIME PEDESTRIAN DETECTION WITH DEEP CASCADES
Real-Time Pedestrian Detection With Deep
Network Cascades
Anelia Angelova1
Alex Krizhevsky1
Vincent Vanhoucke1
Abhijit Ogale2
Dave Ferguson2
Google Research
600 Amphitheatre Parkway
Mountain View, CA, USA
Google X
600 Amphitheatre Parkway
Mountain View, CA, USA"
fdfaf46910012c7cdf72bba12e802a318b5bef5a,Computerized Face Recognition in Renaissance Portrait Art,"Computerized Face Recognition in Renaissance
Portrait Art
Ramya Srinivasan, Conrad Rudolph and Amit Roy-Chowdhury"
fdf9c636f79f146f116bbca392dcad3b535cecb2,Statistical Approaches to Inferring Object Shape from Single Images,"Statistical Approaches to Inferring Object Shape from
Single Images
Ashwini Shikaripur Nadig
Submitted to the Department of Electrical Engineering and Computer Science and the
Graduate Faculty of the University of Kansas
in partial fulllment of the requirements for the degree of
Doctor of Philosophy
Committee members
Dr. Bo Luo, Chairperson
Dr. Brian Potetz, Co-chair
Dr. Luke Huan
Dr. James Miller
Dr. Paul Selden
Date defended:
May 20, 2014"
fd069af1ede370625703f7984e52f282fcd6342e,Guided Feature Transformation (GFT): A Neural Language Grounding Module for Embodied Agents,"Guided Feature Transformation (GFT): A Neural
Language Grounding Module for Embodied Agents
Haonan Yu†, Xiaochen Lian†, Haichao Zhang†, and Wei Xu‡
Baidu Research, Sunnyvale CA USA
Horizon Robotics, Cupertino CA USA"
fdd94d77377df6e55d14e41a28141dc241d8b5d6,Current Status and Future Prospects of Clinical Psychology: Toward a Scientifically Principled Approach to Mental and Behavioral Health Care.,"Current Status and Future Prospects of Clinical Psychology: Toward a Scientifically
Principled Approach to Mental and Behavioral Health Care
Author(s): Timothy B. Baker, Richard M. McFall and  Varda Shoham
Source: Psychological Science in the Public Interest, Vol. 9, No. 2 (November 2008), pp. 67-
Published by: Sage Publications, Inc. on behalf of the Association for Psychological Science
Stable URL: http://www.jstor.org/stable/20697320
Accessed: 07-02-2017 15:41 UTC
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fdca08416bdadda91ae977db7d503e8610dd744f,The Ksera Project (http://www.ksera under the 7th Framework Programme (fp7) for Research and Technological Development under Grant Human Robot Interaction Ksera Project (http://www.ksera-project.eu) Has Received Funding from the European Commission under the 7th Framework Programme (fp7) for Researc,"ICT-2009.7.1
KSERA Project
010-248085
Deliverable D3.1
Deliverable D3.1
Human Robot Interaction
Human Robot Interaction
8 October 2010
Public Document
The KSERA project (http://www.ksera
KSERA project (http://www.ksera-project.eu) has received funding from the European Commission
project.eu) has received funding from the European Commission
under the 7th Framework Programme (FP7) for Research and Technological Development under grant
under the 7th Framework Programme (FP7) for Research and Technological Development under grant
under the 7th Framework Programme (FP7) for Research and Technological Development under grant
greement n°2010-248085."
fd4537b92ab9fa7c653e9e5b9c4f815914a498c0,One-Sided Unsupervised Domain Mapping,
fde3f34a1accadb73269e4beef487611f682b781,"Before A Computer Can Draw, It Must First Learn To See","Before A Computer Can Draw, It Must First Learn To See
Derrall Heath and Dan Ventura
Computer Science Department
Brigham Young University
Provo, UT 84602 USA"
fdee0cf79e9a2695857afeee6526352918c9f315,Quantization for Rapid Deployment of Deep Neural Networks,"Quantization for Rapid Deployment of Deep Neural Networks
Jun Haeng Lee∗, Sangwon Ha∗, Saerom Choi, Won-Jo Lee, Seungwon Lee
Samsung Advanced Institute of Technology
Samsung-ro 130, Suwon-si, Republic of Korea
{junhaeng2.lee,"
fdb33141005ca1b208a725796732ab10a9c37d75,A connectionist computational method for face recognition,"Int.J.Appl. Math. Comput.Sci.,2016,Vol. 26,No. 2,451–465
DOI: 10.1515/amcs-2016-0032
A CONNECTIONIST COMPUTATIONAL METHOD FOR FACE RECOGNITION
FRANCISCO A. PUJOL a, HIGINIO MORA a,∗
, JOS ´E A. GIRONA-SELVA a
Department of Computer Technology
University of Alicante, 03690, San Vicente del Raspeig, Alicante, Spain
e-mail:
In this work, a modified version of the elastic bunch graph matching (EBGM) algorithm for face recognition is introduced.
First, faces are detected by using a fuzzy skin detector based on the RGB color space. Then, the fiducial points for the facial
graph are extracted automatically by adjusting a grid of points to the result of an edge detector. After that, the position of
the nodes, their relation with their neighbors and their Gabor jets are calculated in order to obtain the feature vector defining
each face. A self-organizing map (SOM) framework is shown afterwards. Thus, the calculation of the winning neuron and
the recognition process are performed by using a similarity function that takes into account both the geometric and texture
information of the facial graph. The set of experiments carried out for our SOM-EBGM method shows the accuracy of our
proposal when compared with other state-of the-art methods.
Keywords: pattern recognition, face recognition, neural networks, self-organizing maps.
Introduction
libraries,
In recent years, there has been intensive research carried"
fd4f9955ec28b63443039cb9d4e15bae796defe4,Predictably Angry - Facial Cues Provide a Credible Signal of Destructive Behavior,"Predictably Angry
Facial cues provide a credible signal of destructive behavior
Boris van Leeuwen1, Charles N. Noussair2, Theo Offerman3,
Sigrid Suetens4, Matthijs van Veelen5, and Jeroen van de Ven6
November 2016"
fd51665efe2520a55aa58b2f1863a3bd9870529f,Understanding Compressive Adversarial Privacy,"Understanding Compressive Adversarial Privacy
Xiao Chen, Peter Kairouz, Ram Rajagopal"
fd4b5766a8ace0d89676deb26a098949a96089a3,Supervised Mixed Norm Autoencoder for Kinship Verification in Unconstrained Videos,"Supervised Mixed Norm Autoencoder for Kinship
Verification in Unconstrained Videos
Naman Kohli, Student Member, IEEE, Daksha Yadav, Student Member, IEEE, Mayank Vatsa,
Senior Member, IEEE, Richa Singh, Senior Member, IEEE, and Afzel Noore, Senior Member, IEEE."
fdfff58f62ffe7ab76c2b2cc32ea20099d197194,On the Nonlinear Statistics of Optical Flow,"ON THE NONLINEAR STATISTICS OF OPTICAL FLOW
HENRY ADAMS, JOHNATHAN BUSH, BRITTANY CARR, LARA KASSAB,
AND JOSHUA MIRTH"
fd96432675911a702b8a4ce857b7c8619498bf9f,Improved Face Detection and Alignment using Cascade Deep Convolutional Network,"Improved Face Detection and Alignment using Cascade
Deep Convolutional Network
Weilin Cong†, Sanyuan Zhao†, Hui Tian‡, and Jianbing Shen†
Beijing Key Laboratory of Intelligent Information Technology, School of
Computer Science,Beijing Institute of Technology, Beijing 100081, P.R.China
China Mobile Research Institute, Xuanwu Men West Street, Beijing"
fd615118fb290a8e3883e1f75390de8a6c68bfde,Joint Face Alignment with Non-parametric Shape Models,"Joint Face Alignment with Non-Parametric
Shape Models
Brandon M. Smith and Li Zhang
University of Wisconsin – Madison
http://www.cs.wisc.edu/~lizhang/projects/joint-align/"
fd0e1fecf7e72318a4c53463fd5650720df40281,End-to-End Comparative Attention Networks for Person Re-Identification,"End-to-End Comparative Attention Networks for
Person Re-identification
Hao Liu, Jiashi Feng, Meibin Qi, Jianguo Jiang and Shuicheng Yan, Fellow, IEEE"
fd63df88af4b4a30b315904de22995b3da798b09,Generative Modeling of Multimodal Multi-Human Behavior,"Generative Modeling of Multimodal Multi-Human Behavior
Boris Ivanovic1
Edward Schmerling2
Karen Leung3
Marco Pavone3"
fdaf65b314faee97220162980e76dbc8f32db9d6,Face recognition using both visible light image and near-infrared image and a deep network,"Accepted Manuscript
Face recognition using both visible light image and near-infrared image and a deep
network
Kai Guo, Shuai Wu, Yong Xu
Reference:
S2468-2322(17)30014-8
0.1016/j.trit.2017.03.001
TRIT 41
To appear in:
CAAI Transactions on Intelligence Technology
Received Date: 30 January 2017
Accepted Date: 28 March 2017
Please cite this article as: K. Guo, S. Wu, Y. Xu, Face recognition using both visible light image and
near-infrared image and a deep network, CAAI Transactions on Intelligence Technology (2017), doi:
0.1016/j.trit.2017.03.001.
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to
our customers we are providing this early version of the manuscript. The manuscript will undergo
opyediting, typesetting, and review of the resulting proof before it is published in its final form. Please
note that during the production process errors may be discovered which could affect the content, and all
legal disclaimers that apply to the journal pertain."
fdda5852f2cffc871fd40b0cb1aa14cea54cd7e3,Im2Flow: Motion Hallucination from Static Images for Action Recognition,"Im2Flow: Motion Hallucination from Static Images for Action Recognition
Ruohan Gao
UT Austin
Bo Xiong
UT Austin
Kristen Grauman
UT Austin"
fd1b917476b114919de0ae1b6a4b96a52a410c20,A Memory Based Face Recognition Method,"A Memory Based Face Recognition Method
Alex Pappachen James
B. Tech. (Hons), M. Tech.
Grif‌f‌ith School of Engineering
Science, Environment, Engineering and Technology
Grif‌f‌ith University
Submitted in fulfilment of the requirements of the degree of
Doctor of Philosophy
November 2008"
fdc60fe4654b5efe0752acabef0ec6258062be0f,Multi-Sensor Fusion Adopted 2-D Laser Rangefinder and Camera for Pedestrian Detection,"2nd ITS World Congress, Bordeaux, France, 5–9 October 2015
Paper number ITS-1576
Multi-Sensor Fusion Adopted 2-D Laser Rangefinder and Camera
for Pedestrian Detection
Kuo-Ching Chang*, Chi-Kuo Chen, Pao-Kai Tseng
Automotive Research & Testing Center, Taiwan
+886-4-7811222 Ext. 2323,"
fdbe7c520568d9a32048270d2c87113c635dc7e6,Live Stream Oriented Age and Gender Estimation using Boosted LBP Histograms Comparisons,"Live Stream Oriented Age and Gender Estimation using Boosted LBP
Histograms Comparisons
LAMIA, University of the French West Indies and Guiana, Campus de Fouillole, BP 250, 97157 Pointe `a Pitre, France
Lionel Prevost1, Philippe Phothisane2 and Erwan Bigorgne2
Eikeo, 11 rue L´eon Jouhaux, 75010 Paris, France
Keywords:
Face Analysis, Boosting, Gender Estimation, Age Estimation."
5c1fcee7c31fb2dd54a35670b63cdb2af5726ae6,TUNIR : A Multi-Modal Database for Person Authentication under Near Infrared Illumination,"TUNIR: A Multi-Modal Database for Person
Authentication under Near Infrared Illumination
Shuyan Zhao and Ralph Kricke and Rolf-Rainer Grigat
TUHH Vision Systems (E-2)
Harburger Schloßstr. 20, 21079 Hamburg, Germany
Tel: +49 40 42878-3125, Fax: +49 40 42878-2911
http://www.ti1.tu-harburg.de
in: 6th WSEAS International Conference on Signal Processing, Robotics and Automation (ISPRA
007). See also BIBTEX entry below.
BIBTEX:
uthor = {Shuyan Zhao and Ralph Kricke and Rolf-Rainer Grigat},
title = {TUNIR: A Multi-Modal Database for Person Authentication under Near
Infrared Illumination},
ooktitle = {6th WSEAS International Conference on Signal Processing, Robotics
nd Automation (ISPRA 2007)},
year = {2007},
month = {feb},
url = {http://www.ti1.tu-harburg.de/Publikationen}
© copyright by the author(s)"
5c8ad080ccb3f5e3c999c2948029f0bd005d5635,Engaging Image Captioning,"ENGAGING IMAGE CAPTIONING VIA PERSONALITY
Kurt Shuster, Samuel Humeau, Hexiang Hu, Antoine Bordes, Jason Weston
Facebook AI Research"
5c6ccca19179fd217a74ccb954a4c4370e4203e2,Correspondences of Persistent Feature Points on Near-Isometric Surfaces,"Correspondences of Persistent Feature Points
on Near-Isometric Surfaces
Ying Yang1,2, David G¨unther1,3, Stefanie Wuhrer3,1, Alan Brunton3,4
Ioannis Ivrissimtzis2, Hans-Peter Seidel1, Tino Weinkauf1 (cid:63)
MPI Informatik 2Durham University 3Saarland University 4University of Ottawa"
5c48f97a8a8217025abafeababaef6288fd7ded6,Model syndromes for investigating social cognitive and affective neuroscience: a comparison of Autism and Williams syndrome.,"doi:10.1093/scan/nsl035
SCAN (2006) 1of 8
Model syndromes for investigating social cognitive
nd affective neuroscience: a comparison of
utism and Williams syndrome
Helen Tager-Flusberg, Daniela Plesa Skwerer, and Robert M. Joseph
Boston University School of Medicine, Boston, MA, USA
Autism and Williams syndrome are genetically based neurodevelopmental disorders that present strikingly different social
phenotypes. Autism involves fundamental impairments in social reciprocity and communication, whereas people with Williams
syndrome are highly sociable and engaging. This article reviews the behavioral and neuroimaging literature that has explored the
neurocognitive mechanisms that underlie these contrasting social phenotypes, focusing on studies of face processing. The article
oncludes with a discussion of how the social phenotypes of both syndromes may be characterized by impaired connectivity
etween the amygdala and other critical regions in the ’social brain’.
Keywords: autism; Williams syndrome; face processing; emotion processing; amygdala
INTRODUCTION
For the past two decades autism, (ASD)1 and Williams
syndrome (WMS) have captured the interest and imagina-
tion of cognitive neuroscientists. These neurodevelopmental
disorders present striking phenotypes that hold out the
promise of advancing our understanding of the biological"
5cd34abb1e96e0c11f427364e40b1e87d6fc62c2,Greedy Part-Wise Learning of Sum-Product Networks,"Greedy Part-Wise Learning of Sum-Product
Networks
Robert Peharz, Bernhard C. Geiger and Franz Pernkopf
{robert.peharz, geiger,
Signal Processing and Speech Communication Laboratory
Graz, University of Technology"
5c02bd53c0a6eb361972e8a4df60cdb30c6e3930,Multimedia stimuli databases usage patterns: a survey report,"Multimedia stimuli databases usage patterns: a
survey report
M. Horvat1, S. Popović1 and K. Ćosić1
University of Zagreb, Faculty of Electrical Engineering and Computing
Department of Electric Machines, Drives and Automation
Zagreb, Croatia"
5c435c4bc9c9667f968f891e207d241c3e45757a,"""How old are you?"" : Age Estimation with Tensors of Binary Gaussian Receptive Maps","RUIZ-HERNANDEZ, CROWLEY, LUX: HOW OLD ARE YOU?
""How old are you?"" : Age Estimation with
Tensors of Binary Gaussian Receptive Maps
John A. Ruiz-Hernandez
James L. Crowley
Augustin Lux
INRIA Grenoble Rhones-Alpes
Research Center and Laboratoire
d’Informatique de Grenoble (LIG)
655 avenue de l’Europe
8 334 Saint Ismier Cedex, France"
5cebc83001ea0737cc46360850fd294327c82013,MEMORY-BASED GAIT RECOGNITION 1 Memory-based Gait Recognition,"DANLIUet al.:MEMORY-BASEDGAITRECOGNITION
Memory-based Gait Recognition
Dan Liu
Mao Ye∗
Xudong Li
Feng Zhang
Lan Lin
School of Computer Science and
Engineering,
Center for Robotics,
Key Laboratory for NeuroInformation of
Ministry of Education,
University of Electronic Science and
Technology of China,
Chengdu 611731, P.R. China"
5c81048593a6729b2d0b948a1129a97bdbf82f11,Moving Object Localization Using Optical Flow for Pedestrian Detection from a Moving Vehicle,"Hindawi Publishing Corporation
e Scientific World Journal
Volume 2014, Article ID 196415, 8 pages
http://dx.doi.org/10.1155/2014/196415
Research Article
Moving Object Localization Using Optical Flow for Pedestrian
Detection from a Moving Vehicle
Joko Hariyono, Van-Dung Hoang, and Kang-Hyun Jo
Graduate School of Electrical Engineering, University of Ulsan, Ulsan 680-749, Republic of Korea
Correspondence should be addressed to Kang-Hyun Jo;
Received 9 April 2014; Revised 7 June 2014; Accepted 8 June 2014; Published 10 July 2014
Academic Editor: Yu-Bo Yuan
Copyright © 2014 Joko Hariyono et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
This paper presents a pedestrian detection method from a moving vehicle using optical flows and histogram of oriented gradients
(HOG). A moving object is extracted from the relative motion by segmenting the region representing the same optical flows after
ompensating the egomotion of the camera. To obtain the optical flow, two consecutive images are divided into grid cells 14 × 14
pixels; then each cell is tracked in the current frame to find corresponding cell in the next frame. Using at least three corresponding
ells, affine transformation is performed according to each corresponding cell in the consecutive images, so that conformed optical
flows are extracted. The regions of moving object are detected as transformed objects, which are different from the previously"
5c1e0e94d6cb74448c7b3c1e0db42121be4e9bd6,Saliency Detection using regression trees on hierarchical image segments,"SALIENCY DETECTION USING REGRESSION TREES ON
HIERARCHICAL IMAGE SEGMENTS
G¨okhan Yildirim, Appu Shaji, Sabine S¨usstrunk
School of Computer and Communication Sciences
´Ecole Polytechnique F´ed´erale de Lausanne"
5c6de2d9f93b90034f07860ae485a2accf529285,Compensating for pose and illumination in unconstrained periocular biometrics,"Int. J. Biometrics, Vol. X, No. Y, xxxx
Compensating for pose and illumination in
unconstrained periocular biometrics
Chandrashekhar N. Padole and
Hugo Proença*
Department of Computer Science,
IT – Instituto de Telecomunicações,
University of Beira Interior,
6200-Covilhã, Portugal
Fax: +351-275-319899
E-mail:
E-mail:
*Corresponding author"
5cc9fdd3a588f6e62e46d7884c1dbeef92a782f2,Spontaneous attention to faces in Asperger syndrome using ecologically valid static stimuli.,"Durham Research Online
Deposited in DRO:
6 December 2014
Version of attached le:
Accepted Version
Peer-review status of attached le:
Peer-reviewed
Citation for published item:
Hanley, M. and McPhillips, M. and Mulhern, G. and Riby, D. M. (2013) 'Spontaneous attention to faces in
Asperger Syndrome using ecologically valid static stimuli.', Autism., 17 (6). pp. 754-761.
Further information on publisher's website:
http://dx.doi.org/10.1177/1362361312456746
Publisher's copyright statement:
Use policy
The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for
personal research or study, educational, or not-for-prot purposes provided that:
• a full bibliographic reference is made to the original source
• a link is made to the metadata record in DRO
• the full-text is not changed in any way
The full-text must not be sold in any format or medium without the formal permission of the copyright holders."
5c0dc4dff1dfb5e27b19bef0713bccd9f85ce3b2,Joint probabilistic pedestrian head and body orientation estimation,"014 IEEE Intelligent Vehicles Symposium (IV)
June 8-11, 2014. Dearborn, Michigan, USA
978-1-4799-3637-3/14/$31.00 ©2014 IEEE"
5cead7ba087ebe7314f96d875f3d3dbb8dbed1c7,Automatic Food Intake Assessment Using Camera Phones,"Michigan Technological University
Digital Commons Michigan
Dissertations, Master's Theses and Master's Reports
- Open
Dissertations, Master's Theses and Master's Reports
Automatic Food Intake Assessment Using Camera
Phones
Fanyu Kong
Michigan Technological University
Copyright 2012 Fanyu Kong
Recommended Citation
Kong, Fanyu, ""Automatic Food Intake Assessment Using Camera Phones"", Dissertation, Michigan Technological University, 2012.
http://digitalcommons.mtu.edu/etds/494
Follow this and additional works at: http://digitalcommons.mtu.edu/etds
Part of the Computer Engineering Commons"
5c35ac04260e281141b3aaa7bbb147032c887f0c,Face Detection and Tracking Control with Omni Car,"Face Detection and Tracking Control with Omni Car
Jheng-Hao Chen, Tung-Yu Wu
CS 231A Final Report
June 31, 2016"
5c315aae464602115674716a7f976c4992fcb98e,Teachers’ Perception in the Classroom,"Teachers’ Perception in the Classroom
¨Omer S¨umer1
Patricia Goldberg1
Kathleen St¨urmer1
Tina Seidel3
Peter Gerjets2 Ulrich Trautwein1
Enkelejda Kasneci1
University of T¨ubingen, Germany
Leibniz-Institut f¨ur Wissensmedien, Germany
Technical University of Munich, Germany"
5c4f8972ff0df23161cbdf1d70ea91f0e545d52d,Machine Learning with Dual Process Models,
5c2e264d6ac253693469bd190f323622c457ca05,Improving large-scale face image retrieval using multi-level features,"978-1-4799-2341-0/13/$31.00 ©2013 IEEE
ICIP 2013"
5ca2e14f91dffb4784c443fe5cfe7838c3f3713c,Convolutional Recurrent Predictor: Implicit Representation for Multi-target Filtering and Tracking,"Convolutional Recurrent Predictor:
Implicit Representation for Multi-target Filtering and Tracking
Mehryar Emambakhsh, Alessandro Bay and Eduard Vazquez
{mehryar.emambakhsh, alessandro.bay,
Cortexica Vision Systems
London, UK"
5c271b5f96cfce1b4fdacc728ae8f8ebcbc738f9,A framework for implicit human-centered image tagging inspired by attributed affect,"Vis Comput (2013)
O R I G I NA L  A RT I C L E
A framework for implicit human centered image tagging
inspired by attributed affect
Konstantinos C. Apostolakis · Petros Daras
Published online:
© Springer-Verlag Berlin Heidelberg 2013"
5c09d905f6d4f861624821bf9dfe2aae29137e9c,Women Also Snowboard: Overcoming Bias in Captioning Models,"Women also Snowboard:
Overcoming Bias in Captioning Models
Lisa Anne Hendricks * 1 Kaylee Burns * 1 Kate Saenko 2 Trevor Darrell 1 Anna Rohrbach 1"
5c45a1abc51fe059987bcfba19b1d5076a8d9afb,Autonomous Object Category Learning for Service Robots Using Internet Resources,"Autonomous Object Category
Learning for Service Robots
Using Internet Resources
Md Reaz Ashraful Abedin
November 20, 2016
Master’s Thesis in Computing Science, 30 credits
Supervisor at CS-UmU: Thomas Hellstr¨om
Examiner: Ola Ringdahl
Ume˚a University
Department of Computing Science
SE-901 87 UME˚A
SWEDEN"
5cf12787b9ee536817c0429700c75b98f04192ba,A Self-Supervised Bootstrap Method for Single-Image 3D Face Reconstruction,"A Self-Supervised Bootstrap Method for Single-Image 3D Face Reconstruction
Yifan Xing
Rahul Tewari
Paulo R. S. Mendonc¸a
Amazon Web Services"
5c3fd194ba96c5eea41c0772ad0b2292dedcd197,Understanding the Energy Saving Potential of Smart Scale Selection in the Viola and Jones Facial Detection Algorithm,
5cdc02ed9f456219369fe3115321564c9955b9ae,Real-time Analysis and Visualization of the YFCC 100 m Dataset,"Real-time Analysis and Visualization
of the YFCC100m Dataset
Firstname Lastname
Institute
City, Country"
5c5dbca68946434afb201f0df90011104c85e4c4,Robust 3D Patch-Based Face Hallucination,"Robust 3D Patch-Based Face Hallucination
Chengchao Qu1,2 Christian Herrmann1,2 Eduardo Monari2 Tobias Schuchert2
J¨urgen Beyerer2,1
Vision and Fusion Laboratory (IES), Karlsruhe Institute of Technology (KIT)
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (Fraunhofer IOSB)"
5cff58d081a4732b11e6da498196ed6fbb54d15b,Adversarial Examples for Semantic Segmentation and Object Detection,"Adversarial Examples for Semantic Segmentation and Object Detection
Cihang Xie1*, Jianyu Wang2*, Zhishuai Zhang1∗, Yuyin Zhou1, Lingxi Xie1, Alan Yuille1
Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218 USA
{cihangxie306, wjyouch, zhshuai.zhang, zhouyuyiner, 198808xc,
Baidu Research USA, Sunnyvale, CA 94089 USA"
5c44807fb7a38d4c9c3ef3bdfb950b44c4a02a3f,Viewpoints and keypoints,"Viewpoints and Keypoints
Shubham Tulsiani and Jitendra Malik
University of California, Berkeley - Berkeley, CA 94720"
5c7db2907c586f4f2d6ae5937b0dc0f4d1bc834a,DELIVERABLE D 2 . 1 AUDIO-VISUAL ALGORITHMS FOR PERSON TRACKING AND CHARACTERIZATION ( BASELINE ),"MULTIMODAL MALL ENTERTAINMENT ROBOT
mummer-project.eu
Grant No. 688147. Project started 2016-03-01. Duration 48 months.
DELIVERABLE D2.1
AUDIO-VISUAL ALGORITHMS FOR PERSON
TRACKING AND CHARACTERIZATION (BASELINE)
Jean-Marc Odobez (Idiap), Natalia Lyubova (SBRE),
Olivier Can´evet (Idiap), Kenneth Funes Mora (Idiap),
Weipeng He (Idiap), Angel Martinez Gonzalez (Idiap),
Jean-Marc Montanier (SBRE), Marc Moreaux (SBRE)
Beneficiaries:
Workpackage:
Idiap Research Institute (lead), SoftBank Robotics Europe
Active Multimodal Sensing and Perception
Version:
Nature:
Dissemination level:
Pages:
017-3-3
Draft"
5c9c153f705a02e157adcf49dccf4f1eeb70cf93,Learning Appearance Transfer for Person Re-identification,"Learning Appearance Transfer for Person
Re-identification
Tamar Avraham and Michael Lindenbaum"
5cb343e447c7fd933ff8f57fc9c99c5673cad97d,MoCap-guided Data Augmentation for 3D Pose Estimation in the Wild,"MoCap-guided Data Augmentation
for 3D Pose Estimation in the Wild
Grégory Rogez
Cordelia Schmid
Inria Grenoble Rhône-Alpes, Laboratoire Jean Kuntzmann, France"
5c717afc5a9a8ccb1767d87b79851de8d3016294,A novel eye region based privacy protection scheme,"978-1-4673-0046-9/12/$26.00 ©2012 IEEE
ICASSP 2012"
5cd11d6b6cb7a2b8c00fcb535879edbd6b008a01,Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras,"Large-Scale Direct Sparse Visual Odometry with Stereo Cameras
Stereo DSO:
Rui Wang∗, Martin Schw¨orer∗, Daniel Cremers
Technical University of Munich
{wangr, schwoere,"
5ce40105e002f9cb428a029e8dec6efe8fad380e,Co-design of architectures and algorithms for mobile robot localization and model-based detection of obstacles. (Co-conception d'architectures et d'algorithmes pour la localisation de robots mobiles et la détection d'obstacles basée sur des modèles),"Co-design of architectures and algorithms for mobile
robot localization and model-based detection of obstacles
Daniel Törtei
To cite this version:
Daniel Törtei. Co-design of architectures and algorithms for mobile robot localization and model-based
detection of obstacles. Embedded Systems. Université Paul Sabatier - Toulouse III, 2016. English.
<NNT : 2016TOU30294>. <tel-01477662v2>
HAL Id: tel-01477662
https://tel.archives-ouvertes.fr/tel-01477662v2
Submitted on 16 Feb 2018
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
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broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
5c562ed8d58522d28ac2d749de0bce8de07c1733,A Wavelet-Based Facial Ageing Synthesis Method,"ISSN 1000-9825, CODEN RUXUEW
Journal of Software, Vol.18, No.2, February 2007, pp.469−476
DOI: 10.1360/jos180469
© 2007 by Journal of Software. All rights reserved.
E-mail:
http://www.jos.org.cn
Tel/Fax: +86-10-62562563
一种基于小波的人脸衰老化合成方法
刘剑毅+,    郑南宁,    游屈波
(西安交通大学  人工智能与机器人研究所,陕西  西安    710049)
A Wavelet-Based Facial Ageing Synthesis Method
LIU Jian-Yi+,    ZHENG Nan-Ning,    YOU Qu-Bo
(Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an 710049, China)
+ Corresponding author: Phn: +86-29-82668802 ext 8002, E-mail: http://www.xjtu.edu.cn
Liu JY, Zheng NN, You QB. A wavelet-based facial ageing synthesis method. Journal of Software, 2007,18(2):
69−476. http://www.jos.org.cn/1000-9825/18/469.htm"
5c879f9e2e79d6c6af8d4c821575e73876240a83,DeepFaceLIFT: Interpretable Personalized Models for Automatic Estimation of Self-Reported Pain,"Journal of Machine Learning Research 66 (2017) 1-16
Submitted 5/17; Published 08/17
DeepFaceLIFT: Interpretable Personalized Models
for Automatic Estimation of Self-Reported Pain
Dianbo Liu*2,3
Fengjiao Peng*1
Andrew Shea*3
Ognjen (Oggi) Rudovic1
Rosalind Picard1
Media Lab, MIT, Cambridge, MA, USA
Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA"
7f4bc8883c3b9872408cc391bcd294017848d0cf,The Multimodal Focused Attribute Model : A Nonparametric Bayesian Approach to Simultaneous Object Classification and Attribute Discovery,"Computer
Sciences
Department
The Multimodal Focused Attribute Model:  A Nonparametric
Bayesian Approach to Simultaneous Object Classification and
Attribute Discovery
Jake Rosin
Charles R. Dyer
Xiaojin Zhu
Technical Report #1697
January 2012"
7f217ff1f3c21c84ed116d32e3b8d1509a306fbd,Direct Optimization through arg max for Discrete Variational Auto-Encoder,"Direct Optimization through arg max for Discrete
Variational Auto-Encoder
Guy Lorberbom (Technion), Andreea Gane (MIT),
Tommi Jaakkola (MIT), Tamir Hazan (Technion)."
7f9cacb5fc126f87dbf53dd547a9fb9f58ded557,RoadNet-v2: A 10 ms Road Segmentation Using Spatial Sequence Layer,"RoadNet-v2: A 10 ms Road Segmentation Using
Spatial Sequence Layer
Yecheng Lyu and Xinming Huang
Department of Electrical and Computer Engineering
Worcester Polytechnic Institute
Worcester, MA 01609, USA"
7f44f8a5fd48b2d70cc2f344b4d1e7095f4f1fe5,Sparse Output Coding for Scalable Visual Recognition,"Int J Comput Vis (2016) 119:60–75
DOI 10.1007/s11263-015-0839-4
Sparse Output Coding for Scalable Visual Recognition
Bin Zhao1 · Eric P. Xing1
Received: 15 May 2013 / Accepted: 16 June 2015 / Published online: 26 June 2015
© Springer Science+Business Media New York 2015"
7ff83f10e49e81ce6f66270e8f3f42dd2c6eb3ed,PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report,"PIRM Challenge on Perceptual Image Enhancement
on Smartphones: Report
Andrey Ignatov, Radu Timofte, Thang Van Vu, Tung Minh Luu, Trung X Pham, Cao Van Nguyen,
Yongwoo Kim, Jae-Seok Choi, Munchurl Kim, Jie Huang, Jiewen Ran, Chen Xing, Xingguang Zhou,
Pengfei Zhu, Mingrui Geng, Yawei Li, Eirikur Agustsson, Shuhang Gu, Luc Van Gool, Etienne de Stoutz,
Nikolay Kobyshev, Kehui Nie, Yan Zhao, Gen Li, Tong Tong, Qinquan Gao, Liu Hanwen, Pablo Navarrete
Michelini, Zhu Dan, Hu Fengshuo, Zheng Hui, Xiumei Wang, Lirui Deng, Rang Meng, Jinghui Qin, Yukai
Shi, Wushao Wen, Liang Lin, Ruicheng Feng, Shixiang Wu, Chao Dong, Yu Qiao, Subeesh Vasu, Nimisha
Thekke Madam, Praveen Kandula, A. N. Rajagopalan, Jie Liu, Cheolkon Jung ∗"
7f04b65f2c6f96c7ce000f537fb691a93f61db52,Geometrical and Visual Feature Quantization for 3D Face Recognition,
7f6061c83dc36633911e4d726a497cdc1f31e58a,YouTube-8M: A Large-Scale Video Classification Benchmark,"YouTube-8M: A Large-Scale Video Classification
Benchmark
Sami Abu-El-Haija
George Toderici
Nisarg Kothari
Joonseok Lee
Paul Natsev
Balakrishnan Varadarajan
Sudheendra Vijayanarasimhan
Google Research"
7fdcb6638a9e01986cd8fb4133b4448700087faf,Expression-Invariant Multispectral Face Recognition : You Can Smile Now !,"Expression-Invariant Multispectral Face Recognition:
You Can Smile Now!
Ioannis A. Kakadiarisa, George Passalisa, George Todericia, Yunliang Lua,
Nikos Karampatziakisa, Najam Murtuzaa, Theoharis Theoharisa
Computational Biomedicine Lab, Dept. of Computer Science, Univ. of Houston, TX, USA"
7fa41631cdef8f7fba7e1289dd4c5f3723b172ab,A robust and isotropic curved surface representation for 3D faces description,"A robust and isotropic curved surface representation for 3D faces
description
Majdi Jribi and Faouzi Ghorbel"
7fc5ab3743e6e9a2f4fe70152440e13a673e239b,Improved Face Recognition Rate Using HOG Features and SVM Classifier,"IOSR Journal of Electronics and Communication Engineering (IOSR-JECE)
e-ISSN: 2278-2834,p- ISSN: 2278-8735.Volume 11, Issue 4, Ver. I (Jul.-Aug .2016), PP 34-44
www.iosrjournals.org
Improved Face Recognition Rate Using HOG Features and SVM
Classifier
Harihara Santosh Dadi, Gopala Krishna Mohan Pillutla"
7f3da52c13c70fd5b93c2ccebdd4a4527fa597fa,Deep Multi-task Learning to Recognise Subtle Facial Expressions of Mental States,"Deep Multi-Task Learning to Recognise Subtle Facial Expressions of
Mental States
Hu, G., Liu, L., Yuan, Y., Yu, Z., Hua, Y., Zhang, Z., ... Yang, Y. (2018). Deep Multi-Task Learning to Recognise
Subtle Facial Expressions of Mental States. In European Conference on Computer Vision 2018: Proceedings
(pp. 106-123). (Lecture Notes in Computer Science; Vol. 11216). https://doi.org/10.1007/978-3-030-01258-8_7
Published in:
European Conference on Computer Vision 2018: Proceedings
Document Version:
Peer reviewed version
Queen's University Belfast - Research Portal:
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The Research Portal is Queen's institutional repository that provides access to Queen's research output. Every effort has been made to"
7f9c3ee2d3a3db9922203cbd19f03708067a42ab,A Comparative Analysis of Face Recognition Algorithms,"Gagan Kumar et al. International Journal of Recent Research Aspects ISSN: 2349-7688, Vol. 3, Issue
, June 2016, pp. 201-204
A Comparative Analysis of Face
Recognition Algorithms
Gagan kumar1, Sumit Saurabh2
Assistant Professor, Modern Institute of engineering & technology
Research scholar, Modern Institute of engineering & technology"
7f533bd8f32525e2934a66a5b57d9143d7a89ee1,Audio-Visual Identity Grounding for Enabling Cross Media Search,"Audio-Visual Identity Grounding for Enabling Cross Media Search
Kevin Brady, MIT Lincoln Laboratory
Paper ID 22"
7fd97bc23c85213b8b2e4d28264f04ce6dc84e74,Optimal Transformation Estimation with Semantic Cues,"Optimal Transformation Estimation with Semantic Cues
Danda Pani Paudel
Computer Vision Laboratory
D-ITET, ETH Zurich
Adlane Habed
ICube Laboratory
CNRS, University of Strasbourg
Luc Van Gool
Computer Vision Laboratory
D-ITET, ETH Zurich"
7f3a73babe733520112c0199ff8d26ddfc7038a0,Robust Face Identification with Small Sample Sizes using Bag of Words and Histogram of Oriented Gradients,
7f6cd03e3b7b63fca7170e317b3bb072ec9889e0,A Face Recognition Signature Combining Patch-based Features with Soft Facial Attributes,"A Face Recognition Signature Combining Patch-based
Features with Soft Facial Attributes
L. Zhang, P. Dou, I.A. Kakadiaris
Computational Biomedicine Lab, 4849 Calhoun Rd, Rm 373, Houston, TX 77204"
7ff42ee09c9b1a508080837a3dc2ea780a1a839b,Data Fusion for Real-time Multimodal Emotion Recognition through Webcams and Microphones in E-Learning,"Data Fusion for Real-time Multimodal Emotion Recognition through Webcams
nd Microphones in E-Learning
Kiavash Bahreini*, Rob Nadolski*, Wim Westera*
*Welten Institute, Research Centre for Learning, Teaching and Technology, Faculty of
Psychology and Educational Sciences, Open University of the Netherlands, Valkenburgerweg
77, 6419 AT Heerlen, The Netherlands
{kiavash.bahreini, rob.nadolski,"
7fa62c091a14830ae256dc00b512f7d4b4cf5b94,Stabilizing GAN Training with Multiple Random Projections,"Under review as a conference paper at ICLR 2018
Stabilizing GAN Training with
Multiple Random Projections
Anonymous authors
Paper under double-blind review"
7fbff9fa2ba7a7ff57a433e8bb19cfd99d52132d,A probabilistic framework for car detection in images using context and scale,"RiverCentre, Saint Paul, Minnesota, USA
May 14-18, 2012
978-1-4673-1405-3/12/$31.00 ©2012 IEEE"
7f6a527a3dc2e526aa59a57cadb20ff727124973,A comparison of adaptive matchers for screening of faces in video surveillance,"012 IEEE Symposium on
Computational Intelligence for
Security and Defence Applications
(CISDA 2012)
Ottawa, Ontario, Canada
1 – 13 July 2012
IEEE Catalog Number:
ISBN:
CFP12SDA-PRT
978-1-4673-1416-9"
7ff636c82898a35d3239573f8e3a29da89c73ed4,Automatic Detection of the Uterus and Fallopian Tube Junctions in Laparoscopic Images,"Automatic Detection of the Uterus and
Fallopian Tube Junctions in Laparoscopic Images
Kristina Prokopetc, Toby Collins, and Adrien Bartoli
Image Science for Interventional Techniques (ISIT),
UMR 6284 CNRS, Universit´e d(cid:48)Auvergne, France"
7f3c6bf191a8633d10fad32e23fa06a3c925ffee,The benefits of simply observing: mindful attention modulates the link between motivation and behavior.,"015, Vol. 108, No. 1, 148 –170
0022-3514/15/$12.00
© 2014 American Psychological Association
http://dx.doi.org/10.1037/a0038032
The Benefits of Simply Observing: Mindful Attention Modulates the Link
Between Motivation and Behavior
Esther K. Papies
Utrecht University
Mike Keesman
Utrecht University
Tila M. Pronk
Tilburg University
Lawrence W. Barsalou
Emory University
Mindful attention, a central component of mindfulness meditation, can be conceived as becoming aware
of one’s thoughts and experiences and being able to observe them as transient mental events. Here, we
present a series of studies demonstrating the effects of applying this metacognitive perspective to one’s
spontaneous reward responses when encountering attractive stimuli. Taking a grounded cognition
perspective, we argue that reward simulations in response to attractive stimuli contribute to appetitive
ehavior and that motivational states and traits enhance these simulations. Directing mindful attention at"
7f97a36a5a634c30de5a8e8b2d1c812ca9f971ae,Incremental Classifier Learning with Generative Adversarial Networks,"Incremental Classifier Learning with Generative Adversarial Networks
Yue Wu1 Yinpeng Chen2 Lijuan Wang2 Yuancheng Ye3
Zicheng Liu2 Yandong Guo2 Zhengyou Zhang2 Yun Fu1
Northeastern University 2Microsoft Research 3City University of New York"
7f7c3a99923549601c81cd5e9659ca01e8a42f47,Zero-Shot Learning of Language Models for Describing Human Actions Based on Semantic Compositionality of Actions,"PACLIC 28
Zero-Shot Learning of Language Models for Describing Human Actions
Based on Semantic Compositionality of Actions
Hideki ASOH
National Institute of
Graduate School of Humanities and Sciences,
Ichiro KOBAYASHI
Ochanomizu University
Bunkyo-ku, Tokyo 112-8610 Japan
Advanced Industrial Science and Technology
Tsukuba, Ibaraki 305-8568 Japan"
7fb143927b616726f065a55b4455b822b4cc8d86,Structured Learning for Cell Tracking,"Structured Learning for Cell Tracking
Xinghua Lou, Fred A. Hamprecht
Heidelberg Collaboratory for Image Processing (HCI)
Interdisciplinary Center for Scientific Computing (IWR)
University of Heidelberg, Heidelberg 69115, Germany"
7f92ace1683979018b87f2e472857e152503cc24,Regressor Based Estimation of the Eye Pupil Center,"Regressor Based Estimation of the Eye Pupil Center
Necmeddin Said Karakoc, Samil Karahan, Yusuf Sinan Akgul
Gebze Technical University, Gebze, Kocaeli 41400, Turkey,
GTU Vision Lab: http://vision.gyte.edu.tr"
7f36dd9ead29649ed389306790faf3b390dc0aa2,MOVEMENT DIFFERENCES BETWEEN DELIBERATE AND SPONTANEOUS FACIAL EXPRESSIONS: ZYGOMATICUS MAJOR ACTION IN SMILING.,"MOVEMENT DIFFERENCES BETWEEN DELIBERATE
AND SPONTANEOUS FACIAL EXPRESSIONS:
ZYGOMATICUS MAJOR ACTION IN SMILING
Karen L. Schmidt, Zara Ambadar, Jeffrey F. Cohn, and L. Ian Reed"
7fc70f6dbbbc9221552c8547cd10ffc13d11b276,Respectful cameras: detecting visual markers in real-time to address privacy concerns,"Respectful Cameras: Detecting Visual Markers in
Real-Time to Address Privacy Concerns
Jeremy Schiff, Marci Meingast, Deirdre K. Mulligan, Shankar Sastry, Ken Goldberg"
3347d3e9f8a2da66e1c00f6a1e56bb37d27145ae,devant le jury composé de:,"Spécialité: Informatique et Télécommunications    Ecole doctorale: Informatique, Télécommunications et Electronique de Paris  Présentée par   Raluca-Diana ŞAMBRA-PETRE  Pour obtenir le grade de DOCTEUR DE TELECOM SUDPARIS    MODELISATION ET INFERENCE 2D/3D DE CONNAISSANCES POUR L'ACCES INTELLIGENT AUX CONTENUS VISUELS ENRICHIS     Soutenue le 18 Juin 2013 à Paris                              devant le jury composé de :   Président de jury: Madame le Maître de Conférences, HDR Catherine ACHARD Rapporteur:  Monsieur le Professeur Marc ANTONINI Rapporteur:  Monsieur le Professeur Constantin VERTAN Examinateur:  Monsieur le Professeur Miroslaw BOBER  Examinateur:  Monsieur le Docteur Olivier MARTINOT Directeur de thèse:  Monsieur le Professeur Titus ZAHARIA    Thèse n°: 2013TELE0012      THESE DE DOCTORAT CONJOINT TELECOM SUDPARIS et L'UNIVERSITE PIERRE ET MARIE CURIE"
33919313bb3cf09b00f9fa2253b30af33a52bc51,Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs,"Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs
Anton Osokin∗,1 Jean-Baptiste Alayrac∗,1
Isabella Lukasewitz1 Puneet K. Dokania2 Simon Lacoste-Julien1
INRIA – ´Ecole Normale Sup´erieure, Paris, France
Both authors contributed equally.
INRIA – CentraleSup´elec, Chˆatenay-Malabry, France"
338d0087fbd3bb768e637b4538e6581220387fff,Distributed Averages of Gradients (DAG): A Fast Alternative for Histogram of Oriented Gradients,"Distributed Averages of Gradients (DAG):
A Fast Alternative for Histogram of Oriented
Gradients
M. Hossein Mirabdollah, Mahmoud A. Mohamed, and B¨arbel Mertsching
GET Lab, University of Paderborn, 33098 Paderborn, Germany
{mirabdollah, mahmoud,
http://getwww.upb.de"
3387805b752dadfa34cb8eb63d9dc86aff49934a,"Exploration of Contextual Relationships for Robust Video Analysis: Applications in Camera Networks, Bio-image Analysis and Activity Forecasting","UNIVERSITY OF CALIFORNIA
RIVERSIDE
Exploration of Contextual Relationships for Robust Video Analysis:
Applications in Camera Networks, Bio-image Analysis and Activity Forecasting
A Dissertation submitted in partial satisfaction
of the requirements for the degree of
Doctor of Philosophy
Electrical Engineering
Anirban Chakraborty
August 2014
Dissertation Committee:
Dr. Amit K. Roy-Chowdhury, Chairperson
Dr. Ertem Tuncel
Dr. Stefano Lonardi"
33d045b39bc4645ff2a8bffd83a49697631ff968,Learning Discrete Representations via Information Maximizing Self Augmented Training,"Learning Discrete Representations via Information Maximizing
Self Augmented Training
Weihua Hu 1 Takeru Miyato 2 3 Seiya Tokui 2 1 Eiichi Matsumoto 2 1 Masashi Sugiyama 4 1"
331b2520b0eda715270134ac2ee2a3cbb329aaa1,3D non-rigid reconstruction with prior shape constraints,"D Non-Rigid Reconstruction with
Prior Shape Constraints
Lili Tao
A thesis submitted in partial fulfilment for the requirements for the degree of
Doctor of Philosophy
the University of Central Lancashire
May 2014"
335486cb9bb326e2b33fb03a74d0f9d671490ae7,Real-time pedestrian detection with deformable part models,"Real-time Pedestrian Detection with Deformable Part Models
Hyunggi Cho, Paul E. Rybski, Aharon Bar-Hillel and Wende Zhang"
3389fa2f292b72320f4554261eae34d57e2db7b6,Morphable Reflectance Fields for enhancing face recognition,"MITSUBISHI ELECTRIC RESEARCH LABORATORIES
http://www.merl.com
Morphable Reflectance Fields for Enhancing
Face Recognition
Ritwik Kumar, Michael Jones, Tim Marks
TR2010-039
July 2010"
333be4858994e6d9364341aeb520f7800a0f6a07,Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks,"Unsupervised Pixel–Level Domain Adaptation
with Generative Adversarial Networks
Konstantinos Bousmalis
Google Brain
San Francisco, CA
Nathan Silberman
Google Research
New York, NY
David Dohan
Google Brain
Mountain View, CA
Dumitru Erhan
Google Brain
San Francisco, CA
Dilip Krishnan
Google Research
Cambridge, MA"
3393459600368be2c4c9878a3f65a57dcc0c2cfa,Eigen-PEP for Video Face Recognition,"Eigen-PEP for Video Face Recognition
Haoxiang Li†, Gang Hua†, Xiaohui Shen‡, Zhe Lin‡, Jonathan Brandt‡
Stevens Institute of Technology ‡Adobe Systems Inc."
33e5d1c93e4195a1bfd303a94f0fc3f1c5e233bd,3D Face Recognition Under Expression Variations using Similarity Metrics Fusion,"(cid:176)2007 IEEE. Personal use of this material is permitted.
However, permission to reprint/republish this material for ad-
vertising or promotional purposes or for creating new collec-
tive works for resale or redistribution to servers or lists, or to
reuse any copyrighted component of this work in other works
must be obtained from the IEEE."
33ef644ef5085237b4f529251d6c895d32d6052f,TasselNet: counting maize tassels in the wild via local counts regression network,"Lu et al.
RESEARCH ARTICLES
TasselNet: Counting maize tassels in the wild via
local counts regression network
Hao Lu1, Zhiguo Cao1*, Yang Xiao1, Bohan Zhuang2 and Chunhua Shen2
Part of this work was done when the first author was visiting The University of Adelaide, Australia."
3355aff37b5e4ba40fc689119fb48d403be288be,Deep Private-Feature Extraction,"Deep Private-Feature Extraction
Seyed Ali Osia, Ali Taheri, Ali Shahin Shamsabadi, Kleomenis Katevas, Hamed Haddadi, Hamid R. Rabiee"
339937141ffb547af8e746718fbf2365cc1570c8,Facial Emotion Recognition in Real Time,"Facial Emotion Recognition in Real Time
Dan Duncan
Gautam Shine
Chris English"
33f843eaab92cc04ba6dec0ff8c72026e37cfdb2,Genetic Programming for Multibiometrics,"Genetic Programming for Multibiometrics
Romain Giot∗, Christophe Rosenberger
GREYC Laboratory
ENSICAEN - University of Caen - CNRS
6 Boulevard Mar´echal Juin 14000 Caen Cedex - France"
33d30ab798e518eedbebe7e7569737362fdcefdb,Effect of Purposeful Feature Extraction inHigh-dimensional Kinship Verification Problem,"July 2016, Volume 3, Number 3 (pp. 183–191)
http://www.jcomsec.org
Journal of Computing and Security
Effect of Purposeful Feature Extraction in High-dimensional
Kinship Verification Problem
Pendar Alirezazadeh a, Abdolhossein Fathi a,∗, Fardin Abdali-Mohammadi a
Department of Computer Engineering and Information Technology, Razi University, Kermanshah, Iran.
A R T I C L E I N F O.
A B S T R A C T
Article history:
Received: 31 July 2017
Revised: 20 November 2017
Accepted: 07 January 2018
Published Online: 10 February 2018
Keywords:
Kinship Verification, Purposeful
Feature Extraction, Redundancy,
Feature Selection.
Recently, researchers have shown an increased interest in kinship verification
via facial images in the field of computer vision. The matter of fact is that"
33548dec33abfc66bba40ac3f9651c0605d6b537,CMML : a New Metric Learning Approach for Cross Modal Matching,"CMML: a New Metric Learning Approach for
Cross Modal Matching
Alexis Mignon and Fr´ed´eric Jurie
GREYC, CNRS UMR 6072, Universit´e de Caen Basse-Normandie, France
first name.last"
33ea400ca2105b9a3cd0e3c7c147e06c2d3c6d79,Vision based Decision-Support and Safety Systems for Robotic Surgery,"Vision based Decision-Support and Safety Systems for
Robotic Surgery
Suren Kumar
PhD Candidate
Madusudanan Sathia
Narayanan*
PhD Candidate
Sukumar Misra
Surgical Intern
Sudha Garimella
Assistant Professor
Pankaj Singhal
Director of Robotic Surgery
Jason J. Corso
Assistant Professor"
33c485b59249af2d763d6951cd11e4080f3bbb3d,Fusing 2D Uncertainty and 3D Cues for Monocular Body Pose Estimation,"Fusing 2D Uncertainty and 3D Cues for Monocular Body Pose Estimation
Bugra Tekin
Pablo M´arquez-Neila
Mathieu Salzmann
Pascal Fua
EPFL, Switzerland"
336fe31c25c9128f43f2dfe454041e7c608557d1,H-CNN: Spatial Hashing Based CNN for 3D Shape Analysis.,"H-CNN: Spatial Hashing Based CNN for 3D
Shape Analysis
Tianjia Shao, Yin Yang, Yanlin Weng, Qiming Hou, Kun Zhou"
3333b35ddb698be76dd27bffad131c1daa694bf2,Comparing Robustness of Two-Dimensional PCA and Eigenfaces for Face Recognition,"Comparing Robustness of Two-Dimensional
PCA and Eigenfaces for Face Recognition
Muriel Visani, Christophe Garcia, and Christophe Laurent
France Telecom R&D - DIH/HDM
, rue du Clos Courtel
5512 Cesson-S´evign´e Cedex - France"
33695e0779e67c7722449e9a3e2e55fde64cfd99,Riemannian coding and dictionary learning: Kernels to the rescue,"Riemannian Coding and Dictionary Learning: Kernels to the Rescue
Mehrtash Harandi, Mathieu Salzmann
Australian National University & NICTA
While sparse coding on non-flat Riemannian manifolds has recently become
increasingly popular, existing solutions either are dedicated to specific man-
ifolds, or rely on optimization problems that are difficult to solve, especially
when it comes to dictionary learning. In this paper, we propose to make use
of kernels to perform coding and dictionary learning on Riemannian man-
ifolds. To this end, we introduce a general Riemannian coding framework
with its kernel-based counterpart. This lets us (i) generalize beyond the spe-
ial case of sparse coding; (ii) introduce efficient solutions to two coding
schemes; (iii) learn the kernel parameters; (iv) perform unsupervised and
supervised dictionary learning in a much simpler manner than previous Rie-
mannian coding approaches.
i=1, di ∈ M, be a dictionary on a Rie-
mannian manifold M, and x ∈ M be a query point on the manifold. We
(cid:17)
define a general Riemannian coding formulation as
More specifically, let D = {di}N
(cid:93)N"
334d6c71b6bce8dfbd376c4203004bd4464c2099,Biconvex Relaxation for Semidefinite Programming in Computer Vision,"BICONVEX RELAXATION FOR SEMIDEFINITE PROGRAMMING IN
COMPUTER VISION
SOHIL SHAH*, ABHAY KUMAR*, DAVID JACOBS,
CHRISTOPH STUDER, AND TOM GOLDSTEIN"
33236cd0b9454ab88ec9deddfb8ce8e492056770,Salient social cues are prioritized in autism spectrum disorders despite overall decrease in social attention.,"J Autism Dev Disord
DOI 10.1007/s10803-012-1710-x
O R I G I N A L P A P E R
Salient Social Cues are Prioritized in Autism Spectrum Disorders
Despite Overall Decrease in Social Attention
Coralie Chevallier • Pascal Huguet •
Francesca Happe´ • Nathalie George •
Laurence Conty
Ó Springer Science+Business Media New York 2012"
332339c32d41cc8176d360082b4d9faa90dadffa,"UberNet: Training a Universal Convolutional Neural Network for Low-, Mid-, and High-Level Vision Using Diverse Datasets and Limited Memory","UberNet : Training a ‘Universal’ Convolutional Neural Network for Low-, Mid-,
nd High-Level Vision using Diverse Datasets and Limited Memory
Iasonas Kokkinos
CentraleSup´elec - INRIA"
330a34b8dfb3f6adaf6401c3ececf9f4127505a0,Feature selection for pose invariant lip biometrics,"INTERSPEECH 2010
Feature Selection for Pose Invariant Lip Biometrics
Adrian Pass, Jianguo Zhang, Darryl Stewart
School of Electronics, Electrical Engineering and Computer Science
Queens University Belfast
Belfast BT7 1NN, UK
{apass01, jianguo.zhang,"
330dda431e0343a96f9d630a0b4ee526bd93ad11,Domain Adaptation for Visual Applications: A Comprehensive Survey,"Domain Adaptation for Visual Applications: A Comprehensive
Survey
Gabriela Csurka"
3369692338841f14ce032fc5d0b5b4fe7cc79f1a,Visualising mental representations : A primer on noise-based reverse correlation in social psychology,"European Review of Social Psychology
ISSN: 1046-3283 (Print) 1479-277X (Online) Journal homepage: http://www.tandfonline.com/loi/pers20
Visualising mental representations: A primer
on noise-based reverse correlation in social
psychology
L. Brinkman, A. Todorov & R. Dotsch
To cite this article: L. Brinkman, A. Todorov & R. Dotsch (2017) Visualising mental
representations: A primer on noise-based reverse correlation in social psychology, European
Review of Social Psychology, 28:1, 333-361, DOI: 10.1080/10463283.2017.1381469
To link to this article:  http://dx.doi.org/10.1080/10463283.2017.1381469
© 2017 The Author(s). Published by Informa
UK Limited, trading as Taylor & Francis
Group.
Published online: 16 Oct 2017.
Submit your article to this journal
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http://www.tandfonline.com/action/journalInformation?journalCode=pers20
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33891ca0f8fab0eab503f4b4bcee009a1cf3b880,A video database of human faces under near Infra-Red illumination for human computer interaction applications,"A Video Database of Human Faces under Near Infra-Red
Illumination for Human Computer Interaction Aplications
S L Happy, Anirban Dasgupta, Anjith George, and Aurobinda Routray
Department of Electrical Engineering
Indian Institute of Technology Kharagpur"
33e7bc26047de3c1b607f04a644c2c03920201fd,Learning to Navigate Autonomously in Outdoor Environments : MAVNet,"Learning to Navigate Autonomously in Outdoor Environments :
MAVNet
Saumya Kumaar2, Arpit Sangotra3, Sudakshin Kumar3, Mayank Gupta3, Navaneethkrishnan B2 and S N Omkar1"
330bcf952a5a20aac0e334aad1de4cd6ba6ed6eb,Pedestrian Detection at Day/Night Time with Visible and FIR Cameras: A Comparison,"Article
Pedestrian Detection at Day/Night Time with Visible
nd FIR Cameras: A Comparison
Alejandro González 1,2,*, Zhijie Fang 1,2, Yainuvis Socarras 1,2, Joan Serrat 1,2, David Vázquez 1,2,
Jiaolong Xu 1,2 and Antonio M. López 1,2
Autonomous University of Barcelona, Cerdanyola, Barcelona 08193, Spain; (Z.F.);
(Y.S.); (J.S.); (D.V.); (J.X.);
(A.M.L.)
Computer Vision Center, Cerdanyola, Barcelona 08193, Spain
* Correspondence: Tel.: +34-622-605-455
Academic Editor: Vittorio M. N. Passaro
Received: 17 March 2016; Accepted: 30 May 2016; Published: 4 June 2016"
330126c9dd71b3b0319d6429737186f1f20057a7,Deep Ordinal Regression Based on Data Relationship for Small Datasets,Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
33e20449aa40488c6d4b430a48edf5c4b43afdab,The Faces of Engagement: Automatic Recognition of Student Engagementfrom Facial Expressions,"TRANSACTIONS ON AFFECTIVE COMPUTING
The Faces of Engagement: Automatic
Recognition of Student Engagement from Facial
Expressions
Jacob Whitehill, Zewelanji Serpell, Yi-Ching Lin, Aysha Foster, and Javier R. Movellan"
33691b7e8c980ea2dea5b5d3d7bee661e9623715,Probabilistic Semi-Dense Mapping from Highly Accurate Feature-Based Monocular SLAM,"Probabilistic Semi-Dense Mapping from Highly
Accurate Feature-Based Monocular SLAM
Ra´ul Mur-Artal and Juan D. Tard´os
Instituto de Investigaci´on en Ingenier´ıa de Arag´on (I3A), Universidad de Zaragoza, Spain
{raulmur,"
3323a905a3960a663a9884540e8c3586cf362ba9,Face Hallucination Using Sparse Representation Algorithm,"International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 4 Issue 9, September 2015
Face Hallucination Using Sparse Representation
Algorithm
Sudhir Kumar                                                 Vikram Mutneja"
3361ddc93a6d9f451c1b94cc488c52fd0bdf5c83,Deep Generative Models of Urban Mobility,"Deep Generative Models of Urban Mobility
Ziheng Lin∗
Mogeng Yin†
Civil Systems, CEE, UC Berkeley
Transportation, CEE, UC Berkeley
Civil Systems, CEE, UC Berkeley
Madeleine Sheehan
Transportation, CEE, UC Berkeley
Jean-Francois Paiement
AT&T Research
Sidney Feygin‡
Alexei Pozdnoukhov
CEE, UC Berkeley"
334355b4c82ad477a2f44ba61dd04c68048e78d3,Applications of kernel machines to structured data,"Applications of Kernel Machines to
Structured Data
Vorgelegt von
Diplom-Physiker Jan Eichhorn
us Weimar
Von der Fakult¨at IV - Elektrotechnik und Informatik
der Technischen Universit¨at Berlin
zur Erlangung des akademischen Grades
Doktor der Naturwissenschaften (Dr. rer. nat.)
genehmigte Dissertation
Promotionsausschuß:
Vorsitzender: Prof. Dr. H. Ehrig
Berichter:
Berichter:
Berichter:
Prof. Dr. K.-R. M¨uller
Prof. Dr. K. Obermayer
Prof. Dr. B. Sch¨olkopf
Tag der wissenschaftlichen Aussprache: 27.11.2006
Berlin 2007"
33a9076d5d48208960feebff9d5efdaa2203f872,Face De-Identification,"Face De-identification
Ralph Gross, Latanya Sweeney, Jeffrey Cohn, Fernando de la Torre
nd Simon Baker"
33ae696546eed070717192d393f75a1583cd8e2c,Subspace selection to suppress confounding source domain information in AAM transfer learning,
8d9067da4ba5c57643ee7a84cd5c5d5674384937,Sorting out Lipschitz function approximation,"SORTING OUT LIPSCHITZ FUNCTION APPROXIMATION
Cem Anil ∗
James Lucas∗
Roger Grosse
University of Toronto; Vector Institute
{cemanil, jlucas,"
8d40150c7ec59daba7d1a34eba291ff2eac6388c,Overcoming Dataset Bias : An Unsupervised Domain Adaptation Approach,"Overcoming Dataset Bias:
An Unsupervised Domain Adaptation Approach
Boqing Gong
Dept. of Computer Science
U. of Southern California
Los Angeles, CA 90089
Fei Sha
Dept. of Computer Science
U. of Southern California
Los Angeles, CA 90089
Kristen Grauman
Dept. of Computer Science
U. of Texas at Austin
Austin, TX 78701"
8d384e8c45a429f5c5f6628e8ba0d73c60a51a89,Temporal Dynamic Graph LSTM for Action-Driven Video Object Detection,"Temporal Dynamic Graph LSTM for Action-driven Video Object Detection
Yuan Yuan1 Xiaodan Liang2 Xiaolong Wang2 Dit-Yan Yeung1 Abhinav Gupta2
The Hong Kong University of Science and Technology 2 Carneige Mellon University"
8d44861cfcb5159e0a1afb9d50f3d2c6083f605a,3 D SUPER-RESOLUTION APPROACH FOR SPARSE LASER SCANNER DATA,"D SUPER-RESOLUTION APPROACH FOR SPARSE LASER SCANNER DATA
S. Hosseinyalamdarya∗, A. Yilmaza
Photogrammetric Computer Vision (PCV) Lab
070 Neil avenue, Columbus, OH 43212,
Commission VI, WG VI/4
KEY WORDS: 3D Super-resolution, Geometric Surface Reconstruction, Diffusion Equations, isotropic and anisotropic"
8de2dbe2b03be8a99628ffa000ac78f8b66a1028,Action Recognition in Videos,"´Ecole Nationale Sup´erieure dInformatique et de Math´ematiques Appliqu´ees de Grenoble
INP Grenoble – ENSIMAG
UFR Informatique et Math´ematiques Appliqu´ees de Grenoble
Rapport de stage de Master 2 et de projet de fin d’´etudes
Effectu´e au sein de l’´equipe LEAR, I.N.R.I.A., Grenoble
Action Recognition in Videos
Gaidon Adrien
e ann´ee ENSIMAG – Option I.I.I.
M2R Informatique – sp´ecialit´e I.A.
04 f´evrier 2008 – 04 juillet 2008
LEAR,
I.N.R.I.A., Grenoble
655 avenue de l’Europe
8 334 Montbonnot
France
Responsable de stage
Mme. Cordelia Schmid
Tuteur ´ecole
M. Augustin Lux
M. Roger Mohr"
8de9380536a5f7f29cfe59578041efe7c8ea20bd,Facial image-based gender classification using Local Circular Patterns,"1st International Conference on Pattern Recognition (ICPR 2012)
November 11-15, 2012. Tsukuba, Japan
978-4-9906441-1-6 ©2012 IAPR"
8d3114a3236ec9adabcf0c40613a23f00c272a1c,From 3D Point Clouds to Pose-Normalised Depth Maps,"Int J Comput Vis (2010) 89: 152–176
DOI 10.1007/s11263-009-0297-y
From 3D Point Clouds to Pose-Normalised Depth Maps
Nick Pears · Tom Heseltine · Marcelo Romero
Received: 30 September 2008 / Accepted: 14 September 2009 / Published online: 25 September 2009
© Springer Science+Business Media, LLC 2009"
8d6d0fdf4811bc9572326d12a7edbbba59d2a4cc,SchiNet: Automatic Estimation of Symptoms of Schizophrenia from Facial Behaviour Analysis,"SchiNet: Automatic Estimation of Symptoms of
Schizophrenia from Facial Behaviour Analysis
Mina Bishay, Petar Palasek, Stefan Priebe, and Ioannis Patras"
8d3fbdb9783716c1832a0b7ab1da6390c2869c14,Discriminant Subspace Analysis for Uncertain Situation in Facial Recognition,"Discriminant Subspace Analysis for Uncertain
Situation in Facial Recognition
Pohsiang Tsai, Tich Phuoc Tran, Tom Hintz and Tony Jan
School of Computing and Communications – University of Technology, Sydney
Australia
. Introduction
Facial    analysis  and  recognition  have  received  substential  attention  from  researchers  in
iometrics,  pattern  recognition,  and  computer  vision  communities.  They  have  a  large
number  of  applications,  such  as  security,  communication,  and  entertainment.  Although  a
great deal of efforts has been devoted to automated face recognition systems, it still remains
challenging uncertainty problem. This is because human facial appearance has potentially
of very large intra-subject variations of head pose, illumination, facial expression, occlusion
due to other objects or accessories, facial hair and aging. These misleading variations may
ause classifiers to degrade generalization performance.
It is important for face recognition systems to employ an effective feature extraction scheme
to  enhance  separability  between  pattern  classes  which  should  maintain  and  enhance
features of the input data that make distinct pattern classes separable (Jan, 2004). In general,
there  exist  a  number  of  different  feature  extraction  methods.  The  most  common  feature
extraction  methods  are  subspace  analysis  methods  such  as  principle  component  analysis
(PCA)  (Kirby  &  Sirovich,  1990)  (Jolliffe,  1986)  (Turk  &  Pentland,  1991b),  kernel  principle"
8dc198bcd54ab73936711165a52c6ecc842edc90,Playing for Depth,"Playing for Depth
Mohammad M. Haji-Esmaeili
Gholamali Montazer
Figure 1: Images and Depths extracted from the game Grand Theft Auto V"
8d09c8c6b636ef70633a3f1bb8ff6b4d4136b5cf,3D Twins Expression Challenge,"D Twins Expression Challenge
Vipin Vijayan, Kevin Bowyer, Patrick Flynn
Department of Computer Science and Engineering,
University of Notre Dame.
84 Fitzpatrick Hall,
Notre Dame, IN 46556, USA.
{vvijayan, kwb,
. Introduction
We describe the 3D Twins Expression Challenge (“3D
TEC”) problem in the area of 3D face recognition. The
supporting dataset contains 3D scans of pairs of identical
twins taken with two different facial expressions, neutral
nd smiling. The dataset is smaller than the FRGC v2 [1]
dataset by approximately a factor of ten, but is still more
hallenging than the FRGC v2 dataset due to it containing
twins with different expressions. This challenge problem
will help to push the frontiers of 3D face recognition.
Three dimensional face recognition is an active research
topic in biometrics [2, 3]. While 2D pictures can be cap-
tured quickly, non-intrusively, and easily by widely avail-"
8db43d306a70e23e2a0e6eb2fda60f14b73f65d0,Multi-Commodity Network Flow for Tracking Multiple People,"Multi-Commodity Network Flow
for Tracking Multiple People
Horesh Ben Shitrit, J´erˆome Berclaz, Franc¸ois Fleuret, and Pascal Fua, Fellow, IEEE"
8d2459ada191d496eeee70f1e817d0ba92075160,The evaluation of different approaches towards using Kinect sensor as a Laser scanner,"The evaluation of different approaches towards using
Kinect sensor as a Laser scanner
Bachelor of Science Thesis Software Engineering and Management
KHASHAYAR ABDOLI
ZLATAN HABUL
University of Gothenburg
Chalmers University of Technology
Department of Computer Science and Engineering
Göteborg, Sweden,  June 2014"
8d5945ef2361511a17719c9efe9e2d005247029e,Merced Proceedings of the Annual Meeting of the Cognitive Science Society Title Look Ma ! No Network ! : PCA of Gabor Filters Models the Development of Face Discrimination Permalink,"PCA of Gabor Filters Models the Development of Face Discrimination
Look Ma! No Network!:
Lingyun Zhang and Garrison W. Cottrell
UCSD Computer Science and Engineering
9500 Gilman Dr., La Jolla, CA 92093-0114 USA"
8de06a584955f04f399c10f09f2eed77722f6b1c,Facial Landmarks Localization Estimation by Cascaded Boosted Regression,"Author manuscript, published in ""International Conference on Computer Vision Theory and Applications (VISAPP 2013) (2013)"""
8d9f793bbf6a36285308fdaf2886c9c377f40413,Optimizing process for tracking people in video-camera network. (Optimisation du suivi de personnes dans un réseau de caméras),"Universit´e Nice Sophia Antipolis – UFR Sciences
´Ecole Doctorale STIC
TH`ESE
Pr´esent´ee pour obtenir le titre de :
Docteur en Sciences de l’Universit´e Nice Sophia Antipolis
Sp´ecialit´e : INFORMATIQUE
Julien BADIE
´Equipe d’accueil : STARS – INRIA Sophia Antipolis
OPTIMIZING PROCESS FOR TRACKING PEOPLE IN
VIDEO-CAMERA NETWORK
Th`ese dirig´ee par Franc¸ois BR´EMOND
Soutenance `a l’INRIA le 17 novembre 2015, `a 10h00 devant le jury compos´e de :
Pr´esident : Fr´ed´eric PRECIOSO
Directeur : Franc¸ois BR´EMOND
Professeur, Universit´e
Nice Sophia Antipolis
DR2, INRIA Sophia Antipolis - M´editerran´ee
Rapporteurs : Xavier
ROCA MARVA Directeur du d´epartement des sciences
Laure"
8d4f0517eae232913bf27f516101a75da3249d15,Event-based Dynamic Face Detection and Tracking Based on Activity.,"ARXIV SUBMISSION, MARCH 2018
Event-based Dynamic Face Detection and
Tracking Based on Activity
Gregor Lenz, Sio-Hoi Ieng and Ryad Benosman"
8d97e0102b5d89c62e5c6697eeaaefc82b36c809,Bottom-up attention orienting in young children with autism.,"J Autism Dev Disord (2014) 44:664–673
DOI 10.1007/s10803-013-1925-5
O R I G I N A L P A P E R
Bottom-Up Attention Orienting in Young Children with Autism
Dima Amso • Sara Haas • Elena Tenenbaum •
Julie Markant • Stephen J. Sheinkopf
Published online: 1 September 2013
Ó Springer Science+Business Media New York 2013"
8dffbb6d75877d7d9b4dcde7665888b5675deee1,Emotion Recognition with Deep-Belief Networks,"Emotion Recognition with Deep-Belief
Networks
Tom McLaughlin, Mai Le, Naran Bayanbat
Introduction
For  our  CS229  project,  we  studied  the  problem  of
reliable  computerized  emotion  recognition  in  images  of
human
faces.  First,  we  performed  a  preliminary
exploration using SVM classifiers, and then developed an
pproach based on Deep Belief Nets. Deep Belief Nets, or
DBNs,  are  probabilistic  generative  models  composed  of
multiple  layers  of  stochastic  latent  variables,  where  each
“building block” layer is a Restricted Boltzmann Machine
(RBM).  DBNs  have  a  greedy  layer-wise  unsupervised
learning algorithm as well as a discriminative fine-tuning
procedure  for  optimizing  performance  on  classification
tasks. [1].
We  trained  our  classifier  on  three  databases:  the
Cohn-Kanade Extended Database (CK+) [2], the Japanese
Female  Facial Expression  Database (JAFFE) [3], and the"
8d8afef13a8f6195d3b874231e5e767cf62f3c50,Deep Ranking for Person Re-Identification via Joint Representation Learning,"Deep Ranking for Person Re-identification via
Joint Representation Learning
Shi-Zhe Chen, Chun-Chao Guo, Student Member, IEEE, and Jian-Huang Lai, Senior Member, IEEE"
8d1adf0ac74e901a94f05eca2f684528129a630a,Facial Expression Recognition Using Facial Movement Features,"Facial Expression Recognition Using Facial
Movement Features"
8df3bef321cd1b259cf6fb1ef264a2e885610044,Interactively Learning Visually Grounded Word Meanings from a Human Tutor,"Proceedings of the 5th Workshop on Vision and Language, pages 48–53,
Berlin, Germany, August 12 2016. c(cid:13)2016 Association for Computational Linguistics"
8d7a55d184659ac97d02061a660ae4e30604185b,Penalizing Top Performers: Conservative Loss for Semantic Segmentation Adaptation,"Penalizing Top Performers: Conservative Loss
for Semantic Segmentation Adaptation
Xinge Zhu1, Hui Zhou2, Ceyuan Yang1, Jianping Shi2, Dahua Lin1
CUHK-SenseTime Joint Lab, CUHK
SenseTime Research"
8db9f32b0de29cfb7fd8e3d225be47b801cc9848,Vision-based deep execution monitoring,"Vision-based deep execution monitoring
Francesco Puja, Simone Grazioso, Antonio Tammaro, Valsmis Ntouskos, Marta Sanzari, Fiora Pirri"
4ab87f509fd5c6d4b8c28d1ee6acbb59cd6ce4d8,"MetaStyle: Three-Way Trade-Off Among Speed, Flexibility and Quality in Neural Style Transfer","MetaStyle: Three-Way Trade-Off Among
Speed, Flexibility, and Quality in Neural Style Transfer
Chi Zhang and Yixin Zhu and Song-Chun Zhu
International Center for AI and Robot Autonomy (CARA)"
4a303369828d9334022a0f5e8ad2b1a715d1c0c9,Deep Metric Learning by Online Soft Mining and Class-Aware Attention,"Deep Metric Learning by Online Soft Mining and Class-Aware Attention
Xinshao Wang1,2, Yang Hua1,2, Elyor Kodirov2, Guosheng Hu1,2, Neil M. Robertson1,2
School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, UK
{xwang39, y.hua, {elyor,
Anyvision Research Team, UK"
4aeb87c11fb3a8ad603311c4650040fd3c088832,Self-paced Mixture of Regressions,"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
SamplesSelected SamplesOutliersMoRSPMoR (ours)6361242024Figure1:Inter-componentimbalanceandintra-componentoutliersinMixtureofRegression(MoR)approaches.StandardMoRcannotlearnaccurateregressors(denotedbythedashedlines).Byintroduc-inganovelself-pacedscheme,ourSPMoRapproach(denotedbythesolidlines)selectsbalancedandconfidenttrainingsamplesfromeachcomponent,whilepreventlearningfromtheoutliersthroughoutthetrainingprocedure.theywillbeinevitablybiasedbydatadistribution:lowre-gressionerrorindenselysampledspacewhilehigherrorineverywhereelse.Foraddressingtheissuesofthedatadiscontinuityandheterogeneity,thedivide-and-conquerapproacheswerepro-posedlately.Thecoreideaistolearntocombinemultiplelocalregressors.Forinstance,thehierarchical-based[Hanetal.,2015]andtree-basedregression[HaraandChellappa,2014]makehardpartitionsrecursively,andthesubsetsofsam-plesmaynotbehomogeneousforlearninglocalregressors.WhileMixtureofRegressions(MoR)[Jacobsetal.,1991;JordanandXu,1995]distributesregressionerroramonglocalregressorsbymaximizinglikelihoodinthejointinput-outputspace.Theseapproachesreduceoverallerrorbyfittingre-gressionlocallyandreliefsthebiasbydiscontinuousdatadistribution.Unfortunately,theaforementionedapproachesstillcannotachievesatisfactoryperformancewhenapplyinginsomereal-worldapplications.Themainreasonisthattheseapproachestendtobesensitivetotheintra-componentoutliers(i.e.,thenoisytrainingdataresidingincertaincomponents)andtheinter-componentimbalance(i.e.,thedifferentamountsoftrain-"
4a869781d074f6be7a5001c59e41b25145bdd830,DeltaPhish: Detecting Phishing Webpages in Compromised Websites,"DeltaPhish: Detecting Phishing Webpages
in Compromised Websites∗
Igino Corona1,2, Battista Biggio1,2, Matteo Contini2, Luca Piras1,2, Roberto Corda2, Mauro
Mereu2, Guido Mureddu2, Davide Ariu1,2, and Fabio Roli1,2
Pluribus One, via Bellini 9, 09123 Cagliari, Italy
DIEE, University of Cagliari, Piazza d’Armi 09123, Cagliari, Italy"
4a0f152a07a9becb986b516a1281a4482b38db81,Video Compression for Object Detection Algorithms,"CONFIDENTIAL. Limited circulation. For review only.
Preprint submitted to 24th International Conference on Pattern Recognition.
Received January 22, 2018."
4afd11632db090f518c2591f46523bc7be95ba2e,Exploring outdoor appearance changes with transient scene attributes,"Exploring outdoor appearance changes with transient scene attributes
Pierre-Yves Laffont, James Hays
Brown University∗
Figure 1: Each image in a photo collection is represented as a point in attribute space, where each dimension corresponds to a scene
property which can vary with time, weather, or lighting conditions. Left: projection of all images on the dominant plane of attribute space;
each image is represented as a dot, color-coded according to its value of the “sunniness” attribute. Right: values of a few transient attributes
for three photographs. The scene appearance and its attributes vary widely between the three images, despite the fixed viewpoint.
The appearance of outdoor scenes changes dramatically with light-
ing and weather conditions, time of day, and season. We relate
visual changes to scene attributes, which are human-nameable con-
epts used for high-level description of scenes. They carry semantic
meaning and are more flexible than a categorical representation of
scenes. While the discriminative scene attributes proposed in [Pat-
terson and Hays 2012] distinguish scenes from each other, we fo-
us on transient attributes which describe changes in appearance
within each scene under real-world conditions.
Using online webcams to gather many photographs of outdoor
scenes, crowdsourcing to collect human annotations, and machine
learning to train classifiers, we:
• discover which attributes are likely to vary among images of"
4af36d3ce93f7ed82a7dc321fca926d540691b33,ADVISE: Symbolism and External Knowledge for Decoding Advertisements,[cs.CV]  29 Jul 2018
4abaf7d4b9577131cb2f93e913f8bd83f924da4c,Towards learning through robotic interaction alone: the joint guided search task,"Towards learning through robotic interaction alone:
the joint guided search task
Nick DePalma and Cynthia Breazeal
0 Ames Str. Cambridge MA
Personal Robots Group
MIT Media Lab"
4a4da3d1bbf10f15b448577e75112bac4861620a,"Face, Expression, and Iris Recognition Using Learning-based Approaches Face, Expression, and Iris Recognition Using Learning-based Approaches Special Thanks Are Due to Professors Table of Contents","FACE, EXPRESSION, AND IRIS RECOGNITION
USING LEARNING-BASED APPROACHES
Guodong Guo
A dissertation submitted in partial fulfillment of
the requirements for the degree of
Doctor of Philosophy
(Computer Sciences)
t the
UNIVERSITY OF WISCONSIN–MADISON"
4a53ac7f99a42da17a7f1ba04f5c6d6831e31151,Beyond Bilinear: Generalized Multi-modal Factorized High-order Pooling for Visual Question Answering,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
Beyond Bilinear: Generalized Multi-modal
Factorized High-order Pooling
for Visual Question Answering
Zhou Yu, Jun Yu Member, IEEE, Chenchao Xiang, Jianping Fan, Dacheng Tao Fellow, IEEE"
4a0eb83e1c2b7afbf6d20c7775492764bd039141,Improving a Discriminative Approach to Object Recognition Using Image Patches,"Improving a Discriminative Approach to
Object Recognition using Image Patches
Thomas Deselaers, Daniel Keysers, and Hermann Ney
Lehrstuhl f˜ur Informatik VI { Computer Science Department,
RWTH Aachen University { D-52056 Aachen, Germany
fdeselaers, keysers,"
4a2d54ea1da851151d43b38652b7ea30cdb6dfb2,Direct recognition of motion-blurred faces,"Direct Recognition of Motion Blurred Faces
Kaushik Mitra, Priyanka Vageeswaran and Rama Chellappa"
4a4a3effdfffb51a0f82d3b0904c017086996ac6,Conceptual and methodological challenges for neuroimaging studies of autistic spectrum disorders,"Mazzone and Curatolo Behavioral and Brain Functions 2010, 6:17
http://www.behavioralandbrainfunctions.com/content/6/1/17
REVIEW
Conceptual and methodological challenges for
neuroimaging studies of autistic spectrum
disorders
Luigi Mazzone1*, Paolo Curatolo2
Open Access"
4a855d86574c9bd0a8cfc522bc1c77164819c0bc,PixelCNN Models with Auxiliary Variables for Natural Image Modeling,"PixelCNN Models with Auxiliary Variables for Natural Image Modeling
Alexander Kolesnikov 1 Christoph H. Lampert 1"
4abd49538d04ea5c7e6d31701b57ea17bc349412,Recognizing Fine-Grained and Composite Activities Using Hand-Centric Features and Script Data,"Recognizing Fine-Grained and Composite Activities
using Hand-Centric Features and Script Data
Marcus Rohrbach · Anna Rohrbach · Michaela Regneri ·
Sikandar Amin · Mykhaylo Andriluka · Manfred Pinkal · Bernt Schiele"
4af25075729aa4d0fa4ecf6c948f59ec15bf9565,Analysis and Evaluation of Alternatives and Advanced Solutions for System Elements Contractual Document Evolution Executive Summary,"Project N° IST-2002-507634 - BioSecure
D 9.1.2 - Revision: b3
4 June 2005
Contract Number :
Project Acronym :
Project Title :
Instrument :
Start Date of Project :
Duration :
Deliverable Number :
Title of Deliverable :
Contractual Due Date :
Actual Date of Completion :
IST-2002-507634
BioSecure
Biometrics for Secure Authentication
Network of Excellence
01 June, 2004
6 months
D 9.1.2"
4a1f7905a2d6f218bd39172fb6247ffa9cfe0640,Liveness detection in remote biometrics based on gaze direction estimation,"Proceedings of the Federated Conference on
Computer Science and Information Systems pp. 225–230
DOI: 10.15439/2015F307
ACSIS, Vol. 5
Liveness detection in remote biometrics based on
gaze direction estimation
Krzysztof Adamiak, Dominik ˙Zurek, Krzysztof ´Slot
Lodz University of Technology
ul. Stefanowskiego 18/22, 90-924 Łód´z, Poland
Email:"
4af89578ac237278be310f7660a408b03f12d603,Large-scale geo-facial image analysis,"Islam et al. EURASIP Journal on Image and Video Processing  (2015) 2015:17
DOI 10.1186/s13640-015-0070-9
RESEARCH
Open Access
Large-scale geo-facial image analysis
Mohammad T. Islam1, Connor Greenwell1, Richard Souvenir2 and Nathan Jacobs1*"
4a8085987032e85ac8017d9977a4b76b0d8fa4ac,Object Recognition using Template Matching,"Object Recognition using Template Matching
Nikhil Gupta, Rahul Gupta, Amardeep Singh, Matt Wytock
December 12, 2008
Introduction
Building 3D models
Object Recognition is inherently a hard problem in
omputer vision. Current standard object recogni-
tion techniques require small training data sets of
images and apply sophisticated algorithms. These
methods tend to perform poorly because the small
data set does not reflect the true distribution (selec-
tion bias).
Recently, Torralba et al [1] have proposed to de-
velop a large data set of images (80 million images)
nd apply simple algorithms for object recognition.
Their method performs relatively well for some cer-
tain classes of objects. Nevertheless, their data sets
require very large storage and are noisy.
In this project, we develop precise 3D models of
objects and use these to apply simple learning al-"
4a5b2d5892f630a10b136eab25a1406de81b586b,Adaptive low bit rate facial feature enhanced residual image coding method using SPIHT for compressing personal ID images,"This article appeared in a journal published by Elsevier. The attached
opy is furnished to the author for internal non-commercial research
nd education use, including for instruction at the authors institution
nd sharing with colleagues.
Other uses, including reproduction and distribution, or selling or
licensing copies, or posting to personal, institutional or third party
websites are prohibited.
In most cases authors are permitted to post their version of the
rticle (e.g. in Word or Tex form) to their personal website or
institutional repository. Authors requiring further information
regarding Elsevier’s archiving and manuscript policies are
encouraged to visit:
http://www.elsevier.com/copyright"
4ac3cd8b6c50f7a26f27eefc64855134932b39be,Robust Facial Landmark Detection via a Fully-Convolutional Local-Global Context Network,"Robust Facial Landmark Detection
via a Fully-Convolutional Local-Global Context Network
Daniel Merget
Matthias Rock
Gerhard Rigoll
Technical University of Munich"
4a56d5e483ddea93f14bfbe350a3063b2b9126cb,Iterative Action and Pose Recognition Using Global-and-Pose Features and Action-Specific Models,"Iterative Action and Pose Recognition
using Global-and-Pose Features and Action-specific Models
Norimichi Ukita
Nara Institute of Science and Technology"
4a95dacb1d38a07e73007082b8ed7651a4b5277c,Region labelling using a Point-Based Coherence Criterion,"Region labelling using a Point-Based Coherence Criterion
Hichem Houissa(cid:2) and Nozha Boujemaa(cid:2)
(cid:2)INRIA Rocquencourt, BP 105,78153, Le Chesnay Cedex-France"
4a3d96b2a53114da4be3880f652a6eef3f3cc035,A Dictionary Learning-Based 3D Morphable Shape Model,"A Dictionary Learning-Based
D Morphable Shape Model
Claudio Ferrari
, Giuseppe Lisanti, Stefano Berretti
, Senior Member, IEEE, and Alberto Del Bimbo"
4ada5b80a032f3daa4e97e9f716a5cba7cf80d85,Learning a priori constrained weighted majority votes,"Noname manuscript No.
(will be inserted by the editor)
Learning A Priori Constrained Weighted Majority Votes
Aur´elien Bellet · Amaury Habrard ·
Emilie Morvant · Marc Sebban
Received: date / Accepted: date"
4a1a5316e85528f4ff7a5f76699dfa8c70f6cc5c,Face Recognition using Local Features based on Two-layer Block Model,"MVA2005  IAPR  Conference on Machine VIsion Applications, May 16-18, 2005 Tsukuba Science City, Japan
Face Recognition using Local Features based on Two-layer Block M odel
W onjun Hwang1          Ji-Yeun Kim        Seokcheol Kee
Computing Lab.,
Samsung Advanced Institute of Technology
ombined  by  Yang  and  etc  [7].  The  sparsification  of  LFA
helps the reduction of dimension of image in LDA scheme
nd  local  topological  property  is  more  useful  than  holistic
property of PCA in recognition, but there is still structural
problem  because  the  method  to  select  the  features  is
designed  for  minimization  of  reconstruction  error,  not  for
increasing discriminability in face model.
In  this  paper,  we  proposed  the  novel  recognition
lgorithm  to  merge LFA  and LDA  method. We do  not use
the existing sparsification method for selecting features but
dopt  the  two-layer  block  model  to  make  several  groups
with  topographic  local  features  in  similar  position.  Each
local  block,  flocked  local  features,  can  represent  its  own
local  property  and  at
time  holistic  face"
4a31ca27b987606ae353b300488068b5240633ee,WSABIE: scaling up to large vocabulary image annotation,"WSABIE: Scaling Up To Large Vocabulary Image Annotation
Jason Weston1 and Samy Bengio1 and Nicolas Usunier2
Google, USA
Universit´e Paris 6, LIP6, France"
4a1b67d1f30abeeecb270666605025d9d78971ff,Energy-based adaptive skin segmentation for hand and head detection,"Noname manuscript No.
(will be inserted by the editor)
Energy-based adaptive skin segmentation for hand and
head detection
Michal Kawulok
Received: date / Accepted: date"
4afd04db8eeb6a4cacb616b0dd193819bad8c2b6,Deep Continuous Conditional Random Fields with Asymmetric Inter-object Constraints for Online Multi-object Tracking,"JOURNAL OF LATEX CLASS FILES, DOI 10.1109/TCSVT.2018.2825679
Deep Continuous Conditional Random Fields with
Asymmetric Inter-object Constraints for Online
Multi-object Tracking
Hui Zhou, Wanli Ouyang, Jian Cheng, Xiaogang Wang, and Hongsheng Li"
4a2062ba576ca9e9a73b6aa6e8aac07f4d9344b9,Fusing Deep Convolutional Networks for Large Scale Visual Concept Classification,"Fusing Deep Convolutional Networks for Large
Scale Visual Concept Classification
Hilal Ergun and Mustafa SertB
Department of Computer Engineering
Bas¸kent University
06810 Ankara, TURKEY"
4af133c49d39c8b7aa9d82c17f1fd2c70e36233f,Recognition of Facial Gestures using Gabor Filter,"Recognition of Facial Gestures using Gabor Filter
{tag}                                                                       {/tag}
International Journal of Computer Applications
© 2011 by IJCA Journal
Number 8 - Article 2
Year of Publication: 2011
Authors:
Subhashini Ramalingam
Dr Ilango Paramasivam
Mangayarkarasi Ramiah
10.5120/3153-3990"
4adca62f888226d3a16654ca499bf2a7d3d11b71,Models of Semantic Representation with Visual Attributes,"Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pages 572–582,
Sofia, Bulgaria, August 4-9 2013. c(cid:13)2013 Association for Computational Linguistics"
4a9831e5fec549edee454709048a51997ef60fb7,Did the Model Understand the Question?,"Did the Model Understand the Question?
Pramod K. Mudrakarta
University of Chicago
Ankur Taly
Google Brain
Mukund Sundararajan
Kedar Dhamdhere
Google
Google"
4a75d59c9c57da420441190071ba545eb4a75e1e,Deep Mixture of Diverse Experts for Large-Scale Visual Recognition,"Deep Mixture of Diverse Experts for Large-Scale
Visual Recognition
Tianyi Zhao, Jun Yu, Zhenzhong Kuang, Wei Zhang, Jianping Fan"
4a5014c23e2adb640f4b07b9b47ca2f2a5d427e6,On the Estimation of Face Recognition System Performance using Image Variabil- ity Information,"Accepted Manuscript
Title: On the estimation of face recognition system
performance using image variability information
Authors: Muhammad Aurangzeb Khan, Costas Xydeas,
Hassan Ahmed
Reference:
To appear in:
Received date:
Revised date:
Accepted date:
S0030-4026(17)30206-1
http://dx.doi.org/doi:10.1016/j.ijleo.2017.02.063
IJLEO 58879
8-8-2016
7-2-2017
7-2-2017
Please cite this article as: Muhammad Aurangzeb Khan, Costas Xydeas, Hassan
Ahmed, On the estimation of face recognition system performance using image
variability information, Optik - International Journal for Light and Electron Optics
http://dx.doi.org/10.1016/j.ijleo.2017.02.063"
4a0267f6e840d6632122a60e8fad1f8740eba8ca,Comparative study: face recognition on unspecific persons using linear subspace methods,"Comparative Study: Face Recognition on
Unspeci(cid:2)c Persons using Linear Subspace Methods
Dahua Lin
Shuicheng Yan
Xiaoou Tang
Dept. of Information Engineering
Dept. of Information Engineering
Dept. of Information Engineering
The Chinese University of
The Chinese University of
The Chinese University of
Hong Kong
Shatin, Hong Kong SAR
Email:
Hong Kong
Shatin, Hong Kong SAR
Email:
Hong Kong
Shatin, Hong Kong SAR
Email:"
4a227881f5763d2bda2e545eac346389b2b2017a,Model based image interpretation with application to facial expression recognition,"d d d
d d d d
ddd ddd ddd ddd
Institut für Informatik
der Technischen Universität München
Model-based Image Interpretation with
Application to Facial Expression
Recognition
Dissertation
Matthias Wimmer"
4aa18f3a1c85f7a09d3b0d6b28c0339199892d60,The Application of Neural Networks for Facial Landmarking on Mobile Devices,
4a45b8f8decc178305af06d758ac7428a9070fad,Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data,"Augmented CycleGAN: Learning Many-to-Many Mappings
from Unpaired Data
Amjad Almahairi 1 † Sai Rajeswar 1 Alessandro Sordoni 2 Philip Bachman 2 Aaron Courville 1 3"
4a6fcf714f663618657effc341ae5961784504c7,Scaling Up Class-Specific Kernel Discriminant Analysis for Large-Scale Face Verification,"Scaling up Class-Specific Kernel Discriminant
Analysis for large-scale Face Verification
Alexandros Iosifidis, Senior Member, IEEE, and Moncef Gabbouj, Fellow, IEEE"
4ae3cdba121dec886a84eff146e438a55513002c,Interactive Hausdorff distance computation for general polygonal models,"Interactive Hausdorff Distance Computation for General Polygonal Models
Min Tang∗
Minkyoung Lee†
Ewha Womans University, Seoul, Korea
Young J. Kim‡
http://graphics.ewha.ac.kr/HDIST
Figure 1: Interactive Hausdorff Distance Computation. Our algorithm can compute Hausdorff distance between complicated models at
interactive rates (the first three figures). Here, the green line denotes the Hausdorff distance. This algorithm can also be used to find
penetration depth (PD) for physically-based animation (the last two figures). It takes only a few milli-seconds to run on average."
4a70c6e14bcd7a44838fdabdcdb33bc026c907b4,Allocentric Pose Estimation,"Allocentric Pose Estimation
Jos´e Oramas M.
Luc De Raedt
Tinne Tuytelaars
KU Leuven, ESAT-PSI, iMinds
KU Leuven, CS-DTAI
KU Leuven, ESAT-PSI, iMinds"
4a88237199595feaa3f0e3289cbdd201a3ce28ff,Multi-Domain Pose Network for Multi-Person Pose Estimation and Tracking,"Multi-Domain Pose Network for Multi-Person
Pose Estimation and Tracking
Hengkai Guo1(cid:63), Tang Tang1, Guozhong Luo1, Riwei Chen1, Yongchen Lu1,
nd Linfu Wen1
ByteDance AI Lab"
4a3a9d02999fcf0895db31d644f40c98254ac4b1,Vision-based 3D bicycle tracking using deformable part model and Interacting Multiple Model filter,"Vision-based 3D Bicycle Tracking using Deformable Part Model
nd Interacting Multiple Model Filter
Hyunggi Cho, Paul E. Rybski and Wende Zhang"
4ad51a99e489939755f1d4f5d1f5bc509c49e96d,Preferences for facially communicated big five personality traits and their relation to self-reported big five personality,"Personality and Individual Differences 134 (2018) 195–200
Contents lists available at ScienceDirect
Personality and Individual Differences
journal homepage: www.elsevier.com/locate/paid
Preferences for facially communicated big five personality traits and their
relation to self-reported big five personality
Donald F. Sacco⁎, Mitch Brown
The University of Southern Mississippi, United States of America
A R T I C L E I N F O
A B S T R A C T
Keywords:
Personality
Face perception
Big five
Similarity
Complementarity
A growing body of research has begun to document that core personality traits are associated with specific facial
structures, and that individuals are sensitive to these facial cues, as indexed by preferences for faces commu-
nicating higher or lower levels of specific traits. We explored how self-reported Big Five personality traits in-
fluence preferences for facially-communicated Big Five personality in targets. Participants selected among pairs"
1bc23c771688109bed9fd295ce82d7e702726327,C 2011 Jianchao Yang Sparse Modeling of High-dimensional Data for Learning and Vision,(cid:13) 2011 Jianchao Yang
1b0f11db30e9184da63decbd7711db196753054c,The iNaturalist Species Classification and Detection Dataset-Supplementary Material,"The iNaturalist Species Classification and Detection Dataset
- Supplementary Material
Grant Van Horn1 Oisin Mac Aodha1 Yang Song2 Yin Cui3 Chen Sun2
Alex Shepard4 Hartwig Adam2
Pietro Perona1
Serge Belongie3
Caltech
Google
Cornell Tech
iNaturalist
. Additional Classification Results
We performed an experiment to understand if there was
ny relationship between real world animal size and pre-
diction accuracy. Using existing records for bird [4] and
mammal [2] body sizes we assigned a mass to each of the
lasses in iNat2017 that overlapped with these datasets. For
given species, mass will vary due to the life stage or gen-
der of the particular individual. Here, we simply take the
verage value. This resulted in data for 795 species, from
the small Allen’s hummingbird (Selasphorus sasin) to the"
1b4424e06ac29b72535727b92f261f39d065e858,3D Pictorial Structures Revisited: Multiple Human Pose Estimation,"D Pictorial Structures Revisited:
Multiple Human Pose Estimation
Vasileios Belagiannis, Sikandar Amin, Mykhaylo Andriluka,
Bernt Schiele, Nassir Navab, and Slobodan Ilic"
1b1d9b528c69e082dc5685089090bd2d849d887d,STD-PD: Generating Synthetic Training Data for Pedestrian Detection in Unannotated Videos,"MixedPeds: Pedestrian Detection in Unannotated Videos using Synthetically
Generated Human-agents for Training
Ernest Cheung, Anson Wong, Aniket Bera, Dinesh Manocha
Department of Computer Science
Project Webpage: http://gamma.cs.unc.edu/MixedPeds
The University of North Carolina at Chapel Hill
Email: {ernestc, ahtsans, ab,"
1ba61a4fedc217f7bd052d1b2904567c9985dc44,Person Re-identification for Improved Multi-person Multi-camera Tracking by Continuous Entity Association,"Person Re-identification for Improved
Multi-person Multi-camera Tracking by
Continuous Entity Association
Neeti Narayan, Nishant Sankaran, Devansh Arpit, Karthik
Dantu, Srirangaraj Setlur, Venu Govindaraju
University at Buffalo"
1b9472907f5b7a1815c98b4562dce6c46dd2cf34,Consistent Rank Logits for Ordinal Regression with Convolutional Neural Networks,"Consistent Rank Logits for Ordinal Regression with
Convolutional Neural Networks
Wenzhi Cao 1 Vahid Mirjalili 2 Sebastian Raschka 1"
1bc783d6bc137b7fabcb5cfc9c4542c500a5c3ba,Fast Human Detection Algorithm for High-Resolution CCTV Camera,"Journal  of  the  Korea  Academia-Industrial
ooperation  Society
Vol.  15,  No.  8  pp.  5263-5268,  2014
http://dx.doi.org/10.5762/KAIS.2014.15.8.5263
ISSN  1975-4701  /  eISSN  2288-4688
고해상도  CCTV  카메라를  위한  빠른  사람  검출  알고리즘
박인철1*
호원대학교 국방기술학부
Fast  Human  Detection  Algorithm  for  High-Resolution  CCTV  Camera
In-Cheol  Park1*
Division  of  Defence  Technology,  Howon  University
본  논문은  사람  검출  알고리즘을  고해상도  CCTV  카메라에  적용할  수  있도록  빠른  사람  검출  알고리즘을  제안한다.
HOG  디텍터를  이용한  사람  검출  알고리즘은  영상처리  분야의  최신  기술로  높은  성능을  보인다.  그러나  HOG  특징  추출
과정에서  연산  속도가  느려  실시간  고해상도  영상에  적용하기  어렵다.  이러한  문제를  해결하기  위해  2단계  검출  방법을  제안
한다.  먼저  전처리  과정으로  배경  차감법(Background  subtraction)을  이용하여  사람  후보  영역을  찾는다.  이후  사람  후보
영역에서만  HOG  디텍터를  이용하여  사람/비사람  구분을  수행한다.  이러한  두  단계의  실험  결과  약  2.5배의  검출  속도  향상을
보였으며,  성능  저하는  거의  없음을  확인할  수  있었다."
1b1323b4677c640ae8835a9ccab611ca1e9652e3,Robust object tracking with a hierarchical ensemble framework,"Robust Object Tracking with a Hierarchical Ensemble Framework
Mengmeng Wang1, Yong Liu2 and Rong Xiong2"
1b4bc7447f500af2601c5233879afc057a5876d8,Facial Action Unit Classification with Hidden Knowledge under Incomplete Annotation,"Facial Action Unit Classification with Hidden Knowledge
under Incomplete Annotation
Jun Wang
University of Science and
Technology of China
Hefei, Anhui
Shangfei Wang
University of Science and
Technology of China
Hefei, Anhui
Rensselaer Polytechnic
Qiang Ji
Institute
Troy, NY
P.R.China, 230027
P.R.China, 230027
USA, 12180"
1b2568de7363a9f46094b9cac82f4fe2ec1a4f56,Detection of Fragmented Rectangular Enclosures in Very High Resolution Remote Sensing Images,"Detection of Fragmented Rectangular Enclosures in
Very High Resolution Remote Sensing Images
Igor Zingman, Dietmar Saupe, Otávio A. B. Penatti, and Karsten Lambers"
1b807b6abaeef68edfbdc4200e198bf4e9613198,Image Processing Pipeline for Facial Expression Recognition under Variable Lighting,"Image Processing Pipeline for Facial Expression Recognition under Variable
Lighting
Ralph Ma, Amr Mohamed"
1b0548e52a1ffc7ebffe5200e2111525c9f7fd4a,Novel Views of Objects from a Single Image,"Novel Views of Objects from a Single Image
Konstantinos Rematas, Chuong Nguyen, Tobias Ritschel, Mario Fritz, and Tinne Tuytelaars"
1ba8acf75d84bccc14590251353b369cdd3bd500,Computationally efficient dense moving object detection based on reduced space disparity estimation,"Computationally ef‌f‌icient dense moving
object detection based on reduced space
disparity estimation
Goran Popovi´c, Antea Hadviger, Ivan Markovi´c,
Ivan Petrovi´c ∗
University of Zagreb Faculty of Electrical Engineering and
Computing, Laboratory for Autonomous Systems and Mobile Robotics,
Zagreb, Croatia (e-mail:"
1bb14ddc0326a8e5b44eafd915738c2b1342f392,Title On color texture normalization for active appearance models,"Provided by the author(s) and NUI Galway in accordance with publisher policies. Please cite the published
version when available.
Title
On color texture normalization for active appearance models
Author(s)
Ionita, Mircea C.; Corcoran, Peter M.; Buzuloiu, Vasile
Publication
009-05-12
Publication
Information
Ionita, M. C., Corcoran, P., & Buzuloiu, V. (2009). On Color
Texture Normalization for Active Appearance Models. Image
Processing, IEEE Transactions on, 18(6), 1372-1378.
Publisher
Link to
publisher's
version
http://dx.doi.org/10.1109/TIP.2009.2017163
Item record
http://hdl.handle.net/10379/1350"
1b150248d856f95da8316da868532a4286b9d58e,Analyzing 3D Objects in Cluttered Images,"Analyzing 3D Objects in Cluttered Images
Mohsen Hejrati
UC Irvine
Deva Ramanan
UC Irvine"
1b457646a300c744bf098f0bfde641042602262a,Holistic and Gabor-local Feature-fusion for Face Recognition using Canonical Correlation Analysis ( CCA ),"Holistic and Gabor-local Feature-fusion for Face Recognition
using Canonical Correlation Analysis (CCA)
Hendra Kusuma1), Wirawan2), Adi Soeprijanto3)
Department of Electrical Engineering, Faculty of Industrial Technology ITS Surabaya Indonesia 60111
e-mail :
Abstrak   –  In this paper, we propose a feature fusion
method  based  on  Canonical  Correlation  Analysis
(CCA)  for  combining  two  feature  extractors    to
increase  robustness  of  face  recognition  against  pose
nd  illumination  changes.  At  first  holistic  features,
eigenfaces (PCA) and Gabor phase congruency image
(GPCI)  features  are  extracted  from  facial  images
respectively    and  then  CCA  finds  the  transformation
for  each  extractor  dataset  and  maximizes
orrelation between them. Experiments results on Yale
face  image  and  ORL  databases  have  shown  that  the
fusion  of
exhibit  better
performance.
Keywords:  Feature-fusion,  Local  features,  Holistic"
1b2297ba37fece76568c8b53369e6fd34d63175a,High-Resolution 3 D Layout from a Single View,"High-Resolution 3D Layout from a Single View
M. Zeeshan Zia1, Michael Stark2, and Konrad Schindler1
Photogrammetry and Remote Sensing, ETH Z¨urich, Switzerland
Stanford University and Max Planck Institute for Informatics"
1b2e50412ec151486912f0bfd01703c8ec46b5a7,A geometric approach to face detector combining,"A Geometric Approach to Face Detector
Combining⋆
Nikolay Degtyarev and Oleg Seredin
Tula State University
http://lda.tsu.tula.ru"
1b7a0fffb5ee96adece2f6079f5e9ab79c3bc50e,Spigan: Privileged Adversarial Learning,"Under review as a conference paper at ICLR 2019
SPIGAN: PRIVILEGED ADVERSARIAL LEARNING
FROM SIMULATION
Anonymous authors
Paper under double-blind review"
1bfc74bad04b407d1792a70d73a3f5dc0be0506d,Cross-Dataset Adaptation for Visual Question Answering,"Cross-Dataset Adaptation for Visual Question Answering
Wei-Lun Chao∗
Hexiang Hu∗
Fei Sha
U. of Southern California
U. of Southern California
U. of Southern California
Los Angeles, CA
Los Angeles, CA
Los Angeles, CA"
1b3d5d95e1fcded017f193f5cf9772bf8a1ed108,Using Keystroke Analytics to Improve Pass – Fail Classifiers,"(2017).	 Using
nalytics
http://dx.doi.org/10.18608/jla.2017.42.14
keystrokes
improve
pass-fail
lassifiers.
Journal
Learning	 Analytics,
(2),
89–211.
Using Keystroke Analytics to Improve Pass–Fail Classifiers
Kevin	Casey
Maynooth	University,	Ireland"
1bea531e8271202462c7907f60a8458fa5aec00d,"Ein generisches System zur automatischen Detektion, Verfolgung und Wiedererkennung von Personen in Videodaten","Ein generisches System zur automatischen
Detektion, Verfolgung und Wiedererkennung von
Personen in Videodaten
Zur Erlangung des akademischen Grades eines
Doktor-Ingenieurs
von der Fakult¨at f¨ur
Bauingenieur-, Geo- und Umweltwissenschaften
des Karlsruher Instituts f¨ur Technologie (KIT)
(Institut f¨ur Photogrammetrie und Fernerkundung)
genehmigte
Dissertation
Dipl.-Inform. Kai J¨ungling
us Adenau
Tag der m¨undlichen Pr¨ufung: 24.01.2011
Referent: Prof. Dr.-Ing. Stefan Hinz
Korreferent: Prof. Dr. rer. nat. Maurus Tacke
Korreferent: Prof. Dr.-Ing. Christoph Stiller
Karlsruhe 2011"
1bbec7190ac3ba34ca91d28f145e356a11418b67,Explorer Action Recognition with Dynamic Image Networks,"Action Recognition with Dynamic Image Networks
Citation for published version:
Bilen, H, Fernando, B, Gravves, E & Vedaldi, A 2017, 'Action Recognition with Dynamic Image Networks'
IEEE Transactions on Pattern Analysis and Machine Intelligence. DOI: 10.1109/TPAMI.2017.2769085
Digital Object Identifier (DOI):
0.1109/TPAMI.2017.2769085
Link:
Link to publication record in Edinburgh Research Explorer
Document Version:
Peer reviewed version
Published In:
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s)
nd / or other copyright owners and it is a condition of accessing these publications that users recognise and
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The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer
ontent complies with UK legislation. If you believe that the public display of this file breaches copyright please
ontact providing details, and we will remove access to the work immediately and"
1b1173a3fb33f9dfaf8d8cc36eb0bf35e364913d,Registration Invariant Representations for Expression Detection,"DICTA
DICTA 2010 Submission #147. CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE.
Registration Invariant Representations for Expression Detection
Anonymous DICTA submission
Paper ID 147"
1bdef21f093c41df2682a07f05f3548717c7a3d1,Towards Automated Classification of Emotional Facial Expressions,"Towards Automated Classification of Emotional Facial Expressions
Lewis J. Baker Vanessa LoBue
Elizabeth Bonawitz & Patrick Shafto
Department of Mathematics and Computer Science, 2Department of Psychology
Rutgers University – Newark, 101 Warren St., Newark, NJ, 07102 USA"
1b3b01513f99d13973e631c87ffa43904cd8a821,HMM recognition of expressions in unrestrained video intervals,"HMM RECOGNITION OF EXPRESSIONS IN UNRESTRAINED VIDEO INTERVALS
José Luis Landabaso, Montse Pardàs, Antonio Bonafonte
Universitat Politècnica de Catalunya, Barcelona, Spain"
1b793cc5dceb98c95e816aebc2252205bfd71569,ADNet: A Deep Network for Detecting Adverts,"ADNet: A Deep Network for Detecting Adverts
Murhaf Hossari(cid:63)1, Soumyabrata Dev(cid:63)1, Matthew Nicholson1, Killian McCabe1,
Atul Nautiyal1, Clare Conran1, Jian Tang3, Wei Xu3, and Fran¸cois Piti´e1,2
The ADAPT SFI Research Centre, Trinity College Dublin
Department of Electronic & Electrical Engineering, Trinity College Dublin
Huawei Ireland Research Center, Dublin"
1b6d2f8f9cbbf5e20e445a60cb7840a30975f297,Learning from Noisy Web Data with Category-level Supervision,"Learning from Noisy Web Data with Category-level
Supervision
Li Niu, Qingtao Tang, Ashok Veeraraghavan, and Ashu Sabharwal"
1ba55051d3957895d77257cc9a5885068fb2e43a,High-Resolution Face Verification Using Pore-Scale Facial Features,"High-Resolution Face Verification Using
Pore-Scale Facial Features
Dong Li, Huiling Zhou, and Kin-Man Lam"
1bd80812c58de8cb0127aea915a45ebbff42dc3b,Twins 3D face recognition challenge,"Twins 3D Face Recognition Challenge
Vipin Vijayan 1, Kevin W. Bowyer 1, Patrick J. Flynn 1, Di Huang 2, Liming Chen 2,
Mark Hansen 3, Omar Ocegueda 4, Shishir K. Shah 4, Ioannis A. Kakadiaris 4"
1b2183c2b9608b7f815551c9ba602f22205126b1,Facial Reenactment Project Plan,"Facial Reenactment
Project Plan
Student:
Li Wing Yee
Supervisor:
Dr. Dirk Scheiders"
1b6394178dbc31d0867f0b44686d224a19d61cf4,EPML: Expanded Parts Based Metric Learning for Occlusion Robust Face Verification,"EPML: Expanded Parts based Metric Learning for
Occlusion Robust Face Verification
Gaurav Sharma, Fr´ed´eric Jurie, Patrick P´erez
To cite this version:
Gaurav Sharma, Fr´ed´eric Jurie, Patrick P´erez. EPML: Expanded Parts based Metric Learning
for Occlusion Robust Face Verification. Asian Conference on Computer Vision, Nov 2014, -,
Singapore. pp.1-15, 2014. <hal-01070657>
HAL Id: hal-01070657
https://hal.archives-ouvertes.fr/hal-01070657
Submitted on 2 Oct 2014
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
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L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,
´emanant des ´etablissements d’enseignement et de"
1b4f6f73c70353869026e5eec1dd903f9e26d43f,Robust Subjective Visual Property Prediction from Crowdsourced Pairwise Labels,"Robust Subjective Visual Property Prediction
from Crowdsourced Pairwise Labels
Yanwei Fu, Timothy M. Hospedales, Tao Xiang, Jiechao Xiong,
Shaogang Gong, Yizhou Wang, and Yuan Yao"
1b300a7858ab7870d36622a51b0549b1936572d4,Dynamic Facial Expression Recognition With Atlas Construction and Sparse Representation,"This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TIP.2016.2537215, IEEE
Transactions on Image Processing
Dynamic Facial Expression Recognition with Atlas
Construction and Sparse Representation
Yimo Guo, Guoying Zhao, Senior Member, IEEE, and Matti Pietik¨ainen, Fellow, IEEE"
1be498d4bbc30c3bfd0029114c784bc2114d67c0,Age and Gender Estimation of Unfiltered Faces,"Age and Gender Estimation of Unfiltered Faces
Eran Eidinger, Roee Enbar, Tal Hassner*"
1b3ee5455956a40c6e9e09ccda0f4fb162838629,The Recognition of License Plate Restrictions Based on Faster R-CNN,"017 2nd International Conference on Manufacturing Science and Information Engineering (ICMSIE 2017)
ISBN: 978-1-60595-516-2
The Recognition of License Plate Restrictions
Based on Faster R-CNN
Xi Wang, Lina Xun, Yi Xia, Fenglin Du, Yun Ding, Yuanyuan Li
nd Zhi Yang"
1b90507f02967ff143fce993a5abbfba173b1ed0,Gradient-DCT (G-DCT) descriptors,"Image Processing Theory, Tools and Applications
Gradient-DCT (G-DCT) Descriptors
Radovan Fusek, Eduard Sojka
Technical University of Ostrava, FEECS, Department of Computer Science,
7. listopadu 15, 708 33 Ostrava-Poruba, Czech Republic
e-mail:"
1b92973843c3a791bb5ca5a68405c3ecb3473ded,Grassmann Learning To perform discriminant learning on Grassmann manifolds,"Building Deep Networks on Grassmann Manifolds
Zhiwu Huang†, Jiqing Wu†, Luc Van Gool†‡
Computer Vision Lab, ETH Zurich, Switzerland
VISICS, KU Leuven, Belgium
{zhiwu.huang, jiqing.wu,"
1ba20398e3b0154730590217a0988fbbab19e927,Doubly weighted nonnegative matrix factorization for imbalanced face recognition,"978-1-4244-2354-5/09/$25.00 ©2009 IEEE
ICASSP 2009"
1bed38bc216f80a50617afa5c6d9cc4b2db72519,Face recognition using early biologically inspired features,"Face Recognition Using Early Biologically Inspired Features
Min Li, Shenghua Bao, Weihong Qian, and Zhong Su
IBM China Research Lab, PRC
fminliml,baoshhua,qianwh,
Nalini K. Ratha
IBM Watson Research Center, USA"
1be18a701d5af2d8088db3e6aaa5b9b1d54b6fd3,ENHANCEMENT OF FAST FACE DETECTION ALGORITHM BASED ON A CASCADE OF DECISION TREES,"ENHANCEMENT OF FAST FACE DETECTION ALGORITHM BASED ON A CASCADE OF
DECISION TREES
V. V. Khryashchev a, *, A. A. Lebedev a, A. L. Priorov a
YSU, Yaroslavl, Russia - (vhr,
Commission II, WG II/5
KEY WORDS: Face Detection, Cascade Algorithm, Decision Trees."
1b2dd300a43d0553f1deb578d9aea45d99472136,Fast Approximation of Distance Between Elastic Curves using Kernels,
1bf0b5186af083117af136dfcb08ed28828664d0,"Deep Filter Banks for Texture Recognition, Description, and Segmentation","Int J Comput Vis
DOI 10.1007/s11263-015-0872-3
Deep Filter Banks for Texture Recognition, Description,
nd Segmentation
Mircea Cimpoi1 · Subhransu Maji2 · Iasonas Kokkinos3 · Andrea Vedaldi1
Received: 4 June 2015 / Accepted: 20 November 2015
© The Author(s) 2015. This article is published with open access at Springerlink.com"
1bb73d8f1224a846473d0a2ddc4289ae3e21b61c,A joint particle filter to track the position and head orientation of people using audio visual cues,"© EURASIP, 2010   ISSN 2076-1465
8th European Signal Processing Conference (EUSIPCO-2010)
INTRODUCTION"
1b224ad99c42e696b6d98c05a87f1738e28c6c5e,A Markov Random Field Groupwise Registration Framework for Face Recognition,"A Markov Random Field Groupwise Registration
Framework for Face Recognition
Shu Liao, Dinggang Shen, and Albert C.S. Chung"
1b7a7d291235e4b6e5f97722124070feb26f3cc1,Learning Two-Branch Neural Networks for Image-Text Matching Tasks,"Learning Two-Branch Neural Networks for
Image-Text Matching Tasks
Liwei Wang, Yin Li, Jing Huang, Svetlana Lazebnik"
1b0a071450c419138432c033f722027ec88846ea,Looking at faces in a vehicle: A deep CNN based approach and evaluation,"Windsor Oceanico Hotel, Rio de Janeiro, Brazil, November 1-4, 2016
978-1-5090-1889-5/16/$31.00 ©2016 IEEE"
1b55c4e804d1298cbbb9c507497177014a923d22,Incremental Class Representation Learning for Face Recognition,"Incremental Class Representation
Learning for Face Recognition
Degree’s Thesis
Audiovisual Systems Engineering
Author:
Advisors: Elisa Sayrol, Josep Ramon Morros
Eric Presas Valga
Universitat Politècnica de Catalunya (UPC)
016 - 2017"
1b71d3f30238cb6621021a95543cce3aab96a21b,Fine-grained Video Classification and Captioning,"Fine-grained Video Classification and Captioning
Farzaneh Mahdisoltani1,2, Guillaume Berger2, Waseem Gharbieh2
David Fleet1, Roland Memisevic2
{farzaneh,
University of Toronto1, Twenty Billion Neurons2"
1b3505018e39a794eab032e7e313784b21be42e9,Saliency based Person Re-Identification in Video using Colour Features,"GRD Journals- Global Research and Development Journal for Engineering | Volume 1 | Issue 10 | September 2016
ISSN: 2455-5703
Saliency based Person Re-Identification in Video
using Colour Features
Srujy Krishna A U
PG Student
Shimy Joseph
Assistant Professor
Department of Computer Science and Engineering
Department of Computer Science and Engineering
Federal Institute Of Science and Technology
Federal Institute Of Science and Technology"
1b3587363d37dd197b6adbcfa79d49b5486f27d8,Multimodal Grounding for Language Processing,"Multimodal Grounding for Language Processing
Lisa Beinborn◦∗3
Teresa Botschen∗(cid:52)
Iryna Gurevych (cid:52)
Language Technology Lab, University of Duisburg-Essen
(cid:52) Ubiquitous Knowledge Processing Lab (UKP) and Research Training Group AIPHES
Department of Computer Science, Technische Universit¨at Darmstadt
www.ukp.tu-darmstadt.de"
1b74479f6e597a33703a63161527d55cc5d3096f,Self-Supervised Model Adaptation for Multimodal Semantic Segmentation,"Self-Supervised Model Adaptation for Multimodal
Semantic Segmentation
Abhinav Valada · Rohit Mohan · Wolfram Burgard"
1b8508c6e341dcc803e52ed02968ae944c744f68,Face detection evaluation: a new approach based on the golden ratio $${\Phi}$$,"SIViP manuscript No.
(will be inserted by the editor)
Face Detection Evaluation: A New Approach Based on
the Golden Ratio (cid:8)
M. Hassaballah (cid:1) Kenji Murakami (cid:1) Shun Ido
Received: 1 Jan. 2011 /Revised: 9 March 2011/ Accepted: date"
1b7b95ee13d91e9c768de6417a8919f2a3384599,A Probabilistic U-Net for Segmentation of Ambiguous Images,"A Probabilistic U-Net for Segmentation of Ambiguous
Images
Simon A. A. Kohl1∗,2,, Bernardino Romera-Paredes1, Clemens Meyer1, Jeffrey De Fauw1,
Joseph R. Ledsam1, Klaus H. Maier-Hein2, S. M. Ali Eslami1, Danilo Jimenez Rezende1, and
Olaf Ronneberger1
Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
DeepMind, London, UK"
d488dad9fa81817c85a284b09ebf198bf6b640f9,FCHD: A fast and accurate head detector,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
FCHD: A fast and accurate head detector
Aditya Vora, Johnson Controls Inc."
d4a7259340ece685b9dacb390eea10c6684a05b3,Object Detection based on Region Decomposition and Assembly,"Object Detection based on Region Decomposition and Assembly
Computer Vision Lab., Department of Computer Science and Engineering
Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon, 22012, Korea
Seung-Hwan Bae"
d4343b3c7122f57ecc0bb39887406dad5214dd57,Effective and Efficient Optimization Methods for Kernel Based Classification Problems,"Effective and Efficient Optimization
Methods for Kernel Based Classification
Problems
Aditya Tayal
A thesis
presented to the University of Waterloo
in fulfilment of the
thesis requirement for the degree of
Doctor of Philosophy
Computer Science
Waterloo, Ontario, Canada, 2014
©Aditya Tayal 2014"
d4e4369babdba158bfdce1b605f92d6b1b665be4,The amygdala and the relevance detection theory of autism: an evolutionary perspective,"REVIEW ARTICLE
published: 30 December 2013
doi: 10.3389/fnhum.2013.00894
The amygdala and the relevance detection theory of autism:
n evolutionary perspective
Tiziana Zalla1* and Marco Sperduti 2,3
Institut Jean Nicod, Centre National de la Recherche Scientifique, Ecole Normale Supérieure, Paris, France
Laboratoire Mémoire et Cognition, Institut de Psychologie, Université Paris Descartes, Boulogne-Billancourt, France
Inserm U894, Centre de Psychiatrie et Neurosciences, Université Paris Descartes, Paris, France
Edited by:
Corrado Corradi-Dell’Acqua, University
of Geneva, Switzerland
Reviewed by:
Sebastian B. Gaigg, City University
London, UK
Bhismadev Chakrabarti, University of
Reading, UK
Danilo Bzdok, Research Center Jülich,
Germany
*Correspondence:"
d45fbd818f032566e9e8f8bdc0f658cdd6873e8f,Full-body High-resolution Anime Generation with Progressive Structure-conditional Generative Adversarial Networks,"Full-body High-resolution Anime Generation
with Progressive Structure-conditional
Generative Adversarial Networks
Koichi Hamada, Kentaro Tachibana, Tianqi Li,
Hiroto Honda, and Yusuke Uchida
DeNA Co., Ltd., Tokyo, Japan"
d4207e8fdb053e81f4570ca2da9f6e7b73656ccf,Image Saliency Applied to Infrared Images for Unmanned Maritime Monitoring,"Image Saliency Applied to Infrared Images for
Unmanned Maritime Monitoring
Gon¸calo Cruz1 and Alexandre Bernardino2
Research Center, Portuguese Air Force Academy, Sintra, Portugal
Computer and Robot Vision Laboratory, Instituto de Sistemas e Rob´otica,
Instituto Superior T´ecnico, Lisboa, Portugal"
d4b88be6ce77164f5eea1ed2b16b985c0670463a,A Survey of Different 3 D Face Reconstruction Methods,"TECHNICAL REPORT JAN.15.2016
A Survey of Different 3D Face Reconstruction
Methods
Amin Jourabloo
Department of Computer Science and Engineering"
d4a44459849ec8fcd51f9b5eee196e197d15e005,Novel Simulation Framework of Three-Dimensional Skull BioMetric Measurement,"Shibab A. Hameed et al / International Journal on Computer Science and Engineering Vol.1(3), 2009, 269-274
Novel Simulation Framework of Three-Dimensional Skull Bio-Metric Measurement
Shihab A. Hameed, B.B.Zaidan, A.A.Zaidan, A.W. Naji and Omar Faroq"
d4901683e2c2552fc2d62d4eb3b1f5d5fa60a5ff,ScaleNet: Scale Invariant Network for Semantic Segmentation in Urban Driving Scenes,
d458c49a5e34263c95b3393386b5d76ba770e497,A Comparative Analysis of Gender Classification Techniques,"Middle-East Journal of Scientific Research 20 (1): 01-13, 2014
ISSN 1990-9233
© IDOSI Publications, 2014
DOI: 10.5829/idosi.mejsr.2014.20.01.11434
A Comparative Analysis of Gender Classification Techniques
Sajid Ali Khan, Maqsood Ahmad, Muhammad Nazir and Naveed Riaz
Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad, Pakistan"
d4ced2086ccd9259ade8fabdba14e0e4d9fc0c40,A Mobile Imaging System for Medical Diagnostics,"A Mobile Imaging System for Medical
Diagnostics
Sami Varjo and Jari Hannuksela
The Center for Machine Vision Research
Department of Computer Science and Engineering
P.O. Box 4500, FI-90014 University of Oulu"
d48bd355d091e7ae75ade4e878fe346741e7da1a,Can You Spot the Semantic Predicate in this Video ?,"Can You Spot the Semantic Predicate in this Video?
Christopher Reale, Claire Bonial, Heesung Kwon and Clare R. Voss
U.S. Army Research Lab, Adelphi, Maryland 20783
{claire.n.bonial.civ, heesung.kwon.civ,"
d41c11ebcb06c82b7055e2964914b9af417abfb2,CDI-Type I : Unsupervised and Weakly-Supervised Discovery of Facial Events 1,"CDI-Type I: Unsupervised and Weakly-Supervised
Introduction
Discovery of Facial Events
The face is one of the most powerful channels of nonverbal communication. Facial expression has been a
focus of emotion research for over a hundred years [12]. It is central to several leading theories of emotion
[18, 31, 54] and has been the focus of at times heated debate about issues in emotion science [19, 24, 50].
Facial expression figures prominently in research on almost every aspect of emotion, including psychophys-
iology [40], neural correlates [20], development [11], perception [4], addiction [26], social processes [30],
depression [49] and other emotion disorders [55], to name a few. In general, facial expression provides cues
bout emotional response, regulates interpersonal behavior, and communicates aspects of psychopathology.
Because of its importance to behavioral science and the emerging fields of computational behavior
science, perceptual computing, and human-robot interaction, significant efforts have been applied toward
developing algorithms that automatically detect facial expression. With few exceptions, previous work on
facial expression relies on supervised approaches to learning (i.e. event categories are defined in advance
in labeled training data). While supervised learning has important advantages, two critical limitations may
e noted. One, because labeling facial expression is highly labor intensive, progress in automated facial
expression recognition and analysis is slowed. For the most detailed and comprehensive labeling or coding
systems, such as Facial Action Coding System (FACS), three to four months is typically required to train
coder (’coding’ refers to the labeling of video using behavioral descriptors). Once trained, each minute
of video may require 1 hour or more to code [9]. No wonder relatively few databases are yet available,"
d42142285c46207a16bd4294e437d504e419a9b7,Varying image description tasks : spoken versus written descriptions,"Varying image description tasks: spoken versus written descriptions
Emiel van Miltenburg
Vrije Universiteit Amsterdam
Ruud Koolen
Tilburg University
Emiel Krahmer
Tilburg University"
d44ca9e7690b88e813021e67b855d871cdb5022f,"Selecting, Optimizing and Fusing `Salient' Gabor Features for Facial Expression Recognition","QUT Digital Repository:
http://eprints.qut.edu.au/
Zhang, Ligang and Tjondronegoro, Dian W. (2009) Selecting, optimizing and
fusing ‘salient’ Gabor features for facial expression recognition. In: Neural
Information Processing (Lecture Notes in Computer Science), 1-5 December
009, Hotel Windsor Suites Bangkok, Bangkok.
©  Copyright 2009 Springer-Verlag GmbH Berlin Heidelberg"
d46b790d22cb59df87f9486da28386b0f99339d3,Learning Face Deblurring Fast and Wide,"Learning Face Deblurring Fast and Wide
Meiguang Jin
University of Bern
Switzerland
Michael Hirsch†
Amazon Research
Germany
Paolo Favaro
University of Bern
Switzerland"
d4712c75a1a51ecbc74e362747926a16a2cd36ed,Automated Human Recognition by Gait using Neural Network,"Image Processing Theory, Tools & Applications
Automated Human Recognition by Gait using Neural Network
Jang-Hee Yoo
Information Security
Research Division, ETRI
S. Korea
Ki-Young Moon
Information Security
Research Division, ETRI
S. Korea"
d497b9e50dc2aacfb1693ca4de6ebf904404d98d,Patch Based Approaches for the Recognition of Visual Object Classes - A Survey,"ALBERT-LUDWIGS-UNIVERSIT ¨AT FREIBURG
INSTITUT F ¨UR INFORMATIK
Lehrstuhl f¨ur Mustererkennung und Bildverarbeitung
Patch Based Approaches for the Recognition of Visual Object
Classes - A Survey
Internal Report 2/06
Alexandra Teynor
November, 2006"
d444368421f456baf8c3cb089244e017f8d32c41,CNN for IMU assisted odometry estimation using velodyne LiDAR,"CNN for IMU Assisted Odometry Estimation using Velodyne LiDAR
Martin Velas, Michal Spanel, Michal Hradis, and Adam Herout"
d4f8168242f688af29bcbbe1cc5aec7cd12a601c,Edinburgh Research Explorer Visually Grounded Meaning Representations,"Visually Grounded Meaning Representations
Citation for published version:
Silberer, C, Ferrari, V & Lapata, M 2016, 'Visually Grounded Meaning Representations' IEEE Transactions
on Pattern Analysis and Machine Intelligence. DOI: 10.1109/TPAMI.2016.2635138
Digital Object Identifier (DOI):
0.1109/TPAMI.2016.2635138
Link:
Link to publication record in Edinburgh Research Explorer
Document Version:
Peer reviewed version
Published In:
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s)
nd / or other copyright owners and it is a condition of accessing these publications that users recognise and
bide by the legal requirements associated with these rights.
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The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer
ontent complies with UK legislation. If you believe that the public display of this file breaches copyright please
ontact providing details, and we will remove access to the work immediately and"
d4dd4600e8f4ecfd11fa4a4a702b1f08bc9ec6f7,Combining intention and emotional state inference in a dynamic neural field architecture for human-robot joint action,"Special issue on Grounding Emotions in Robots
Combining intention and emotional
state inference in a dynamic neural
field architecture for human-robot
joint action
Adaptive Behavior
016, Vol. 24(5) 350–372
Ó The Author(s) 2016
Reprints and permissions:
sagepub.co.uk/journalsPermissions.nav
DOI: 10.1177/1059712316665451
db.sagepub.com
Rui Silva1, Luı´s Louro1, Tiago Malheiro1, Wolfram Erlhagen2 and
Estela Bicho1"
d409d8978034de5e5e8f9ee341d4a00441e3d05f,Annual research review: re-thinking the classification of autism spectrum disorders.,"Journal of Child Psychology and Psychiatry 53:5 (2012), pp 490–509
doi:10.1111/j.1469-7610.2012.02547.x
Annual Research Review: Re-thinking the
lassification of autism spectrum disorders
Center for Autism and the Developing Brain, Weill-Cornell Medical College and New York Presbyterian Hospital/
Westchester Division, White Plains, NY, USA
Catherine Lord and Rebecca M. Jones
Background: The nosology of autism spectrum disorders (ASD) is at a critical point in history as the
field seeks to better define dimensions of social-communication deficits and restricted/repetitive
ehaviors on an individual
level for both clinical and neurobiological purposes. These different
dimensions also suggest an increasing need for quantitative measures that accurately map their dif-
ferences, independent of developmental factors such as age, language level and IQ. Method: Psycho-
metric measures, clinical observation as well as genetic, neurobiological and physiological research
from toddlers, children and adults with ASD are reviewed. Results: The question of how to conceptu-
lize ASDs along dimensions versus categories is discussed within the nosology of autism and the
proposed changes to the DSM-5 and ICD-11. Differences across development are incorporated into the
new classification frameworks. Conclusions: It is crucial to balance the needs of clinical practice in
ASD diagnostic systems, with neurobiologically based theories that address the associations between
social-communication and restricted/repetitive dimensions in individuals. Clarifying terminology,"
d8af6a45eaea68adda8597ae65f91ece152f7b21,Sparse and Dense Data with CNNs: Depth Completion and Semantic Segmentation,"Sparse and Dense Data with CNNs:
Depth Completion and Semantic Segmentation
Maximilian Jaritz1, 2, Raoul de Charette1, Emilie Wirbel2, Xavier Perrotton2, Fawzi Nashashibi1
{maximilian.jaritz, raoul.de-charette,
Inria RITS Team
{emilie.wirbel,
Valeo"
d809c0ab068861c139a544e5d8eeaa73cc8a3f6b,Monocular Semantic Occupancy Grid Mapping with Convolutional Variational Encoder-Decoder Networks,"Monocular Semantic Occupancy Grid Mapping
with Convolutional Variational Encoder-Decoder Networks
Chenyang Lu1, Ren´e van de Molengraft2, and Gijs Dubbelman1"
d8b568392970b68794a55c090c4dd2d7f90909d2,PDA Face Recognition System Using Advanced Correlation Filters,"PDA Face  Recognition  System
Using  Advanced  Correlation
Filters
Chee  Kiat  Ng
Advisor:  Prof.  Khosla/Reviere"
d850aff9d10a01ad5f1d8a1b489fbb3998d0d80e,Recognizing and Segmenting Objects in the Presence of Occlusion and Clutter,"UNIVERSITY OF CALIFORNIA,
IRVINE
Recognizing and Segmenting Objects in the Presence of Occlusion and Clutter
DISSERTATION
submitted in partial satisfaction of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
in Computer Science
Golnaz Ghiasi
Dissertation Committee:
Professor Charless Fowlkes, Chair
Professor Deva Ramanan
Professor Alexander Ihler"
d8b3bebe39f5f69d68cdb5c04b44aba3f9b5ae02,Active Vision System with Human Detection Using RGB-D images and machine learning algorithms,"Active Vision System with Human Detection
Using RGB-D images and machine learning algorithms
Master’s thesis in Applied Physics and in Biomedical Engineering
ANDREAS BERGGREN
ERIC BJ ¨ORKLUND
Department of Applied Mechanics
Division of Vehicle Engineering and Autonomous Systems
CHALMERS UNIVERSITY OF TECHNOLOGY
Gothenburg, Sweden 2012
Master’s thesis 2012:28"
d8c04365ed0627a5043996cdd26c1a56b5a630b8,Learning Monocular Depth Estimation with Unsupervised Trinocular Assumptions,"Learning monocular depth estimation with unsupervised trinocular assumptions
Matteo Poggi, Fabio Tosi, Stefano Mattoccia
University of Bologna, Department of Computer Science and Engineering
Viale del Risorgimento 2, Bologna, Italy
{m.poggi, fabio.tosi5,"
d8f0bda19a345fac81a1d560d7db73f2b4868836,Activity Understanding and Labeling in Natural Videos,"UNIVERSITY OF CALIFORNIA
RIVERSIDE
Online Activity Understanding and Labeling in Natural Videos
A Dissertation submitted in partial satisfaction
of the requirements for the degree of
Doctor of Philosophy
Computer Science
Md Mahmudul Hasan
August 2016
Dissertation Committee:
Dr. Amit K. Roy-Chowdhury, Chairperson
Dr. Eamonn Keogh
Dr. Evangelos Christidis
Dr. Christian Shelton"
d861c658db2fd03558f44c265c328b53e492383a,Automated face extraction and normalization of 3D Mesh Data,"Automated Face Extraction and Normalization of 3D Mesh Data
Jia Wu1, Raymond Tse2, Linda G. Shapiro1"
d881a59d00971c754e02bfaaf4c48ec6dfbc1343,Neighborhood Sensitive Mapping for Zero-Shot Classification using Independently Learned Semantic Embeddings,"Neighborhood Sensitive Mapping for Zero-Shot
Classification using Independently Learned
Semantic Embeddings
Gaurav Singh1, Fabrizio Silvestri2, and John Shawe-Taylor1
UCL, UK
Yahoo, UK"
d813ec3a3442f2885b76ac0133c4c5d76f9f8065,Panoptic Studio: A Massively Multiview System for Social Interaction Capture,"Panoptic Studio: A Massively Multiview System
for Social Interaction Capture
Hanbyul Joo, Tomas Simon, Xulong Li, Hao Liu, Lei Tan, Lin Gui, Sean Banerjee, Timothy Godisart,
Bart Nabbe, Iain Matthews, Takeo Kanade, Shohei Nobuhara, and Yaser Sheikh"
d88eb94d7054d2668b1a8dfa311721f37ae1f059,Straight to the Facts: Learning Knowledge Base Retrieval for Factual Visual Question Answering,"Straight to the Facts: Learning Knowledge Base
Retrieval for Factual Visual Question Answering
Medhini Narasimhan, Alexander G. Schwing
University of Illinois Urbana-Champaign"
d8b8e165279ca2091d5af1440ed974db4792250f,Mean Response-Time Minimization of a Soft-Cascade Detector,
d83ae5926b05894fcda0bc89bdc621e4f21272da,Frugal Forests : Learning a Dynamic and Cost Sensitive Feature Extraction Policy for Anytime Activity Classification,"The Thesis committee for Joshua Allen Kelle certifies that this is the approved
version of the following thesis:
Frugal Forests: Learning a Dynamic and Cost Sensitive
Feature Extraction Policy for Anytime Activity Classification
APPROVED BY
SUPERVISING COMMITTEE:
Kristen Grauman, Supervisor
Peter Stone"
d81dbc2960e527e91c066102aabdaf9eb8b15f85,Deep Directed Generative Models with Energy-Based Probability Estimation,"Deep Directed Generative Models
with Energy-Based Probability Estimation
Taesup Kim, Yoshua Bengio∗
Department of Computer Science and Operations Research
Université de Montréal
Montréal, QC, Canada"
d8671247f6188620c6e382ffcd15d3e909647c63,Multicamera human detection and tracking supporting natural interaction with large-scale displays,"DOI 10.1007/s00138-012-0408-6
ORIGINAL PAPER
Multicamera human detection and tracking supporting natural
interaction with large-scale displays
Xenophon Zabulis · Dimitris Grammenos ·
Thomas Sarmis · Konstantinos Tzevanidis ·
Pashalis Padeleris · Panagiotis Koutlemanis ·
Antonis A. Argyros
Received: 8 March 2011 / Revised: 9 January 2012 / Accepted: 17 January 2012
© Springer-Verlag 2012"
d8904955fa93ad434f5156235ac94452eec57f64,Facial expression recognition in the wild based on multimodal texture features,"Downloaded From: https://www.spiedigitallibrary.org/journals/Journal-of-Electronic-Imaging on 4/20/2018 Terms of Use: https://www.spiedigitallibrary.org/terms-of-use
FacialexpressionrecognitioninthewildbasedonmultimodaltexturefeaturesBoSunLiandongLiGuoyanZhouJunHeBoSun,LiandongLi,GuoyanZhou,JunHe,“Facialexpressionrecognitioninthewildbasedonmultimodaltexturefeatures,”J.Electron.Imaging25(6),061407(2016),doi:10.1117/1.JEI.25.6.061407."
d827c72d6c9e35066b40bd205bbd71ce487a1c39,ENSEMBLE OF FACE / EYE DETECTORS FOR ACCURATE AUTOMATIC FACE DETECTION 1,"International Journal of Latest Research in Science
Volume 4, Issue 3: Page No.8-18, May-June 2015
http://www.mnkjournals.com/ijlrst.htm
nd Technology           ISSN (Online):2278-5299
ENSEMBLE OF FACE/EYE DETECTORS FOR
ACCURATE AUTOMATIC FACE DETECTION
Loris Nanni, 2Alessandra Lumini, 3Sheryl Brahnam
Department of Information Engineering at the University of Padua, Padua, Italy
DISI, University of Bologna, Cesena, Italy
Computer Information Systems, Missouri State University, USA"
d853f490cd3d552d3a6d4a90d1b76c84e746a061,Hierarchically grouped 2D local features applied to edge contour localisation,"Liu, Yuan (2016) Hierarchically grouped 2D local features applied to
edge contour localisation. PhD thesis
http://theses.gla.ac.uk/7335/
Copyright and moral rights for this thesis are retained by the author
A copy can be downloaded for personal non-commercial research or
study, without prior permission or charge
This thesis cannot be reproduced or quoted extensively from without first
obtaining permission in writing from the Author
The content must not be changed in any way or sold commercially in any
format or medium without the formal permission of the Author
When referring to this work, full bibliographic details including the
uthor, title, awarding institution and date of the thesis must be given.
Glasgow Theses Service
http://theses.gla.ac.uk/"
d89cfed36ce8ffdb2097c2ba2dac3e2b2501100d,Robust Face Recognition via Multimodal Deep Face Representation,"Robust Face Recognition via Multimodal Deep
Face Representation
Changxing Ding, Student Member, IEEE, Dacheng Tao, Fellow, IEEE"
d8f7b26d25a026fe43487b6f77993e11b8b333e0,Photo Indexing and Retrieval based on Content and Context,"PhD Dissertation
International Doctorate School in Information and
Communication Technologies
DISI - University of Trento
Photo Indexing and Retrieval
ased on Content and Context
Mattia Broilo
Advisor:
Prof. Francesco G. B. De Natale
Universit`a degli Studi di Trento
February 2011"
d80564cea654d11b52c0008891a0fd2988112049,Semi-supervised Conditional GANs,"Semi-supervised Conditional GANs
Kumar Sricharan∗1, Raja Bala1, Matthew Shreve1,
Hui Ding1, Kumar Saketh2, and Jin Sun1
Interactive and Analytics Lab, Palo Alto Research Center, Palo Alto, CA
Verizon Labs, Palo Alto, CA
August 22, 2017"
d8cfc7d9b13a5e1b21fc81e9f14697ac445c3698,Dynamic and Robust Object Tracking for Activity Recognition. (Suivi dynamique et robuste d'objets pour la reconnaissance d'activités),"Dynamic and Robust Object Tracking for Activity
Recognition
Duc Phu Chau
To cite this version:
Duc Phu Chau. Dynamic and Robust Object Tracking for Activity Recognition. Computer Vision
nd Pattern Recognition [cs.CV]. Institut National de Recherche en Informatique et en Automatique
(INRIA), 2012. English. <tel-00695567>
HAL Id: tel-00695567
https://tel.archives-ouvertes.fr/tel-00695567
Submitted on 22 Nov 2012
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
d888895cd56d336aa1367fac8072da782bdbc0fb,AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks,"AttnGAN: Fine-Grained Text to Image Generation
with Attentional Generative Adversarial Networks
Tao Xu∗1, Pengchuan Zhang2, Qiuyuan Huang2,
Han Zhang3, Zhe Gan4, Xiaolei Huang1, Xiaodong He2
Lehigh University 2Microsoft Research 3Rutgers University 4Duke University
{tax313, {penzhan, qihua,"
d8e8730231dc0e77f3ad61385f918df3d93bd266,Efficient face detection method with eye region judgment,"Lin and Lin EURASIP Journal on Image and Video Processing 2013, 2013:34
http://jivp.eurasipjournals.com/content/2013/1/34
R ES EAR CH
Efficient face detection method with eye region
judgment
Chun-Fu Lin1,2 and Sheng-Fuu Lin1*
Open Access"
d84568d42a02b6d365889451f208f423edb1f0f3,Age Synthesis and Estimation From Face Image Ms,"www.ijecs.in
International Journal Of Engineering And Computer Science ISSN:2319-7242
Volume 3 Issue 4 April, 2014 Page No. 5462-5466
Age Synthesis and Estimation From Face Image
Ms. Deepali R. gadbail1, Prof. S.S. Dhande2, Prof.Kanchan M. Pimple3
M s. Deepali R Gadbail,
Computer Science and Engineering Department,
Sipna COET,Amravati.
Prof. S. S. Dhande,
Computer Science and Engineering Department,
Sipna COET,Amravati.
Prof.Kanchan M . Pimple,
IBSS College of engg. & tech.,Amravati"
d833c48334e906537f21757b6f9fa44da66f6c76,MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement,"MEMC-Net: Motion Estimation and Motion
Compensation Driven Neural Network for
Video Interpolation and Enhancement
Wenbo Bao, Wei-Sheng Lai, Xiaoyun Zhang, Zhiyong Gao, and Ming-Hsuan Yang"
d865c5e85191cfc0da714290d8583a2fb1179fd4,"Learning Hierarchical Space Tiling for Scene Modeling, Parsing and Attribute Tagging","Learning Hierarchical Space Tiling for Scene
Modeling, Parsing and Attribute Tagging
Shuo Wang, Yizhou Wang, and Song-Chun Zhu"
d83c5c0fa648de9ea0f8f6f92ce2096d5fa04808,Multi-appearance segmentation and extended 0-1 programming for dense small object tracking,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, JULY 2016
Multi-appearance Segmentation and Extended 0-1
Program for Dense Small Object Tracking
Longtao Chen, Jing Lou, Wei Zhu, Qingyuan Xia, Mingwu Ren"
d87ccfc42cf6a72821d357aab0990e946918350b,Exploiting the Potential of Standard Convolutional Autoencoders for Image Restoration by Evolutionary Search,"Exploiting the Potential of Standard Convolutional Autoencoders
for Image Restoration by Evolutionary Search
Masanori Suganuma 1 2 Mete Ozay 1 Takayuki Okatani 1 2"
d88d43504f2be7e26ab1ec731dfc8af6e407aa59,"Model-based Optical Flow: Layers, Learning, and Geometry","Model-based Optical Flow:
Layers, Learning, and Geometry
Dissertation
der Mathematisch-Naturwissenschaftlichen Fakult¨at
der Eberhard Karls Universit¨at T¨ubingen
zur Erlangung des Grades eines
Doktors der Naturwissenschaften
(Dr. rer. nat.)
vorgelegt von
Dipl.-Ing. Jonas Wulff
us Warburg (Westfalen)
T¨ubingen"
d84263e22c7535cb1a2a72c88780d5a407bd9673,Stability of Scattering Decoder For Nonlinear Diffractive Imaging,"Stability of Scattering Decoder for Nonlinear Diffractive Imaging
Yu Sun1 and Ulugbek S. Kamilov1,2
Department of Computer Science & Engineering, Washington University in St Louis.
Department of Electrical & Systems Engineering, Washington University in St. Louis"
d88e3d5ca820cb240de4b662f0a6fd1172a678c7,Image Quality-based Adaptive Illumination Normalisation for Face Recognition,"Harin Sellahewa and Sabah A. Jassim, ""Image quality-based adaptive illumination normalisation for face recognition"",
Proc.  SPIE  7306,  Optics  and  Photonics  in  Global  Homeland  Security  V  and  Biometric  Technology  for  Human
Identification VI, 73061V (May 05, 2009); doi:10.1117/12.819087; http://dx.doi.org/10.1117/12.819087
Copyright 2009 Society of Photo Optical Instrumentation Engineers. One print or electronic copy may be made for
personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for
ommercial purposes, or modification of the content of the paper are prohibited.” (http://spie.org/x1125.xml)"
d86478cb6c79d8b6c74e6caeb20c7456266cf50e,Statistical Transformation Techniques for Face Verification Using Faces Rotated in Depth,"STATISTICAL TRANSFORMATION
TECHNIQUES FOR FACE
VERIFICATION USING FACES
ROTATED IN DEPTH
Conrad Sanderson (a)
Samy Bengio (b)
IDIAP–RR 04-04
FEBRUARY 2004
SUBMITTED FOR PUBLICATION
D a l l e M o l l e
I n s t i t u t e
f o r P e r c e p t u a l A r t i f i c i a l
Intelligence • P.O.Box 592 •
Martigny • Valais • Switzerland
phone +41 − 27 − 721 77 11
+41 − 27 − 721 77 12
e-mail
internet http://www.idiap.ch"
d8b58c5b403dc28437af8244ec812efdfbc6b2e0,MVOR: A Multi-view RGB-D Operating Room Dataset for 2D and 3D Human Pose Estimation,"MVOR: A Multi-view RGB-D Operating Room
Dataset for 2D and 3D Human Pose Estimation
Vinkle Srivastav1, Thibaut Issenhuth1, Abdolrahim Kadkhodamohammadi1,
Michel de Mathelin1, Afshin Gangi1,2, and
Nicolas Padoy1
ICube, University of Strasbourg, CNRS, IHU Strasbourg, France
Radiology Department, University Hospital of Strasbourg, France"
d8abf01fce0d44665949e7a73716fff7731fa6da,Places: An Image Database for Deep Scene Understanding,"Places: An Image Database for Deep Scene
Understanding
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Antonio Torralba and Aude Oliva"
d8db46f1775641051d8596dad3d37d1d731558f7,Survey on Deep Learning Techniques for Person Re-Identification Task,
5bfc32d9457f43d2488583167af4f3175fdcdc03,ijsr . net Local Gray Code Pattern ( LGCP ) : A Robust Feature Descriptor for Facial Expression Recognition,"International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064
Local Gray Code Pattern (LGCP): A Robust
Feature Descriptor for Facial Expression
Recognition
Mohammad Shahidul Islam
Atish Dipankar University of Science & Technology, School, Department of Computer Science and Engineering, Dhaka, Bangladesh."
5bed19eaba0a6667e16859de8b78173e99568e1f,Deep Neural Networks In Fully Connected CRF For Image Labeling With Social Network Metadata,"Deep Neural Networks In Fully Connected CRF For Image Labeling With Social
Network Metadata
Chengjiang Long
Kitware Inc.
Roddy Collins
Eran Swears
Anthony Hoogs
{chengjiang.long, roddy.collins, eran.swears,
8 Corporate Drive, Clifton Park, NY 12065"
5b01d4338734aefb16ee82c4c59763d3abc008e6,A Robust Face Recognition Algorithm Based on Kernel Regularized Relevance-Weighted Discriminant Analysis,"DI WU: A ROBUST FACE RECOGNITION ALGORITHM BASED ON KERNEL REGULARIZED RELEVANCE …
A Robust Face Recognition Algorithm Based on Kernel Regularized
Relevance-Weighted Discriminant Analysis
Di WU 1, 2
2 Hunan Provincial Key Laboratory of Wind Generator and Its Control, Hunan Institute of Engineering, Xiangtan, China.
College of Electrical and Information Engineering,
[e-mail:
I. INTRODUCTION
interface  and  security
recognition
their
this  paper,  we  propose  an  effective"
5b14abbea83270282ef94fcf3f3a73e7d8fee023,Experiments about the Generalization Ability of Common Vector based methods for Face Recognition,"Experiments about the Generalization Ability of
Common Vector based methods for Face
Recognition ?
Marcelo Armengot, Francesc J. Ferri, and Wladimiro D´ıaz
Dept. d’Inform`atica, Universitat de Val`encia
Dr Moliner, 50 46100 Burjassot, Spain"
5b25b9053ceafe1cf8258d8daa818a2da80c800f,Assigning affinity-preserving binary hash codes to images,"Assigning af‌f‌inity-preserving
inary hash codes to images
Jason Filippou
Varun Manjunatha
June 10, 2014"
5bb87c7462c6c1ec5d60bde169c3a785ba5ea48f,Targeting Ultimate Accuracy: Face Recognition via Deep Embedding,"Targeting Ultimate Accuracy: Face Recognition via Deep Embedding
Jingtuo Liu     Yafeng Deng     Tao Bai     Zhengping Wei     Chang Huang
Baidu Research – Institute of Deep Learning"
5b3dc81a490b1d9e69d7be20c4e8e1de886b5ca3,Improving Object Localization with Fitness NMS and Bounded IoU Loss,"Improving Object Localization with Fitness NMS and Bounded IoU Loss
Lachlan Tychsen-Smith, Lars Petersson
CSIRO (Data61)
CSIRO-Synergy Building, Acton, ACT, 2601"
5bc5cfc2622f6b0a0003d7b115726d075205a2cc,AUTO LANDING PROCESS FOR AUTONOMOUS FLYING ROBOT BY USING IMAGE PROCESSING BASED ON EDGE DETECTION,"AUTO LANDING PROCESS FOR
AUTONOMOUS FLYING ROBOT BY USING
IMAGE PROCESSING BASED ON EDGE
DETECTION
Bahram Lavi Sefidgari1 and Sahand Pourhassan Shamchi2
Department of Computer Engineering, EMU, Famagusta, Cyprus
Department of Mechanical Engineering, EMU, Famagusta, Cyprus"
5b516862b93052cc2335d78a832641a38304beed,Foresee: Attentive Future Projections of Chaotic Road Environments with Online Training,"Foresee: Attentive Future Projections of Chaotic
Road Environments with Online Training
Indraprastha Institute of Information Technology, Delhi
Anil Sharma and Prabhat Kumar
{anils,"
5b73bc1660b7eef0c12694db935854dba0829f9e,A Probabilistic Model for Face Transformation with Application to Person Identification,"EURASIP Journal on Applied Signal Processing 2004:4, 510–521
(cid:1) 2004 Hindawi Publishing Corporation
A Probabilistic Model for Face Transformation
with Application to Person Identification
Florent Perronnin
Multimedia Communications Department, Institut Eur´ecom, BP 193, 06904 Sophia Antipolis Cedex, France
Email:
Jean-Luc Dugelay
Multimedia Communications Department, Institut Eur´ecom, BP 193, 06904 Sophia Antipolis Cedex, France
Email:
Kenneth Rose
Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA 93106-9560, USA
Email:
Received 30 October 2002; Revised 23 June 2003
A novel approach for content-based image retrieval and its specialization to face recognition are described. While most face recog-
nition techniques aim at modeling faces, our goal is to model the transformation between face images of the same person. As a
global face transformation may be too complex to be modeled directly, it is approximated by a collection of local transforma-
tions with a constraint that imposes consistency between neighboring transformations. Local transformations and neighborhood
onstraints are embedded within a probabilistic framework using two-dimensional hidden Markov models (2D HMMs). We fur-
ther introduce a new ef‌f‌icient technique, called turbo-HMM (T-HMM) for approximating intractable 2D HMMs. Experimental"
5bcff482bd9652420f8f6b0e6e58ab59a562046e,Bit-Scalable Deep Hashing With Regularized Similarity Learning for Image Retrieval and Person Re-Identification,"Bit-Scalable Deep Hashing with Regularized
Similarity Learning for Image Retrieval and Person
Re-identification
Ruimao Zhang, Liang Lin, Rui Zhang, Wangmeng Zuo, and Lei Zhang"
5b818c73ce5681e523d6fe9ed8603c7afc0a9089,Improving Shape Retrieval by Spectral Matching and Meta Similarity,"Improving Shape retrieval by Spectral
Matching and Meta Similarity
Amir Egozi (BGU),
Yosi Keller (BIU)
nd Hugo Guterman (BGU)
Department of Electrical and Computer Engineering,
Ben-Gurion University of the Negev
/ 21"
5b3eee498ba40423536c3ca487f7ac10d94092d2,Multimodal fusion of polynomial classifiers for automatic person recognition,"Multimodal fusion of polynomial classifiers for automatic person
recognition
Charles C. Brouna and Xiaozheng Zhangb
Motorola Labs – Human Interface Lab, Phoenix, Arizona
The Georgia Institute of Technology, Atlanta, Georgia"
5b9d9f5a59c48bc8dd409a1bd5abf1d642463d65,An evolving spatio-temporal approach for gender and age group classification with Spiking Neural Networks,"(will be inserted by the editor)
An evolving spatio-temporal approach for gender and age
group classification with Spiking Neural Networks
Fahad Bashir Alvi, Russel Pears, Nikola Kasabov
Received: date / Accepted: date"
5bae9822d703c585a61575dced83fa2f4dea1c6d,MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking,"MOTChallenge 2015:
Towards a Benchmark for Multi-Target Tracking
Laura Leal-Taix´e∗, Anton Milan∗, Ian Reid, Stefan Roth, and Konrad Schindler"
5be74c6fa7f890ea530e427685dadf0d0a371fc1,Deep Co-attention based Comparators For Relative Representation Learning in Person Re-identification,"Deep Co-attention based Comparators For Relative
Representation Learning in Person Re-identification
Lin Wu, Yang Wang, Junbin Gao, Dacheng Tao, Fellow, IEEE"
5b6c603fba0a66fb3c037632079bdca82ec3bf91,Alternating Co-Quantization for Cross-Modal Hashing,"Alternating Co-Quantization for Cross-modal Hashing
Go Irie
Hiroyuki Arai
Yukinobu Taniguchi
NTT Corporation
{irie.go, arai.hiroyuki,"
5bfde88767331eee8f06b296791d21e2260deee0,Les modèles génératifs en classification supervisée et applications à la catégorisation d'images et à la fiabilité industrielle. (Generative models in supervised statistical learning with applications to digital image categorization and structural reliability),"Universit´e Joseph Fourier – Grenoble 1
Les mod`eles g´en´eratifs en classification
supervis´ee et applications `a la
at´egorisation d’images et `a la fiabilit´e
industrielle.
TH`ESE
Soutenance en 2005
pour l’obtention du
Doctorat de l’universit´e Joseph Fourier – Grenoble 1
(sp´ecialit´e math´ematiques appliqu´ees)
Guillaume Bouchard
Composition du jury
Directeur de thèse :
Gilles Celeux
INRIA
Co-directeur de thèse : William Triggs CNRS
Institut National Recherche en Informatique et Automatique"
5bf9493564d1ed173aee4dc701d4e62d5f926fe3,Bonnet: An Open-Source Training and Deployment Framework for Semantic Segmentation in Robotics using CNNs,"Bonnet: An Open-Source Training and Deployment Framework
for Semantic Segmentation in Robotics using CNNs
Andres Milioto
Cyrill Stachniss"
5b7870359b8b9934453f8e772ab7c3f9df3a5035,LF Indoor Location and Identification System,"LF Indoor Location and Identification System
Antti Ropponen, Matti Linnavuo, Raimo Sepponen
Helsinki University of Technology
Department of Electronics
PL 3340, 02015 TKK Finland
Emails:"
5b94093939ac42aba54ab41eb1725aeba1bd5c34,Aalborg Universitet RGB-D Segmentation of Poultry,"Aalborg Universitet
RGB-D Segmentation of Poultry Entrails
Philipsen, Mark Philip; Jørgensen, Anders; Guerrero, Sergio Escalera; Moeslund, Thomas B.
Published in:
IX International Conference on Articulated Motion and Deformable Objects
DOI (link to publication from Publisher):
0.1007/978-3-319-41778-3_17
Publication date:
Document Version
Accepted author manuscript, peer reviewed version
Link to publication from Aalborg University
Citation for published version (APA):
Philipsen, M. P., Jørgensen, A., Guerrero, S. E., & Moeslund, T. B. (2016). RGB-D Segmentation of Poultry
Entrails. In IX International Conference on Articulated Motion and Deformable Objects (pp. 168-174). Springer.
(Lecture Notes in Computer Science, Vol. 9756). DOI: 10.1007/978-3-319-41778-3_17
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5b0552a8e0ffdf1b6e7f2573640f888815391dec,Part-level fully convolutional networks for pedestrian detection,"978-1-5090-4117-6/17/$31.00 ©2017 IEEE
ICASSP 2017"
5ba51674897afa2a1bc0646fddada7510bd9c0ce,Video-based face recognition evaluation in the CHIL project - Run 1,"Video-Based Face Recognition Evaluation
in the CHIL Project – Run 1
Hazım Kemal Ekenel
Universität Karlsruhe (TH)
Interactive Systems Labs
76131, Karlsruhe, Germany
Aristodemos Pnevmatikakis
Athens Information Technology
Autonomic and Grid Computing Group
9002, Peania, Athens, Greece"
5b9c849c2acbdea6e3cfc730def4f083f169521c,A Method for Face Detection based on Wavelet Transform and optimised feature selection using Ant Colony Optimisation in Support Vector Machine,"ISSN (Print)   : 2320 – 9798
ISSN (Online) : 2320 – 9801
International Journal of Innovative  Research in Computer and Communication Engineering
Vol. 1, Issue 2, April  2013
A Method for Face Detection based on Wavelet
Transform and optimised feature selection using Ant
Colony Optimisation in Support Vector Machine
Sanjay Kumar Pal1, Uday Chourasia 2 and Manish Ahirwar3
Department of CSE, University Institute of Technology, RGPV, Bhopal, India1,2,3"
5b1d78b160560db5f581e65289ce5e2f99eb9b1f,Twitter100k: A Real-World Dataset for Weakly Supervised Cross-Media Retrieval,"Twitter100k: A Real-world Dataset for Weakly
Supervised Cross-Media Retrieval
Yuting Hu, Liang Zheng, Yi Yang, and Yongfeng Huang"
5b3725c8b5e058ec3a383b621aa9316b90738b2e,Gaussian Conditional Random Field Network for Semantic Segmentation,"MITSUBISHI ELECTRIC RESEARCH LABORATORIES
http://www.merl.com
Gaussian Conditional Random Field Network for Semantic
Segmentation
Vemulapalli, R.; Tuzel, C.O.; Liu, M.-Y.; Chellappa, R.
TR2016-078
June 2016"
5bf4f97b631937b2176db9c80dee965e2e2286be,From Classical to Generalized Zero-Shot Learning: a Simple Adaptation Process,"From Classical to Generalized Zero-Shot
Learning: a Simple Adaptation Process
Yannick Le Cacheux
Herv´e Le Borgne
CEA LIST
CEA LIST
Michel Crucianu
CEDRIC Lab – CNAM
September 27, 2018"
5be3cc1650c918da1c38690812f74573e66b1d32,Relative Parts: Distinctive Parts for Learning Relative Attributes,"Relative Parts: Distinctive Parts for Learning Relative Attributes
Ramachandruni N. Sandeep
Yashaswi Verma
C. V. Jawahar
Center for Visual Information Technology, IIIT Hyderabad, India - 500032"
5b7c0f8ae03e23573049e71329a4ba4166d016c9,Learning Context For Semantic Segmentation and Applications,"Dissertation
Learning Context For Semantic Segmentation
And Applications
Vladimir Haltakov
Technische Universit¨at M ¨unchen
Department of Computer Science
Chair for Computer Aided Medical Procedures and Augmented Reality
ampar.cs.tum.edu"
5bca2c751526665469e9e405d6143d13e7472f7d,Differentiable Particle Filters: End-to-End Learning with Algorithmic Priors,"Differentiable Particle Filters:
End-to-End Learning with Algorithmic Priors
Rico Jonschkowski, Divyam Rastogi, and Oliver Brock
Robotics and Biology Laboratory, Technische Universit¨at Berlin, Germany"
5be6340c55d4a45e96e811bdeac3972328ca9247,People Identification and Tracking Through Fusion of Facial and Gait Features,"Original citation:
Guan, Yu (Researcher in Computer Science), Wei, Xingjie, Li, Chang-Tsun and Keller,
Y. (2014) People identification and tracking through fusion of facial and gait features. In:
Cantoni, Virginio and Dimov, Dimo and Tistarell, Massimo, (eds.) Biometric
Authentication : First International Workshop, BIOMET 2014, Sofia, Bulgaria, June 23-
4, 2014. Revised Selected Papers. Lecture Notes in Computer Science . Springer
International Publishing, pp. 209-221. ISBN 9783319133850
Permanent WRAP url:
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metadata page and the content is not changed in any way."
5b6bdf478860b1e3f797858e71abd14f98684b61,Distributed neural computation for the visual perception of motion. (Calcul neuronal distribué pour la perception visuelle du mouvement),"Distributed neural computation for the visual
perception of motion
Mauricio Cerda
To cite this version:
Mauricio Cerda. Distributed neural computation for the visual perception of motion. Computer
science. Universit´e Nancy II, 2011. English. <tel-00642818>
HAL Id: tel-00642818
https://tel.archives-ouvertes.fr/tel-00642818
Submitted on 18 Nov 2011
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,
´emanant des ´etablissements d’enseignement et de
recherche fran¸cais ou ´etrangers, des laboratoires"
5ba7882700718e996d576b58528f1838e5559225,Predicting Personalized Image Emotion Perceptions in Social Networks,"This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TAFFC.2016.2628787, IEEE
Transactions on Affective Computing
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, VOL. X, NO. X, OCTOBER 2016
Predicting Personalized Image Emotion
Perceptions in Social Networks
Sicheng Zhao, Hongxun Yao, Yue Gao, Senior Member, IEEE, Guiguang Ding and Tat-Seng Chua"
5b6f0a508c1f4097dd8dced751df46230450b01a,Finding lost children,"Finding Lost Children
Ashley Michelle Eden
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2010-174
http://www.eecs.berkeley.edu/Pubs/TechRpts/2010/EECS-2010-174.html
December 20, 2010"
5ba1db56bccc090ce5eceb13f46f2cd15ba3aa55,Interpretable Counting for Visual Question Answering,"Under review as a conference paper at ICLR 2018
INTERPRETABLE COUNTING IN VISUAL QUESTION
ANSWERING
Anonymous authors
Paper under double-blind review"
5bb14bba7510c590164007d7e3aa1bf88cb3faec,Learning to Match Appearances by Correlations in a Covariance Metric Space,"Learning to Match Appearances by Correlations
in a Covariance Metric Space
Sªawomir B¡k, Guillaume Charpiat, Etienne Corvée, François Brémond,
Monique Thonnat
INRIA Sophia Antipolis, STARS group
004, route des Lucioles, BP93
06902 Sophia Antipolis Cedex - France"
f5083b4e28e42a2da7bafd2a742ab8e21c12559f,Deep Learning for Automated Image Classification of Seismic Damage to Built Infrastructure,"Eleventh U.S. National Conference on Earthquake Engineering
Integrating Science, Engineering & Policy
June 25-29, 2018
Los Angeles, California
DEEP LEARNING FOR AUTOMATED
IMAGE CLASSIFICATION OF SEISMIC
DAMAGE TO BUILT INFRASTRUCTURE
B. Patterson1 , G. Leone1, M. Pantoja1, and A. Behrouzi2"
f580b0e1020ad67bdbb11e8d99a59c21a8df1e7d,Compressed Sensing using Generative Models,"Compressed Sensing using Generative Models
Ashish Bora∗
Ajil Jalal†
Eric Price‡
Alexandros G. Dimakis§"
f553f8022b1417bc7420523220924b04e3f27b8e,Finding your Lookalike: Measuring Face Similarity Rather than Face Identity,"Finding your Lookalike:
Measuring Face Similarity Rather than Face Identity
Amir Sadovnik, Wassim Gharbi, Thanh Vu
Lafayette College
Easton, PA
Andrew Gallagher
Google Research
Mountain View, CA"
f5770dd225501ff3764f9023f19a76fad28127d4,Real Time Online Facial Expression Transfer with Single Video Camera,"Real Time Online Facial Expression Transfer
with Single Video Camera"
f5748711db00d82469ff60e05f62319f1eac90c5,Comparing Apples and Oranges: Off-Road Pedestrian Detection on the NREC Agricultural Person-Detection Dataset,"Comparing Apples and Oranges:
Off-Road Pedestrian Detection on the NREC Agricultural
Person-Detection Dataset
Zachary Pezzementi∗
Trenton Tabor∗
Peiyun Hu†
Jonathan K. Chang∗
Deva Ramanan†
Carl Wellington∗
Benzun P. Wisely Babu∗
Herman Herman∗"
f5c83679b73ab59c2ada2b72610acdd63669b226,2D-3D Pose Invariant Face Recognition System for Multimedia Applications,"D-3D POSE INVARIANT FACE RECOGNITION
SYSTEM FOR MULTIMEDIA APPLICATIONS
Authors:
Antonio Rama1, Francesc Tarrés1
Jürgen Rurainsky2
{tonirama,
Department of Signal Theory and Communications
Universitat Politècnica de Catalunya (UPC)
Image Processing Department
Fraunhofer Institute for Telecommunications
Heinrich-Hertz-Institut (HHI)
Automatic  Face  recognition  of  people  is  a  challenging  problem  which  has  re-
eived much attention during the recent years due to its potential multimedia ap-
plications in different fields such as 3D videoconference, security applications or
video indexing. However, there is no technique that provides a robust solution to
ll  situations  and  different  applications,  yet.  Face  recognition  includes  a  set  of
hallenges like expression variations, occlusions of facial parts, similar identities,
resolution of the acquired images, aging of the subjects and many others. Among
ll these challenges, most of the face recognition techniques have evolved in order
to overcome two main problems: illumination and pose variation. Either of these"
f541dac9d0d49cadb3cfd018e87b26e03e3f13aa,Trio Constrained Adaptive Noise Removal ( TCANR ) Mechanism for Salt and Pepper Noise in Image Classification,"International Journal of Advanced Research in Computer and Communication Engineering
IJARCCE
ISSN (Online) 2278-1021
ISSN (Print) 2319 5940
ISO 3297:2007 Certified
Vol. 6, Issue 3, March 2017
Trio Constrained Adaptive Noise Removal
(TCANR) Mechanism for Salt and Pepper Noise
in Image Classification
G Muthu Krishnan1, Capt.Dr.S.Santhosh Baboo2
Research Scholar, Dravidian University,  Kuppam1
Associate Professor,  P.G. & Research Department of Computer science, D.G. Vaishnav College, Chennai2"
f5050ffebf973d4d848049dcf661891acd950b82,"Face and object discrimination in autism, and relationship to IQ and age.","J Autism Dev Disord
DOI 10.1007/s10803-013-1955-z
O R I G I N A L P A P E R
Face and Object Discrimination in Autism, and Relationship to IQ
nd Age
Pamela M. Pallett • Shereen J. Cohen •
Karen R. Dobkins
Ó Springer Science+Business Media New York 2013
faces, yet"
f5adb841e30eb635b91e95c03575f3b8767c9ed5,Learning Optimal Parameters For Multi-target Tracking,"WANG, FOWLKES: LEARNING MULTI-TARGET TRACKING
Learning Optimal Parameters
For Multi-target Tracking
Shaofei Wang
Charless Fowlkes
Dept of Computer Science
University of California
Irvine, CA, USA"
f51771c6cd9061acc9c468e7b44d5d3b6c552b32,"Sparse Representation, Discriminative Dictionaries and Projections for Visual Classification",
f558a3812106764fb1af854a02da080cc42c197f,Amygdala volume and nonverbal social impairment in adolescent and adult males with autism.,"ORIGINAL ARTICLE
Amygdala Volume and Nonverbal Social Impairment
in Adolescent and Adult Males With Autism
Brendon M. Nacewicz, BS; Kim M. Dalton, PhD; Tom Johnstone, PhD; Micah T. Long, BS; Emelia M. McAuliff, BS;
Terrence R. Oakes, PhD; Andrew L. Alexander, PhD; Richard J. Davidson, PhD
Background: Autism is a syndrome of unknown cause,
marked by abnormal development of social behavior. At-
tempts to link pathological features of the amygdala, which
plays a key role in emotional processing, to autism have
shown little consensus.
Objective: To evaluate amygdala volume in individu-
ls with autism spectrum disorders and its relationship
to laboratory measures of social behavior to examine
whether variations in amygdala structure relate to symp-
tom severity.
Design: We conducted 2 cross-sectional studies of amyg-
dala volume, measured blind to diagnosis on high-
resolution, anatomical magnetic resonance images. Par-
ticipants were 54 males aged 8 to 25 years, including 23
with autism and 5 with Asperger syndrome or pervasive"
f5bd11c5c5a455df04b171e37acd1fbdbf3dacd5,African American and Caucasian males ' evaluation of racialized female facial averages,"UNLV Theses, Dissertations, Professional Papers, and Capstones
5-2010
African American and Caucasian males' evaluation
of racialized female facial averages
Rhea M. Watson
University of Nevada Las Vegas
Follow this and additional works at: http://digitalscholarship.unlv.edu/thesesdissertations
Part of the Cognition and Perception Commons, Race and Ethnicity Commons, and the Social
Psychology Commons
Repository Citation
Watson, Rhea M., ""African American and Caucasian males' evaluation of racialized female facial averages"" (2010). UNLV Theses,
Dissertations, Professional Papers, and Capstones. 366.
http://digitalscholarship.unlv.edu/thesesdissertations/366
This Thesis is brought to you for free and open access by Digital It has been accepted for inclusion in UNLV Theses, Dissertations,
Professional Papers, and Capstones by an authorized administrator of Digital For more information, please contact"
f56674e6a4d89bf8855499eea0a043fa14fead70,Prediction of Pedestrian Trajectories Final Report,"Prediction of Pedestrian Trajectories Final Report
Mingchen Li (limc), Yiyang Li (yiyang7), Gendong Zhang (zgdsh29)
December 15, 2017
Introduction
As the industry of automotive vehicles growing rapidly, the ability of those vehicles to predict trajectories
of pedestrians becomes more crucial than ever. Any autonomous vehicle navigating such a scene should
e able to foresee the future positions of pedestrians and accordingly adjust its path to avoid collisions
[1]. As stated in [1], this trajectory prediction problem can be viewed as a sequence generation task,
where we are interested in predicting the future trajectory of people based on their past positions. In
this project, we present comparisons among the performances of different machine learning models.
The input to our algorithm is arbitrary number of people’s previous positions in x-y coordinates and
the output is the people’s next position. The methods been applied are K-Nearest Neighbors (KNN)
ombined with linear regression, Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU).
The latter two are different cell types of Recurrent Neural Network (RNN).
Related Works
The problem of predicting pedestrian trajectories has been studied by many research groups around
the world for decades and many models have been proposed. Some groups used traditional machine
learning algorithms by fitting data into different models, such as Gaussian mixture model [2], Mixed
Markov-chain model [3], and Gaussian process regression [4]. Other groups considered the social forces
etween pedestrians. Helbing et. al. [5] modeled pedestrian motions with attractive and repulsive forces."
f56edb6f2bf4f5bc9d54284289212b8d4a437c1b,Detection and Localization of Texture-less Objects with Deep Neural Networks,"Bachelor Thesis
Czech
Technical
University
in Prague
Faculty of Electrical Engineering
Department of Cybernetics
Detection and Localization of Texture-less
Objects with Deep Neural Networks
Pavel Haluza
Supervisor: Ing. Tomáš Hodaň
May 2017"
f5c99652c4c89e56156faf2bed361a15de6162d5,Towards Large-Scale Multimedia Retrieval Enriched by Knowledge about Human Interpretation Retrospective Survey,"Noname manuscript No.
(will be inserted by the editor)
Towards Large-Scale Multimedia Retrieval Enriched
y Knowledge about Human Interpretation
Retrospective Survey
Kimiaki Shirahama · Marcin Grzegorzek
Received: date / Accepted: date"
f565ac8e175e4659fadd3b5b6507ebac2d90a2b7,Interpretable Visual Question Answering by Reasoning on Dependency Trees,"IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. X, NO. X, XXX
Interpretable Visual Question Answering by
Reasoning on Dependency Trees
Qingxing Cao, Xiaodan Liang, Bailin Li and Liang Lin"
b8a5839f6b1e051f430f2b89d5a1a7e49a10655a,DCFNet: Deep Neural Network with Decomposed Convolutional Filters,"DCFNet: Deep Neural Network with Decomposed Convolutional Filters
Qiang Qiu 1 Xiuyuan Cheng 1 Robert Calderbank 1 Guillermo Sapiro 1"
b8a53daa97fb917a89c351c47f0b197573e20023,Recognizing Faces---An Approach Based on Gabor Wavelets,"Recognizing Faces --- An Approach Based on Gabor
Wavelets
By LinLin Shen, BSc, MSc
Thesis submitted to the University of Nottingham
for the degree of Doctor of Philosophy
July 2005"
b86d16c9df3791ed29ae8219ac419447fde82270,PART-BASED PEDESTRIAN DETECTION AND TRACKING USING HOG-SVM CLASSIFICATION Miss .,"A. Sanofer Nisha et al, International Journal of Computer Science and Mobile Applications,
Vol.2 Issue. 1, January- 2014, pg. 142-155                              ISSN: 2321-8363
PART-BASED PEDESTRIAN
DETECTION AND TRACKING USING
HOG-SVM CLASSIFICATION
Miss. A. Sanofer Nisha(1)
Mrs. K. Thulasimani(2)
ME II Year
AP/CSE
Department of Computer Science and
Department of Computer Science and
Engineering
Engineering
Government College of Engineering
Government College of Engineering
Tirunelveli
Tirunelveli"
b8ad1f7e5753473e3d5231a08c980fc2bae3af0b,Image Background Matching for Identifying Suspects,"Chapter 24
IMAGE BACKGROUND MATCHING
FOR IDENTIFYING SUSPECTS
Paul Fogg, Gilbert Peterson and Michael Veth"
b8969d6e5658b360111f33d3f85eac63afcd7252,WESPE: Weakly Supervised Photo Enhancer for Digital Cameras,"WESPE: Weakly Supervised Photo Enhancer for Digital Cameras
Andrey Ignatov, Nikolay Kobyshev, Kenneth Vanhoey, Radu Timofte, Luc Van Gool
ETH Zurich
{andrey, nk, vanhoey, timofter,"
b87eeb3b873d27c68a5a1cdfd9409c14db352d92,Hierarchical Cellular Automata for Visual Saliency,"Noname manuscript No.
(will be inserted by the editor)
Hierarchical Cellular Automata for Visual Saliency
Yao Qin* · Mengyang Feng* · Huchuan Lu · Garrison W. Cottrell
Received: date / Accepted: date"
b8abc0573208786550e0bfbce4bbcac9d048537e,Context-Patch for Difficult Face Recognition Anonymous ICB 2012 submission,"CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE.
Context-Patch for Difficult Face Recognition
Anonymous ICB 2012 submission"
b8f09ff53e5a1700492100b8cd1b9e9783485376,Clustered Multitask Feature Learning for Attribute Prediction Anonymous CVPR submission,"#1105
CVPR 2016 Submission #1105. CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE.
#1105
Clustered Multi-task Feature Learning for Attribute Prediction
Anonymous CVPR submission
Paper ID 1105"
b8612b5c1aa0970b5d99340ad19d7fcede1b0854,"Fusion of Speech, Faces and Text for Person Identification in TV Broadcast","Fusion of speech, faces and text for
person identification in TV broadcast
Herv´e Bredin1, Johann Poignant2, Makarand Tapaswi3, Guillaume Fortier4,
Viet Bac Le5, Thibault Napoleon6, Hua Gao3, Claude Barras1, Sophie Rosset1,
Laurent Besacier2, Jakob Verbeek4, Georges Qu´enot2, Fr´ed´eric Jurie6, and
Hazim Kemal Ekenel3
Univ Paris-Sud / CNRS-LIMSI UPR 3251, BP 133, F-91403 Orsay, France
UJF-Grenoble 1 / UPMF-Grenoble 2 / Grenoble INP / CNRS-LIG UMR 5217,
F-38041 Grenoble, France
Karlsruher Institut fur Technologie, Karlsruhe, Germany
INRIA Rhone-Alpes, 655 Avenue de lEurope, F-38330 Montbonnot, France
5 Vocapia Research, 3 rue Jean Rostand, Parc Orsay Universit´e, F-91400 Orsay,
6 Universit´e de Caen / GREYC UMR 6072, F-14050 Caen Cedex, France
France"
b800f6b02c32c54cb07e6b8655171bbb2ca5cc0e,Computer Vision : Visual Extent of an Object,"IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 14, Issue 4 (Sep. - Oct. 2013), PP 22-27
www.iosrjournals.org
Computer Vision: Visual Extent of an Object
Akshit Chopra1, Ayushi Sharma2
(Department Of Computer Science, Maharaja Surajmal Institute Of Technology – Guru Gobind Singh
(Department Of Computer Science, Maharaja Surajmal Institute Of Technology – Guru Gobind Singh
Indraprastha University, India)
Indraprastha University, India)"
b85901174fa83c76ae994603228ba5b4f299a1af,"SOS, LOST IN A HIGH DIMENSIONAL SPACE","SOS, LOST IN A HIGH DIMENSIONAL SPACE
Anne Hendrikse"
b85580ff2d8d8be0a2c40863f04269df4cd766d9,HCMUS team at the Multimodal Person Discovery in Broadcast TV Task of MediaEval 2016,"HCMUS team at the Multimodal Person Discovery in
Broadcast TV Task of MediaEval 2016
Vinh-Tiep Nguyen, Manh-Tien H. Nguyen, Quoc-Huu Che, Van-Tu Ninh,
Tu-Khiem Le, Thanh-An Nguyen, Minh-Triet Tran
Faculty of Information Technology
University of Science, Vietnam National University-Ho Chi Minh city
{nhmtien, cqhuu, nvtu,"
b8b46df1b013c30d791972ee109425a94e3adc06,"Automaticity , Control , and the Social Brain","C H A P T E R   1 9
Automaticity, Control,
nd the Social Brain
Robert P. Spunt and Matthew D. Lieberman
The  social  world  is  good  at  keeping  the
human  brain  busy,  posing  cognitive  chal-
lenges that are complex, frequent, and enor-
mously important to our well-being. In fact,
the  computational  demands  of  the  social
world  may  be  the  principal  reason  why
the  human  brain  has  evolved  to  its  present
form and function relative to other primates
(Dunbar,  1993).  Importantly,  the  human
rain  is  often  able  to  make  sense  of  the
social world without having to do too much
work. This is because many of its processes
re  automatically  initiated  by  the  presence
of relevant social stimuli and run to comple-
tion  without  much,  if  any,  conscious  inter-
vention (Bargh & Chartrand, 1999; Gilbert,"
b878518814fee31ce8cb61040301e7a921892156,A Gaussian Feature Adaptive Integrated PCA-ICA Approach for Facial Recognition,"Vaishali et al, International Journal of Computer Science and Mobile Computing, Vol.4 Issue.5, May- 2015, pg. 401-406
Available Online at www.ijcsmc.com
International Journal of Computer Science and Mobile Computing
A Monthly Journal of Computer Science and Information Technology
IJCSMC, Vol. 4, Issue. 5, May 2015, pg.401 – 406
RESEARCH ARTICLE
ISSN 2320–088X
A Gaussian Feature Adaptive Integrated PCA-ICA
Approach for Facial Recognition
Student, Dept. of ECE, ITM University Gurgaon Haryana
Vaishali
Dr. Rekha Vig
Asstt. Prof, Dept. of ECE, ITM University Gurgaon Haryana"
b856c493c2e5cbb71791f56763886e5e0d40295c,Unsupervised Domain Adaptive Re-Identification: Theory and Practice,"Unsupervised Domain Adaptive Re-Identification:
Theory and Practice
Liangchen Song12∗ Cheng Wang23∗ Lefei Zhang1 Bo Du1
Qian Zhang2 Chang Huang2 Xinggang Wang3
Wuhan University 2Horizon Robotics
Huazhong Univ. of Science and Technology"
b86bcc153e097838fdb31ffa6c363f1d883512ae,ON-LINE UNSUPERVISED ADAPTATION FOR FACE VERIFICATION USING GAUSSIAN MIXTURE MODELS WITH MULTIPLE USER MODELS,"ON-LINE UNSUPERVISED ADAPTATION FOR
FACE VERIFICATION USING GAUSSIAN
MIXTURE MODELS WITH MULTIPLE USER
MODELS
Niklas Johansson        Chris McCool        Sébastien Marcel
Idiap-RR-07-2011
MARCH 2011
Centre du Parc, Rue Marconi 19, P.O. Box 592, CH - 1920 Martigny
T +41 27 721 77 11  F +41 27 721 77 12   www.idiap.ch"
b89862f38fff416d2fcda389f5c59daba56241db,A Web Survey for Facial Expressions Evaluation,"A Web Survey for Facial Expressions Evaluation
Matteo Sorci
Gianluca Antonini
Jean-Philippe Thiran
Ecole Polytechnique Federale de Lausanne
Signal Processing Institute
Ecublens, 1015 Lausanne, Switzerland
Ecole Polytechnique Federale de Lausanne, Operation Research Group
Michel Bierlaire
Ecublens, 1015 Lausanne, Switzerland
June 9, 2008"
b8471908880c916ebc70ac900e9446705ed258f4,Transitional and translational studies of risk for anxiety.,"Review
TRANSITIONAL AND TRANSLATIONAL STUDIES
OF RISK FOR ANXIETY
B. J. Casey Ph.D.,
Erika J. Ruberry B.S., Victoria Libby B.A., Charles E. Glatt M.D., Ph.D., Todd Hare Ph.D.,
Fatima Soliman M.D., Ph.D., Stephanie Duhoux Ph.D., Helena Frielingsdorf M.D., Ph.D., and Nim Tottenham
Ph.D.
Adolescence reflects a period of increased rates of anxiety, depression, and
suicide. Yet most teens emerge from this period with a healthy, positive outcome.
In this article, we identify biological factors that may increase risk for some
individuals during this developmental period by: (1) examining changes in
neural circuitry underlying core phenotypic features of anxiety as healthy
individuals transition into and out of adolescence; (2) examining genetic factors
that may enhance the risk for psychopathology in one individual over another
using translation from mouse models to human neuroimaging and behavior;
nd (3) examining the effects of early experiences on core phenotypic features of
nxiety using human neuroimaging and behavioral approaches. Each of these
pproaches alone provides only limited information on genetic and environ-
mental influences on complex human behavior across development. Together,
they reflect an emerging field of translational developmental neuroscience in"
b856c0eb039effce7da9ff45c3f5987f18928bef,Pedestrian Alignment Network for Large-scale Person Re-identification,"Noname manuscript No.
(will be inserted by the editor)
Pedestrian Alignment Network for
Large-scale Person Re-identification
Zhedong Zheng · Liang Zheng · Yi Yang
Received: date / Accepted: date"
b8b202fa955801da840afc9f523d439d14d87cc1,A Novel Approach for Monocular 3 D Object Tracking in Cluttered Environment 853 Monocular Video Sequences,"International Journal of Computational Intelligence Research
ISSN 0973-1873 Volume 13, Number 5 (2017), pp. 851-864
© Research India Publications
http://www.ripublication.com
A Novel Approach for Monocular 3D Object
Tracking in Cluttered Environment
Navneet S. Ghedia
Research scholar, Gujarat Technological University, Gujarat, India.
Dr. C.H. Vithalani
Professor and Head of EC Dept., Government Engineering College, Rajkot, India.
Dr. Ashish Kothari
Associate Professor and Head of EC Dept., Atmiya Institute of Technology and
Science, Rajkot, Gujarat, India."
b8da30e8f6149baf1201e98b2ecbe847cf49a872,Open Framework for Combined Pedestrian Detection,"Open Framework for Combined Pedestrian Detection
Floris De Smedt and Toon Goedem´e
EAVISE, KU Leuven, Sint-Katelijne-Waver, Belgium
Keywords:
Pedestrian Detection, Real-time, Framework."
b8ccc5341a1b0214e9d155b019962023f344c2ee,Incremental Learning of Object Detectors without Catastrophic Forgetting,"Incremental Learning of Object Detectors without Catastrophic Forgetting
Konstantin Shmelkov
Cordelia Schmid
Karteek Alahari
Inria∗"
b88771387d5c0f09ea9a2ccc743b11471fb257b4,An interactive facial-expression training platform for individuals with autism spectrum disorder,"An Interactive Facial-Expression Training Platform
for Individuals with Autism Spectrum Disorder
Christina Tsangouri*, Wei Li+, Zhigang Zhu*
* Dept. of Comp. Sci.. and +Dept of Electrical Eng..
City College of New York, New York, USA"
b8d361d45f6fe4d8dc6129d205b0ae8c8e615939,2 FACE SKETCH SYNTHESIS USING THE MULTISCALE MARKOV RANDOM FIELDS MODEL,"IEEE TRANS. ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Face Photo-Sketch Synthesis and Recognition
Xiaogang Wang, Student Member, IEEE, and Xiaoou Tang, Senior Member, IEEE"
b8aef59bac4035013bcdaa9b56d665fc8b4e187d,Optimal Bayes Classification of High Dimensional Data in Face Recognition,"Optimal Bayes Classification of High Dimensional Data in Face
Recognition
GRIFT Research Group, CRISTAL Laboratory, National School of Computer Sciences, University of Manouba,
Wissal Drira and Faouzi Ghorbel
Manouba, Tunisia
Keywords:
Face  Classification,  Bayes,  Feature  Extraction,  Reduction  Dimension,  L2  Probabilistic  Dependence
Measure."
b8053da77bf1a5b4c87fddf6140be0a612cfc164,Multi-Pose Face Recognition Using Hybrid Face Features Descriptor,"MULTI-POSE FACE RECOGNITION USING
HYBRID FACE FEATURES DESCRIPTOR
I Gede Pasek Suta WIJAYA[1,2], Keiichi UCHIMURA[2] and Gou KOUTAKI[2]"
b8a4e7c21c3163b7595dac0cb00cf518e2dd82b5,Coupling Fall Detection and Tracking in Omnidirectional Cameras,"Coupling Fall Detection and Tracking in
Omnidirectional Cameras
removed for blind review
No Institute Given"
b8a829b30381106b806066d40dd372045d49178d,A Probabilistic Framework for Joint Pedestrian Head and Body Orientation Estimation,"A Probabilistic Framework for Joint Pedestrian Head
nd Body Orientation Estimation
Fabian Flohr, Madalin Dumitru-Guzu, Julian F. P. Kooij, and Dariu M. Gavrila"
b871d1b8495025ff8a6255514ed39f7765415935,Application of Completed Local Binary Pattern for Facial Expression Recognition on Gabor Filtered Facial Images,"Application of Completed Local Binary Pattern for Facial Expression
Recognition on Gabor Filtered Facial Images
Tanveer Ahsan, 2Rifat Shahriar, *3Uipil Chong
Dept. of Electrical and Computer Engineering, University of Ulsan, Ulsan, Republic of Korea"
b8f3f6d8f188f65ca8ea2725b248397c7d1e662d,Selfie Detection by Synergy-Constraint Based Convolutional Neural Network,"Selfie Detection by Synergy-Constriant Based
Convolutional Neural Network
Yashas Annadani, Vijaykrishna Naganoor, Akshay Kumar Jagadish and Krishnan Chemmangat
Electrical and Electronics Engineering, NITK-Surathkal, India."
b8e35566129299c3591af0fd4f127e5e0d0b5774,3D Facial Image Comparison using Landmarks,"D Facial Image Comparison using Landmarks
A study to the discriminating value of the characteristics
of 3D facial landmarks and their automated detection.
Alize Scheenstra
Master thesis: INF/SCR-04-54
Netherlands Forensic Institute
Institute of Information and Computing Sciences
Utrecht University
February 2005"
b8dba0504d6b4b557d51a6cf4de5507141db60cf,Comparing Performances of Big Data Stream Processing Platforms with RAM3S,"Comparing Performances of Big Data Stream
Processing Platforms with RAM3S"
b8e76cadc9ad20c242718be4dd3c5af0e34b29bf,Fusing Body Posture with Facial Expressions for Joint Recognition of Affect in Child-Robot Interaction,"Fusing Body Posture with Facial Expressions for Joint Recognition of Affect in
Child-Robot Interaction
Panagiotis P. Filntisis 1,3
Niki Efthymiou 1,3
Petros Koutras 1,3
Gerasimos Potamianos 2,3
Petros Maragos 1,3
School of E.C.E., NTUA, Greece
E.C.E. Department, UTH, Greece
Athena RC, Maroussi, Greece"
4106c49eb96b506ea1125c27e2b2f32ad79f8c48,"Markovian Tracking-by-Detection from a Single , Uncalibrated Camera","Markovian Tracking-by-Detection from a Single, Uncalibrated Camera
Michael D. Breitenstein1 Fabian Reichlin1 Bastian Leibe1,2 Esther Koller-Meier1 Luc Van Gool1,3
ETH Zurich
RWTH Aachen
KU Leuven"
4129e1075c7856d8bebbf0655ae00a4843109429,A Tale of Two Losses : Discriminative Deep Feature Learning for Person Re-Identification,"A Tale of Two Losses: Discriminative Deep Feature Learning for
Person Re-Identification
Borgia, A., Hua, Y., & Robertson, N. (2017). A Tale of Two Losses: Discriminative Deep Feature Learning for
Person Re-Identification. In Irish Machine Vision and Image Processing Conference 2017: Proceedings
Published in:
Irish Machine Vision and Image Processing Conference 2017: Proceedings
Document Version:
Peer reviewed version
Queen's University Belfast - Research Portal:
Link to publication record in Queen's University Belfast Research Portal
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opyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated
with these rights.
Take down policy
The Research Portal is Queen's institutional repository that provides access to Queen's research output. Every effort has been made to
ensure that content in the Research Portal does not infringe any person's rights, or applicable UK laws. If you discover content in the"
41a6196f88beced105d8bc48dd54d5494cc156fb,Using facial images for the diagnosis of genetic syndromes: A survey,"015 International Conference on
Communications, Signal
Processing, and their Applications
(ICCSPA 2015)
Sharjah, United Arab Emirates
7-19 February 2015
IEEE Catalog Number:
ISBN:
CFP1574T-POD
978-1-4799-6533-5"
41ed93fd97aa76b4abfda7a09168ad1799f34664,Video Event Detection: From Subvolume Localization to Spatiotemporal Path Search,"This document is downloaded from DR-NTU, Nanyang Technological
University Library, Singapore.
Title
Video event detection : from subvolume localization to
spatio-temporal path search
Author(s)
Tran, Du; Yuan, Junsong; Forsyth, David
Citation
Tran, D., Yuan, J., & Forsyth, D. (2014). Video Event
Detection: From Subvolume Localization to
Spatiotemporal Path Search. IEEE Transactions on
Pattern Analysis and Machine Intelligence, 36(2), 404-
http://hdl.handle.net/10220/19322
Rights
© 2014 IEEE. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other
uses, in any current or future media, including
reprinting/republishing this material for advertising or
promotional purposes, creating new collective works, for
resale or redistribution to servers or lists, or reuse of any"
41235b815a3a69eb5ef48199e7ea7e98495e56a9,Learning Discriminative Local Patterns with Unrestricted Structure for Face Recognition,"Learning discriminative local patterns with unrestricted
structure for face recognition
Author
Brown, Douglas, Gao, Yongsheng, Zhou, Jun
Published
Conference Title
013 International Conference on Digital Image Computing: Techniques and Applications
(DICTA)
https://doi.org/10.1109/DICTA.2013.6691504
Copyright Statement
© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be
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41de109bca9343691f1d5720df864cdbeeecd9d0,Facial Emotion Recognition: A Survey and Real-World User Experiences in Mixed Reality,"Article
Facial Emotion Recognition: A Survey and
Real-World User Experiences in Mixed Reality
Dhwani Mehta, Mohammad Faridul Haque Siddiqui and Ahmad Y. Javaid * ID
EECS Department, The University of Toledo, Toledo, OH 43606, USA; (D.M.);
(M.F.H.S.)
* Correspondence: Tel.: +1-419-530-8260
Received: 10 December 2017; Accepted: 26 January 2018; Published: 1 Febuary 2018"
4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06,Nighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching,"Nighttime Face Recognition at Long Distance:
Cross-distance and Cross-spectral Matching
Hyunju Maenga, Shengcai Liaob, Dongoh Kanga, Seong-Whan Leea,
Anil K. Jaina;b
Dept. of Brain and Cognitive Eng. Korea Univ., Seoul, Korea
Dept. of Comp. Sci. & Eng. Michigan State Univ., E. Lansing, MI, USA 48824"
4180978dbcd09162d166f7449136cb0b320adf1f,Real-time head pose classification in uncontrolled environments with Spatio-Temporal Active Appearance Models,"Real-time head pose classification in uncontrolled environments
with Spatio-Temporal Active Appearance Models
Miguel Reyes∗ and Sergio Escalera+ and Petia Radeva +
Matematica Aplicada i Analisi ,Universitat de Barcelona, Barcelona, Spain
+ Matematica Aplicada i Analisi, Universitat de Barcelona, Barcelona, Spain
+ Matematica Aplicada i Analisi, Universitat de Barcelona, Barcelona, Spain"
41ddd29d9e56bb87b9f988afc75cd597657b2600,R 4-A . 3 : Human Detection & Re-Identification for Mass Transit Environments,"R4-A.3: Human Detection & Re-Identification for
Mass Transit Environments
PARTICIPANTS
Rich Radke
Title
Faculty/Staff
Institution
Graduate, Undergraduate and REU Students
Srikrishna Karanam
Eric Lam
Degree Pursued
Institution
Email
Month/Year of Graduation
5/2017
5/2017
PROJECT DESCRIPTION
Project Overview
The computer vision research problem of human re-identification or “re-id” is generally posed as follows:
Given a cropped rectangle of pixels representing a human in one view, a re-id algorithm produces a similarity"
4189aa74550c1761dd5927442d0a98ff3d3d1134,Residual Conv-Deconv Grid Network for Semantic Segmentation,"FOURURE ET AL.: RESIDUAL CONV-DECONV GRIDNET
Residual Conv-Deconv Grid Network for
Semantic Segmentation
Univ Lyon, UJM Saint-Etienne,
CNRS UMR 5516,
Hubert Curien Lab, F-42023
Saint-Etienne, France
INSA-Lyon,
LIRIS UMR CNRS 5205,
F-69621,
France
Damien Fourure1
Rémi Emonet1
Elisa Fromont1
Damien Muselet1
Alain Tremeau1
Christian Wolf2"
4152d2c8585f7e3f85d3b3d84036171de104cbd7,Rethinking ImageNet Pre-training,"Rethinking ImageNet Pre-training
Kaiming He Ross Girshick
Piotr Doll´ar
Facebook AI Research (FAIR)"
41fafb5392ad5e33e5169d870812ab5edca301a1,Tree-Structured Stick Breaking Processes for Hierarchical Data,"TREE-STRUCTURED STICK BREAKING PROCESSES
FOR HIERARCHICAL DATA
By Ryan P. Adams, Zoubin Ghahramani and Michael I. Jordan
Many data are naturally modeled by an unobserved hierarchical
structure. In this paper we propose a flexible nonparametric prior over
processes to allow for trees of unbounded width and depth, where data
an live at any node and are infinitely exchangeable. One can view
our model as providing infinite mixtures where the components have a
dependency structure corresponding to an evolutionary diffusion down
tree. By using a stick-breaking approach, we can apply Markov chain
Monte Carlo methods based on slice sampling to perform Bayesian
inference and simulate from the posterior distribution on trees. We
pply our method to hierarchical clustering of images and topic
modeling of text data.
. Introduction. Structural aspects of models are often critical to ob-
taining flexible, expressive model families. In many cases, however, the
structure is unobserved and must be inferred, either as an end in itself or
to assist in other estimation and prediction tasks. This paper addresses an
important instance of the structure learning problem: the case when the
data arise from a latent hierarchy. We take a direct nonparametric Bayesian"
414715421e01e8c8b5743c5330e6d2553a08c16d,PoTion : Pose MoTion Representation for Action Recognition,"PoTion: Pose MoTion Representation for Action Recognition
Philippe Weinzaepfel2
Inria∗
NAVER LABS Europe
J´erˆome Revaud2 Cordelia Schmid1
Vasileios Choutas1,2"
41367bca49675d0dd078dcd9a140b92d05379900,Survey on Emotional Body Gesture Recognition,"JOURNAL OF IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, VOL. XX, NO. X, XXX 201X
Survey on Emotional Body Gesture Recognition
Fatemeh Noroozi, Ciprian Adrian Corneanu, Dorota Kami´nska, Tomasz Sapi´nski, Sergio Escalera,
nd Gholamreza Anbarjafari,"
4131aa28d640d17e1d63ca82e55cc0b280db0737,COULOMB GANS: PROVABLY OPTIMAL NASH EQUI-,"Under review as a conference paper at ICLR 2018
COULOMB GANS: PROVABLY OPTIMAL NASH EQUI-
LIBRIA VIA POTENTIAL FIELDS
Anonymous authors
Paper under double-blind review"
41a174c27f0b431d62d0f50051bce7f5b3b4ce64,A System for Object Class Detection,"A system for object class detection
Daniela Hall
INRIA Rh^one-Alpes, 655, ave de l’Europe,
8320 St. Ismier, France"
41b541ff747817dade4653fe6ffcdc50e7b3135b,A Stochastic Graph Evolution Framework for Robust Multi-target Tracking,"A Stochastic Graph Evolution Framework for
Robust Multi-Target Tracking
Bi Song, Ting-Yueh Jeng, Elliot Staudt, and Amit K. Roy-Chowdhury (cid:63)
Dept. of Electrical Engineering, University of California, Riverside, CA 92521, USA"
416c647cd9f8c1d77db8676195dff7ae5dfc1fd8,Grammatical Facial Expressions Recognition with Machine Learning,"Grammatical Facial Expressions Recognition with Machine Learning
Fernando de Almeida Freitas
Incluir Tecnologia
Itajub´a, MG, Brazil
Universidade de S˜ao Paulo
S˜ao Paulo, SP, Brazil
Clodoaldo Aparecido de Moraes Lima
Sarajane Marques Peres
Felipe Venˆancio Barbosa
Universidade de S˜ao Paulo
S˜ao Paulo, SP, Brazil"
41a5e043d499967f405e823b959e2ac4fdf9ff71,Extending Recognition in a Changing Environment,"Extending Recognition in a Changing Environment
Department of Computer Science and Applied Mathematics, The Weizmann Institue of Science, Rehovot, Israel
Daniel Harari and Shimon Ullman
{danny.harari,
Keywords:
Object Recognition, Video Analysis, Dynamic Model Update, Unsupervised Learning, Bayesian Model."
418b468b804379e8a600bca0395e01bffb7e08de,Class-specific kernel linear regression classification for face recognition under low-resolution and illumination variation conditions,"Chou et al. EURASIP Journal on Advances in Signal Processing  (2016) 2016:28
DOI 10.1186/s13634-016-0328-0
Open Access
R ES EAR CH
Class-specific kernel linear regression
lassification for face recognition under
low-resolution and illumination variation
onditions
Yang-Ting Chou, Shih-Ming Huang and Jar-Ferr Yang*"
4196e0b77f88ea01cd868c535befb52c2722454f,3D Facial similarity: Automatic assessment versus perceptual judgments,"D Facial Similarity: Automatic Assessment versus Perceptual
Judgments
Anush K. Moorthy, Anish Mittal, Sina Jahanbin, Kristen Grauman and Alan C. Bovik"
41308edf82ae645923efea2d6979d076b975ee25,Convolutional Scale Invariance for Semantic Segmentation,"Convolutional Scale Invariance
for Semantic Segmentation
Ivan Kre(cid:20)so, Denis (cid:20)Cau(cid:20)sevi(cid:19)c, Josip Krapac and Sini(cid:20)sa (cid:20)Segvi(cid:19)c
Faculty of Electrical Engineering and Computing
University of Zagreb, Croatia"
41f8dd3de3380d49ed3809c582b139d9be5176e9,The Price of Fair PCA: One Extra dimension,"The Price of Fair PCA: One Extra Dimension
Samira Samadi
Georgia Tech
Uthaipon Tantipongpipat
Georgia Tech
Jamie Morgenstern
Georgia Tech
Mohit Singh
Georgia Tech
Santosh Vempala
Georgia Tech"
418b106e1f072c4da400b516079f429d84cd7305,Model-based Face Computation Project Role in Support of Imsc Strategic Plan Discussion of Methodology Used,"Model-Based Face Computation
Research Team
Project Leader:
Prof. Ulrich Neumann, IMSC and Computer Science
Post Doc(s):
John P. Lewis
Graduate Students:
Hea-juen Hwang, Zhenyao Mo, Gordon Thomas
Statement of Project Goals
Prior knowledge of the canonical structure of the human face can aid in various automated face-
processing tasks.  In this project we have developed a statistical appearance model for faces and
re  exploring  its  application  to  several  problems:   stylized  face  rendering,  caricature,  and
reconstruction of occluded face images.
Project Role in Support of IMSC Strategic Plan
Model-Based Face computation is part of the general IMSC effort towards expressive human
interaction  in  virtual  and  augmented  reality  environments.   While  this  project  involves
processing based on the structure of the face, the complementary Data-Driven Facial Animation
project is directed toward deriving models of facial movement (including non-speech gestures)
directly from data.
Discussion of Methodology Used"
41690be86b39c55a26ea056261513ddd726d6601,Heterogeneous microarchitectures trump voltage scaling for low-power cores,"Heterogeneous Microarchitectures Trump Voltage Scaling
for Low-Power Cores
Andrew Lukefahr, Shruti Padmanabha, Reetuparna Das, Ronald Dreslinski Jr.,
Thomas F. Wenisch, and Scott Mahlke
Advanced Computer Architecture Laboratory
Ann Arbor, MI, USA
{lukefahr, shrupad, reetudas, rdreslin, twenisch,"
41e6dfe1a87f49c8539f725daa44256c19f31004,Audio-visual speaker identification using the CUAVE database,"COVER SHEET
Dean, David and Lucey, Patrick and Sridharan, Sridha (2005) Audio-visual speaker identification
using the CUAVE database. In Vatikiotis-Bateson, Eric and Burnham, Denis and Fels, Sidney, Eds.
Proceedings Auditory-Visual Speech Processing 2005, British Columbia, Canada.
Accessed from   http://eprints.qut.edu.au
Copyright 2005 the authors"
413160257096b9efcd26d8de0d1fa53133b57a3d,Customer satisfaction measuring based on the most significant facial emotion,"Customer satisfaction measuring based on the most
significant facial emotion
Mariem Slim, Rostom Kachouri, Ahmed Atitallah
To cite this version:
Mariem Slim, Rostom Kachouri, Ahmed Atitallah. Customer satisfaction measuring based on the
most significant facial emotion. 15th IEEE International Multi-Conference on Systems, Signals
Devices (SSD 2018), Mar 2018, Hammamet, Tunisia. <hal-01790317>
HAL Id: hal-01790317
https://hal-upec-upem.archives-ouvertes.fr/hal-01790317
Submitted on 11 May 2018
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
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L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
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émanant des établissements d’enseignement et de"
41dd2ca8929bfdae49a4bf85de74df4723ef9c3b,Correction by Projection: Denoising Images with Generative Adversarial Networks.,"WITH GENERATIVE ADVERSARIAL NETWORKS
Subarna Tripathi
Zachary C. Lipton
Truong Q. Nguyen
UC San Diego
UC San Diego
UC San Diego"
414722ddd809b460d5b397eaf454fbb697cfb881,Dimensionality Reduction and Classification through PCA and LDA,"International Journal of Computer Applications (0975 – 8887)
Volume 122 – No.17, July 2015
Dimensionality Reduction and Classification
through PCA and LDA
Telgaonkar Archana H.
PG Student
Department of CS and IT
Dr. BAMU, Aurangabad"
417890cc6d43a3082a6ab2ac64527f8db5b0125b,Generalized Earley Parser: Bridging Symbolic Grammars and Sequence Data for Future Prediction,"microwavetake foodput into microwaveParse TreeFuture prediction...Sequenceinput dataClassifierraw outputFinal outputGrammarGeneralized Earley algorithmmicrowave foodFigure1.TheinputofthegeneralizedEarleyparserisamatrixofprobabilitiesofeachlabelforeachframe,givenbyanarbitraryclassifier.Theparsersegmentsandlabelsthesequencedataintoalabelsentenceinthelanguageofagivengrammar.Futurepredictionsarethenmadebasedonthegrammar.grammarstoparseandlabelsequencedata.Traditionalgrammarparserstakesymbolicsentencesasinputsinsteadofnoisysequencedatalikevideosoraudios.Thedatahastobei)segmentedandii)labeledtoapplyexistinggram-marparsersto.Onenaivesolutionistofirstsegmentandlabelthedatausingaclassifierandthusgeneratingalabelsentence.Thengrammarparserscanbeappliedontopofitforprediction.Butthisisapparentlynon-optimal,sincethegrammarrulesarenotconsideredintheclassificationpro-cess.Itmaynotevenbepossibletoparsethislabelsentence,becausetheyareveryoftengrammaticallyincorrect.Inthispaper,wedesignagrammar-basedparsingalgorithmthatdirectlyoperatesonsequenceinputdata,whichgoesbeyondthescopeofsymbolicstringinputs.Specifically,weproposeageneralizedEarleyparserbasedontheEarleyparser(Earley,1970).Thealgorithmfindstheoptimalseg-mentationandlabelsentenceaccordingtobothasymbolicgrammarandaclassifieroutputofprobabilitiesoflabelsforeachframeasshowninFigure1.Optimalityheremeansmaximizingtheprobabilityofthelabelsentenceaccordingtotheclassifieroutputwhilebeinggrammaticallycorrect.Thedifficultyofachievingthisoptimalityliesinthejointoptimizationofboththegrammarstructureandtheparsinglikelihoodoftheoutputlabelsentence.Forexample,anexpectation-maximization-typealgorithmwillnotworkwell"
41ea92251c668a99d2b9a31935fc71e6b6d82b6d,Canonical Correlation Analysis of Datasets With a Common Source Graph,"Canonical Correlation Analysis of Datasets
with a Common Source Graph
Jia Chen, Gang Wang, Student Member, IEEE,
Yanning Shen, Student Member, IEEE, and Georgios B. Giannakis, Fellow, IEEE"
413a184b584dc2b669fbe731ace1e48b22945443,Human Pose Co-Estimation and Applications,"Human Pose Co-Estimation and Applications
Marcin Eichner and Vittorio Ferrari"
41f7c03519a2b108c064a2126daf627edde14c1e,Generic Object Detection using AdaBoost,"Generic Object Detection using AdaBoost
Ben Weber
Department of Computer Science
University of California, Santa Cruz
Santa Cruz, CA 95064"
419a6fca4c8d73a1e43003edc3f6b610174c41d2,A component based approach improves classification of discrete facial expressions over a holistic approach,"A Component Based Approach Improves Classification of Discrete
Facial Expressions Over a Holistic Approach
Kenny Hong, and Stephan K. Chalup, Senior Member, IEEE and Robert A.R. King"
41d9a240b711ff76c5448d4bf4df840cc5dad5fc,Image Similarity Using Sparse Representation and Compression Distance,"JOURNAL DRAFT, VOL. X, NO. X, APR 2013
Image Similarity Using Sparse Representation
nd Compression Distance
Tanaya Guha, Student Member, IEEE, and Rabab K Ward, Fellow, IEEE"
41f6368bc4ec5e334c81a9d16185205b3acecee3,Machine Learning Methods from Group to Crowd Behaviour Analysis,"Machine learning methods from group to crowd
ehaviour analysis
Luis Felipe Borja-Borja1, Marcelo Saval-Calvo2, and Jorge Azorin-Lopez2
Universidad Central del Ecuador,
Ciudadela Universitaria Av. Am´erica, Quito, Ecuador
Computer Technology Department, University of Alicante,
Carretera San Vicente s/n, 03690, San Vicente del Raspeig (Spain)"
410017a1810308564dc54cb986b12f079428f966,A functional pipeline framework for landmark identification on 3D surface extracted from volumetric data,"RESEARCH ARTICLE
A functional pipeline framework for landmark
identification on 3D surface extracted from
volumetric data
Pan Zheng1,2*, Bahari Belaton2*, Iman Yi Liao3, Zainul Ahmad Rajion4,5
Faculty of Engineering, Computing and Science, Swinburne University of Technology Sarawak Campus,
Kuching, Malaysia, 2 School of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia, 3 School of
Computer Science, The University of Nottingham Malaysia Campus, Semenyih, Malaysia, 4 School of Dental
Sciences, Universiti Sains Malaysia, Kubang Kerian, Malaysia, 5 College of Dentistry, King Saud bin
Abdulaziz University for Health Sciences, Riyadh, Kingdom of Saudi Arabia
* (PZ); (BB)"
413a1a00f0eab2fcc3dcc0d821fb2f34e85f5d7a,Pedestrian detection by scene dependent classifiers with generative learning,"June 23-26, 2013, Gold Coast, Australia
978-1-4673-2754-1/13/$31.00 ©2013 IEEE"
413c960e57ec3fe713e7b3e070cb6072726874bd,A Search Space Strategy for Pedestrian Detection and Localization in World Coordinates,
41ab4939db641fa4d327071ae9bb0df4a612dc89,Interpreting Face Images by Fitting a Fast Illumination-Based 3D Active Appearance Model,"Interpreting Face Images by Fitting a Fast
Illumination-Based 3D Active Appearance
Model
Salvador E. Ayala-Raggi, Leopoldo Altamirano-Robles, Janeth Cruz-Enriquez
Instituto Nacional de Astrof´ısica, ´Optica y Electr´onica,
Luis Enrique Erro #1, 72840 Sta Ma. Tonantzintla. Pue., M´exico
Coordinaci´on de Ciencias Computacionales
{saraggi, robles,"
41b2c5ad11a3f55d72def07d44cb32a44701ecd1,Weighted Self-Incremental Transfer Learning for 3 D-Semantic Segmentation,"Weighted Self-Incremental Transfer Learning for
D-Semantic Segmentation
Anonymous Author(s)
Affiliation
Address
email"
8458efc65d0b2ef9b23c0f4f2a41f206fcaa787c,Indexing of the CNN features for the large scale image search,"Noname manuscript No.
(will be inserted by the editor)
Indexing of the CNN Features for the Large Scale
Image Search
Ruoyu Liu · Shikui Wei · Yao Zhao · Yi
Received: date / Accepted: date"
843f873c08df64431baefd79e83e4b70236427de,Exploring and Understanding the High Dimensional and Sparse Image Face Space : a Self-Organized Manifold Mapping,"Exploring and Understanding the High
Dimensional and Sparse Image Face Space:
Self-Organized Manifold Mapping
Edson C. Kitani1, Emilio M. Hernandez1,
Gilson A. Giraldi2 and Carlos E. Thomaz3
Universidade de São Paulo, São Paulo, São Paulo,
Laboratório Nacional de Computação Científica, Petrópolis, Rio de Janeiro,
Centro Universitário da FEI, São Bernardo do Campo, São Paulo,
Brazil
. Introduction
Face recognition has motivated several research studies in the last years owing not only to
its applicability and multidisciplinary inherent characteristics, but also to its important role
in human relationship.  Despite extensive studies on face recognition, a number of related
problems has still remained challenging in this research topic.  It is well known that humans
an  overcome  any  computer  program  in  the  task  of  face  recognition  when  artefacts  are
present such as changes in pose, illumination, occlusion, aging and etc.  For instance, young
hildren can robustly identify their parents, friends and common social groups without any
previous explicit teaching or learning.
Some  recent  research  in Neuroscience  (Kandel  et  al.,  2000;  Bakker  et  al.,  2008)  has shown
that there is some new information about how humans deal with such high dimensional and"
84adff86191a1942ec165654fa1d484555d1e6f2,Implementation of an Intentional Vision System to Support Cognitive Architectures,"Implementation of an Intentional Vision System to
Support Cognitive Architectures
Ignazio Infantino, Carmelo Lodato, Salvatore Lopes and Filippo Vella
Istituto di Calcolo e Reti ad Alte Prestazioni
edif. 11, Viale delle Scienze, 90128, Palermo, Italy
Consiglio Nazionale delle Ricerche
ICAR-CNR sede di Palermo"
84fd7c00243dc4f0df8ab1a8c497313ca4f8bd7b,Perceived Age Estimation from Face Images,"We are IntechOpen,
the world’s leading publisher of
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84a69f6357b137028e3aa51376ce2dffad5e0179,"UPSALIENSIS UPPSALA 2018 Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Social Sciences 152 Visual Attention to Faces , Eyes and Objects Studies of Typically and Atypically Developing Children JOHAN","Digital Comprehensive Summaries of Uppsala Dissertations
from the Faculty of Social Sciences 152
Visual Attention to Faces, Eyes and
Objects
Studies of Typically and Atypically Developing
Children
JOHAN L. KLEBERG
ISSN 1652-9030
ISBN 978-91-513-0244-7
urn:nbn:se:uu:diva-342578
UNIVERSITATIS
UPSALIENSIS
UPPSALA"
84124eba5ccd5a25d2275c3dd6d2f15e30225ef7,People counting with image retrieval using compressed sensing,"014 IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP)
978-1-4799-2893-4/14/$31.00 ©2014 Crown
Homa Foroughi, Nilanjan Ray, Hong Zhang
Index  Terms—  compressed  sensing,  people  counting,
. INTRODUCTION"
8468af265ef8c296764d26a69a7d35e6ccd68fa5,Adaptive Regression Splines Models for Predicting Facial Image Verification and Quality Assessment Scores,"BALKAN JOURNAL OF ELECTRICAL & COMPUTER ENGINEERING,  2015,  Vol.3, No.1
Adaptive Regression Splines Models for
Predicting Facial Image Verification and
Quality Assessment Scores
A. A. Abayomi-Alli, E. O. Omidiora, S. O. Olabiyisi and J. A. Ojo"
84f6f20496fadb975922b47528fd94c71e872950,Dissimilarity-based people re-identification and search for intelligent video surveillance,"Ph.D. in Electronic and Computer Engineering
Dept. of Electrical and Electronic Engineering
University of Cagliari
Dissimilarity-based people
re-identification and search for
intelligent video surveillance
Riccardo Satta
Advisor: Prof. Fabio Roli
Co-advisor: Prof. Giorgio Fumera
Curriculum: ING-INF/05 - Sistemi di Elaborazione delle Informazioni
XXV Cycle
April 2013"
84c8b29103480cf6f2b93e2fd4225b0d9d535ed6,Playing hide and seek with a mobile companion robot,"Playing Hide and Seek with a Mobile
Companion Robot
Michael Volkhardt, Steffen Mueller, Christof Schroeter, Horst-Michael Gross
Neuroinformatics and Cognitive Robotics Lab
Ilmenau University of Technology
98684 Ilmenau, Germany
Email:"
84c8eb2db35f7fd38c906ced741e2c5470ba7544,Deep Control - a simple automatic gain control for memory efficient and high performance training of deep convolutional neural networks,"Deep Control - a simple automatic gain control for memory
efficient and high performance training of deep
onvolutional neural networks
Brendan Ruff
Submitted to BMVC 2017, 2nd May 2017
Patent application GB1619779.0, 23rd Nov 2016"
84508e846af3ac509f7e1d74b37709107ba48bde,Use of the Septum as a Reference Point in a Neurophysiologic Approach to Facial Expression Recognition,"Use of the Septum as a Reference Point in a Neurophysiologic Approach to
Facial Expression Recognition
Igor Stankovic and Montri Karnjanadecha
Department of Computer Engineering, Faculty of Engineering,
Prince of Songkla University, Hat Yai, Songkhla, 90112 Thailand
Telephone: (66)080-7045015, (66)074-287-357
E-mail:"
8411fe1142935a86b819f065cd1f879f16e77401,Facial Recognition using Modified Local Binary Pattern and Random Forest,"International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 4, No. 6, November 2013
Facial Recognition using Modified Local Binary
Pattern and Random Forest
Brian O’Connor and Kaushik Roy
Department of Computer Science,
North Carolina A&T State University,
Greensboro, NC 27411"
84e4b7469f9c4b6c9e73733fa28788730fd30379,Projective complex matrix factorization for facial expression recognition,"Duong et al. EURASIP Journal on Advances in Signal Processing  (2018) 2018:10
DOI 10.1186/s13634-017-0521-9
EURASIP Journal on Advances
in Signal Processing
R ES EAR CH
Projective complex matrix factorization for
facial expression recognition
Viet-Hang Duong1, Yuan-Shan Lee1, Jian-Jiun Ding2, Bach-Tung Pham1, Manh-Quan Bui1, Pham The Bao2
nd Jia-Ching Wang1,3*
Open Access"
84968d6488e87c99b8560ab33110a5bf85aa5761,Object category learning and retrieval with weak supervision,"Object category learning and retrieval with
weak supervision
Steven Hickson, Anelia Angelova, Irfan Essa, Rahul Sukthankar
Google Brain / Google Research
(shickson, anelia, irfanessa,"
84187adc5e6412123405102bb3c2f0428713593c,Quad-Tree based Image Encoding Methods for Data-Adaptive Visual Feature Learning,"IPSJ SIG Technical Report
Quad-Tree based Image Encoding Methods for
Data-Adaptive Visual Feature Learning
Cuicui Zhang1,a) Xuefeng Liang1,b) Takashi Matsuyama1,c)"
844568d9e49ec34536502bb8c66d5578c962abd6,From Virtual to Real World Visual Perception using Domain Adaptation - The DPM as Example,"Invited book chapter to appear in Domain Adaptation in Computer Vision Applications, Springer Series: Advances
in Computer Vision and Pattern Recognition, Edited by Gabriela Csurka. Written during Summer 2016.
From Virtual to Real World Visual Perception using Domain
Adaptation – The DPM as Example
Computer Vision Center (CVC) and Dpt. Ci`encies de la Computaci´o (DCC),
Antonio M. L´opez
Universitat Aut`onoma de Barcelona (UAB)
Jiaolong Xu
Jos´e L. G´omez
David V´azquez
CVC and DCC, UAB
CVC and DCC, UAB
CVC and DCC, UAB
Germ´an Ros
CVC and DCC, UAB
December 30, 2016"
84c35fc21db3bcd407a4ffb009912b6ac5a47e3c,MGAN: TRAINING GENERATIVE ADVERSARIAL NETS,"Under review as a conference paper at ICLR 2018
MGAN: TRAINING GENERATIVE ADVERSARIAL NETS WITH
MULTIPLE GENERATORS
Anonymous authors
Paper under double-blind review"
847a1fc7c29ca91282a676fc6381056b8dec65a6,People as Sensors: Imputing Maps from Human Actions,"People as Sensors: Imputing Maps from Human Actions
Oladapo Afolabi*, Katherine Driggs-Campbell*, Roy Dong, Mykel J. Kochenderfer, and S. Shankar Sastry"
845c03910c7cfd02de7df9622a9973e8b085c0d8,Interactive Generation of Realistic Facial Wrinkles from Sketchy Drawings,"EUROGRAPHICS 2015 / O. Sorkine-Hornung and M. Wimmer
(Guest Editors)
Volume 34 (2015), Number 2
Interactive Generation of Realistic Facial Wrinkles from
Sketchy Drawings
Hyeon-Joong Kim 1,3, A. Cengiz Öztireli2, Il-Kyu Shin1, Markus Gross2, Soo-Mi Choi†1
Sejong University, Korea 2 ETH Zurich, Switzerland 3 3D Systems, Korea
Figure 1: We use statistics extracted from example faces to augment interactively drawn concept sketches for synthesizing
realistic facial wrinkles."
84f911432ba8a3356013b3abfbf1947f1145c953,Online Object Tracking with Proposal Selection,"Online Object Tracking with Proposal Selection
Yang Hua
Karteek Alahari
Inria∗
Cordelia Schmid"
84a20d0a47c0d826b77f73075530d618ba7573d2,Look at Boundary: A Boundary-Aware Face Alignment Algorithm,"(68	points)	COFW	(29	points)	AFLW	(19	points)	Figure1:Thefirstcolumnshowsthefaceimagesfromdifferentdatasetswithdifferentnumberoflandmarks.Thesecondcolumnillustratestheuniversallydefinedfacialboundariesestimatedbyourmethods.Withthehelpofboundaryinformation,ourapproachachieveshighaccuracylocalisationresultsacrossmultipledatasetsandannotationprotocols,asshowninthethirdcolumn.Differenttofacedetection[45]andrecognition[75],facealignmentidentifiesgeometrystructureofhumanfacewhichcanbeviewedasmodelinghighlystructuredout-put.Eachfaciallandmarkisstronglyassociatedwithawell-definedfacialboundary,e.g.,eyelidandnosebridge.However,comparedtoboundaries,faciallandmarksarenotsowell-defined.Faciallandmarksotherthancornerscanhardlyremainthesamesemanticallocationswithlargeposevariationandocclusion.Besides,differentannotationschemesofexistingdatasetsleadtoadifferentnumberoflandmarks[28,5,66,30](19/29/68/194points)andanno-tationschemeoffuturefacealignmentdatasetscanhardlybedetermined.Webelievethereasoningofauniquefacial"
842e42d30dc31de1833047c268f0a5cdff16f2ce,Face Compression and Recognition using Spherical Wavelet Parametrization,"(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 3, No.9, 2012
D Face Compression and Recognition using
Spherical Wavelet Parametrization
Rabab M. Ramadan
College of Computers and Information Technology
University of Tabuk
Tabuk, KSA
into  multi-resolution  sub"
84fa126cb19d569d2f0147bf6f9e26b54c9ad4f1,Improved Boosting Performance by Explicit Handling of Ambiguous Positive Examples,"Improved Boosting Performance by Explicit
Handling of Ambiguous Positive Examples
Miroslav Kobetski and Josephine Sullivan"
84be05dd82a7208a6e7b3d238df27b123cc917ce,Revisiting Visual Question Answering Baselines,"Revisiting Visual Question Answering Baselines
Allan Jabri, Armand Joulin, and Laurens van der Maaten
Facebook AI Research"
42ecfc3221c2e1377e6ff849afb705ecd056b6ff,Pose Invariant Face Recognition Under Arbitrary Unknown Lighting Using Spherical Harmonics,"Pose Invariant Face Recognition under Arbitrary
Lei Zhang and Dimitris Samaras
Department of Computer Science,
SUNY at Stony Brook, NY, 11790
{lzhang,"
42b56c77e4b154364763d4024baa8129da75151f,Deep Detection of People and their Mobility Aids for a Hospital Robot,"Deep Detection of People and their Mobility Aids for a Hospital Robot
Andres Vasquez
Marina Kollmitz
Andreas Eitel
Wolfram Burgard"
42f4653f0693f16e087e4b913407d9b0278154c9,3D Human Action Recognition with Siamese-LSTM Based Deep Metric Learning,"D Human Action Recognition with Siamese-
LSTM Based Deep Metric Learning
VisLab, Department of Computer Engineering, Gebze Technical University, Kocaeli, Turkey
Seyma Yucer and Yusuf Sinan Akgul
Email: {syucer,"
42e793b1dd6669b74ad106071c432aa5015b8631,How do people think about interdependence? A multidimensional model of subjective outcome interdependence.,"tapraid5/z2g-perpsy/z2g-perpsy/z2g99917/z2g4623d17z
xppws S⫽1
8/10/17
:53 Art: 2016-0710
APA NLM
017, Vol. 0, No. 999, 000
0022-3514/17/$12.00
© 2017 American Psychological Association
http://dx.doi.org/10.1037/pspp0000166
How Do People Think About Interdependence? A Multidimensional Model
of Subjective Outcome Interdependence
Fabiola H. Gerpott, Daniel Balliet,
Simon Columbus, and Catherine Molho
Vrije Universiteit Amsterdam
Reinout E. de Vries
Vrije Universiteit Amsterdam and University of Twente
Interdependence is a fundamental characteristic of social interactions. Interdependence Theory states that
6 dimensions describe differences between social situations. Here we examine if these 6 dimensions
describe how people think about their interdependence with others in a situation. We find that people (in
situ and ex situ) can reliably differentiate situations according to 5, but not 6, dimensions of interde-"
424e918134ed7c70fa73450bd6af1bd982071a27,Final Report : Localized object detection with Convolutional Neural Networks,"Final Report: Localized object detection with Convolutional
Computer Vision
Neural Networks
Bardia Doosti
Vijay Hareesh Avula
May 5, 2016"
423aacfe7467961e32f012bc6de10d636ebc0236,Breaking the interactive bottleneck in multi-class classification with active selection and binary feedback,"MITSUBISHI ELECTRIC RESEARCH LABORATORIES
http://www.merl.com
Breaking the Interactive Bottleneck in
Multi-Class Classification with Active
Selection and Binary Feedback
Ajay Joshi, Fatih Porikli, Nikolaos Papanikolopoulos
TR2010-037
July 2010"
4264342722c48bb334d19b993400c5a133819e51,Nasal Patches and Curves for Expression-Robust 3D Face Recognition,"Nasal Patches and Curves for
Expression-robust 3D Face Recognition
Mehryar Emambakhsh and Adrian Evans"
4209783b0cab1f22341f0600eed4512155b1dee6,Accurate and Efficient Similarity Search for Large Scale Face Recognition,"Accurate and Efficient Similarity Search for Large Scale Face Recognition
Ce Qi
Zhizhong Liu
Fei Su"
4213502d0f226b9845b00c2882851ba4c57742ab,Does Rabbit Antithymocyte Globulin (Thymoglobuline®) Have a Role in Avoiding Delayed Graft Function in the Modern Era of Kidney Transplantation?,"Hindawi
Journal of Transplantation
Volume 2018, Article ID 4524837, 11 pages
https://doi.org/10.1155/2018/4524837
Review Article
Does Rabbit Antithymocyte Globulin (ThymoglobulineD)
Have a Role in Avoiding Delayed Graft Function in the Modern
Era of Kidney Transplantation?
Lluís Guirado
Department of Renal Transplantation, Fundaci´o Puigvert, Barcelona, Spain
Correspondence should be addressed to Llu´ıs Guirado;
Received 12 April 2018; Accepted 20 June 2018; Published 12 July 2018
Academic Editor: Andreas Zuckermann
Copyright © 2018 Llu´ıs Guirado. This is an open access article distributed under the Creative Commons Attribution License, which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Delayed graft function (DGF) increases the risk of graft loss by up to 40%, and recent developments in kidney donation have
increased the risk of its occurrence. Lowering the risk of DGF, however, is challenging due to a complicated etiology in which
ischemia-reperfusion injury (IRI) leads to acute tubular necrosis. Among various strategies explored, the choice of induction
therapy is one consideration. Rabbit antithymocyte globulin (rATG [Thymoglobuline]) has complex immunomodulatory effects
that are relevant to DGF. In addition to a rapid and profound T-cell depletion, rATG inhibits leukocyte migration and adhesion."
42ab6c438bf5a6e0e74cc2dd9192a12f2406ca33,Nonlinear Dimensionality Reduction by Manifold Unfolding,"Nonlinear Dimensionality Reduction
y Manifold Unfolding
Pooyan Khajehpour Tadavani
A thesis
presented to the University of Waterloo
in fulfillment of the
thesis requirement for the degree of
Doctor of Philosophy
Computer Science
Waterloo, Ontario, Canada, 2013
(cid:13) Pooyan Khajehpour Tadavani 2013"
428017f7a6df4d667275c7ac9b3feba39b70e4ae,CNN-RNN: A Unified Framework for Multi-label Image Classification,"CNN-RNN: A Unified Framework for Multi-label Image Classification
Jiang Wang1 Yi Yang1
Junhua Mao2
Zhiheng Huang3∗ Chang Huang4∗ Wei Xu1
Baidu Research
University of California at Los Angles
Facebook Speech
Horizon Robotics"
42cc8637a5e7b8203722ba0dca995814f6dfd525,PETS 2016: Dataset and Challenge,"PETS 2016: Dataset and Challenge
Luis Patino*, Tom Cane**, Alain Vallee*** and James Ferryman*
*University of Reading, Computational Vision Group, Reading RG6 6AY, United Kingdom,
{j.l.patinovilchis,
**BMT Group Ltd., Teddington TW11 8LZ. United Kingdom,
***SAGEM, 92659 Boulogne-Billancourt, France,"
429b8d5bb05e1a580fad0222b9e9496985465e40,"See No Evil, Say No Evil: Description Generation from Densely Labeled Images","Proceedings of the Third Joint Conference on Lexical and Computational Semantics (*SEM 2014), pages 110–120,
Dublin, Ireland, August 23-24 2014.
(Count:3) Isa: ride, vehicle,… Doing: parking,… Has: steering wheel,… Attrib: black, shiny,… children (Count:2) Isa: kids, children … Doing: biking, riding … Has: pants, bike … Attrib: young, small … bike (Count:1) Isa: bike, bicycle,… Doing: playing,… Has: chain, pedal,… Attrib: silver, white,… women(Count:3) Isa: girls, models,… Doing: smiling,...  Has: shorts, bags,… Attrib: young, tan,… purses(Count:3) Isa: accessory,… Doing: containing,… Has: body, straps,… Attrib: black, soft,… sidewalk(Count:1) Isa: sidewalk, street,… Doing: laying,… Has: stone, cracks,… Attrib: flat, wide,… woman(Count:1) Isa: person, female,… Doing: pointing,… Has: nose, legs,… Attrib: tall, skinny,… tree(Count:1) Isa: plant,… Doing: growing,… Has: branches,… Attrib: tall, green,… kids(Count:5) Isa: group, teens,… Doing: walking,… Has: shoes, bags,… Attrib: young,… Fiveyoungpeopleonthestreet,twosharingabicycle.Severalyoungpeoplearewalkingnearparkedvehicles.Threegirlswithlargehandbagswalkingdownthesidewalk.Threewomenwalkdownacitystreet,asseenfromabove.Threeyoungwomanwalkingdownasidewalklookingup.Figure1:Anannotatedimagewithhumangeneratedsen-tencedescriptions.Eachboundingpolygonencompassesoneormoreobjectsandisassociatedwithacountandtextla-bels.Thisimagehas9highlevelobjectsannotatedwithover250textuallabels.tomuchofthevisualcontentneededtogeneratecomplete,human-likesentences.Inthispaper,weinsteadstudygenerationwithmorecompletevisualsupport,asprovidedbyhu-manannotations,allowingustodevelopmorecomprehensivemodelsthanpreviouslyconsid-ered.Suchmodelshavethedualbenefitof(1)providingnewinsightsintohowtoconstructmorehuman-likesentencesand(2)allowingustoper-formexperimentsthatsystematicallystudythecontributionofdifferentvisualcuesingeneration,suggestingwhichautomaticdetectorswouldbemostbeneficialforgeneration.Inanefforttoapproximaterelativelycompletevisualrecognition,wecollectedmanuallylabeledrepresentationsofobjects,parts,attributesandac-tivitiesforabenchmarkcaptiongenerationdatasetthatincludesimagespairedwithhumanauthored"
4263630a35c5ee34ccf9dbd81c0541d92d0c7d5b,Shape Variation-Based Frieze Pattern for Robust Gait Recognition,"Shape Variation-Based Frieze Pattern for Robust Gait Recognition
Seungkyu Lee*                    Yanxi Liu*                     Robert Collins
Dept. of Computer Science and Eng. *Dept. of Electrical Eng.
The Penn State University"
42cc9ea3da1277b1f19dff3d8007c6cbc0bb9830,Coordinated Local Metric Learning,"Coordinated Local Metric Learning
Shreyas Saxena
Jakob Verbeek
Inria∗"
421387011b5cdd2cb4a1fdf04728d350741a0ac1,Incidental memory for faces in children with different genetic subtypes of Prader-Willi syndrome,"Social Cognitive and Affective Neuroscience, 2017, 918–927
doi: 10.1093/scan/nsx013
Advance Access Publication Date: 17 February 2017
Original article
Incidental memory for faces in children with different
genetic subtypes of Prader-Willi syndrome
Alexandra P. Key,1,2 and Elisabeth M. Dykens1,3
Vanderbilt Kennedy Center for Research on Human Development, 2Department of Hearing and Speech
Sciences, Vanderbilt University Medical Center, and 3Department of Psychology and Human Development,
Vanderbilt University, Nashville, TN 37203, USA
Correspondence should be addressed to Alexandra P. Key, Vanderbilt Kennedy Center, Peabody Box 74, Vanderbilt University, Nashville, TN 37203, USA.
E-mail:"
421b3a33ec70af2d733310f6c83ad713a314951d,Using nasal curves matching for expression robust 3D nose recognition,"Emambakhsh, M., Evans, A. and Smith, M. (2013) Using nasal curves
matching for expression robust 3D nose recognition. In: IEEE Con-
ference on Biometrics: Theory, Applications and Systems (BTAS2013),
Washington DC, USA, September 29th - October 2, 2013. Available
from: http://eprints.uwe.ac.uk/20812
We recommend you cite the published version.
The publisher’s URL is:
http://eprints.uwe.ac.uk/20812/
Refereed: Yes
(no note)
Disclaimer
UWE has obtained warranties from all depositors as to their title in the material
deposited and as to their right to deposit such material.
UWE makes no representation or warranties of commercial utility, title, or fit-
ness for a particular purpose or any other warranty, express or implied in respect
of any material deposited.
UWE makes no representation that the use of the materials will not infringe
ny patent, copyright, trademark or other property or proprietary rights.
UWE accepts no liability for any infringement of intellectual property rights
in any material deposited but will remove such material from public view pend-"
426b47af132293e9ffe6071a3ede59cfdc1aa3fb,Promoting social behavior with oxytocin in high-functioning autism spectrum disorders.,"Promoting social behavior with oxytocin in high-
functioning autism spectrum disorders
Elissar Andaria, Jean-René Duhamela, Tiziana Zallab, Evelyn Herbrechtb, Marion Leboyerb, and Angela Sirigua,1
Centre de Neuroscience Cognitive, Unité Mixte de Recherche 5229, Centre National de la Recherche Scientifique, 69675 Bron, France; and bInstitut National
de la Santé et de la Recherche Médicale U 841, Department of Psychiatry, Hôpital Chenevier-Mondor, 94000 Créteil, France
Edited by Leslie G. Ungerleider, National Institute of Mental Health, Bethesda, MD, and approved January 7, 2010 (received for review September 8, 2009)
Social adaptation requires specific cognitive and emotional compe-
tences. Individuals with high-functioning autism or with Asperger
syndrome cannot understand or engage in social situations despite
preserved intellectual abilities. Recently, it has been suggested that
oxytocin, a hormone known to promote mother-infant bonds, may
e implicated in the social deficit of autism. We investigated the
ehavioral effects of oxytocin in 13 subjects with autism.
simulated ball game where participants interacted with fictitious
partners, we found that after oxytocin inhalation, patients
exhibited stronger interactions with the most socially cooperative
partner and reported enhanced feelings of trust and preference.
Also, during free viewing of pictures of faces, oxytocin selectively
increased patients’ gazing time on the socially informative region of
the face, namely the eyes. Thus, under oxytocin, patients respond"
423cfa55a14cd92ada32245b416b587ef9c29308,Visually-Grounded Bayesian Word Learning,"Visually-Grounded Bayesian Word Learning
Yangqing Jia
Joshua Abbott
Joseph Austerweil
Thomas Griffiths
Trevor Darrell
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2012-202
http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-202.html
October 17, 2012"
422d352a7d26fef692a3cd24466bfb5b4526efea,Pedestrian interaction in tracking : the social force model and global optimization methods,"Pedestrian interaction in tracking: the social
force model and global optimization methods
Laura Leal-Taix´e and Bodo Rosenhahn"
42d8a6b1ef5acaaf4640a8974c6f99d60b56090c,Markerless Motion Capture of Multiple Characters Using Multiview Image Segmentation,"SUBMIT TO IEEE TRANS. PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. XX, NO. XX, AUGUST 2012
Markerless Motion Capture of Multiple Characters
Using Multi-view Image Segmentation
Yebin Liu, Juergen Gall Member, IEEE, Carsten Stoll, Qionghai Dai Senior Member, IEEE,
Hans-Peter Seidel, and Christian Theobalt"
42afe5fd3f7b1d286a20e9306c6bc8624265f658,FACE DETECTION USING THE 3×3 BLOCK RANK PATTERNS OF GRADIENT MAGNITUDE IMAGES,"Signal & Image Processing : An International Journal (SIPIJ) Vol.4, No.5, October 2013
FACE DETECTION USING THE 3×3 BLOCK RANK
PATTERNS OF GRADIENT MAGNITUDE IMAGES
Kang-Seo Park, Young-Gon Kim, and Rae-Hong Park
Department of Electronic Engineering, School of Engineering, Sogang University,
5 Baekbeom-ro (Sinsu-dong), Mapo-gu, Seoul 121-742, Korea"
4297deda7ea77fb90de2509c763738584b2353de,Beyond one billion time series: indexing and mining very large time series collections with $$i$$ SAX2+,"Knowl Inf Syst
DOI 10.1007/s10115-012-0606-6
REGULAR PAPER
Beyond one billion time series: indexing and mining very
large time series collections with iSAX2+
Alessandro Camerra · Jin Shieh · Themis Palpanas ·
Thanawin Rakthanmanon · Eamonn Keogh
Received: 23 March 2012 / Revised: 23 September 2012 / Accepted: 28 December 2012
© Springer-Verlag London 2013"
428e42f8d5cbffc068e2e5fe8f697c9c9ee113a9,Deep Multimodal Subspace Clustering Networks,"IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, VOL. X, NO. X, SEPTEMBER 21, 2018
Deep Multimodal Subspace Clustering Networks
Mahdi Abavisani, Student Member, IEEE and Vishal M. Patel, Senior Member, IEEE"
42f8ef9d5ebf969a7e2b4d1eef4b332db562e5d4,Which Training Methods for GANs do actually Converge?,"Which Training Methods for GANs do actually Converge?
Lars Mescheder 1 Andreas Geiger 1 2 Sebastian Nowozin 3"
42d9cb791b8aa3a8658e3ac34e41a1bad1935610,Tracking Sports Players with Context-Conditioned Motion Models,"Tracking Sports Players with Context-Conditioned Motion Models
Jingchen Liu1
The Pennsylvania State University
Peter Carr2"
423e8cc1a7501066b7e0e5bb1beb5b9592337023,Accurate eye center localization using Snakuscule,"Accurate Eye Center Localization using Snakuscule
Abhinav Tripathi
Microsoft Research India
Edward Cutrell
Microsoft Research India
Sanyam Garg
Microsoft Research India"
4275ef99c717c5dedd88f2e0b578df5216da2183,Facial Face Recognition Method using Fourier Transform Filters,"International Conference on Intelligent Systems and Data Processing (ICISD) 2011
Proceedings published by International Journal of Computer Applications® (IJCA)
Facial Face Recognition Method using Fourier
Transform Filters Gabor and R_LDA
Anissa Bouzalmat
Arsalane Zarghili
Jamal Kharroubi
Sidi Mohamed Ben Abdellah
Sidi Mohamed Ben Abdellah
Sidi Mohamed Ben Abdellah
University
University
University
Department of Computer
Department of Computer
Department of Computer
Science Faculty of Science and
Science Faculty of Science and
Science Faculty of Science and
Technology"
42966f6d506f09f990a42fe422f69895235f9bee,Video-Based Face Recognition and Tracking from a Robot Companion,"1st October 2008
0:25 WSPC/INSTRUCTION FILE
Video-based Face Recognition and Tracking from a Robot Companion
T.Germa†, F.Lerasle†, T.Simon¶
LAAS-CNRS, Universit´e de Toulouse, Toulouse, FRANCE
¶ IUT Figeac, LRPmip-Perceval, avenue de Nayrac, 46100 Figeac, France
http://www.laas.fr/∼tgerma/hri
This paper deals with video-based face recognition and tracking from a camera mounted
on a mobile robot companion. All persons must be logically identified before being
uthorized to interact with the robot while continuous tracking is compulsory in order
to estimate the person’s approximate position. A first contribution relates to experiments
of still-image-based face recognition methods in order to check which image projection
nd classifier associations give the highest performance of the face database acquired
from our robot. Our approach, based on Principal Component Analysis (PCA) and
Support Vector Machines (SVM) improved by genetic algorithm optimization of the free-
parameters, is found to outperform conventional appearance-based holistic classifiers
(eigenface and Fisherface) which are used as benchmarks. Relative performances are
nalyzed by means of Receiver Operator Characteristics which systematically provide
optimized classifier free-parameter settings. Finally, for the SVM-based classifier, we
propose a non-dominated sorting genetic algorithm to obtain optimized free-parameter"
4265269bc894caa97efbfcfe5b83da7413f86a30,Asymmetric Tri-training for Unsupervised Domain Adaptation,"Asymmetric Tri-training for Unsupervised Domain Adaptation
Kuniaki Saito 1 Yoshitaka Ushiku 1 Tatsuya Harada 1"
426c39358d592be673b72d26dad4560b06ec5d89,Human Trajectory Prediction using Spatially aware Deep Attention Models,"Human Trajectory Prediction using Spatially aware
Deep Attention Models
Daksh Varshneya
IIIT-B∗
G. Srinivasaraghavan
IIIT-B ∗"
4273a9d1605a69ac66440352b92ebeb230fd34f6,Simple Test Procedure for Image-based Biometric Veriication Systems,"SimpleTestProcedureforImage-BasedBiometric
Veri(cid:12)cationSystems
C.L.Wilson,R.M.McCabe
InformationTechnologyLaboratory
NationalInstituteofStandardsandTechnology
Gaithersburg,MD"
429d4848d03d2243cc6a1b03695406a6de1a7abd,"Identification Mode, it is process of one-to-many comparison of the test set with database to identify an unknown","Face Recognition based on Logarithmic Fusion
International Journal of Soft Computing and Engineering (IJSCE)
ISSN: 2231-2307, Volume-2, Issue-3, July 2012
of SVD and KT
Ramachandra A C, Raja K B, Venugopal K R, L M Patnaik"
42a6ae6827f8cc92e15191e53605b0aa4f875fb9,Challenges in Autonomous Vehicle Testing and Validation,"Preprint: 2016 SAE World Congress
016-01-0128 / 16AE-0265
Challenges in Autonomous Vehicle Testing and Validation
Philip Koopman & Michael Wagner
Carnegie Mellon University; Edge Case Research LLC"
424e52158b43e40f356af7eafb35c91a9e13db30,"Impact Factor : 3 . 449 ( ISRA ) , Impact Factor : 2 .","[Randive, 4(1): January, 2015]
ISSN: 2277-9655
Scientific Journal Impact Factor: 3.449
(ISRA), Impact Factor: 2.114
IJESRT
INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH
TECHNOLOGY
AN INNOVATIVE APPROACH FOR PLASTIC SURGERY FACE RECOGNITION-A
Mahendra P. Randive *, Prof. Umesh W. Hore
REVIEW
*Student of M.E. Department of Electronics & Telecommunication Engineering, P. R. Patil College of
Engineering, Amravati Maharashtra – India."
42e898ca773dbd9e085ffa824c21d0bfda245345,LOTS about attacking deep features,"This is a pre-print of the original paper accepted at the International Joint Conference on Biometrics (IJCB) 2017.
LOTS about Attacking Deep Features
Andras Rozsa, Manuel G¨unther, and Terrance E. Boult
Vision and Security Technology (VAST) Lab
University of Colorado, Colorado Springs, USA"
4270460b8bc5299bd6eaf821d5685c6442ea179a,"Partial Similarity of Objects, or How to Compare a Centaur to a Horse","Int J Comput Vis (2009) 84: 163–183
DOI 10.1007/s11263-008-0147-3
Partial Similarity of Objects, or How to Compare a Centaur
to a Horse
Alexander M. Bronstein · Michael M. Bronstein · Alfred
M. Bruckstein · Ron Kimmel
Received: 30 September 2007 / Accepted: 3 June 2008 / Published online: 26 July 2008
© Springer Science+Business Media, LLC 2008"
42e155ea109eae773dadf74d713485be83fca105,Sparse reconstruction of facial expressions with localized gabor moments,
42c645df49106b68a71abe757ac13245db4be394,A New Method of Illumination Normalization for Robust Face Recognition,"A New Method of Illumination Normalization
for Robust Face Recognition
Young Kyung Park, Bu Cheon Min, and Joong Kyu Kim
School of Information and Communication Engineering, SungKyunKwan University.
00, Chun-Chun-Dong, Chang-An-Ku, Suwon, Korea 440-746
{multipym,"
4212a93f011aa47c6344c0cdc3e991740d8c7c04,Zero-Shot Kernel Learning,"Zero-Shot Kernel Learning
Hongguang Zhang∗,2,1
Piotr Koniusz∗,1,2
Data61/CSIRO, 2Australian National University
nu.edu.au2}"
42832bcb36ee3f69327c38d0d17e6e2a73aaa2a6,SUN Database: Exploring a Large Collection of Scene Categories,"Int J Comput Vis
DOI 10.1007/s11263-014-0748-y
SUN Database: Exploring a Large Collection of Scene Categories
Jianxiong Xiao · Krista A. Ehinger · James Hays ·
Antonio Torralba · Aude Oliva
Received: 9 June 2013 / Accepted: 2 July 2014
© Springer Science+Business Media New York 2014"
42dadeee13686e555435f9426cb9840fc085b23a,Challenges in Designing Datasets and Validation for Autonomous Driving,"CHALLENGES IN DESIGNING DATASETS AND VALIDATION FOR
AUTONOMOUS DRIVING
Michal Uˇriˇc´aˇr1, David Hurych1, Pavel Kˇr´ıˇzek1 and Senthil Yogamani2
Valeo R&D DVS, Prague, Czech Republic
{michal.uricar, david.hurych,
Valeo Vision Systems, Tuam, Ireland
Keywords:
Visual Perception, Design of Datasets, Validation Scheme, Automated Driving."
42e0d7fe2039b075ac2372d883fa994eb0a68b48,Learning human actions in video,"Learning human actions in video
Alexander Klaser
To cite this version:
Alexander Klaser. Learning human actions in video. Modeling and Simulation. Institut Na-
tional Polytechnique de Grenoble - INPG, 2010. English. <tel-00514814>
HAL Id: tel-00514814
https://tel.archives-ouvertes.fr/tel-00514814
Submitted on 3 Sep 2010
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,
´emanant des ´etablissements d’enseignement et de
recherche fran¸cais ou ´etrangers, des laboratoires
publics ou priv´es."
42954bb6babed6fc16646c6f34083b720593cce9,Bringing Order in the Bag of Words,"BRINGING ORDER IN THE BAG OF WORDS
Shihong Zhang, Rahat Khan, Damien Muselet and Alain Tr´emeau
Universit´e de Lyon, F-42023, Saint- ´Etienne, France
CNRS, UMR 5516, Laboratoire Hubert Curien, F-42000, Saint- ´Etienne, France
Universit´e de Saint- ´Etienne, Jean-Monnet, F-42000, Saint- ´Etienne, France
Keywords:
Bag-of-words, Object Categorization, Spatial Information."
42df75080e14d32332b39ee5d91e83da8a914e34,Illumination Compensation Using Oriented Local Histogram Equalization and its Application to Face Recognition,"Illumination Compensation Using Oriented
Local Histogram Equalization and
Its Application to Face Recognition
Ping-Han Lee, Szu-Wei Wu, and Yi-Ping Hung"
426840ccf74bbd8b087cf357efdb80ecc85ea2ab,Reduced Analytic Dependency Modeling: Robust Fusion for Visual Recognition,"Noname manuscript No.
(will be inserted by the editor)
Reduced Analytic Dependency Modeling: Robust Fusion for Visual
Recognition
Andy J Ma · Pong C Yuen
Received: date / Accepted: date"
42dc36550912bc40f7faa195c60ff6ffc04e7cd6,Visible and Infrared Face Identification via Sparse Representation,"Hindawi Publishing Corporation
ISRN Machine Vision
Volume 2013, Article ID 579126, 10 pages
http://dx.doi.org/10.1155/2013/579126
Research Article
Visible and Infrared Face Identification via
Sparse Representation
Pierre Buyssens1 and Marinette Revenu2
LITIS EA 4108-QuantIF Team, University of Rouen, 22 Boulevard Gambetta, 76183 Rouen Cedex, France
GREYC UMR CNRS 6072 ENSICAEN-Image Team, University of Caen Basse-Normandie, 6 Boulevard Mar´echal Juin,
4050 Caen, France
Correspondence should be addressed to Pierre Buyssens;
Received 4 April 2013; Accepted 27 April 2013
Academic Editors: O. Ghita, D. Hernandez, Z. Hou, M. La Cascia, and J. M. Tavares
Copyright © 2013 P. Buyssens and M. Revenu. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
ited.
We present a facial recognition technique based on facial sparse representation. A dictionary is learned from data, and patches
extracted from a face are decomposed in a sparse manner onto this dictionary. We particularly focus on the design of dictionaries
that play a crucial role in the final identification rates. Applied to various databases and modalities, we show that this approach"
fab0d19c58815eccb0db7215fe45d6a32066ca1c,Inferring Human Attention by Learning Latent Intentions,"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
the mug's statuschecking the book's statuslocating the dispenserFigure1:Humanattentionandintentionsina3Dscene.thedispenser,hisattentionsweepsfromthetabletothedis-penser;whilefetchingwaterfromthedispenser,hisintentionistocheckifthemugisfullandhisattentionsteadilyfocusesonthemug.Thedrivingrulesofintentionsactingonattentioncanbeindependentofactivitycategories.Forexample,inFigure1,theattentiondrivenbytheintentioncheckingstatusalwayspresentsassteadilyfocusing,evenindifferentactivities.Thisphenomenonmakesitpossibletoinfertheattentionwiththesamerulesacrossdifferentactivities.However,thesedrivingrulesarehiddenandshouldbelearnedfromdata.Thispaperproposesaprobabilisticmethodtoinfer3Dhu-manattentionbyjointlymodelingattention,intentions,andtheirinteractions.Theattentionandintentionarerepresent-edwithfeaturesextractedfromhumanskeletonsandscenevoxels.Humanintentionsaretakenaslatentvariableswhichguidethemotionsandformsofhumanattention.Conversely,thehumanattentionrevealstheintentionfeatures.Attentioninferenceismodeledasajointoptimizationwithlatenthu-manintentions.WeadoptanEM-based[Bishop,2006]approachtolearnthemodelparametersandminethelatentintentions.Giv-enanRGB-DvideowithhumanskeletonscapturedbytheKinectcamera,ajoint-statedynamicprogrammingalgorithm"
fa08a4da5f2fa39632d90ce3a2e1688d147ece61,Supplementary material for “ Unsupervised Creation of Parameterized Avatars ” 1 Summary of Notations,"Supplementary material for
“Unsupervised Creation of Parameterized Avatars”
Summary of Notations
Tab. 1 itemizes the symbols used in the submission. Fig. 2,3,4 of the main text illustrate many of these
symbols.
DANN results
Fig. 1 shows side by side samples of the original image and the emoji generated by the method of [1].
As can be seen, these results do not preserve the identity very well, despite considerable effort invested in
finding suitable architectures.
Multiple Images Per Person
Following [4], we evaluate the visual quality that is obtained per person and not just per image, by testing
TOS on the Facescrub dataset [3]. For each person p, we considered the set of their images Xp, and selected
the emoji that was most similar to their source image, i.e., the one for which:
||f (x) − f (e(c(G(x))))||.
rgmin
Fig. 2 depicts the results obtained by this selection method on sample images form the Facescrub dataset
(it is an extension of Fig. 7 of the main text). The figure also shows, for comparison, the DTN [4] result for
the same image.
Detailed Architecture of the Various Networks
In this section we describe the architectures of the networks used in for the emoji and avatar experiments."
faf40ce28857aedf183e193486f5b4b0a8c478a2,Automated Human Identification Using Ear Imaging,"Imperial Journal of Interdisciplinary Research (IJIR)
Vol.2, Issue-1 , 2016
ISSN : 2454-1362 , www.onlinejournal.in
Automated Human Identification Using Ear Imaging
Priya Thakare
SITS.Narhe
Abhijit Patil
SITS, Narhe.
Priya More
SITS, Narhe.
Vivek Patil
SITS, Narhe.
Akshay Shende
SITS, Narhe.
Reliability
in  human  authentication
from  airport  surveillance
important aspect for the security requirements in various
pplications  ranging
electronic  banking.  Many  physical  characteristics  of"
fab83bf8d7cab8fe069796b33d2a6bd70c8cefc6,Draft : Evaluation Guidelines for Gender Classification and Age Estimation,"Draft: Evaluation Guidelines for Gender
Classification and Age Estimation
Tobias Gehrig, Matthias Steiner, Hazım Kemal Ekenel
{tobias.gehrig,
July 1, 2011
Introduction
In previous research on gender classification and age estimation did not use a
standardised evaluation procedure. This makes comparison the different ap-
proaches dif‌f‌icult.
Thus we propose here a benchmarking and evaluation protocol for gender
lassification as well as age estimation to set a common ground for future re-
search in these two areas.
The evaluations are designed such that there is one scenario under controlled
labratory conditions and one under uncontrolled real life conditions.
The datasets were selected with the criteria of being publicly available for
research purposes.
File lists for the folds corresponding to the individual benchmarking proto-
ols will be provided over our website at http://face.cs.kit.edu/befit. We
will provide two kinds of folds for each of the tasks and conditions: one set of
folds using the whole dataset and one set of folds using a reduced dataset, which"
fae4185a5fc540b057ea9e0402223e653327d0f9,Structured Edge Detection for Improved Object Localization using the Discriminative Generalized Hough Transform,
fa11590fea86049fff1eb412642753422738c584,Depression-related difficulties disengaging from negative faces are associated with sustained attention to negative feedback during social evaluation and predict stress recovery,"RESEARCH ARTICLE
Depression-related difficulties disengaging
from negative faces are associated with
sustained attention to negative feedback
during social evaluation and predict stress
recovery
Alvaro Sanchez*, Nuria Romero, Rudi De Raedt
Department of Experimental Clinical and Health Psychology, Ghent University, Ghent, Belgium"
fa60521dabd2b64137392b4885e4d989f4b86430,Physics-Based Generative Adversarial Models for Image Restoration and Beyond,"Physics-Based Generative Adversarial Models
for Image Restoration and Beyond
Jinshan Pan, Yang Liu, Jiangxin Dong, Jiawei Zhang,
Jimmy Ren, Jinhui Tang, Yu-Wing Tai and Ming-Hsuan Yang"
faccce1a55c0c0ac767b74782c862a3eed0d1065,SIGNet: Semantic Instance Aided Unsupervised 3D Geometry Perception,"SIGNet: Semantic Instance Aided Unsupervised 3D Geometry Perception
Yue Meng1
Yongxi Lu1
Tara Javidi1
Aman Raj1
Gaurav Bansal2
Samuel Sunarjo1
Dinesh Bharadia1
Rui Guo2
UC San Diego
Toyota InfoTechnology Center
{yum107, yol070, amraj, ssunarjo, tjavidi,"
fabbc7f921d77b5aa9157310df29ad81367fe92d,Efficient Image and Video Representations for Retrieval,
fad895771260048f58d12158a4d4d6d0623f4158,Audio-visual emotion recognition for natural human-robot interaction,"Audio-Visual Emotion
Recognition For Natural
Human-Robot Interaction
Dissertation zur Erlangung des akademischen Grades
Doktor der Ingenieurwissenschaften (Dr.-Ing.)
vorgelegt von
Ahmad Rabie
n der Technischen Fakultät der Universität Bielefeld
5. März 2010"
fafa7bbd6b37dc97237155654e1a4d1f1aba70f8,Radial Basis Function Neuroscaling Algorithms for Efficient Facial Image Recognition,"Machine Learning Research
017; 2(4): 152-168
http://www.sciencepublishinggroup.com/j/mlr
doi: 10.11648/j.mlr.20170204.16
Radial Basis Function Neuroscaling Algorithms for Efficient
Facial Image Recognition
Vincent A. Akpan1, *, Joshua B. Agbogun2, Michael T. Babalola3, Bamidele A. Oluwade4
Department of Biomedical Technology, The Federal University of Technology, Akure, Nigeria
Department of Computer Science, Kogi State University, Anyigba, Nigeria
Department of Physics Electronics, Afe Babalola University, Ado-Ekiti, Nigeria
Department of Computer Science, University of Ilorin, Ilorin, Nigeria
Email address:
(V. A. Akpan), (J. B. Agbogun), (M. T. Babalola),
(B. A. Oluwade)
*Corresponding author
To cite this article:
Vincent A. Akpan, Joshua B. Agbogun, Michael T. Babalola, Bamidele A. Oluwade. Radial Basis Function Neuroscaling Algorithms for
Efficient Facial Image Recognition. Machine Learning Research. Vol. 2, No. 4, 2017, pp. 152-168. doi: 10.11648/j.mlr.20170204.16
Received: September 30, 2017; Accepted: November 9, 2017; Published: December 28, 2017"
facdb71e8175c33ec54c2248fa6cfc319e27cfa5,Accelerating Machine Learning Research with MI-Prometheus,"Accelerating Machine Learning Research with
MI-Prometheus
Tomasz Kornuta Vincent Marois Ryan L. McAvoy Younes Bouhadjar
Alexis Asseman
Vincent Albouy
IBM Research AI, Almaden Research Center, San Jose, USA
T.S. Jayram Ahmet S. Ozcan
{tkornut, vmarois, mcavoy, byounes, jayram,
{alexis.asseman,"
fa307dac56600e8fe8a0ee03830ed72b014208e3,Descripteurs d ’ images locaux et méthodes par noyaux pour la classification de textures et de catégories d ’ objets : une étude approfondie,"INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET EN AUTOMATIQUE
Local Features and Kernels for Classification of
Texture and Object Categories: An In-Depth Study
Jianguo Zhang — Marcin Marszałek — Svetlana Lazebnik — Cordelia Schmid
N° 5737
Octobre 2005
Th`eme COG
p p o r t  (cid:13)
(cid:13) d e  r e c h e r c h e (cid:13)"
fafe69a00565895c7d57ad09ef44ce9ddd5a6caa,Gaussian Mixture Models for Human Face Recognition under Illumination Variations,"Applied Mathematics, 2012, 3, 2071-2079
http://dx.doi.org/10.4236/am.2012.312A286 Published Online December 2012 (http://www.SciRP.org/journal/am)
Gaussian Mixture Models for Human Face Recognition
under Illumination Variations
Information Systems and Decision Sciences Department, Mihaylo College of Business and Economics,
California State University, Fullerton, USA
Email:
Sinjini Mitra
Received August 18, 2012; revised September 18, 2012; accepted September 25, 2012"
fac36fa1b809b71756c259f2c5db20add0cb0da0,Transferring GANs: Generating Images from Limited Data,"Transferring GANs: generating images from
limited data
Yaxing Wang, Chenshen Wu, Luis Herranz, Joost van de Weijer,
Abel Gonzalez-Garcia, Bogdan Raducanu
{yaxing, chenshen, lherranz, joost, agonzgarc,
Computer Vision Center
Universitat Aut`onoma de Barcelona, Spain"
fa2603efaf717974c77162c93d800defae61a129,Face recognition/detection by probabilistic decision-based neural network,"Face Recognition/Detection by Probabilistic
Decision-Based Neural Network
Shang-Hung Lin, Sun-Yuan Kung, Fellow, IEEE, and Long-Ji Lin"
faca1c97ac2df9d972c0766a296efcf101aaf969,Sympathy for the Details: Dense Trajectories and Hybrid Classification Architectures for Action Recognition,"Sympathy for the Details: Dense Trajectories and Hybrid
Classification Architectures for Action Recognition
C´esar Roberto de Souza1,2, Adrien Gaidon1, Eleonora Vig3, Antonio Manuel L´opez2
Computer Vision Group, Xerox Research Center Europe, Meylan, France
Centre de Visi´o per Computador, Universitat Aut`onoma de Barcelona, Bellaterra, Spain
German Aerospace Center, Wessling, Germany
{cesar.desouza,"
facf25e1880d23eb993d4ad507256ebbc7e0d82d,CURE-OR: Challenging Unreal and Real Environments for Object Recognition,"Citation D. Temel, J. Lee, and G. AlRegib, “CURE-OR: Challenging unreal and real environments
for object recognition,” 2018 17th IEEE International Conference on Machine Learning
nd Applications (ICMLA), Orlando, Florida, USA, 2018.
Dataset
https://ghassanalregib.com/cure-or/
ICMLA,
uthor={D. Temel and J. Lee and G. AlRegib},
ooktitle={2018 17th IEEE International Conference on Machine Learning and Applications
(ICMLA)},
title={CURE-OR: Challenging unreal and real environments for object recognition},
year=2018,}
Copyright c(cid:13)2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be
obtained for all other uses, in any current or future media, including reprinting/republishing
this material for advertising or promotional purposes, creating new collective works, for
resale or redistribution to servers or lists, or reuse of any copyrighted component of this
work in other works.
Contact
https://ghassanalregib.com/
http://cantemel.com/"
fa24bf887d3b3f6f58f8305dcd076f0ccc30272a,Interval Insensitive Loss for Ordinal Classification,"JMLR: Workshop and Conference Proceedings 39:189–204, 2014
ACML 2014
Interval Insensitive Loss for Ordinal Classification
Kostiantyn Antoniuk
Vojtˇech Franc
V´aclav Hlav´aˇc
Center for Machine Perception, Department of Cybernetics, Faculty of Electrical Engineering, Czech
Technical University in Prague, Technick´a 2, 166 27 Prague 6 Czech Republic
Editor: Dinh Phung and Hang Li"
fa496716a5b8520e94a0126b5baa4f636623c997,Revisiting Knowledge Transfer for Training Object Class Detectors,"Revisiting knowledge transfer for training object class detectors
Jasper R. R. Uijlings
S. Popov
V. Ferrari
Google AI Perception"
fad721b7af838964c98bbb3ebb3f6265b83f950d,Adult Image Content Classification Using Global Features and Skin Region Detection,"Adult Image Content Classification Using Global
Features and Skin Region Detection
Hakan Sevimli1,2, Ersin Esen1,3, Tuğrul K. Ateş1,3, Ezgi C. Ozan1,3,
Mashar Tekin1,2, K. Berker Loğoğlu1,4, Ayça Müge Sevinç1,4, Ahmet Saracoğlu1,3,
Adnan Yazıcı2 and A. Aydın Alatan3
TÜBİTAK Space Technologies Research Institute
Department of Computer Engineering, M.E.T.U.
Department of Electrical and Electronics Engineering, M.E.T.U.
Graduate School of Informatics, M.E.T.U.
{hakan.sevimli, ersin.esen, tugrul.ates, ezgican.ozan, mashar.tekin, berker.logoglu,
muge.sevinc,"
faa111d749eb228c686643e4667dd1bc21c724f2,Condensed from Video Sequences for Place Recognition,"Boosting Descriptors Condensed from Video Sequences for Place Recognition
Tat-Jun Chin, Hanlin Goh and Joo-Hwee Lim
Institute for Infocomm Research
1 Heng Mui Keng Terrace, Singapore 119613.
{tjchin, hlgoh,"
fac0151ed0494caf10c7d778059f176ba374e29c,Recognising Complex Mental States from Naturalistic Human-Computer Interactions,"Copyright and use of this thesis
This thesis must be used in accordance with the
provisions of the Copyright Act 1968.
Reproduction of material protected by copyright
may be an infringement of copyright and
opyright owners may be entitled to take
legal action against persons who infringe their
opyright.
Section 51 (2) of the Copyright Act permits
n authorized officer of a university library or
rchives to provide a copy (by communication
or otherwise) of an unpublished thesis kept in
the library or archives, to a person who satisfies
the authorized officer that he or she requires
the reproduction for the purposes of research
or study.
The Copyright Act grants the creator of a work
number of moral rights, specifically the right of
ttribution, the right against false attribution and
the right of integrity."
fa23122db319440fb5a7253e19709f992b4571b9,Human Age Estimation via Geometric and Textural Features,"HUMAN AGE ESTIMATION VIA GEOMETRIC
AND TEXTURAL FEATURES
Merve Kilinc1 and Yusuf Sinan Akgul2
TUBITAK BILGEM UEKAE, Anibal Street, 41470, Gebze, Kocaeli, Turkey
GIT Vision Lab∗, Department of Computer Engineering, Gebze Institute of Technology, 41400, Kocaeli, Turkey
Keywords:
Age Estimation, Age Classification, Geometric Features, LBP, Gabor, LGBP, Cross Ratio, FGNET, MORPH."
fa4ff855ca125b986bcb2bc6b71bef2ae8fde1cf,"3d Integral Invariant Signatures and Their Application on Face Recognition Dedication I Am Grateful for the Support and Guidance I Have Received from Dr. Irina A. Kogan, and I Also Express My Gratitude To",
fa4544e5dce135c3d0517304ec9e620c78267891,LIDAR-based driving path generation using fully convolutional neural networks,"LIDAR-based Driving Path Generation
Using Fully Convolutional Neural Networks
Luca Caltagirone∗, Mauro Bellone, Lennart Svensson, Mattias Wahde"
fab7f1af3d67c7b7cf76ec1d8dfcb265da61a572,Towards Recommender Systems for Police Photo Lineup,"Towards Recommender Systems for Police Photo Lineup
Ladislav Peska
Department of Software Engineering
Hana Trojanova
Department of Psychology
Faculty of Mathematics and Physics, Charles University, Prague
Faculty of Arts, Charles University, Prague
Czech Republic
Czech Republic"
fab6e12a913223b69e1b9f0672df6c89275b1ed0,Initial Development of a Learners ’ Ratified Acceptance of Multibiometrics Intentions Model ( RAMIM ),"Interdisciplinary Journal of E-Learning and Learning Objects
IJELLO special series of Chais Conference 2009 best papers
Volume 5, 2009
Initial Development of a Learners’ Ratified
Acceptance of Multibiometrics Intentions Model
(RAMIM)
Yair Levy
GSCIS,
Nova Southeastern University,
Ft. Lauderdale, FL, USA
Michelle M. Ramim
Nova Southeastern University,
Huizenga School of Business,
Ft. Lauderdale, FL, USA"
fa4e1906e120c07116858f059c17abfbfa7f145b,Face Recognition Using Chain Codes,"Journal of Signal and Information Processing, 2013, 4, 154-157
doi:10.4236/jsip.2013.43B027 Published Online August 2013 (http://www.scirp.org/journal/jsip)
Face Recognition Using Chain Codes
Nazmeen B. Boodoo-Jahangeer, Sunilduth Baichoo
Department of Computer Science, University of Mauritius, Reduit, Mauritius.
Email:
Received May, 2013."
fa9f1b236d0a252d4a56e26e8a9a41d496803413,Face Recognition Method with Two-Dimensional HMM,"FACE RECOGNITION METHOD WITH
TWO-DIMENSIONAL HMM
Janusz Bobulski1
Czestochowa University of Technology
Institute of Computer and Information Science
Dabrowskiego Street 73, 42-200 Czestochowa, Poland."
fa95ce52d821499547a68048d35f35a0dd171a25,Forecast the Plausible Paths in Crowd Scenes,Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
670637d0303a863c1548d5b19f705860a23e285c,Face swapping: automatically replacing faces in photographs,"Face Swapping: Automatically Replacing Faces in Photographs
Dmitri Bitouk
Neeraj Kumar
Samreen Dhillon∗
Columbia University†
Peter Belhumeur
Shree K. Nayar
Figure 1: We have developed a system that automatically replaces faces in an input image with ones selected from a large collection of
face images, obtained by applying face detection to publicly available photographs on the internet. In this example, the faces of (a) two
people are shown after (b) automatic replacement with the top three ranked candidates. Our system for face replacement can be used for face
de-identification, personalized face replacement, and creating an appealing group photograph from a set of “burst” mode images. Original
images in (a) used with permission from Retna Ltd. (top) and Getty Images Inc. (bottom).
Rendering, Computational Photography
Introduction
Advances in digital photography have made it possible to cap-
ture large collections of high-resolution images and share them
on the internet. While the size and availability of these col-
lections is leading to many exciting new applications,
lso creating new problems. One of the most
important of"
67c30688bd46d305c610a83a0b28e86e10ef5cc4,Ship Detection in Harbour Surveillance based on Large-Scale Data and CNNs,
67dca0d4b87ab2a4f18b5a1ef76f6ba17b599245,Top-Down Regularization of Deep Belief Networks,"Top-Down Regularization of Deep Belief Networks
Hanlin Goh∗, Nicolas Thome, Matthieu Cord
Laboratoire d’Informatique de Paris 6
UPMC – Sorbonne Universit´es, Paris, France
Joo-Hwee Lim†
Institute for Infocomm Research
A*STAR, Singapore"
6740f4918d594094f5eca3c0c65006c9c6d6c1d4,Class Room Attendance System Using Facial Recognition System,"The International Journal of Mathematics, Science, Technology and Management
(ISSN : 2319-8125) Vol. 2 Issue 3
Class Room Attendance System Using Facial
Recognition System
Abhishek Jha
ABES Engineering College, Ghaziabad"
67751b7ce7f934ffadcf095f4189b31f890e9fdc,Pilot Comparative Study of Different Deep Features for Palmprint Identification in Low-Quality Images,"Ninth Hungarian Conference on Computer Graphics and Geometry, Budapest, 2018
Pilot Comparative Study of Different Deep Features
for Palmprint Identification in Low-Quality Images
A.S. Tarawneh1, D. Chetverikov1,2 and A.B. Hassanat3
Eötvös Loránd University, Budapest, Hungary
Institute for Computer Science and Control, Budapest, Hungary
Mutah University, Karak, Jordan"
677585ccf8619ec2330b7f2d2b589a37146ffad7,A flexible model for training action localization with varying levels of supervision,"A flexible model for training action localization
with varying levels of supervision
Guilhem Chéron∗ 1 2
Jean-Baptiste Alayrac∗ 1
Ivan Laptev1
Cordelia Schmid2"
67126ad0af544740c455311d08cb180aec830a6c,Generating Descriptions of Spatial Relations between Objects in Images,"Proceedings of the 15th European Workshop on Natural Language Generation (ENLG), pages 100–104,
Brighton, September 2015. c(cid:13)2015 Association for Computational Linguistics"
67484723e0c2cbeb936b2e863710385bdc7d5368,Anchor Cascade for Efficient Face Detection,"Anchor Cascade for Efficient Face Detection
Baosheng Yu and Dacheng Tao, Fellow, IEEE"
6737a429dd615a0d9ac78d836c6b65bfd9ec36e8,Image Classification by Transfer Learning Based on the Predictive Ability of Each Attribute,"Image Classification by Transfer Learning Based
on the Predictive Ability of Each Attribute
Masahiro Suzuki, Haruhiko Sato, Satoshi Oyama, and Masahito Kurihara"
6733adb12458678c606759233f6f55782bace372,Photogenic Facial Expression Discrimination,"PHOTOGENIC FACIAL EXPRESSION DISCRIMINATION
Luana Bezerra Batista and Herman Martins Gomes
Departamento de Sistemas e Computação
João Marques de Carvalho
Departamento de Engenharia Elétrica
Universidade Federal de Campina Grande
Campina Grande, Paraíba, Brasil, 58.109-970
Keywords:
Facial Expression Recognition, Photogeny, Principal Component Analysis, Multi-Layer Perceptron."
67490b6f34c827f107b046adeef0f5476132d4f8,"How good are detection proposals, really?","J. HOSANG ET AL.: HOW GOOD ARE DETECTION PROPOSALS, REALLY?
How good are detection proposals, really?
Jan Hosang
http://mpi-inf.mpg.de/~jhosang
Rodrigo Benenson
http://mpi-inf.mpg.de/~benenson
Bernt Schiele
http://mpi-inf.mpg.de/~schiele
MPI Informatics
Saarbrücken, Germany"
675b2caee111cb6aa7404b4d6aa371314bf0e647,AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions,"AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
Chunhui Gu∗
Yeqing Li∗
Chen Sun∗
David A. Ross∗
Sudheendra Vijayanarasimhan∗
Carl Vondrick∗
George Toderici∗
Caroline Pantofaru∗
Susanna Ricco∗
Rahul Sukthankar∗
Cordelia Schmid† ∗
Jitendra Malik‡ ∗"
679aded003bce19aa7821295117693b9a5b4f0ef,Fluid Annotation: A Human-Machine Collaboration Interface for Full Image Annotation,"Fluid Annotation: A Human-Machine Collaboration Interface
for Full Image Annotation
Mykhaylo Andriluka∗
Jasper R. R. Uijlings∗
Google Research
Z¨urich, Switzerland
Vi(cid:138)orio Ferrari"
67bf0b6bc7d09b0fe7a97469f786e26f359910ef,Abnormal use of facial information in high-functioning autism.,"J Autism Dev Disord
DOI 10.1007/s10803-006-0232-9
O R I G I N A L P A P E R
Abnormal Use of Facial Information in High-Functioning
Autism
Michael L. Spezio Æ Ralph Adolphs Æ
Robert S. E. Hurley Æ Joseph Piven
Ó Springer Science+Business Media, LLC 2006"
678b367b2d5250f278c994238bbf816098252d9d,IrisDenseNet: Robust Iris Segmentation Using Densely Connected Fully Convolutional Networks in the Images by Visible Light and Near-Infrared Light Camera Sensors,"Article
IrisDenseNet: Robust Iris Segmentation Using
Densely Connected Fully Convolutional Networks in
the Images by Visible Light and Near-Infrared Light
Camera Sensors
Muhammad Arsalan, Rizwan Ali Naqvi, Dong Seop Kim, Phong Ha Nguyen, Muhammad Owais
nd Kang Ryoung Park *
Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu,
Seoul 100-715, Korea; (M.A.); (R.A.N.);
(D.S.K.); (P.H.N.); (M.O.)
* Correspondence: Tel.: +82-10-3111-7022; Fax: +82-2-2277-8735
Received: 2 April 2018; Accepted: 8 May 2018; Published: 10 May 2018"
67e00f7e928e6eab0faf1917252778b36bf64e39,Sparse radial sampling LBP for writer identification,"Sparse Radial Sampling LBP for Writer
Identification
Anguelos Nicolaou∗, Andrew D. Bagdanov∗, Marcus Liwicki†, and Dimosthenis Karatzas∗
Computer Vision Center, Edifici O, Universitad Autonoma de Barcelona,Bellaterra, Spain
DIVA research group, Department of Informatics, University of Fribourg, Switzerland
Email:"
67bee729d046662c6ebd9d3d695823c9d820343a,Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus,"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pages 588–598,
Berlin, Germany, August 7-12, 2016. c(cid:13)2016 Association for Computational Linguistics"
674fcadf1b895e3a79380d3ac5afb43d406fd31a,Facial Asymmetry Assessment from 3D Shape Sequences: The Clinical Case of Facial Paralysis,
6769cfbd85329e4815bb1332b118b01119975a95,Tied Factor Analysis for Face Recognition Across Large Pose Changes,"Tied factor analysis for face recognition across
large pose changes"
67f88f37e4853b870debef2bd29b257b5b19f255,EgoSampling: Wide View Hyperlapse from Single and Multiple Egocentric Videos,"EgoSampling: Wide View Hyperlapse from
Single and Multiple Egocentric Videos
Tavi Halperin Yair Poleg Chetan Arora Shmuel Peleg"
67a6bd37e91f2c334b1092fd9e9b16be93f82377,Data Driven Visual Recognition,"Data Driven Visual Recognition
OMID AGHAZADEH
Doctoral Thesis
Stockholm, Sweden, 2014"
6768b558cc58e113096540c123ef3b2c2d2469a1,Maximum Margin Linear Classifiers in Unions of Subspaces,"LYU, ZEPEDA, PÉREZ: US-SVM
Maximum Margin Linear Classifiers in
Unions of Subspaces
Xinrui Lyu1,2
Joaquin Zepeda1
Patrick Pérez1
Technicolor
5576, Cesson-Sevigne, France
École Polytechnique Fédérale de
Lausanne (EPFL)
CH-1015, Lausanne, Switzerland"
67da607541b8e380c1665c2158e5e0dd4a6f0e49,Learning to Localize Sound Source in Visual Scenes,
6752b59da83c03e64c73f9248a67304713b6efa9,Chapter 3 Re-identification by Covariance Descriptors,"Chapter 3
Re-identification by Covariance Descriptors
Sławomir B ˛ak and François Brémond"
67f42733a8d147bc432bc79171a7c976e99374b3,Motion Prediction of Traffic Actors for Autonomous Driving using Deep Convolutional Networks,"Short-term Motion Prediction of Traffic Actors for
Autonomous Driving using Deep Convolutional Networks
Nemanja Djuric, Vladan Radosavljevic, Henggang Cui,
Thi Nguyen, Fang-Chieh Chou, Tsung-Han Lin and Jeff Schneider1"
67c78fbef7ebcde1b8c4e42415e595fb78317133,Optimization of Face Recognition Algorithms for Smartphone Environment,"International Journal of Security and Its Applications
Vol.7, No.6 (2013), pp.303-308
http://dx.doi.org/10.14257/ijsia.2013.7.6.31
Optimization of Face Recognition Algorithms for Smartphone
Environment
Kanghun Jeong, Dongil Han and Hyeonjoon Moon
School of Computer Science and Engineering, Sejong University, Seoul, Korea
E-mail:"
67c703a864aab47eba80b94d1935e6d244e00bcb,Face Retrieval Based On Local Binary Pattern and Its Variants : A Comprehensive Study,"(IJACSA) International Journal of Advanced Computer Science and Applications
Vol. 7, No. 6, 2016
Face Retrieval Based On Local Binary Pattern and Its
Variants: A Comprehensive Study
Department of Computer Vision and Robotics, University of Science, VNU-HCM, Viet Nam
Phan Khoi, Lam Huu Thien, Vo Hoai Viet
face  searching,"
6720edcea05b31a9b9a6db98ee71e8ed31efdc38,Practices in source code sharing in astrophysics,"Practices
source
sharing
astrophysics
Shamir1,
Wallin2,
Alice
Allen3,
Bruce
Berriman4,
Peter
Teuben5,
Robert
Nemiroff6,
Jessica
Mink7,
Robert
Hanisch8,
Kimberly
DuPrie3"
6757254d27b761ada5dbd88642bd0112fcb962cf,Gait Recognition Using Wearable Motion Recording Sensors,"Hindawi Publishing Corporation
EURASIP Journal on Advances in Signal Processing
Volume 2009, Article ID 415817, 16 pages
doi:10.1155/2009/415817
Research Article
Gait Recognition Using Wearable Motion Recording Sensors
Davrondzhon Gafurov and Einar Snekkenes
Norwegian Information Security Laboratory, Gjøvik University College, P.O. Box 191, 2802 Gjøvik, Norway
Correspondence should be addressed to Davrondzhon Gafurov,
Received 1 October 2008; Revised 26 January 2009; Accepted 26 April 2009
Recommended by Natalia A. Schmid
This paper presents an alternative approach, where gait is collected by the sensors attached to the person’s body. Such wearable
sensors record motion (e.g. acceleration) of the body parts during walking. The recorded motion signals are then investigated for
person recognition purposes. We analyzed acceleration signals from the foot, hip, pocket and arm. Applying various methods,
the best EER obtained for foot-, pocket-, arm- and hip- based user authentication were 5%, 7%, 10% and 13%, respectively.
Furthermore, we present the results of our analysis on security assessment of gait. Studying gait-based user authentication (in case
of hip motion) under three attack scenarios, we revealed that a minimal effort mimicking does not help to improve the acceptance
hances of impostors. However, impostors who know their closest person in the database or the genders of the users can be a
threat to gait-based authentication. We also provide some new insights toward the uniqueness of gait in case of foot motion. In
particular, we revealed the following: a sideway motion of the foot provides the most discrimination, compared to an up-down or"
67a56dd94906a5460c263e1a1b87fa3a52c4b453,FACE ANALYSIS BY LOCAL DIRECTIONAL NUMBER PATTERN,"International Journal of Engineering Research and General Science Volume 3, Issue 1,  January-February, 2015
ISSN 2091-2730
FACE ANALYSIS BY LOCAL DIRECTIONAL NUMBER PATTERN
Manjunatha S B, Guruprasad A M, Vineesh P
Coorg Institute of Technology, Ponnampet, Coorg-District, Karnataka, 9611962024"
67ba3524e135c1375c74fe53ebb03684754aae56,A compact pairwise trajectory representation for action recognition,"978-1-5090-4117-6/17/$31.00 ©2017 IEEE
ICASSP 2017"
ebabf19e66ef1253fda8d39a0569787c65e60a9e,Multi-person Tracking with Sparse Detection and Continuous Segmentation,"Multi-Person Tracking with Sparse Detection and
Continuous Segmentation
Dennis Mitzel1, Esther Horbert1, Andreas Ess2, Bastian Leibe1
UMIC Research Centre RWTH Aachen University, Germany
Computer Vision Laboratory, ETH Zurich, Switzerland"
ebd5df2b4105ba04cef4ca334fcb9bfd6ea0430c,Fast Localization of Facial Landmark Points,"Fast Localization of Facial Landmark Points
Nenad Markuˇs*, Miroslav Frljak*, Igor S. Pandˇzi´c*, J¨orgen Ahlberg†, and Robert Forchheimer†
* University of Zagreb, Faculty of Electrical Engineering and Computing, Unska 3, 10000 Zagreb, Croatia
Link¨oping University, Department of Electrical Engineering, SE-581 83 Link¨oping, Sweden
March 28, 2014"
eb33adf3f8eb5c07b58a1433734ab1fee5d77c93,"Singleton, C. J., Ashwin, C. and Brosnan, M. (2014) Physiological Responses to Social and Nonsocial Stimuli in Neurotypical Adults With High and Low Levels of Autistic Traits:Implications for Understanding Nonsocial Drive in Autism Spectrum","Singleton, C. J., Ashwin, C. and Brosnan, M. (2014) Physiological
Responses to Social and Nonsocial Stimuli in Neurotypical
Adults With High and Low Levels of Autistic Traits:Implications
for Understanding Nonsocial Drive in Autism Spectrum
Disorders. Autism Research, 7 (6). pp. 695-703. ISSN 1939-3792
Link to official URL (if available): http://dx.doi.org/10.1002/aur.1422
Opus: University of Bath Online Publication Store
http://opus.bath.ac.uk/
This version is made available in accordance with publisher policies.
Please cite only the published version using the reference above.
See http://opus.bath.ac.uk/ for usage policies.
Please scroll down to view the document."
eba31ad9871c6dd5c2e7c62a121bbb417dcb1223,Adaptive Ensemble Selection for Face Re-identification under Class Imbalance,"Adaptive Ensemble Selection for Face
Re-Identification Under Class Imbalance(cid:63)
Paulo Radtke1, Eric Granger1, Robert Sabourin1 and Dmitry Gorodnichy2
. Laboratoire d’imagerie, de vision et d’intelligence artificielle
´Ecole de technologie sup´erieure, Universit´e du Qu´ebec, Montreal, Canada
{eric.granger,
. Science and Engineering Directorate, Canada Border Services Agency
Ottawa, Canada,"
eb0e5db282f88d47b65f98df70c2e7c78b8647a6,Image Provenance Analysis at Scale,"Image Provenance Analysis at Scale
Daniel Moreira, Aparna Bharati, Student Member, IEEE, Joel Brogan, Student Member, IEEE,
Allan Pinto, Student Member, IEEE, Michael Parowski, Kevin W. Bowyer, Fellow, IEEE,
Patrick J. Flynn, Fellow, IEEE, Anderson Rocha, Senior Member, IEEE,
nd Walter J. Scheirer, Senior Member, IEEE"
eb566490cd1aa9338831de8161c6659984e923fd,From Lifestyle Vlogs to Everyday Interactions,"From Lifestyle Vlogs to Everyday Interactions
David F. Fouhey, Wei-cheng Kuo, Alexei A. Efros, Jitendra Malik
EECS Department, UC Berkeley"
ebc2643567b1c614727cd7ecf1d0604972572568,Robust Subspace Estimation Using Low-rank,"ROBUST SUBSPACE ESTIMATION USING LOW-RANK OPTIMIZATION.
THEORY AND APPLICATIONS IN SCENE RECONSTRUCTION, VIDEO
DENOISING, AND ACTIVITY RECOGNITION.
OMAR OREIFEJ
B.S. University of Jordan, 2006
M.S. University of Central Florida, 2009
A dissertation submitted in partial fulfillment of the requirements
for the degree of Doctor of Philosophy
in the Department of Electrical Engineering and Computer Science
in the College of Engineering and Computer Science
t the University of Central Florida
Orlando, Florida
Spring Term
Major Professor: Mubarak Shah"
eb0e0a40372db32d30ceaefad046b213fac977f4,SCENE UNDERSTANDING USING BACK PROPAGATION BY NEURAL NETWORK,"Scene Understanding Using Back Propagation by Neural Network
SCENE UNDERSTANDING USING BACK PROPAGATION BY
NEURAL NETWORK
ARTI TIWARI1 & JAGVIR VERMA2
,2Department of Elex & Telecomm. Engg.Chouksey Engg. College,Bilaspur
intelligent  human-computer"
eb526174fa071345ff7b1fad1fad240cd943a6d7,Deeply vulnerable: a study of the robustness of face recognition to presentation attacks,"Deeply Vulnerable – A Study of the Robustness of Face Recognition to
Presentation Attacks
Amir Mohammadi, Sushil Bhattacharjee, and S´ebastien Marcel ∗†"
ebf877db5fb6aadbc09d74325f4f9d29a192018a,Embedding Model for Stereo Matching Costs,"A Deep Visual Correspondence Embedding Model
for Stereo Matching Costs
Zhuoyuan Chen, Xun Sun, Liang Wang,
Baidu Research – Institute of Deep Learning
Yinan Yu, Chang Huang
Horizon Robotics"
eb90ebf766dc36c3eca3f85b12d4b8fdd2ddbf1e,Modelling Uncertainty in Representation of Facial Features for Face Recognition,"We are IntechOpen,
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eb716dd3dbd0f04e6d89f1703b9975cad62ffb09,Visual Object Category Discovery in Images and Videos,"Copyright
Yong Jae Lee"
ebcd23bf99ead285b9f064bc47e713cd6e5dd599,On Demand Video Sharing System,"Imperial Journal of Interdisciplinary Research (IJIR)
Vol-2, Issue-11, 2016
ISSN: 2454-1362, http://www.onlinejournal.in
On Demand Video Sharing System
Tejas Benke1, Pratik Wakchaure2, Vaibhav Deshmukh3
Amol Sonawane4   & Prof. Kahate S. A.5
2,3,4 Student, SPCOE, Department Of Computer Engineering, Dumbarwadi, Otur
5 Assistant Professor, SPCOE, Department Of Computer Engineering, Dumbarwadi, Otur"
eb69f89588e9538194750f12bf8c8df6d5301f3b,Object Tracking by a Combination of Discriminative Global and Generative Multi-Scale Local Models,"Article
Object Tracking by a Combination of Discriminative
Global and Generative Multi-Scale Local Models
Zhiguo Song *, Jifeng Sun and Jialin Yu
Wushan Road, Tianhe District, Guangzhou 510640, China; (J.S.); (J.Y.)
* Correspondence:
Academic Editor: Willy Susilo
Received: 6 February 2017; Accepted: 5 April 2017; Published: 11 April 2017"
ebf204e0a3e137b6c24e271b0d55fa49a6c52b41,Visual Tracking Using Deep Motion Features,"Master of Science Thesis in Electrical Engineering
Department of Electrical Engineering, Linköping University, 2016
Visual Tracking Using
Deep Motion Features
Susanna Gladh"
eb4edbec8cb122de07951e3cf54c33fc30dd1c19,Examining the Effects of Supervision for Transfer from Synthetic to Real Driving Domains Vashisht Madhavan,"Examining the Effects of Supervision for Transfer from Synthetic to Real
Driving Domains
Vashisht Madhavan"
eb3f47ed113752eaa4c989bb92aa0e3e4e0bf339,Tracking of dolphins in a basin using a constrained motion model,"Tracking of Dolphins in a Basin Using a
Constrained Motion Model
Clas Veibäck, Gustaf Hendeby and Fredrik Gustafsson
Linköping University Post Print
N.B.: When citing this work, cite the original article.
Original Publication:
Clas Veibäck, Gustaf Hendeby and Fredrik Gustafsson, Tracking of Dolphins in a Basin Using
Constrained  Motion  Model,  2015,  Proceedings  of  the  18th  International  Conference  of
Information Fusion.
Copyright: The Authors.
Preprint ahead of print available at: Linköping University Electronic Press
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-120373"
eb4d2ec77fae67141f6cf74b3ed773997c2c0cf6,A new soft biometric approach for keystroke dynamics based on gender recognition,"Int. J. Information Technology and Management, Vol. 11, Nos. 1/2, 2012
A new soft biometric approach for keystroke
dynamics based on gender recognition
Romain Giot* and Christophe Rosenberger
GREYC Research Lab,
ENSICAEN – Université de Caen Basse Normandie – CNRS,
4000 Caen, France
Fax: +33-231538110
E-mail:
E-mail:
*Corresponding author"
eb2ab9caa61b021c1cd7aff6d08163768faba99e,Cleaning Up Multiple Detections Caused by Sliding Window Based Object Detectors,"Cleaning Up Multiple Detections Caused
y Sliding Window Based Object Detectors
Arne Ehlers, Bj¨orn Scheuermann, Florian Baumann, and Bodo Rosenhahn
Institut f¨ur Informationsverarbeitung (TNT)
Leibniz Universit¨at Hannover, Germany"
eb48a58b873295d719827e746d51b110f5716d6c,Face Alignment Using K-Cluster Regression Forests With Weighted Splitting,"Face Alignment Using K-cluster Regression Forests
With Weighted Splitting
Marek Kowalski and Jacek Naruniec"
529baf1a79cca813f8c9966ceaa9b3e42748c058,Triangle wise Mapping Technique to Transform one Face Image into Another Face Image,"Triangle Wise Mapping Technique to Transform one Face Image into Another Face Image
{tag}                                                                           {/tag}
International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 87 - Number 6
Year of Publication: 2014
Authors:
Rustam Ali Ahmed
Bhogeswar Borah
10.5120/15209-3714
{bibtex}pxc3893714.bib{/bibtex}"
524890eef6beaeb2e206c7b1bf51b58298eb55ec,Florian et al_ICMCSSE 2012_3,"Efficient and Effective Gabor Feature
Representation for Face Detection
Yasuomi D. Sato, Yasutaka Kuriya"
526ce5c72af5e1f93b8029a26e2eed7d1ac009f5,0 Constructing Kernel Machines in the Empirical Kernel Feature Space,"We are IntechOpen,
the world’s leading publisher of
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Numbers displayed above are based on latest data collected."
520e4b21302b99e5f289075541f9fe4959a639a4,Framewise approach in multimodal emotion recognition in OMG challenge,"Framewise approach in multimodal emotion
recognition in OMG challenge
Grigoriy Sterling1, 2, Andrey Belyaev1, 3, and Maxim Ryabov1
Institute for Information Transmission Problems, Moscow, Russian Federation
Moscow State Univercity, Moscow, Russian Federation
Neurodata Lab LLC, USA
May 4, 2018"
527d596a56aa238dfc450c3ebfdae31e82c6c175,Face detection methods,"Face Detection Methods
ZYAD SHAABAN
Department of Information Technology
College of Computers and Information Technology
University of Tabuk
Tabuk 71491
KINGDOM OF SAUDI ARABIA"
5209758096819efee15751c8875121bd27f2ee78,Finding Person Relations in Image Data of the Internet Archive,"Finding Person Relations in Image Data of the
Internet Archive
Eric M¨uller-Budack1,2[0000−0002−6802−1241],
Kader Pustu-Iren1[0000−0003−2891−9783], Sebastian Diering1, and
Ralph Ewerth1,2[0000−0003−0918−6297]
Leibniz Information Centre for Science and Technology (TIB), Hannover, Germany
L3S Research Center, Leibniz Universit¨at Hannover, Germany"
52012b4ecb78f6b4b9ea496be98bcfe0944353cd,Using Support Vector Machine and Local Binary Pattern for Facial Expression Recognition,"JOURNAL OF COMPUTATION IN BIOSCIENCES AND ENGINEERING
Journal homepage: http://scienceq.org/Journals/JCLS.php
Research Article
Using  Support  Vector  Machine  and  Local  Binary  Pattern  for  Facial  Expression
Recognition
Open Access
Ayeni Olaniyi Abiodun 1, Alese Boniface Kayode1, Dada Olabisi Matemilayo2
1. Department of Computer Science, Federal University Technology Akure, PMB 704, Akure, Nigeria.
. Department of computer science, Kwara state polytechnic Ilorin, Kwara-State, Nigeria.
. *Corresponding author:  Ayeni Olaniyi Abiodun  Mail Id:
Received:    September 22, 2015, Accepted: December 14, 2015, Published: December 14, 2015."
5265be9c7b8b22f4e06a01736bbedf171caee74e,Covariance of Motion and Appearance Featuresfor Spatio Temporal Recognition Tasks,"Covariance of Motion and Appearance Features
for Human Action and Gesture Recognition
Subhabrata Bhattacharya, Nasim Souly and Mubarak Shah"
5223f3485b96bffe7dd4b3aa71e63fd2b049fcf0,Is the Pedestrian going to Cross? Answering by 2D Pose Estimation,"Is the Pedestrian going to Cross? Answering by 2D Pose Estimation
Zhijie Fang and Antonio M. L´opez"
5251cb5349e37495b3ca29b06e6ed7422f12d126,A Pedestrian Detector Using Histograms of Oriented Gradients and a Support Vector Machine Classifier,"Proceedings of the 2007 IEEE
Intelligent Transportation Systems Conference
Seattle, WA, USA, Sept. 30 - Oct. 3, 2007
MoD2.2
-4244-1396-6/07/$25.00 ©2007 IEEE."
5293960de53b0118ef3c8b410d27b23b9cec9bf7,Online Multi-Object Tracking with Dual Matching Attention Networks,"Online Multi-Object Tracking with
Dual Matching Attention Networks
Ji Zhu1,2, Hua Yang1(cid:63), Nian Liu3, Minyoung Kim4,
Wenjun Zhang1, and Ming-Hsuan Yang5,6
Northwestern Polytechnical University 4Massachusetts Institute of Technology
Shanghai Jiao Tong University 2Visbody Inc
5University of California, Merced 6Google Inc
{jizhu1023,"
529341eb910ca5125b4aa6aa83bfc5fc8bf44fe3,V&L Net 2014 The 3rd Annual Meeting Of The EPSRC Network On Vision & Language and The 1st Technical Meeting of the European Network on Integrating Vision and Language,"V&LNet2014The3rdAnnualMeetingOfTheEPSRCNetworkOnVision&LanguageandThe1stTechnicalMeetingoftheEuropeanNetworkonIntegratingVisionandLanguageAWorkshopofthe25thInternationalConferenceonComputationalLinguistics(COLING2014)ProceedingsAugust23,2014Dublin,Ireland"
52f60abde42428d4aa8c5824a749398a1aa73be8,3D Skull Recognition Using 3D Matching Technique,"JOURNAL OF COMPUTING, VOLUME 2, ISSUE 1, JANUARY 2010, ISSN 2151-9617
HTTPS://SITES.GOOGLE.COM/SITE/JOURNALOFCOMPUTING/
D Skull Recognition Using 3D Matching
Technique
Hamdan.O.Alanazi, B.B Zaidan, A.A Zaidan"
52f71cc9c312aa845867ad1695c25a6d1d94ba0e,The invariance assumption in process-dissociation models: an evaluation across three domains.,"Journal of Experimental Psychology: General
015, Vol. 144, No. 1, 198 –221
0096-3445/15/$12.00
© 2014 American Psychological Association
http://dx.doi.org/10.1037/xge0000044
The Invariance Assumption in Process-Dissociation Models:
An Evaluation Across Three Domains
Karl Christoph Klauer, Kerstin Dittrich,
nd Christine Scholtes
Albert-Ludwigs-Universität Freiburg
Andreas Voss
Universität Heidelberg
The class of process-dissociation models, a subset of the class of multinomial processing-tree models, is
one of the best understood classes of models used in experimental psychology. A number of prominent
debates have addressed fundamental assumptions of process-dissociation models, leading, in many cases,
to conceptual clarifications and extended models that address identified issues. One issue that has so far
defied empirical clarification is how to evaluate the invariance assumption for the dominant process.
Violations of the invariance assumption have, however, the potential to bias conventional process-
dissociation analyses in different ways, and they can cause misleading theoretical interpretations and
onclusions. Based on recent advances in multinomial modeling, we propose new approaches to examine"
52884a0c7913be319c1a2395f009cea47b03f128,Explorer Learning Grounded Meaning Representations with Autoencoders,"Learning Grounded Meaning Representations with Autoencoders
Citation for published version:
Silberer, C & Lapata, M 2014, 'Learning Grounded Meaning Representations with Autoencoders'. in
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long
Papers). Association for Computational Linguistics, Baltimore, Maryland, pp. 721-732.
Link:
Link to publication record in Edinburgh Research Explorer
Document Version:
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Published In:
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long
Papers)
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ontent complies with UK legislation. If you believe that the public display of this file breaches copyright please
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52ed30920f2f96970c4f79d6768436ed855dad42,Active image pair selection for continuous person re-identification,"ACTIVE IMAGE PAIR SELECTION FOR CONTINUOUS PERSON RE-IDENTIFICATION
Abir Das, Rameswar Panda, Amit Roy-Chowdhury
Electrical and Computer Engineering Department, University of California, Riverside, USA"
52c9617414f29551dca35fb7a7ba18b58640a4eb,The Multimedia Satellite Task at MediaEval 2018,"The Multimedia Satellite Task at MediaEval 2018
Emergency Response for Flooding Events
Benjamin Bischke1, 2, Patrick Helber1, 2, Zhengyu Zhao3, Jens de Bruijn4, Damian Borth1
German Research Center for Artificial Intelligence (DFKI), Germany
TU Kaiserslautern, Germany
Radboud University, The Netherlands
VU University Amsterdam, The Netherlands"
522fab628aab972f39835521e31564b4b6c64fe5,Vehicle Classification on Low-resolution and Occluded images : A low-cost labeled dataset for augmentation,"Vehicle Classification on Low-resolution and
Occluded images: A low-cost labeled dataset for
ugmentation
Anonymous Author(s)
Affiliation
Address
email"
52e0c03dd661d032865dfedd91ca49542ccfc2a3,Improving Human Action Recognition Using Score Distribution and Ranking,"Improving Human Action Recognition
using Score Distribution and Ranking
Minh Hoai1,2 and Andrew Zisserman1
Visual Geometry Group, Dept. Engineering Science, University of Oxford.
Department of Computer Science, Stony Brook University."
52b6df1fe810d36fd615eb7c47aa1fd29376e769,Graph Mining for Object Tracking in Videos,"Graph Mining for Object Tracking in Videos
Fabien Diot, Elisa Fromont, Baptiste Jeudy, Emmanuel Marilly, Olivier
Martinot
To cite this version:
Fabien Diot, Elisa Fromont, Baptiste Jeudy, Emmanuel Marilly, Olivier Martinot. Graph
Mining for Object Tracking in Videos. European Conference on Machine Learning and Prin-
iples and Practice of Knowledge Discovery in Databases, Sep 2012, Bristol, United Kingdom.
Springer, LNCS (LNAI 6321), pp.394-409, 2012. <hal-00714705v2>
HAL Id: hal-00714705
https://hal.archives-ouvertes.fr/hal-00714705v2
Submitted on 20 Sep 2012
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
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52cd3eb77a5e259289d2442205f1731eafd581f9,Human Detection Based on the Generation of a Background Image and Fuzzy System by Using a Thermal Camera,"Article
Human Detection Based on the Generation of
Background Image and Fuzzy System by Using
Thermal Camera
Eun Som Jeon, Jong Hyun Kim, Hyung Gil Hong, Ganbayar Batchuluun and Kang Ryoung Park *
Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu,
Seoul 100-715, Korea; (E.S.J.); (J.H.K.); (H.G.H.);
(G.B.)
* Correspondence: Tel.: +82-10-3111-7022; Fax: +82-2-2277-8735
Academic Editors: Vincenzo Spagnolo and Dragan Indjin
Received: 3 January 2016; Accepted: 24 March 2016; Published: 30 March 2016"
52417b0406886154f0b4e2343ad6ac18c0484ec4,Ecological legacies of civil war : 35-year increase in savanna tree cover following wholesale large-mammal declines,"Journal of Ecology 2016, 104, 79–89
doi: 10.1111/1365-2745.12483
Ecological legacies of civil war: 35-year increase in
savanna tree cover following wholesale large-mammal
declines
Joshua H. Daskin1*, Marc Stalmans2 and Robert M. Pringle1
Department of Ecology and Evolutionary Biology, 106A Guyot Hall, Princeton University Princeton, NJ 08540, USA;
nd 2Department of Scientific Services, Gorongosa National Park, Sofala Province, Mozambique
Summary
. Large mammalian herbivores (LMH) exert strong effects on plants in tropical savannas, and
many wild LMH populations are declining. However, predicting the impacts of these declines on
vegetation structure remains challenging.
. Experiments suggest that tree cover can increase rapidly following LMH exclusion. Yet it is
unclear whether these results scale up to predict ecosystem-level impacts of LMH declines, which
often alter fire regimes, trigger compensatory responses of other herbivores and accompany anthro-
pogenic land-use changes. Moreover, theory predicts that grazers and browsers should have oppos-
ing effects on tree cover, further complicating efforts to forecast the outcomes of community-wide
declines.
. We used the near-extirpation of grazing and browsing LMH from Gorongosa National Park dur-
ing the Mozambican Civil War (1977–1992) as a natural experiment to test whether megafaunal col-"
52969cdd2c5eaccb534fe1296a61517b7ec42a54,Human Identification based on Ear Recognition,"Human Identification based on Ear Recognition
S. Gangaram1, and S. Viriri1,2"
524634e1055637b7c22b29e7e36437f4ba80df04,Thermal to Visible Synthesis of Face Images Using Multiple Regions,"Thermal to Visible Synthesis of Face Images using Multiple Regions
Benjamin S. Riggan1,*
Nathaniel J. Short1,2
Shuowen Hu1
U.S. Army Research Laboratory, 2800 Powder Mill Rd., Adelphi, MD 20783
Booz Allen Hamilton, 8283 Grennsboro Dr., McLean, VA 22102
*Corresponding author:"
5293df3b1543e79f88eea70b8766dc34e1f406e0,A Comparative Evaluation of Fusion Strategies for Multimodal Biometric Verification,"A Comparative Evaluation of Fusion Strategies for
Multimodal Biometric Verification
J. Fierrez-Aguilar, J. Ortega-Garcia, D. Garcia-Romero and J. Gonzalez-Rodriguez
www.atvs.diac.upm.es
Universidad Politecnica de Madrid, Spain
Biometrics Research Lab., ATVS"
5232a1ab263e4feaa4989b8b257830650403dfa5,On Finding Differences Between Faces,"On Finding Differences Between Faces
Manuele Bicego1, Enrico Grosso1, and Massimo Tistarelli2
DEIR - University of Sassari, via Sardegna 58 - 07100 Sassari - Italy
DAP - University of Sassari, piazza Duomo 6 - 07041 Alghero (SS) - Italy"
527ed756eba3bc77eb58d22d4cfe27da04d3bbbb,Adaptive skew-sensitive fusion of ensembles and their application to face re-identification,"Adaptive Skew-Sensitive Fusion of Ensembles and
their Application to Face Re-Identification
Miguel De-la-Torre∗†, Eric Granger∗, Robert Sabourin∗
´Ecole de technologie sup´erieure, Universit´e du Qu´ebec, Montr´eal, Canada
Centro Universitario de Los Valles, Universidad de Guadalajara, Ameca, M´exico"
52887969107956d59e1218abb84a1f834a314578,Travel Recommendation by Mining People Attributes and Travel Group Types From Community-Contributed Photos,"Travel Recommendation by Mining People
Attributes and Travel Group Types From
Community-Contributed Photos
Yan-Ying Chen, An-Jung Cheng, and Winston H. Hsu, Senior Member, IEEE"
521120c3907677e17708c17c5b6bab9087e61c5b,"l2, 1-Norm Regularized Discriminative Feature Selection for Unsupervised Learning","(cid:2)2,1-Norm Regularized Discriminative Feature
Selection for Unsupervised Learning
Yi Yang1, Heng Tao Shen1, Zhigang Ma2, Zi Huang1, Xiaofang Zhou1
School of Information Technology & Electrical Engineering, The University of Queensland.
Department of Information Engineering & Computer Science, University of Trento.
yangyi {huang,"
52a723061175ad141d73cb3979788e8afb7291db,Canonical Correlation Analysis of Datasets With a Common Source Graph,"Canonical Correlation Analysis of Datasets
with a Common Source Graph
Jia Chen, Gang Wang, Student Member, IEEE,
Yanning Shen, Student Member, IEEE, and Georgios B. Giannakis, Fellow, IEEE"
bd2d7c7f0145028e85c102fe52655c2b6c26aeb5,Attribute-based People Search: Lessons Learnt from a Practical Surveillance System,"Attribute-based People Search: Lessons Learnt from a
Practical Surveillance System
Rogerio Feris
IBM Watson
http://rogerioferis.com
Russel Bobbitt
IBM Watson
Lisa Brown
IBM Watson
Sharath Pankanti
IBM Watson"
bd96c3af9c433b4eaf95c8a28f072e1b0fc2de1a,A Study on Facial Expression Recognition Model using an Adaptive Learning Capability,"We are IntechOpen,
the world’s leading publisher of
Open Access books
Built by scientists, for scientists
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Open access books available
International  authors and editors
Downloads
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Selection of our books indexed in the Book Citation Index
in Web of Science™ Core Collection (BKCI)
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Contact
Numbers displayed above are based on latest data collected."
bd37ff771acd72ebdf4024043cb62fcacdd3a82b,Cycle-Consistent Deep Generative Hashing for Cross-Modal Retrieval,"Cycle-Consistent Deep Generative Hashing for
Cross-Modal Retrieval
Lin Wu, Yang Wang and Ling Shao Senior Member, IEEE"
bd86306ccb6698e17f00f208cf4fbc7a0aae39a9,An incremental nonparametric Bayesian clustering-based traversable region detection method,"Auton Robot
DOI 10.1007/s10514-016-9588-7
An incremental nonparametric Bayesian clustering-based
traversable region detection method
Honggu Lee1 · Kiho Kwak2 · Sungho Jo1
Received: 23 October 2015 / Accepted: 23 June 2016
© Springer Science+Business Media New York 2016"
bda61e9bcf02d02f61882790dbbdad8e4fed0986,Face Recognition through Combined SVD and LBP Features,"Face Recognition through Combined SVD and LBP
International Journal of Computer Applications (0975 – 8887)
Volume 88 – No.9, February 2014
Features
Rahul Kumar Mittal
M.Tech. Scholar
BGIET, Sangrur
Punjab (India)
Anupam Garg
Assistant Professor
BGIET, Sangrur
Punjab (India)"
bd76a71c6b1f414ada026ba82726024fbd58e9fe,DEEPSTEREOBRUSH : INTERACTIVE DEPTH MAP CREATION,"DEEPSTEREOBRUSH: INTERACTIVE DEPTH MAP CREATION
Sebastian Knorr1,3,∗, Matis Hudon1,∗, Julian Cabrera2,∗, Thomas Sikora3, Aljosa Smolic1 †
Trinity College Dublin, 2Universidad Polit´ecnica de Madrid, 3Technische Universit¨at Berlin"
bd0a6bea1985ece3388b1dae47fa76aab3562d6d,One Deep Music Representation to Rule Them All? : A comparative analysis of different representation learning strategies,"Noname manuscript No.
(will be inserted by the editor)
One Deep Music Representation to Rule Them All?
A comparative analysis of different representation learning strategies
Jaehun Kim · Juli´an Urbano ·
Cynthia C. S. Liem · Alan Hanjalic
Received: date / Accepted: date"
bd95446d4b1423b9f9c9243d8b5dddfbd045a30d,Cosy Cognitive Systems for Cognitive Assistants Dr.4.2 System for Supervised Mapping Executive Summary Role of (topic of Deliverable) in Cosy Relation to the Demonstrators Cosy Fp6-004250 1 Introduction Cosy Fp6-004250,"FP6-004250
Cognitive Systems for Cognitive Assistants
Integrated Project
Information Society Technologies
DR.4.2
System for Supervised Mapping
Due date of deliverable: 31/8/2005
Actual submission date: 16/10/2005
Start date of project: September 1st, 2004
Duration: 48 months
Organisation name of lead contractor for this deliverable:
Revision: draft V.1., final, etc...
Dissemination Level: PU"
bdb74f1b633b2c48d5e9d101e09bad2db8d68be6,Medical Image Annotation 1,"Chapter 1
Medical image annotation 1
Thanks to the rapid development of modern medical devices and the use of
digital systems, more and more medical images are being generated. This
has lead to an increase in the demand for automatic methods to index, com-
pare, analyze and annotate them. Until 2005, automatic categorization of
medical images was often restricted to a small number of classes. The Image-
CLEF medical image annotation challenge was born in this scenario, propos-
ing a task reflecting real life constraints of content based image classification
in medical applications. In this chapter we report about our experience first
s participants, then as co-organizers. This research activity started in 2007,
supported by a 1-year IM2 fellowship. By leveraging over the initial IM2
support, in 2008 a 4-year project started (EMMA, Enhanced Multimodal
Medical data Access), sponsored by the Halser foundation. Since 2009, B.
Caputo has been an ImageCLEF task organizers, respectively for the medi-
al annotation and robot vision tasks. Since 2013, she is main organizer of
ImageCLEF.
Introduction
This chapter presents the algorithms and results of the Idiap team partici-
pation to the ImageCLEFmed annotation task in 2007, 2008 and 2009. The"
bd17d6ba5525dec8762dbaacf6cc3e0cc3f5ff90,Necst: Neural Joint Source-channel Coding,"Under review as a conference paper at ICLR 2019
NECST: NEURAL JOINT SOURCE-CHANNEL CODING
Anonymous authors
Paper under double-blind review"
bdfb5f11d497b44b17d0315c3b6892f835723832,Object Captioning and Retrieval with Natural Language,"Object Captioning and Retrieval with Natural Language
Anh Nguyen1, Thanh-Toan Do2, Ian Reid2, Darwin G. Caldwell1, Nikos G. Tsagarakis1"
bd3a3884718015cde3eb8b0fdeae94eb1702a233,Hierarchical Compositional Model for Representation and Sketching of High-Resolution Human Images,"UNIVERSITY OF CALIFORNIA
Los Angeles
A Hierarchical Compositional Model for
Representation and Sketching
of High-Resolution Human Images
A dissertation submitted in partial satisfaction
of the requirements for the degree
Doctor of Philosophy in Statistics
Zijian Xu"
bd07d1f68486052b7e4429dccecdb8deab1924db,Face representation under different illumination conditions,
bd74c7b6d17e2515583cc26f26933a785045690f,Navigation assistance and guidance of older adults across complex public spaces: the DALi approach,"Intel Serv Robotics (2015) 8:77–92
DOI 10.1007/s11370-015-0169-y
ORIGINAL RESEARCH PAPER
Navigation assistance and guidance of older adults across complex
public spaces: the DALi approach
Luigi Palopoli1 · Antonis Argyros2 · Josef Birchbauer3 · Alessio Colombo1 · Daniele Fontanelli1 ·
Axel Legay4 · Andrea Garulli5 · Antonello Giannitrapani5 · David Macii1 · Federico Moro1 ·
Payam Nazemzadeh1 · Pashalis Padeleris2 · Roberto Passerone1 · Georg Poier3 ·
Domenico Prattichizzo5 · Tizar Rizano1 · Luca Rizzon1 · Stefano Scheggi5 · Sean Sedwards4
Received: 26 November 2014 / Accepted: 5 March 2015 / Published online: 22 March 2015
© Springer-Verlag Berlin Heidelberg 2015"
bdbba95e5abc543981fb557f21e3e6551a563b45,Speeding up the Hyperparameter Optimization of Deep Convolutional Neural Networks,"Vol. 17, No. 2 (2018) 1850008 (15 pages)
#.c The Author(s)
DOI: 10.1142/S1469026818500086
Speeding up the Hyperparameter Optimization of Deep
Convolutional Neural Networks
Tobias Hinz*, Nicolas Navarro-Guerrero†, Sven Magg‡
nd Stefan Wermter§
Knowledge Technology, Department of Informatics
Universit€at Hamburg
Vogt-K€olln-Str. 30, Hamburg 22527, Germany
Received 15 August 2017
Accepted 23 March 2018
Published 18 June 2018
Most learning algorithms require the practitioner to manually set the values of many hyper-
parameters before the learning process can begin. However, with modern algorithms, the
evaluation of a given hyperparameter setting can take a considerable amount of time and the
search space is often very high-dimensional. We suggest using a lower-dimensional represen-
tation of the original data to quickly identify promising areas in the hyperparameter space. This
information can then be used to initialize the optimization algorithm for the original, higher-
dimensional data. We compare this approach with the standard procedure of optimizing the"
bd2752acf6821282655933d1946f43bb4ac5e901,Flexible Network Binarization with Layer-Wise Priority,"Flexible Network Binarization with Layer-wise Priority
Lixue Zhuang*, Yi Xu*, Bingbing Ni*, Hongteng Xu†
Shanghai Jiao Tong University*, Duke University†
{qingliang, xuyi,"
bd67c08fae1d32f605585b0f375ad17c9695c9bd,Real Time Face Tracking and Recognition ( RTFTR,"Real Time Face Tracking and Recognition
(RTFTR)
http://rtftr.sourceforge.net
http://collaborate.d2labs.org/projects/rtftr
Open Software Challenge Nepal(OSCN) - 2009
Submitted to
July 01, 2009
Abhishek Dutta Anjan Nepal Bibek Shrestha Lakesh Kansakar
{ adutta.np, anjan.nepal, bibekshrestha, lakesh.kansakar } gmail.com
Institute of Engineering - Pulchowk Campus
Tribhuvan University, Nepal"
bd88bb2e4f351352d88ee7375af834360e223498,HDA dataset-DRAFT 1 A Multi-camera video data set for research on High-Definition surveillance,"HDA dataset - DRAFT
A Multi-camera video data set for research on
High-Definition surveillance
Athira Nambiar, Matteo Taiana, Dario Figueira,
Jacinto Nascimento and Alexandre Bernardino
Computer and Robot Vision Lab, Institute for Systems and Robotics
Instituto Superior Técnico
Lisbon, Portugal"
bd13f50b8997d0733169ceba39b6eb1bda3eb1aa,Occlusion Coherence: Detecting and Localizing Occluded Faces,"Occlusion Coherence: Detecting and Localizing Occluded Faces
Golnaz Ghiasi, Charless C. Fowlkes
University of California at Irvine, Irvine, CA 92697"
bd0e100a91ff179ee5c1d3383c75c85eddc81723,Okutama-Action: An Aerial View Video Dataset for Concurrent Human Action Detection,"Okutama-Action: An Aerial View Video Dataset for Concurrent Human Action
Detection∗
Mohammadamin Barekatain1, Miquel Mart´ı2,3, Hsueh-Fu Shih4, Samuel Murray2, Kotaro Nakayama5,
Yutaka Matsuo5, Helmut Prendinger6
Technical University of Munich, Munich, 2KTH Royal Institute of Technology, Stockholm,
Polytechnic University of Catalonia, Barcelona, 4National Taiwan University, Taipei, 5University of
Tokyo, Tokyo, 6National Institute of Informatics, Tokyo"
a56c1331750bf3ac33ee07004e083310a1e63ddc,Efficient Point-to-Subspace Query in ℓ1 with Application to Robust Object Instance Recognition,"Vol. xx, pp. x
(cid:13) xxxx Society for Industrial and Applied Mathematics
Ef‌f‌icient Point-to-Subspace Query in (cid:96)1 with Application to Robust Object
Instance Recognition
Ju Sun∗, Yuqian Zhang†, and John Wright‡"
a511463a423f842bdb524009f6ce6c6b0ffa0f77,Kernel diff-hash,"Kernel diff-hash
Michael M. Bronstein
Institute of Computational Science
Faculty of Informatics,
Universit`a della Svizzera Italiana
Via G. Buf‌f‌i 13, Lugano 6900, Switzerland
November 3, 2011"
a5c33de108cf7903a0f7d20daf22fb0794adba43,Study and Analysis of Convolutional Neural Networks for Pedestrian Detection in Autonomous Vehicles,"UPTEC F 18020
Examensarbete 30 hp
Juni 2018
Study and Analysis of Convolutional
Neural Networks for Pedestrian
Detection in Autonomous Vehicles
Louise Augustsson"
a565990d6b176bf9c82eec9354b0936fb141e631,Scheduling on Heterogeneous Multi-core Processors Using Stable Matching Algorithm,"(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 7, No. 6, 2016
Scheduling on Heterogeneous Multi-core Processors
Using Stable Matching Algorithm
Muhammad  Rehman  Zafar
Department  of  Computer  Science
Bahria  University
Islamabad,  Pakistan
Muhammad  Asfand-e-Yar
Department  of  Computer  Science
Bahria  University
Islamabad,  Pakistan"
a5006c29b0609296b5c1368ff1113eeb12b119ad,In-flight launch of unmanned aerial vehicles,"In-flight launch of unmanned aerial vehicles
Niels Nauwynck, Haris Balta, Geert De Cubber, and Hichem Sahli"
a588d38ec81c0337b445931eadf6f443aea13380,Functional Map of the World,"Functional Map of the World
Gordon Christie1
Neil Fendley1
The Johns Hopkins University Applied Physics Laboratory
James Wilson2
Ryan Mukherjee1
DigitalGlobe"
a52d6daf72281521ee99dabd82cd80093e8d6f4a,Person re-identification across different datasets with multi-task learning,"Person re-identification across different datasets
with multi-task learning
Matthieu Ospici, Antoine Cecchi
Atos BDS R&D"
a5ae7fe2bb268adf0c1cd8e3377f478fca5e4529,Exemplar Hidden Markov Models for classification of facial expressions in videos,"Exemplar Hidden Markov Models for Classification of Facial Expressions in
Videos
Univ. of California San Diego
Univ. of Canberra, Australian
Univ. of California San Diego
Abhinav Dhall
Marian Bartlett
Karan Sikka
California, USA
National University
Australia
California, USA"
a5dd647ff98d8ac9642a884c501de9a7aaf9a1b7,ICANet : a simple cascade linear convolution network for face recognition,"Zhang et al. EURASIP Journal on Image and Video
Processing  (2018) 2018:51
https://doi.org/10.1186/s13640-018-0288-4
EURASIP Journal on Image
nd Video Processing
RESEARCH
Open Access
ICANet: a simple cascade linear
onvolution network for face recognition
Yongqing Zhang1,2, Tianyu Geng3*, Xi Wu1, Jiliu Zhou1 and Dongrui Gao1"
a50b4d404576695be7cd4194a064f0602806f3c4,Efficiently Estimating Facial Expression and Illumination in Appearance-based Tracking,"In Proceedings of BMVC, Edimburgh, UK, September 2006
Efficiently estimating facial expression and
illumination in appearance-based tracking
Jos´e M. Buenaposada†, Enrique Mu˜noz‡, Luis Baumela‡
ESCET, U. Rey Juan Carlos
C/ Tulip´an, s/n
8933 M´ostoles, Spain
Facultad Inform´atica, UPM
Campus de Montegancedo s/n
8660 Boadilla del Monte, Spain
http://www.dia.fi.upm.es/~pcr"
a55efc4a6f273c5895b5e4c5009eabf8e5ed0d6a,"Continuous Head Movement Estimator for Driver Assistance: Issues, Algorithms, and On-Road Evaluations","Continuous Head Movement Estimator for
Driver Assistance: Issues, Algorithms,
nd On-Road Evaluations
Ashish Tawari, Student Member, IEEE, Sujitha Martin, Student Member, IEEE, and
Mohan Manubhai Trivedi, Fellow, IEEE"
a52d9e9daf2cb26b31bf2902f78774bd31c0dd88,Understanding and Designing Convolutional Networks for Local Recognition Problems,"Understanding and Designing Convolutional Networks
for Local Recognition Problems
Jonathan Long
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2016-97
http://www.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-97.html
May 13, 2016"
a51d5c2f8db48a42446cc4f1718c75ac9303cb7a,Cross-validating Image Description Datasets and Evaluation Metrics,"Cross-validating Image Description Datasets and Evaluation Metrics
Josiah Wang and Robert Gaizauskas
Department of Computer Science
University of Sheffield, UK
{j.k.wang,"
a5d77dd6ed07bd9688d13eac9d7848d19fdfb39b,PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point Cloud,"PointSeg: Real-Time Semantic Segmentation
Based on 3D LiDAR Point Cloud
Yuan Wang1
Tianyue Shi2
Peng Yun1
Lei Tai1
Ming Liu1"
a55ec6bade29f23f8cb1337edf417b2da2f48695,Deep Asymmetric Networks with a Set of Node-wise Variant Activation Functions,"Deep Asymmetric Networks with a Set of
Node-wise Variant Activation Functions
Jinhyeok Jang, Hyunjoong Cho, Jaehong Kim, Jaeyeon Lee, and Seungjoon Yang"
a5fd72b3bdda02c1d706167e98d088ffbdfefae4,RSR2015: Database for Text-Dependent Speaker Verification using Multiple Pass-Phrases,"The RSR2015: Database for Text-Dependent Speaker Verification
using Multiple Pass-Phrases
Anthony Larcher, Kong Aik Lee, Bin Ma, Haizhou Li
Institute for Infocomm Research (I2R)
A(cid:63)STAR, Singapore"
a5ad7ce9b8bba0a6bd8e6c26ccc5d6133d748c44,NEAREST-NEIGHBOR BASED METRIC FUNCTIONS FOR INDOOR SCENE RECOGNITION,"NEAREST-NEIGHBOR BASED METRIC
FUNCTIONS FOR INDOOR SCENE
RECOGNITION
thesis
submitted to the department of computer engineering
nd the graduate school of engineering and science
of b˙Ilkent university
in partial fulfillment of the requirements
for the degree of
master of science
Fatih C¸ akır
July, 2011"
a5da6a6d4243a89e974a6467cb5c6df6d914a946,Static and Dynamic Approaches for Pain Intensity Estimation using Facial Expressions,
a55dea7981ea0f90d1110005b5f5ca68a3175910,"Are 1, 000 Features Worth A Picture? Combining Crowdsourcing and Face Recognition to Identify Civil War Soldiers","Combining Crowdsourcing and Face Recognition to Identify Civil War Soldiers
Are 1,000 Features Worth A Picture?
Vikram Mohanty, David Thames, Kurt Luther
Department of Computer Science and Center for Human-Computer Interaction
Virginia Tech, Arlington, VA, USA"
a5625cfe16d72bd00e987857d68eb4d8fc3ce4fb,VFSC: A Very Fast Sparse Clustering to Cluster Faces from Videos,"VFSC: A Very Fast Sparse Clustering to Cluster Faces
from Videos
Dinh-Luan Nguyen, Minh-Triet Tran
University of Science, VNU-HCMC, Ho Chi Minh city, Vietnam"
a5d525d27a55c38879c4becda7af7ad04406708e,Feature Multi-Selection among Subjective Features,"Feature Multi-Selection among Subjective Features
Sivan Sabato
Adam Kalai
Microsoft Research New England, 1 Memorial Dr., Cambridge, MA"
a54e0f2983e0b5af6eaafd4d3467b655a3de52f4,Face Recognition Using Convolution Filters and Neural Networks.,"Face Recognition Using Convolution Filters and
Neural Networks
V. Rihani
Head, Dept. of E&E,PEC
Sec-12, Chandigarh – 160012
Amit Bhandari
Department of CSE & IT, PEC
Sec-12, Chandigarh – 160012
C.P. Singh
Physics Department, CFSL,
Sec-36, Chandigarh - 160036
to:  (a)
potential  method"
a546fd229f99d7fe3cf634234e04bae920a2ec33,Fast Fight Detection,"RESEARCH ARTICLE
Fast Fight Detection
Ismael Serrano Gracia1*, Oscar Deniz Suarez1*, Gloria Bueno Garcia1*, Tae-Kyun Kim2
Department of Systems Engineering and Automation, E.T.S.I. Industriales, Ciudad Real, Castilla-La
Mancha, Spain, 2 Department of Electrical and Electronic Engineering, Imperial College, London, UK
* (ISG); (ODS); (GBG)"
a5be204b71d1daaf6897270f2373d1a5e37c3010,Improving Spatiotemporal Self-supervision by Deep Reinforcement Learning,"Improving Spatiotemporal Self-Supervision
y Deep Reinforcement Learning
Uta B¨uchler(cid:63), Biagio Brattoli(cid:63), and Bj¨orn Ommer
Heidelberg University, HCI / IWR, Germany"
a5e5094a1e052fa44f539b0d62b54ef03c78bf6a,Detection without Recognition for Redaction,"Detection without Recognition for Redaction
Shagan Sah1, Ram Longman1, Ameya Shringi1, Robert Loce2, Majid Rabbani1, and Raymond Ptucha1
Rochester Institute of Technology - 83 Lomb Memorial Drive, Rochester, NY USA, 14623
Conduent, Conduent Labs - US, 800 Phillips Rd, MS128, Webster, NY USA, 14580
Email:"
a5a44a32a91474f00a3cda671a802e87c899fbb4,Moments in Time Dataset: one million videos for event understanding,"Moments in Time Dataset: one million
videos for event understanding
Mathew Monfort, Bolei Zhou, Sarah Adel Bargal,
Alex Andonian, Tom Yan, Kandan Ramakrishnan, Lisa Brown,
Quanfu Fan, Dan Gutfruend, Carl Vondrick, Aude Oliva"
a5531b5626c1ee3b6f9aed281a98338439d06d12,Multichannel Attention Network for Analyzing Visual Behavior in Public Speaking,"Multichannel Attention Network for Analyzing
Visual Behavior in Public Speaking
Rahul Sharma, Tanaya Guha and Gaurav Sharma
IIT Kanpur
{rahus, tanaya,"
a59e338fec32adee012e31cdb0513ec20d6c8232,Phase Retrieval Under a Generative Prior,"Phase Retrieval Under a Generative Prior
Paul Hand∗, Oscar Leong∗, and Vladislav Voroninski†
July 12, 2018"
e018c7f468a9b61cd6e7dcbc40b332a8a25808ae,Face Recognition by Face Bunch Graph Method,"Face Recognition by Face Bunch Graph Method
JIRI STASTNY*, VLADISLAV SKORPIL**
* Department of Automation and Computer Science,
** Department of Telekommunications,
Brno University of Technology,
Purkynova 118,  612 00 Brno,
CZECH  REPUBLIC,"
e0181f7596b475f7c7d31fd1eccad8e9b7379180,Facial Expression Recognition for Traumatic Brain Injured Patients,
e076f818b090e42036821c69727cfa3b7da49373,Social Groups Detection in Crowd through Shape-Augmented Structured Learning,"Social Groups Detection in Crowd Through
Shape-Augmented Structured Learning
Francesco Solera and Simone Calderara
DIEF University of Modena and Reggio Emilia, Italy"
e000dd1aec1c7b1e9e781ec7ea66f2bde72faa5e,Ear Recognition : A Complete System,"Ear Recognition: A Complete System
Ayman Abazaa,b and MaryAnn F. Harrisona
West Virginia High Tech Foundation, 1000 Technology Drive, Fairmont, USA;
Cairo University, Cairo, Egypt"
e0739088d578b2abf583e30953ffa000620cca98,Efficient Pedestrian Detection in Urban Traffic Scenes,"Efficient Pedestrian Detection in Urban Traffic Scenes
Dissertation
Erlangung des Doktorgrades (Dr. rer. nat.)
Mathematisch-Naturwissenschaftlichen Fakult¨at
Rheinischen Friedrich-Wilhelms-Universit¨at Bonn
vorgelegt von
Shanshan Zhang
Jiangxi, V.R. China
Bonn, 2014"
e013c650c7c6b480a1b692bedb663947cd9d260f,Robust Image Analysis With Sparse Representation on Quantized Visual Features,"Robust Image Analysis With Sparse Representation
on Quantized Visual Features
Bing-Kun Bao, Guangyu Zhu, Jialie Shen, and Shuicheng Yan, Senior Member, IEEE"
e00bdb0b046c4d21517ca808a4233a6fd5f3faee,Efficient Retina-like Resampling from Cartesian Images,"VII Workshop de Vis˜ao Computacional – WVC 2011
Efficient Retina-like Resampling from Cartesian Images
Hugo Vieira Neto, Diogo Rosa Kuiaski and Gustavo Benvenutti Borba
Graduate School of Electrical Engineering and Applied Computer Science
Federal University of Technology - Paran´a, Brazil"
e065a2cb4534492ccf46d0afc81b9ad8b420c5ec,SFace: An Efficient Network for Face Detection in Large Scale Variations,"SFace: An Ef‌f‌icient Network for Face Detection
in Large Scale Variations
Jianfeng Wang12∗, Ye Yuan 1†, Boxun Li†, Gang Yu† and Sun Jian†
College of Software, Beihang University∗
Megvii Inc. (Face++)†"
e0f3b8cbf90096bb0d4b36324c6fe7db89f580b9,Ré-identification de personnes : Application aux réseaux de caméras à champs disjoints,"Ré-identification de personnes : Application aux réseaux
de caméras à champs disjoints
Boris Meden
To cite this version:
Boris Meden. Ré-identification de personnes : Application aux réseaux de caméras à champs disjoints.
Robotique [cs.RO]. Université Paul Sabatier - Toulouse III, 2013. Français. <tel-00822779>
HAL Id: tel-00822779
https://tel.archives-ouvertes.fr/tel-00822779
Submitted on 15 May 2013
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de
recherche français ou étrangers, des laboratoires"
e0082ae9e466f7c855fb2c2300215ced08f61432,Generative Temporal Models with Spatial Memory for Partially Observed Environments,"Generative Temporal Models with Spatial Memory
for Partially Observed Environments
Marco Fraccaro 1 * Danilo Jimenez Rezende 2 Yori Zwols 2 Alexander Pritzel 2 S. M. Ali Eslami 2 Fabio Viola 2"
e04a5a6860b80e3a7fc293d495f3b9a822c3f98d,Exploration of the Influence of Smiling on Initial Reactions Across Levels of Facial Attractiveness,"American Journal of Undergraduate Research
(cid:90)(cid:90)(cid:90)(cid:17)(cid:68)(cid:77)(cid:88)(cid:85)(cid:82)(cid:81)(cid:79)(cid:76)(cid:81)(cid:72)(cid:17)(cid:82)(cid:85)(cid:74)
Exploration of the Influence of Smiling on Initial Reactions Across Levels
of Facial Attractiveness
Stephanie M. Shields* a, Caitlin E. Morse a, Paige Arringtonb, and David F. Nicholsa
Department of Psychology, Roanoke College, Salem, VA
Graduate Program in Liberal Arts, Hollins University, Roanoke, VA
Students:
Mentor:"
e0939b4518a5ad649ba04194f74f3413c793f28e,Mind-reading machines : automated inference of complex mental states Rana,"Technical Report
UCAM-CL-TR-636
ISSN 1476-2986
Number 636
Computer Laboratory
Mind-reading machines:
utomated inference
of complex mental states
Rana Ayman el Kaliouby
July 2005
5 JJ Thomson Avenue
Cambridge CB3 0FD
United Kingdom
phone +44 1223 763500
http://www.cl.cam.ac.uk/"
e0aa9ab8f00b2bf0dd1b6ffd5c00e5a15b6a67e1,Robust Visual Tracking via Hierarchical Convolutional Features,"Robust Visual Tracking
via Hierarchical Convolutional Features
Chao Ma, Jia-Bin Huang, Xiaokang Yang, and Ming-Hsuan Yang"
e0a57676ca5f7fced9dcf885a60a1967cc21070c,Development of a Computer Interface for People with Disabilities based on Computer Vision,
e05444e51d292bda871388c22b97400ed4cf73a8,An Overview of Recent Approaches in Person Re-Identification,An Overview of Recent Approaches in Person Re-Identification
e0eb1d66f244456063409264ed795d9893565011,Inhibited Softmax for Uncertainty Estimation in Neural Networks,"Electronic Preprint
INHIBITED SOFTMAX FOR UNCERTAINTY ESTIMATION
IN NEURAL NETWORKS
Marcin Mo˙zejko, Mateusz Susik & Rafał Karczewski
Sigmoidal"
e0ad3408ed47261a9d0fbc2f037b395fb41b88bf,MOTION VECTOR FIELD ESTIMATION USING BRIGHTNESS CONSTANCY ASSUMPTION AND EPIPOLAR GEOMETRY CONSTRAINT,"ISPRS Technical Commission I Symposium, Sustaining Land Imaging: UAVs to Satellites
7 – 20 November 2014, Denver, Colorado, USA, MTSTC1-109"
e0e511a5d58a8d090ad169be4fcfdbeaef097a70,Leveraging Cognitive Computing for Gender and Emotion Detection,"Leveraging Cognitive Computing for Gender and
Emotion Detection
Andrea Corriga1, Simone Cusimano1, Francesca M. Malloci1, Lodovica
Marchesi1 and Diego Reforgiato Recupero1
Department of Mathematics and Computer Science,
University of Cagliari, Via Ospedale 72, 09124, Cagliari"
e0515dc0157a89de48e1120662afdd7fe606b544,Perception Science in the Age of Deep Neural Networks,"SPECIALTY GRAND CHALLENGE
published: 02 February 2017
doi: 10.3389/fpsyg.2017.00142
Perception Science in the Age of
Deep Neural Networks
Rufin VanRullen 1, 2*
Centre National de la Recherche Scientifique, UMR 5549, Faculté de Médecine Purpan, Toulouse, France, 2 Université de
Toulouse, Centre de Recherche Cerveau et Cognition, Université Paul Sabatier, Toulouse, France
Keywords: perception, neuroscience, psychology, neural networks, deep learning, artificial intelligence
For decades, perception was considered a unique ability of biological systems, little understood in
its inner workings, and virtually impossible to match in artificial systems. But this status quo was
upturned in recent years, with dramatic improvements in computer models of perception brought
bout by “deep learning” approaches. What does all the ruckus about a “new dawn of artificial
intelligence” imply for the neuroscientific and psychological study of perception? Is it a threat, an
opportunity, or maybe a little of both?
WHILE WE WERE SLEEPING...
My personal journey in the field of perception science started about 20 years ago. For as long as
I can remember, we perception scientists have exploited in our papers and grant proposals the
lack of human-level artificial perception systems, both as a justification for scientific inquiry, and
s a convenient excuse for using a cautious, methodical approach—i.e., “baby steps.” Visual object"
e01058388d139e027482a7d89a2997606f7ef4fd,Global-residual and Local-boundary Refinement Networks for Rectifying Scene Parsing Predictions,"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
Input (b) FCN Based Model (c) GRN (d) Input (f) LRN (e) FCN Based Model Figure1:ResultofFCNbasedmodel(b)hasinconsistentlabelsinwall,curtainandbedsidetable,whichcanberefinedbytheproposedGRN(c).ResultofFCNbasedmodel(e)hasimpreciseanddiscon-tinuousobjectboundariesofcabinet,tableandchairs,whichcanberefinedbytheproposedLRN(f).stepinmanypracticalframeworks.Forexample,inobjectdetection,bounding-boxrefinement[GidarisandKomodakis,2015]iswidelyusedin[Heetal.,2016][Belletal.,2016][Shrivastavaetal.,2016],bringingsignificantimprovementofbounding-boxlocalizationandscoring.Inspiredbyitssuccess,wedesigntwonewrefinementnetworksparticularlyforrectifyingtheparsingpredictions,frombothglobalandlocalviewsrespectively.Eachofthetwonetworkscanbeemployedaftertheexistingparsingframeworksindividually.Moreover,cascadingthemtogetherforrefinementcangainmorepreciseparsingresults.Firstly,weconsiderperformingrefinementfromtheglobalview.Inconsistentparsingresultsareverycommoninpre-dictionsofexistingsceneparsingframeworks,asshowninFigure1(b).Toaddressthisproblem,wedesigntheGlobal-residualRefinementNetwork(GRN)throughexploit-ingglobalcontextualinformationandspatiallayoutrelation-shipsduringrefining.ThisnetworktakestheoriginalimagesandtheKconfidencemaps(i.e.,theoutputofthelastlayerbeforeSoftMaxlayer,eachforoneoftheKsemanticclasses)asinput.Thenoutputstheglobalparsingresidual,whichwillbeaddedtotheinputconfidencemapstoobtaintheglobalrectifyingresults.Thisnetworkeffectivelycapturesglobalcontextualinformationbyiterativelyusingadeepneuralnet-workwithlargereceptivefields.AfterglobalrefinementbyGRN,somemislabelingcanbecorrectedandsomeinconsis-"
e09c7bbf1bef602018928acb395f09448a0366b8,Learning beautiful (and ugly) attributes.,"MARCHESOTTI, PERRONNIN: LEARNING BEAUTIFUL (AND UGLY) ATTRIBUTES
Learning beautiful (and ugly) attributes
Luca Marchesotti
Florent Perronnin
Xerox Research Centre Europe
Meylan, France"
e096b11b3988441c0995c13742ad188a80f2b461,DeepProposals: Hunting Objects and Actions by Cascading Deep Convolutional Layers,"Noname manuscript No.
(will be inserted by the editor)
DeepProposals: Hunting Objects and Actions by Cascading
Deep Convolutional Layers
Amir Ghodrati · Ali Diba · Marco Pedersoli · Tinne Tuytelaars · Luc
Van Gool
Received: date / Accepted: date"
e0e71b59a34c97d15e5ff148fb9a43b892d45bd5,Facial Expression Emotion Detection for Real-Time Embedded Systems †,"Article
Facial Expression Emotion Detection for Real-Time
Embedded Systems †
Saeed Turabzadeh 1, Hongying Meng 1,* ID , Rafiq M. Swash 1 ID , Matus Pleva 2 ID and Jozef Juhar 2 ID
Department of Electronic and Computer Engineering, Brunel University London, Uxbridge UB8 3PH, UK;
(S.T.); (R.M.S.)
Department of Electronics and Multimedia Telecommunications, Technical University of Kosice, Letna 9,
04001 Kosice, Slovakia; (M.P.); (J.J.)
* Correspondence: Tel.: +44-1895-265496
This paper is an extended version of our paper in Proceedings of Innovative Computing Technology
(INTECH 2017), Luton, UK, 16–18 August 2017; with permission from IEEE.
Received: 15 December 2017; Accepted: 22 January 2018; Published: 26 January 2018"
e0152a4e30f4ce73fd7a3d56f9d796da6dad4bdc,Indexation audio-visuelle des personnes dans un contexte de télévision. (Audio-visual indexing of people in TV-context),"Indexation audio-visuelle des personnes dans un
ontexte de télévision
Meriem Bendris
To cite this version:
Meriem Bendris. Indexation audio-visuelle des personnes dans un contexte de télévision. Traitement
du signal et de l’image. Télécom ParisTech, 2011. Français. <pastel-00661662>
HAL Id: pastel-00661662
https://pastel.archives-ouvertes.fr/pastel-00661662
Submitted on 20 Jan 2012
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de
recherche français ou étrangers, des laboratoires"
e043d79f4dc41c9decaf637d8ffdd11f8ed59f2b,Distance metric learning for image and webpage comparison. (Apprentissage de distance pour la comparaison d'images et de pages Web),"Distance metric learning for image and webpage
omparison
Marc Teva Law
To cite this version:
Marc Teva Law. Distance metric learning for image and webpage comparison. Image Processing. Uni-
versité Pierre et Marie Curie - Paris VI, 2015. English. <NNT : 2015PA066019>. <tel-01135698v2>
HAL Id: tel-01135698
https://tel.archives-ouvertes.fr/tel-01135698v2
Submitted on 18 Mar 2015
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de
recherche français ou étrangers, des laboratoires"
e01ac06aa1f0b193a620bf70c5dad91128a1bc90,CAPTAIN: Comprehensive Composition Assistance for Photo Taking,"International Journal on Computer Vision manuscript No.
(will be inserted by the editor)
CAPTAIN: Comprehensive Composition Assistance for Photo
Taking
Farshid Farhat · Mohammad Mahdi Kamani · James Z. Wang
Received: date / Accepted: date"
e0cac58f3855cd84b9d28f508b2f7711e0d7e44a,3 A : A PERSON RE-IDENTIFICATION SYSTEM VIA ATTRIBUTE AUGMENTATION AND AGGREGATION,"978-1-5090-4117-6/17/$31.00 ©2017 IEEE
ICASSP 2017"
e00526ff149bd61f6811ba2f2145ed22d9306319,Personal Space Regulation in Childhood Autism Spectrum Disorders,"Personal Space Regulation in Childhood Autism
Spectrum Disorders
Erica Gessaroli1,2, Erica Santelli3, Giuseppe di Pellegrino1,4*, Francesca Frassinetti1,2*
Department of Psychology, University of Bologna, Bologna, Italy, 2 Fondazione Salvatore Maugeri, Clinica del Lavoro e della Riabilitazione, Istituto di Ricovero
e  Cura  a  Carattere  Scientifico,  Mantova,  Castel  Goffredo,  Italy,  3  Centro  Autismo,  Reggio  Emilia,  Italy,  4  Center  for  Studies  and  Research  in  Cognitive
Neuroscience, Cesena, Italy"
6898b0934d2bc34acc61a3c63fbb20337d7b9a95,Learning Styles and Emotion Recognition in a Fuzzy Expert System,"Learning Styles and Emotion Recognition in a Fuzzy
Expert System
Ramón Zatarain-Cabada, M. Lucía Barrón-Estrada, Rosalío Zatarain-Cabada
Instituto Tecnológico de Culiacán, Juan de Dios Bátiz s/n, Col. Guadalupe, Culiacán Sinaloa,
80220, Mexico
{rzatarain,"
68ba19afe924699b4a0c84af91c05deb5b03e3bd,Do Characteristics of Faces That Convey Trustworthiness and Dominance Underlie Perceptions of Criminality?,"Do Characteristics of Faces That Convey Trustworthiness
nd Dominance Underlie Perceptions of Criminality?
Heather D. Flowe*
College of Medicine, Biological Sciences and Psychology, University of Leicester, Leicester, United Kingdom"
683260bf133c282439b91ac4427d42d73a5988b5,"Optimizing Program Performance via Similarity, Using Feature-aware and Feature-agnostic Characterization Approaches","UNIVERSITY OF CALIFORNIA,
IRVINE
Optimizing Program Performance via Similarity,
Using Feature-aware and Feature-agnostic Characterization Approaches
DISSERTATION
submitted in partial satisfaction of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
in Information and Computer Science
Rosario Cammarota
Dissertation Committee:
Professor Alexander V. Veidenbaum, Chair
Professor Alexandru Nicolau
Professor Nikil Dutt"
6821a3fa67d9d58655c26e24b568fda1229ac5be,Fast and robust object segmentation with the Integral Linear Classifier,"Fast and Robust Object Segmentation with the Integral Linear Classifier
David Aldavert
Computer Vision Center
Dept. Computer Science
Arnau Ramisa
INRIA-Grenoble
Artificial Intelligence Research
Univ. Aut`onoma de Barcelona
Institute (IIIA-CSIC)
Ramon Lopez de Mantaras
Artificial Intelligence Research
Institute (IIIA-CSIC)
Campus UAB
Ricardo Toledo
Computer Vision Center
Dept. Computer Science
Univ. Aut`onoma de Barcelona"
68ee4a2a4aecff38598cf99e72bc21a6ecbbcd1f,Approximating Discrete Probability Distribution of Image Emotions by Multi-Modal Features Fusion,Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
6888f3402039a36028d0a7e2c3df6db94f5cb9bb,CLASSIFIER-TO-GENERATOR ATTACK: ESTIMATION,"Under review as a conference paper at ICLR 2018
CLASSIFIER-TO-GENERATOR ATTACK: ESTIMATION
OF TRAINING DATA DISTRIBUTION FROM CLASSIFIER
Anonymous authors
Paper under double-blind review"
68d08ed9470d973a54ef7806318d8894d87ba610,Drive Video Analysis for the Detection of Traffic Near-Miss Incidents,"Drive Video Analysis for the Detection of Traffic Near-Miss Incidents
Hirokatsu Kataoka1, Teppei Suzuki1
, Shoko Oikawa3, Yasuhiro Matsui4 and Yutaka Satoh1"
68e4ed4daa2ae94c789443ed222601a4a47f9a45,BUILDING EXTRACTION FROM POLARIMETRIC INTERFEROMETRIC SAR DATA USING BAYESIAN NETWORK,"BUILDING EXTRACTION FROM POLARIMETRIC INTERFEROMETRIC SAR DATA
USING BAYESIAN NETWORK
Wenju He and Olaf Hellwich
Berlin University of Technology
{wenjuhe,
. INTRODUCTION
Many researches have been done to extract buildings from high resolution Synthetic Aperture Radar (SAR) data. The extraction
problem is far from solved due to many constraints, e.g. SAR side-look imaging, speckle, and lack of object extent in SAR
images. Building detection algorithms usually use intensity information or textures. Layovers and shadows can be discriminated
from other objects since they have distinct appearances. The detection is hindered by the small geometric extent of buildings
in SAR images and the orientation dependency of reflections. Many buildings are occluded with surrounding environments.
The interactions between radar and various buildings are hard to model. Polarimetric SAR data can resolve some ambiguities
ecause polarimetry can be used to analyze physical scattering properties. Scatterers formed by buildings have strong double-
ounce reflections. Polarimetric SAR data also allow us to extract rich features for object detection. Polarimetric interferometric
SAR (PolinSAR) data are more promising since they are able to provide object height information. Furthermore, coherent
scatterer and permanent scatterer analysis using interferometric SAR (InSAR) data are powerful in urban change detection
pplications. As to building localization, a height map retrieved from PolinSAR data is very advantageous. PolinSAR data are
expected to further resolve ambiguities in building detection problems.
For meter-resolution PolinSAR data, however, it is hard to retrieve phases of building roofs from interferometric phase
ecause of complex scattering mechanisms and building geometries. Building height image was derived from InSAR digital"
6889d649c6bbd9c0042fadec6c813f8e894ac6cc,Analysis of Robust Soft Learning Vector Quantization and an application to Facial Expression Recognition,"Analysis of Robust Soft Learning Vector
Quantization and an application to Facial
Expression Recognition"
68484ae8a042904a95a8d284a7f85a4e28e37513,Spoofing Deep Face Recognition with Custom Silicone Masks,"Spoofing Deep Face Recognition with Custom Silicone Masks
Sushil Bhattacharjee Amir Mohammadi
S´ebastien Marcel
Idiap Research Institute. Centre du Parc, Rue Marconi 19, Martigny (VS), Switzerland
{sushil.bhattacharjee; amir.mohammadi;"
68b44eb4c7440046783146064ae9e715a72766dc,An Investigation of Physiological Arousal in Children with Autism and Co-morbid Challenging Behaviour,"An Investigation of Physiological Arousal in Children with
Autism and Co-morbid Challenging Behaviour
Sinéad Lydon
A thesis submitted to Trinity College Dublin, the University of Dublin,
in partial fulfillment of the requirements for the Degree of Doctor of
Philosophy (PhD) in Psychology
Supervisors: Dr. Olive Healy (Trinity College Dublin) and
Professor Brian Hughes (National University of Ireland, Galway)."
68333b73613c59914bfe1264a440b3cf854dc15c,Mugeetion: Musical Interface Using Facial Gesture and Emotion,"Mugeetion: Musical Interface Using Facial Gesture and Emotion
Eunjeong Stella Koh
Music Department
UC San Diego"
688f5cb02dc6c779fa9fd18f44b792f9626bdcd0,Visual pattern discovery in image and video data: a brief survey,"Visual Pattern Discovery in Image and Video Data:
A Brief Survey
Hongxing Wang, Gangqiang Zhao and Junsong Yuan"
68b01afed57ed7130d993dffc03dcbfa36d4e038,Adversarial Learning with Local Coordinate Coding,"Adversarial Learning with Local Coordinate Coding
Jiezhang Cao * 1 Yong Guo * 1 Qingyao Wu * 1 Chunhua Shen 2 Junzhou Huang 3 Mingkui Tan 1"
68d40176e878ebffbc01ffb0556e8cb2756dd9e9,Locality Repulsion Projection and Minutia Extraction Based Similarity Measure for Face Recognition,"International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622
International Conference on Humming Bird ( 01st March 2014)
RESEARCH ARTICLE
OPEN ACCESS
Locality Repulsion Projection and Minutia Extraction Based
Similarity Measure for Face Recognition
Agnel AnushyaP.1,RamyaP.2
AgnelAnushya P. is currently pursuing M.E (Computer Science and engineering) at Vins Christian college of
Ramya P. is currently working as an Asst. Professor in the dept. of Information Technology at Vins Christian
Engineering.
ollege of Engineering."
688cb9fd33769b152806c04ef6fc276629a9f300,LocNet: Improving Localization Accuracy for Object Detection,"LocNet: Improving Localization Accuracy for Object Detection
Spyros Gidaris
Universite Paris Est, Ecole des Ponts ParisTech
Nikos Komodakis
Universite Paris Est, Ecole des Ponts ParisTech"
68a05a845b6ace756d51c5bbce927479c9b9ab95,A Moral Framework for Understanding of Fair ML through Economic Models of Equality of Opportunity,"A Moral Framework for Understanding of Fair ML
through Economic Models of Equality of Opportunity
Hoda Heidari
ETH Z¨urich
Krishna P. Gummadi
MPI-SWS
Michele Loi
University of Z¨urich
Andreas Krause
ETH Z¨urich"
687ef116d7115498f12dff1b3338d959f164ef6b,Using Thought-Provoking Children's Questions to Drive Artificial Intelligence Research,"Using Thought-Provoking Children’s Questions
to Drive Artificial Intelligence Research
Erik T. Mueller and Henry Minsky
Minsky Institute for Artificial Intelligence
http://minskyinstitute.org/
September 14, 2015 00:09"
68c279d4fcc02710056e73a3b0d0d564a7615cad,Unified framework for fast exact and approximate search in dissimilarity spaces,"Unified Framework for Fast Exact and
Approximate Search in Dissimilarity Spaces
TOM´AˇS SKOPAL
Charles University in Prague
In multimedia systems we usually need to retrieve DB objects based on their similarity to a query
object, while the similarity assessment is provided by a measure which defines a (dis)similarity
score for every pair of DB objects. In most existing applications, the similarity measure is required
to be a metric, where the triangle inequality is utilized to speedup the search for relevant objects
y use of metric access methods (MAMs), e.g. the M-tree. A recent research has shown, however,
that non-metric measures are more appropriate for similarity modeling due to their robustness and
ease to model a made-to-measure similarity. Unfortunately, due to the lack of triangle inequality,
the non-metric measures cannot be directly utilized by MAMs. From another point of view, some
sophisticated similarity measures could be available in a black-box non-analytic form (e.g. as an
lgorithm or even a hardware device), where no information about their topological properties is
provided, so we have to consider them as non-metric measures as well. From yet another point
of view, the concept of similarity measuring itself is inherently imprecise and we often prefer fast
ut approximate retrieval over an exact but slower one.
To date, the mentioned aspects of similarity retrieval have been solved separately, i.e. exact
vs. approximate search or metric vs. non-metric search. In this paper we introduce a similarity
retrieval framework which incorporates both of the aspects into a single unified model. Based on"
6872615b0298aa01affa3b8d71e4d5547244278f,WEIGHTED FOURIER IMAGE ANALYSIS AND MODELING,"WEIGHTED FOURIER IMAGE ANALYSIS
AND MODELING
Shubing Wang
A dissertation submitted in partial fulfillment of the
requirements for the degree of
Doctor of Philosophy
(Statistics)
t the
UNIVERSITY OF WISCONSIN – MADISON"
688680d9902f688b9ac2d47c399ceebd1014d785,GIS-supported people tracking re-acquisition in a multi-camera environment,"GIS-supported People Tracking Re-Acquisition in a Multi-Camera
Environment
Anastasios Dimou1, Vasileios Lovatsis1, Andreas Papadakis2, Stelios Pantelopoulos2 and Petros
Daras1
CERTH-ITI, 6th kilometer Harilaou-Thermi, Thessaloniki, Greece
SingularLogic, Athens, Greece
Keywords:
GIS, Re-Identification, Multi-camera."
6844a700aee36bd809d1188f6f9e81707c513f19,Interactive model-based reconstruction of the human head using an RGB-D sensor,"Interactive Model-based Reconstruction of the
Human Head using an RGB-D Sensor
M. Zollh¨ofer, J. Thies, M. Colaianni, M. Stamminger, G. Greiner
Computer Graphics Group, University Erlangen-Nuremberg, Germany"
68becbe61cf30ef93b2679866d3a511e919ffb2f,"Motor, emotional, and cognitive empathy in children and adolescents with autism spectrum disorder and conduct disorder.","J Abnorm Child Psychol (2013) 41:425–443
DOI 10.1007/s10802-012-9689-5
Motor, Emotional, and Cognitive Empathy in Children
nd Adolescents with Autism Spectrum Disorder
nd Conduct Disorder
Danielle Bons & Egon van den Broek & Floor Scheepers &
Pierre Herpers & Nanda Rommelse & Jan K. Buitelaaar
Published online: 25 October 2012
# Springer Science+Business Media New York 2012"
68df1f746a3434ee8bcc8918d46809ddaad38b12,Subspace learning in minimax detection,"014 IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP)
978-1-4799-2893-4/14/$31.00 ©2014 IEEE
Email: {raja.fazliza, david.mary,
SUBSPACE LEARNING IN MINIMAX DETECTION
Raja Fazliza R. Suleiman, David Mary and Andr´e Ferrari
Campus Valrose, 06108 Nice Cedex 02, FRANCE
Laboratoire J.-L. Lagrange, UMR7293,
. INTRODUCTION AND PRIOR WORKS
(cid:26) H0"
68caf5d8ef325d7ea669f3fb76eac58e0170fff0,Long-term face tracking in the wild using deep learning,
68d4056765c27fbcac233794857b7f5b8a6a82bf,Example-Based Face Shape Recovery Using the Zenith Angle of the Surface Normal,"Example-Based Face Shape Recovery Using the
Zenith Angle of the Surface Normal
Mario Castel´an1, Ana J. Almaz´an-Delf´ın2, Marco I. Ram´ırez-Sosa-Mor´an3,
nd Luz A. Torres-M´endez1
CINVESTAV Campus Saltillo, Ramos Arizpe 25900, Coahuila, M´exico
Universidad Veracruzana, Facultad de F´ısica e Inteligencia Artificial, Xalapa 91000,
ITESM, Campus Saltillo, Saltillo 25270, Coahuila, M´exico
Veracruz, M´exico"
683fbd7593cf5c22ef54004bb89c469eab2f656e,URJC&UNED at ImageCLEF 2013 Photo Annotation Task,"URJCyUNED at ImageCLEF 2012 Photo
Annotation task⋆
Jes´us S´anchez-Oro1, Soto Montalvo1, Antonio S. Montemayor1, Ra´ul Cabido1,
Juan J. Pantrigo1, Abraham Duarte1, V´ıctor Fresno2, and Raquel Mart´ınez2
Universidad Rey Juan Carlos, M(cid:19)ostoles, Spain
Universidad Nacional de Educaci(cid:19)on a Distancia, Madrid, Spain"
6848a0993b0754b27750a78196fa91e3cf2bbebb,Accurate and Robust Neural Networks for Security Related Applications Exampled by Face Morphing Attacks,"Accurate and Robust Neural Networks for
Security Related Applications Exampled by Face
Morphing Attacks
Clemens Seibold1, Wojciech Samek1, Anna Hilsmann1 and Peter Eisert1,2
Fraunhofer HHI, Einsteinufer 37, 10587 Berlin, Germany
Humboldt University Berlin, Unter den Linden 6, 10099 Berlin, Germany"
68ae4db6acf5361486f153ee0c0d540e0823682a,FlashReport Memory conformity for con fi dently recognized items : The power of social in fl uence on memory reports,"Journal of Experimental Social Psychology 48 (2012) 783–786
Contents lists available at SciVerse ScienceDirect
Journal of Experimental Social Psychology
j o u r n a l h o m e pa ge : w ww . e l s e v i e r . c o m/ l o c a t e / j e s p
FlashReport
Memory conformity for confidently recognized items: The power of social influence
on memory reports
Ruth Horry ⁎, Matthew A. Palmer 1, Michelle L. Sexton, Neil Brewer
Flinders University, Australia
r t i c l e
i n f o
b s t r a c t
Article history:
Received 14 September 2011
Revised 9 December 2011
Available online 22 December 2011
Keywords:
Memory conformity
Confidence
Face recognition"
682f735ef796370f510218eb7afb4d2a36cd1256,On Offline Evaluation of Vision-Based Driving Models,
68f61154a0080c4aae9322110c8827978f01ac2e,"Recognizing blurred , non-frontal , illumination and expression variant partially occluded faces","Research Article
Journal of the Optical Society of America A
Recognizing blurred, non-frontal, illumination and
expression variant partially occluded faces
ABHIJITH PUNNAPPURATH1* AND AMBASAMUDRAM NARAYANAN RAJAGOPALAN1
Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai 600036, India.
*Corresponding author:
Compiled June 26, 2016
The focus of this paper is on the problem of recognizing faces across space-varying motion blur, changes
in pose, illumination, and expression, as well as partial occlusion, when only a single image per subject
is available in the gallery. We show how the blur incurred due to relative motion between the camera and
the subject during exposure can be estimated from the alpha matte of pixels that straddle the boundary
etween the face and the background. We also devise a strategy to automatically generate the trimap re-
quired for matte estimation. Having computed the motion via the matte of the probe, we account for pose
variations by synthesizing from the intensity image of the frontal gallery, a face image that matches the
pose of the probe. To handle illumination and expression variations, and partial occlusion, we model the
probe as a linear combination of nine blurred illumination basis images in the synthesized non-frontal
pose, plus a sparse occlusion. We also advocate a recognition metric that capitalizes on the sparsity of the
occluded pixels. The performance of our method is extensively validated on synthetic as well as real face
data. © 2016 Optical Society of America"
68c17aa1ecbff0787709be74d1d98d9efd78f410,GENDER CLASSIFICATION FROM FACE IMAGES USING MUTUAL INFORMATION AND FEATURE FUSION,"International Journal of Optomechatronics, 6: 92–119, 2012
Copyright # Taylor & Francis Group, LLC
ISSN: 1559-9612 print=1559-9620 online
DOI: 10.1080/15599612.2012.663463
GENDER CLASSIFICATION FROM FACE IMAGES
USING MUTUAL INFORMATION AND FEATURE
FUSION
Claudio Perez, Juan Tapia, Pablo Este´vez, and Claudio Held
Department of Electrical Engineering and Advanced Mining Technology
Center, Universidad de Chile, Santiago, Chile
In this article we report a new method for gender classification from frontal face images
using feature selection based on mutual information and fusion of features extracted from
intensity, shape, texture, and from three different spatial scales. We compare the results of
three different mutual information measures: minimum redundancy and maximal relevance
(mRMR), normalized mutual information feature selection (NMIFS), and conditional
mutual information feature selection (CMIFS). We also show that by fusing features
extracted from six different methods we significantly improve the gender classification
results relative to those previously published, yielding 99.13% of the gender classification
rate on the FERET database.
Keywords: Feature fusion, feature selection, gender classification, mutual information, real-time gender"
688ae87c5e40583ecf9ec6d06d4d15a3e62f5556,A New Angle on L2 Regularization,"A New Angle on L2 Regularization
(interactive version available at https://thomas-tanay.github.io/post--L2-regularization/)
Thomas Tanay
Lewis D Grif‌f‌in
CoMPLEX, UCL
CoMPLEX, UCL
Deep neural networks have been shown to be vulnerable to the
dversarial example phenomenon: all models tested so far can have their
lassifications dramatically altered by small image perturbations [1, 2].
The following predictions were for instance made by a state-of-the-art
network trained to recognize celebrities [3]:"
684c8acd49148020e9bf9c4f4aefc03708a6dac0,Video-Based Person Re-Identification With Accumulative Motion Context,"Video-based Person Re-identification with
Accumulative Motion Context
Hao Liu, Zequn Jie, Karlekar Jayashree, Meibin Qi, Jianguo Jiang and Shuicheng Yan, Fellow, IEEE, Jiashi Feng"
68b6ec13d06facacf5637f90828ab5b6e352be60,Neural Proximal Gradient Descent for Compressive Imaging,"Neural Proximal Gradient Descent for Compressive
Imaging
Morteza Mardani1,2, Qingyun Sun4, Shreyas Vasawanala2, Vardan Papyan3,
Hatef Monajemi3, John Pauly1, and David Donoho3
Depts. of Electrical Eng., Radiology, Statistics, and Mathematics; Stanford University"
6864b089c8586b0e3f6bd6736cabea96b1c4a28a,Robust classification for occluded ear via Gabor scale feature-based non-negative sparse representation,"Robust classification for occluded ear via
Gabor scale feature-based non-negative
sparse representation
Baoqing Zhang
Zhichun Mu
Chen Li
Hui Zeng
Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 01/02/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx"
685f8df14776457c1c324b0619c39b3872df617b,Face Recognition with Preprocessing and Neural Networks,"Master of Science Thesis in Electrical Engineering
Department of Electrical Engineering, Linköping University, 2016
Face Recognition with
Preprocessing and Neural
Networks
David Habrman"
68eb5404a22fcca595cc6360e9a77a4b09156eb2,Appearance-based person reidentification in camera networks: problem overview and current approaches,"J Ambient Intell Human Comput (2011) 2:127–151
DOI 10.1007/s12652-010-0034-y
O R I G I N A L R E S E A R C H
Appearance-based person reidentification in camera networks:
problem overview and current approaches
Gianfranco Doretto • Thomas Sebastian •
Peter Tu • Jens Rittscher
Received: 30 January 2010 / Accepted: 4 October 2010 / Published online: 14 January 2011
Ó Springer-Verlag 2011"
c9bbe64ae797b8d522eac5cc115ac31e8e5491bf,Vision-Aided Absolute Trajectory Estimation Using an Unsupervised Deep Network with Online Error Correction,"Vision-Aided Absolute Trajectory Estimation Using an Unsupervised
Deep Network with Online Error Correction
E. Jared Shamwell1, Sarah Leung2, William D. Nothwang3"
c9c3ba7bebee553490a9ddbc6840292ed5aed90b,SCHOOL OF COMPUTER ENGINEERING PhD Confirmation Report on Object Detection in Real Images,"SCHOOL OF COMPUTER ENGINEERING
PhD Confirmation Report
Object Detection in Real Images
Submitted by: Dilip Kumar Prasad
Research Student (PhD)
School of Computer Engineering
E-mail:
Supervisor:     Dr. Maylor K. H. Leung
Associate Professor,
School of Computer Engineering
E-mail:
August 2010"
c94c2cf52fef0503c09268c7d1faee60465ee08e,BenchIP: Benchmarking Intelligence Processors,"BENCHIP: Benchmarking Intelligence
Processors
Jinhua Tao1, Zidong Du1,2, Qi Guo1,2, Huiying Lan1, Lei Zhang1
Shengyuan Zhou1, Lingjie Xu3, Cong Liu4, Haifeng Liu5, Shan Tang6
Allen Rush7,Willian Chen7, Shaoli Liu1,2, Yunji Chen1, Tianshi Chen1,2
ICT CAS,2Cambricon,3Alibaba Infrastructure Service, Alibaba Group
IFLYTEK,5JD,6RDA Microelectronics,7AMD"
c9ea71631540dfc13079338fb534c6eb78198d4e,Automatic Visual Integration: Defragmenting the Face,"Automatic Visual Integration: Defragmenting the Face
Electrical & Computer Engineering Department, University of California, San Diego
Luke Barrington
La Jolla, CA 92093 USA
Computer Science & Engineering Department, University of California, San Diego
Garrison W. Cottrell
La Jolla, CA 92093 USA"
c9b13a5d5c7a688b567e08d933a8098724c75325,Motion in action : optical flow estimation and action localization in videos. (Le mouvement en action : estimation du flot optique et localisation d'actions dans les vidéos),"Motion in action : optical flow estimation and action
localization in videos
Philippe Weinzaepfel
To cite this version:
Philippe Weinzaepfel. Motion in action : optical flow estimation and action localization in videos.
Computer Vision and Pattern Recognition [cs.CV]. Université Grenoble Alpes, 2016. English. <NNT :
016GREAM013>. <tel-01407258>
HAL Id: tel-01407258
https://tel.archives-ouvertes.fr/tel-01407258
Submitted on 1 Dec 2016
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
c97774191be232678a45d343a25fcc0c96c065e7,Co-Training of Audio and Video Representations from Self-Supervised Temporal Synchronization,"Co-Training of Audio and Video Representations from
Self-Supervised Temporal Synchronization
Undergraduate Thesis
written by
Bruno Korbar
under the supervision of Professor Lorenzo Torresani and Du Tran, and
submitted to the Committee as a culminating experience for the degree of
Bachelor of Arts in Computer Science
t Dartmouth College.
Date of the public presentation: Members of the Thesis Committee:
May 29, 2018
Prof Lorenzo Torresani
Prof Saeed Hassanpour
Prof Venkatramanan Siva Subrahmanian
Dartmouth Computer Science Technical Report TR2018-849"
c9d9e91bec48a13048a2a0626892e00575e236f5,A Two Dimensional Facial Features Analysis for Gender-based Comparison Using Morphometrics Approach,"International Journal of Engineering & Technology, 7 (4.31) (2018) 214-219
International Journal of Engineering & Technology
Website: www.sciencepubco.com/index.php/IJET
Research paper
A Two Dimensional Facial Features Analysis for Gender-based
Comparison Using Morphometrics Approach
Olalekan Agbolade1*, Azree Shahrel Ahmad Nazri2
Faculty of Computer Science & Information Technology,
Universiti Putra Malaysia, Serdang, Selangor, Malaysia
*Corresponding author E-mail:"
c9876861cc0e33fffe8c3ce7484ae27d3b2eeb75,A Corpus for Analyzing Linguistic and Paralinguistic Features in Multi-Speaker Spontaneous Conversations – EVA Corpus,"A Corpus for Analyzing Linguistic and Paralinguistic Features in
Multi-Speaker Spontaneous Conversations – EVA Corpus
IZIDOR MLAKAR, ZDRAVKO KAČIČ, MATEJ ROJC
Faculty of Electrical Engineering and Computer Science, University of Maribor
SLOVENIA"
c93996cb126589b30c04bf1256c97a4431c0e8b6,Robustness Analysis of Pedestrian Detectors for Surveillance,"Robustness Analysis of Pedestrian Detectors
for Surveillance
Yuming Fang, Senior Memmber, IEEE, Guanqun Ding, Yuan Yuan, Weisi Lin, Fellow, IEEE,
nd Haiwen Liu, Senior Memmber, IEEE"
c924137ca87e8b4e1557465405744f8b639b16fc,Seeding Deep Learning using Wireless Localization,"ADDRESSING TRAINING BIAS VIA AUTOMATED IMAGE ANNOTATION
Zhujun Xiao 1 Yanzi Zhu 2 Yuxin Chen 1 Ben Y. Zhao 1 Junchen Jiang 1 Haitao Zheng 1"
c91103e6612fa7e664ccbc3ed1b0b5deac865b02,Automatic facial expression recognition using statistical-like moments,"Automatic facial expression recognition using
statistical-like moments
Roberto D’Ambrosio, Giulio Iannello, and Paolo Soda
{r.dambrosio, g.iannello,
Integrated Research Center, Universit`a Campus Bio-Medico di Roma,
Via Alvaro del Portillo, 00128 Roma, Italy"
c933c4bef57be3585abb13bacb74aca29588a6ac,People Detection in Color and Infrared Video Using HOG and Linear SVM,"People Detection in Color and Infrared Video
using HOG and Linear SVM
Pablo Tribaldos1, Juan Serrano-Cuerda1, Mar´ıa T. L´opez1;2,
Antonio Fern´andez-Caballero1;2, and Roberto J. L´opez-Sastre3
Instituto de Investigaci(cid:19)on en Inform(cid:19)atica de Albacete (I3A), 02071-Albacete, Spain
Universidad de Castilla-La Mancha, Departamento de Sistemas Inform(cid:19)aticos,
02071-Albacete, Spain
Universidad de Alcal(cid:19)a, Dpto. de Teor(cid:19)(cid:16)a de la se~nal y Comunicaciones,
8805-Alcal(cid:19)a de Henares (Madrid), Spain"
c9d7219d54eccb9e49b72044d805e103fe17ba80,Towards Information-Seeking Agents,"Under review as a conference paper at ICLR 2017
TOWARDS INFORMATION-SEEKING AGENTS
Philip Bachman∗
phil.bachman
Alessandro Sordoni∗
lessandro.sordoni
Adam Trischler
dam.trischler
Maluuba Research
Montréal, QC, Canada"
c99a23a5bb5d5b10098395f59e9f8f79c79a75bd,Prediction Using Audience Chat Reactions,"Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 972–978
Copenhagen, Denmark, September 7–11, 2017. c(cid:13)2017 Association for Computational Linguistics"
c90b109301244e59771fec431a8d50a78e395956,Alternative face models for 3 D face registration,"Alternative face models for 3D face registration
Albert Ali Salah, Ne¸se Aly¨uz, Lale Akarun
Bo˘gazi¸ci University, 34342 Bebek, ˙Istanbul, Turkey"
c9bbf31afbec278ca735e91cf5e9c70dd3aa41a4,Enhancing 3D Face Recognition By Mimics Segmentation,"Enhancing 3D Face Recognition By Mimics Segmentation
Boulbaba Ben Amor, Mohsen Ardabilian, and Liming Chen
MI Department, LIRIS Laboratory, CNRS 5205
Ecole Centrale de Lyon, 36 av. Guy de Collongue, 69134 Lyon , France
{Boulbaba.Ben-Amor, Mohsen.Ardabilian,"
c94ae3d1c029a70cabdab906fe1460d84fd42acd,"Comparison of wavelet , Gabor and curvelet transform for face recognition","Optica Applicata, Vol. XLI, No. 1, 2011
Comparison of wavelet, Gabor and curvelet
transform for face recognition
JIULONG ZHANG, YINGHUI WANG, ZHIYU ZHANG, CHUNLI XIA
Computer Science and Engineering School, Xian University of Technology,
Xi'an, 710048, P.R. China
There has been much research about using Gabor wavelet for face recognition. Other multiscale
geometrical tools, such as curvelet and contourlet, have also been used for face recognition, thus
it is interesting to know which method performs best, especially under illumination and expression
hanges.  In  this  paper,  we  make  a  systematic  comparison  of  wavelet,  Gabor  and  curvelet  for
recognition,  and  find  the  best  subband  irrelevant  to  expression  and  illumination  changes.  We
ombine the multiscale analysis with subspace decomposition as our algorithm. Experiments show
that for expression changes, the properties of the coarse layer of curvelet and wavelet are very
good. Whilst for illumination changes, the low frequency parts of the two methods are similarly
influenced, but the detail coefficients of curvelet and the high frequency of wavelet work fine with
PCA, with the former outperforming the latter. When these two factors change simultaneously,
the detail layer of curvelet is better relative to the others.
Keywords: wavelet transform, Gabor wavelet, curvelet transform, face recognition, multiscale analysis.
. Introduction
Among the so many popular methods for face recognition, the wavelet transform is"
c92e701c908908bda407f12edf6984b283e8c258,Where Should You Attend While Driving?,"Where Should You Attend While Driving?
Simone Calderara
Stefano Alletto
Andrea Palazzi∗
Francesco Solera∗
Rita Cucchiara
University of Modena and Reggio Emilia"
c9b139b78e5337580047138d7fc2dff3b8fcf31f,Offline Face Recognition System Based on Gabor- Fisher Descriptors and Hidden Markov Models,"Offline Face Recognition System Based on Gabor-
Fisher Descriptors and Hidden Markov Models
Zineb Elgarrai1, Othmane Elmeslouhi2, Mustapha Kardouchi3, Hakim Allali1, Sid-Ahmed Selouani4
FST of Hassan 1st University Settat /LAVETTE Laboratory,
FPO of Ibnou Zohr University /LabSIE Laboratory
Université de Moncton /Département d’Informatique,
Université de Moncton/Département de Gestion de l’Information"
c936b9a958a67cdd5665b923569d9d786c934029,Software Specification Document For,"Software Specification
Document
Crowd_Count++
Version 1.0
November 2015
Juan Mejia      Michael Safdieh      Rosario Antunez
Prepared by:"
c9311a0c5045d86a617bd05a5cc269f44e81508d,Accurate Eye Centre Localisation by Means of Gradients,"ACCURATE EYE CENTRE LOCALISATION BY MEANS OF
GRADIENTS
Institute for Neuro- and Bioinformatics, University of L¨ubeck, Ratzeburger Allee 160, D-23538 L¨ubeck, Germany
Pattern Recognition Company GmbH, Innovations Campus L¨ubeck, Maria-Goeppert-Strasse 1, D-23562 L¨ubeck, Germany
{timm,
Fabian Timm and Erhardt Barth
Keywords:"
c96f012f4915398259e7e223810c57898b5e1a76,Fast LIDAR-based Road Detection Using Convolutional Neural Networks,"Fast LIDAR-based Road Detection
Using Convolutional Neural Networks
Luca Caltagirone1, Samuel Scheidegger2, Lennart Svensson3, Mattias Wahde4
{luca.caltagirone, samsch, lennart.svensson,"
c95d8b9bddd76b8c83c8745747e8a33feedf3941,Image Ordinal Classification and Understanding: Grid Dropout with Masking Label,"label:(1, 0, 1, 0, 1, 1, 1, 1, 1)Masking label:(0, 1, 1, 1, 0, 1, 1, 1, 1)Entire imageInput imageNeuron dropout’s gradCAMGrid dropout’s gradCAMFig.1.Above:imageordinalclassificationwithrandomlyblackoutpatches.Itiseasyforhumantorecognizetheageregardlessofthemissingpatches.Themaskinglabelisalsousefultoimageclassification.Bottom:griddropout’sgrad-CAMisbetterthanthatofneurondropout.Thatistosay,griddropoutcanhelplearningfeaturerepresentation.problem[1].Withtheproliferationofconvolutionalneuralnetwork(CNN),workshavebeencarriedoutonordinalclas-sificationwithCNN[1][2][3].Thoughgoodperformanceshavebeenloggedwithmoderndeeplearningapproaches,therearetwoproblemsinimageordinalclassification.Ononehand,theamountofordinaltrainingdataisverylim-itedwhichprohibitstrainingcomplexmodelsproperly,andtomakemattersworse,collectinglargetrainingdatasetwithordinallabelisdifficult,evenharderthanlabellinggenericdataset.Therefore,insufficienttrainingdataincreasestheriskofoverfitting.Ontheotherhand,lessstudiesareconductedtounderstandwhatdeepmodelshavelearnedonordinaldata978-1-5386-1737-3/18/$31.00c(cid:13)2018IEEE"
c95c30fb990576704f2ccb3dc3335aaf43208856,CS 231 A Project report,"CS231A Project report
Cecile Foret
March 19, 2014."
c9b90cf9cdd901bd3072d6dfd8ddc523c55944b1,Adversarial Generator-Encoder Networks,"Adversarial Generator-Encoder Networks
Dmitry Ulyanov 1 2 Andrea Vedaldi 3 Victor Lempitsky 1"
e810ddd9642db98492bd6a28b08a8655396c1555,Facing facts: neuronal mechanisms of face perception.,"Review
Acta Neurobiol Exp 2008, 68: 229–252
Facing facts: Neuronal mechanisms of face perception
Monika Dekowska1, Michał Kuniecki2, and Piotr Jaśkowski3*
Kazimierz Wielki University of Bydgoszcz, Poland; 2Department of Psychophysiology, Jagiellonian University,
Kraków, Poland; 3Department of Cognitive Psychology, University of Finance and Management, Warszawa, Poland,
*Email:
The face is one of the most important stimuli carrying social meaning. Thanks to the fast analysis of faces, we are able to
judge physical attractiveness and features of their owners’ personality, intentions, and mood. From one’s facial expression
we can gain information about danger present in the environment. It is obvious that the ability to process efficiently one’s
face is crucial for survival. Therefore, it seems natural that in the human brain there exist structures specialized for face
processing.  In  this  article,  we  present  recent  findings  from  studies  on  the  neuronal  mechanisms  of  face  perception  and
recognition in the light of current theoretical models. Results from brain imaging (fMRI, PET) and electrophysiology (ERP,
MEG) show that in face perception particular regions (i.e. FFA, STS, IOA, AMTG, prefrontal and orbitofrontal cortex) are
involved.  These  results  are  confirmed  by  behavioral  data  and  clinical  observations  as  well  as  by  animal  studies.  The
developmental findings reviewed in this article lead us to suppose that the ability to analyze face-like stimuli is hard-wired
nd improves during development. Still, experience with faces is not sufficient for an individual to become an expert in face
perception.  This  thesis  is  supported  by  the  investigation  of  individuals  with  developmental  disabilities,  especially  with
utistic spectrum disorders (ASD).
Key words: face perception, emotion perception"
e8691980eeb827b10cdfb4cc402b3f43f020bc6a,Segmentation Guided Attention Networks for Visual Question Answering,"Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics- Student Research Workshop, pages 43–48
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics- Student Research Workshop, pages 43–48
Vancouver, Canada, July 30 - August 4, 2017. c(cid:13)2017 Association for Computational Linguistics
Vancouver, Canada, July 30 - August 4, 2017. c(cid:13)2017 Association for Computational Linguistics
https://doi.org/10.18653/v1/P17-3008
https://doi.org/10.18653/v1/P17-3008"
e811550cc83a3650bee8764d5fab4bd26109ee73,Discriminant Phase Component for Face Recognition,"Hindawi Publishing Corporation
Journal of Electrical and Computer Engineering
Volume 2012, Article ID 718915, 12 pages
doi:10.1155/2012/718915
Research Article
Discriminant Phase Component for Face Recognition
Naser Zaeri
Faculty of Computer Studies, Arab Open University, P.O. Box 3322, Safat 13033, Kuwait
Correspondence should be addressed to Naser Zaeri,
Received 1 September 2011; Revised 14 December 2011; Accepted 14 December 2011
Academic Editor: Somaya Al-Maadeed
Copyright © 2012 Naser Zaeri. This is an open access article distributed under the Creative Commons Attribution License, which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Numerous face recognition techniques have been developed owing to the growing number of real-world applications. Most of
urrent algorithms for face recognition involve considerable amount of computations and hence they cannot be used on devices
onstrained with limited speed and memory. In this paper, we propose a novel solution for ef‌f‌icient face recognition problem
for systems that utilize small memory capacities and demand fast performance. The new technique divides the face images into
omponents and finds the discriminant phases of the Fourier transform of these components automatically using the sequential
floating forward search method. A thorough study and comprehensive experiments relating time consumption versus system
performance are applied to benchmark face image databases. Finally, the proposed technique is compared with other known"
e8f753208fc354fa9aeb3fa9c6acb3d45e7eac7b,Definite Description Lexical Choice: taking Speaker's Personality into account,"Definite Description Lexical Choice:
taking Speaker’s Personality into account
Alex Gwo Jen Lan, Ivandr´e Paraboni
University of S˜ao Paulo, School of Arts, Sciences and Humanities
S˜ao Paulo, Brazil"
e8039e1531dd86da960be26d59718d2452f9943b,Scene Parsing and Fusion-Based Continuous Traversable Region Formation,"Scene parsing and fusion-based continuous
traversable region formation
Xuhong Xiao, Gee Wah Ng, Yuan Sin Tan, Yeo Ye Chuan
0 Science Park Drive, DSO national Laboratories, Singapore 118230"
e8af37ac6e0a5b7f04b6824bb1f74e4f363b99b5,On the replication of CycleGAN,"Bachelor thesis
Computer Science
Radboud University
On the replication of CycleGAN
Author:
Robin Elbers
s4225678
First supervisor/assessor:
MSc. Jacopo Acquarelli
Second assessor:
Prof. Tom Heskes
August 10, 2018"
e855856d4b61b6a732005418f543c49195cb1542,Novel Method for Eyeglasses Detection in Frontal Face Images,"Novel Method for Eyeglasses Detection in Frontal
Face Images
R. L. Parente, L. V. Batista
Centro de Inform´atica - CI
Universidade Federal da Para´ıba - UFPB
Jo˜ao Pessoa, Brazil
I. Andreza, E. Borges, R. Marques
VSoft Research Group
VSoft Technology
Jo˜ao Pessoa, Brazil
{igorlpa90, erickvagnerr,"
e8bcef31648bcde5a97c770dddbbcd1e09086930,A direct approach for object detection with catadioptric omnidirectional cameras,"Noname manuscript No.
(will be inserted by the editor)
A Direct Approach for Object Detection with Catadioptric
Omnidirectional Cameras
Ibrahim Cinaroglu · Yalin Bastanlar
Received: date / Accepted: date"
e8dda897372e6b4cf903234c7a9c40117711d8d8,What do you think of my picture? Investigating factors of influence in profile images context perception,"What do you think of my picture? Investigating factors
of influence in profile images context perception
Filippo Mazza, Matthieu Perreira da Silva, Patrick Le Callet, Ingrid
Heynderickx
To cite this version:
Filippo Mazza, Matthieu Perreira da Silva, Patrick Le Callet, Ingrid Heynderickx. What do you
think of my picture? Investigating factors of influence in profile images context perception. Human
Vision and Electronic Imaging XX, Mar 2015, San Francisco, United States. Proc. SPIE 9394, Hu-
man Vision and Electronic Imaging XX, 9394, <http://spie.org/EI/conferencedetails/human-vision-
electronic-imaging>. <10.1117/12.2082817>. <hal-01149535>
HAL Id: hal-01149535
https://hal.archives-ouvertes.fr/hal-01149535
Submitted on 7 May 2015
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est"
e819a577c57c83a133a0a0e81180d14dc13b82e9,Pyramid Histogram of Oriented Gradients based Human Ear Identification,"Pyramid Histogram of Oriented Gradients based Human Ear Identification
Pyramid Histogram of Oriented Gradients based Human
Ear Identification
Partha Pratim Sarangi1, B.S.P. Mishra1 and Sachidanada Dehuri2
School of Computer Engineering KIIT University, Bhubaneswar , Emails:
Department of ICT FM University, Balasore, Email:"
e825811cfd92a8be5be2caee67fc7a48ed2f5df0,Meta-Analysis of Face Recognition Algorithms,"Meta-Analysis of Face Recognition Algorithms
P. Jonathon Phillips
00 Bureau Dr., STOP 8940
Gaithersburg, Md. 20899-8940
Elaine M. Newton
01 N. Craig St., Suite 102
Pittsburgh, Pa. 15213"
e8acbe49ecf7e0a77f4f5874674ac40b1e35bf8a,EXPERIMENTAL EVALUATION OF TEXT-INDEPENDENT SPEAKER VERIFICATION ON LABORATORY AND FIELD TEST DATABASES IN THE M2VTS PROJECT,"EXPERIMENTAL EVALUATION OF TEXT-INDEPENDENT
SPEAKER VERIFICATION ON LABORATORY AND FIELD
TEST DATABASES IN THE M2VTS PROJECT
L. Besacie
, J. Luett
, G. Maîtr
, E. Meurv
(1) IMT, Neuchâtel (CH) -
(2) IDIAP, Martigny (CH) -
(3) now at EIV, Sion (CH) -
(4) now at EPFL, Lausanne (CH) -"
e8304700fd89461ec9ecf471179ad87f08f3c2f7,Learning to Learn New Models of Human Activities in Indoor Settings 1 1.1 Introduction,"Chapter 1
Learning to learn new
models of human activities in
indoor settings1
Introduction
Biological cognitive systems have the great capability to recognize and in-
easily within their existing knowledge base. Autonomous artificial agents to
large extent still lack such capacities. In this paper, we work towards this
direction, as we do not only detect abnormal situations, but are also able to
learn new concepts during runtime.
We aim at the interpretation of human behavior in indoor environments.
Possible applications go from the main IM2 scenario, i.e. analysis and un-
derstanding of meetings, to monitoring of elderly or handicapped people in
their homes in order to ensure their well-being. The indoor setting triggers
interesting issues, such as the adaptation of pre-trained knowledge to a par-
with an individual behavior style, whereas real abnormalities must still be
detected.
One main limitation of automated surveillance approaches is their need
for an of‌f‌line prior training with many labeled data. Furthermore, no train-
ing sequence contains a comprehensive set of all the situations to expect"
e8e43abbc8bee64a53af64ceca90bfb687f7bb9d,Fast Object Class Labelling via Speech,"Fast Object Class Labelling via Speech
Michael Gygli
Google Research
Z¨urich, Switzerland
Vittorio Ferrari
Google Research
Z¨urich, Switzerland"
e8686663aec64f4414eba6a0f821ab9eb9f93e38,Improving shape-based face recognition by means of a supervised discriminant Hausdorff distance,"IMPROVING SHAPE-BASED FACE RECOGNITION BY MEANS OF A SUPERVISED
DISCRIMINANT HAUSDORFF DISTANCE
J.L. Alba
, A. Pujol
, A. L´opez
nd J.J. Villanueva
Signal Theory and Communications Department, University of Vigo, Spain
Centre de Visio per Computador, Universitat Autonoma de Barcelona, Spain
Digital Pointer MVT"
e8867f819f39c1838bba7d446934258035d4101c,Face recognition performance with superresolution.,"Face recognition performance with superresolution
Shuowen Hu,1,* Robert Maschal,1 S. Susan Young,1 Tsai Hong Hong,2
nd P. Jonathon Phillips2
United States Army Research Laboratory, 2800 Powder Mill Road, Adelphi, Maryland 20783, USA
NIST, 100 Bureau Drive, Gaithersburg, Maryland 20899, USA
*Corresponding author:
Received 29 September 2011; revised 19 April 2012; accepted 24 April 2012;
posted 30 April 2012 (Doc. ID 155384); published 20 June 2012
With the prevalence of surveillance systems, face recognition is crucial to aiding the law enforcement com-
munity and homeland security in identifying suspects and suspicious individuals on watch lists. However,
face recognition performance is severely affected by the low face resolution of individuals in typical sur-
veillance footage, oftentimes due to the distance of individuals from the cameras as well as the small pixel
ount of low-cost surveillance systems. Superresolution image reconstruction has the potential to improve
face recognition performance by using a sequence of low-resolution images of an individual’s face in the
same pose to reconstruct a more detailed high-resolution facial image. This work conducts an extensive
performance evaluation of superresolution for a face recognition algorithm using a methodology and ex-
perimental setup consistent with real world settings at multiple subject-to-camera distances. Results show
that superresolution image reconstruction improves face recognition performance considerably at the
examined midrange and close range.
OCIS codes:"
e8ffef3d4d74720e766e506e175e533bdc8ee705,Object Detection Networks on Convolutional Feature Maps,"Object Detection Networks on
Convolutional Feature Maps
Shaoqing Ren, Kaiming He, Ross Girshick, Xiangyu Zhang, and Jian Sun"
e8a5800db4b7609e3a55ec4b904b263cd359df2e,Face Recognition using Neural Network and Eigenvalues with Distinct Block Processing,"International Journal of Scientific & Engineering Research Volume 2, Issue 5, May-2011                                                                  1
ISSN 2229-5518
Face Recognition using Neural Network
nd Eigenvalues with Distinct Block
Processing
Prashant Sharma, Amil Aneja, Amit Kumar, Dr.Shishir Kumar"
e80635b9b48df5ad263c51ecec62d7d4bd7327fd,"Playful Robot for Research , Therapy , and Entertainment","Int J Soc Robot (2009) 1: 3–18
DOI 10.1007/s12369-008-0009-8
O R I G I N A L PA P E R
Keepon
A Playful Robot for Research, Therapy, and Entertainment
Hideki Kozima · Marek P. Michalowski ·
Cocoro Nakagawa
Accepted: 28 October 2008 / Published online: 19 November 2008
© Springer 2008"
e81705e6100759b75eb71839ea61abc257b0b6a9,Enhanced Spatial Pyramid Matching Using Log-Polar-Based Image Subdivision and Representation,"Enhanced Spatial Pyramid Matching Using Log-Polar-Based Image Subdivision
nd Representation
Edmond Zhang, Michael Mayo
Department of Computer Science
The University of Waikato
Hamilton, New Zealand"
e862577cb654c33c4817c31b445264614485413c,Grassmannian Learning: Embedding Geometry Awareness in Shallow and Deep Learning,"Grassmannian Learning: Embedding Geometry
Awareness in Shallow and Deep Learning
Jiayao Zhang, Guangxu Zhu, Robert W. Heath Jr., and Kaibin Huang
Modern machine learning algorithms have been adopted in
range of signal-processing applications spanning computer
vision, natural language processing, and artificial intelligence.
Many relevant problems involve subspace-structured features,
orthogonality constrained or low-rank constrained objective
functions, or subspace distances. These mathematical charac-
teristics are expressed naturally using the Grassmann manifold.
Unfortunately, this fact is not yet explored in many traditional
learning algorithms. In the last few years, there have been
growing interests in studying Grassmann manifold to tackle
new learning problems. Such attempts have been reassured by
substantial performance improvements in both classic learning
nd learning using deep neural networks. We term the former
s shallow and the latter deep Grassmannian learning. The aim
of this paper is to introduce the emerging area of Grassman-
nian learning by surveying common mathematical problems
nd primary solution approaches, and overviewing various"
e8fdacbd708feb60fd6e7843b048bf3c4387c6db,Deep Learning,"Deep Learning
Andreas Eilschou
Hinnerup Net A/S
www.hinnerup.net
July 4, 2014
Introduction
Deep learning is a topic in the field of artificial intelligence (AI) and is a relatively
new research area although based on the popular artificial neural networks (supposedly
mirroring brain function). With the development of the perceptron in the 1950s and
960s by Frank RosenBlatt, research began on artificial neural networks. To further
mimic the architectural depth of the brain, researchers wanted to train a deep multi-
layer neural network – this, however, did not happen until Geoffrey Hinton in 2006
introduced Deep Belief Networks [1].
Recently, the topic of deep learning has gained public interest. Large web companies such
s Google and Facebook have a focused research on AI and an ever increasing amount
of compute power, which has led to researchers finally being able to produce results
that are of interest to the general public. In July 2012 Google trained a deep learning
network on YouTube videos with the remarkable result that the network learned to
recognize humans as well as cats [6], and in January this year Google successfully used
deep learning on Street View images to automatically recognize house numbers with"
e8d898a6adcd526874e0a41840b69760506a98a1,Computer Vision Methods as an Aid to Visually Impaired Users Title: Computer Vision Methods as an Aid to Visually Impaired Users,"Dipartimento di Informatica, Bioingegneria,
Robotica ed Ingegneria dei Sistemi
Computer Vision methods as an aid to visually impaired users
Giovanni Fusco
Theses Series
DIBRIS-TH-2013-03
DIBRIS, Universit`a di Genova
Via Opera Pia, 13 16145 Genova, Italy
http://www.dibris.unige.it/"
e8632e5bf43f7c59f4e1978833db8aa405c76c58,Saliency and Gist Features for Target Detection in Satellite Images,"Saliency and Gist Features for Target
Detection in Satellite Images
Zhicheng Li and Laurent Itti"
e82693e9e7b1176ecb48a775cf2548e3d68ffd3a,Linear versus nonlinear neural modeling for 2-D pattern recognition,"IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART A: SYSTEMS AND HUMANS, VOL. 35, NO. 6, NOVEMBER 2005
[7] X. J. Li et al., “CAD-vision-range-based self-localization for mobile robot
using one landmark,” J. Intell. Robot. Syst., vol. 35, no. 1, pp. 61–82,
002.
[8] J. M. Perez, C. Urdiales, A. Bandera, and F. Sandoval, “A Hough-based
solution to the simultaneous localization and map building problem,”
in Proc. 1st Eur. Conf. Mobile Robots (ECMR), Radziejowice, Poland,
003, pp. 53–58.
[9] A. Bonci, G. Ippoliti, L. Jetto, T. Leo, and S. Longhi, “Methods and
lgorithms for sensor data fusion aimed at improving the autonomy of
mobile robot,” in Advances in Control of Articulated Mobile Robots,
B. Siciliano et al., Eds. Heidelberg, Germany: Springer-Verlag, Springer
Tracts in Advanced Robotics (STAR), 2004, pp. 191–222.
[10] P. V. C. Hough, “Methods and means for recognising complex patterns,”
U.S. Patent 3 069 654, Dec. 18, 1962.
[11] R. O. Duda and P. E. Hart, “Use of the Hough Transform to detect
lines and curves in pictures,” Commun. ACM, vol. 15, no. 1, pp. 11–15,
Jan. 1972.
[12] F. O’Gorman and M. B. Clowes, “Finding picture edges through collinear-
ity of feature points,” IEEE Trans. Comput., vol. C-25, no. 4, pp. 449–454,"
e8e8f40ceff8b71d5dafa6b680d40690dfae940c,title : Guidelines for studying developmental prosopagnosia in adults and children,"Article	type:		Focus	Article
Article	title:	Guidelines	for	studying	developmental	prosopagnosia	in	adults
nd	children
First	author:	Full	name	and	affiliation;	plus	email	address	if
orresponding	author
Kirsten	A.	Dalrymple*
Institute	of	Child	Development,	University	of	Minnesota,	Minneapolis,	USA
Second	author:	Full	name	and	affiliation;	plus	email	address	if
orresponding	author
Romina	Palermo*
School	of	Psychology,	and	ARC	Centre	of	Excellence	in	Cognition	and	its	Disorders
University	of	Western	Australia,	Crawley,	Australia
Please	note	that	both	authors	would	like	to	be	listed	as	“corresponding	authors”."
e84e49c9530897fad7927a06ac4a48ddaf0adf0f,Searching for Efficient Multi-Scale Architectures for Dense Image Prediction,"Searching for Efficient Multi-Scale
Architectures for Dense Image Prediction
Liang-Chieh Chen Maxwell D. Collins
Barret Zoph
Florian Schroff
Yukun Zhu
Hartwig Adam
George Papandreou
Jonathon Shlens
Google Inc."
2a7b7de7488211471a001044a3a249a117af488a,Physical Attribute Prediction Using Deep Residual Neural Networks,"Physical Attribute Prediction Using Deep Residual
Neural Networks
st Rashidedin Jahandideh
dept. Computer Science
Shahid Beheshti University
Tehran, Iran
nd Alireza Tavakoli Targhi
dept. Computer Science
Shahid Beheshti University
Tehran, Iran
rd Maryam Tahmasbi
dept. Computer Science
Shahid Beheshti University
Tehran, Iran"
2a259fd1b4442a71cd127afac417a650ffc379d9,Human upper body posture recognition and upper limbs motion parameters estimation,"Human Upper Body Posture Recognition and Upper
Limbs Motion Parameters Estimation
Jun-Yang Huang1 Shih-Chung Hsu1and Chung-Lin Huang1,2
.  Department Of Electrical Engineering, National Tsing-Hua University, Hsin-Chu, Taiwan
.  Department of Applied Informatics and Multimedia, Asia Univeristy, Tai-Chung, Taiwan.
Email:"
2ad2af8e3bdeb0302de07defc3fec9b387414a27,Don ' t Look Back : Post-hoc Category Detection via Sparse Reconstruction,"Don't Look Back: Post-hoc Category Detection via
Sparse Reconstruction
Hyun Oh Song
Mario Fritz
Tim Althoff
Trevor Darrell
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2012-16
http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-16.html
January 24, 2012"
2a86bc520586f611771c2052b50ac52239414dd2,CrowdHuman: A Benchmark for Detecting Human in a Crowd,"CrowdHuman: A Benchmark for Detecting Human in a Crowd
Shuai Shao∗ Zijian Zhao∗ Boxun Li
Tete Xiao Gang Yu Xiangyu Zhang
Jian Sun
{shaoshuai, zhaozijian, liboxun, xtt, yugang, zhangxiangyu,
Megvii Inc. (Face++)"
2ab9c36e19090ed9ac5295b3704708bdce80462d,Zero-Shot Learning via Category-Specific Visual-Semantic Mapping and Label Refinement,"Zero-Shot Learning via Category-Specific
Visual-Semantic Mapping
Li Niu, Jianfei Cai, and Ashok Veeraraghavan"
2aa08ab3d6c227e3b071dc470a2f36dc5d4a2403,Ensembling Visual Explanations for VQA,"To Appear In Proceedings of the NIPS 2017 workshop on Visually-Grounded
Interaction and Language (ViGIL), December 2017."
2a7bca56e2539c8cf1ae4e9da521879b7951872d,Exploiting Unrelated Tasks in MultiTask Learning,"Exploiting Unrelated Tasks in Multi-Task Learning
Anonymous Author 1
Anonymous Author 2
Anonymous Author 3"
2a6c7d5aa087233ff8a09bdaa34d5f76f3330a4f,A Survey of Efficient Regression of General-Activity Human Poses from Depth Images,"A Survey of Efficient Regression of General-Activity Hu-
man Poses from Depth Images
Wenye He
This paper presents a comprehensive review on regression-based method for human pose es-
timation. The problem of human pose estimation has been intensively studied and enabled
many application from entertainment to training. Traditional methods often rely on color im-
ge only which cannot completely ambiguity of joint’s 3D position, especially in the complex
ontext. With the popularity of depth sensors, the precision of 3D estimation has significant
improvement. In this paper, we give a detailed analysis of state-of-the-art on human pose
estimation, including depth image based and RGB-D based approaches. The experimental
results demonstrate their advantages and limitation for different scenarios.
Introduction
Human pose estimation from images has been studied for decades in computer vision. As recent
development in cameras and sensors, depth images receive a wide spread of notice from researchers
from body pose estimation 1 to 3D reconstruction 2. Girshick et al.1 present an approach to find the
joints position in human body from depth images. They address the problem of general-activity
pose estimation. Their regression-based approach sucessfully computes the joint positions even
with occlusion. Their method can be view as a new combination of two existing works, implicit
shape models3 and Hough forest4. The following sections cover related works, explanation on the
method from testing to training, and result and comparison."
2ae139b247057c02cda352f6661f46f7feb38e45,Combining modality specific deep neural networks for emotion recognition in video,"Combining Modality Specific Deep Neural Networks for
Emotion Recognition in Video
Samira Ebrahimi Kahou1, Christopher Pal1, Xavier Bouthillier2, Pierre Froumenty1,
Ça˘glar Gülçehre2,∗ , Roland Memisevic2, Pascal Vincent2, Aaron Courville2, & Yoshua Bengio2
École Polytechique de Montréal, Université de Montréal, Montréal, Canada
Laboratoire d’Informatique des Systèmes Adaptatifs, Université de Montréal, Montréal, Canada
{samira.ebrahimi-kahou, christopher.pal,
{bouthilx, gulcehrc, memisevr, vincentp, courvila,"
2a218c17944d72bfdc7f078f0337cab67536e501,Detection bank: an object detection based video representation for multimedia event recognition,"Detection Bank: An Object Detection Based Video
Representation for Multimedia Event Recognition
Tim Althoff, Hyun Oh Song, Trevor Darrell
UC Berkeley EECS/ICSI
Multimedia Event Detection
Birthday Party      vs      Wedding Ceremony
● ObjectBank omits the following steps that are
standard in a detection pipeline:
● Thresholding of score maps
● Non-maximum suppression
● Pooling across all scales
● We compute different detection count statistics to
apture e.g. max number of detections, sum of
detection scores, probablity of detection based on
the detection images from a large number of
windowed object detectors.
Detection Count Statistics
Look for: Balloon, Candle, Birthday Cake vs.
Bride, Groom, Wedding Gown, Wedding Cake
Illustration"
2a152dae1ba70d0cc605b0f7418392ed1a294a4a,Head pose detection using Fast Robust PCA for Side Active Appearance Models under Occlusion,"Head Pose Detection Using Fast Robust PCA
for Side Active Appearance Models Under Occlusion
Anıl Yüce1, Matteo Sorci2, and Jean-Philippe Thiran1
Signal Processing Laboratory (LTS5)
École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
nViso Sàrl, Lausanne, Switzerland"
2a83a51c9596ed796da52bdac49ca30e4eb04345,Eclectic Genetic Algorithm for Holistic Face Recognition in L ∞ Space,"Eclectic Genetic Algorithm for Holistic Face
Recognition in L∞ Space
C. Villegas, J. Climent, C.R. Murillo, A. Otero, C.R. Villegas"
2a93ce4284c7f8605e1d9bc0a8b86036073ebf61,Learning and Detection of Multiple Objects in Video Sequences,"Master Thesis
Czech
Technical
University
in Prague
Faculty of Electrical Engineering
Department of Cybernetics
Tracking, Learning and Detection of
Multiple Objects in Video Sequences
Filip Naiser
Supervisor: prof. Ing. Jiří Matas, Ph.D.
January 2017"
2acf7e58f0a526b957be2099c10aab693f795973,Bosphorus Database for 3D Face Analysis,"Bosphorus Database for 3D Face Analysis
Arman Savran1, Neşe Alyüz2, Hamdi Dibeklioğlu2, Oya Çeliktutan1, Berk Gökberk3,
Bülent Sankur1, and Lale Akarun2
Boğaziçi University, Electrical and Electronics Engineering Department
Boğaziçi University, Computer Engineering Department
Philips Research, Eindhoven, The Netherlands"
2a2232f2972191a0606d588aa4f13c9f27d1972d,InstanceCut: From Edges to Instances with MultiCut,"InstanceCut: from Edges to Instances with MultiCut
Alexander Kirillov1 Evgeny Levinkov2 Bjoern Andres2 Bogdan Savchynskyy1 Carsten Rother1
TU Dresden, Dresden, Germany
MPI for Informatics, Saarbr¨ucken, Germany"
2a2b99fc9583419931681acfd83ac953a3df3270,Estimating the quality of face localization for face verification,"ESTIMATING THE QUALITY OF FACE LOCALIZATION FOR FACE VERIFICATION
Yann Rodriguez
Fabien Cardinaux
Samy Bengio
Johnny Mari´ethoz
IDIAP
CP 592, rue du Simplon 4
920 Martigny, Switzerland"
2aec012bb6dcaacd9d7a1e45bc5204fac7b63b3c,Robust Registration and Geometry Estimation from Unstructured Facial Scans,"Robust Registration and Geometry Estimation from Unstructured
Facial Scans
Maxim Bazik1 and Daniel Crispell2"
2a067874fc1ec318b6d23f34bdb13ea4e95d5ca6,An Evaluation of Image-Based Verb Prediction Models against Human Eye-Tracking Data,"New Orleans, Louisiana, June 1 - 6, 2018. c(cid:13)2018 Association for Computational Linguistics
Proceedings of NAACL-HLT 2018, pages 758–763"
2a1d3e1baf323e61da517a054b9571559815a651,Temporal normalization of videos using visual speech,"Temporal Normalization of Videos Using Visual Speech
Usman Saeed
EURECOM Sophia Antipolis
229 Route Des Cretes
Sophia Antipolis, France
+33(0)493008248
Jean-Luc Dugelay
EURECOM Sophia Antipolis
229 Route Des Cretes
Sophia Antipolis, France
+33(0)493008141"
2ac986ec18c3572ee4f922ba9a90ae374563491c,A New Approach of Human Segmentation from Photo Images,"International Journal of Scientific and Research Publications, Volume 5, Issue 1, January 2015
ISSN 2250-3153
A New Approach of Human Segmentation from Photo
Images
Ashwini Magar*, Prof.J.V.Shinde**
* Computer Department, Late G .N. Sapkal College Of Engineering, Savitribai Phule Pune University
** Computer Department, Late G .N .Sapkal College Of Engineering, Savitribai Phule Pune University"
2ae1c0d4898cf7d5446039e639d95a7e27f4d957,Visual place recognition with probabilistic voting,
2aca60ee8a43d88be24ab9dad373dd555fd080a7,Reduced-Gate Convolutional LSTM Using Predictive Coding for Spatiotemporal Prediction,"Reduced-Gate Convolutional LSTM Using
Predictive Coding for Spatiotemporal Prediction
Nelly Elsayed, Anthony S. Maida, and Magdy Bayoumi"
2acf319c5eac89cc9e0ed24633e4408dbd4a8a5b,The Effect of Distance Measures on the Recognition Rates of PCA and LDA Based Facial Recognition,"The Effect of Distance Measures on the Recognition Rates of PCA
nd LDA Based Facial Recognition
Philip Miller, Jamie Lyle
Digitial Image Processing
Clemson Universtiy
{pemille,"
2a6b5b8afee8289e454db9447fdaebc1282c1fea,Automatic Face Recognition and Identification Tools in the Forensic Science Domain,"Automatic Face Recognition and Identification Tools
in the Forensic Science Domain
Angelo Salici(✉) and Claudio Ciampini
Raggruppamento Carabinieri Investigazioni Scientifiche, RIS di Messina,
S.S.114 Km 6,400, 98128 Messina, Italy"
2a08147bf88041c6e0354e26762b4e4d65d5163f,Trimmed Event Recognition ( Moments in Time ) : Submission to ActivityNet Challenge 2018,"Trimmed Event Recognition (Moments in Time):
Submission to ActivityNet Challenge 2018
Dongyang Cai"
2a06341b40b3fd27483b2a8d8cbf86fddf45e423,Automatic generation of ground truth for the evaluation of obstacle detection and tracking techniques,"Automatic generation of ground truth for the evaluation of obstacle detection
nd tracking techniques
Hatem Hajri∗, Emmanuel Doucet∗†, Marc Revilloud∗, Lynda Halit∗, Benoit Lusetti∗,
Mohamed-Cherif Rahal∗
Automated Driving Research Team, Institut VEDECOM, Versailles, France
InnoCoRe Team, Valeo, Bobigny, France"
2a1deffc67ccb5f8ca5897ac3f31dac09af70f05,Robust Subspace Clustering via Tighter Rank Approximation,"Robust Subspace Clustering via Tighter Rank
Approximation
Zhao Kang
Computer Science Dept.
Southern Illinois University
Carbondale, IL, USA
Chong Peng
Computer Science Dept.
Southern Illinois University
Carbondale, IL, USA
Qiang Cheng
Computer Science Dept.
Southern Illinois University
Carbondale, IL, USA"
2a87f95e36938ca823b33c72a633d8d902d5cb86,Oxytocin Improves “Mind-Reading” in Humans,"PRIORITY COMMUNICATION
Oxytocin Improves “Mind-Reading” in Humans
Gregor Domes, Markus Heinrichs, Andre Michel, Christoph Berger, and Sabine C. Herpertz
Background: The ability to “read the mind” of other individuals, that is, to infer their mental state by interpreting subtle social cues, is
indispensable in human social interaction. The neuropeptide oxytocin plays a central role in social approach behavior in nonhuman
mammals.
Methods: In a double-blind, placebo-controlled, within-subject design, 30 healthy male volunteers were tested for their ability to infer
the affective mental state of others using the Reading the Mind in the Eyes Test (RMET) after intranasal administration of 24 IU oxytocin.
Results: Oxytocin improved performance on the RMET compared with placebo. This effect was pronounced for difficult compared with
easy items.
Conclusions: Our data suggest that oxytocin improves the ability to infer the mental state of others from social cues of the eye region.
Oxytocin might play a role in the pathogenesis of autism spectrum disorder, which is characterized by severe social impairment.
Key Words: Emotion, oxytocin, peptide, social cognition, theory of
T he ability to infer the internal state of another person to
dapt one’s own behavior is a cornerstone of all human
social interactions. Humans have to infer internal states
from external cues such as facial expressions in order to make
sense of or predict another person’s behavior, an ability that is
referred  to  as  “mind-reading”  (Siegal  and  Varley  2002;  Stone  et  al
998).  In  particular,  individuals  with  autism  have  distinct  diffi-"
2a8aedea2031128868f1c6dd44329c5bb7afc419,A Convex Duality Framework for GANs,"A Convex Duality Framework for GANs
Farzan Farnia∗
David Tse∗"
2aa362740ac9a2b304a74122da820e3829689842,"Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age","Past, Present, and Future of Simultaneous
Localization And Mapping: Towards the
Robust-Perception Age
Cesar Cadena, Luca Carlone, Henry Carrillo, Yasir Latif,
Davide Scaramuzza, Jos´e Neira, Ian Reid, John J. Leonard"
2a5efa83ea5c4733757b838b84ba6519f873b826,A Continuous Learning for Solving a Face Recognition Problem,"International Journal of Computer Information Systems and Industrial Management Applications.
ISSN 2150-7988 Volume 4 (2012) pp. 570-577
© MIR Labs, www.mirlabs.net/ijcisim/index.html
A Continuous Learning for Solving a Face
Recognition Problem
Aldo Franco Dragoni, Germano Vallesi and Paola Baldassarri
Università Politecnica delle Marche, Italy
{a.f.dragoni, g.vallesi,"
2a7e2cda27807d24b845f5b5080fb1296c302bfe,Personal Authentication Using Signature Recognition,"Personal Authentication Using Signature Recognition
Diana Kalenova
Department of Information Technology, Laboratory of Information Processing,
Lappeenranta University of Technology"
2a4f3536cd21fb7193662faef2612384f429c6a7,1 Performance Evaluation of Photometric Normalization Techniques for Illumination Invariant Face Recognition,"Performance Evaluation of Photometric
Normalization Techniques for Illumination
Invariant Face Recognition
Avtor: Vitomir Štruc
Internal Report: LUKS"
2af2aa21538783e46911fb857a23dbb88ed90c2b,A Study on Deep Learning Based Sauvegrain Method for Measurement of Puberty Bone Age,"A Study on Deep Learning Based
Sauvegrain Method for Measurement
of Puberty Bone Age
Keum Gang Cha∗
Seung Bin Baik∗
Plani Inc.
Plani Inc.
September 20, 2018"
2a56a51490f6ccfaf6fcbdf546a5515bef5203a1,"Attention, please!: Comparing Features for Measuring Audience Attention Towards Pervasive Displays","Attention, please! Comparing Features for Measuring
Audience Attention Towards Pervasive Displays
Florian Alta, Andreas Bullingb, Lukas Meckea, Daniel Buscheka
LMU Munich
Munich, Germany"
cef6cffd7ad15e7fa5632269ef154d32eaf057af,Emotion Detection Through Facial Feature Recognition,"Emotion Detection Through Facial Feature
Recognition
James Pao
through  consistent"
cee700093d6672df48d169ef194861026fe31e8e,Hashing on Nonlinear Manifolds,"Hashing on Nonlinear Manifolds
Fumin Shen, Chunhua Shen, Qinfeng Shi, Anton van den Hengel, Zhenmin Tang, Heng Tao Shen
in the Hamming space. This means that many algorithms
which are based on such pairwise comparisons can be made
more efficient, and applied to much larger datasets. Due to the
flexibility of hash codes, hashing techniques can be applied
in many ways. one can, for example, efficiently perform
similarity search by exploring only those data points falling
into the close-by buckets to the query by the Hamming
distance, or use the binary representations for other tasks like
image classification."
cea85314294f9731661a419f627cb99331ad9c50,Race recognition using local descriptors,"978-1-4673-0046-9/12/$26.00 ©2012 IEEE
ICASSP 2012"
ce9799830a24412f4bd9ad30a9d6e2a50215f8f8,Beef Cattle Instance Segmentation Using Fully Convolutional Neural Network,"Aram Ter-Sarkisov1
John Kelleher1
Bernadette Earley2
Michael Keane2
Robert Ross1
TER-SARKISOV ET AL.: BEEF CATTLE INSTANCE SEGMENTATION USING FCN
Beef Cattle Instance Segmentation Using
Fully Convolutional Neural Network
School of Computing
Dublin Institute of Technology
Dublin, Ireland
TEAGASC
Grange, Dunsany, Co. Meath, Ireland"
cef092bf9beed65e379ab48ef2b43498d4aaea92,Process Monitoring in the Intensive Care Unit: Assessing Patient Mobility Through Activity Analysis with a Non-Invasive Mobility Sensor,"Process Monitoring in the Intensive Care Unit:
Assessing Patient Mobility Through Activity
Analysis with a Non-Invasive Mobility Sensor
Austin Reiter1(B), Andy Ma1, Nishi Rawat2, Christine Shrock2,
nd Suchi Saria1
The Johns Hopkins University, Baltimore, MD, USA
Johns Hopkins Medical Institutions, Baltimore, MD, USA"
ceee9ba72a021ae5604db04a93fdcff421d60216,Encoder Based Lifelong Learning,"Encoder Based Lifelong Learning
Amal Rannen Triki ∗† Rahaf Aljundi∗ Mathew B. Blaschko
Tinne Tuytelaars
KU Leuven
KU Leuven, ESAT-PSI, iMinds, Belgium"
ce391bcdb64f7659ddc5a0c2e5c73854c1e8031c,Zur Erlangung Des Grades Des,"FILTERING AND OPTIMIZATION
STRATEGIES FOR MARKERLESS
HUMAN MOTION CAPTURE WITH
SKELETON-BASED SHAPE MODELS.
DISSERTATION
ZUR ERLANGUNG DES GRADES DES
DOKTORS DER INGENIEURWISSENSCHAFTEN (DR.-ING.)
DER NATURWISSENSCHAFTLICH-TECHNISCHEN FAKULT ¨ATEN
DER UNIVERSIT ¨AT DES SAARLANDES
VORGELEGT VON
JUERGEN GALL
SAARBR ¨UCKEN"
cebf73d590e0c0021f09bdbd59778bd574e96da7,First Impressions and the Reference Encounter : The In fl uence of Affect and Clothing on Librarian Approachability,"The University of Maine
Library Staff Publications
Fogler Library
First Impressions and the Reference Encounter:
The Influence of Affect and Clothing on Librarian
Approachability
Jennifer Bonnet
University of Maine - Main,
Ben McAlexander
Trihydro Corporation,
Follow this and additional works at: https://digitalcommons.library.umaine.edu/lib_staffpub
Part of the Library and Information Science Commons
Repository Citation
Bonnet, Jennifer and McAlexander, Ben, ""First Impressions and the Reference Encounter: The Influence of Affect and Clothing on
Librarian Approachability"" (2013). Library Staff Publications. 16.
https://digitalcommons.library.umaine.edu/lib_staffpub/16
This Article is brought to you for free and open access by It has been accepted for inclusion in Library Staff Publications by
n authorized administrator of For more information, please contact"
ce0dbe6b1abecb54dcc98dbe652aa63d190dbc94,Part-Based Models for Finding People and Estimating Their Pose,"Part-based models for finding people and
estimating their pose
Deva Ramanan"
ce54e891e956d5b502a834ad131616786897dc91,Face Recognition Using LTP Algorithm,"International Journal of Science and Research (IJSR)
ISSN (Online): 2319-7064
Index Copernicus Value (2013): 6.14 | Impact Factor (2014): 5.611
Face Recognition Using LTP Algorithm
Richa Sharma1, Rohit Arora2
ECE & KUK
Assistant Professor (ECE)
Volume 4 Issue 12, December 2015
Licensed Under Creative Commons Attribution CC BY
www.ijsr.net
  Variation  in  luminance:  Third  main  challenge  that
ppears in face recognition process is the luminance. Due
to variation in the luminance the representation get varied
from  the  original  image.  The  person  with  same  poses
expression and seen from same viewpoint can be appear
very different due to variation in lightening."
ceba512cd64951fa49ca2ee19295561cd2493f18,Visual and Semantic Knowledge Transfer for Large Scale Semi-Supervised Object Detection,"Visual and Semantic Knowledge Transfer for
Large Scale Semi-supervised Object Detection
Yuxing Tang, Josiah Wang, Xiaofang Wang, Boyang Gao, Emmanuel Dellandr´ea, Robert Gaizauskas
nd Liming Chen Senior Member,"
cee5e1a4e5a79ed93a3f5a6cc0b22e38f1c8d389,Occluded Facial Image Retrieval Based on a Similarity Measurement,"Publishing CorporationMathematical Problems in EngineeringVolume 2015, Article ID 217568, 11 pageshttp://dx.doi.org/10.1155/2015/217568"
ce06015fc0eb2add064ef93c9b97ad063c03aef4,Person Re-identification in Surveillance Videos using Multi-part Color Descriptor,"International Journal of Computer Applications (0975 – 8887)
Volume 121 – No.16, July 2015
Person Re-identification in Surveillance Videos
using Multi-part Color Descriptor
P.K. Sathish
S. Balaji
Computer Science and Engineering Dept.
Centre for Emerging Technologies, Jain University
Christ University
Bengaluru- 560074"
ce12bbb8ce974df4b64f18e478d7fa99b722de03,A Hybrid Data Association Framework for Robust Online Multi-Object Tracking,"A Hybrid Data Association Framework for Robust
Online Multi-Object Tracking
Min Yang, Yuwei Wu∗, and Yunde Jia Member, IEEE,"
ce20f81374e2058b01910a4d028b79c07ce7e994,Discriminating Characteristics of Gabor Phase-Face and Improved Methods for Face Recognition,"Noname manuscript No.
(will be inserted by the editor)
Discriminating Characteristics of Gabor Phase-Face and
Improved Methods for Face Recognition
Iqbal Nouyed · Bruce Poon · M. Ashraful Amin · Hong Yan
the date of receipt and acceptance should be inserted later"
cea50611ba73b5775cc2fe1e9c27990a0bb20cf8,Gabor Feature Based Sparse Representation for Face Recognition with Gabor Occlusion Dictionary,"Gabor Feature based Sparse Representation for
Face Recognition with Gabor Occlusion
Dictionary
Meng Yang, Lei Zhang ⋆
Biometric Research Center, Dept. of Computing, The Hong Kong Polytechnic
University, Hong Kong,"
ce6f459462ea9419ca5adcc549d1d10e616c0213,A Survey on Face Identification Methodologies in Videos,"A Survey on Face Identification Methodologies in
Videos
Student, M.Tech  CSE ,Department of Computer Science
& Engineering ,G.H.Raisoni College of Engineering &
Technology for Women, Nagpur, Maharashtra, India.
Deepti Yadav"
ce3ee08f4d937a6dcb2d6dd0a1ca100920f312e6,Literature Survey On Contactless Palm Vein Recognition,"International Journal of Computer Science Trends and Technology (IJCST) – Volume 3 Issue 5, Sep-Oct 2015
RESEARCH  ARTICLE
Literature Survey On Contactless Palm Vein Recognition
Roshni C Rahul [1], Merin  Cherian [2], Manu Mohan C M [3]
Department of Computer Science [1], Department of Science [2], Department of Electronics [3]
OPEN  ACCESS
Mahatma Gandhi University
Kerala - India"
ce13aaf9ab0d3ec3dd9637b2dd5122b4aa711fd7,Local Feature-based Person Re-Identification in Video,"UNIVERSITÄT KARLSRUHE (TH)
FAKULTÄT FÜR INFORMATIK
INSTITUT FÜR ANTHROPOMATIK
Prof. Dr. Rainer Stiefelhagen
DIPLOMA THESIS
Local Feature-based Person
Re-Identification in Video
SUBMITTED BY
Martin Bäuml
MAY 2009
ADVISORS
Prof. Dr. Rainer Stiefelhagen
Dipl. Inf. Keni Bernardin
Dr. Jie Yang"
ced4853617ba6af27f5447f9c4de07c3e05e8c3b,Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations,"Real-Time Joint Semantic Segmentation and Depth Estimation Using
Asymmetric Annotations
Vladimir Nekrasov1, Thanuja Dharmasiri2, Andrew Spek2, Tom Drummond2, Chunhua Shen1 and Ian Reid1"
ce316d2366ec1b95ee91a98b4f426e6c00cdcdc4,Hierarchical Energy-transfer Features,"Hierarchical Energy-Transfer Features
Radovan Fusek, Eduard Sojka, Karel Mozdˇreˇn and Milan ˇSurkala
Technical University of Ostrava, FEECS, Department of Computer Science
7. listopadu 15, 708 33 Ostrava-Poruba, Czech Republic
{radovan.fusek, eduard.sojka, karel.mozdren,
Keywords:
Object Detection, Recognition, SVM, Image Descriptors, Feature Selection."
ce57cc478421adf85a9058a0cc8fad8ebfd81c52,Multimodal Attribute Extraction,"Multimodal Attribute Extraction
Robert L. Logan IV
University of California
Irvine, CA
Samuel Humeau
Diffbot
Mountain View, CA
Sameer Singh
University of California
Irvine, CA
Introduction
Given the large collections of unstructured and semi-structured data available on the web, there is a
rucial need to enable quick and efficient access to the knowledge content within them. Traditionally,
the field of information extraction has focused on extracting such knowledge from unstructured text
documents, such as job postings, scientific papers, news articles, and emails. However, the content
on the web increasingly contains more varied types of data, including semi-structured web pages,
tables that do not adhere to any schema, photographs, videos, and audio. Given a query by a user,
the appropriate information may appear in any of these different modes, and thus there’s a crucial
need for methods to construct knowledge bases from different types of data, and more importantly,
Motivated by this goal, we introduce the task of multimodal attribute extraction. Provided contextual"
ce6d23894f88349443e7c9fe512ca81291bb2e00,VIENA2: A Driving Anticipation Dataset,"VIENA2: A Driving Anticipation Dataset
Mohammad Sadegh Aliakbarian1,2,4, Fatemeh Sadat Saleh1,4, Mathieu
Salzmann3, Basura Fernando2, Lars Petersson1,4, and Lars Andersson4
ANU, 2ACRV, 3CVLab, EPFL, 4Data61-CSIRO"
ceaa5eb51f761b5f84bd88b58c8f484fcd2a22d6,UC San Diego UC San Diego Electronic Theses and Dissertations Title Interactive learning and prediction algorithms for computer vision applications,"UC San Diego
UC San Diego Electronic Theses and Dissertations
Title
Inhibitions of ascorbate fatty acid derivatives on three rabbit muscle glycolytic enzymes
Permalink
https://escholarship.org/uc/item/8x33n1gj
Author
Pham, Duyen-Anh
Publication Date
011-01-01
Peer reviewed|Thesis/dissertation
eScholarship.org
Powered by the California Digital Library
University of California"
cef2b5ab841568755233994b12cf046c408f881e,TECHNIQUES FOR STATISTICAL SHAPE MODEL BUILDING AND FUSION,"TECHNIQUES
STATISTICAL SHAPE MODEL
BUILDING AND FUSION
Constantine Butakoff
(Kostantyn Butakov)"
ce073cb70eec80d87c9e07a4ec2d4162d91e23a6,Positive Definite Matrices : Data Representation and Applications to Computer Vision,"Positive Definite Matrices: Data Representation
nd Applications to Computer Vision
Anoop Cherian and Suvrit Sra"
ce4853f2214ee1f4c47a97ff45d4e53f6ffd5087,MODELS AND METHODS FOR BAYESIAN OBJECT MATCHING,"Helsinki University of Technology Laboratory of Computational Engineering Publications
Teknillisen korkeakoulun Laskennallisen tekniikan laboratorion julkaisuja
Espoo 2005
REPORT B52
MODELS AND METHODS FOR BAYESIAN OBJECT
MATCHING
Toni Tamminen
AB TEKNILLINEN KORKEAKOULU
TEKNISKA H(cid:214)GSKOLAN
HELSINKI UNIVERSITY OF TECHNOLOGY
TECHNISCHE UNIVERSIT˜T HELSINKI
UNIVERSITE DE TECHNOLOGIE D’HELSINKI"
ce6d34010f04afa4cf3018f51bad8f480ebc759c,"ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras","DOI: 10.1109/TRO.2017.2705103
IEEE Xplore: http://ieeexplore.ieee.org/document/7946260/
(cid:13)2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any
urrent or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new
ollective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other
works."
ceac97de889ed2f65af62f61a007651d03b36b6c,Diagnostic Accuracy of Content Based Dermatoscopic Image Retrieval with Deep Classification Features,"Diagnostic Accuracy of Content Based Dermatoscopic Image Retrieval with
Deep Classification Features
Tschandl P, Argenziano G, Razmara M, Yap J
Final version available at https://doi.org/10.1111/bjd.17189
Citation:
tschandl cbir2018,
Author=”Tschandl, P. and Argenziano, G. and Razmara, M. and Yap, J. ”,
Title=”Diagnostic Accuracy of Content Based Dermatoscopic Image Retrieval with Deep Classification Features”,
Journal=”Br J Dermatol”,
Year=”2018”"
ce933821661a0139a329e6c8243e335bfa1022b1,Temporal Modeling Approaches for Large-scale Youtube-8M Video Understanding,"Temporal Modeling Approaches for Large-scale
Youtube-8M Video Understanding
Fu Li, Chuang Gan, Xiao Liu, Yunlong Bian, Xiang Long, Yandong Li, Zhichao Li, Jie Zhou, Shilei Wen
Baidu IDL & Tsinghua University"
cefd107b19201cd9f403e2f9332c690e81f770b5,A Survey on Databases for Facial Expression Analysis,
ced61099a0306d555486162670f1213f2c72b020,Multi-Vehicle Trajectories Generation for Vehicle-to-Vehicle Encounters,"Multi-Vehicle Trajectories Generation for Vehicle-to-Vehicle Encounters
Wenhao Ding, Wenshuo Wang, and Ding Zhao"
ceedb191328ac4d968853b948a32b5689c2ac2a2,Semisupervised Dimensionality Reduction and Classification Through Virtual Label Regression,"IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 41, NO. 3, JUNE 2011
Semisupervised Dimensionality Reduction and
Classification Through Virtual Label Regression
Feiping Nie, Dong Xu, Xuelong Li, Senior Member, IEEE, and Shiming Xiang"
cebfafea92ed51b74a8d27c730efdacd65572c40,Matching 2.5D face scans to 3D models,"JANUARY 2006
Matching 2.5D Face Scans to 3D Models
Xiaoguang Lu, Student Member, IEEE, Anil K. Jain, Fellow, IEEE, and
Dirk Colbry, Student Member, IEEE"
ce8c8e9fdbdd84adc096018bb0edb49b6913b946,Learning Discriminative Features for Speaker Identification and Verification,"Interspeech 2018
-6 September 2018, Hyderabad
0.21437/Interspeech.2018-1015"
ce0cc5f078c5224b9599caf518d74ae3023be0a6,Review on computer vision techniques in emergency situations,"(will be inserted by the editor)
Review on Computer Vision Techniques in Emergency Situations
Laura Lopez-Fuentes · Joost van de Weijer · Manuel Gonz´alez-Hidalgo · Harald
Skinnemoen · Andrew D. Bagdanov
Received: date / Accepted: date"
fc1e37fb16006b62848def92a51434fc74a2431a,A Comprehensive Analysis of Deep Regression,"DRAFT
A Comprehensive Analysis of Deep Regression
St´ephane Lathuili`ere, Pablo Mesejo, Xavier Alameda-Pineda, Member IEEE, and Radu Horaud"
fc2f12bacd3714c02a52ba183309d1f5dd8b292e,Generalization Abilities of Appearance-Based Subspace Face Recognition Algorithms,"2th Int. Workshop on Systems, Signals & Image Processing, 22-24 September 2005, Chalkida, Greece
Generalization Abilities of
Appearance-Based Subspace
Face Recognition Algorithms
Kresimir Delac *, Mislav Grgic and Sonja Grgic
Department of Wireless Communications, Faculty of Electrical Engineering and
Computing, University of Zagreb, Croatia
E-mail:
* Corresponding author"
fcf8bb1bf2b7e3f71fb337ca3fcf3d9cf18daa46,Feature Selection via Sparse Approximation for Face Recognition,"MANUSCRIPT SUBMITTED TO IEEE TRANS. PATTERN ANAL. MACH. INTELL., JULY 2010
Feature Selection via Sparse Approximation for
Face Recognition
Yixiong Liang, Lei Wang, Yao Xiang, and Beiji Zou"
fc74e14a3195fdf91157d5ea86d35c576fcf01d6,Detection and Handling of Occlusion in an Object Detection System,"Detection and Handling of Occlusion in an
Object Detection System
R.M.G. Op het Velda, R.G.J. Wijnhovenb, Y. Bondarauc and Peter H.N. de Withd
,bViNotion B.V., Horsten 1, 5612 AX, Eindhoven, The Netherlands;
,c,dEindhoven University of Technology, Den Dolech 2, 5612 AZ, Eindhoven, The Netherlands"
fcd3d69b418d56ae6800a421c8b89ef363418665,Effects of Aging over Facial Feature Analysis and Face Recognition,"Effects of Aging over Facial Feature Analysis and Face
Recognition
Bilgin Esme & Bulent Sankur
Bogaziçi Un. Electronics Eng. Dept. March 2010"
fcbedf2113c91fb71fe7bfcf9644a2f7f74ddff3,Street Viewer: An Autonomous Vision Based Traffic Tracking System,"Article
Street Viewer: An Autonomous Vision Based Traffic
Tracking System
Andrea Bottino, Alessandro Garbo, Carmelo Loiacono and Stefano Quer *
Dipartimento di Automatica ed Informatica, Politecnico di Torino, Torino 10129, Italy;
(A.B.); (A.G.); (C.L.)
* Correspondence: Tel.: +39-011-090-7076
Academic Editor: Andrea Zanella
Received: 8 March 2016; Accepted: 27 May 2016; Published: 3 June 2016"
fc30d7dbf4c3cdd377d8cd4e7eeabd5d73814b8f,Multiple Object Tracking by Efficient Graph Partitioning,"Multiple Object Tracking
y Ef‌f‌icient Graph Partitioning
Ratnesh Kumar, Guillaume Charpiat, Monique Thonnat
STARS Team, INRIA, Sophia Antipolis, France"
fcb64ef4421cebb80eb33f62c7726f339eb2bb62,Deep View-Aware Metric Learning for Person Re-Identification,Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18)
fc068f7f8a3b2921ec4f3246e9b6c6015165df9a,Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline),"Beyond Part Models: Person Retrieval with Refined Part Pooling
(and A Strong Convolutional Baseline)
Yifan Sun†, Liang Zheng‡, Yi Yang‡, Qi Tian§, Shengjin Wang†∗
Tsinghua University ‡University of Technology Sydney §University of Texas at San Antonio
{liangzheng06,"
fc49b2b0bfc81df697aebf704f8bb68e5fa3cc44,Implementation of Gabor Filters Combined with Binary Features for Gender Recognition,"International Journal of Electrical and Computer Engineering (IJECE)
Vol. 4, No. 1, Feburary 2014, pp. 108~115
ISSN: 2088-8708
      108
Implementation of Gabor Filters Combined with Binary
Features for Gender Recognition
Milad Jafari Barani*, Karim Faez**, Fooad Jalili***
* Department of Electrical Computer and Biomedical Engineering Qazvin Branch, Islamic Azad University Qazvin, Iran
** Electrical Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), Hafez Avenue, Tehran
**Department of Electrical Computer and Biomedical Engineering Qazvin Branch, Islamic Azad University Qazvin, Iran
Article Info
Article history:
Received Sep 8, 2013
Revised Nov 25, 2013
Accepted Dec 21, 2013
Keyword:
Gender classification
Self-organizing networks
Geometric characteristics
Gabor filters"
fca14b3ad0efa7bdc6ab7c1f1d58016d2be634dc,Combining vocal and visual cues in an identity verification system using k-NN based classifiers,"COMBININGVOCALANDVISUALCUES
INANIDENTITYVERIFICATIONSYSTEM
USINGK-NNBASEDCLASSIFIERS
G(cid:19)erardChollet
PatrickVerlinde
CNRSURA-
SignalandImageCenter
ENST/TSIDepartment
RoyalMilitaryAcademy
Paris,France
Brussels,Belgium"
fcabf1c0f4a26431d4df95ddeec2b1dff9b3e928,Semantic Segmentation using Adversarial Networks,
fc73090889036a0e42ea40827ac835cd5e135b16,Deep Learning based Large Scale Visual Recommendation and Search for E-Commerce,"Deep Learning based Large Scale Visual Recommendation and
Search for E-Commerce
Devashish Shankar, Sujay Narumanchi, Ananya H A,
Pramod Kompalli, Krishnendu Chaudhury
Flipkart Internet Pvt. Ltd.,
Bengaluru, India."
fc50c9392fd23b6c88915177c6ae904a498aacea,Scaling Egocentric Vision: The EPIC-KITCHENS Dataset,"Scaling Egocentric Vision:
The EPIC-KITCHENS Dataset
Dima Damen1, Hazel Doughty1, Giovanni Maria Farinella2, Sanja Fidler3,
Antonino Furnari2, Evangelos Kazakos1, Davide Moltisanti1,
Jonathan Munro1, Toby Perrett1, Will Price1, and Michael Wray1
Uni. of Bristol, UK 2Uni. of Catania, Italy,
Uni. of Toronto, Canada"
fc4b50b96761f1746efe286ec1b27b0d44d5fc75,ILLUMINATION INVARIANT FACE RECOGNITION SYSTEM,"International Journal For Technological Research In Engineering
Volume 1, Issue 10, June-2014
ISSN (Online): 2347 - 4718
ILLUMINATION INVARIANT FACE RECOGNITION SYSTEM
Chande Anita1, Shah Khushbu2
Computer Engineering Department,
L.J.I.E.T, G.T.U, Ahmedabad, Gujarat, India."
fc04a50379e08ddde501816eb1f9560c36d01a39,Image Pre-processing Using OpenCV Library on MORPH-II Face Database,"Image Pre-processing Using OpenCV Library on MORPH-II Face Database
B. Yip, R. Towner, T. Kling, C. Chen, and Y. Wang"
fc27c2c8a2486f5918451fbef198f46b5bf45d2c,Robust Real-Time Multi-View Eye Tracking,"IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. XX, NO. XX, 2018
Robust Real-Time Multi-View Eye Tracking
Nuri Murat Arar, Student Member, IEEE, and Jean-Philippe Thiran, Senior Member, IEEE"
fcd77f3ca6b40aad6edbd1dab9681d201f85f365,Machine Learning Based Attacks and Defenses in Computer Security : Towards Privacy and Utility Balance in Sensor Environments,"(cid:13)Copyright 2014
Miro Enev"
fcd9221f8ef306155f59817a3b0bdae05e9e0ae2,GEFeWS: A Hybrid Genetic-Based Feature Weighting and Selection Algorithm for Multi-Biometric Recognition,"GEFeWS: A Hybrid Genetic-Based Feature Weighting and
Selection Algorithm for Multi-Biometric Recognition
Aniesha Alford+, Khary Popplewell#, Gerry Dozier#, Kelvin Bryant#, John Kelly+,
Josh Adams#, Tamirat Abegaz^, and Joseph Shelton#
Center for Advanced Studies in Identity Sciences
+Electrical and Computer Engineering Department,
#Computer Science Department
^Computational Science and Engineering Department
North Carolina A & T State University
601 E Market St., Greensboro, NC  27411"
fcbf808bdf140442cddf0710defb2766c2d25c30,Unsupervised Semantic Action Discovery from Video Collections,"IJCV manuscript No.
(will be inserted by the editor)
Unsupervised Semantic Action Discovery from Video
Collections
Ozan Sener · Amir Roshan Zamir · Chenxia Wu · Silvio Savarese ·
Ashutosh Saxena
Received: date / Accepted: date"
fc9e60f370252bc9a6120a6b2c39703ac1fee810,Critical Points to Determine Persistence Homology,"Critical Points to Determine Persistence
Homology
Charmin Asirimath, Jayampathy Ratnayake, and Chathuranga Weeraddana"
fcc6fd9b243474cd96d5a7f4a974f0ef85e7ddf7,InclusiveFaceNet: Improving Face Attribute Detection with Race and Gender Diversity,"Improving Face Attribute Detection with Race and Gender Diversity
InclusiveFaceNet:
Hee Jung Ryu 1 Hartwig Adam * 1 Margaret Mitchell * 1"
fc5fb5d0fb7c654e8fda140e5bc53f4f422a5d6e,Computationally Efficient Invariant Facial Expression Recognition,"Research Journal of Recent Sciences _________________________________________________ ISSN 2277-2502
Vol. 3(2), 61-68, February (2014)
Res.J.Recent Sci.
Computationally Efficient Invariant Facial Expression Recognition
Muhammad Hussain, Sajid Ali Khan, Nadeem Ullah, Naveed Riaz and Muhammad Nazir
Department of Computer Science, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad, PAKISTAN
Available online at: www.isca.in, www.isca.me
Received 28th June 2013, revised 12th July 2013, accepted 13th August 2013"
fc3e097ea7dd5daa7d314ecebe7faad9af5e62fb,Variational Inference and Model Selection with Generalized Evidence Bounds,"Variational Inference and Model Selection
with Generalized Evidence Bounds
Chenyang Tao * Liqun Chen * Ruiyi Zhang Ricardo Henao Lawrence Carin"
fc7627e57269e7035e4d56105358211076fe4f04,The Association of Quantitative Facial Color Features with Cold Pattern in Traditional East Asian Medicine,"Hindawi
Evidence-Based Complementary and Alternative Medicine
Volume 2017, Article ID 9284856, 9 pages
https://doi.org/10.1155/2017/9284856
Research Article
The Association of Quantitative Facial Color Features with
Cold Pattern in Traditional East Asian Medicine
Sujeong Mun, Ilkoo Ahn, and Siwoo Lee
Mibyeong Research Center, Korea Institute of Oriental Medicine, 1672 Yuseong-daero, Yuseong-gu, Daejeon 305-811, Republic of Korea
Correspondence should be addressed to Siwoo Lee;
Received 30 June 2017; Accepted 13 September 2017; Published 17 October 2017
Academic Editor: Kenji Watanabe
Copyright © 2017 Sujeong Mun et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Introduction. Facial diagnosis is a major component of the diagnostic method in traditional East Asian medicine. We investigated
the association of quantitative facial color features with cold pattern using a fully automated facial color parameterization system.
Methods. The facial color parameters of 64 participants were obtained from digital photographs using an automatic color correction
nd color parameter calculation system. Cold pattern severity was evaluated using a questionnaire. Results. The 𝑎∗ values of the
whole face, lower cheek, and chin were negatively associated with cold pattern score (CPS) (whole face: 𝐵 = −1.048, 𝑃 = 0.021;
lower cheek: 𝐵 = −0.494, 𝑃 = 0.007; chin: 𝐵 = −0.640, 𝑃 = 0.031), while 𝑏∗ value of the lower cheek was positively associated"
90e994a802a0038f24c8e3735d7619ebb40e6e93,Semantic Foggy Scene Understanding with Synthetic Data,"Noname manuscript No.
(will be inserted by the editor)
Semantic Foggy Scene Understanding with Synthetic Data
Christos Sakaridis · Dengxin Dai · Luc Van Gool
Received: date / Accepted: date"
90e56a8515c8c2ff16f5c79c69811e283be852c7,Boosting face recognition via neural Super-Resolution,"Boosting face recognition via neural Super-Resolution
Guillaume Berger, Cl´ement Peyrard and Moez Baccouche
Orange Labs - 4 rue du Clos Courtel, 35510 Cesson-S´evign´e - France"
90fb58eeb32f15f795030c112f5a9b1655ba3624,OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS ISSN 2320-7345 FACE AND IRIS RECOGNITION IN A VIDEO SEQUENCE USING DBPNN AND ADAPTIVE HAMMING DISTANCE,"INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS
www.ijrcar.com
Vol.4 Issue 6, Pg.: 12-27
June 2016
INTERNATIONAL JOURNAL OF
RESEARCH IN COMPUTER
APPLICATIONS AND ROBOTICS
ISSN 2320-7345
FACE AND IRIS RECOGNITION IN A
VIDEO SEQUENCE USING DBPNN AND
ADAPTIVE HAMMING DISTANCE
S. Revathy, 2Mr. L. Ramasethu
PG Scholar, Hindusthan College of Engineering and Technology, Coimbatore, India.
Assistant Professor, Hindusthan College of Engineering and Technology, Coimbatore, India.
Email id:"
904b322a61d9be9c0b1023946320f9245533085e,Multi-Residual Networks,"Multi-Residual Networks
Masoud Abdi and Saeid Nahavandi*"
90ce227ec08053ea6acf9f9f9f53d8b7169574f2,An Introduction to Evaluating Biometric Systems,"C O V E R   F E A T U R E
An Introduction to
Evaluating
Biometric
Systems
O n the basis of media hype alone, you might
onclude that biometric passwords will soon
replace  their  alphanumeric  counterparts
with versions that cannot be stolen, forgot-
ten, lost, or given to another person. But
what if the performance estimates of these systems are
far more impressive than their actual performance?
P. Jonathon
Phillips
Alvin Martin
C.L. Wilson
Przybocki
National
Institute of
Standards and"
9043df1de4f6e181875011c1379d1a7f68a28d6c,People Detection from Overhead Cameras,"People Detection from Overhead
Cameras
A study of impact of occlusion on
performance
Lu Liu
in partial fulfillment of the requirements for the degree of
Master of Science
t the Delft University of Technology,
to be defended publicly on Friday August 31, 2018 at 01:00 PM.
Student number:
Thesis committee: Dr. Hayley Hung (supervisor)
621832
EEMCS
Laura Cabrera-Quiros (mentor) EEMCS
EEMCS
Prof. Marcel Reinders,
Dr. Julian Kooij,"
90c26eca18194e23cfd3c3bbf341b133e4bf5f6b,Localization from semantic observations via the matrix permanent,"Article
Localization from semantic observations
via the matrix permanent
The International Journal of
Robotics Research
Ó The Author(s) 2015
Reprints and permissions:
sagepub.co.uk/journalsPermissions.nav
DOI: 10.1177/0278364915596589
ijr.sagepub.com
Nikolay Atanasov, Menglong Zhu, Kostas Daniilidis and George J. Pappas"
90cb074a19c5e7d92a1c0d328a1ade1295f4f311,Fully Automatic Upper Facial Action Recognition,"MIT. Media Laboratory Affective Computing Technical Report #571
Appears in IEEE International Workshop on Analysis and Modeling of Faces and Gestures , Oct 2003
Fully Automatic Upper Facial Action Recognition
Ashish Kapoor Yuan Qi Rosalind W. Picard
MIT Media Laboratory
Cambridge, MA 02139"
909f91c1957ce2bf9d76ee2109a865e87bf17057,GMCP-Tracker: Global Multi-object Tracking Using Generalized Minimum Clique Graphs,"GMCP-Tracker: Global Multi-object Tracking
Using Generalized Minimum Clique Graphs
Amir Roshan Zamir, Afshin Dehghan, and Mubarak Shah
UCF Computer Vision Lab, Orlando, FL 32816, USA"
90eb9f6a1b7e3dae24e438b201e6b1f671a87eb5,Single-Camera Automatic Landmarking for People Recognition with an Ensemble of Regression Trees,"Single-Camera Automatic Landmarking for People Recognition
with an Ensemble of Regression Trees
Karla Trejo, Cecilio Angulo
Universitat Polit`ecnica de Catalunya, Barcelona,
Spain
(AAM)
Active Appearance Model"
907fbe706ec14101978a63c6252e0d75e657e8dd,The Unreasonable Effectiveness of Texture Transfer for Single Image Super-resolution,"The Unreasonable Effectiveness of Texture Transfer
for Single Image Super-resolution
Muhammad Waleed Gondal
Max Planck Institute for Intelligent Systems.
Bernhard Schölkopf
Max Planck Institute for Intelligent Systems.
Michael Hirsch
Amazon Research."
9099de23a4842b37b1612e90db18ec43d2e6250f,A comprehensive comparison of features and embedding methods for face recognition,"Turk J Elec Eng & Comp Sci
(2016) 24: 313 { 340
⃝ T (cid:127)UB_ITAK
doi:10.3906/elk-1301-65
A comprehensive comparison of features and embedding methods for face
recognition
(cid:3)
, Hakan C(cid:24) EV_IKALP, Rifat ED_IZKAN
Hasan Serhan YAVUZ
Department of Electrical and Electronics Engineering, Eski(cid:24)sehir Osmangazi University, Eski(cid:24)sehir, Turkey
Received: 11.01.2013
(cid:15)
Accepted/Published Online: 09.11.2013
(cid:15)
Final Version: 01.01.2016"
90443ec362dc553f29fbf824b4d13fd7f26f2a32,A Vote-and-Verify Strategy for Fast Spatial Verification in Image Retrieval,"A Vote-and-Verify Strategy for Fast Spatial
Verification in Image Retrieval
Johannes L. Sch¨onberger1(cid:63), True Price2(cid:63), Torsten Sattler1(cid:63),
Jan-Michael Frahm2, Marc Pollefeys1,3
ETH Z¨urich, 2 UNC Chapel Hill, 3 Microsoft"
903bc3588be010a5a166b48d3f52d0ee521dd4f9,"Discrete Energy Minimization, beyond Submodularity: Applications and Approximations","Thesis for the degree Doctor of Philosophy By Shai Bagon Advisor: Prof. Michal Irani September, 2012 Submitted to the Scientific Council of the Weizmann Institute of Science Rehovot, Israel ,תויטרקסיד תויגרנא רועזימ תתל רֶבֵעֵמ-:תויראלודומ םיבוריקו תויצקילפא  Discrete Energy Minimization, beyond Submodularity: Applications and Approximations ראותל (הזת) רמג תדובע היפוסוליפל רוטקוד תאמ שןוגב י ירשתג""עשת , שגומת לש תיעדמה הצעומל עדמל ןמציו ןוכמ לארשי ,תובוחר חנמ:ה ינריא לכימ 'פורפ"
9095f633a153c0e3a5503c0373c9c1dfeeefb0cc,Fast 3D face reconstruction based on uncalibrated photometric stereo,"Multimed Tools Appl
DOI 10.1007/s11042-013-1791-3
Fast 3D face reconstruction based on uncalibrated
photometric stereo
Yujuan Sun & Junyu Dong & Muwei Jian & Lin Qi
# Springer Science+Business Media New York 2013"
907475a4febf3f1d4089a3e775ea018fbec895fe,Statistical modeling for facial expression analysis and synthesis,"STATISTICAL MODELING FOR FACIAL EXPRESSION ANALYSIS AND SYNTHESIS
Bouchra Abboud, Franck Davoine, Mˆo Dang
Heudiasyc Laboratory, CNRS, University of Technology of Compi`egne.
BP 20529, 60205 COMPIEGNE Cedex, FRANCE.
E-mail:"
90d8dbaa799430d7384425061317e0fa55bf5cbb,Representation Models and Machine Learning Techniques for Scene Classificatio,"Representation Models and
Machine Learning Techniques
for Scene Classificatio
Giovanni Maria Farinella and Sebastiano Battiato
Image Processing Lab, Dipartimento di Matematica e Informatica,
Universit`a degli Studi di Catania, Viale A. Doria 6, 95125 Catania, Italy;
E-mail: {gfarinella,"
90de946acdc1dd6886c51f48031e2d5f3b4b8b28,Nonlinear Dimensionality Reduction with Locally Linear Embedding and Isomap,"Nonlinear Dimensionality Reduction
with Locally Linear Embedding
nd Isomap
Tobias Friedrich
Department of Computer Science
The University of Sheffield
September 2002
THIS DISSERTATION IS A PART REQUIREMENT
FOR THE MSC IN ADVANCED COMPUTER SCIENCE."
9015fd773526e21e352037663de3f586ccf4e907,Fused Deep Neural Networks for Efficient Pedestrian Detection,"Fused Deep Neural Networks for Efficient
Pedestrian Detection
Xianzhi Du, Mostafa El-Khamy, Vlad I. Morariu, Jungwon Lee, and Larry Davis"
90a226bcd72ac3bd7a0e12d416055b6299613169,Classifying content-based Images using Self Organizing Map Neural Networks Based on Nonlinear Features,"Int. J. Advanced Networking and Applications
Volume: 6 Issue: 1   Pages: 2135-2140   (2014) ISSN : 0975-0290
Classifying content-based Images using Self
Organizing Map Neural Networks Based on
Nonlinear Features
Ebrahim Parcham
Electrical and Computer Engineering Department, Tehran Science & Research University Tehran, Iran
Department of Electrical Computer and BiomedicalEngineering,Qazvin Branch Islamic Azad University Qazvin,Iran
Department of Electrical Computer and BiomedicalEngineering,Qazvin Branch Islamic Azad University Qazvin,Iran
Email:
Monireh Pournazari
Email:
Mina Hojati
Email:
Mehrdad Jalili Monir
Shatel Isp technical Employer
Email:
Bahareh Mirzaei
Sohrevardi private high educational institute,school of computer engineering Qazvin,Iran
Email:"
90a545980b3bb2f001298fe5091847c9738be8a0,Automated vehicle's behavior decision making using deep reinforcement learning and high-fidelity simulation environment,"Automated Vehicle’s behavior decision making using deep
reinforcement learning and high-fidelity simulation environment
Yingjun Yea, Xiaohui Zhanga, Jian Suna
Department of Traffic Engineering & Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji
University, Shanghai, China."
90915cc93248174c4729be65159fb946d2ad5f72,"Relative Dense Tracklets for Human Action Recognition Piotr Bilinski Etienne Corvee Slawomir Bak Francois Bremond INRIA Sophia Antipolis , STARS team 2004 Route des Lucioles , BP 93 , 06902 Sophia Antipolis , France","Relative Dense Tracklets for Human Action Recognition
Piotr Bilinski
Etienne Corvee
Slawomir Bak
Francois Bremond
INRIA Sophia Antipolis, STARS team
004 Route des Lucioles, BP93, 06902 Sophia Antipolis, France"
903210406f14a12b481524d543b14f16114797e2,Pretest of images for the beauty dimension,"Análise Psicológica (2015), 4 (XXXIII): 453-466
doi: 10.14417/ap.1052
Pretest of images for the beauty dimension
Joana Mello* / Filipe Loureiro*
* ISPA – Instituto Universitário
In this work, we present norms concerning the perceived association of two sets of image stimuli with
the concept of “beauty”: 40 objects (Study 1) and 40 photos of human faces (Study 2)1. Participants
were presented with a set of words associated with the construct of “beauty” and were subsequently
sked to judge each image on how much they considered them to be related with this construct on a
7-point scale (1 – Not at all related; 7 – Very related). The interpretation of means’ confidence intervals
distinguish between 40 images, evaluated as “ugly” – with low scores on the beauty dimension – (20
objects  and  20  faces),  and  28  images  evaluated  as  “beautiful”  –  with  high  scores  on  the  beauty
dimension – (12 objects and 16 faces). Results are summarized and photos made available to support
future research requiring beauty and/or ugly stimulus.
Key words: Norms, Beauty, Ugly, People, Objects.
Introduction
The objective of this work consists on the presentation of beauty norms of a set of images from
two categories (people and objects) for further use in different contexts and experimental settings.
Our main purpose was to present norms of a set of updated to present-days photos of faces and
objects regarding its level of activation of the “beauty” construct, i.e., of the perceived association"
9064e178864208cb0e89fecdc8d26b846ccc8e55,Localizing Moments in Video with Temporal Language,"Localizing Moments in Video with Temporal Language
Lisa Anne Hendricks1∗, Oliver Wang2, Eli Shechtman2,
Josef Sivic2,3∗ , Trevor Darrell1, Bryan Russell2
UC Berkeley, 2 Adobe Research, 3 INRIA"
904a8241ef400bd85b1ad10267a1177bbde1c048,Image-Text Dataset Generation for Image Annotation and Retrieval ⋆,"II Congreso Español de Recuperación de  la Información
CERI 2012
Image-Text Dataset Generation for Image
Annotation and Retrieval⋆
Mauricio Villegas and Roberto Paredes
Institut Tecnol`ogic d’Inform`atica
Universitat Polit`ecnica de Val`encia
Cam´ı de Vera s/n, 46022 Val`encia (Spain)"
9070045c1a9564a5f25b42f3facc7edf4c302483,Everybody needs somebody: Modeling social and grouping behavior on a linear programming multiple people tracker,"Everybody needs somebody: Modeling social and grouping behavior on a linear
programming multiple people tracker
Laura Leal-Taix´e, Gerard Pons-Moll and Bodo Rosenhahn
Institute for Information Processing (TNT)
Leibniz University Hannover, Germany"
900175d24928921600d09985211b6b9bfea44ce0,Person re-identification by pose priors,"Person re-identification by pose priors
Sławomir Bąk
Filipe Martins
Francois Brémond
INRIA Sophia Antipolis, STARS team, 2004, route des Lucioles, BP93
06902 Sophia Antipolis Cedex - France"
90dd6e7051a2dd8639d6f2d9f7b02acb43eb94c7,BlitzNet: A Real-Time Deep Network for Scene Understanding,
90dd771829094dad1230e32b8bc4385bfe86c4e5,A Comparison of Word Embeddings for the Biomedical Natural Language Processing,[cs.IR]  18 Jul 2018
90818e0ab85b6a5f03cd28bc5c23c90a0971c36e,Maximum Entropy-based Thresholding algorithm for Face image segmentation,"Maximum Entropy-based Thresholding algorithm for Face
image segmentation
Kittikhun Meethongjan
Department of Computer Graphic, Faculty of Computer
Science & Information System, University Technology of
Malaysia, 81310 Skudai, Johor, Malaysia. +60127204314"
902d1b14b076120cb21029b51ed8e63529fe686d,PERFORMANCE ANALYSIS FOR FACIAL EXPRESSION RECOGNITION UNDER SALT AND PEPPER NOISE WITH MEDIAN FILTER APPROACH,"PERFORMANCE ANALYSIS FOR FACIAL EXPRESSION
RECOGNITION UNDER SALT AND PEPPER NOISE WITH
MEDIAN FILTER APPROACH
AZRINI BINTI IDRIS
A project report submitted in partial
fulfillment of the requirement for the award of the
Degree of Master of Electrical Engineering
Facultyof Electrical and Electronic Engineering
UniversitiTun Hussein Onn Malaysia
JULY 2013"
90f0646c0801f1dad43d2374d1145be8e005bdbf,Raised Middle-Finger: Electrocortical Correlates of Social Conditioning with Nonverbal Affective Gestures,"Raised Middle-Finger: Electrocortical Correlates of Social
Conditioning with Nonverbal Affective Gestures
Matthias J. Wieser1*, Tobias Flaisch2, Paul Pauli1
Department of Psychology, University of Wu¨ rzburg, Wu¨ rzburg, Germany, 2 Department of Psychology, University of Konstanz, Konstanz, Germany"
90a70b38c5a1b40ac16e18628a7772923cdc5cb5,Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation,"Exact Subspace Segmentation and Outlier Detection by
Low-Rank Representation
Anonymous Author 1
Anonymous Author 2
Anonymous Author 3"
90d8bf2199e7fd972dab3bd3dc6fb67536fa509b,Performance and Energy Modeling of Heterogeneous Many-core Architectures,"PERFORMANCE AND ENERGY MODELING OF HETEROGENEOUS MANY-CORE ARCHITECTURES
Performance and Energy Modeling of
Heterogeneous Many-core Architectures
Rui Pedro Gaspar Pinheiro"
90e36f66c25a4c73a252102c6c6c329c36d82676,Probably Unknown: Deep Inverse Sensor Modelling In Radar,"Probably Unknown: Deep Inverse Sensor Modelling Radar
Rob Weston, Sarah Cen, Paul Newman and Ingmar Posner"
9040fa6a62f6c185473f4043846de3aa7920624b,THERMAL IMAGING AS A BIOMETRICS APPROACH TO FACIAL SIGNATURE AUTHENTICATION 1,"October 2015, Volume 2, Issue 10
THERMAL IMAGING AS A BIOMETRICS APPROACH TO FACIAL SIGNATURE
JETIR  (ISSN-2349-5162)
AUTHENTICATION
.MundeBibhishanUttamrao                                  2.HONRAO S.B.
[M.Tech.] Aditya college                                           Associate Prof. , Aditya College
difficulty  in  detecting  facial  disguises.  The  light  variability
leads to problems in matching.
1) Proposed Method
This paper  realizes the potential of thermal MWIR imagery
for human identification using the vein structure of hands in
“Biometric verification using thermal images of palm-dorsa
vein  patterns,”  and  by  using  finger  vein  patterns  in
“Artificial  immune  system  for  personal  identification  with
finger  vein  pattern”.  Thermal  images  have  been  used  to
identify  the  affective  state  of  humans  in  the  previous  work
on  “Classifying  affective  states  using  thermal  infrared
imaging of the human face"".
thermal
templates"
9028fbbd1727215010a5e09bc5758492211dec19,Solving the Uncalibrated Photometric Stereo Problem Using Total Variation,"Solving the Uncalibrated Photometric Stereo
Problem using Total Variation
Yvain Qu´eau1, Fran¸cois Lauze2, and Jean-Denis Durou1
IRIT, UMR CNRS 5505, Toulouse, France
Dept. of Computer Science, Univ. of Copenhagen, Denmark"
90c80317fa68784a3fe4fc3136bb188895b09fa4,Sparsity Invariant CNNs,"Sparsity Invariant CNNs
Jonas Uhrig(cid:63),1,2 Nick Schneider(cid:63),1,3
Lukas Schneider1,4
Uwe Franke1
Thomas Brox2 Andreas Geiger4,5
The first two authors contributed equally to this work
Daimler R&D Sindelfingen
University of Freiburg
KIT Karlsruhe
ETH Z¨urich
5MPI T¨ubingen
September 1, 2017"
904c53ea063d7d1e13b99d55257801d69d073775,Combined Object Detection and Segmentation,"International Journal of Machine Learning and Computing, Vol. 3, No. 1, February 2013
Combined Object Detection and Segmentation
Jarich Vansteenberge, Masayuki Mukunoki, and Michihiko Minoh"
365b72a225a18a930b96e7c0b215b9fede8a0968,Storyline Reconstruction for Unordered Images Final Paper,"Storyline Reconstruction for Unordered Images
Final Paper
Sameedha Bairagi, Arpit Khandelwal, Venkatesh Raizaday
Introduction:
Storyline reconstruction is a relatively new topic and has not been researched extensively. The
main objective is to take a stream of images as input and re-shuffle them in chronological order.
The recent growth of online multimedia data has generated lots and lots of unstructured data on
the web. Image streams are generated daily on websites like Flicker, Instagram etc. and almost
00 hours of video is uploaded on YouTube on a daily basis.
In  this paper,  we  try  and  implement  an  algorithm  which  uses  the  property of  videos  of being
temporally adept to sort a stream of unordered images. The basic process is as follows:
-  Generate  key  frames/video  summary  of  a  video  from  multiple  instances  of  the  same
ategory.
-  Cluster these key frames on the basis of the action being performed in them.
-  Create a graph from these clusters using temporal data from the videos.
-  Take an input stream of images and assign each image to its most probable cluster.
-  Use the graph to assign ordering to the images.
In the following sections, we will try and go deep into each of the step mentioned above and
discuss multiple approaches we implemented to do the same.
Background and Related work:"
361367838ee5d9d5c9a77c69c1c56b1c309ab236,Salient Object Detection: A Survey,"Salient Object Detection: A Survey
Ali Borji, Ming–Ming Cheng, Huaizu Jiang and Jia Li"
363913a335053c837d5fc279032d28c418dda1dc,ECE 533 – Image Processing Project Face Recognition Techniques,"ECE533 – Image Processing Project
Face Recognition
Techniques
Jorge Orts"
36f039e39efde3558531b99d85cd9e3ab7d396b3,9 Efficiency of Recognition Methods for Single Sample per Person Based Face Recognition,"Efficiency of Recognition Methods for Single
Sample per Person Based Face Recognition
Miloš Oravec, Jarmila Pavlovičová, Ján Mazanec,
Ľuboš Omelina, Matej Féder and Jozef Ban
Faculty of Electrical Engineering and Information Technology
Slovak University of Technology in Bratislava
Slovakia
. Introduction
Even for the present-day computer technology, the biometric recognition of human face is
difficult  task  and  continually  evolving  concept  in  the  area  of  biometric  recognition.  The
rea of face recognition is well-described today in many papers and books, e.g. (Delac et al.,
008), (Li & Jain, 2005), (Oravec et al., 2010). The idea that two-dimensional still-image face
recognition  in  controlled  environment  is  already  a  solved  task  is  generally  accepted  and
several  benchmarks  evaluating  recognition  results  were  done  in  this  area  (e.g.  Face
Recognition  Vendor  Tests,  FRVT  2000,  2002,  2006,  http://www.frvt.org/).  Nevertheless,
many  tasks  have  to  be  solved,  such  as  recognition  in  unconstrained  environment,
recognition of non-frontal images, single sample per person problem, etc.
This chapter deals with single sample per person face recognition (also called one sample
per  person  problem).  This  topic  is  related  to  small  sample  size  problem  in  pattern
recognition. Although there are also advantages of single sample – fast and easy creation of"
36c8f798145902583592a03df88a6043baa11fe7,Score Fusion of SIFT & SURF Descriptors for Face Recognition Using Wavelet Transforms,"I.J. Image, Graphics and Signal Processing, 2017, 10, 22-28
Published Online October 2017 in MECS (http://www.mecs-press.org/)
DOI: 10.5815/ijigsp.2017.10.03
Score Fusion of SIFT & SURF Descriptors for
Face Recognition Using Wavelet Transforms
Musa M.Ameen
Ishik University/Computer Engineering Department, Erbil, 44001, Iraq
Email:
Alaa Eleyan
Avrasya University/Electrical and Electronics Engineering Department, Trabzon, 61000, Turkey
Received: 09 June 2017; Accepted: 12 September 2017; Published: 08 October 2017
Email:
human
intervention.
the  automated"
366c14f477bf2ed16b1498d1c56a7e1f2af08e69,Comparative Analysis of Statistical Shape Spaces,"Comparative Analysis of Statistical Shape Spaces
Alan Brunton∗
Augusto Salazar†
Timo Bolkart†
Stefanie Wuhrer†"
365866dc937529c3079a962408bffaa9b87c1f06,Facial Feature Expression Based Approach for Human Face Recognition : A Review,"IJISET - International Journal of Innovative Science, Engineering & Technology, Vol. 1 Issue 3, May 2014.
www.ijiset.com
ISSN 2348 – 7968
Facial Feature Expression Based Approach for Human Face
Recognition: A Review
Jageshvar K. Keche1, Mahendra P. Dhore2
Department of Computer Science, SSESA, Science College, Congress Nagar, Nagpur, (MS)-India,
Department of Electronics & Computer Science, RTM Nagpur University, Campus Nagpur, (MS)-India.
required
extraction  of"
36ab143da8b6f6d49811afaaa7bcbf81c22a210e,Modeling Multimodal Clues in a Hybrid Deep Learning Framework for Video Classification,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
Modeling Multimodal Clues in a Hybrid Deep
Learning Framework for Video Classification
Yu-Gang Jiang, Zuxuan Wu, Jinhui Tang, Zechao Li, Xiangyang Xue, Shih-Fu Chang"
36939e6a365e9db904d81325212177c9e9e76c54,"Assessing the Accuracy of Four Popular Face Recognition Tools for Inferring Gender, Age, and Race","Assessing the Accuracy of Four Popular Face Recognition Tools for
Inferring Gender, Age, and Race
Soon-Gyo Jung, Jisun An, Haewoon Kwak, Joni Salminen, Bernard J. Jansen
Qatar Computing Research Institute, HBKU
HBKU Research Complex, Doha, P.O. Box 34110, Qatar"
367b5b814aa991329c2ae7f8793909ad8c0a56f1,Performance evaluation of random set based pedestrian tracking algorithms,"Performance Evaluation of Random Set Based
Pedestrian Tracking Algorithms
Branko Ristic
ISR Division
Australia
Jamie Sherrah
ISR Division
Australia
´Angel F. Garc´ıa-Fern´andez
Department of Signals and Systems
Chalmers University of Technology
Sweden"
362bfeb28adac5f45b6ef46c07c59744b4ed6a52,Incorporating Scalability in Unsupervised Spatio- Temporal Feature Learning,"INCORPORATING SCALABILITY IN UNSUPERVISED SPATIO-TEMPORAL FEATURE
LEARNING
Sujoy Paul, Sourya Roy and Amit K. Roy-Chowdhury
Dept. of Electrical and Computer Engineering, University of California, Riverside, CA 92521"
364584f8313e7601b1f5134d371e98aeb61110e8,An invariant bipolar representation for 3 D surfaces,"An invariant bipolar representation for 3D surfaces
M. JRIBI and F. GHORBEL
CRSITAL Laboratory / GRIFT research group,
Ecole Nationale des Sciences de l’Informatique (ENSI),
La Manouba University, 2010 La Manouba, Tunisia"
36513f869e5ba2928369014244dff998ab93728c,Discriminative cluster analysis,"Chapter 1
Discriminative Cluster Analysis
Fernando De la Torre and Takeo Kanade"
361b19d2c00d086fa8ef860374f5e1d862fd2f30,Learning to Refine Object Segments,"Learning to Refine Object Segments
Pedro O. Pinheiro(cid:63), Tsung-Yi Lin(cid:63), Ronan Collobert, Piotr Doll´ar
Facebook AI Research (FAIR)"
362250566948f17693b737122fc1434173982da8,Automatic Image Annotation using Weakly Labelled Web Data,"Automatic Image Annotation using
Weakly Labelled Web Data
Pravin Kakar, Xiangyu Wang and Alex Yong-Sang Chia
Social Media and Internet Vision Analytics Lab,
Institute for Infocomm Research,
#21-01, 1 Fusionopolis Way,
{kakarpv, wangx,
Singapore 138632."
3687bad2caa2d323941e6ec343e9156fca9cf606,Super Resolution of Images and Video,"MOBK071-FM
MOBKXXX-Sample.cls
April 16, 2007
5:48
Super Resolution of
Images and Video"
36ca4ad185e68db34b0bbfa1057ebdaa9177c131,Segmented AAMs Improve Person-Indepedent Face Fitting,"Segmented AAMs Improve
Person-Independent Face Fitting
Julien Peyras1 Adrien Bartoli2 Hugo Mercier3 Patrice Dalle3
Dipartimento di Scienze dell’Informazione, Milano, Italy
LASMEA, Clermont-Ferrand, France
IRIT, Toulouse, France"
369c4a308ec9e56746f7cc1b164208b917e31a22,Scene Classification in Indoor Environments for Robots using Context Based Word Embeddings,"Scene Classification in Indoor Environments for Robots using Context
Based Word Embeddings
Bao Xin Chen, Raghavender Sahdev, Dekun Wu, Xing Zhao, Manos Papagelis and John K. Tsotsos"
36d8cc038db71a473d0c94c21f2b68a840dff21c,Unsupervised Detector Adaptation by Joint Dataset Feature Learning,"
	


	
		

	
	

	
				



!∀∀
##!∃%&∋()
		
∗+,
			
#−./!0!∀
!!2!342
,"
3646b42511a6a0df5470408bc9a7a69bb3c5d742,Detection of Facial Parts based on ABLATA,"International Journal of Computer Applications (0975 – 8887)
Applications of Computers and Electronics for the Welfare of Rural Masses (ACEWRM) 2015
Detection of Facial Parts based on ABLATA
Siddhartha Choubey
Shri Shankaracharya
Technical Campus, Bhilai
Vikas Singh
Shri Shankaracharya
Technical Campus, Bhilai
Abha Choubey
Shri Shankaracharya
Technical Campus, Bhilai"
363ca0a3f908859b1b55c2ff77cc900957653748,Local Binary Patterns and Linear Programming using Facial Expression,"International Journal of Computer Trends and Technology (IJCTT) – volume 1 Issue 3 Number 4 – Aug 2011
Local Binary Patterns and Linear Programming using
Facial Expression
Ms.P.Jennifer
#MCA Department, Bharath Institute of Science and Technology
+B.Tech (C.S.E), Bharath University , Chennai – 73.
Dr. A. Muthu kumaravel
#MCA Department, Bharath Institute of Science and Technology
+B.Tech (C.S.E), Bharath University , Chennai – 73."
3678dac7e9998567b92f526046a16e2910ced55d,Talking Robots : grounding a shared lexicon in an unconstrained environment,"Berthouze, L., Prince, C. G., Littman, M., Kozima, H., and Balkenius, C. (2007).
Proceedings of the Seventh International Conference on Epigenetic Robotics: Modeling
Cognitive Development in Robotic Systems. Lund University Cognitive Studies, 135.
Talking Robots: grounding a shared lexicon in an
unconstrained environment
Matthieu Nottale
Jean-Christophe Baillie
ENSTA-UEI cognitive robotics lab."
36973330ae638571484e1f68aaf455e3e6f18ae9,Scale-Aware Fast R-CNN for Pedestrian Detection,"Scale-aware Fast R-CNN for Pedestrian Detection
Jianan Li, Xiaodan Liang, ShengMei Shen, Tingfa Xu, and Shuicheng Yan"
36018404263b9bb44d1fddaddd9ee9af9d46e560,OCCLUDED FACE RECOGNITION BY USING GABOR FEATURES,"OCCLUDED FACE RECOGNITION BY USING GABOR
FEATURES
Burcu Kepenekci 1,2, F. Boray Tek 1,2, Gozde Bozdagi Akar 1
Department of Electrical And Electronics Engineering, METU, Ankara, Turkey
7h%ł7$.(cid:3)%ł/7(1(cid:15)(cid:3)$QNDUD(cid:15)(cid:3)7XUNH\"
36fe39ed69a5c7ff9650fd5f4fe950b5880760b0,Tracking von Gesichtsmimik mit Hilfe von Gitterstrukturen zur Klassifikation von schmerzrelevanten Action Units,"Tracking von Gesichtsmimik
mit Hilfe von Gitterstrukturen
zur Klassifikation von schmerzrelevanten Action
Units
Christine Barthold1, Anton Papst1, Thomas Wittenberg1
Christian K¨ublbeck1, Stefan Lautenbacher2, Ute Schmid2, Sven Friedl1,3
Fraunhofer-Institut f¨ur Integrierte Schaltungen IIS, Erlangen,
Otto-Friedrich-Universit¨at Bamberg, 3Universit¨atsklinkum Erlangen
Kurzfassung. In der Schmerzforschung werden schmerzrelevante Mi-
mikbewegungen von Probanden mittels des Facial Action Coding System
klassifiziert. Die manuelle Klassifikation hierbei ist aufw¨andig und eine
utomatische (Vor-)klassifikation k¨onnte den diagnostischen Wert dieser
Analysen erh¨ohen sowie den klinischen Workflow unterst¨utzen. Der hier
vorgestellte regelbasierte Ansatz erm¨oglicht eine automatische Klassifika-
tion ohne große Trainingsmengen vorklassifizierter Daten. Das Verfahren
erkennt und verfolgt Mimikbewegungen, unterst¨utzt durch ein Gitter,
und ordnet diese Bewegungen bestimmten Gesichtsarealen zu. Mit die-
sem Wissen kann aus den Bewegungen auf die zugeh¨origen Action Units
geschlossen werden.
Einleitung"
367231b80e8201fc9c461fbb42047b20e89ea961,Impatient DNNs - Deep Neural Networks with Dynamic Time Budgets,"MANUEL AMTHOR, ERIK RODNER, AND JOACHIM DENZLER: IMPATIENT DNNS
Impatient DNNs – Deep Neural Networks
with Dynamic Time Budgets
Manuel Amthor
Erik Rodner
Joachim Denzler
Computer Vision Group
Friedrich Schiller University Jena
Germany
www.inf-cv.uni-jena.de"
362cfe79a6822f9e317555c5e3469dd038b9053f,Damped Gauss-Newton algorithm for nonnegative Tucker decomposition,"978-1-4577-0568-7/11/$26.00 ©2011 IEEE
DY, An	, G (cid:2) (cid:12)Y 
 G  A	(cid:12)2
DECOMPOSITION
. INTRODUCTION"
3674f3597bbca3ce05e4423611d871d09882043b,Facial Expression Spacial Charts for Describing Dynamic Diversity of Facial Expressions,"ISSN 1796-2048
Volume 7, Number 4, August 2012
Contents
Special Issue: Multimedia Contents Security in Social Networks Applications
Guest Editors: Zhiyong Zhang and Muthucumaru Maheswaran
Guest Editorial
Zhiyong Zhang and Muthucumaru Maheswaran
SPECIAL ISSUE PAPERS
DRTEMBB: Dynamic Recommendation Trust Evaluation Model Based on Bidding
Gang Wang and Xiao-lin Gui
Block-Based Parallel Intra Prediction Scheme for HEVC
Jie Jiang, Baolong, Wei Mo, and Kefeng Fan
Optimized LSB Matching Steganography Based on Fisher Information
Yi-feng Sun, Dan-mei Niu, Guang-ming Tang, and Zhan-zhan Gao
A Novel Robust Zero-Watermarking Scheme Based on Discrete Wavelet Transform
Yu Yang, Min Lei, Huaqun Liu, Yajian Zhou, and Qun Luo
Stego Key Estimation in LSB Steganography
Jing Liu and Guangming Tang
REGULAR PAPERS
Facial Expression Spacial Charts for Describing Dynamic Diversity of Facial Expressions"
368132f8dfcbd6e857dfc1b7dce2ab91bd9648ad,"Simultaneous Localization And Mapping: Present, Future, and the Robust-Perception Age","Simultaneous Localization And Mapping:
Present, Future, and the Robust-Perception Age
Cesar Cadena, Luca Carlone, Henry Carrillo, Yasir Latif,
Davide Scaramuzza, Jos´e Neira, Ian D. Reid, John J. Leonard"
36cd55cdb1b032c8f29e011ed0637923afc46d3f,Strategies to Improve Activity Recognition Based on Skeletal Tracking: Applying Restrictions Regarding Body Parts and Similarity Boundaries †,"Article
Strategies to Improve Activity Recognition Based on
Skeletal Tracking: Applying Restrictions Regarding
Body Parts and Similarity Boundaries †
Carlos Gutiérrez-López-Franca *, Ramón Hervás and Esperanza Johnson
MAmI Research Lab, University of Castilla-La Mancha, Paseo de la Universidad 4, 13071 Ciudad Real, Spain;
(R.H.); (E.J.)
* Correspondence:
This paper is an extended version of our paper published in Gutiérrez López de la Franca, C.; Hervás, R.;
Johnson, E.; Bravo, J. Findings about Selecting Body Parts to Analyze Human Activities through Skeletal
Tracking Joint Oriented Devices. In Proceedings of the 10th International Conference on Ubiquitous
Computing and Ambient Intelligence (UCAMI 2016), Gran Canaria, Spain, 29 November–2 December 2016.
Received: 4 April 2018; Accepted: 17 May 2018; Published: 22 May 2018"
3607afdb204de9a5a9300ae98aa4635d9effcda2,Face Description with Local Binary Patterns: Application to Face Recognition,"Face Description with Local Binary Patterns:
Application to Face Recognition
Timo Ahonen, Student Member, IEEE, Abdenour Hadid,
nd Matti Pietik¨ainen, Senior Member, IEEE"
367008b91eb57c5ea64ef7520dfcabc0c5c85532,"Person Re-identification: Past, Present and Future","JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
Person Re-identification:
Past, Present and Future
Liang Zheng, Yi Yang, and Alexander G. Hauptmann"
36b9faf0d6c4c6296193b8d5d7833624a181624c,Real-Time Multiple Human Perception With Color-Depth Cameras on a Mobile Robot,"Real-Time Multiple Human Perception
with Color-Depth Cameras on a Mobile Robot
Hao Zhang, Student Member, IEEE, Christopher Reardon, Student Member, IEEE, and Lynne E. Parker, Fellow, IEEE"
362a70b6e7d55a777feb7b9fc8bc4d40a57cde8c,A partial least squares based ranker for fast and accurate age estimation,"978-1-4799-9988-0/16/$31.00 ©2016 IEEE
ICASSP 2016"
36ca720185b62e92a7f3cce75418356a5a125d24,Template aging in 3D and 2D face recognition,"Template Aging in 3D and 2D Face Recognition
Ishan Manjani∗
Hakki Sumerkan†
Patrick J. Flynn†
Kevin W. Bowyer†"
36fa002f36e14ab7d24ebcdd99b6589ed726b383,Detecting conversational gaze aversion using unsupervised learning,"Detecting Conversational Gaze Aversion Using
Unsupervised Learning
Matthew Roddy, Naomi Harte
ADAPT Centre, School of Engineering
Trinity College Dublin, Ireland"
36ce0b68a01b4c96af6ad8c26e55e5a30446f360,Facial expression recognition based on a mlp neural network using constructive training algorithm,"Multimed Tools Appl
DOI 10.1007/s11042-014-2322-6
Facial expression recognition based on a mlp neural
network using constructive training algorithm
Hayet Boughrara · Mohamed Chtourou ·
Chokri Ben Amar · Liming Chen
Received: 5 February 2014 / Revised: 22 August 2014 / Accepted: 13 October 2014
© Springer Science+Business Media New York 2014"
36cbcd70af6f2fd3e700e0a710acd5f1f6abebcf,Matching People across Camera Views using Kernel Canonical Correlation Analysis,"Matching People across Camera Views using
Kernel Canonical Correlation Analysis
Giuseppe Lisanti , Iacopo Masi , Alberto Del Bimbo
Media Integration and Communication Center (MICC), Università degli Studi di Firenze
Viale Morgagni 65 - 50134 Firenze, Italy"
36c9731f24e5daa42c1e2c6c68258567dfa78a0a,Movement tracking in terrain conditions accelerated with CUDA,"Proceedings of the 2014 Federated Conference on
Computer Science and Information Systems pp. 709–717
DOI: 10.15439/2014F282
ACSIS, Vol. 2
978-83-60810-58-3/$25.00 c(cid:13) 2014, IEEE"
363e5a0e4cd857e98de72a726ad6f80cea9c50ab,Fast Landmark Localization With 3D Component Reconstruction and CNN for Cross-Pose Recognition,"Fast Landmark Localization
with 3D Component Reconstruction and CNN for
Cross-Pose Recognition
Gee-Sern (Jison) Hsu, Hung-Cheng Shie, Cheng-Hua Hsieh"
369bd35ab8bad4c7bc5e376cc776a5366d97b12e,An Object Detector Trained on Line Drawings,"Bachelor’s Thesis
An Object Detector
Trained on Line Drawings
Patric Tippmann
August 2012
Albert-Ludwigs-Universität Freiburg
Department of Computer Science
Computer Vision Group
Supervisor: Prof. Dr. Thomas Brox"
36688a79cc8926f489ccb6e6dadba15afbb4b6a4,Linear discriminant analysis for the small sample size problem: an overview,"Int. J. Mach. Learn. & Cyber.
DOI 10.1007/s13042-013-0226-9
O R I G I N A L A R T I C L E
Linear discriminant analysis for the small sample size problem:
n overview
Alok Sharma • Kuldip K. Paliwal
Received: 19 March 2013 / Accepted: 26 December 2013
Ó Springer-Verlag Berlin Heidelberg 2014"
3600f9def4e619e154a59df50dffe3cb23300e42,A Grey Wolf Optimizer for Modular Granular Neural Networks for Human Recognition,"Hindawi
Computational Intelligence and Neuroscience
Volume 2017, Article ID 4180510, 26 pages
https://doi.org/10.1155/2017/4180510
Research Article
A Grey Wolf Optimizer for Modular Granular Neural Networks
for Human Recognition
Daniela Sánchez, Patricia Melin, and Oscar Castillo
Tijuana Institute of Technology, Tijuana, BC, Mexico
Correspondence should be addressed to Oscar Castillo;
Received 25 February 2017; Revised 17 June 2017; Accepted 10 July 2017; Published 14 August 2017
Academic Editor: Jos´e Alfredo Hern´andez-P´erez
Copyright © 2017 Daniela S´anchez et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
A grey wolf optimizer for modular neural network (MNN) with a granular approach is proposed. The proposed method performs
optimal granulation of data and design of modular neural networks architectures to perform human recognition, and to prove
its effectiveness benchmark databases of ear, iris, and face biometric measures are used to perform tests and comparisons against
other works. The design of a modular granular neural network (MGNN) consists in finding optimal parameters of its architecture;
these parameters are the number of subgranules, percentage of data for the training phase, learning algorithm, goal error, number
of hidden layers, and their number of neurons. Nowadays, there is a great variety of approaches and new techniques within the"
367c571480ac46d48be050dee4e6103a0ebb5db5,Multimedia Content Based Image Retrieval Iii : Local Tetra,"Manas M N et al Int. Journal of Engineering Research and Applications                       www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 6( Version 3), June 2014, pp.104-107
RESEARCH ARTICLE
OPEN ACCESS
Multimedia  Content  Based  Image  Retrieval  Iii:  Local  Tetra
Pattern
Nagaraja G S1, Rajashekara Murthy S2, Manas M N3, Sridhar N H4
(Department of CSE, RVCE, Visvesvaraya Technological University, Bangalore-59, Karnataka, India)
(Department of ISE, RVCE, Visvesvaraya Technological University, Bangalore-59, Karnataka, India)
(M.  Tech,  Department  of  CSE,  RVCE,  Visvesvaraya  Technological  University,  Bangalore-59,  Karnataka,
India)
(Research  Scholar,  Department  of  CSE,  RVCE,  Visvesvaraya  Technological  University,  Bangalore-59,
Karnataka, India)"
36b322095bd0953d6076096111e4a020f427793b,Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation,"This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.
Large Displacement Optical Flow:
Descriptor Matching in Variational
Motion Estimation
Thomas Brox, Jitendra Malik, Fellow, IEEE"
36b2aa7248152fdad7bc7f670d0b577c9728d466,Data-dependent Initializations of Convolutional Neural Networks,"Under review as a conference paper at ICLR 2016
DATA-DEPENDENT INITIALIZATIONS OF
CONVOLUTIONAL NEURAL NETWORKS
Philipp Kr¨ahenb¨uhl1, Carl Doersch1,2, Jeff Donahue1, Trevor Darrell1
Department of Electrical Engineering and Computer Science, UC Berkeley
Machine Learning Department, Carnegie Mellon"
366595171c9f4696ec5eef7c3686114fd3f116ad,Algorithms and Representations for Visual Recognition,"Algorithms and Representations for Visual
Recognition
Subhransu Maji
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2012-53
http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-53.html
May 1, 2012"
36918b2ef6b20ffb8cffe458c0067742500c6149,"""Look, some Green Circles!"": Learning to Quantify from Images","Proceedings of the 5th Workshop on Vision and Language, pages 75–79,
Berlin, Germany, August 12 2016. c(cid:13)2016 Association for Computational Linguistics"
36119c10f75094e0568cae8256400c94546d973b,The CASIA NIR-VIS 2.0 Face Database,"The CASIA NIR-VIS 2.0 Face Database
Stan Z. Li, Dong Yi, Zhen Lei and Shengcai Liao
Center for Biometrics and Security Research & National Laboratory of Pattern Recognition
Institute of Automation, Chinese Academy of Sciences (CASIA)
szli, dyi, zlei,"
36358eff7c34de64c0ce8aa42cf7c4da24bf8e93,Deep Metric Learning for Person Re-identification,"Deep Metric Learning for Person Re-Identification
(Invited Paper)
Dong Yi, Zhen Lei, Shengcai Liao and Stan Z. Li
Center for Biometrics and Security Research & National Laboratory of Pattern Recognition
Institute of Automation, Chinese Academy of Sciences (CASIA)"
73599349402bf8f0d97f51862d11d128cdba44ef,Affective analysis of videos: detecting emotional content in real-life scenarios,"Affective Analysis of Videos:
Detecting Emotional Content in Real-Life Scenarios
vorgelegt von
Master of Science
Esra Acar Celik
geb. in Afyonkarahisar
Von der Fakultät IV – Elektrotechnik und Informatik –
der Technischen Universität Berlin
zur Erlangung des akademischen Grades
Doktor der Ingenieurwissenschaften
– Dr.-Ing. –
genehmigte Dissertation
Promotionsausschuss:
Vorsitzender:
Berichter:
Berichter:
Berichter:
Prof. Dr. Thomas Wiegand
Prof. Dr. Dr. h.c. Sahin Albayrak
Prof. Dr. Adnan Yazıcı"
73d23c0e81c39b25cc43521a8f2697912d6cff94,Detecting Adversarial Examples in Deep Networks with Adaptive Noise Reduction,"IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING, MANUSCRIPT ID
Detecting Adversarial Image Examples in Deep
Neural Networks with Adaptive Noise Reduction
Bin Liang, Hongcheng Li, Miaoqiang Su, Xirong Li, Wenchang Shi and Xiaofeng Wang"
73351b313df89572afe1332625044f7e5dd0ce06,High-level Feature Learning by Ensemble Projection for Image Classification with Limited Annotations I,"High-level Feature Learning by Ensemble Projection for Image
Classification with Limited Annotations $
Dengxin Dai∗, Luc Van Gool
Computer Vision Lab, ETH Z¨urich, CH-8092, Switzerland"
73704242a548e8725926762faf7333e5598d0228,Surveillance of Super-Extended Objects : Bimodal Approach,"World Academy of Science, Engineering and Technology
International Journal of Mechanical and Mechatronics Engineering
Vol:8, No:9, 2014
Surveillance of Super-Extended Objects: Bimodal
Approach
Andrey V. Timofeev, Dmitry Egorov"
732e8d8f5717f8802426e1b9debc18a8361c1782,Unimodal Probability Distributions for Deep Ordinal Classification,"Unimodal Probability Distributions for Deep Ordinal Classification
Christopher Beckham 1 Christopher Pal 1"
73200504c7381c48c900894455995b9188676cd5,Weakly-Supervised Image Annotation and Segmentation with Objects and Attributes,"Weakly-Supervised Image Annotation and
Segmentation with Objects and Attributes
Zhiyuan Shi, Yongxin Yang, Timothy M. Hospedales, Tao Xiang"
734cdda4a4de2a635404e4c6b61f1b2edb3f501d,Automatic landmark point detection and tracking for human facial expressions,"Tie and Guan EURASIP Journal on Image and Video Processing 2013, 2013:8
http://jivp.eurasipjournals.com/content/2013/1/8
R ES EAR CH
Open Access
Automatic landmark point detection and tracking
for human facial expressions
Yun Tie* and Ling Guan"
73ec2d5a6b4bee0f268b793ff646330507497e38,Is an Image Worth More than a Thousand Words? On the Fine-Grain Semantic Differences between Visual and Linguistic Representations,"Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers,
pages 2807–2817, Osaka, Japan, December 11-17 2016."
7373c4a23684e2613f441f2236ed02e3f9942dd4,Feature extraction through Binary Pattern of Phase Congruency for facial expression recognition,"This document is downloaded from DR-NTU, Nanyang Technological
University Library, Singapore.
Title
Feature extraction through binary pattern of phase
ongruency for facial expression recognition
Author(s)
Shojaeilangari, Seyedehsamaneh; Yau, Wei-Yun; Li, Jun;
Teoh, Eam Khwang
Citation
Shojaeilangari, S., Yau, W. Y., Li, J., & Teoh, E. K.
(2012). Feature extraction through binary pattern of
phase congruency for facial expression recognition. 12th
International Conference on Control Automation Robotics
& Vision (ICARCV), 166-170.
http://hdl.handle.net/10220/18012
Rights
© 2012 IEEE. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other
uses, in any current or future media, including
reprinting/republishing this material for advertising or"
73a4fe5072a30c132e8a0a18384caae4c112f198,What is typical is good: the influence of face typicality on perceived trustworthiness.,"554955 PSSXXX10.1177/0956797614554955Sofer et al.What Is Typical Is Good
research-article2014
Research Article
What Is Typical Is Good: The Influence
of Face Typicality on Perceived
Trustworthiness
015, Vol. 26(1) 39 –47
© The Author(s) 2014
Reprints and permissions:
sagepub.com/journalsPermissions.nav
DOI: 10.1177/0956797614554955
pss.sagepub.com
Carmel Sofer1,2, Ron Dotsch2,3, Daniel H. J. Wigboldus2, and
Alexander Todorov1,2
Department of Psychology, Princeton University; 2Behavioural Science Institute, Radboud University
Nijmegen; and 3Department of Psychology, Utrecht University"
73bbbfac7b144f835840fe7f7b5139283bf4f3f1,Do we spontaneously form stable trustworthiness impressions from facial appearance?,"ATTITUDES AND SOCIAL COGNITION
Do We Spontaneously Form Stable Trustworthiness Impressions From
Facial Appearance?
André Klapper
Radboud University
Ron Dotsch
Utrecht University and Radboud University
Iris van Rooij and Daniël H. J. Wigboldus
Radboud University
It is widely assumed among psychologists that people spontaneously form trustworthiness impressions of
newly encountered people from their facial appearance. However, most existing studies directly or
indirectly induced an impression formation goal, which means that the existing empirical support for
spontaneous facial trustworthiness impressions remains insufficient. In particular, it remains an open
question whether trustworthiness from facial appearance is encoded in memory. Using the ‘who said
what’ paradigm, we indirectly measured to what extent people encoded the trustworthiness of observed
faces. The results of 4 studies demonstrated a reliable tendency toward trustworthiness encoding. This
was shown under conditions of varying context-relevance, and salience of trustworthiness. Moreover,
evidence for this tendency was obtained using both (experimentally controlled) artificial and (naturalistic
varying) real faces. Taken together, these results suggest that there is a spontaneous tendency to form
relatively stable trustworthiness impressions from facial appearance, which is relatively independent of"
7306d42ca158d40436cc5167e651d7ebfa6b89c1,Transductive Zero-Shot Action Recognition by Word-Vector Embedding,"Noname manuscript No.
(will be inserted by the editor)
Transductive Zero-Shot Action Recognition by
Word-Vector Embedding
Xun Xu · Timothy Hospedales · Shaogang Gong
Received: date / Accepted: date"
73be334ecc48751269443b0db2629086125e69f5,Robust Face Recognition under Difficult Lighting Conditions,"International Journal of Technological Exploration and Learning (IJTEL)
Volume 1 Issue 1 (August 2012)
Robust Face Recognition under Difficult Lighting
Conditions
S.S. Ghatge1,V.V. Dixit2
Department of Electronics &Telecomunication1, 2
Sinhgad College of Engineering1, 2
University of Pune, India1, 2"
73390af668a6c9662d15cdf84b2ddefaa3f7826f,FACE TRACKING FOR A SYSTEM OF COLLECTING STATISTICS ON VISITORS AND QUALITY ASSESSMENT OF ITS FUNCTIONING,"Journal of Theoretical and Applied Information Technology
31st January 2015. Vol.71 No.3
© 2005 - 2015 JATIT & LLS. All rights reserved.
ISSN: 1992-8645                                                       www.jatit.org                                                          E-ISSN: 1817-3195
FACE TRACKING FOR A SYSTEM OF COLLECTING
STATISTICS ON VISITORS AND QUALITY ASSESSMENT
OF ITS FUNCTIONING
A.S.SAMOYLOV, D.M.MIKHAYLOV, P.E.MININ, A.D.EGOROV
Engineering Centre of the National Research Nuclear University MEPhI (Moscow Engineering Physics
Institute), Kashirskoye Highway 31, 115409, Moscow, Russian Federation
E-mail:  , , ,"
738d5a6491ae0fef5d2debc17f951534061cf6f8,Advances in Learning Visual Saliency: From Image Primitives to Semantic Contents,"Chapter 14
Advances in Learning Visual Saliency:
From Image Primitives to Semantic Contents
Qi Zhao and Christof Koch"
73713880d4d1ec4c8f4608a94f67ea9e9f9a97a5,Visual query attributes suggestion,"Visual Query Attributes Suggestion
Jingwen Bian
National University of
Singapore, Singapore
Zheng-Jun Zha
National University of
Singapore, Singapore
Hanwang Zhang
National University of
Singapore, Singapore
Qi Tian
University of Texas at San
Antonio, USA"
73ed64803d6f2c49f01cffef8e6be8fc9b5273b8,Cooking in the kitchen: Recognizing and Segmenting Human Activities in Videos,"Noname manuscript No.
(will be inserted by the editor)
Cooking in the kitchen: Recognizing and Segmenting Human
Activities in Videos
Hilde Kuehne · Juergen Gall · Thomas Serre
Received: date / Accepted: date"
73c13ba142588f45aaa92805fe75ca2691ac981b,A Comparative Study of Social Scene Parsing Strategies between Children with and without Autism Spectrum Disorder,"96                                                                                  Jul 2016 Vol 9 No.3                                    North American Journal of Medicine and Science
Original Research
A Comparative Study of Social Scene Parsing
Strategies between Children with and
without Autism Spectrum Disorder
Chen Song;1 Aosen Wang;1 Kathy Ralabate Doody, PhD;2* Michelle Hartley-
McAndrew, MD;3 Jana Mertz, MBA;4 Feng Lin, PhD;1 Wenyao Xu, PhD1
Computer Science and Engineering, SUNY, University at Buffalo, Buffalo NY
Exceptional Education, SUNY, Buffalo State, Buffalo, NY
Jacobs School of Medicine and Biomedical Sciences, SUNY, University at Buffalo Women and Children's Hospital of Buffalo, Buffalo, NY
Children’s Guild Foundation Autism Spectrum Disorder Center, Women and Children’s Hospital of Buffalo, Buffalo, NY
Autism  spectrum  disorder  (ASD)  is  a  complex  developmental  disability  characterized  by  deficits  in  social
interaction. Gaze behavior is of great interest because it reveals the parsing strategy the participant uses to
chieve social content. The legacy features in gaze fixation, such as time and area-of-interest, however, cannot
omprehensively reveal the way the participant may cognize the social scene. In this work, we investigate the
dynamic components within the gaze behavior of children with ASD upon the carefully-selected social scene.
A cohort of child participants (n = 51) were recruited between 2 and 10 years. The results suggest significant
differences in the social scene parsing strategies of children with ASD, giving added insight into the way they
may decode and interpret the social scenarios.
[N A J Med Sci. 2016;9(3):96-103.   DOI:  10.7156/najms.2016.0903096]"
73d57e2c855c39b4ff06f2d7394ab4ea35f597d4,First Order Generative Adversarial Networks,"First Order Generative Adversarial Networks
Calvin Seward 1 2 Thomas Unterthiner 2 Urs Bergmann 1 Nikolay Jetchev 1 Sepp Hochreiter 2"
73fbdd57270b9f91f2e24989178e264f2d2eb7ae,Kernel linear regression for low resolution face recognition under variable illumination,"978-1-4673-0046-9/12/$26.00 ©2012 IEEE
ICASSP 2012"
73c72161969a070b3caa40d4f075ba501a1b994b,Expression-Invariant 3D Face Recognition Using Patched Geodesic Texture Transform,"Expression-Invariant 3D Face Recognition using Patched
Geodesic Texture Transform
Author
Hajati, Farshid, Raie, Abolghasem, Gao, Yongsheng
Published
Conference Title
Proceedings 2010 Digital Image Computing: Techniques and Applications DICTA 2010
https://doi.org/10.1109/DICTA.2010.52
Copyright Statement
© 2010 IEEE. Personal use of this material is permitted. However, permission to reprint/
republish this material for advertising or promotional purposes or for creating new collective
works for resale or redistribution to servers or lists, or to reuse any copyrighted component of
this work in other works must be obtained from the IEEE.
Downloaded from
http://hdl.handle.net/10072/37733
Link to published version
http://dicta2010.conference.nicta.com.au/
Griffith Research Online
https://research-repository.griffith.edu.au"
73c9cbbf3f9cea1bc7dce98fce429bf0616a1a8c,Unsupervised Learning of Object Landmarks by Factorized Spatial Embeddings,"imagesViewpoint factorizationLearned landmarksFigure1.Wepresentanovelmethodthatcanlearnviewpointin-variantlandmarkswithoutanysupervision.Themethodusesaprocessofviewpointfactorizationwhichlearnsadeeplandmarkdetectorcompatiblewithimagedeformations.Itcanbeappliedtorigidanddeformableobjectsandobjectcategories.terns.Achievingadeeperunderstandingofobjectsrequiresmodelingtheirintrinsicviewpoint-independentstructure.Oftenthisstructureisdefinedmanuallybyspecifyingen-titiessuchaslandmarks,parts,andskeletons.Givensuffi-cientmanualannotations,itispossibletoteachdeepneuralnetworksandothermodelstorecognizesuchstructuresinimages.However,theproblemoflearningsuchstructureswithoutmanualsupervisionremainslargelyopen.Inthispaper,wecontributeanewapproachtolearnviewpoint-independentrepresentationsofobjectsfromim-ageswithoutmanualsupervision(fig.1).Weformulatethistaskasafactorizationproblem,wheretheeffectsofimagedeformations,forexamplearisingfromaviewpointchange,areexplainedbythemotionofareferenceframeattachedtotheobjectandindependentoftheviewpoint.Afterdescribingthegeneralprinciple(sec.3.1),wein-1"
73866bdb723841da93b6ad93afe3d72817e2b377,Dense and Low-Rank Gaussian CRFs Using Deep Embeddings,"Dense and Low-Rank Gaussian CRFs Using Deep Embeddings
Siddhartha Chandra1
Nicolas Usunier2
Iasonas Kokkinos2
INRIA GALEN, CentraleSup´elec
Facebook AI Research, Paris"
73052a2bf7b41b7be2447fadc13c29be1d994708,"Pedestrian tracking using probability fields and a movement feature space Publicación científica Negri ,","Pedestrian tracking using probability fields and a movement feature space 1
Pablo Negri a & Damián Garayalde b
Universidad Argentina de la Empresa (UADE). CONICET. Buenos Aires, Argentina.
Instituto Tecnológico de Buenos Aires (ITBA), Buenos Aires, Argentina.
Received: April 18th, 2016. Received in revised form: November 1rd, 2016. Accepted: December 2nd, 2016."
73a7ccf0facccd8943f7e54d19478f2bef9b7dab,Number 16,"Number 16
{tag}                                                 {/tag}
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 132
Number 16
Year of Publication: 2015
Authors:
Pronaya Prosun Das, Taskeed Jabid, S.M. Shariar Mahamud
10.5120/ijca2015907690
{bibtex}2015907690.bib{/bibtex}"
73d8fafee6be9d4fa789ece2192f259199f00e60,Face Recognition Using Radon Transform and Factorial Discriminant Analysis ( FDA ),"Volume 3, Issue 7, July 2013                                    ISSN: 2277 128X
International Journal of Advanced Research in
Computer Science and Software Engineering
Research Paper
Available online at: www.ijarcsse.com
D Face Recognition Using Radon Transform and Factorial
Discriminant Analysis (FDA)
P. S. Hiremath , Manjunatha Hiremath
Department of Computer Science
Gulbarga University, Gulbarga-585106
Karnataka, India."
731840289e35c61c6e21ae18f2da2751bd8e2f20,Event-related potential (ERP) correlates of face processing in verbal children with autism spectrum disorders (ASD) and their first-degree relatives: a family study,"Sysoeva et al. Molecular Autism  (2018) 9:41
https://doi.org/10.1186/s13229-018-0220-x
Open Access
R ES EAR CH
Event-related potential (ERP) correlates of
face processing in verbal children with
utism spectrum disorders (ASD) and their
first-degree relatives: a family study
Olga V. Sysoeva1,2, John N. Constantino1*
nd Andrey P. Anokhin1"
3e08d000ba3dd382c16e4295435ef8264235ccbc,Multiple People Tracking in Smart Camera Networks by Greedy Joint-Likelihood Maximization,
3ed186b4337f48e263ef60acffb49f16d5a85511,Discriminatively learned filter bank for acoustic features,"978-1-4799-9988-0/16/$31.00 ©2016 IEEE
ICASSP 2016"
3e2588aaa719c63e48fe599a7f0dbea10a41b4eb,Using Sparse Semantic Embeddings Learned from Multimodal Text and Image Data to Model Human Conceptual Knowledge,"Using sparse semantic embeddings learned from multimodal text and
image data to model human conceptual knowledge
Steven Derby1
Paul Miller1
Brian Murphy1,2
Barry Devereux1
Queen’s University Belfast, Belfast, United Kingdom
{sderby02, p.miller, brian.murphy,
BrainWaveBank Ltd., Belfast, United Kingdom"
3ea8d289313b0fe14031ea0d29f517f92a3b0fd3,Probability-based Detection Quality (PDQ): A Probabilistic Approach to Detection Evaluation,"Probability-based Detection Quality (PDQ): A Probabilistic Approach to
Detection Evaluation
David Hall1,2, Feras Dayoub1,2, John Skinner1,2, Peter Corke1,2, Gustavo Carneiro1,3, Niko S¨underhauf1,2
Australian Centre for Robotic Vision
Queensland University of Technology (QUT), 3University of Adelaide
{d20.hall, feras.dayoub, j6.skinner, peter.corke,"
3ec0d2c66ba2f00d90470c03969372e610986833,EEG Data of Face Recognition in Case of Biological Compatible Changes: A Pilot Study on Healthy People,
3ede3ed28329bf48fbd06438a69c4f855bef003f,Large-scale geo-facial image analysis,"Islam et al. EURASIP Journal on Image and Video Processing  (2015) 2015:17
DOI 10.1186/s13640-015-0070-9
RESEARCH
Open Access
Large-scale geo-facial image analysis
Mohammad T. Islam1, Connor Greenwell1, Richard Souvenir2 and Nathan Jacobs1*"
3e50e351687779c05390daf117f0394d1556cd3c,Die Detektion interessanter Objekte unter Verwendung eines objektbasierten Aufmerksamkeitsmodells,"Die Detektion interessanter Objekte unter Verwendung
eines objektbasierten Aufmerksamkeitsmodells
Dissertation
zur Erlangung des Grades eines
D o k t o r s d e r I n g e n i e u r w i s s e n s c h a f t e n
der Technischen Universit¨at Dortmund
n der Fakult¨at f¨ur Informatik
Fabian Naße
Dortmund"
3e67058c6ddd0afae692b7665f82124945ea2c5a,On the Learning of Deep Local Features for Robust Face Spoofing Detection,"On the Learning of Deep Local Features for
Robust Face Spoofing Detection
Gustavo Botelho de Souza1, Jo˜ao Paulo Papa2 and Aparecido Nilceu Marana2 - in Proc. of SIBGRAPI 2018
UFSCar - Federal University of S˜ao Carlos. Rod. Washington Lu´ıs, Km 235. S˜ao Carlos (SP), Brazil. 13565-905.
UNESP - S˜ao Paulo State University. Av. Eng. Luiz Edmundo Carrijo Coube, 14-01. Bauru (SP), Brazil. 17033-360.
E-mail: {papa,"
3e687d5ace90c407186602de1a7727167461194a,Photo Tagging by Collection-Aware People Recognition,"Photo Tagging by Collection-Aware People Recognition
Cristina Nader Vasconcelos
Vinicius Jardim
Asla S´a
Paulo Cezar Carvalho"
3ea8a6dc79d79319f7ad90d663558c664cf298d4,Automatic Facial Expression Recognition from Video Sequences Using Temporal Information Table of Contents,"(cid:13) Copyright by Ira Cohen, 2000"
3ee4076041fe2412d50e84d6778a974d997d8660,FACE RECOGNITION BASED ON OPTIMAL KERNEL MINIMAX PROBABILITY MACHINE,"Journal of Theoretical and Applied Information Technology
28th February 2013. Vol. 48 No.3
© 2005 - 2013 JATIT & LLS. All rights reserved.
ISSN: 1992-8645                                                       www.jatit.org                                                          E-ISSN: 1817-3195
FACE RECOGNITION BASED ON OPTIMAL KERNEL
MINIMAX PROBABILITY MACHINE
School of Information Science and Engineering, Henan University of Technology, Zhengzhou China
ZHIQIANG ZHOU, 2ZIQIANG WANG, 3XIA SUN
E-mail:"
3e0a12352fe3e9fb9246ee0f81ff7fbf0600f818,Facial Surface Analysis using Iso-Geodesic Curves in Three Dimensional Face Recognition System,"Facial Surface Analysis using Iso-Geodesic Curves
in Three Dimensional Face Recognition System
Rachid AHDID, El Mahdi BARRAH, Said SAFI and Bouzid MANAUT"
3e4bd583795875c6550026fc02fb111daee763b4,Convolutional Sketch Inversion,"Convolutional Sketch Inversion
Ya˘gmur G¨u¸cl¨ut¨urk∗, Umut G¨u¸cl¨u∗, Rob van Lier, and Marcel A. J.
van Gerven
Radboud University, Donders Institute for Brain, Cognition and
Behaviour, Nijmegen, the Netherlands
Figure 1: Example results of our convolutional sketch inversion models. Our models
invert face sketches to synthesize photorealistic face images. Each row shows the sketch
inversion / photo synthesis pipeline that transforms a different sketch of the same face
to a different image of the same face via a different deep neural network. Each deep
neural network layer is represented by the top three principal components of its feature
maps."
3e4ec7bdd279573d328a26b720854894e68230ed,Efficient Relative Attribute Learning Using Graph Neural Networks,"Ef‌f‌icient Relative Attribute Learning using
Graph Neural Networks
Zihang Meng1, Nagesh Adluru1, Hyunwoo J. Kim1⋆,
Glenn Fung2, and Vikas Singh1
University of Wisconsin – Madison
American Family Insurance"
3e4f84ce00027723bdfdb21156c9003168bc1c80,A co-training approach to automatic face recognition,"© EURASIP, 2011  -  ISSN 2076-1465
9th European Signal Processing Conference (EUSIPCO 2011)
INTRODUCTION"
3e685704b140180d48142d1727080d2fb9e52163,Single Image Action Recognition by Predicting Space-Time Saliency,"Single Image Action Recognition by Predicting
Space-Time Saliency
Marjaneh Safaei and Hassan Foroosh"
3e0db33884ca8c756b26dc0df85c498c18d5f2ec,Exploiting pedestrian interaction via global optimization and social behaviors,"Exploiting pedestrian interaction via global optimization
nd social behaviors
Laura Leal-Taix´e, Gerard Pons-Moll, and Bodo Rosenhahn
Leibniz Universit¨at Hannover, Appelstr. 9A, Hannover, Germany"
3e04feb0b6392f94554f6d18e24fadba1a28b65f,14 Subspace Image Representation for Facial Expression Analysis and Face Recognition and its Relation to the Human Visual System,"Subspace Image Representation for Facial
Expression Analysis and Face Recognition
nd its Relation to the Human Visual System
Ioan Buciu1,2 and Ioannis Pitas1
Department of Informatics, Aristotle University of Thessaloniki GR-541 24,
Thessaloniki, Box 451, Greece.
Electronics Department, Faculty of Electrical Engineering and Information
Technology, University of Oradea 410087, Universitatii 1, Romania.
Summary. Two main theories exist with respect to face encoding and representa-
tion in the human visual system (HVS). The first one refers to the dense (holistic)
representation of the face, where faces have “holon”-like appearance. The second one
laims that a more appropriate face representation is given by a sparse code, where
only a small fraction of the neural cells corresponding to face encoding is activated.
Theoretical and experimental evidence suggest that the HVS performs face analysis
(encoding, storing, face recognition, facial expression recognition) in a structured
nd hierarchical way, where both representations have their own contribution and
goal. According to neuropsychological experiments, it seems that encoding for face
recognition, relies on holistic image representation, while a sparse image represen-
tation is used for facial expression analysis and classification. From the computer
vision perspective, the techniques developed for automatic face and facial expres-"
3e0415f0e8c36f20042d6a1f8b7c216fb5543c3a,RGB-D Segmentation of Poultry Entrails,"Aalborg Universitet
RGB-D Segmentation of Poultry Entrails
Philipsen, Mark Philip; Jørgensen, Anders; Guerrero, Sergio Escalera; Moeslund, Thomas B.
Published in:
IX International Conference on Articulated Motion and Deformable Objects
DOI (link to publication from Publisher):
0.1007/978-3-319-41778-3_17
Publication date:
Document Version
Accepted author manuscript, peer reviewed version
Link to publication from Aalborg University
Citation for published version (APA):
Philipsen, M. P., Jørgensen, A., Guerrero, S. E., & Moeslund, T. B. (2016). RGB-D Segmentation of Poultry
Entrails. In IX International Conference on Articulated Motion and Deformable Objects (pp. 168-174). Springer.
Lecture Notes in Computer Science, Vol.. 9756, DOI: 10.1007/978-3-319-41778-3_17
General rights
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nd it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.
? Users may download and print one copy of any publication from the public portal for the purpose of private study or research.
? You may not further distribute the material or use it for any profit-making activity or commercial gain"
3e3f305dac4fbb813e60ac778d6929012b4b745a,Feature sampling and partitioning for visual vocabulary generation on large action classification datasets.,"Feature sampling and partitioning for visual vocabulary
generation on large action classification datasets.
Michael Sapienza1, Fabio Cuzzolin1, and Philip H.S. Torr2
Department of Computing and Communications Technology, Oxford Brookes University.
Department of Engineering Science, University of Oxford."
3eec9e8d5051e84624ea7e009a8947403dee99d1,"Material Recognition Meets 3D Reconstruction: Novel Tools for Efficient, Automatic Acquisition Systems","Material Recognition Meets 3D
Reconstruction: Novel Tools for Efficient,
Automatic Acquisition Systems
Dissertation
Erlangung des Doktorgrades (Dr. rer. nat.)
Mathematisch-Naturwissenschaftlichen Fakultät
der Rheinischen Friedrich-Wilhelms-Universität Bonn
vorgelegt von
Dipl.-Ing. Michael Weinmann
us Karlsruhe
Bonn, Dezember 2015"
3e309126c78261f242d21826bfac37412f5437cd,Attribute CNNs for Word Spotting in Handwritten,"International Journal on Document Analysis and Recognition manuscript No.
(will be inserted by the editor)
Attribute CNNs for Word Spotting in Handwritten
Documents
Sebastian Sudholt · Gernot A. Fink
Received: date / Accepted: date"
3e63e93b46a403f4bdbf3f7e497dc53c23b6824c,Elastic appearance models,"HANSEN ET AL.: ELASTIC APPEARANCE MODELS
Elastic Appearance Models
Mads Fogtmann Hansen
http://www.imm.dtu.dk/~mfh
Jens Fagertun
http://www.imm.dtu.dk/~jenf
Rasmus Larsen
http://www.imm.dtu.dk/~rl
DTU Informatic
Technical University of Denmark
Kgs. Lyngby, Denmark"
3e30c59cfdf9ce2c89481d81912e179a9bd6cbee,Boosting Shape Classifiers Accuracy by Considering the Inverse Shape,"Journal of Pattern Recognition Research ??? (2016) 1-14
Boosting Shape Classifiers Accuracy by Considering
the Inverse Shape
Sébastien Piérard and Antoine Lejeune and Marc Van Droogenbroeck
{Sebastien.Pierard, Antoine.Lejeune,
INTELSIG Laboratory, Montefiore Institute, University of Liège, Belgium
Received ???. Received in revised form ???. Accepted ???."
3e3ba138edbcf594cd0479ac2cddd5a8e3ee6a18,Edge detection for facial expression recognition,"Edge Detection for Facial Expression Recognition
Jesús García-Ramírez, Ivan Olmos-Pineda, J. Arturo Olvera-López, Manuel Martín
Ortíz
Faculty of Computer Science, Benemérita Universidad Autónoma de Puebla, Av. San Claudio
olvera,
y 14 sur. Puebla, Pue. C.P. 72570, México"
3edf3a996790fef8957e21c68ddf48b52238e662,Product of tracking experts for visual tracking of surgical tools,"Product of Tracking Experts for Visual Tracking of Surgical Tools
Suren Kumar, Madusudanan Sathia Narayanan, Pankaj Singhal, Jason J. Corso and Venkat Krovi
State University of New York (SUNY) at Buffalo"
3e4a54adb53d69984bb1e113eb1a8184be4abe99,Person Authentication from Video of Faces: A Behavioral and Physiological Approach Using Pseudo Hierarchical Hidden Markov Models,"Person Authentication from Video of Faces: A
Behavioral and Physiological Approach Using
Pseudo Hierarchical Hidden Markov Models
Manuele Bicego1, Enrico Grosso1, and Massimo Tistarelli2
DEIR - University of Sassari, via Torre Tonda 34 - 07100 Sassari - Italy
DAP - University of Sassari, piazza Duomo 6 - 07041 Alghero (SS) - Italy"
3edb0fa2d6b0f1984e8e2c523c558cb026b2a983,Automatic Age Estimation Based on Facial Aging Patterns,"Automatic Age Estimation Based on
Facial Aging Patterns
Xin Geng, Zhi-Hua Zhou, Senior Member, IEEE,
Kate Smith-Miles, Senior Member, IEEE"
3e5ba104e9fce5d57751d4314cf32118398d1f22,MODNet: Moving Object Detection Network with Motion and Appearance for Autonomous Driving,"MODNet: Motion and Appearance based Moving
Object Detection Network for Autonomous Driving
Mennatullah Siam, Heba Mahgoub, Mohamed Zahran,
Senthil Yogamani, Martin Jagersand"
3e6b70e5be3dbe688866d8dd4382ce05b201fd28,Evaluation of Face Recognition Techniques,"PIAGENG 2009: Image Processing and Photonics for Agricultural Engineering, edited by Honghua Tan, Qi Luo,
Proc. of SPIE Vol. 7489, 74890M · © 2009 SPIE · CCC code: 0277-786X/09/$18 · doi: 10.1117/12.836686
Proc. of SPIE Vol. 7489  74890M-1
Downloaded from SPIE Digital Library on 24 Jan 2010 to 130.194.78.137. Terms of Use:  http://spiedl.org/terms"
3e9d04b62d3469fb155e02c1f30b8900381e1419,"Fast and Accurate, Convolutional Neural Network Based Approach for Object Detection from UAV","FAST AND ACCURATE, CONVOLUTIONAL
NEURAL NETWORK BASED APPROACH FOR
OBJECT DETECTION FROM UAV
Xiaoliang Wang
Department of Technology
College of Engineering and
Technology
Virginia State University
Petersburg, VA, USA
Peng Cheng
Department of Technology
College of Engineering and
Technology
Virginia State University
Petersburg, VA, USA
Xinchuan Liu
Department of Technology
College of Engineering and
Technology
Virginia State University"
3e4acf3f2d112fc6516abcdddbe9e17d839f5d9b,Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs,"Deep Value Networks Learn to
Evaluate and Iteratively Refine Structured Outputs
Michael Gygli 1 * Mohammad Norouzi 2 Anelia Angelova 2"
3eff18934f5870b27f80c8b1d7104967460e3035,Driver hand localization and grasp analysis: A vision-based real-time approach,
3e18b439a6fff09a0e4c245eb1298531cc766a72,"Semi-automatic Face Image Finding Method , Which Uses the 3 D Model of the Head for Recognising an Unknown Face","Technologies of Computer Control
doi: 10.7250/tcc.2015.001
______________________________________________________________________________________________ 2015 / 16
Semi-automatic Face Image Finding Method, Which
Uses the 3D Model of the Head for Recognising an
Olga Krutikova1, Aleksandrs Glazs2
, 2 Riga Technical University"
3efb04937f6d87ab9540700e04d8133102c67bc0,Ask Your Neurons: A Deep Learning Approach to Visual Question Answering,"myjournal
Ask Your Neurons:
A Deep Learning Approach to Visual Question Answering
Mateusz Malinowski · Marcus Rohrbach · Mario Fritz
Received: date / Accepted: date"
3e93e41ccd22596ca478f336727f6340b12e7572,LEARNING PHYSICAL DYNAMICS,"Published as a conference paper at ICLR 2017
A COMPOSITIONAL OBJECT-BASED APPROACH TO
LEARNING PHYSICAL DYNAMICS
Michael B. Chang*, Tomer Ullman**, Antonio Torralba*, and Joshua B. Tenenbaum**
*Department of Electrical Engineering and Computer Science, MIT
**Department of Brain and Cognitive Sciences, MIT"
3ee522805e16bf7816ec4abfaf0c7648b5cb5c95,From Numerical Sensor Data to Semantic Representations :,"From Numerical Sensor Data to Semantic Representations:
A Data-driven Approach for Generating Linguistic Descriptions
Hadi Banaee
Akademisk avhandling
Avhandling för filosofie doktorsexamen i datavetenskap,
som kommer att försvaras offentligt
fredag den 20 april 2018 kl. 13.15,
Hörsal T, Örebro universitet, Örebro
Opponent: Prof. Antonio Chella
University of Palermo
Italy
Örebro universitet
Institutionen för Naturvetenskap och Teknik
701 82 Örebro"
3e3ce21b1ef9e4c7199522d2c923e3771dbae930,EXT . ZIP : C OMPRESSING TEXT CLASSIFICATION MODELS,"Under review as a conference paper at ICLR 2017
FASTTEXT.ZIP:
COMPRESSING TEXT CLASSIFICATION MODELS
Armand Joulin, Edouard Grave, Piotr Bojanowski, Matthijs Douze, Herv´e J´egou & Tomas Mikolov
Facebook AI Research"
3e8de2f904dea8368477daebab0c0dc97e0229f4,Detection and Classification of Vehicles from Omnidirectional Videos using Temporal Average of Silhouettes,"Detection and Classification of Vehicles from Omnidirectional Videos
using Temporal Average of Silhouettes
Computer Vision Research Group, Department of Computer Engineering, Izmir Institute of Technology, 35430,
Hakki Can Karaimer and Yalin Bastanlar
Izmir, Turkey
{cankaraimer,
Keywords:
Omnidirectional  Camera,  Omnidirectional  Video,  Object  Detection,  Vehicle  Detection,  Vehicle
Classification."
3efea06ad6398f9db07acf34479c81a99479e80b,Localizing Moments in Video with Natural Language,"Localizing Moments in Video with Natural Language
Lisa Anne Hendricks1
, Oliver Wang2, Eli Shechtman2, Josef Sivic2
, Trevor Darrell1, Bryan Russell2
UC Berkeley, 2Adobe Research, 3INRIA
https://people.eecs.berkeley.edu/˜lisa_anne/didemo.html
Figure 1: We consider localizing moments in video with natural language and demonstrate that incorporating local and
global video features is important for this task. To train and evaluate our model, we collect the Distinct Describable Moments
(DiDeMo) dataset which consists of over 40,000 pairs of localized video moments and corresponding natural language."
3e7b5b07da3465103929b4347852d456c0f0ed58,Video Processing From Electro-Optical Sensors for Object Detection and Tracking in a Maritime Environment: A Survey,"Video Processing from Electro-optical Sensors for
Object Detection and Tracking in Maritime
Environment: A Survey
Dilip K. Prasad1,∗, Deepu Rajan2, Lily Rachmawati3, Eshan Rajabally4, and Chai Quek2"
3e56a9b6c6aced2cb14f9cd7f89d145851c44113,Zero and Few Shot Learning with Semantic Feature Synthesis and Competitive Learning,"Zero and Few Shot Learning with Semantic
Feature Synthesis and Competitive Learning
Zhiwu Lu, Jiechao Guan, Aoxue Li, Tao Xiang, An Zhao, and Ji-Rong Wen"
08ff3e9f5ad47e59592ad993348b817003b9c0e4,A Sequential Classifier for Hand Detection in the Framework of Egocentric Vision,"A Sequential Classifier for Hand Detection in the Framework of Egocentric Vision
Alejandro Betancourt1,2
Miriam M. L´opez1
Carlo S. Regazzoni1
Matthias Rauterberg2
Department of Naval, Electric, Electronic and Telecommunications Engineering - University of Genoa, Italy
Designed Intelligence Group, Department of Industrial Design - Eindhoven University of Technology, The Netherlands"
08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7,Understanding Kin Relationships in a Photo,"Understanding Kin Relationships in a Photo
Siyu Xia, Ming Shao, Student Member, IEEE, Jiebo Luo, Fellow, IEEE, and Yun Fu, Senior Member, IEEE"
0857281a3b6a5faba1405e2c11f4e17191d3824d,Face recognition via edge-based Gabor feature representation for plastic surgery-altered images,"Chude-Olisah et al. EURASIP Journal on Advances in Signal Processing 2014, 2014:102
http://asp.eurasipjournals.com/content/2014/1/102
R ES EAR CH
Face recognition via edge-based Gabor feature
representation for plastic surgery-altered images
Chollette C Chude-Olisah1*, Ghazali Sulong1, Uche A K Chude-Okonkwo2 and Siti Z M Hashim1
Open Access"
08903bf161a1e8dec29250a752ce9e2a508a711c,Joint Dimensionality Reduction and Metric Learning: A Geometric Take,"Joint Dimensionality Reduction and Metric Learning: A Geometric Take
Mehrtash Harandi 1 2 Mathieu Salzmann 3 Richard Hartley 2 1"
084f1a6c62a3464b1a9b745fee40af2895920301,Capitalize on dimensionality increasing techniques for improving face recognition grand challenge performance,"Capitalize on Dimensionality Increasing
Techniques for Improving Face Recognition
Grand Challenge Performance
Chengjun Liu"
085ba9f82e15603f1fe2a29dfa0182d46465a591,Face Recognition In Presence Of Occlusion Using Machine Learning Classifier Vandana,"International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 4, April 2014
Face Recognition In Presence Of Occlusion
Using Machine Learning Classifier
Vandana P, Manjunath C N
chieve"
083a2bc86e0984968b06593ba06654277b252f00,Neural evidence for the contribution of holistic processing but not attention allocation to the other-race effect on face memory.,"Cognitive, Affective, & Behavioral Neuroscience (2018) 18:1015–1033
https://doi.org/10.3758/s13415-018-0619-z
Neural evidence for the contribution of holistic processing but not
ttention allocation to the other-race effect on face memory
Grit Herzmann 1 & Greta Minor 1 & Tim Curran 2
Published online: 25 June 2018
# Psychonomic Society, Inc. 2018"
08ff81f3f00f8f68b8abd910248b25a126a4dfa4,Symmetric Subspace Learning for Image Analysis,"Papachristou, K., Tefas, A., & Pitas, I. (2014). Symmetric Subspace Learning
5697. DOI: 10.1109/TIP.2014.2367321
Peer reviewed version
Link to published version (if available):
0.1109/TIP.2014.2367321
Link to publication record in Explore Bristol Research
PDF-document
This is the author accepted manuscript (AAM). The final published version (version of record) is available online
via Institute of Electrical and Electronic Engineers at http://dx.doi.org/10.1109/TIP.2014.2367321. Please refer to
ny applicable terms of use of the publisher.
University of Bristol - Explore Bristol Research
General rights
This document is made available in accordance with publisher policies. Please cite only the published
version using the reference above. Full terms of use are available:
http://www.bristol.ac.uk/pure/about/ebr-terms"
088aabe3da627432fdccf5077969e3f6402f0a80,CLASSIFIER-TO-GENERATOR ATTACK: ESTIMATION,"Under review as a conference paper at ICLR 2018
CLASSIFIER-TO-GENERATOR ATTACK: ESTIMATION
OF TRAINING DATA DISTRIBUTION FROM CLASSIFIER
Anonymous authors
Paper under double-blind review"
08f46d6a91e513edd57a0ef15d5367b5d0545c1b,"How do targets, nontargets, and scene context influence real-world object detection?","Atten Percept Psychophys
DOI 10.3758/s13414-017-1359-9
How do targets, nontargets, and scene context influence
real-world object detection?
Harish Katti 1
& Marius V. Peelen 2 & S. P. Arun 1
# The Psychonomic Society, Inc. 2017"
08b76e6923eea74ab0ed149811b3144fa21c7c73,Scalable Laplacian K-modes,"Scalable Laplacian K-modes
Imtiaz Masud Ziko ∗
ÉTS Montreal
Eric Granger
ÉTS Montreal
Ismail Ben Ayed
ÉTS Montreal"
08030f9d34cc96384f672d9f9f296914d594335b,Multiple Object Tracking: A Review,"Multiple Object Tracking: A Literature Review
Wenhan Luo, Junliang Xing, Anton Milan, Xiaoqin Zhang, Wei Liu, Xiaowei Zhao and Tae-Kyun Kim"
0861f86fb65aa915fbfbe918b28aabf31ffba364,An Efficient Facial Annotation with Machine Learning Approach,"International Journal of Computer Trends and Technology (IJCTT) – volume 22 Number 3–April 2015
An Efficient Facial Annotation with Machine Learning Approach
A.Anusha,2R.Srinivas
Final M.Tech Student, 2Associate Professor
,2Dept of CSE ,Aditya Institute of Technology And Management, Tekkali, Srikakulam , Andhra Pradesh"
0816cbac9ea8f4425d9b57fd46174cb35cd5d7cc,People tracking in RGB-D data with on-line boosted target models,"People Tracking in RGB-D Data
With On-line Boosted Target Models
Matthias Luber
Luciano Spinello
Kai O. Arras"
0856622ce2fcc4e39fd396427abae90cddf78fd0,Abnormal activation of the social brain during face perception in autism.,"Abnormal Activation of the Social Brain During
Face Perception in Autism
Nouchine Hadjikhani,1,2* Robert M. Joseph,3 Josh Snyder,1
nd Helen Tager-Flusberg3
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital,
Division of Health Sciences and Technology, Harvard-Massachusetts Institute of Technology,
Harvard Medical School, Charlestown, Massachusetts
Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston,
Cambridge, Massachusetts
Massachusetts"
08f48d8dd64328ec6c91cf0de8d19e80e65ad52c,Face recognition based on polar frequency features,"Face Recognition Based on Polar Frequency Features
YOSSI ZANA1
Dept. of Computer Science – IME, University of São Paulo, São Paulo - SP, Brazil
ROBERTO M. CESAR-JR
Dept. of Computer Science – IME, University of São Paulo, São Paulo - SP, Brazil
________________________________________________________________________
A  novel  biologically  motivated  face  recognition  algorithm  based  on  polar  frequency  is  presented.  Polar
frequency descriptors are extracted from face images by Fourier-Bessel transform (FBT). Next, the Euclidean
distance between all images is computed and each image is now represented by its dissimilarity to the other
images. A Pseudo-Fisher Linear Discriminant was built on this dissimilarity space. The performance of Discrete
Fourier  transform  (DFT)  descriptors,  and  a  combination  of  both  feature  types  was  also  evaluated.  The
lgorithms  were  tested  on  a  40-  and  1196-subjects  face  database  (ORL  and  FERET,  respectively).  With  5
images per subject in the training and test datasets, error rate on the ORL database was 3.8, 1.25 and 0.2% for
the FBT, DFT, and the combined classifier, respectively, as compared to 2.6% achieved by the best previous
lgorithm.  The  most  informative  polar  frequency  features  were  concentrated  at  low-to-medium  angular
frequencies  coupled  to  low  radial  frequencies.  On  the  FERET  database,  where  an  affine  normalization  pre-
processing was applied, the FBT algorithm outperformed only the PCA in a rank recognition test. However, it
chieved  performance  comparable  to  state-of-the-art  methods  when  evaluated  by  verification  tests.  These
results  indicate  the  high  informative  value  of  the  polar  frequency  content  of  face  images  in  relation  to
recognition and verification tasks, and that the Cartesian frequency content can complement information about"
08b70ab782141a2d7003226a0f438a6aea0a0d46,Parametrizing Fully Convolutional Nets,"Under review as a conference paper at ICLR 2019
PARAMETRIZING FULLY CONVOLUTIONAL NETS
WITH A SINGLE HIGH-ORDER TENSOR
Anonymous authors
Paper under double-blind review"
0888b6904ef12bc7a3c59fa59c4051d5002de80f,Learning with Shared Information for Image and Video Analysis,"DEPARTMENT OF INFORMATION ENGINEERING AND COMPUTER SCIENCE
ICT International Doctoral School
LEARNING WITH SHARED INFORMATION FOR IMAGE
AND VIDEO ANALYSIS
Gaowen Liu
Advisor
Prof. Nicu Sebe
Universit`a degli Studi di Trento"
08aedeb74dda306a14c699ffcef4f434a60f34e8,3d spatial layout and geometric constraints for scene understanding,(cid:13) 2011 Varsha Chandrashekhar Hedau
08d2f655361335bdd6c1c901642981e650dff5ec,Automatic Cast Listing in Feature-Length Films with Anisotropic Manifold Space,"This is the published version:
Arandjelovic,	Ognjen	and	Cipolla,	R.	2006,	Automatic	cast	listing	in	feature‐length	films	with
Anisotropic	Manifold	Space,	in	CVPR	2006	:	Proceedings	of	the	Computer	Vision	and	Pattern
Recognition	Conference	2006,	IEEE,	Piscataway,	New	Jersey,	pp.	1513‐1520.
http://hdl.handle.net/10536/DRO/DU:30058435
Reproduced	with	the	kind	permission	of	the	copyright	owner.
Copyright	:	2006,	IEEE
Available from Deakin Research Online:"
08ff22f76a567fcbc1afec6bfbf957a560cfadc7,Exploring Person Context and Local Scene Context for Object Detection.,"Exploring Person Context and Local Scene Context for Object Detection
Saurabh Gupta∗
UC Berkeley
Bharath Hariharan∗
Facebook AI Research
Jitendra Malik
UC Berkeley"
08ae1f8dea9b5ce7923db6469443f43f2c290510,Progressive sparse representation-based classification using local discrete cosine transform evaluation for image recognition,"Progressive sparse representation-
ased classification using local
discrete cosine transform evaluation
for image recognition
Xiaoning Song
Zhen-Hua Feng
Guosheng Hu
Xibei Yang
Jingyu Yang
Yunsong Qi
Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 02/27/2017 Terms of Use: http://spiedigitallibrary.org/ss/termsofuse.aspx"
081b6b99aabe36f8b5530163f991bae3f8015ff8,Deep Leaf Segmentation Using Synthetic Data,"Deep Leaf Segmentation Using Synthetic
Daniel Ward
Peyman Moghadam
Nicolas Hudson
Robotics and Autonomous Systems
The Commonwealth Scientific and
Industrial Research Organisation
(CSIRO), Data61
Brisbane, Australia"
08ae100805d7406bf56226e9c3c218d3f9774d19,Predicting the Sixteen Personality Factors (16PF) of an individual by analyzing facial features,"Gavrilescu and Vizireanu EURASIP Journal on Image and Video Processing  (2017) 2017:59
DOI 10.1186/s13640-017-0211-4
EURASIP Journal on Image
nd Video Processing
R ES EAR CH
Predicting the Sixteen Personality Factors
(16PF) of an individual by analyzing facial
features
Mihai Gavrilescu* and Nicolae Vizireanu
Open Access"
08f00e5adaba03628144dbc97daefa8ceb6e5322,Machine Vision based Fruit Classification and Grading-A Review,"International Journal of Computer Applications (0975 – 8887)
Volume 170 – No.9, July 2017
Machine Vision based Fruit Classification and
Grading - A Review
Sapan Naik
Babu Madhav Institute of Information Technology
Uka Tarsadia University,
Bardoli, Surat, Gujarat, India."
082d339e29b1b1a9a800a1d72b401f69b6a157c5,Webly Supervised Joint Embedding for Cross-Modal Image-Text Retrieval,"Webly Supervised Joint Embedding for Cross-Modal
Image-Text Retrieval
Niluthpol Chowdhury Mithun
University of California, Riverside, CA
Evangelos E. Papalexakis
University of California, Riverside, CA
Rameswar Panda
University of California, Riverside, CA
Amit K. Roy-Chowdhury
University of California, Riverside, CA"
0854b445973f5df79978cf4d4b031af696244ffb,Optimal Weighting of Landmarks for Face Recognition,"Optimal Weighting of Landmarks
for Face Recognition
Mechatronics Research Group, Department of Mechanical and Manufacturing Engineering,
Rajinda S. Senaratne, and Saman K. Halgamuge
The University of Melbourne, Melbourne, Australia
Email:"
08847df8ea5b22c6a2d6d75352ef6270f53611de,Using k-Poselets for Detecting People and Localizing Their Keypoints,"Using k-poselets for detecting people and localizing their keypoints
Georgia Gkioxari∗, Bharath Hariharan∗, Ross Girshick and Jitendra Malik
University of California, Berkeley - Berkeley, CA 94720"
0834dff6e1d37ecb36137e019f8e2c933d5e74f6,Building Part-Based Object Detectors via 3D Geometry,"BUILDING PART-BASED OBJECT DETECTORS VIA 3D GEOMETRY
Experimental Results
Qualitative Results
Input Image
DPM Detection
Test Set: NYU v2 RGB Images
gDPM Detection  Predicted Geometry
Bed gDPM Model 3
Sofa gDPM Model 3
Table gDPM Model 3
Discriminative Part-based Models
Supervised Parts
Unsupervised Parts
Key-point/part annotation, e.g.,
Heuristic initialization, e.g., gradient
natomical.
magnitudes.
. Overview
. Overview
As input to the system, at training, we use RGB images"
08c6943a17f267ef27316cff9248b3036a7059f3,We are not contortionists: Coupled adaptive learning for head and body orientation estimation in surveillance video,"We are not Contortionists: Coupled Adaptive Learning
for Head and Body Orientation Estimation in Surveillance Video
Cheng Chen
Jean-Marc Odobez
Idiap Research Institute – CH-1920, Martigny, Switzerland
(cid:3)"
089ad31ad5eef41bd179bb0a142d3386a8de5564,Continuous memories for representing sets of vectors and image collections. (Mémoires continues représentant des ensembles de vecteurs et des collections d'images),"Continuous memories for representing sets of vectors
nd image collections
Ahmet Iscen
To cite this version:
Ahmet Iscen. Continuous memories for representing sets of vectors and image collections. Com-
puter Vision and Pattern Recognition [cs.CV]. Université Rennes 1, 2017. English. <NNT :
017REN1S039>. <tel-01661319>
HAL Id: tel-01661319
https://tel.archives-ouvertes.fr/tel-01661319
Submitted on 11 Dec 2017
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
08e24f9df3d55364290d626b23f3d42b4772efb6,Enhancing facial expression classification by information fusion,"ENHANCING FACIAL EXPRESSION CLASSIFICATION BY INFORMATION
FUSION
I. Buciu1, Z. Hammal 2, A. Caplier2, N. Nikolaidis 1, and I. Pitas 1
AUTH/Department of Informatics/ Aristotle University of Thessaloniki
phone: + 30(2310)99.6361, fax: + 30(2310)99.8453, email:
GR-54124, Thessaloniki, Box 451, Greece
Laboratoire des Images et des Signaux / Institut National Polytechnique de Grenoble
phone: + 33(0476)574363, fax: + 33(0476)57 47 90, email:
web: http://www.aiia.csd.auth.gr
8031 Grenoble, France
web: http://www.lis.inpg.fr"
08ab862450b42595be34510f8da8defcfaec3d2e,Object class recognition using multiple layer boosting with heterogeneous features,"Object Class Recognition Using Multiple Layer Boosting
with Heterogeneous Features
Wei Zhang1
Bing Yu1
Gregory J. Zelinsky2
Dimitris Samaras1
Dept. of Computer Science
SUNY at Stony Brook
Stony Brook, NY 11794
{wzhang, ybing,
Dept. of Psychology
SUNY at Stony Brook
Stony Brook, NY 11794"
081d6ac51bbb7df142e3db6649fb5d663e90d569,Generalized zero-shot learning for action recognition with web-scale video data,"Noname manuscript No.
(will be inserted by the editor)
Generalized Zero-Shot Learning for Action
Recognition with Web-Scale Video Data
Kun Liu · Wu Liu · Huadong Ma ·
Wenbing Huang · Xiongxiong Dong
Received: date / Accepted: date"
0874a262c2ec7082658cbfc55892ec6e5ca6a374,CaTDet: Cascaded Tracked Detector for Efficient Object Detection from Video,"CATDET: CASCADED TRACKED DETECTOR FOR EFFICIENT OBJECT
DETECTION FROM VIDEO
Huizi Mao 1 Taeyoung Kong 1 William J. Dally 1 2"
08bdb84d5c66265b3b6d33e8f95c4cc27caf33ad,Detecting Visual Relationships Using Box Attention,"Detecting Visual Relationships Using Box Attention
Alexander Kolesnikov∗
Google AI
Christoph H. Lampert
IST Austria
Vittorio Ferrari
Google AI"
081093b0b3195e3f6bfa283b49fee26b606d4f67,Object Co-detection,"Object Co-detection
Sid Yingze Bao, Yu Xiang, Silvio Savarese
University of Michigan at Ann Arbor, USA
{yingze, yuxiang,"
08b0664fd37cd434201a1b37c20c0919833a6ff1,Online Multi-Object Tracking with Historical Appearance Matching and Scene Adaptive Detection Filtering,"Online Multi-Object Tracking with Historical Appearance Matching and
Scene Adaptive Detection Filtering
Young-chul Yoon Abhijeet Boragule Young-min Song Kwangjin Yoon Moongu Jeon
Gwangju Institute of Science and Technology
23 Cheomdangwagi-ro, Buk-gu, Gwangju, 61005, South Korea
{zerometal9268, abhijeet, sym, yoon28,
(cid:11)(cid:36)(cid:57)(cid:54)(cid:54)(cid:3)(cid:21)(cid:19)(cid:20)(cid:27)(cid:12)
(cid:20)(cid:17)(cid:3)(cid:44)(cid:81)(cid:87)(cid:85)(cid:82)(cid:71)(cid:88)(cid:70)(cid:87)(cid:76)(cid:82)(cid:81)(cid:3)(cid:11)(cid:87)(cid:72)(cid:80)(cid:83)(cid:82)(cid:85)(cid:68)(cid:79)(cid:3)(cid:72)(cid:85)(cid:85)(cid:82)(cid:85)(cid:86)(cid:3)(cid:71)(cid:88)(cid:85)(cid:76)(cid:81)(cid:74)(cid:3)(cid:87)(cid:85)(cid:68)(cid:70)(cid:78)(cid:76)(cid:81)(cid:74)(cid:12)"
082a8642455b9a5cfb27c07cf9969106f8a7bf3c,Face recognition is similarly affected by viewpoint in school-aged children and adults,"Face recognition is similarly affected by
viewpoint in school-aged children and
dults
Marisa Nordt and Sarah Weigelt
Department of Developmental Neuropsychology, Institute of Psychology, Ruhr-Universität Bochum,
Bochum, Germany"
08d40ee6e1c0060d3b706b6b627e03d4b123377a,Towards Weakly-Supervised Action Localization,"Human Action Localization
with Sparse Spatial Supervision
Philippe Weinzaepfel, Xavier Martin, and Cordelia Schmid, Fellow, IEEE"
080c204edff49bf85b335d3d416c5e734a861151,CLAD: A Complex and Long Activities Dataset with Rich Crowdsourced Annotations,"CLAD: A Complex and Long Activities
Dataset with Rich Crowdsourced
Annotations
Jawad Tayyub1, Majd Hawasly2∗, David C. Hogg1 and Anthony G. Cohn1
Journal Title
XX(X):1–6
(cid:13)The Author(s) 2016
Reprints and permission:
sagepub.co.uk/journalsPermissions.nav
DOI: 10.1177/ToBeAssigned
www.sagepub.com/"
08809165154c9c557d368cddfa3ae66ccaceaed9,Taming VAEs,"Taming VAEs
Danilo J. Rezende ∗
Fabio Viola ∗
{danilor,
DeepMind, London, UK"
0871982db35e924506d41de97ba4d909bb727f50,Recognizing Rotated Faces from Two Orthogonal Views in Mugshot Databases,"Recognizing Rotated Faces from Two Orthogonal Views in
Mugshot Databases
Author
Zhang, Paul, Gao, Yongsheng, Zhang, Bai-ling
Published
Conference Title
Proceedings: The 18th International Conference of Pattern Recognition
https://doi.org/10.1109/ICPR.2006.978
Copyright Statement
© 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/
republish this material for advertising or promotional purposes or for creating new collective
works for resale or redistribution to servers or lists, or to reuse any copyrighted component of
this work in other works must be obtained from the IEEE.
Downloaded from
http://hdl.handle.net/10072/13122
Griffith Research Online
https://research-repository.griffith.edu.au"
08fbe3187f31b828a38811cc8dc7ca17933b91e9,Statistical Computations on Grassmann and Stiefel Manifolds for Image and Video-Based Recognition,"MITSUBISHI ELECTRIC RESEARCH LABORATORIES
http://www.merl.com
Statistical Computations on Grassmann and
Stiefel Manifolds for Image and Video-Based
Recognition
Turaga, P.; Veeraraghavan, A.; Srivastava, A.; Chellappa, R.
TR2011-084 April 2011"
085ca7f8935808986ae1c6afbbb62f6804049f26,Universität Augsburg Monocular 3d Human Pose Estimation by Classification Institut F ¨ Ur Informatik D-86135 Augsburg Monocular 3d Human Pose Estimation by Classification,"Universit¨at Augsburg
Monocular 3D Human Pose Estimation
y Classification
T. Greif, D. Sengupta, R. Lienhart
Report 2011-09
M¨arz 2011
Institut f¨ur Informatik
D-86135 Augsburg"
081456e22734a2cdef442345f80182e84d1c6124,Approaches for Multi-Class Discriminant Analysis for Ranking Principal Components,"Approaches for Multi-Class Discriminant Analysis
for Ranking Principal Components
Tiene Andre Filisbino
Laborat´orio Nacional
Gilson Antonio Giraldi
Laborat´orio Nacional
Carlos Eduardo Thomaz
Departamento de Engenharia El´etrica
de Computac¸˜ao Cient´ıfica - LNCC
de Computac¸˜ao Cient´ıfica - LNCC
Centro Universit´ario da FEI
Petr´opolis, RJ 25651-075
Email:
Petr´opolis, RJ 25651-075
Email:
S˜ao Bernardo do Campo, SP 09850-901
Email:"
08bbb59036c4b85a2418f9702ccd37929c5dd154,Understanding and Predicting the Memorability of Natural Scene Images,"Understanding and Predicting the Memorability of
Natural Scene Images
Jiaxin Lu, Mai Xu, Senior Member, IEEE, Ren Yang and Zulin Wang"
08f4832507259ded9700de81f5fd462caf0d5be8,Geometric Approach for Human Emotion Recognition using Facial Expression,"International Journal of Computer Applications (0975 – 8887)
Volume 118 – No.14, May 2015
Geometric Approach for Human Emotion
Recognition using Facial Expression
S. S. Bavkar
Assistant Professor
VPCOE Baramati
J. S. Rangole
Assistant Professor
VPCOE Baramati
V. U. Deshmukh
Assistant Professor
VPCOE Baramati"
087337fdad69caaab8ebd8ae68a731c5bf2e8b14,Fully Convolutional Networks for Semantic Segmentation,"Fully Convolutional Networks for Semantic Segmentation
Jonathan Long∗
Evan Shelhamer∗
UC Berkeley
Trevor Darrell"
0875af310ab8c850b3232b3f6b84535ffff84e5d,A Novel Technique to Detect Faces in a Group Photo,"International Journal of Computer Applications (0975 – 8887)
Volume 54– No.1, September 2012
A Novel Technique to Detect Faces in a Group Photo
Saravanan Chandran
Assistant Professor, National Institute of Technology, Durgapur, West Bengal, India."
085fce160b0fa279597bf23b518c56c735d9e7ff,Joint detection and recognition of human actions in wireless surveillance camera networks,"Joint Detection and Recognition of Human Actions in Wireless
Surveillance Camera Networks
Nikhil Naikal1, Pedram Lajevardi2 and Shankar. S. Sastry1"
084bd219dd239dc4c9a02621a5333d3bc1446566,DeepTrack: Learning Discriminative Feature Representations Online for Robust Visual Tracking,"DeepTrack: Learning Discriminative Feature
Representations Online for Robust Visual Tracking
Hanxi Li, Yi Li, Fatih Porikli"
82a610a59c210ff77cfdde7fd10c98067bd142da,Human attention and intent analysis using robust visual cues in a Bayesian framework,"UC San Diego
UC San Diego Electronic Theses and Dissertations
Title
Human attention and intent analysis using robust visual cues in a Bayesian framework
Permalink
https://escholarship.org/uc/item/1cb8d7vw
Author
McCall, Joel Curtis
Publication Date
006-01-01
Peer reviewed|Thesis/dissertation
eScholarship.org
Powered by the California Digital Library
University of California"
82a2a523c4488c34b486c920046f4ebbf8ea828e,Vision-Based System for Human Detection and Tracking in Indoor Environment,"Author manuscript, published in ""International Journal of Social Robotics 2, 1 (2010) 41-52""
DOI : 10.1007/s12369-009-0040-4"
821ba3eba1e36a29cc482f5378f4a0d0f6893159,Unsupervised Domain Adaptation for Learning Eye Gaze from a Million Synthetic Images: An Adversarial Approach,"Unsupervised Domain Adaptation for Learning Eye Gaze from a
Million Synthetic Images: An Adversarial Approach
Avisek Lahiri∗
Abhinav Agarwalla
Prabir Kumar Biswas
Dept. of E&ECE, IIT Kharagpur
Dept. of E&ECE, IIT Kharagpur
Dept. of Mathematics, IIT Kharagpur"
82417d8ec8ac6406f2d55774a35af2a1b3f4b66e,Some Faces are More Equal than Others: Hierarchical Organization for Accurate and Efficient Large-Scale Identity-Based Face Retrieval,"Some faces are more equal than others:
Hierarchical organization for accurate and
ef‌f‌icient large-scale identity-based face retrieval
Binod Bhattarai1, Gaurav Sharma2, Fr´ed´eric Jurie1, Patrick P´erez2
GREYC, CNRS UMR 6072, Universit´e de Caen Basse-Normandie, France1
Technicolor, Rennes, France2"
82ec2ff0bef7db7e5ea48c42336200fb0e44dbf9,Reconstruction of 3D Human Facial Images Using Partial Differential Equations,"Reconstruction of 3D Human Facial Images
Using Partial Differential Equations
University of Bradford/EIMC Department, Richmond Road, BD7 1DP, Bradford, UK
Email: {E.Elyan,
Eyad Elyan, Hassan Ugail
(PDE).  Here"
828b73e8a4d539eeae82601b5f5a4392818c6430,Long-Term Tracking by Decision Making,"UNIVERSITY OF CALIFORNIA,
IRVINE
Long-Term Tracking by Decision Making
DISSERTATION
submitted in partial satisfaction of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
in Computer Science
James Supanˇciˇc, III
Dissertation Committee:
Deva Ramanan, Chair
Charless Fowlkes
Alexander Ihler"
82319857563e7b578bcb66ec4df1c85decd6a624,Cooperative Tracking of Cyclists Based on Smart Devices and Infrastructure,"Cooperative Tracking of Cyclists Based on
Smart Devices and Infrastructure
G¨unther Reitberger, Maarten Bieshaar, Stefan Zernetsch, Konrad Doll, Bernhard Sick, and Erich Fuchs"
82a4562d9ef19aec3aeaf9bd9f0ac4e09bdf5c86,Putting Out a HIT: Crowdsourcing Malware Installs,"Putting Out a HIT: Crowdsourcing Malware Installs
Chris Kanich
UC San Diego
Stephen Checkoway
UC San Diego
Keaton Mowery
UC San Diego"
8285e1b5536ce11d55462ae757f61c75ec6773c6,The Frontiers of Fairness in Machine Learning,"The Frontiers of Fairness in Machine Learning
Alexandra Chouldechova∗
Aaron Roth†
October 23, 2018"
82fae97673a353271b1d4c001afda1af6ef6dc23,Semantic contours from inverse detectors,"Semantic Contours from Inverse Detectors∗
Bharath Hariharan1, Pablo Arbel´aez1, Lubomir Bourdev1
, Subhransu Maji1 and Jitendra Malik1
EECS, U.C. Berkeley, Berkeley, CA 94720
Adobe Systems, Inc., 345 Park Ave, San Jose, CA 95110
{bharath2, arbelaez, lbourdev, smaji,"
82d5656c74362d6c5c5fd889fc48f7816bbb033a,Contemplating Visual Emotions: Understanding and Overcoming Dataset Bias,"Contemplating Visual Emotions: Understanding
nd Overcoming Dataset Bias
Rameswar Panda1, Jianming Zhang2, Haoxiang Li3, Joon-Young Lee2, Xin
Lu2, and Amit K. Roy-Chowdhury1
Department of ECE, UC Riverside.
Adobe Research.
Aibee."
8209445ce555d166580159ee18059fa41c0433cd,Real-world Object Recognition with Off-the-shelf Deep Conv Nets: How Many Objects can iCub Learn?,"Real-world Object Recognition with
Off-the-shelf Deep Conv Nets:
How Many Objects can iCub Learn?
Giulia Pasquale ∗
Carlo Ciliberto †
Francesca Odone ‡
Lorenzo Rosasco † ‡
Lorenzo Natale ∗"
82c303cf4852ad18116a2eea31e2291325bc19c3,Fusion Based FastICA Method : Facial Expression Recognition,"Journal of Image and Graphics, Volume 2, No.1, June, 2014
Fusion Based FastICA Method: Facial Expression
Recognition
Humayra B. Ali and David M W Powers
Computer Science, Engineering and Mathematics School, Flinders University, Australia
Email: {ali0041,"
825bfa844e4493f205f66782c6ca68aa69018d9c,In-Place Activated BatchNorm for Memory-Optimized Training of DNNs,"In-Place Activated BatchNorm for Memory-Optimized Training of DNNs
Samuel Rota Bulò, Lorenzo Porzi, Peter Kontschieder
Mapillary Research"
823f4300ddf64a95324db89035946638ecb02aa0,MX-LSTM: mixing tracklets and vislets to jointly forecast trajectories and head poses,"MX-LSTM: mixing tracklets and vislets to jointly forecast
trajectories and head poses
Irtiza Hasan1,2, Francesco Setti1, Theodore Tsesmelis1,2,3, Alessio Del Bue3,
Fabio Galasso2, and Marco Cristani1
University of Verona (UNIVR)
OSRAM GmbH
Istituto Italiano di Tecnologia (IIT)"
8239e4a37825979f66ff0419ccd50a08aebfbadf,Tracing the Colors of Clothing in Paintings with Image Analysis,"Tracing the Colors of Clothing in Paintings with
Image Analysis
Cihan Sarı1, Albert Ali Salah2, and Alkım Almıla Akda˘g Salah3
Bo˘gazi¸ci University, Systems and Control Engineering,
Bo˘gazi¸ci University, Computer Engineering,
{cihan.sari,
Istanbul S¸ehir University, College of Communications
Introduction
The history of color is full of instances of how and why certain colors become to
e associated with certain concepts, ideas, politics, status and power. Sometimes
the connotations occur arbitrarily, like in the instance when pink was assigned
to baby girls, and blue started to be associated with baby boys at the turn of
9th Century [Paoletti, 1987]. Sometimes though, color associations have very
tangible reasons, such as in the case of Marian blue and why over the centuries
it was reserved only for painting Virgin Mary. The reason is to be found in the
scarcity of the rock lapis lazuli -even more valuable than gold-, from which the
lue pigments were extracted. Individual colors have convoluted and contested
histories, since they have been attached to many symbols at any given time.
John Gage, an art historian who has devoted 30 years of research on the topic
of color, explains the conundrum of what he terms as “politics of color” in a"
82f6cc54ddb4df9fae811467bdf25f25985c7e2f,CNN features are also great at unsupervised classification,"CNN features are also great at unsupervised
lassification
Joris Guérin∗
Arts et Métiers ParisTech
59000, Lille, France
Eric Nyiri∗
Arts et Métiers ParisTech
59000, Lille, France
Olivier Gibaru∗
Arts et Métiers ParisTech
59000, Lille, France
Stéphane Thiery∗
Arts et Métiers ParisTech
59000, Lille, France"
82a922e775ec3a83d2d5637030860f587697ae42,Dense Multiperson Tracking with Robust Hierarchical Linear Assignment,"Dense Multiperson Tracking with Robust Hierarchical Linear
Assignment
McLaughlin, N., Martinez-del-Rincon, J., & Miller, P. (2015). Dense Multiperson Tracking with Robust
https://doi.org/10.1109/TCYB.2014.2348314
Published in:
Document Version:
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Queen's University Belfast - Research Portal:
Link to publication record in Queen's University Belfast Research Portal
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Copyright 2014 IEEE.
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829f390b3f8ad5856e7ba5ae8568f10cee0c7e6a,A Robust Rotation Invariant Multiview Face Detection in Erratic Illumination Condition,"International Journal of Computer Applications (0975 – 8887)
Volume 57– No.20, November 2012
A Robust Rotation Invariant Multiview Face Detection in
Erratic Illumination Condition
G.Nirmala Priya
Associate Professor, Department of ECE
Sona College of Technology
Salem"
82f6dad08432a5f1b737ba91dd002ff1f89170f7,c○2013 The Association for Computational Linguistics Order copies of this and other ACL proceedings from:,"ACL201351stAnnualMeetingoftheAssociationforComputationalLinguisticsProceedingsoftheConferenceSystemDemonstrationsAugust4-9,2013Sofia,Bulgaria"
82a4a35b2bae3e5c51f4d24ea5908c52973bd5be,Real-time emotion recognition for gaming using deep convolutional network features,"Real-time emotion recognition for gaming using
deep convolutional network features
S´ebastien Ouellet"
825198ad69b2203997341fa903ce56ea28451644,Developing crossmodal expression recognition based on a deep neural model,"Special issue on Grounding Emotions in Robots
Developing crossmodal expression
recognition based on a deep neural
model
Pablo Barros and Stefan Wermter
Adaptive Behavior
016, Vol. 24(5) 373–396
Ó The Author(s) 2016
Reprints and permissions:
sagepub.co.uk/journalsPermissions.nav
DOI: 10.1177/1059712316664017
db.sagepub.com"
8210fd10ef1de44265632589f8fc28bc439a57e6,Single Sample Face Recognition via Learning Deep Supervised Autoencoders,"Single Sample Face Recognition via Learning Deep
Supervised Auto-Encoders
Shenghua  Gao,  Yuting  Zhang,  Kui  Jia,  Jiwen  Lu,  Yingying  Zhang"
8239a0b4cdb480c9fb913c7476f12825418b0909,People detection in RGB-D data,"People Detection in RGB-D Data
Luciano Spinello
Kai O. Arras"
82d2af2ffa106160a183371946e466021876870d,A Novel Space-Time Representation on the Positive Semidefinite Cone for Facial Expression Recognition,"A Novel Space-Time Representation on the Positive Semidefinite Cone
for Facial Expression Recognition
Anis Kacem1, Mohamed Daoudi1, Boulbaba Ben Amor1, and Juan Carlos Alvarez-Paiva2
IMT Lille Douai, Univ. Lille, CNRS, UMR 9189 – CRIStAL –
Centre de Recherche en Informatique Signal et Automatique de Lille, F-59000 Lille, France
Univ. Lille, CNRS, UMR 8524, Laboratoire Paul Painlev´e, F-59000 Lille, France."
82d3dc1dd35e7d2d13bc43614b575dce61b0aba3,Head Pose Estimation from Passive Stereo Images,"Head Pose Estimation
from Passive Stereo Images
M. D. Breitenstein1, J. Jensen2, C. Høilund2, T. B. Moeslund2, L. Van Gool1
ETH Zurich, Switzerland1 Aalborg University, Denmark2"
826c66bd182b54fea3617192a242de1e4f16d020,Action-vectors: Unsupervised movement modeling for action recognition,"978-1-5090-4117-6/17/$31.00 ©2017 IEEE
ICASSP 2017"
820b1349751d7e932b74c3de94b96557fa2534cf,BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography,"BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography
Michael J. Wilber1,2
Chen Fang1
John Collomosse1
Adobe Research
Aaron Hertzmann1
Hailin Jin1
Serge Belongie2
Cornell Tech"
8263834bbe6e986a703370810f9b963e2d25a7f7,Towards Head Motion Compensation Using Multi-Scale Convolutional Neural Networks,"Towards Head Motion Compensation Using Multi-Scale
Convolutional Neural Networks
O. Rajput1∗, N. Gessert1∗, M. Gromniak1, L. Matth¨aus2, A. Schlaefer1
Institute of Medical Technology, Hamburg University of Technology, Hamburg, Germany
eemagine Medical Imaging Solutions GmbH, Berlin, Germany
Both authors contributed equally.
Contact:"
82fc08f5658e0e37aeb36177717621605e11cda1,CNN-Based Patch Matching for Optical Flow with Thresholded Hinge Embedding Loss,"CNN-based Patch Matching for Optical Flow with Thresholded Hinge
Embedding Loss
Christian Bailer1
Kiran Varanasi1
Didier Stricker1,2
German Research Center for Artificial Intelligence (DFKI), 2University of Kaiserslautern"
82eff71af91df2ca18aebb7f1153a7aed16ae7cc,MSU-AVIS dataset : Fusing Face and Voice Modalities for Biometric Recognition in Indoor Surveillance Videos,"MSU-AVIS dataset:
Fusing Face and Voice Modalities for Biometric
Recognition in Indoor Surveillance Videos
Anurag Chowdhury*, Yousef Atoum+, Luan Tran*, Xiaoming Liu*, Arun Ross*
*Michigan State University, USA
+Yarmouk University, Jordan"
82ab819815c86e85128a2a055a0c0fcd1146b696,Sampled Image Tagging and Retrieval Methods on User Generated Content,[cs.CV]  23 Nov 2016
82752700f496d4575163b2c59a547d24eb916baf,Similarity Search on Spatio-Textual Point Sets,"Series ISSN: 2367-2005
0.5441/002/edbt.2016.31
o1, {shop,jeans}u2, o2, {football,match,stadium}u3, o3, {shop,market}u2, o5, {hurry, tube, time}u1, o4, {tube,ride}u3, o6, {thames,bridge}u3, o7, {bus,ride}spatial thresholdu2, o8, {football,derby}Figure1:STPSJoinqueryscenario.Multipleobjectsarespatiallyortextuallysimilar,butonlyusersu1andu3haveobjectswhicharemutuallysimilar.dayfrom100millionactiveusers.Useractivitiesintheseplatformsgeneratecontentthathastextualcomponent,e.g.,statusupdates,shortmessages,ortags,and,followingthewidespreadadoptionofGPSinmobiledevices,ageospatialcomponent,e.g.,geotaggedtweets,photos,andusercheck-ins.Thus,theactionsofusersaredocumentedbytheirmessagesinsocialnetworksandassuchgenerate“traces”,whichconsistofspatio-textualobjects.Ef‌f‌icientindexingandqueryingofspatio-textualdatahasreceivedalotofattentionoverthepastyears,duetothehighimportanceofsuchcontentinlocation-basedservices,suchasnearbysearchandrecommendations.Inparticu-lar,multipletypesofspatio-textualquerieshavebeenex-tensivelystudied,includingbooleanrangequeries,top-kqueries,k-nearestneighborqueries,andmorerecently,spatio-textualsimilarityjoins[11,7].Nevertheless,inexistingworks,spatio-textualentitiesaretypicallytreatedasisolatedobservations.Atypicalexamplequeryistofindnearbyrestaurantsorhotelsmatchingcertaincriteria.Theworkin[7]dealswithfindingpairsofentitiesthatarebothspatiallycloseandtextuallysimilar.Exampleusecasesarede-duplicatingPoints-of-Interestacrossdatasets,orfindingmatchingphotostakenatroughlythesameloca-tionandhavingsimilartags.Nowconsiderlookingforsimilarusersinsocialnetworks.Here,auserischaracterizedbythemessagestheygenerateand,ifavailable,respectivelocationinformation.Assuch,eachmessagecanbeconsideredaspatio-textualobject,e.g.,ageotaggedphotoortweet.Witheachuserbeingcharacter-"
82ffd9024dd6890e5469b587e3516cd07786d6d4,Using Image Fairness Representations in Diversity-Based Re-ranking for Recommendations,"Using Image Fairness Representations in Diversity-Based
Re-ranking for Recommendations
Chen Karako
Shopify Inc.
90 Rue de la Gauchetiere O.
Montreal, QC H2Z 0B2
Putra Manggala
Shopify Inc.
90 Rue de la Gauchetiere O.
Montreal, QC H2Z 0B2"
82d781b7b6b7c8c992e0cb13f7ec3989c8eafb3d,Robust Facial Expression Recognition Using a State-based Model of Spatially-localized Facial,"REFERENCES
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8291491723d24fd242a3a93248f6475cb084999c,MobileFace: 3D Face Reconstruction with Efficient CNN Regression,"MobileFace: 3D Face Reconstruction
with Ef‌f‌icient CNN Regression
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VisionLabs, Amsterdam, The Netherlands
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Research University, ENS/INRIA/CNRS UMR 8548, Paris, France"
61b17f719bab899dd50bcc3be9d55673255fe102,Detecting Sarcasm in Multimodal Social Platforms,"Detecting Sarcasm in Multimodal Social Platforms
Rossano Schifanella
University of Turin
Corso Svizzera 185
0149, Turin, Italy
Paloma de Juan
Yahoo
29 West 43rd Street
New York, NY 10036
Joel Tetreault
Yahoo
29 West 43rd Street
New York, NY 10036
Liangliang Cao
Yahoo
29 West 43rd Street
New York, NY 10036
inc.com"
617c4e23fc7ca51d98dacb28779214b3e79e9720,Open-Ended Visual Question-Answering,"Open-Ended Visual
Question-Answering
Escola T`ecnica Superior d’Enginyeria de Telecomunicaci´o de Barcelona
Submitted to the Faculty of the
A Degree Thesis
In partial fulfilment
of the requirements for the degree in
SCIENCE AND TELECOMMUNICATION TECHNOLOGIES
ENGINEERING
Author:
Advisors: Xavier Gir´o i Nieto, Santiago Pascual de la Puente
Issey Masuda Mora
Universitat Polit`ecnica de Catalunya (UPC)
June 2016"
610a4451423ad7f82916c736cd8adb86a5a64c59,A Survey on Search Based Face Annotation Using Weakly Labelled Facial Images,"Volume 4, Issue 11, November 2014                                  ISSN: 2277 128X
International Journal of Advanced Research in
Computer Science and Software Engineering
Research Paper
Available online at: www.ijarcsse.com
A Survey on Search Based Face Annotation Using Weakly
Labelled Facial Images
Shital A. Shinde*, Prof. Archana Chaugule
Department of Computer Engg, DYPIET Pimpri,
Savitri Bai Phule Pune University, Maharashtra India"
61c07d7387dcbfb8fa697f15316e3b265d78a2fa,Multi-modal Approach for Affective Computing,"Multi-modal Approach for Affective Computing
Siddharth1,2, Tzyy-Ping Jung2 and Terrence J. Sejnowski2"
61c4969c78cff37357ac794af5ac8e439751b39f,Midrange Geometric Interactions for Semantic Segmentation Constraints for Continuous Multi-label Optimization,"Int J Comput Vis
DOI 10.1007/s11263-015-0828-7
Midrange Geometric Interactions for Semantic Segmentation
Constraints for Continuous Multi-label Optimization
Julia Diebold1 · Claudia Nieuwenhuis2 · Daniel Cremers1
Received: 1 June 2014 / Accepted: 15 May 2015
© Springer Science+Business Media New York 2015"
61366c2eed49519e3adef44e8b7146db1fcc2113,Convex NMF on Non-Convex Massiv Data,"Convex NMF on Non-Convex Massiv Data
Kristian Kersting1 and Mirwaes Wahabzada1 and Christian Thurau2 and Christian Bauckhage2
Knowledge Discovery Department, 2Vision and Social Media Group
Fraunhofer IAIS, Schloss Birlinghoven, 53754 Sankt Augustin, Germany"
614524b27188bb8869ec7a5b374c2a9874f96ec5,A new covariance estimate for Bayesian classifiers in biometric recognition,"A New Covariance Estimate for Bayesian
Classifiers in Biometric Recognition
Carlos E. Thomaz, Duncan F. Gillies, and Raul Q. Feitosa"
61f04606528ecf4a42b49e8ac2add2e9f92c0def,Deep Deformation Network for Object Landmark Localization,"Deep Deformation Network for Object Landmark
Localization
Xiang Yu, Feng Zhou and Manmohan Chandraker
NEC Laboratories America, Department of Media Analytics"
610c341985633b2d31368f8642519953c39ff7e8,Computational Load Balancing on the Edge in Absence of Cloud and Fog,"Computational Load Balancing on the Edge in Absence of Cloud
nd Fog
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Sthapit, S, Thompson, J, Robertson, NM & Hopgood, J 2018, 'Computational Load Balancing on the Edge
in Absence of Cloud and Fog' IEEE Transactions on Mobile Computing. DOI: 10.1109/TMC.2018.2863301
Digital Object Identifier (DOI):
0.1109/TMC.2018.2863301
Link:
Link to publication record in Edinburgh Research Explorer
Document Version:
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Published In:
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ontent complies with UK legislation. If you believe that the public display of this file breaches copyright please"
619eaaa60f0194d456591983a6f26b04cd9e9a52,"Munafo, M. (2017). Impaired Recognition of Basic Emotions from Facial Expressions in Young People with Autism Spectrum Disorder: Assessing the Importance of Expression","Griffiths, S. L., Jarrold, C., Penton-Voak, I., Woods, A., Skinner, A., &
Munafo, M. (2017). Impaired Recognition of Basic Emotions from Facial
Expressions in Young People with Autism Spectrum Disorder: Assessing the
Importance of Expression Intensity. Journal of Autism and Developmental
Disorders. DOI: 10.1007/s10803-017-3091-7
Publisher's PDF, also known as Version of record
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version using the reference above. Full terms of use are available:
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61c256071d3344cce6602afcf4f6c27593a2d93e,Online pedestrian group walking event detection using spectral analysis of motion similarity graph,"Online Pedestrian Group Walking Event Detection Using Spectral Analysis of
Motion Similarity Graph
Vahid Bastani, Damian Campo, Lucio Marcenaro and Carlo Regazzoni
University of Genoa, DITEN
{vahid.bastani, {lucio.marcenaro,
Via all’Opera Pia, 11A - 16145 Genova (GE)"
610c62bd933c82b609555692ca7f8e9b77934034,DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data,"Arango-Argoty et al. Microbiome  (2018) 6:23
DOI 10.1186/s40168-018-0401-z
SO F T WA R E
DeepARG: a deep learning approach for
predicting antibiotic resistance genes from
metagenomic data
Gustavo Arango-Argoty1, Emily Garner2, Amy Pruden2, Lenwood S. Heath1, Peter Vikesland2 and Liqing Zhang1*
Open Access"
61c76ac08113e3f732e65a3593471c3e94ddda7b,Active Shape Models with Invariant Optimal Features (IOF-ASMs),"Active Shape Models
with Invariant Optimal Features (IOF-ASMs)
Federico Sukno1,2, Sebasti´an Ord´as1, Costantine Butakoff1,
Santiago Cruz2, and Alejandro Frangi1
Department of Technology, Pompeu Fabra University, Barcelona, Spain
Aragon Institute of Engineering Research, University of Zaragoza, Spain"
61f4e08b938986ea80f711c73cadbc84e1811181,Development of an Efficient Face Recognition System based on Linear and Nonlinear Algorithms,"Development of an Efficient Face Recognition System based on Linear and Nonlinear Algorithms
{tag}                                                 {/tag}
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 134
Number 7
Year of Publication: 2016
Authors:
Filani Araoluwa S., Adetunmbi Adebayo O.
10.5120/ijca2016907932
{bibtex}2016907932.bib{/bibtex}"
619f9c1552f8f4f7c5927a7369c79e34d6294083,A Volumetric/Iconic Frequency Domain Representation for Objects With Application for Pose Invariant Face Recognition,"AVolumetric/IconicFrequencyDomain
RepresentationforObjects
withapplicationfor
PoseInvariantFaceRecognition

AppearedinIEEETrans.onPatternAnalysisandMachineIntelligence
Vol.,No.,May
		,pp.	-.
JezekielBen-ArieandDibyenduNandy
DepartmentofElectricalEngineeringandComputerScience
TheUniversityofIllinoisatChicago
ContactAddress:
Dr.JezekielBen-Arie
TheUniversityofIllinoisatChicago
DepartmentofElectricalEngineeringandComputerScience(M/C
)

SouthMorganStreetChicago,IL-
Phone:(
)		-
Fax:(
)		-

ThisworkwassupportedbytheNationalScienceFoundationunderGrantNo.IRI-		
ndGrantNo.IRI-	

	."
6198c7d579726fcc0d4c62ac156b503fc9e39251,SEARCHINGWITH EXPECTATIONS Harsimrat Sandhawalia,"978-1-4244-4296-6/10/$25.00 ©2010 IEEE
ICASSP 2010"
61dfebbb02dad16b56cd9e6c54b5da3ab41caf1c,Exploiting Local Class Information in Extreme Learning Machine,"Iosifidis, A., Tefas, A., & Pitas, I. (2014). Exploiting Local Class Information
in Extreme Learning Machine. Paper presented at International Joint
Conference on Computational Intelligence (IJCCI), Rome, Italy.
Peer reviewed version
Link to publication record in Explore Bristol Research
PDF-document
University of Bristol - Explore Bristol Research
General rights
This document is made available in accordance with publisher policies. Please cite only the published
version using the reference above. Full terms of use are available:
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618c13f1e13cc5346ed5c069a77acaa720b6a1a8,Learning More Universal Representations for Transfer-Learning,"SUBMISSION TO PAMI, SEPTEMBER 2018
Learning More Universal Representations
for Transfer-Learning
Youssef Tamaazousti, Hervé Le Borgne, Céline Hudelot, Mohamed-El-Amine Seddik
nd Mohamed Tamaazousti"
610e0bee525a6573932e077f091505f54a5c4ede,"The Wisdom of MaSSeS: Majority, Subjectivity, and Semantic Similarity in the Evaluation of VQA","Majority, Subjectivity, and Semantic Similarity in the Evaluation of VQA
The Wisdom of MaSSeS:
Shailza Jolly∗
SAP SE, Berlin
TU Kaiserslautern
Sandro Pezzelle∗
SAP SE, Berlin
CIMeC - University of Trento
Tassilo Klein
SAP SE, Berlin
Andreas Dengel
DFKI, Kaiserslautern
CS Department, TU Kaiserslautern
Moin Nabi
SAP SE, Berlin"
6155d504d59c52dc3a6b8ad6aeae8bf249afd5ac,Analysis of Feature Fusion Based on HIK SVM and Its Application for Pedestrian Detection,Hindawi Publishing Corporation
611f9faa6f3aeff3ccd674d779d52c4f9245376c,Multiresolution models for object detection,"Multiresolution models for object detection
Dennis Park, Deva Ramanan, and Charless Fowlkes
UC Irvine, Irvine CA 92697, USA,"
61e97d8440627bdc9772b3b2083c65f44a51107d,Oxytocin and vasopressin in the human brain: social neuropeptides for translational medicine,"R E V I E W S
Oxytocin and vasopressin in the
human brain: social neuropeptides
for translational medicine
Andreas Meyer‑Lindenberg*, Gregor Domes‡, Peter Kirsch* and Markus Heinrichs‡"
61f0cb2e3fdc6a5d0719184e51d2dc483a945ac1,Bilinear Attention Networks,"Bilinear Attention Networks
Jin-Hwa Kim1∗, Jaehyun Jun2, Byoung-Tak Zhang2,3
SK T-Brain, 2Seoul National University, 3Surromind Robotics"
6163381244823241373f6741a282f2c4a868b59c,Multimodal biometrics for identity documents (MBioID).,"Multimodal Biometrics for Identity
Documents 1
State-of-the-Art
Research Report
PFS 341-08.05
(Version 2.0)
Damien Dessimoz
Prof. Christophe Champod
Jonas Richiardi
Dr. Andrzej Drygajlo
{damien.dessimoz,
{jonas.richiardi,
June 2006
This project was sponsored by the Foundation Banque Cantonale Vaudoise."
61c425bdda0e053074e96c3e6761ff1d7e0dd469,A Framework for Understanding Unintended Consequences of Machine Learning,"A Framework for Understanding Unintended Consequences of Machine Learning
Harini Suresh
John V. Guttag"
61764c068ad7d2ec988e6ec315d6ed2ed7489c2e,PhD Forum: Dynamic Camera Positioning and Reconfiguration for Multi Camera Networks,"Dynamic Camera Positioning and
Reconfiguration for Multi Camera
Networks
Krishna Reddy Konda
Advisor: Dr Nicola Conci
February 2015"
617b719e6c31cdfe7c5c485a755435b95f0c4991,Visual Classification of Images by Learning Geometric Appearances Through Boosting,"Visual Classification of Images by Learning
Geometric Appearances through Boosting
Martin Antenreiter, Christian Savu-Krohn, and Peter Auer
Chair of Information Technology (CiT)
University of Leoben, Austria"
61a5ba0935e31dbc4cd448504f9b15455922c1f4,"Pengenalan wajah adalah salah satu teknologi biometrik yang telah banyak diaplikasikan dalam system security selain pengenalan retina mata, pengenalan sidik jari dan iris mata. Dalam aplikasinya sendiri pengenalan wajah menggunakan sebuah kamera untuk menangkap wajah seseorang kemudian dibandingkan ","Jarot Dwiprasetyo1, Mochamad Hariadi2
,2Fakultas Teknik Industri, Elektro,  Institut Teknologi Sepuluh Nopember, Surabaya 60111
E-mail :1 ,2
ABSTRAK
Seminar Nasional Teknologi Informasi & Komunikasi Terapan 2012 (Semantik 2012)
Semarang, 23 Juni 2012
ISBN 979 - 26 - 0255 -  0
PENGENALAN WAJAH DAN KOMPUTER VISION
Pengenalan wajah adalah salah satu teknologi biometrik yang telah banyak diaplikasikan dalam  system security selain
pengenalan retina mata, pengenalan sidik jari dan iris mata. Dalam aplikasinya sendiri pengenalan wajah menggunakan
sebuah kamera untuk menangkap wajah seseorang kemudian dibandingkan dengan wajah yang sebelumnya telah disimpan
di dalam database tertentu. Ada beberapa macam metoda pengenalan wajah yaitu neural network, jaringan syaraf tiruan,
neuro fuzzy adaptif dan eigenface. Secara khusus dalam paper ini metoda yang akan dijelaskan adalah metoda eigenface.
Pada paper  akhir ini menawarkan metode Eigenface, dan menggunakan webcam untuk menangkap gambar secara real-
time. Metode eigenface berfungsi untuk menghitung eigenvalue dan eigenvector yang akan digunakan sebagai fitur dalam
melakukan pengenalan. Metode Euclidean distance digunakan untuk mencari jarak dengan data fitur yang telah didapat ,
dan jarak terkecil adalah hasilnya.
Kata kunci : eigenface, gambar, pengenalan wajah, PCA
.   PENDAHULUAN
Teknologi biometrik adalah metode otomatis untuk mengidentifikasi manusia berdasarkan beberapa karakteristik biologis"
61c4b35443b152679c923d5db6c26daaec304172,Fast and stable human detection using multiple classifiers based on subtraction stereo with HOG features,"Fast and Stable Human Detection Using Multiple Classifiers
Based on Subtraction Stereo with HOG Features
Makoto Arie, Alessandro Moro, Yuma Hoshikawa, Toru Ubukata, Kenji Terabayashi, Kazunori Umeda"
614a7c42aae8946c7ad4c36b53290860f6256441,Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks,"Joint Face Detection and Alignment using
Multi-task Cascaded Convolutional Networks
Kaipeng Zhang, Zhanpeng Zhang, Zhifeng Li, Senior Member, IEEE, and Yu Qiao, Senior Member, IEEE"
61b0cfd75f5bce59cf79abb7b602e404fa5584e7,Person Re-Identification by Semantic Region Representation and Topology Constraint,"IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
Person Re-Identification by Semantic Region
Representation and Topology Constraint
Jianjun Lei, Senior Member, IEEE, Lijie Niu, Huazhu Fu, Senior Member, IEEE, Bo Peng,
Qingming Huang, Fellow, IEEE, and Chunping Hou"
617253f275f14490c61dc9d8cb23ceb9c9d4ba35,A coarse-to-fine curvature analysis-based rotation invariant 3D face landmarking,"A coarse-to-fine curvature analysis-based rotation invariant 3D face
landmarking
Przemyslaw Szeptycki, Mohsen Ardabilian and Liming Chen"
61ab6e3f999269731b26155605f38bea6d3557f2,Unsupervised object discovery and co-localization by deep descriptor transformation,"Noname manuscript No.
(will be inserted by the editor)
Unsupervised Object Discovery and Co-Localization
y Deep Descriptor Transforming
Xiu-Shen Wei · Chen-Lin Zhang · Jianxin Wu · Chunhua Shen ·
Zhi-Hua Zhou
Received: date / Accepted: date"
6106028c73d22570a01212814e1e4f4edb4abed6,Counting moving people in crowds using motion statistics of feature-points,"Multimed Tools Appl
DOI 10.1007/s11042-013-1367-2
Counting moving people in crowds using motion
statistics of feature-points
Mahdi Hashemzadeh· Gang Pan· Min Yao
© Springer Science+Business Media New York 2013"
614f4f8fe47e7c0bcf64aa0ad39dc371e4b4ab7b,promoting access to White Rose research papers,"promoting access to White Rose research papers
Universities of Leeds, Sheffield and York
http://eprints.whiterose.ac.uk/
This is an author produced version of a paper published in Journal of Autism
nd Developmental Disorders.
White Rose Research Online URL for this paper:
http://eprints.whiterose.ac.uk/10325
Published paper
Freeth, M., Chapman, P., Ropar, D., Mitchell, P. (2010) Do gaze cues in complex
scenes capture and direct the attention of high functioning adolescents with ASD?
evidence from eye-tracking, Journal of Autism and Developmental Disorders (In
Press)
http://dx.doi.org/10.1007/s10803-009-0893-2
White Rose Research Online"
61183c50509f7ad284ae52ef4768aced2af3d260,Data Fusion For Biometric Verification System,"Data Fusion For Biometric Verification System
RICHARD A. WASNIOWSKI
Computer Science Department
California State University – Dominguez Hills
Carson, CA 90747, USA"
618d3ad69c677016547098e01b9c6e94c260de1d,What are customers looking at?,"What are customers looking at?∗
Xiaoming Liu Nils Krahnstoever
Ting Yu
Peter Tu
Visualization and Computer Vision Lab
General Electric Global Research Center
Niskayuna, NY, 12309, USA"
617a6935643615f09ef2b479609baa0d5f87cd67,To Be Taken At Face Value ? Computerised Identification,"Information and Communications Technology Law
To Be Taken At Face Value?
Computerised Identification
Michael Bromby
Joseph Bell Centre for Forensic Statistics and Legal Reasoning
Glasgow Caledonian University and University of Edinburgh
Scientific  evidence  such  as  fingerprints,  blood,  hair  and  DNA  samples  are  often
presented  during  legal  proceedings.    Without  such  evidence,  a  description  provided  by
the  victim  or  any  eyewitnesses  is  often  the  only  means  to    identify  a  suspect.    With  the
dvent  of  closed  circuit  television  (CCTV),  many  crimes  are  now  recorded  by
ameras  in  the  public  or  private  domain,  leading  to  a  new  form  of  forensic
identification  –  facial  biometrics.  Decisions  on  how  to  view  and  interpret  biometric
evidence  are  important  for  both  prosecution  and  defence,  not  least  for  the  judge  and
jury  who  must  decide  the  case.    A  jury  may  accept  eyewitnesses  as  reliable  sources  of
evidence  more  readily
False
eyewitness  accounts  appear  reliable  when  confidently  presented  to  a  mock  jury.    The
decision-making  process  of  the  judge  and  jury  may  be  seriously  flawed  if  an
eyewitness  has  made  a  genuine  mistake.    Using  computerised  recognition,  the  judicial
decision  of  whether  to  accept  an  alibi  or  whether  to  accept  the  eyewitness  account"
617159ad6dfbadd396751058a1bb79e663b44a52,A Photometric Stereo Approach to Face Recognition,"A Photometric Stereo Approach to
Face Recognition
Roger Woodman
[ www.brl.ac.uk/~rwoodman ]
A dissertation submitted in partial fulfilment of the requirements of the University of the
West of England, Bristol for the Degree of Master of Science
Faculty of Computing, Engineering and Mathematical Sciences
November 2007"
61692cffff60568da43780df38876c11390ccdc8,Gabor Orientation Histogram for Face Representation and Recognition,"Gabor Orientation Histogram for Face
Representation and Recognition
Jun Yi and Fei Su"
61bab86023de164bca3e35fc22944a7262970e1d,Child Facial Expression Detection,"CHILD FACIAL EXPRESSION
DETECTION
Eden Benhamou
Deborah Wolhandler
Supervisors:
Alon Zvirin
Michal Zivan
Spring 2018"
e70ebb9971b1fece8760293e61ed42e2372b1d19,An Evaluation of Large-scale Methods for Image Instance and Class Discovery,"An evaluation of large-scale methods
for image instance and class discovery
Matthijs Douze, Herv´e J´egou, Jeff Johnson
Facebook AI Research
ontact:"
e7cac91da51b78eb4a28e194d3f599f95742e2a2,"Positive Feeling, Negative Meaning: Visualizing the Mental Representations of In-Group and Out-Group Smiles","RESEARCH ARTICLE
Positive Feeling, Negative Meaning:
Visualizing the Mental Representations of In-
Group and Out-Group Smiles
Andrea Paulus1☯*, Michaela Rohr1☯, Ron Dotsch2,3, Dirk Wentura1
Saarland University, Saarbrücken, Germany, 2 Utrecht University, Utrecht, the Netherlands,
Behavioural Science Institute, Radboud University, Nijmegen, the Netherlands
☯ These authors contributed equally to this work."
e7265c560b3f10013bf70aacbbf0eb4631b7e2aa,Look at Boundary: A Boundary-Aware Face Alignment Algorithm,"(68	points)	COFW	(29	points)	AFLW	(19	points)	Figure1:Thefirstcolumnshowsthefaceimagesfromdifferentdatasetswithdifferentnumberoflandmarks.Thesecondcolumnillustratestheuniversallydefinedfacialboundariesestimatedbyourmethods.Withthehelpofboundaryinformation,ourapproachachieveshighaccuracylocalisationresultsacrossmultipledatasetsandannotationprotocols,asshowninthethirdcolumn.Differenttofacedetection[45]andrecognition[75],facealignmentidentifiesgeometrystructureofhumanfacewhichcanbeviewedasmodelinghighlystructuredout-put.Eachfaciallandmarkisstronglyassociatedwithawell-definedfacialboundary,e.g.,eyelidandnosebridge.However,comparedtoboundaries,faciallandmarksarenotsowell-defined.Faciallandmarksotherthancornerscanhardlyremainthesamesemanticallocationswithlargeposevariationandocclusion.Besides,differentannotationschemesofexistingdatasetsleadtoadifferentnumberoflandmarks[28,5,66,30](19/29/68/194points)andanno-tationschemeoffuturefacealignmentdatasetscanhardlybedetermined.Webelievethereasoningofauniquefacial"
e79847c3bf3ffefe9304e212d8dda7aaa29eaada,From Deterministic to Generative: Multi-Modal Stochastic RNNs for Video Captioning,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
From Deterministic to Generative: Multi-Modal
Stochastic RNNs for Video Captioning
Jingkuan Song, Yuyu Guo, Lianli Gao, Xuelong Li, IEEE Fellow Alan Hanjalic, IEEE Fellow Heng Tao Shen"
e7928bd33d09fd00a588617736b102063ca9d070,A Non-Technical Survey on Deep Convolutional Neural Network Architectures,"A Non-Technical Survey on Deep Convolutional
Neural Network Architectures
Felix Altenberger
Technical University of Munich
85748 Garching, Germany
Email:
Claus Lenz
Cognition Factory GmbH
80797 Munich, Germany
Email:"
e74bddccc40e65b31081a1599cbe7385d5d3e1c0,Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering,"Bottom-Up and Top-Down Attention for Image Captioning
nd Visual Question Answering
Peter Anderson1∗
Xiaodong He2
Chris Buehler3
Damien Teney4
Mark Johnson5
Stephen Gould1
Lei Zhang3
Australian National University 2JD AI Research 3Microsoft Research 4University of Adelaide 5Macquarie University"
e79a34f9942172ad97c5fadca3701db3e29d32e2,Fusiform Correlates of Facial Memory in Autism,"NIH Public Access
Author Manuscript
Behav Sci (Basel). Author manuscript; available in PMC 2014 April 21.
Published in final edited form as:
Behav Sci (Basel). ; 3(3): 348–371. doi:10.3390/bs3030348.
Fusiform Correlates of Facial Memory in Autism
Haley G. Trontel1, Tyler C. Duffield2, Erin D. Bigler2,3,4,*, Alyson Froehlich5, Molly B.D.
Prigge5, Jared A. Nielsen5, Jason R. Cooperrider5, Annahir N. Cariello5, Brittany G.
Travers6, Jeffrey S. Anderson7, Brandon A. Zielinski8, Andrew Alexander6,11, Nicholas
Lange9,10, and Janet E. Lainhart11,12
Department of Psychology, University of Montana, Missoula, MT 59812, USA;
Department of Psychology, Brigham Young University, Provo, UT 84604,
USA; (T.C.D.); (E.D.B.) 3Neuroscience Center,
Brigham Young University, Provo, UT 84604, USA 4The Brain Institute of Utah, University of
Utah, Salt Lake City, UT 84112, USA 5Department of Psychiatry, University of Utah, Salt Lake
City, UT 84112, USA; (A.F.);
(M.B.D.P); (J.A.N.); (J.R.C.);
(A.N.C.) 6Department of Medical Physics, University of Wisconsin,
Madison, WI 53706, USA; (B.G.T.); (A.A.)
7Department of Radiology, University of Utah, Salt Lake City, UT 84112, USA;"
e72e852dca333d66559dbcfb050140fac5affe4f,Anatomical Landmark Tracking by One-shot Learned Priors for Augmented Active Appearance Models,"DataωLDAFull AAMAUGMENTED AAMSubset AAMLocal TrackingextractionlearnmodelOne-shotDetectortraintrainLower LegContraintsEipolar ConstraintsDistance ConstraintsTorso ConstraintsFigure1:BasedonfewannotatedbiplanarrecordedtrainingimagesanAugmentedAAM(HaaseandDenzler,2013)istrained,consistingofanatomicalknowledge,afullmulti-viewAAMmodel,anAAMmodelofthetorsoland-marksubset,epipolarconstraintsandalocaltracking-by-detectionpriorintroducedinthispaper.In(HaaseandDenzler,2013)ActiveAppearanceModels(AAM)(Cootesetal.,2001)havebeenap-pliedtoseveralbipedalbirdlocomotiondatasets.OnecrucialconclusionofthisworkisthatAAMsneedsubstantialconstraintsfromvarioussources.Withthesupportofadditionalanatomicalknowledge,i.e.re-gionsegmentation,multi-viewacquisition,andlocallandmarktracking,fortheanimalslowerlimbsys-tem,theresultingAugmentedAAM(HaaseandDen-zler,2013)providesrobustresultsforthemajorityoftheprocesseddatasets.However,theappliedonlinetrackingapproach(Amthoretal.,2012)suffersfrom246MothesO.andDenzlerJ.AnatomicalLandmarkTrackingbyOne-shotLearnedPriorsforAugmentedActiveAppearanceModels.DOI:10.5220/0006133302460254InProceedingsofthe12thInternationalJointConferenceonComputerVision,ImagingandComputerGraphicsTheoryandApplications(VISIGRAPP2017),pages246-254ISBN:978-989-758-227-1Copyrightc(cid:13)2017bySCITEPRESS–ScienceandTechnologyPublications,Lda.Allrightsreserved"
e7dc0d5545e6e028b03a82d2f5bb3bccc995a0d7,A New Fast and Efficient HMM-Based Face Recognition System Using a 7-State HMM Along With SVD Coefficients,"Archive of SID
A  New  Fast  and  Efficient  HMM-Based  Face  Recognition
System Using a 7-State HMM Along With SVD Coefficients
H. Miar-Naimi* and P. Davari*"
e7b6887cd06d0c1aa4902335f7893d7640aef823,Modeling of facial aging and kinship: A survey,"Modelling of Facial Aging and Kinship: A Survey
Markos Georgopoulos, Yannis Panagakis, and Maja Pantic,"
e76798bddd0f12ae03de26b7c7743c008d505215,Joint Max Margin and Semantic Features for Continuous Event Detection in Complex Scenes,
e74c4cf90c5bbb88a8ae77aaa5709984f7e6a80f,Viewpoint Unconstrained Face Recognition Based on Affine Local Descriptors and Probabilistic Similarity,"J Inf Process Syst, Vol.11, No.4, pp.643~654, December 2015
ISSN 1976-913X (Print)
ISSN 2092-805X (Electronic)
Viewpoint Unconstrained Face Recognition Based on
Affine Local Descriptors and Probabilistic Similarity
Yongbin Gao* and Hyo Jong Lee*,**"
e741bced5c1d6368530ce8cdfcdcaa0d9e07c31c,On-Board Detection of Pedestrian Intentions,"Article
On-Board Detection of Pedestrian Intentions
Zhijie Fang 1,2,*, David Vázquez 2 and Antonio M. López 1,2
Computer Science Department, Universitat Autònoma Barcelona (UAB), 08193 Barcelona, Spain
Computer Vision Center (CVC), Universitat Autònoma Barcelona (UAB), 08193 Barcelona, Spain;
(D.V.); (A.M.L.)
* Correspondence: Tel.: +34-93-581-1828
Received: 4 August 2017; Accepted: 20 September 2017; Published: 23 September 2017"
e778e618862ea1c9a97e89e942228c4de98c9a86,Automated Pruning for Deep Neural Network Compression,"Automated Pruning for Deep Neural Network Compression
Franco Manessi1†, Alessandro Rozza1†, Simone Bianco2, Paolo Napoletano2, Raimondo Schettini2
lastminute.com group — Strategic Analytics
{first name.last
Universit`a degli Studi di Milano Bicocca — DISCo {first name.last"
e74e2004d8b7357c35d727cb4c92ca97142759f0,Comparison of Image Transform-Based Features for Visual Speech Recognition in Clean and Corrupted Videos,"Comparison of Image Transform-Based Features for Visual Speech
Recognition in Clean and Corrupted Videos
Seymour, R., Stewart, D., & Ji, M. (2008). Comparison of Image Transform-Based Features for Visual Speech
Recognition in Clean and Corrupted Videos. EURASIP Journal on Image and Video Processing, 2008, 1-9.
[810362]. DOI: 10.1155/2008/810362
Published in:
EURASIP Journal on Image and Video Processing
Document Version:
Peer reviewed version
Queen's University Belfast - Research Portal:
Link to publication record in Queen's University Belfast Research Portal
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Copyright for the publications made accessible via the Queen's University Belfast Research Portal is retained by the author(s) and / or other
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ensure that content in the Research Portal does not infringe any person's rights, or applicable UK laws. If you discover content in the
Research Portal that you believe breaches copyright or violates any law, please contact
Download date:06. Nov. 2018"
e7f00f6e5994c5177ec114ee353cc7064d40a78f,Back to Basic: Do Children with Autism Spontaneously Look at Screen Displaying a Face or an Object?,"Hindawi Publishing Corporation
Autism Research and Treatment
Volume 2013, Article ID 835247, 7 pages
http://dx.doi.org/10.1155/2013/835247
Research Article
Back to Basic: Do Children with Autism Spontaneously Look at
Screen Displaying a Face or an Object?
Marie Guimard-Brunault,1,2,3,4 Nadia Hernandez,3 Laetitia Roché,3 Sylvie Roux,3
Catherine Barthélémy,1,2,3 Joëlle Martineau,2,3 and Frédérique Bonnet-Brilhault1,2,3
CHRU de Tours, Centre Universitaire de P´edopsychiatrie, 2 Boulevard Tonnell´e, 37044 Tours Cedex 9, France
Universit´e Franc¸ois Rabelais de Tours, 60 rue du Plat D’Etain, 37020 Tours Cedex 1, France
UMR Inserm U 930, ´Equipe 1: Imagerie et Cerveau, Universit´e Franc¸ois Rabelais de Tours, Tours, France
UMR Inserm U 930, ´Equipe 1: Imagerie et Cerveau, CHRU de Tours-Hˆopital Bretonneau, 2 boulevard Tonnell´e,
Bˆat B1A, 1er Etage, 37044 Tours Cedex 9, France
Correspondence should be addressed to Marie Guimard-Brunault;
Received 29 June 2013; Revised 29 September 2013; Accepted 21 October 2013
Academic Editor: Elizabeth Aylward
Copyright © 2013 Marie Guimard-Brunault et al. This is an open access article distributed under the Creative Commons
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is
properly cited."
e78394213ae07b682ce40dc600352f674aa4cb05,Expression-invariant three-dimensional face recognition,"Expression-invariant three-dimensional face recognition
Alexander M. Bronstein
Email:
Michael M. Bronstein
Ron Kimmel
Computer Science Department,
Technion – Israel Institute of Technology,
Haifa 32000, Israel
One of the hardest problems in face recognition is dealing with facial expressions. Finding an
expression-invariant representation of the face could be a remedy for this problem. We suggest
treating faces as deformable surfaces in the context of Riemannian geometry, and propose to ap-
proximate facial expressions as isometries of the facial surface. This way, we can define geometric
invariants of a given face under different expressions. One such invariant is constructed by iso-
metrically embedding the facial surface structure into a low-dimensional flat space. Based on this
pproach, we built an accurate three-dimensional face recognition system that is able to distinguish
etween identical twins under various facial expressions. In this chapter we show how under the
near-isometric model assumption, the dif‌f‌icult problem of face recognition in the presence of facial
expressions can be solved in a relatively simple way.
0.1 Introduction
It is well-known that some characteristics or behavior patterns of the human body are strictly"
e73f1b6143dabf90fb7a45923b7808a5c35bfbcf,DeepMoTIon: Learning to Navigate Like Humans,
e72f626074252b7e17ebc48d9fd4a4cd9d231359,Deep Feature Learning for Medical Image 2 Analysis with Convolutional Autoencoder 3 Neural Network,"IEEE TRANSACTIONS ON BIG DATA, VOL. 3, NO. X, XXXXX 2017
Deep Feature Learning for Medical Image
Analysis with Convolutional Autoencoder
Neural Network
Index Terms—Convolutional autoencoder neural network, lung nodule, feature learning, hand-craft feature, unsupervised learning
6 1 INTRODUCTION
Min Chen, Senior Member, IEEE, Xiaobo Shi, Yin Zhang, Senior Member, IEEE,
Di Wu, Senior Member, IEEE, and Mohsen Guizani, Fellow, IEEE"
e75cd1379b07d77358e5a2f4a042f624066603b6,Weakly-Supervised Learning of Visual Relations,"Weakly-supervised learning of visual relations
Julia Peyre1,2
Ivan Laptev1,2
Cordelia Schmid2,4
Josef Sivic1,2,3"
e746c8eec81384bd37dede9700be9c8a3700f936,Context Encoding for Semantic Segmentation,"Context Encoding for Semantic Segmentation
Hang Zhang 1,2 Kristin Dana 1
Jianping Shi 3 Zhongyue Zhang 2
Xiaogang Wang 4 Ambrish Tyagi 2 Amit Agrawal 2
Rutgers University 2Amazon Inc 3SenseTime 4The Chinese University of Hong Kong"
e72d35ae7c1f477ce4341a5fb3a15bcfe0481a0e,Behavioral Consistency Extraction for Face Verification,"Behavioral Consistency Extraction for Face
Verification
Hui Fang and Nicholas Costen
Manchester Metropolitan University
Department of Computing and Mathematics,
Manchester, U.K."
e7906370eae8655fb69844ae1a3d986c9f37c902,Face recognition using Deep Learning,"POLYTECHNIC UNIVERSITY OF CATALONIA
MASTER THESIS
Face recognition using Deep
Learning
Author:
Xavier SERRA
Advisor:
Javier CASTÁN
Tutor:
Sergio ESCALERA
This master thesis has been developed at GoldenSpear LLC
January 2017"
e719e1ed86bf2214512d5631e31716effe2e23d2,Learning to Estimate 3D Human Pose and Shape from a Single Color Image,"Learning to Estimate 3D Human Pose and Shape from a Single Color Image
Georgios Pavlakos1, Luyang Zhu2, Xiaowei Zhou3, Kostas Daniilidis1
University of Pennsylvania 2 Peking University 3 Zhejiang University"
e746447afc4898713a0bcf2bb560286eb4d20019,Leveraging Virtual and Real Person for Unsupervised Person Re-identification,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, NOVEMBER 2018
Leveraging Virtual and Real Person for
Unsupervised Person Re-identification
Fengxiang Yang, Zhun Zhong, Zhiming Luo, Sheng Lian, and Shaozi Li"
e7f4951c1106bff0460665ef67d11fb9c2d07c41,Machine Vision-Based Analysis of Gaze and Visual Context : an Application to Visual Behavior of Children with Autism Spectrum Disorders by,"Machine Vision-Based Analysis of Gaze and
Visual Context: an Application to Visual
Behavior of Children with Autism Spectrum
Disorders
Basilio Noris
MSc/BSc in Computer Science, Université de Lausanne, 2005
Dissertation
Submitted to the School of Engineering
in partial fulfillment of the requirements for the degree of
Doctor of Philosophy
Ecole Polytechnique Fédérale de Lausanne (EPFL)
t the
(Swiss Federal Insitute of Technology Lausanne)
Supervisor:
Prof. Aude Billard
Examiners:
Prof. Thierry Pun
Prof. Jacqueline Nadel
Prof. Nouchine Hadjikhani
President of the jury:"
e7721f40fed05aae4d49d84e9ebc94ced7015aac,Design and Implementation of Resampling Techniques for Face Recognition using Classical LDA Algorithm in MATLAB,"International Journal of Computer Applications (0975 – 8887)
Volume 152 – No.6, October 2016
Design and Implementation of Resampling Techniques
for Face Recognition using Classical LDA Algorithm in
MATLAB
S. R Bichwe
Dept. of Electronics &
Communication
Kavikulguru Institute of
Technology & Science,
Ramtek, Maharashtra
Sugandha Satija
Dept. of Information
Technology
Kavikulguru Institute of
Technology & Science,
Ramtek, Maharashtra
Madhavi R. Bichwe
Dept of Computer Science &
Technology"
e72c5fb54c3d14404ebd1bf993e51d0056f6c429,Tempered Adversarial Networks,
7b8aa3ebeae17e5266dac23e87f603a5d5f7b1e3,Open Set Logo Detection and Retrieval,"Open Set Logo Detection and Retrieval
Andras T¨uzk¨o1, Christian Herrmann1,2, Daniel Manger1, J¨urgen Beyerer1,2
Fraunhofer IOSB, Karlsruhe, Germany
Karlsruhe Institute of Technology KIT, Vision and Fusion Lab, Karlsruhe, Germany
Keywords:
Logo Detection, Logo Retrieval, Logo Dataset, Trademark Retrieval, Open Set Retrieval, Deep Learning."
7b965bf132e5971dfa95c67bc7685b73b32e07df,Pedestrian Detection via Mixture of CNN Experts and Thresholded Aggregated Channel Features,"Pedestrian Detection via Mixture of CNN Experts and thresholded Aggregated
Channel Features
Ankit Verma, Ramya Hebbalaguppe, Lovekesh Vig, Swagat Kumar, and Ehtesham Hassan
TCS Innovation Labs,
New Delhi"
7b522c5d6d2d0699c4183a543b8e65b1a66d9e74,Understanding critical factors in appearance-based gender categorization,"Understanding Critical Factors in
Appearance-based Gender Categorization
Enrico Grosso, Andrea Lagorio, Luca Pulina, and Massimo Tistarelli
POLCOMING – University of Sassari
Viale Mancini, 5 – 07100 Sassari, Italy"
7b07a87ff71b85f3493d1944034a960917b8482f,Alternating BackPropagation for Generator Network,"Alternating Back-Propagation for Generator Network
Tian Han†, Yang Lu†, Song-Chun Zhu, and Ying Nian Wu
Department of Statistics, University of California, Los Angeles, USA"
7b6b49adf60d56d1b33b428fdf66aff7426fca6e,Survey on Deep Learning Techniques for Person Re-Identification Task,
7b66dababebd800e95d23a1fde299d44a52e98ed,Dual Recurrent Attention Units for Visual Question Answering,"Under review for Computer Vision and Image Understanding
DRAU: Dual Recurrent Attention Units for Visual Question Answering
Ahmed Osmana,, Wojciech Sameka,
Fraunhofer Heinrich Hertz Institute, Einsteinufer 37, Berlin 10587, Germany"
7b67c38a6f49e02c03e1cea98146a506f607b0d7,Using Facial Symmetry to Handle Pose Variations in Real-World 3D Face Recognition,"Using Facial Symmetry to Handle Pose
Variations in Real-World 3D Face Recognition
Georgios Passalis1,2, Panagiotis Perakis1,2, Theoharis Theoharis1,2
nd Ioannis A. Kakadiaris2, Senior Member, IEEE"
7b3a63d030d03e536ddcbc217bc8d6fd630e3b53,xView: Objects in Context in Overhead Imagery,"xView: Objects in Context in Overhead Imagery
Darius Lam1
Richard Kuzma2
Matthew Klaric4
Kevin McGee3
Yaroslav Bulatov5
Samuel Dooley4 Michael Laielli4
Brendan McCord2"
7b9a5d9d7386d47c51cb473f6338988bd6e9f2b1,An Individual-Specific Strategy for Management of Reference Data in Adaptive Ensembles for Person Re-Identification,"An Individual-Specific Strategy for Management of Reference Data
in Adaptive Ensembles for Person Re-Identification
Miguel De-la-Torre*†, Eric Granger*, Robert Sabourin*, Dmitry O. Gorodnichy‡
* ´Ecole de technologie sup´erieure, Universit´e du Qu´ebec, Montr´eal, Canada,
Centro Universitario de Los Valles, Universidad de Guadalajara, Ameca, M´exico
Science and Engineering Directorate, Canada Border Services Agency, Ottawa, Canada,
Keywords: Multi-Classifier Systems; Adaptive Biometrics; Face
Recognition; Video Surveillance; Person Re-Identification"
7be6fe8c58ca12974c563689b7230b933dfca432,Design of radial basis function network as classifier in face recognition using eigenfaces,"SBRN’98 – Simpósio Brasileiro de Redes Neurais, Belo Horizonte, Minas Gerais, dezembro de 1998.
Design of Radial Basis Function Network as Classifier in Face Recognition Using
Eigenfaces
Carlos Eduardo Thomaz
Raul Queiroz Feitosa
Álvaro Veiga
PUC RJ- Pontifícia Universidade Católica do Rio de Janeiro
Departamento de Engenharia Elétrica
Rua Marquês de São Vicente, 225, 22453-900 Rio de Janeiro, RJ, Brasil"
7b358ed87f39a12d737070dc22b4c547ce378648,Color Features for Boosted Pedestrian Detection,"Institutionen för systemteknik
Department of Electrical Engineering
Examensarbete
Color Features for Boosted Pedestrian Detection
Examensarbete utfört i Datorseende
vid Tekniska högskolan vid Linköpings universitet
Niklas Hansson
LiTH-ISY-EX--15/4899--SE
Linköping 2015
Department of Electrical Engineering
Linköpings universitet
SE-581 83 Linköping, Sweden
Linköpings tekniska högskola
Linköpings universitet
581 83 Linköping"
7bcd98ee2df3d14eae7bbed713208cb7da7b5db0,Unsupervised data association for metric learning in the context of multi-shot person re-identification,"Unsupervised data association for Metric Learning in the context of Multi-shot
Person Re-identification
Furqan M. Khan, Francois Bremond
INRIA Sophia Antipolis-Mediterrannee
004 Route des Lucioles, Sophia Antipolis Cedex, France
{furqan.khan |"
7b0e81249159686337ca2cfe81662123906b6b26,An Automatic Eye Detection Method for Gray Intensity Facial Images,"IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 4, No 2, July 2011
ISSN (Online): 1694-0814
www.IJCSI.org
An Automatic Eye Detection Method for Gray Intensity Facial
Images
M. Hassaballah1,2 , Kenji Murakami1, Shun Ido1
Department of Computer Science, Ehime University, 790-8577, Japan
Department of Mathematics, Faculty of Science, South Valley University, Qena, 83523, Egypt"
7bd18f70586a735313fb38f2cb5bb0113265567d,Fusing Saliency Maps with Region Proposals for Unsupervised Object Localization,"Fusing Saliency Maps with Region Proposals for Unsupervised Object
Localization
Hakan Karao˘guz1 and Patric Jensfelt1"
7b3b7769c3ccbdf7c7e2c73db13a4d32bf93d21f,"On the design and evaluation of robust head pose for visual user interfaces: algorithms, databases, and comparisons","On the Design and Evaluation of Robust Head Pose for
Visual User Interfaces: Algorithms, Databases, and
Comparisons
Sujitha Martin
Laboratory of Intelligent and
Safe Automobiles
UCSD - La Jolla, CA, USA
Ashish Tawari
Laboratory of Intelligent and
Safe Automobiles
UCSD - La Jolla, CA, USA
Erik Murphy-Chutorian
Laboratory of Intelligent and
Safe Automobiles
UCSD - La Jolla, CA, USA
Shinko Y. Cheng
Laboratory of Intelligent and
Safe Automobiles
UCSD - La Jolla, CA, USA
Mohan Trivedi"
7b83867b7f79cbfbfc71996bcf07fe7ee7a7600c,Object detection through search with a foveated visual system,"Object Detection Through Exploration With A
Foveated Visual Field
Emre Akbas, Miguel P. Eckstein"
7be351e731eb9c3b71ad0c2a47ee8d300f7049be,Recognition for Objects by Relationships Between Attributes,"Journal of Computer Science Technology Updates, 2016, 3, 15-21
Recognition for Objects by Relationships Between Attributes
Hiroka Horiguchi*, Kazuo Ikeshiro and Hiroki Imamura
Graduate School of Engineering, Soka University, Hachioji-Shi, Tokyo, Japan"
7b9b3794f79f87ca8a048d86954e0a72a5f97758,Passing an Enhanced Turing Test - Interacting with Lifelike Computer Representations of Specific Individuals,"DOI 10.1515/jisys-2013-0016      Journal of Intelligent Systems 2013; 22(4): 365–415
Avelino J. Gonzalez*, Jason Leigh, Ronald F. DeMara, Andrew
Johnson, Steven Jones, Sangyoon Lee, Victor Hung, Luc
Renambot, Carlos Leon-Barth, Maxine Brown, Miguel Elvir,
James Hollister and Steven Kobosko
Passing an Enhanced Turing Test –
Interacting with Lifelike Computer
Representations of Specific Individuals"
7b0f1fc93fb24630eb598330e13f7b839fb46cce,Learning to Find Eye Region Landmarks for Remote Gaze Estimation in Unconstrained Settings,"Learning to Find Eye Region Landmarks for Remote Gaze
Estimation in Unconstrained Settings
Seonwook Park
ETH Zurich
Xucong Zhang
MPI for Informatics
Andreas Bulling
MPI for Informatics
Otmar Hilliges
ETH Zurich"
7bbaa09c9e318da4370a83b126bcdb214e7f8428,"FaaSter, Better, Cheaper: The Prospect of Serverless Scientific Computing and HPC","FaaSter, Better, Cheaper: The Prospect of
Serverless Scientific Computing and HPC
Josef Spillner1, Cristian Mateos2, and David A. Monge3
Zurich University of Applied Sciences, School of Engineering
Service Prototyping Lab (blog.zhaw.ch/icclab/), 8401 Winterthur, Switzerland
ISISTAN Research Institute - CONICET - UNICEN
Campus Universitario, Paraje Arroyo Seco, Tandil (7000), Buenos Aires, Argentina
ITIC Research Institute, National University of Cuyo
Padre Jorge Contreras 1300, M5502JMA Mendoza, Argentina"
7bce4f4e85a3bfcd6bfb3b173b2769b064fce0ed,A Psychologically-Inspired Match-Score Fusion Model for Video-Based Facial Expression Recognition,"A Psychologically-Inspired Match-Score Fusion Model
for Video-Based Facial Expression Recognition
Albert Cruz, Bir Bhanu, Songfan Yang,
VISLab, EBUII-216, University of California Riverside,
Riverside, California, USA, 92521-0425
{acruz, bhanu,"
7b95bd44db15f7cf20bfc051c353841f3fcea383,Low-Complexity Face Recognition using a Multilevel DWT and Two States of Continuous HMM to recognize Noisy Images,"Low-Complexity Face Recognition using a
Multilevel DWT and Two States of
Continuous HMM to recognize Noisy
Images
Hameed R. Farhan1, Mahmuod H. Al-Muifraje2, Thamir R. Saeed2
Department of Electrical and Electronic Engineering, University of Kerbala, Kerbala, Iraq
Department of Electrical Engineering, University of Technology, Baghdad, Iraq"
7b47ca13af16bdc1f4b88e9b68dd3ea52d959199,Online nonparametric discriminant analysis for incremental subspace learning and recognition,"Pattern Anal Applic (2008) 11:259–268
DOI 10.1007/s10044-008-0131-0
T H E O R E T I C A L A D V A N C E S
Online nonparametric discriminant analysis for incremental
subspace learning and recognition
B. Raducanu Æ J. Vitria`
Received: 15 December 2006 / Accepted: 20 January 2008 / Published online: 24 July 2008
Ó Springer-Verlag London Limited 2008"
7ba6ac1b769ad7098037c07a5b7399fe9d97fcc8,Moving Object Detection in Heterogeneous Conditions in Embedded Systems,"Article
Moving Object Detection in Heterogeneous
Conditions in Embedded Systems
Alessandro Garbo and Stefano Quer *
Dipartimento di Automatica ed Informatica, Politecnico di Torino, 10129 Torino, Italy;
* Correspondence: Tel.: +39-011-090-7076
Received: 25 May 2017; Accepted: 27 June 2017; Published: 1 July 2017"
7bd6d0bca27ff68621acd10d6d1709f084f97602,Learning to Detect and Track Visible and Occluded Body Joints in a Virtual World,"Learning to Detect and Track Visible and
Occluded Body Joints in a Virtual World
Matteo Fabbri(cid:63), Fabio Lanzi(cid:63), Simone Calderara(cid:63), Andrea Palazzi, Roberto
Vezzani, and Rita Cucchiara
Department of Engineering “Enzo Ferrari”
University of Modena and Reggio Emilia, Italy"
7b8e9c50f74ce6ca66a8ab61fb18ca31d26cf13f,Nonlinear Channels Aggregation Networks for Deep Action Recognition,"Under review as a conference paper at ICLR 2019
Nonlinear Channels Aggregation Networks
for Deep Action Recognition
Anonymous authors
Paper under double-blind review"
7b1af8cc9c2c43fa9d528bcfb05142d714df3700,"Modeling shape, appearance and motion for human movement analysis",
7b2e0c87aece7ff1404ef2034d4c5674770301b2,Discriminative Feature Learning with Foreground Attention for Person Re-Identification,"Discriminative Feature Learning with Foreground
Attention for Person Re-Identification
Sanping Zhou, Jinjun Wang, Deyu Meng, Yudong Liang, Yihong Gong, Nanning Zheng"
7b3e725ff30fb9e70482af0873f46c599ac0f675,Deep Learning with Long Short-Term Memory for Time Series Prediction,"Deep Learning with Long Short-Term Memory for
Time Series Prediction
Yuxiu Hua, Zhifeng Zhao, Rongpeng Li, Xianfu Chen, Zhiming Liu,
nd Honggang Zhang"
7b9961094d3e664fc76b12211f06e12c47a7e77d,Bridging biometrics and forensics,"Bridging Biometrics and Forensics
Yanjun Yan and Lisa Ann Osadciw
EECS, Syracuse University, Syracuse, NY, USA
{yayan,"
6b78f2ece211c2d1eb6699e1e057b7beb3e0b4a7,GM-PHD-Based Multi-Target Visual Tracking Using Entropy Distribution and Game Theory,"GM-PHD-Based Multi-Target Visual Tracking
Using Entropy Distribution and Game Theory
Xiaolong Zhou, Youfu Li, Senior Member, IEEE, Bingwei He, and Tianxiang Bai"
6bfae88bea2301f2abeb6d1ed62c8b9a99b251c0,CNRS TELECOM ParisTech at ImageCLEF 2015 Scalable Concept Image Annotation Task: Concept Detection with Blind Localization Proposals,"CNRS TELECOM ParisTech at ImageCLEF
015 Scalable Concept Image Annotation Task:
Concept Detection with Blind Localization
Proposals
Hichem SAHBI
CNRS TELECOM ParisTech"
6b5438161cfe55d1bd44829db81f396819e9e6b9,Wasserstein Dictionary Learning: Optimal Transport-based unsupervised non-linear dictionary learning,"Wasserstein Dictionary Learning:
Optimal Transport-Based Unsupervised Nonlinear Dictionary Learning
Morgan A. Schmitz∗ , Matthieu Heitz† , Nicolas Bonneel† , Fred Ngol`e‡ , David Coeurjolly† ,
Marco Cuturi§ , Gabriel Peyr´e¶, and Jean-Luc Starck∗"
6b6943a138938c31b285c1bb11213b87404feddf,Multiple Instance Learning-Based Birdsong Classification Using Unsupervised Recording Segmentation,"Multiple Instance Learning-Based Birdsong Classification
Using Unsupervised Recording Segmentation
J. F. Ruiz-Mu˜noz, Mauricio Orozco-Alzate, G. Castellanos-Dominguez
Universidad Nacional de Colombia - Sede Manizales
{jfruizmu, morozcoa,"
6b55153f8d87bfd0dfb2f24eb2aa61d40e314cae,"Track, Then Decide: Category-Agnostic Vision-Based Multi-Object Tracking","Track, then Decide: Category-Agnostic Vision-based
Multi-Object Tracking
Aljoˇsa Oˇsep, Wolfgang Mehner, Paul Voigtlaender, and Bastian Leibe"
6b7f27cff688d5305c65fbd90ae18f3c6190f762,Generative networks as inverse problems with Scattering transforms,"Published as a conference paper at ICLR 2018
GENERATIVE NETWORKS AS INVERSE PROBLEMS
WITH SCATTERING TRANSFORMS
Tom´as Angles & St´ephane Mallat
´Ecole normale sup´erieure, Coll`ege de France, PSL Research University
75005 Paris, France"
6b0b10836197d7934f53080a39787b7d8d2b81f2,Detecting Granger-causal relationships in global spatio-temporal climate data via multitask learning,"Detecting Granger-causal relationships in global
spatio-temporal climate data via multi-task learning
Matthias Demuzere
Christina Papagiannopoulou
Diego G. Miralles
Ghent University
Ghent University
Ghent University
Niko E. C. Verhoest
Ghent University
Willem Waegeman
Ghent University"
6b59716a193d3f91f88277e4c8a0f4cd0b6873c4,Detection of Deception in the Mafia Party Game,"Detection of Deception in the Mafia Party Game
Sergey Demyanov
James Bailey
Kotagiri
Ramamohanarao
Christopher Leckie
Department of Computing and Information Systems
The University of Melbourne, Melbourne, VIC, Australia"
6b6e2c2ff6fcc5837523940c69cf2e9e94bc0503,Unsupervised Deep Video Hashing with Balanced Rotation,Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
6b2db002cbc5312e4796de4d4b14573df2c01648,Learning Hierarchical Features from Deep Generative Models,"Learning Hierarchical Features from Deep Generative Models
Shengjia Zhao 1 Jiaming Song 1 Stefano Ermon 1"
6b6493551017819a3d1f12bbf922a8a8c8cc2a03,Pose Normalization for Local Appearance-Based Face Recognition,"Pose Normalization for Local Appearance-Based
Face Recognition
Hua Gao, Hazım Kemal Ekenel, and Rainer Stiefelhagen
Computer Science Department, Universit¨at Karlsruhe (TH)
Am Fasanengarten 5, Karlsruhe 76131, Germany
http://isl.ira.uka.de/cvhci"
6b089627a4ea24bff193611e68390d1a4c3b3644,PAID I CROSS-POLLINATION OF NORMALISATION TECHNIQUES FROM SPEAKER TO FACE AUTHENTICATION USING GAUSSIAN MIXTURE MODELS,"CROSS-POLLINATION OF NORMALISATION
TECHNIQUES FROM SPEAKER TO FACE
AUTHENTICATION USING GAUSSIAN
MIXTURE MODELS
Roy Wallace        Mitchell McLaren        Chris McCool
Sébastien Marcel
Idiap-RR-03-2012
JANUARY 2012
Centre du Parc, Rue Marconi 19, P.O. Box 592, CH - 1920 Martigny
T +41 27 721 77 11  F +41 27 721 77 12   www.idiap.ch"
6bf57ae6c63873253d1b95782f8c6b7bbc91b9ac,Semantic face segmentation from video streams in the wild,"UNIVERSITAT POLITÈCNICA DE CATALUNYA
Universitat de Barcelona
Universitat Rovira i Virgili
MASTER THESIS
Semantic face segmentation from video
streams in the wild
Author:
Deividas SKIPARIS
Academic Supervisor:
Dr. Sergio ESCALERA
Industry Supervisor:
Dr. Pascal LANDRY
A thesis submitted in fulfillment of the requirements
for the degree of Master of Artificial Intelligence
in the
Facultat d’Informàtica de Barcelona (FIB)
Facultat de Matemàtiques (UB)
Escola Tècnica Superior d’Enginyeria (URV)
June 16, 2017"
6b95a3dbec92071c8552576930e69455c70e529c,BEGAN: Boundary Equilibrium Generative Adversarial Networks,"BEGAN: Boundary Equilibrium Generative
Adversarial Networks
David Berthelot, Thomas Schumm, Luke Metz
Google"
6bcfcc4a0af2bf2729b5bc38f500cfaab2e653f0,Facial Expression Recognition in the Wild Using Improved Dense Trajectories and Fisher Vector Encoding,"Facial expression recognition in the wild using improved dense trajectories and
Fisher vector encoding
Sadaf Afshar1
Albert Ali Salah2
Computational Science and Engineering Program, Bo˘gazic¸i University, Istanbul, Turkey
Department of Computer Engineering, Bo˘gazic¸i University, Istanbul, Turkey
{sadaf.afshar,"
6bf4f5f6f1fc30e7886812cc352ce6c2c95ae6e9,MGS2 : Optimisation multicrit ères de contours actifs par algorithmes génétiques,"MGS2 : Optimisation multicrit`eres de contours actifs par algorithmes
g´en´etiques
Nicolas CLADEL, Renaud S ´EGUIER
´Equipe SCEE (Supelec - IETR)
Supelec, avenue de la boulaie, BP 81127, 35511 Cesson-S´evign´e Cedex, France
R´esum´e – Dans cet article nous proposons une ´evolution de notre pr´ec´edent travail sur l’optimisation multicrit`eres de contours actifs. Cette
pproche permet une exploration globale de l’image et une gestion efficace de plusieurs ´energies par la repr´esentation de Pareto. Notre nouvel
lgorithme, le MultiObjective Genetic Snakes 2 (MGS2), associe l’algorithme multicrit`eres NSGA2 [4] au codage des contours actifs des MGS
[3]. Les MGS2 exploitent de nouvelles ´energies d’attache `a l’image afin d’assurer la convergence des contours vers un objet creux en environ-
nement bruit´e `a partir d’une initialisation al´eatoire. Nous proc´edons ici `a l’´etude de ces ´energies et `a l’analyse de la convergence de l’algorithme
`a travers une s´erie de tests sur une base d’objets synth´etiques. Nous pr´esenterons ´egalement des r´esultats de l’application de MGS2 `a la lecture
labiale."
6badfb6f273dce55ed058abe19c292f2519d12f6,Parts-based face detection at multiple views,"Savakis A. and Higgs D. (2007).
PARTS-BASED FACE DETECTION AT MULTIPLE VIEWS.
In Proceedings of the Second International Conference on Computer Vision Theory and Applications - IU/MTSV, pages 298-301
Copyright c(cid:13) SciTePress"
6bb55ed3761eb1556acbd1a0d15c2c9099bab0b7,Temporally Coherent Bayesian Models for Entity Discovery in Videos by Tracklet Clustering,"Temporally Coherent Chinese Restaurant Process
for Discovery of Persons and Corresponding
Tracklets from User-generated Videos"
6b6ff9d55e1df06f8b3e6f257e23557a73b2df96,Survey of Threats to the Biometric Authentication Systems and Solutions,"International Journal of Computer Applications (0975 – 8887)
Volume 61– No.17, January 2013
Survey of Threats to the Biometric Authentication
Systems and Solutions
Sarika Khandelwal
Research Scholor,Mewar
University,Chitorgarh. (INDIA)
P.C.Gupta
Kota University,Kota(INDIA)
Khushboo Mantri
M.tech.student, Arya College of
engineering ,Jaipur(INDIA)"
6bcc2b50e32bdbb0c668f75000badf21e6cd0839,Knowledge Projection for Deep Neural Networks,"Knowledge Projection for Effective Design of
Thinner and Faster Deep Neural Networks
Zhi Zhang, Guanghan Ning, and Zhihai He"
6b8a5a2d018356b396301b27156fd69dd18b1d82,A Study on the Impact of Wavelet Decomposition on Face Recognition Methods,"International Journal of Computer Applications (0975 – 8887)
Volume 87 – No.3, February 2014
A Study on the Impact of Wavelet Decomposition on
Face Recognition Methods
M. M. Mohie El-Din1, Neveen. I. Ghali2, Ahmed. A. A. G1 and H. A. El Shenbary 1
Department of Mathematics and Computer Science, Faculty of Science, Al-Azhar University, Cairo, Egypt
Assoc. Prof Computer Science, Faculty of Science, Al-Azhar University, Cairo. Egypt"
6b3c9c0e4d47bd960c0adc4d13ae524a5d9b94d1,Visual Multiple-Object Tracking for Unknown Clutter Rate,"Visual Multiple-Object Tracking for Unknown
Clutter Rate
Du Yong Kim"
6b4da897dce4d6636670a83b64612f16b7487637,Learning from Simulated and Unsupervised Images through Adversarial Training,"This paper has been submitted for publication on November 15, 2016.
Learning from Simulated and Unsupervised Images through Adversarial
Training
Ashish Shrivastava, Tomas Pfister, Oncel Tuzel, Josh Susskind, Wenda Wang, Russ Webb
Apple Inc"
6bca057c25b48fa7d1607e5701c46392ec906822,An ordered topological representation of 3D triangular mesh facial surface: Concept and applications,"Werghi et al. EURASIP Journal on Advances in Signal Processing 2012, 2012:144
http://asp.eurasipjournals.com/content/2012/1/144
RESEARCH
Open Access
An ordered topological representation of 3D
triangular mesh facial surface: concept and
pplications
Naoufel Werghi1*, Mohamed Rahayem2 and Johan Kjellander2"
6bf58047438f54720e03252d50984d1a340a116a,Discriminative Autoencoders for Small Targets Detection,"Discriminative Autoencoders
for Small Targets Detection.
Sebastien Razakarivony
SAGEM D.S. – SAFRAN Group
CNRS UMR 6072 – University of Caen – ENSICAEN
Email:
Fr´ed´eric Jurie
CNRS UMR 6072 – University of Caen – ENSICAEN
Email:"
6bd6460ec06adc1bd69d9517d116fd1545c04ac7,Small sample scene categorization from perceptual relations,"In the Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (CVPR), 2012
Small Sample Scene Categorization from Perceptual Relations
Ilan Kadar and Ohad Ben-Shahar
Dept. of Computer Science, Ben-Gurion University
Beer-Sheva, Israel"
6b02d73f097d745e58bb99a880e559b78c4594a1,Cross-Domain Face Verification: Matching ID Document and Self-Portrait Photographs,"Cross-Domain Face Verification:
Matching ID Document and Self-Portrait Photographs
Guilherme Folego 1,2 ∗ Marcus A. Angeloni 1,2
Jos´e Augusto Stuchi 2,3 Alan Godoy 1,2 Anderson Rocha 2
CPqD Foundation, Brazil
University of Campinas (Unicamp), Brazil
Phelcom Technologies, Brazil"
6bbcec054017a6fd64af8bf325cb6e3e7244ba55,On the Benefits and the Limits of ` p-norm Multiple Kernel Learning In Image Classification,"On the Benefits and the Limits of (cid:96)p-norm Multiple Kernel Learning In Image
Classification
Alexander Binder
Technical University of Berlin
Franklinstr. 28/29, 10587 Berlin, Germany
Shinichi Nakajima
NIKON Corporation
Optical Research Laboratory, Tokyo, Japan
Marius Kloft
Technical University of Berlin
Christina M¨uller
Technical University of Berlin
Wojciech Samek
Technical University of Berlin
Ulf Brefeld
Yahoo! Research
Barcelona, Spain
Klaus-Robert M¨uller
Technical University of Berlin
Motoaki Kawanabe"
6bee77418af305d632b21eb03872a0d268eeebac,Understanding the Intrinsic Memorability of Images,"Understanding the Intrinsic Memorability of Images
Phillip Isola
Devi Parikh
TTI-Chicago
Antonio Torralba
Aude Oliva"
6b5850c5a288fd26480ebcbbfc43172597e0d442,PHARMACOLOGICAL EFFECTS ON SOCIAL INTERACTION 1 Effects of Pharmacological Manipulations on Natural Social Interaction in Rhesus Macaques : A Pilot Investigation,"PHARMACOLOGICAL EFFECTS ON SOCIAL INTERACTION
Effects of Pharmacological Manipulations on
Natural Social Interaction in Rhesus Macaques: A Pilot Investigation
Angelica Fuentes
Spring, 2017
Cognitive Science
Advisor: Steve W. Chang"
6b18628cc8829c3bf851ea3ee3bcff8543391819,Face recognition based on subset selection via metric learning on manifold,"Hong Shao, Shuang Chen, Jie-yi Zhao, Wen-cheng Cui, Tian-shu Yu, 2015.
Face recognition based on subset selection via metric learning on manifold.
058.  [doi:10.1631/FITEE.1500085]
Face recognition based on subset
selection via metric learning on manifold
Key words: Face recognition, Sparse representation, Manifold structure,
Metric learning, Subset selection
Contact: Shuang Chen
E-mail:
ORCID: http://orcid.org/0000-0001-7441-4749
Front Inform Technol & Electron Eng"
6b6791c0a3f06c356035747f7e5f87d54bc5a657,A Neuro Fuzzy approach for Facial Expression Recognition using LBP Histograms,"International Journal of Computer Theory and Engineering, Vol. 2, No. 2 April, 2010
793-8201
A Neuro Fuzzy approach for Facial Expression
Recognition using LBP Histograms
V. Gomathi, Dr. K. Ramar, and A. Santhiyaku Jeevakumar"
6b6946ce943da5ba4bf6471609d3355cadec172e,Improvement of Facial Emotion Recognition Using Skin Color and Face Components kowsar,"International journal of Computer Science & Network Solutions                                         April.2014-Volume 2.No4
http://www.ijcsns.com
ISSN 2345-3397
Improvement of Facial Emotion Recognition
Using Skin Color and Face Components
Department of Computer Engineering, khouzestan Science and Research Branch, Islamic Azad
kowsar azadmanesh, Reza javidan,  S. Enayatolah Alavi
Computer Engineering and IT Department Shiraz University of Technology, Shiraz, Iran,
Department of computer Engineering, shahid chamran university, Ahvaz, Iran,
University, Ahvaz, Iran,"
6b4d1c0ddf606c84148edd889db231f67703ef3e,A comparison of techniques for robust gender recognition,"Repositorio Institucional de la Universidad Autónoma de Madrid
https://repositorio.uam.es
Esta es la versión de autor del congreso publicado en:
This is an author produced version of a paper published in:
Image Processing (ICIP), 2011 18th IEEE International Conference on.
DOI:  http://dx.doi.org/10.1109/ICIP.2011.6116610
IEEE, 2011. 561 - 564
Copyright:  © 2011 IEEE
El acceso a la versión del editor puede requerir la suscripción del recurso
Access to the published version may require subscription"
e4a5ff03ac258f1bcc9c214c30497610b3d5faa2,DropBlock: A regularization method for convolutional networks,"DropBlock: A regularization method for
onvolutional networks
Golnaz Ghiasi
Google Brain
Tsung-Yi Lin
Google Brain
Quoc V. Le
Google Brain"
e4d08ef1b4350c7e03bdfb716200370c2ea87a6a,A novel approach for face recognition using fused GMDH-based networks,"The International Arab Journal of Information Technology, Vol. 15, No. 3, May 2018                                                            369
A Novel Approach for Face Recognition Using
Fused GMDH-Based Networks
El-Sayed El-Alfy1, Zubair Baig2, and Radwan Abdel-Aal1
College of Computer Sciences and Engineering, King Fahd University of Petroleum and Minerals, KSA
School of Science and Security Research Institute, Edith Cowan University, Australia"
e467f7e2434ca74bdd4b19808a6b3d78b8c5ba1a,Feature Construction Using Evolution-COnstructed Features for General Object Recognition,"Feature Construction Using Evolution-COnstructed Features
for General Object Recognition
Kirt Dwayne Lillywhite
A dissertation submitted to the faculty of
Brigham Young University
in partial fulfillment of the requirements for the degree of
Doctor of Philosophy
Dah-Jye Lee, Chair
James K Archibald
Bryan S. Morse
Dan A. Ventura
Brent E. Nelson
Department of Electrical and Computer Engineering
Brigham Young University
April 2012
Copyright c(cid:13) 2012 Kirt Dwayne Lillywhite
All Rights Reserved"
e4501da190012623d5048d57b7e650de27643b8d,Learning Actionlet Ensemble for 3D Human Action Recognition,"Chapter 2
Learning Actionlet Ensemble for 3D Human
Action Recognition"
e4d4346bd415c6fa9187c16a9b7f5c69f48f1ec4,Towards High Performance Video Object Detection for Mobiles,"Towards High Performance
Video Object Detection for Mobiles
Xizhou Zhu(cid:63)
Jifeng Dai Xingchi Zhu(cid:63) Yichen Wei
Lu Yuan
Microsoft Research Asia"
e4d33362b4f99ab77fd6ceaafa183c087c79faea,Design and implementation of a high performance pedestrian detection,"June 23-26, 2013, Gold Coast, Australia
978-1-4673-2754-1/13/$31.00 ©2013 Crown"
e475e857b2f5574eb626e7e01be47b416deff268,Facial Emotion Recognition Using Nonparametric Weighted Feature Extraction and Fuzzy Classifier,"Facial Emotion Recognition Using Nonparametric
Weighted Feature Extraction and Fuzzy Classifier
Maryam Imani and Gholam Ali Montazer"
e4afc03c818bc9e357ea2fb23ebf73496f1ffc81,Mafalda Libório Baio Morais Alves,"Universidade do Minho
Escola de Psicologia
Mafalda Libório Baio Morais Alves
Physical attractiveness: sexual satisfaction,
promiscuity and infidelity
janeiro de 2018"
e46732f0c818b059420f68162363c9d1a9dc5395,Geometric and Physical Constraints for Head Plane Crowd Density Estimation in Videos.,"Geometric and Physical Constraints for
Head Plane Crowd Density Estimation in Videos
Weizhe Liu(cid:63) Krzysztof Lis Mathieu Salzmann
Pascal Fua
Computer Vision Laboratory, ´Ecole Polytechnique F´ed´erale de Lausanne
{weizhe.liu, krzysztof.lis, mathieu.salzmann,
(EPFL)"
e4cbe39daed8700a1d6f4a25a3a98645c4f231d0,A nonconvex formulation for low rank subspace clustering: algorithms and convergence analysis,"Comput Optim Appl (2018) 70:395–418
https://doi.org/10.1007/s10589-018-0002-6
A nonconvex formulation for low rank subspace
lustering: algorithms and convergence analysis
Hao Jiang1 · Daniel P. Robinson1
René Vidal1 · Chong You1
Received: 14 July 2017 / Published online: 27 March 2018
© Springer Science+Business Media, LLC, part of Springer Nature 2018"
e44f59da5b123da999738d10954a54e2fb635eda,Combined Correlation Rules to Detect Skin based on Dynamic Color Clustering,
e43cc682453cf3874785584fca813665878adaa7,Face Recognition using Local Derivative Pattern Face Descriptor,"www.ijecs.in
International Journal Of Engineering And Computer Science ISSN:2319-7242
Volume 3 Issue 10 October, 2014 Page No.8830-8834
Face Recognition using Local Derivative Pattern Face
Descriptor
Pranita R. Chavan1,  Dr. Dnyandeo J. Pete2
Department of Electronics and Telecommunication
Datta Meghe College of Engineering
Airoli, Navi Mumbai, India 1,2
Mob: 99206746061
Mob: 99870353142"
e48fa574960b23ba65b7ff1a732cc521213b5120,Mining Automatically Estimated Poses from Video Recordings of Top Athletes,"Mining Automatically Estimated Poses from Video Recordings
of Top Athletes
Rainer Lienhart∗
University of Augsburg
uni-augsburg.de
Moritz Einfalt
University of Augsburg
uni-augsburg.de
Dan Zecha
University of Augsburg"
e4d0e87d0bd6ead4ccd39fc5b6c62287560bac5b,Implicit video multi-emotion tagging by exploiting multi-expression relations,"Implicit Video Multi-Emotion Tagging by Exploiting Multi-Expression
Relations
Zhilei Liu, Shangfei Wang*, Zhaoyu Wang and Qiang Ji"
e480f8c00dfe217653c2569d0eec6e2ffa836d59,The “Something Something” Video Database for Learning and Evaluating Visual Common Sense,"The “something something” video database
for learning and evaluating visual common sense
Raghav Goyal
Samira Ebrahimi Kahou
Vincent Michalski
Joanna Materzy´nska
Susanne Westphal
Heuna Kim
Valentin Haenel
Ingo Fruend
Peter Yianilos
Moritz Mueller-Freitag
Florian Hoppe
Christian Thurau
Ingo Bax
Roland Memisevic"
e4896772d51a66b743e0d072d53cf26f6b61fc75,Automated Identification of Trampoline Skills Using Computer Vision Extracted Pose Estimation,"Automated Identification of Trampoline Skills
Using Computer Vision Extracted Pose Estimation
Paul W. Connolly, Guenole C. Silvestre and Chris J. Bleakley
School of Computer Science, University College Dublin, Belfield, Dublin 4, Ireland."
e45bcda905b897513f4cff9e5c0a5bf475674a02,"Domain Stylization: A Strong, Simple Baseline for Synthetic to Real Image Domain Adaptation","Domain Stylization: A Strong, Simple Baseline for
Synthetic to Real Image Domain Adaptation
Aysegul Dundar, Ming-Yu Liu, Ting-Chun Wang, John Zedlewski, Jan Kautz
NVIDIA"
e40007540c4813c81bc8b54dda4dd6f6c21deaa8,D Face Recognition using Patch Geodesic Derivative Pattern,"International Journal of  Smart Electrical Engineering, Vol.2, No.3, Summer 2013                   ISSN:  2251-9246
pp.127:132
D Face Recognition using Patch Geodesic Derivative Pattern"
e443cb55dcc54de848e9f0c11a6194568a875011,From passive to interactive object learning and recognition through self-identification on a humanoid robot,"From passive to interactive object learning and
recognition through self-identification on a humanoid
robot
Natalia Lyubova, Serena Ivaldi, David Filliat
To cite this version:
Natalia Lyubova, Serena Ivaldi, David Filliat. From passive to interactive object learning and
recognition through self-identification on a humanoid robot. Autonomous Robots, Springer
Verlag, 2015, pp.23. .
HAL Id: hal-01166110
https://hal.archives-ouvertes.fr/hal-01166110
Submitted on 22 Jun 2015
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,"
e496d6be415038de1636bbe8202cac9c1cea9dbe,Facial Expression Recognition in Older Adults using Deep Machine Learning,"Facial Expression Recognition in Older Adults using
Deep Machine Learning
Andrea Caroppo, Alessandro Leone and Pietro Siciliano
National Research Council of Italy, Institute for Microelectronics and Microsystems, Lecce,
Italy"
e4abc40f79f86dbc06f5af1df314c67681dedc51,Head Detection with Depth Images in the Wild,"Head Detection with Depth Images in the Wild
Diego Ballotta, Guido Borghi, Roberto Vezzani and Rita Cucchiara
Department of Engineering ”Enzo Ferrari”
University of Modena and Reggio Emilia, Italy
Keywords:
Head Detection, Head Localization, Depth Maps, Convolutional Neural Network"
e4dc24e4926df4de3e8d7ca7cd1f4115e91f03e1,Instance-level video segmentation from object tracks Anonymous CVPR submission,"CVPR 2016 Submission #185. CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE.
Instance-level video segmentation from object tracks
Anonymous CVPR submission
Paper ID 185"
e42e7735f94a8f498ef0bf790ab43a668f904848,Low-Latency Detec on and Tracking of Aircra in Very High-Resolu on Video Feeds,"Linköping University | Department of Computer and Information Science
Master thesis, 30 ECTS | Datateknik
018 | LIU-IDA/LITH-EX-A--18/022--SE
Low-Latency Detec(cid:415)on and
Tracking of Aircra(cid:332) in Very
High-Resolu(cid:415)on Video Feeds
Låglatent detek(cid:415)on och spårning av flygplan i högupplösta
videokällor
Jarle Mathiesen
Supervisor : Magnus Bång
Examiner : Erik Berglund
Linköpings universitet
SE–581 83 Linköping
+46 13 28 10 00 , www.liu.se"
e4d90019c312ed87a236a11374caeea9cc4e6940,Comparison Comparison PCA Train GMM Feature Reduction Classify GMM Threshold,"COVER SHEET
Cook, Jamie and Chandran, Vinod and Sridharan, Sridha and Fookes, Clinton (2004) Face
Recognition from 3D Data using Iterative Closest Point Algorithm and Gaussian Mixture Models.
In Proceedings 3D Data Processing, Visualisation and Transmission, Thessaloniki, Greece.
Accessed from  http://eprints.qut.edu.au
Copyright 2004 the authors."
e4391993f5270bdbc621b8d01702f626fba36fc2,Head Pose Estimation Using Multi-scale Gaussian Derivatives,"Author manuscript, published in ""18th Scandinavian Conference on Image Analysis (2013)""
DOI : 10.1007/978-3-642-38886-6_31"
e4c2f8e4aace8cb851cb74478a63d9111ca550ae,Distributed One-Class Learning,"DISTRIBUTED ONE-CLASS LEARNING
Ali Shahin Shamsabadi(cid:63), Hamed Haddadi†, Andrea Cavallaro(cid:63)
(cid:63)Queen Mary University of London,†Imperial College London"
e4f032ee301d4a4b3d598e6fa6cffbcdb9cdfdd1,Facial Landmark Point Localization using Coarse-to-Fine Deep Recurrent Neural Network,"MAHPOD et al.: CCNN
Facial Landmark Point Localization using
Coarse-to-Fine Deep Recurrent Neural Network
Shahar Mahpod, Rig Das, Emanuele Maiorana, Yosi Keller, and Patrizio Campisi,"
e4485930357db8248543eb78ce3bc9f32050694e,Drawn to danger: trait anger predicts automatic approach behaviour to angry faces.,"Cognition and Emotion
ISSN: 0269-9931 (Print) 1464-0600 (Online) Journal homepage: http://www.tandfonline.com/loi/pcem20
Drawn to danger: trait anger predicts automatic
pproach behaviour to angry faces
Lotte Veenstra, Iris K. Schneider, Brad J. Bushman & Sander L. Koole
To cite this article: Lotte Veenstra, Iris K. Schneider, Brad J. Bushman & Sander L. Koole (2016):
Drawn to danger: trait anger predicts automatic approach behaviour to angry faces, Cognition
nd Emotion, DOI: 10.1080/02699931.2016.1150256
To link to this article:  http://dx.doi.org/10.1080/02699931.2016.1150256
Published online: 19 Feb 2016.
Submit your article to this journal
Article views: 39
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Date: 04 April 2016, At: 13:19"
e4d8ba577cabcb67b4e9e1260573aea708574886,Um Sistema De Recomendaç˜ao Inteligente Baseado Em V ´ Idio Aulas Para Educaç˜ao a Distˆancia an Intelligent Recommendation System Based on Video Lectures for Distance Education (revelation),"UM SISTEMA DE RECOMENDAC¸ ˜AO INTELIGENTE BASEADO EM V´IDIO
AULAS PARA EDUCAC¸ ˜AO A DIST ˆANCIA
Gaspare Giuliano Elias Bruno
Tese de Doutorado apresentada ao Programa
de P´os-gradua¸c˜ao em Engenharia de Sistemas e
Computa¸c˜ao, COPPE, da Universidade Federal
do Rio de Janeiro, como parte dos requisitos
necess´arios `a obten¸c˜ao do t´ıtulo de Doutor em
Engenharia de Sistemas e Computa¸c˜ao.
Orientadores: Edmundo Albuquerque de
Souza e Silva
Rosa Maria Meri Le˜ao
Rio de Janeiro
Janeiro de 2016"
e48432872be1e0449f50c6807b274d57c87a641f,Human Body Extraction from Single Images Using Images Processing Techniques,"Human Body Extraction from Single Images Using Images
Processing Techniques
T.Ravichandra Babu
Associate Professor & HOD,
Department of ECE,
Katravath Rajendhar
PG Scholar-SSP,
Department of ECE,
Krishnamurthy Institute of Technology and
Krishnamurthy Institute of Technology and
Engineering.
Engineering.
that  can
images
to  cope  with"
e4d2cc8fe567e8e1f2e0c5eb751ff9e9361346c0,ALTERED BRAIN ACTIVITY IN AUTISTIC CHILDREN VERSUS HEALTHY CONTROLS WHILE PERFORMING SIMPLE TASKS USING fMRI by Donald,"Copyright Warning & Restrictions
The copyright law of the United States (Title 17, United
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rchives are authorized to furnish a photocopy or other
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opying order if, in its judgment, fulfillment of the order
would involve violation of copyright law.
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New Jersey Institute of Technology reserves the right to
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Printing note: If you do not wish to print this page, then select
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e4660f26d3d752349ec72c1f7ad6d14b13ab95c9,LIMO: Lidar-Monocular Visual Odometry,"LIMO: Lidar-Monocular Visual Odometry
Johannes Graeter1, Alexander Wilczynski1 and Martin Lauer1"
e43ea078749d1f9b8254e0c3df4c51ba2f4eebd5,Facial Expression Recognition Based on Constrained Local Models and Support Vector Machines,"Facial Expression Recognition Based on Constrained
Local Models and Support Vector Machines
Nikolay Neshov1, Ivo Draganov2, Agata Manolova3"
eefb8768f60c17d76fe156b55b8a00555eb40f4d,Subspace Scores for Feature Selection in Computer Vision,"Subspace Scores for Feature Selection in Computer Vision
Cameron Musco
Christopher Musco"
eec95bb8825f0f761706f2a89c6d078d6438590e,LandmarkBoost: Efficient Visual Context Classifiers for Robust Localization,"LandmarkBoost: Efficient Visual Context Classifiers
for Robust Localization
Marcin Dymczyk∗, Igor Gilitschenski‡, Juan Nieto∗, Simon Lynen∗†, Bernhard Zeisl†, and Roland Siegwart∗
Autonomous Systems Lab, ETH Z¨urich, †Google Inc., Z¨urich, ‡CSAIL, MIT"
ee92d36d72075048a7c8b2af5cc1720c7bace6dd,Face recognition using mixtures of principal components,"FACE RECOGNITION USING MIXTURES OF PRINCIPAL COMPONENTS
Deepak S. Turaga and Tsuhan Chen
Video and Display Processing
Philips Research USA
Briarcliff Manor, NY 10510"
ee87aa52d9642607d86f011c0d7326c4bdc63121,Automatic Detection of Facial Midline as a Guide for Facial Feature Extraction,"Automatic Detection of Facial Midline
s a Guide for Facial Feature Extraction
Nozomi Nakao, Wataru Ohyama, Tetsushi Wakabayashi and Fumitaka Kimura
Graduate School of Engineering, Mie University
577 Kurimamachiya-cho, Tsu-shi, Mie, 5148507, Japan"
eee8a37a12506ff5df72c402ccc3d59216321346,Volume C,"Uredniki:
dr. Tomaž Erjavec
Odsek za tehnologije znanja
Institut »Jožef Stefan«, Ljubljana
dr. Jerneja Žganec Gros
Alpineon d.o.o, Ljubljana
Založnik: Institut »Jožef Stefan«, Ljubljana
Tisk: Birografika BORI d.o.o.
Priprava zbornika: Mitja Lasič
Oblikovanje naslovnice: dr. Damjan Demšar
Tiskano iz predloga avtorjev
Naklada:  50
Ljubljana, oktober 2008
Konferenco IS 2008 sofinancirata
Ministrstvo za visoko šolstvo, znanost in tehnologijo
Institut »Jožef Stefan«
Informacijska družba
ISSN 1581-9973
CIP - Kataložni zapis o publikaciji
Narodna in univerzitetna knjižnica, Ljubljana"
ee9385efb66ee0b1bee31c1632141729bb7fb6f5,Numerical simplification for bloat control and analysis of building blocks in genetic programming,"Noname manuscript No.
(will be inserted by the editor)
Numerical Simplification for Bloat Control and Analysis of
Building Blocks in Genetic Programming
David Kinzett · Mark Johnston · Mengjie Zhang
the date of receipt and acceptance should be inserted later"
eeec69e910430bebe3808773f5a6a155d77059a0,Multi-shot Pedestrian Re-identification via Sequential Decision Making,"Multi-shot Pedestrian Re-identification via Sequential Decision Making
Jianfu Zhang1, Naiyan Wang2 and Liqing Zhang1
Shanghai Jiao Tong University∗, 2TuSimple"
eed1dd2a5959647896e73d129272cb7c3a2e145c,The Elements of Fashion Style,"INPUTSTYLE DOCUMENTTOP ITEMS“             ”I need an outfit for a beach wedding that I'm going to early this summer. I'm so excited -- it's going to be warm and exotic and tropical... I want my outfit to look effortless, breezy, flowy, like I’m floating over the sand! Oh, and obviously no white! For a tropical spot, I think my outfit should be bright and"
eedfb384a5e42511013b33104f4cd3149432bd9e,Multimodal probabilistic person tracking and identification in smart spaces,"Multimodal Probabilistic Person
Tracking and Identification
in Smart Spaces
zur Erlangung des akademischen Grades eines
Doktors der Ingenieurwissenschaften
der Fakultät für Informatik
der Universität Fridericiana zu Karlsruhe (TH)
genehmigte
Dissertation
Keni Bernardin
us Karlsruhe
Tag der mündlichen Prüfung: 20.11.2009
Erster Gutachter:
Zweiter Gutachter:
Prof. Dr. A. Waibel
Prof. Dr. R. Stiefelhagen"
ee4fd1a1df6a01e7dabe82090b1024e2eb6d78a1,Effective Emotional Classification Combining Facial Classifiers and User Assessment,"Effective Emotional Classification Combining Facial
Classifiers and User Assessment
Isabelle Hupont1, Sandra Baldassarri2, Rafael Del Hoyo1, and Eva Cerezo2
Instituto Tecnológico de Aragón, Zaragoza (Spain)
Departamento de Informática e Ingeniería de Sistemas,
Instituto de Investigación en Ingeniería de Aragón, Universidad de Zaragoza (Spain)"
eebe66c4d1a41b3c7830846306044c8f3fe0d350,Domain adaptation networks for noisy image classification Master Thesis,"Faculty of Electrical Engineering, Mathematics and Computer Science
Department of Intelligent Systems
Domain adaptation
networks for noisy image
lassification
Master Thesis
Chengqiu Zhang
Committee:
Supervisors:
Dr. Jan van Gemert
Prof. Martha Larson
Dr. Silvia-Laura Pintea Dr. Jan van Gemert
Dr. Ildiko Suveg
Dr. Marco Loog
Dr. Silvia-Laura Pintea
Dr. Adriana Gonzalez
Eindhoven, Aug 2017"
eed98eb53b34820df736203d62076eff81de926e,Scene Segmentation and Object Classification for Place Recognition,"Dr. Mongi A. Abidi,  Major Professor
To the Graduate Council:
I  am  submitting  herewith  a  dissertation    written  by  Chang  Cheng  entitled  “Scene
Segmentation  and  Object  Classification  for  Place  Recognition.”  I  have  examined  the  final
electronic copy of this dissertation for form and content and recommend that it be accepted in
partial fulfillment of the requirements for the degree of Doctor of Philosophy, with a major in
Electrical Engineering.
We have read this dissertation
nd recommend its acceptance:
Dr. Seddik M. Djouadi
Dr. Andreas Koschan
Dr. Hairong Qi
Dr. Timothy M. Young
Accepted for the Council:
Carolyn R. Hodges
Vice Provost and Dean of the Graduate School"
eef297f46b0f6deedde6b74fcd0b44a7a8df0d8b,"Performance evaluation of face alignment algorithms on "" inthe-wild "" selfies","ISSN 2464-4617 (print)
ISSN 2464-4625 (CD)
Computer Science Research Notes
CSRN 2802
Short Papers Proceedings
http://www.WSCG.eu
Performance evaluation of face alignment algorithms on
""in-the-wild"" selfies
Ivan Babanin
Aleksandr Mashrabov
Moscow Institute of Physics and
Technology, Adorable Inc.
Department of Innovations and High
Moscow Institute of Physics and
Technology, Adorable Inc.
Department of Innovations and High
Technology
Institutskiy Pereulok, 9
region, Dolgoprudny
Technology"
eef82228530a70a3158e59be2b0b07d6dccd8a88,Nonlinear supervised dimensionality reduction via smooth regular embeddings,"Nonlinear Supervised Dimensionality Reduction via
Smooth Regular Embeddings
Department of Electrical and Electronics Engineering, METU, Ankara
Cem ¨Ornek and Elif Vural"
eed25d9b5b5b28e8454a359d54c9de5a05cc4682,Context-aware home monitoring system for Parkinson ' s disease patients : ambient and wearable sensing for freezing of gait detection,"Context-aware Home Monitoring System
for Parkinson’s Disease Patients
Ambient and Wearable Sensing for Freezing of Gait Detection
B(cid:2456)(cid:2459)(cid:2450)(cid:2460) T(cid:2442)(cid:2452)(cid:2442)(cid:20)(cid:2444)"
eec466317c83e8093a32b978e753c3fc8f21d21b,Performance Characterization in Computer Vision A Tutorial,"Performance Characterization in Computer Vision
A Tutorial
Adrian F. Clark and Christine Clark
VASE Laboratory, Electronic Systems Engineering
University of Essex, Colchester, CO4 3SQ, UK
This document provides a tutorial on performance characterization in computer
vision. It explains why learning to characterize the performances of vision tech-
niques is crucial to the discipline’s development. It describes the usual proce-
dure for evaluating vision algorithms and its statistical basis. The use of a soft-
ware tool, a so-called test harness, for performing such evaluations is described.
The approach is illustrated on an example technique.
Contents
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The Performance Assessment and Characterization Processes . . .
Assessing an Individual Algorithm . . . . . . . . . . . . . . . . . . .
.1 The Receiver Operating Characteristic Curve . . . . . . . . . . . .
.2 The Detection Error Trade-off Curve . . . . . . . . . . . . . . . .
.3 Confusion Matrices . . . . . . . . . . . . . . . . . . . . . . . . . .
Comparing Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . ."
ee3a905ec8cd2e62dc642fad33d6f5f8516968a8,It depends: Approach and avoidance reactions to emotional expressions are influenced by the contrast emotions presented in the task.,"tapraid5/zfn-xhp/zfn-xhp/zfn00515/zfn3313d15z
xppws S⫽1
8/4/15
5:44 Art: 2014-0213
APA NLM
Journal of Experimental Psychology:
Human Perception and Performance
015, Vol. 41, No. 5, 000
0096-1523/15/$12.00
© 2015 American Psychological Association
http://dx.doi.org/10.1037/xhp0000130
It Depends: Approach and Avoidance Reactions to Emotional Expressions
re Influenced by the Contrast Emotions Presented in the Task
AQ: au
Andrea Paulus and Dirk Wentura
Saarland University
Studies examining approach and avoidance reactions to emotional expressions have yielded conflicting
results. For example, expressions of anger have been reported to elicit approach reactions in some studies
ut avoidance reactions in others. Nonetheless, the results were often explained by the same general
underlying process, namely the influence that the social message signaled by the expression has on"
ee463f1f72a7e007bae274d2d42cd2e5d817e751,Automatically Extracting Qualia Relations for the Rich Event Ontology,"Automatically Extracting Qualia Relations for the Rich Event Ontology
Ghazaleh Kazeminejad1, Claire Bonial2, Susan Windisch Brown1 and Martha Palmer1
{ghazaleh.kazeminejad, susan.brown,
University of Colorado Boulder, 2U.S. Army Research Lab"
7897f6a19d5211bf6387f5c9e141c90a0cc84566,One-shot Texture Segmentation,"One-shot Texture Segmentation
Ivan Ustyuzhaninov
University of Tübingen
Claudio Michaelis
University of Tübingen
Wieland Brendel∗
University of Tübingen
Matthias Bethge∗
University of Tübingen"
789c76749a15614d97ac8f4ec18b3ce7d80a2d28,Explorer Multiplicative LSTM for sequence modelling,"Multiplicative LSTM for sequence modelling
Citation for published version:
Krause, B, Murray, I, Renals, S & LU, L 2017, Multiplicative LSTM for sequence modelling. in  International
Conference on Learning Representations - ICLR 2017 - Workshop Track. pp. 2872-2880.
Link:
Link to publication record in Edinburgh Research Explorer
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Download date: 02. Sep. 2017"
78045e2b93745b16a174137074e430ccd5ff53ff,Hedging Deep Features for Visual Tracking.,"This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
Hedging Deep Features for Visual Tracking
Yuankai Qi, Shengping Zhang, Lei Qin, Qingming Huang, Hongxun Yao, Jongwoo Lim, and Ming-Hsuan Yang"
7854876ab5d87248ace94615731ed3e3e56af769,MixedPeds: Pedestrian Detection in Unannotated Videos Using Synthetically Generated Human-Agents for Training,
780772a69b1556d5f725630dff8e79ec3ccb46bb,FieldSAFE: Dataset for Obstacle Detection in Agriculture,"FieldSAFE: Dataset for Obstacle Detection in Agriculture
Mikkel Kragh∗1, Peter Christiansen∗1, Morten S. Laursen1, Morten Larsen2, Kim
A. Steen3, Ole Green3, Henrik Karstoft1 and Rasmus N. Jørgensen1
Department of Engineering, Aarhus University, Denmark
Conpleks Innovation ApS, Struer, Denmark
AgroIntelli, Aarhus, Denmark"
7858410077f9ba94ca60d0f6b4d29509e46a4ef9,Predicting Visual Exemplars of Unseen Classes for Zero-Shot Learning,"Predicting Visual Exemplars of Unseen Classes for Zero-Shot Learning
Soravit Changpinyo
U. of Southern California
Los Angeles, CA
Wei-Lun Chao
Los Angeles, CA
U. of Southern California
U. of Southern California
Fei Sha
Los Angeles, CA"
78f438ed17f08bfe71dfb205ac447ce0561250c6,Bridging the Semantic Gap : Image and video Understanding by Exploiting Attributes,
788eceb4d1b7556d1c9033224da2348b4402d6ca,An Empirical Evaluation of Visual Question Answering for Novel Objects,"An Empirical Evaluation of Visual Question Answering for Novel Objects
Santhosh K. Ramakrishnan1,2 Ambar Pal1 Gaurav Sharma1 Anurag Mittal2
IIT Kanpur∗
IIT Madras†"
786e57ed6877dc8491b1bb9253f8b82c02732977,Efficient approach to de-identifying faces in videos,"Page 1 of 8
An Efficient Approach to De-Identifying Faces in Videos
Li Meng *, Zongji Sun, Odette Tejada Collado
School of Engineering and Technology, University of Hertfordshire, College Lane, Hatfield, UK"
783f22f9ad77e437438f24f2d0a7c1397468ec88,A New Quadratic Classifier Applied to Biometric Recognition,"A New Quadratic Classifier applied to Biometric Recognition
Carlos E. Thomaz1, Duncan F. Gillies1 and Raul Q. Feitosa2
Imperial College of Science Technology and Medicine, Department of Computing,
80 Queen’s Gate, London SW7 2BZ, United Kingdom
State University of Rio de Janeiro, Department of Computer Engineering,
r. São Francisco Xavier, Rio de Janeiro 20559-900, Brazil
Catholic University of Rio de Janeiro, Department of Electrical Engineering,
r. Marques de Sao Vicente 225, Rio de Janeiro 22453-900, Brazil"
78a2a964b61308f683fae6f3a62e3a8aece51bae,EXECUTIVE NEURAL CIRCUITRY IN INDIVIDUALS WITH HIGH-FUNCTIONING AUTISM,"FUNCTIONAL NEUROIMAGING OF THE INTERACTION BETWEEN SOCIAL
AND EXECUTIVE NEURAL CIRCUITRY IN INDIVIDUALS WITH HIGH-
FUNCTIONING AUTISM
Kimberly Lynn Hills Carpenter
A dissertation submitted to the faculty of the University of North Carolina at Chapel
Hill in partial fulfillment of the requirements for the degree of Doctor of Philosophy in
the Curriculum in Neurobiology
Chapel Hill
Approved By:
Dr. Aysenil Belger
Dr. Jim Bodfish
Dr. Gabriel Dichter
Dr. Kevin LaBar
Dr. Joseph Piven
Dr. Aldo Rustioni"
78df7d3fdd5c32f037fb5cc2a7c104ac1743d74e,Temporal Pyramid Pooling-Based Convolutional Neural Network for Action Recognition,"TEMPORAL PYRAMID POOLING CNN FOR ACTION RECOGNITION
Temporal Pyramid Pooling Based Convolutional
Neural Network for Action Recognition
Peng Wang, Yuanzhouhan Cao, Chunhua Shen, Lingqiao Liu, and Heng Tao Shen"
78c91d969c55a4a61184f81001c376810cdbd541,A Spike and Slab Restricted Boltzmann Machine,"A Spike and Slab Restricted Boltzmann Machine
Aaron Courville
James Bergstra
Yoshua Bengio
DIRO, Universit´e de Montr´eal, Montr´eal, Qu´ebec, Canada"
787c1bb6d1f2341c5909a0d6d7314bced96f4681,"Face Detection and Verification in Unconstrained Videos : Challenges , Detection , and Benchmark Evaluation","Face Detection and Verification in Unconstrained
Videos: Challenges, Detection, and Benchmark
Evaluation
Mahek Shah
IIIT-D-MTech-CS-GEN-13-106
July 16, 2015
Indraprastha Institute of Information Technology, Delhi
Thesis Advisors
Dr. Mayank Vatsa
Dr. Richa Singh
Submitted in partial fulfillment of the requirements
for the Degree of M.Tech. in Computer Science
(cid:13) Shah, 2015
Keywords: face recognition, face detection, face verification"
7808937b46acad36e43c30ae4e9f3fd57462853d,Describing people: A poselet-based approach to attribute classification,"Describing People: A Poselet-Based Approach to Attribute Classification ∗
Lubomir Bourdev1,2, Subhransu Maji1 and Jitendra Malik1
EECS, U.C. Berkeley, Berkeley, CA 94720
Adobe Systems, Inc., 345 Park Ave, San Jose, CA 95110"
78598c69201cccfc060d47fc0415f2f9365035fc,A Taught-Obesrve-Ask (TOA) Method for Object Detection with Critical Supervision,"A Taught-Obesrve-Ask (TOA) Method for Object
Detection with Critical Supervision
Chi-Hao Wu, Qin Huang, Siyang Li, and C.-C. Jay Kuo, Fellow, IEEE"
78fdf2b98cf6380623b0e20b0005a452e736181e,Dense Wide-Baseline Stereo with Varying Illumination and its Application to Face Recognition,
78c9a63be8e07dc6acb90f4fe3f06821719eaa34,Hierarchical online domain adaptation of deformable part-based models,"Hierarchical online domain adaptation of deformable part-based models
Jiaolong Xu1, David V´azquez2, Krystian Mikolajczyk3 and Antonio M. L´opez1"
788a3faa14ca191d7f187b812047190a70798428,Interpretable Set Functions,"Interpretable Set Functions
Andrew Cotter, Maya Gupta, Heinrich Jiang,
James Muller, Taman Narayan, Serena Wang, Tao Zhu
600 Amphitheatre Parkway, Mountain View, CA 94043
Google Research"
7882c67f555b761e10ecc70216db25382890d9d7,Automated Characterization of Stenosis in Invasive Coronary Angiography Images with Convolutional Neural Networks,"Automated Characterization of Stenosis in Invasive Coronary Angiography Images with Convolutional
Neural Networks"
78749b58299ecebf100e2512872029f89878449b,One-class Selective Transfer Machine for Personalized Anomalous Facial Expression Detection,
782188821963304fb78791e01665590f0cd869e8,Automatic Spatially-Aware Fashion Concept Discovery,"sleevelengthincreasing dress length+ mini =(b) Structured product browsing(c) Attribute-feedback product retrieval(a) Concept discoveryminimidimaxisleevelessshort-sleevelong-sleeveblueblackredyellowFigure1.(a)Weproposeaconceptdiscoveryapproachtoauto-maticallyclusterspatially-awareattributesintomeaningfulcon-cepts.Thediscoveredspatially-awareconceptsarefurtherutilizedfor(b)structuredproductbrowsing(visualizingimagesaccordingtoselectedconcepts)and(c)attribute-feedbackproductretrieval(refiningsearchresultsbyprovidingadesiredattribute).variousfeedback,includingtherelevanceofdisplayedim-ages[20,4],ortuningparameterslikecolorandtexture,andthenresultsareupdatedcorrespondingly.However,rel-evancefeedbackislimitedduetoitsslowconvergencetomeetthecustomerrequirements.Inadditiontocolorandtexture,customersoftenwishtoexploithigher-levelfea-tures,suchasneckline,sleevelength,dresslength,etc.Semanticattributes[13],whichhavebeenappliedef-fectivelytoobjectcategorization[15,27]andfine-grainedrecognition[12]couldpotentiallyaddresssuchchallenges.Theyaremid-levelrepresentationsthatdescribesemanticproperties.Recently,researchershaveannotatedclotheswithsemanticattributes[9,2,8,16,11](e.g.,material,pat-tern)asintermediaterepresentationsorsupervisorysignalstobridgethesemanticgap.However,annotatingsemanticattributesiscostly.Further,attributesconditionedonob-jectpartshaveachievedgoodperformanceinfine-grainedrecognition[3,33],confirmingthatspatialinformationiscriticalforattributes.Thisalsoholdsforclothingimages.Forexample,thenecklineattributeusuallycorrespondstothetoppartinimageswhilethesleeveattributeordinarily1"
78f7304ba4c853c568dc4e38fef35aa2c003e3f3,Modeling correlations in spontaneous activity of visual cortex with centered Gaussian-binary deep Boltzmann machines.,"visual cortex with centered Gaussian-binary deep
Boltzmann machines
Nan Wang
Institut f¨ur Neuroinformatik
Ruhr-Universit¨at Bochum
Bochum, 44780, Germany
Dirk Jancke
Institut f¨ur Neuroinformatik
Ruhr-Universit¨at Bochum
Bochum, 44780, Germany
Laurenz Wiskott
Institut f¨ur Neuroinformatik
Ruhr-Universit¨at Bochum
Bochum, 44780, Germany"
78a144d5dce1a61c92420e77c11116f541a7617f,Box Aggregation for Proposal Decimation: Last Mile of Object Detection,"Box Aggregation for Proposal Decimation: Last Mile of Object Detection
The Chinese University of Hong Kong ♯Stanford University ‡Shanghai Jiao Tong University
Shu Liu† Cewu Lu♯,‡
Jiaya Jia†"
789389dce27ad72adad251c81734bdb6c274c30f,3D facial feature localization for registration,"D Facial Feature Localization for Registration
Albert Ali Salah and Lale Akarun
Bo˘gazi¸ci University
Computer Engineering Department, Turkey
Perceptual Intelligence Laboratory
{salah,"
781d3550f54f3b4bfbd99ca9957aba6d6dec990e,Regularized Kernel Discriminant Analysis With a Robust Kernel for Face Recognition and Verification,"This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.
Brief Papers
Regularized Kernel Discriminant Analysis With a Robust
Kernel for Face Recognition and Verification
Stefanos Zafeiriou, Georgios Tzimiropoulos, Maria Petrou,
nd Tania Stathaki"
7803206f024ba6887d93e8aec91dd0097ffc5165,Automatic detection of facial actions from 3D data,"Automatic Detection of Facial Actions from 3D Data
Arman Savran
Electrical and Electronics Engineering Department
Bo˘gazic¸i University, Istanbul, Turkey
B¨ulent Sankur"
78d00241fc9798eba895d41a0068715212a70489,Person identification from gait analysis with a depth camera at home,978-1-4244-9270-1/15/$31.00 ©2015 IEEE
781c2553c4ed2a3147bbf78ad57ef9d0aeb6c7ed,Tubelets: Unsupervised Action Proposals from Spatiotemporal Super-Voxels,"Int J Comput Vis
DOI 10.1007/s11263-017-1023-9
Tubelets: Unsupervised Action Proposals from Spatiotemporal
Super-Voxels
Mihir Jain1
Cees G. M. Snoek1
· Jan van Gemert2 · Hervé Jégou3 · Patrick Bouthemy3 ·
Received: 25 June 2016 / Accepted: 18 May 2017
© The Author(s) 2017. This article is an open access publication"
783f3fccde99931bb900dce91357a6268afecc52,Adapted Active Appearance Models,"Hindawi Publishing Corporation
EURASIP Journal on Image and Video Processing
Volume 2009, Article ID 945717, 14 pages
doi:10.1155/2009/945717
Research Article
Adapted Active Appearance Models
Renaud S´eguier,1 Sylvain Le Gallou,2 Gaspard Breton,2 and Christophe Garcia2
SUP ´ELEC/IETR, Avenue de la Boulaie, 35511 Cesson-S´evign´e, France
Orange Labs—TECH/IRIS, 4 rue du clos courtel, 35 512 Cesson S´evign´e, France
Correspondence should be addressed to Renaud S´eguier,
Received 5 January 2009; Revised 2 September 2009; Accepted 20 October 2009
Recommended by Kenneth M. Lam
Active Appearance Models (AAMs) are able to align ef‌f‌iciently known faces under duress, when face pose and illumination are
ontrolled. We propose Adapted Active Appearance Models to align unknown faces in unknown poses and illuminations. Our
proposal is based on the one hand on a specific transformation of the active model texture in an oriented map, which changes the
AAM normalization process; on the other hand on the research made in a set of different precomputed models related to the most
dapted AAM for an unknown face. Tests on public and private databases show the interest of our approach. It becomes possible
to align unknown faces in real-time situations, in which light and pose are not controlled.
Copyright © 2009 Renaud S´eguier et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly"
49957368eceaa751c0b9c49251512ca6a8800cff,Accurate Object Localization with Shape Masks,"Accurate Object Localization with Shape Masks
Marcin Marsza(cid:7)ek
Cordelia Schmid
INRIA, LEAR - LJK
665 av de l’Europe, 38330 Montbonnot, France"
499ec985f911bfdd44ca67af223e08916f9ee8ea,AN ALGEBRAIC FRAMEWORK FOR CLASSIFIER DEVELOPMENT AND ITS APPLICATION IN FACE RECOGNITION,"AN ALGEBRAIC FRAMEWORK FOR CLASSIFIER
DEVELOPMENT AND ITS APPLICATION IN FACE
RECOGNITION
A THESIS
Submitted by
K. R. SUJITH
in ful(cid:2)llment for the award of the degree
MASTER OF SCIENCE (BY RESEARCH)
FACULTY OF INFORMATION AND
COMMUNICATION ENGINEERING
ANNA UNIVERSITY: CHENNAI 600 025
DECEMBER 2005"
49d4cb2e1788552a04c7f8fec33fbfabb3882995,Visually-Enabled Active Deep Learning for (Geo) Text and Image Classification: A Review,"Article
Visually-Enabled Active Deep Learning for
(Geo) Text and Image Classification: A Review
Liping Yang 1,*, Alan M. MacEachren 1,* ID , Prasenjit Mitra 2 and Teresa Onorati 3
Department of Geography and Institute for CyberScience, The Pennsylvania State University,
University Park, PA 16802, USA
College of Information Sciences and Technology, The Pennsylvania State University, University Park,
PA 16802, USA;
Computer Science Department, Universidad Carlos III de Madrid, 28911-Leganés, Madrid, Spain;
* Correspondence: (L.Y.); (A.M.M.)
Received: 29 December 2017; Accepted: 17 February 2018; Published: 20 February 2018"
496074fcbeefd88664b7bd945012ca22615d812e,Driver Distraction Using Visual-Based Sensors and Algorithms,"Review
Driver Distraction Using Visual-Based Sensors
nd Algorithms
Alberto Fernández 1,*, Rubén Usamentiaga 2, Juan Luis Carús 1 and Rubén Casado 2
Grupo TSK, Technological Scientific Park of Gijón, 33203 Gijón, Asturias, Spain;
Department of Computer Science and Engineering, University of Oviedo, Campus de Viesques, 33204 Gijón,
Asturias, Spain; (R.U.); (R.C.)
* Corrospondence: Tel.: +34-984-29-12-12; Fax: +34-984-39-06-12
Academic Editor: Gonzalo Pajares Martinsanz
Received: 14 July 2016; Accepted: 24 October 2016; Published: 28 October 2016"
49004f22a420e0897f7b811239c1e098b0c655bf,Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering,"Out of the Box: Reasoning with Graph Convolution
Nets for Factual Visual Question Answering
Medhini Narasimhan, Svetlana Lazebnik, Alexander G. Schwing
University of Illinois Urbana-Champaign
{medhini2, slazebni,"
4919663c62174a9bc0cc7f60da8f96974b397ad2,Human age estimation using enhanced bio-inspired features (EBIF),"HUMAN AGE ESTIMATION USING ENHANCED BIO-INSPIRED FEATURES (EBIF)
Mohamed Y.El Dib and Motaz El-Saban
Faculty of Computers and Information, Cairo University, Cairo, Egypt"
499842b3df387b81dbb2436c764d22b1a3f42cae,Collaborative feature learning from social media,"Collaborative Feature Learning from Social Media
Chen Fang1, Hailin Jin2, Jianchao Yang3, Zhe Lin2
Department of Computer Science, Dartmouth College. 2Adobe Research. 3Snapchat.
Image feature representation plays an essential role in image recognition
nd related tasks. The current state-of-the-art feature learning paradigm
is supervised learning from labeled data [3], which surpasses other well-
known hand-crafted feature based methods [4, 5]. However, this paradigm
requires large datasets with category labels to train properly, which limits its
pplicability to new problem domains where labels are hard to obtain.
In this paper, we ask an interesting research question: Are category-level
labels the only way for data driven feature learning?
There is a surge of social media websites in the last ten years. Most
social media websites such as Pinterest have been collecting content data
that the users share as well as behavior data of the users. User behavior
data are the activities of individual users, such as likes, comments, or view
histories and they carry rich information about corresponding content data.
For instance, two photos of a similar style on Pinterest tend to be pinned by
the same user. If we aggregate the user behavior data across many users, we
may recover interesting properties of the content. For instance, the photos
liked by a group of users of similar interests tend to have very similar styles."
494e736c05ddf500830e9c51b5fb42be9b9bff1a,Learning Depth from Monocular Videos using Direct Methods,
494c1630c93e74aca3169ae33734f2f733c95e05,The Iris Challenge Evaluation 2005,"The Iris Challenge Evaluation 2005
P. Jonathon Phillips, Kevin W. Bowyer, Patrick J. Flynn, Xiaomei Liu, W. Todd Scruggs"
49df381ea2a1e7f4059346311f1f9f45dd997164,Client-Specific Anomaly Detection for Face Presentation Attack Detection,"On the Use of Client-Specific Information for Face
Presentation Attack Detection Based on Anomaly
Detection
Shervin Rahimzadeh Arashloo and Josef Kittler,"
49e1aa3ecda55465641b2c2acc6583b32f3f1fc6,Support Vector Machine for age classification,"International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, Volume 2, Issue 5, May 2012)
Support Vector Machine for age classification
Sangeeta Agrawal1, Rohit Raja2, Sonu Agrawal3
Assistant Professor, CSE, RSR RCET, Kohka Bhilai
,3 Sr. Assistant Professor, CSE, SSCET, Junwani Bhilai"
49b2545b8b9ed81cc547ec974e0b61d01b7bc759,Examplers based image fusion features for face recognition,"Examplers based  image  fusion  features for  face
recognition
Alex  Pappachen  James*1  and  Sima  Dimitrijev2
*1   Asst.  Professor  and   Group Lead, Machine Intelligence Group,  Indian  Institute  of
Information  Technology and   Management-Kerala,  India.  www.mirgroup.co.cc,
Professor  and   Deputy Director,Queensland  Micro-  and   Nanotechnology Center,  Griffith
University,   Australia,  www.gu.edu.au/qmnc"
499343a2fd9421dca608d206e25e53be84489f44,Face Recognition with Name Using Local Weber‟s Law Descriptor,"Anil Kumar.C, et.al, International Journal of Technology and Engineering Science [IJTES]TM
Volume 1[9], pp: 1371-1375, December 2013
Face Recognition with Name Using Local Weber‟s
Law Descriptor
C.Anil kumar,2A.Rajani,3I.Suneetha
M.Tech Student,2Assistant Professor,3Associate Professor
Department of ECE, Annamacharya Institute of Technology and Sciences, Tirupati, India-517520
on  FERET"
4928d4a458355bbf2a9e8a7567125dd06e459cf8,198 : 500 Light Seminar : Readings on Generic Object Recognition,"98:500 Light Seminar: Readings on Generic
Object Recognition
Organizer: Ahmed Elgammal
Description
The field of computer vision witnesses recently a great interest focused on solving
the generic object recognition problem. Traditionally, object recognition has
een at the center of the computer vision field. The explosion of digital imaging
nd digital video that we are witnessing makes a huge urgent demand for systems
nd applications that are able to understand images and videos at the semantic
level. The goal of this reading seminar is to catch up with the state of the art in
this field and understand the achievements and challenges towards solving the
generic object recognition problem.
Class Time: Thursday 2-3pm
Class Location: Hill 254
Extended Reading List
Sept 19: Holistic appearance-based approaches: Murase, Hiroshi, and Nayar [1995]
Presented by: Chan Su Lee
Sept 27: Part-based Models: Schmid and Mohr [1997], Agarwal, Awan, and Roth
[2004]
Presented by: Afzal Mazhar and Marwan A. Torki"
494a71a5d0df506ea5803d1f4e691d4b10eae506,Chapter 2 Face Recognition in Subspaces,"Chapter 2
Face Recognition in Subspaces
Gregory Shakhnarovich and Baback Moghaddam
.1 Introduction
Images of faces, represented as high-dimensional pixel arrays, often belong to a
manifold of intrinsically low dimension. Face recognition, and computer vision re-
search in general, has witnessed a growing interest in techniques that capitalize on
this observation and apply algebraic and statistical tools for extraction and analy-
sis of the underlying manifold. In this chapter, we describe in roughly chronologic
order techniques that identify, parameterize, and analyze linear and nonlinear sub-
spaces, from the original Eigenfaces technique to the recently introduced Bayesian
method for probabilistic similarity analysis. We also discuss comparative experi-
mental evaluation of some of these techniques as well as practical issues related to
the application of subspace methods for varying pose, illumination, and expression.
.2 Face Space and Its Dimensionality
Computer analysis of face images deals with a visual signal (light reflected off the
surface of a face) that is registered by a digital sensor as an array of pixel values.
The pixels may encode color or only intensity. In this chapter, we assume the latter
ase (i.e., gray-level imagery). After proper normalization and resizing to a fixed
m-by-n size, the pixel array can be represented as a point (i.e., vector) in an mn-"
49a7949fabcdf01bbae1c2eb38946ee99f491857,A concatenating framework of shortcut convolutional neural networks,"A CONCATENATING FRAMEWORK OF SHORTCUT
CONVOLUTIONAL NEURAL NETWORKS
Yujian Li Ting Zhang, Zhaoying Liu, Haihe Hu"
4926af10d590686f4f5706b450515caaa1ddea54,Adaptive Deep Supervised Autoencoder Based Image Reconstruction for Face Recognition,"Publishing CorporationMathematical Problems in EngineeringVolume 2016, Article ID 6795352, 14 pageshttp://dx.doi.org/10.1155/2016/6795352"
4913477a16c8354f032546b1444728c592823586,Web Image Retrieval Search Engine based on Semantically Shared Annotation,"Web Image Retrieval Search Engine based on Semantically
Shared Annotation
Alaa Riad1, Hamdy Elminir2 and Sameh Abd-Elghany3
Vice dean of Students Affair, Faculty of Computers and Information Sciences, Mansoura University
Mansoura, Egypt
Mansoura, Egypt
Mansoura, Egypt
Head of Electronic and Communication Dept, Misr Higher Institute of Engineering and Technology
Faculty of Computers and Information Sciences, Mansoura University"
498fd231d7983433dac37f3c97fb1eafcf065268,Linear Disentangled Representation Learning for Facial Actions,"LINEAR DISENTANGLED REPRESENTATION LEARNING FOR FACIAL ACTIONS
Xiang Xiang1 and Trac D. Tran2
Dept. of Computer Science
Dept. of Electrical & Computer Engineering
Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA
Fig. 1. The separability of the neutral face yn and expression
omponent ye. We find yn is better for identity recognition
than y and ye is better for expression recognition than y."
4941f92222d660f9b60791ba95796e51a7157077,Conditional CycleGAN for Attribute Guided Face Image Generation,"Conditional CycleGAN for Attribute Guided
Face Image Generation
Yongyi Lu
HKUST
Yu-Wing Tai
Tencent
Chi-Keung Tang
HKUST"
490a0b6ff5b982e884622bb9c81250f05c069f32,Template Aging in 3 D and 2 D Face Recognition,"Template Aging in 3D and 2D Face Recognition
Ishan Manjani∗
Hakki Sumerkan†
Patrick J. Flynn†
Kevin W. Bowyer†"
490a217a4e9a30563f3a4442a7d04f0ea34442c8,An SOM-based Automatic Facial Expression Recognition System,"International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.2, No.4, August 2013
An SOM-based Automatic Facial Expression
Recognition System
Mu-Chun Su1, Chun-Kai Yang1, Shih-Chieh Lin1,De-Yuan Huang1, Yi-Zeng
Hsieh1, andPa-Chun Wang2
Department of Computer Science &InformationEngineering,National Central
University,Taiwan, R.O.C.
Cathay General Hospital, Taiwan, R.O.C.
E-mail:"
49d76cef9a31d18eda22057cfc99f7eb9e25bc7c,Implicit Non-linear Similarity Scoring for Recognizing Unseen Classes,"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18)
Feature ExtractionElephantDolphinLionClass Feature ExtractionImage Feature SpaceClass Feature SpaceSimilarity MeasureJoint Embedding Space0.110.040.94(cid:2030)(cid:2038)Figure1:ThebasicframeworkofexistingZSLapproaches.labeledexemplar.Inaddition,manynewconceptsemergeeverydayanditisdifficulttocollectlabeleddataforthem.Inthesecases,therecognitionmodelhastorecognizeclass-eswhichareunseenbefore.Therefore,howtotrainimagerecognitionmodelsthatiscapableofgeneralizingwellforunseenclasses,i.e.,zero-shotlearning,hasrecentlybecomeahotresearchtopicandisanopenissue[Xianetal.,2017].ThebasicideaofZSListolearnafunctiontomeasurethecorrespondence/similaritybetweenanimageandaclassusingseenclasseswhichhavesufficientlabeledimages,andthentransferthesimilarityfunctiontounseenones.Ifanim-ageisverysimilartoaclass,itislikelytobelongtothisclass.WesummarizetheframeworkinFigure1.Eachimageisrepresentedbyafeaturevector(imagefeatureextraction),likedeepfeature[Donahueetal.,2014].Eachclassisal-sogivenafeaturevector(classfeatureextraction),liketheword2vecoutputusingtheclassnameasinputortheclassattributes[Lampertetal.,2014].Thenthekeystepistoconstructanimagefeatureprojectionfunction(cid:30)andatextfeatureprojectionfunction’tomapthemintoajointem-beddingspacesuchthatthesimilaritybetweenthemcanbedirectlymeasured.Thejointembeddingspacecanbetheimagefeaturespace[Guoetal.,2017],theclassfeatures-pace[Socheretal.,2013],oranewlatentspace[Akataetal.,2016].Afterthejointembeddingstep,thesimilaritybetweenanimageandaclasscanbemeasureddirectlyinthisspace,wheretheexplicitlinearscoringiswidelyadopted,forexam-ple,innerproductsimilarity[Romera-ParedesandTorr,2015;ZhangandSaligrama,2016;Xianetal.,2016].Becausetheseenclassesandunseenclassesarerelatedandsimilar,al-thoughdifferent,thefunctionlearningonseenclassescanworkwellonunseenclasses.Inthisway,themodelisabletorecognizeunseenclassesbasedonthetransferredknowledge."
4906d54807947dcdbf2da174fd0cd716ea195006,Augmented Reality in Road Navigation,"TECHNION INSTITUTE OF TECHNOLOGY
PROJECT IN IMAGE PROCESSING AND ANALYSIS
34329
Augmented Reality in Road
Navigation
Author:
Doron Halevy
Supervisor:
Gil Shamai
May 8, 2016"
49d7fd8975413fb2912e111093749733712210dd,Vpliv kakovosti vhodnih slik na zanesljivost samodejnega razpoznavanja obrazov,"Elektrotehniški vestnik 74(3): 145-150, 2007
Electrotechnical Review: Ljubljana, Slovenija
Vpliv kakovosti vhodnih slik na zanesljivost samodejnega
razpoznavanja obrazov
Vitomir Štruc, Nikola Paveši(cid:29)
Univerza v Ljubljani, Fakulteta za elektrotehniko, Tržaška 25, 1001 Ljubljana, Slovenija
E-pošta:
Povzetek.  Zanesljivost  samodejnega  razpoznavanja  obrazov  je  odvisna  od  številnih  dejavnikov,  med  katerimi  so
najpomembnejši  natan(cid:24)nost  dolo(cid:24)itve  slikovnega  obmo(cid:24)ja  obraza  in  njegova  odpornost  na  slabšo  kakovost  slik,
izbira ustreznega postopka izpeljave obraznih zna(cid:24)ilk ter  uporaba primernega algoritma za izra(cid:24)un podobnosti in
sprejetje  odlo(cid:24)itve  o  identiteti  osebe.  V  (cid:24)lanku  predstavljamo  rezultate  vrednotenja  napak,  ki  jih  v  biometri(cid:24)ni
sistem  vnašajo  razli(cid:24)ne  degradacije  vhodnih  slik.  Njihov  vpliv  smo  prou(cid:24)ili  za  tri  na  podro(cid:24)ju  razpoznavanja
obrazov  pogosteje  uporabljene  postopke  izpeljave  zna(cid:24)ilk  (analizo  glavnih  komponent  –  PCA,  analizo  linearne
diskriminante  –  LDA  ter  analizo  neodvisnih  komponent  –  ICA),  pri  (cid:24)emer  smo  za  dolo(cid:24)itev  zanesljivosti
razpoznavanja (verifikacije) uporabili bazo XM2VTS; za ovrednotenje napak, ki jih v biometri(cid:24)ni sistem vnašajo
spremembe v kakovosti slik, pa njene degradirane razli(cid:24)ice.
Klju ne  besede:  razpoznavanje  obrazov,  analiza  glavnih  komponent,  analiza  linearne  diskriminante,  analiza
neodvisnih komponent, zanesljivost razpoznavanja, kakovost vhodnih slik
Impact of image degradations on the face recognition accuracy"
49e85869fa2cbb31e2fd761951d0cdfa741d95f3,Adaptive Manifold Learning,"Adaptive Manifold Learning
Zhenyue Zhang, Jing Wang, and Hongyuan Zha"
493abaf881c3a6e9d3bf47a8d6e75abb4e75f557,Introspective Evaluation of Perception Performance for Parameter Tuning without Ground Truth,"Introspective Evaluation of Perception Performance for Parameter Tuning
without Ground Truth
Humphrey Hu† and George Kantor†"
4967b0acc50995aa4b28e576c404dc85fefb0601,An Automatic Face Detection and Gender Classification from Color Images using Support Vector Machine,"Vol. 4, No. 1 Jan 2013                                                                                                 ISSN 2079-8407
Journal of Emerging Trends in Computing and Information Sciences
©2009-2013 CIS Journal. All rights reserved.
An Automatic Face Detection and Gender Classification from
http://www.cisjournal.org
Color Images using Support Vector Machine
Md. Hafizur Rahman, 2 Suman Chowdhury, 3 Md. Abul Bashar
, 2, 3 Department of Electrical & Electronic Engineering, International
University of Business Agriculture and Technology, Dhaka-1230, Bangladesh"
492f3def325296164cd32b80d19a591b72b480cd,Metric Learning,"Computer Vision Group
Metric Learning
Technical University of Munich
Department of Informatics
Computer Vision Group
June 9, 2017
M.Sc. John Chiotellis: Metric Learning
/ 46"
4987ac5638e1fdb116cc76626465f166998d7536,Polysemous codes.,"Polysemous codes
Matthijs Douze, Herv´e J´egou and Florent Perronnin
Facebook AI Research"
490fa9ee39614e1ef1d74162e698e4a1f0e5f916,In Good Shape: Robust People Detection based on Appearance and Shape,"PISHCHULIN et al.: PEOPLE DETECTION USING APPEARANCE AND SHAPE
In Good Shape: Robust People Detection
ased on Appearance and Shape
Computer Vision and
Multimodal Computing
MPI Informatics
Saarbrücken, Germany
Leonid Pishchulin
Arjun Jain
Christian Wojek
Thorsten Thormählen
Bernt Schiele"
4914f51bc2f5a35c0d15924e39a51975c53f9753,Recovered from a Single 2 D Palmprint Image,"A 3D Feature Descriptor Recovered from a
Single 2D Palmprint Image
Qian Zheng1,2, Ajay Kumar1, and Gang Pan2"
491cf4d86ed895000a35ba96f46261984c0bdf7c,Facial Expression Recognition for Domestic Service Robots,"Facial Expression Recognition for Domestic
Service Robots
Geovanny Giorgana and Paul G. Ploeger
Bonn-Rhein-Sieg University of Applied Sciences,
Grantham-Allee 20 53757 Sankt Augustin, Germany"
23d55061f7baf2ffa1c847d356d8f76d78ebc8c1,Generic and attribute-specific deep representations for maritime vessels,"Solmaz et al. IPSJ Transactions on Computer Vision and
Applications  (2017) 9:22
DOI 10.1186/s41074-017-0033-4
IPSJ Transactions on Computer
Vision and Applications
RESEARCH PAPER
Open Access
Generic and attribute-specific deep
representations for maritime vessels
Berkan Solmaz*†
, Erhan Gundogdu†, Veysel Yucesoy and Aykut Koc"
2322ec2f3571e0ddc593c4e2237a6a794c61251d,Four not six: Revealing culturally common facial expressions of emotion.,"Jack, R. E. , Sun, W., Delis, I., Garrod, O. G. B. and Schyns, P. G. (2016)
Four not six: revealing culturally common facial expressions of
emotion.Journal of Experimental Psychology: General, 145(6), pp. 708-
730.  (doi:10.1037/xge0000162)
This is the author’s final accepted version.
There may be differences between this version and the published version.
You are advised to consult the publisher’s version if you wish to cite from
http://eprints.gla.ac.uk/116592/
Deposited on: 20 April 2016
Enlighten – Research publications by members of the University of Glasgow
http://eprints.gla.ac.uk"
2306b2a8fba28539306052764a77a0d0f5d1236a,Surveillance Face Recognition Challenge,"Noname manuscript No.
(will be inserted by the editor)
Surveillance Face Recognition Challenge
Zhiyi Cheng · Xiatian Zhu · Shaogang Gong
Received: date / Accepted: date"
23e1746c449e675a4ffa3833b0ac5c5a7b743f7f,Development of an Efficient Face Recognition System based on Linear and Nonlinear Algorithms,"Development of an Efficient Face Recognition System based on Linear and Nonlinear Algorithms
{tag}                                                 {/tag}
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 134
Number 7
Year of Publication: 2016
Authors:
Filani Araoluwa S., Adetunmbi Adebayo O.
10.5120/ijca2016907932
{bibtex}2016907932.bib{/bibtex}"
23a2b75c92123b3e7bbaf1d98e434845167fe259,Multimodal Biometrics for Identity Documents 1 State-ofthe-Art Research Report PFS 341-08 . 05 ( Version 1 . 0 ),"Forensic Science International 167 (2007) 154–159
www.elsevier.com/locate/forsciint
Multimodal biometrics for identity documents (
Damien Dessimoz a,*, Jonas Richiardi b, Christophe Champod a, Andrzej Drygajlo b
Institut de Police Scientifique, E´ cole des Sciences Criminelles, Universite´ de Lausanne, Switzerland
Speech Processing and Biometrics Group, Signal Processing Institute, E´ cole Polytechnique Fe´de´rale de Lausanne, Switzerland
Received 9 June 2006; accepted 14 June 2006
Available online 4 August 2006"
239df42479c69cf95e7194cc0ec3d8cf7d4a98e8,Face Detection and Extraction from Low Resolution Surveillance Video Using Motion Segmentation,"Face Detection and Extraction from Low
Resolution Surveillance Video Using
Motion Segmentation
Vikram Mutneja1
I.K. Gujral Punjab Technical University, Kapurthala, Punjab (India)
Ph.D. Research Scholar,
I.K. Gujral Punjab Technical University Main Campus, Kapurthala, Punjab (India)
Dr. Satvir Singh2,
Associate Professor,"
230c4a30f439700355b268e5f57d15851bcbf41f,EM Algorithms for Weighted-Data Clustering with Application to Audio-Visual Scene Analysis,"EM Algorithms for Weighted-Data Clustering
with Application to Audio-Visual Scene Analysis
Israel D. Gebru, Xavier Alameda-Pineda, Florence Forbes and Radu Horaud"
23b93f3b237481bd1d36941ca3312bb16f4beb58,Reconnaissance d'événements et d'actions à partir de la profondeur thermique 3D. (Event and action recognition from thermal and 3D depth Sensing),"Reconnaissance d’événements et d’actions à partir de la
profondeur thermique 3D
Adnan Al Alwani
To cite this version:
Adnan Al Alwani. Reconnaissance d’événements et d’actions à partir de la profondeur thermique
D. Vision par ordinateur et reconnaissance de formes [cs.CV]. Université de Caen Normandie, 2016.
Français. <tel-01418369>
HAL Id: tel-01418369
https://hal.archives-ouvertes.fr/tel-01418369
Submitted on 16 Dec 2016
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
236db916e2c73eccfe8821110274affcc9b54360,From Virtual to Reality: Fast Adaptation of Virtual Object Detectors to Real Domains,"BMVC 2014 BAOCHEN SUN, KATE SAENKO: FROM VIRTUAL TO REALITY
From Virtual to Reality: Fast Adaptation of
Virtual Object Detectors to Real Domains
Baochen Sun
http://www.cs.uml.edu/~bsun
Kate Saenko
http://www.cs.uml.edu/~saenko
Computer Science Department
University of Massachusetts Lowell
Lowell, Massachusetts, US"
23fd82c04852b74d655015ff0876e6c5defc6e61,Deep-based Ingredient Recognition for Cooking Recipe Retrieval,"Deep-based Ingredient Recognition for
Cooking Recipe Retrieval
Jingjing Chen
City University of HongKong
Kowloon, HongKong
Chong-Wah Ngo
City University of HongKong
Kowloon, HongKong"
2396ff03c41c498ff20e3a0e5419afa45e4a9d41,MIT Autonomous Vehicle Technology Study: Large-Scale Deep Learning Based Analysis of Driver Behavior and Interaction with Automation,"MIT Autonomous Vehicle Technology Study:
Large-Scale Deep Learning Based Analysis of
Driver Behavior and Interaction with Automation
Lex Fridman∗, Daniel E. Brown, Michael Glazer, William Angell, Spencer Dodd, Benedikt Jenik,
Andrew Sipperley, Anthony Pettinato, Bobbie Seppelt, Linda Angell, Bruce Mehler, Bryan Reimer∗
Jack Terwilliger, Julia Kindelsberger, Li Ding, Sean Seaman, Hillary Abraham, Alea Mehler,"
2331df8ca9f29320dd3a33ce68a539953fa87ff5,Extended Isomap for Pattern Classification,"Extended Isomap for Pattern Classification
Ming-Hsuan Yang
Honda Fundamental Research Labs
Mountain View, CA 94041"
2312bc2d48a0f68bd5ab1b024d5726786455da3a,Learning Deep Context-Aware Features over Body and Latent Parts for Person Re-identification,"Learning Deep Context-aware Features over Body and Latent Parts
for Person Re-identification
Supplementary Materials
Dangwei Li1,2, Xiaotang Chen1,2, Zhang Zhang1,2, Kaiqi Huang1,2,3
CRIPAC & NLPR, CASIA 2University of Chinese Academy of Sciences
CAS Center for Excellence in Brain Science and Intelligence Technology
{dangwei.li, xtchen, zzhang,
. Market1501 dataset
To further understand the results on Market1501 [8], we show mean Average Precision (mAP) and Rank-1 identification
rate between camera pairs in Figure 1 and Figure 2. Compared to the BOW methods, the proposed method improves mean
mAP and Rank-1 identification rate between camera pairs by 35.09% and 40.01% respectively. In addition, we show some
searching results with different query images in Figure 3. The dataset is challenging and the returned images have very similar
ppearances and some pedestrians have large backgrounds and occlusions. For the query image in first row of Figure 3, even
though the query person has large occlusions and some groundtruth images have large backgrounds, our proposed method
an still return the right results. This shows the effectiveness of our proposed method.
. CUHK03 dataset
CUHK03 [3] is one of the largest person re-identification datasets. It provides two types of pedestrian bounding boxes,
including detected and manually annotated. In this paragraph, we show the overall Cumulated Matching Characteristics
(CMC) on both detected and labeled datasets in Figure 4. For the GateSCNN [5] in Figure 4(a), we use the singe-query
results to approximate the single-shot results. The DGD [6] is trained using multiple datasets. In this paper, we use the"
23172f9a397f13ae1ecb5793efd81b6aba9b4537,Defining Visually Descriptive Language,"Proceedings of the 2015 Workshop on Vision and Language (VL’15), pages 10–17,
Lisbon, Portugal, 18 September 2015. c(cid:13)2015 Association for Computational Linguistics."
23e881c9b791fd17e248b1fb4fc980710dd005d7,An Unbiased Temporal Representation for Video-Based Person Re-Identification,"AN UNBIASED TEMPORAL REPRESENTATION FOR VIDEO-BASED PERSON
RE-IDENTIFICATION
Xiu Zhang and Bir Bhanu
Center for Research in Intelligent Systems
University of California, Riverside, Riverside, CA 92521, USA"
23ea8a34570342855611a78a4ff00ddd902e6123,Gradient-based global features and its application to image retargeting,"Gradient-based Global Features and Its Application
to Image Retargeting
Izumi Ito
Tokyo Institute of Technology Tokyo, 152-8552 Japan
+81-3-5734-2997"
23cb53fd272c105e98111e66502120216ae1788c,Incremental learning for robot perception through HRI,"Incremental Learning for Robot Perception through HRI
Sepehr Valipour∗, Camilo Perez∗ and Martin Jagersand"
2333cf918f50ac2ae201a837166d310adf3a00b0,Optimally Training a Cascade Classifier,"Optimally Training a Cascade Classifier
Chunhua Shen, Peng Wang, and Anton van den Hengel"
23e0571fd42347ca2bdf424b8562c6c6f72b88fc,Classification of Things in DBpedia using Deep Neural Networks,"International Journal of Web & Semantic Technology (IJWesT) Vol.9, No.1, January 2018
CLASSIFICATION OF THINGS IN DBPEDIA
USING DEEP NEURAL NETWORKS
Rahul Parundekar
Elevate.do"
235f4fad10a5d9e043759354a7cb94122a8f10fc,"Multi-perspective vehicle detection and tracking: Challenges, dataset, and metrics","Windsor Oceanico Hotel, Rio de Janeiro, Brazil, November 1-4, 2016
978-1-5090-1889-5/16/$31.00 ©2016 IEEE"
23d7833d63ed3e416bbb237ea39f751bc9bfb703,"THE MULTI-BIOMETRIC , MULTI-DEVICE AND MULTILINGUAL ( M 3 ) CORPUS","THE MULTI-BIOMETRIC, MULTI-DEVICE AND MULTILINGUAL (M3) CORPUS
Helen Meng1, P.C. Ching1, Tan Lee1, Man Wai Mak2, Brian Mak3, Y.S. Moon1, Man-Hung Siu3,
Xiaoou Tang1, Henry P.S. Hui1, Andrew Lee1, Wai-Kit Lo1, Bin Ma1 and Eddie K.T. Sio1
The Chinese University of Hong Kong (CUHK), 2The Hong Kong Polytechnic University (HKPolyU),
Hong Kong University of Science and Technology (HKUST)
Email:"
239492717385758c0e64afe4d31b789d000c18f2,Technical Report for Real-Time Certified Probabilistic Pedestrian Forecasting,"Technical Report for Real-Time Certified Probabilistic Pedestrian Forecasting
Henry O. Jacobs, Owen K. Hughes, Matthew Johnson-Roberson, and Ram Vasudevan"
23e707600c3e9a240e24eaa4ed4b0e4ec6a436c1,Automatic foreground extraction via joint CRF and online learning,"Automatic foreground extraction via joint CRF and
online learning
Wenbin Zou, Kidiyo Kpalma, Joseph Ronsin
To cite this version:
Wenbin Zou, Kidiyo Kpalma, Joseph Ronsin. Automatic foreground extraction via joint CRF
nd online learning. Electronics Letters, IET, 2013, 49 (18), pp.1140 - 1142. .
HAL Id: hal-00875339
https://hal.archives-ouvertes.fr/hal-00875339
Submitted on 21 Oct 2013
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
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destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,
´emanant des ´etablissements d’enseignement et de
recherche fran¸cais ou ´etrangers, des laboratoires"
23ba9e462151a4bf9dfc3be5d8b12dbcfb7fe4c3,"Determining Mood from Facial Expressions CS 229 Project , Fall 2014","CS 229 Project, Fall 2014
Matthew Wang
Spencer Yee
Determining Mood from Facial Expressions
Introduction
Facial expressions play an extremely important role in human communication. As
society continues to make greater use of human-machine interactions, it is important for
machines to be able to interpret facial expressions in order to improve their
uthenticity. If machines can be trained to determine mood to a better extent than
humans can, especially for more subtle moods, then this could be useful in fields such as
ounseling. This could also be useful for gauging reactions of large audiences in various
ontexts, such as political talks.
The results of this project could also be applied to recognizing other features of facial
expressions, such as determining when people are purposefully suppressing emotions or
lying. The ability to recognize different facial expressions could also improve technology
that recognizes to whom specific faces belong. This could in turn be used to search a
large number of pictures for a specific photo, which is becoming increasingly difficult, as
storing photos digitally has been extremely common in the past decade. The possibilities
re endless.
II  Data and Features"
2311cdd241c118395a510776ec226aff7725ebc8,Hunting Nessie - Real-time abnormality detection from webcams,"Hunting Nessie – Real-Time Abnormality Detection from Webcams
Michael D. Breitenstein1 Helmut Grabner1 Luc Van Gool1,2
Computer Vision Laboratory
ETH Zurich
ESAT-PSI / IBBT
KU Leuven"
23943197f4124aa6c5a263bf2042169c9b816906,Crafting GBD-Net for Object Detection,"MANUSCRIPT
Crafting GBD-Net for Object Detection
Xingyu Zeng*,Wanli Ouyang*,Junjie Yan, Hongsheng Li,Tong Xiao, Kun Wang, Yu Liu, Yucong Zhou, Bin
Yang, Zhe Wang,Hui Zhou, Xiaogang Wang,"
231a12de5dedddf1184ae9caafbc4a954ce584c3,Closed and Open World Multi-shot Person Re-identification. (Ré-identification de personnes à partir de multiples images dans le cadre de bases d'identités fermées et ouvertes),"Closed and Open World Multi-shot Person
Re-identification
Solène Chan-Lang
To cite this version:
Solène Chan-Lang. Closed and Open World Multi-shot Person Re-identification. Systems and Control
[cs.SY]. Université Pierre et Marie Curie - Paris VI, 2017. English. <NNT : 2017PA066389>. <tel-
01810504>
HAL Id: tel-01810504
https://tel.archives-ouvertes.fr/tel-01810504
Submitted on 8 Jun 2018
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
2349eab05cd0c6f94ba5314c037d198aa12c2f0f,Eigen-profiles of spatio-temporal fragments for adaptive region-based tracking,"978-1-4673-0046-9/12/$26.00 ©2012 IEEE
ICASSP 2012"
237316762470d72a02795a7f57de9279e9cda16a,Dimensionality-reduced subspace clustering,"Dimensionality-reduced subspace clustering
Reinhard Heckel, Michael Tschannen, and Helmut B¨olcskei
December 15, 2015"
23095c6fc92f41a86f93276d446cfc72c7ce7b23,Stereo-based Pedestrian Detection using Multiple Patterns,"HATTORI et al.: STEREO-BASED PEDESTRIAN DETECTION USING MULTI-PATTERNS
Stereo-based Pedestrian Detection using
Multiple Patterns
Research & Development Center,
TOSHIBA Corporation, JAPAN
Hiroshi Hattori
Akihito Seki
Manabu Nishiyama
Tomoki Watanabe"
230ad73e8bd1d3268d56c66a83442d24176b864d,ORB-SLAM: A Versatile and Accurate Monocular SLAM System,"IEEE Xplore: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7219438
DOI: 10.1109/TRO.2015.2463671
(cid:13)2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any
urrent or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new
ollective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other
works."
230527d37421c28b7387c54e203deda64564e1b7,Person Re-identification: System Design and Evaluation Overview,"Person Re-identification: System Design and
Evaluation Overview
Xiaogang Wang and Rui Zhao"
23a8d02389805854cf41c9e5fa56c66ee4160ce3,Influence of low resolution of images on reliability of face detection and recognition,"Multimed Tools Appl
DOI 10.1007/s11042-013-1568-8
Influence of low resolution of images on reliability
of face detection and recognition
Tomasz Marciniak· Agata Chmielewska·
Radoslaw Weychan· Marianna Parzych·
Adam Dabrowski
© The Author(s) 2013. This article is published with open access at SpringerLink.com"
2310202b10c535b7228c9029e11a24d33deafdb2,Wavelet-based fingerprint image retrieval,"Accepted Manuscript
Wavelet-based fingerprint image retrieval
Javier A. Montoya Zegarra, Neucimar J. Leite, Ricardo da Silva Torres
Reference:
S0377-0427(08)00112-X
0.1016/j.cam.2008.03.017
CAM 6702
To appear in:
Journal of Computational and Applied Mathe-
matics
Received date: 4 April 2006
Revised date:
0 September 2007
Please cite this article as: J.A. Montoya Zegarra, N.J. Leite, R. da Silva Torres, Wavelet-based
fingerprint image retrieval, Journal of Computational and Applied Mathematics (2008),
doi:10.1016/j.cam.2008.03.017
This is a PDF file of an unedited manuscript that has been accepted for publication. As a
service to our customers we are providing this early version of the manuscript. The manuscript
will undergo copyediting, typesetting, and review of the resulting proof before it is published in
its final form. Please note that during the production process errors may be discovered which"
23a68a8b181a2d29172a5b2fe61c72cecdc15638,Local Feature Based on Moment Invariants for Blurred Image Matching,"International Journal of Electronics and Computer Science Engineering              376
Available Online at www.ijecse.org                                                                          ISSN- 2277-1956
Local Feature Based on Moment Invariants for
Blurred Image Matching
Qiang Tong  1 , Terumasa Aoki  1, 2
Graduate School of Information Sciences, Tohoku University，Japan
2   New Industry Creation Hatchery Center, Tohoku University，Japan"
23120f9b39e59bbac4438bf4a8a7889431ae8adb,Aalborg Universitet Improved RGB-D-T based Face Recognition,"Aalborg Universitet
Improved RGB-D-T based Face Recognition
Oliu Simon, Marc;   Corneanu, Ciprian; Nasrollahi, Kamal; Guerrero, Sergio Escalera;
Nikisins, Olegs; Sun, Yunlian; Li, Haiqing; Sun, Zhenan; Moeslund, Thomas B.; Greitans,
Modris
Published in:
IET Biometrics
DOI (link to publication from Publisher):
0.1049/iet-bmt.2015.0057
Publication date:
Document Version
Accepted manuscript, peer reviewed version
Link to publication from Aalborg University
Citation for published version (APA):
Oliu Simon, M.,   Corneanu, C., Nasrollahi, K., Guerrero, S. E., Nikisins, O., Sun, Y., ... Greitans, M. (2016).
Improved RGB-D-T based Face Recognition. IET Biometrics, (2047-4946 ). DOI: 10.1049/iet-bmt.2015.0057
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237ec7e6d20025c32069e41f8007bb97931a7fc6,Learning real-time object detectors: probabilistic generative approaches,
2315371408e02cdff6f54359f159f192009d1600,Effective Pedestrian Detection Using Center-symmetric Local Binary/Trinary Patterns,"SEPTEMBER 2010
Effective Pedestrian Detection Using
Center-symmetric Local Binary/Trinary Patterns
Yongbin Zheng, Chunhua Shen, Richard Hartley, Fellow, IEEE, and Xinsheng Huang"
f1c2ba8c7797c4844fa61068b3ce9d319e6ced3f,Human Head Tracking Based on Inheritance and Evolution Concept,"MVA2009 IAPR Conference on Machine Vision Applications, May 20-22, 2009, Yokohama, JAPAN
Human Head Tracking Based on Inheritance and Evolution Concept
Yi Hu,      Tetsuya Takamori
Fujifilm Corporation, Japan
798, Miyanodai, Kaisei-machi, Ashigarakami-gun, Kanagawa, 258-8538 JAPAN
{yi_hu,"
f1ba2fe3491c715ded9677862fea966b32ca81f0,Face Tracking and Recognition in Videos : HMM Vs KNN,"ISSN: 2321-7782 (Online)
Volume 1, Issue 7, December 2013
International Journal of Advance Research in
Computer Science and Management Studies
Research Paper
Available online at: www.ijarcsms.com
Face Tracking and Recognition in Videos:
HMM Vs KNN
Madhumita R. Baviskar
Assistant Professor
Department of Computer Engineering
MIT College of Engineering (Pune University)
Pune - India"
f196a79c5e4b570013e4aa031cdd0fc0c98fc07d,Interactively Picking Real-World Objects with Unconstrained Spoken Language Instructions,"Interactively Picking Real-World Objects with
Unconstrained Spoken Language Instructions
Jun Hatori∗, Yuta Kikuchi∗, Sosuke Kobayashi∗, Kuniyuki Takahashi∗,
Yuta Tsuboi∗, Yuya Unno∗, Wilson Ko, Jethro Tan†"
f1bb2c95dc270ffa9c2f88e29ae5d2178b4459cb,A Generative Model of People in Clothing,"A Generative Model of People in Clothing
Christoph Lassner1, 2
Gerard Pons-Moll2
Peter V. Gehler3,*
BCCN, Tübingen
MPI for Intelligent Systems, Tübingen 3University of Würzburg
Figure 1: Random examples of people generated with our model. For each row, sampling is conditioned on the silhouette
displayed on the left. Our proposed framework also supports unconditioned sampling as well as conditioning on local
ppearance cues, such as color."
f13552e2e2843716e7a1c7c2492cfcc6e86aa03c,Reinforced Pipeline Optimization: Behaving Optimally,"Under review as a conference paper at ICLR 2019
REINFORCED PIPELINE OPTIMIZATION: BEHAVING
OPTIMALLY WITH NON-DIFFERENTIABILITIES
Anonymous authors
Paper under double-blind review"
f1aaf4f80c2bd42f14d8feeec34bcb0af48a61db,PiEye in the Wild: Exploring Eye Contact Detection for Small Inexpensive Hardware,"Teknik och samhälle
Datavetenskap
Examensarbete
5 högskolepoäng, grundnivå
PiEye in the Wild: Exploring Eye Contact Detection for
Small Inexpensive Hardware
PiEye: En Undersökning av Ögonkontakts-igenkänning för Liten och Billig
Hårdvara
Karl Casserfelt
Ragnar Einestam
Examen: kandidatexamen 180 hp
Huvudområde: Datavetenskap
Program: Datavetenskap och Applikation-
sutveckling
Datum för slutseminarium: 2017-05-30
Handledare: Shahram Jalalinya
Examinator: Erik Pineiro"
f131a654bbf4c8de0679d3c6054c10bba4a919d4,Vision-based Driver Assistance Systems,"Vision-based Driver Assistance Systems
.enpeda.. (Environment Perception and Driver Assistance) Project
CITR, Auckland, New Zealand
Reinhard Klette
5 February 2015"
f1d090fcea63d9f9e835c49352a3cd576ec899c1,Single-Hidden Layer Feedforward Neual Network Training Using Class Geometric Information,"Iosifidis, A., Tefas, A., & Pitas, I. (2015). Single-Hidden Layer Feedforward
Neual Network Training Using Class Geometric Information. In . J. J.
Merelo, A. Rosa, J. M. Cadenas, A. Dourado, K. Madani, & J. Filipe (Eds.),
Computational Intelligence: International Joint Conference, IJCCI 2014
Rome, Italy, October 22-24, 2014 Revised Selected Papers. (Vol. III, pp.
51-364). (Studies in Computational Intelligence; Vol. 620). Springer. DOI:
0.1007/978-3-319-26393-9_21
Peer reviewed version
Link to published version (if available):
0.1007/978-3-319-26393-9_21
Link to publication record in Explore Bristol Research
PDF-document
University of Bristol - Explore Bristol Research
General rights
This document is made available in accordance with publisher policies. Please cite only the published
version using the reference above. Full terms of use are available:
http://www.bristol.ac.uk/pure/about/ebr-terms.html"
f157daaffa1754aae5963d9c49247142b07c8d4a,DCT-BASED REDUCED FACE FOR FACE RECOGNITION,"International Journal of Information Technology and Knowledge Management
January-June 2012, Volume 5, No. 1, pp. 97-100
DCT-BASED  REDUCED  FACE  FOR  FACE  RECOGNITION
Vikas Maheshkar1, Sushila Kamble2, Suneeta Agarwal3, and Vinay Kumar Srivastava4"
f16921c1c6e8bce89bce7679cbd824d65b494e4d,The face of love: spontaneous accommodation as social emotion regulation.,"Personality and Social Psychology
Bulletin
http://psp.sagepub.com/
The Face of Love : Spontaneous Accommodation as Social Emotion Regulation
Pers Soc Psychol Bull
Michael Häfner and Hans IJzerman
2011 37: 1551 originally published online 21 July 2011
DOI: 10.1177/0146167211415629
The online version of this article can be found at:
http://psp.sagepub.com/content/37/12/1551
Published by:
http://www.sagepublications.com
On behalf of:
Society for Personality and Social Psychology
Additional services and information for
Personality and Social Psychology Bulletin
can be found at:
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f19ab817dd1ef64ee94e94689b0daae0f686e849,Blickrichtungsunabhängige Erkennung von Personen in Bild- und Tiefendaten,"TECHNISCHE UNIVERSIT¨AT M ¨UNCHEN
Lehrstuhl f¨ur Mensch-Maschine-Kommunikation
Blickrichtungsunabh¨angige Erkennung von
Personen in Bild- und Tiefendaten
Andre St¨ormer
Vollst¨andiger Abdruck der von der Fakult¨at f¨ur Elektrotechnik und Informationstechnik
der Technischen Universit¨at M¨unchen zur Erlangung des akademischen Grades eines
Doktor-Ingenieurs (Dr.-Ing.)
genehmigten Dissertation.
Vorsitzender:
Univ.-Prof. Dr.-Ing. Thomas Eibert
Pr¨ufer der Dissertation:
. Univ.-Prof. Dr.-Ing. habil. Gerhard Rigoll
. Univ.-Prof. Dr.-Ing. Horst-Michael Groß,
Technische Universit¨at Ilmenau
Die Dissertation wurde am 16.06.2009 bei der Technischen Universit¨at M¨unchen einge-
reicht und durch die Fakult¨at f¨ur Elektrotechnik und Informationstechnik am 30.10.2009
ngenommen."
f1498ba94d64ee221e27b658bcd407c160cf0897,LS-VO: Learning Dense Optical Subspace for Robust Visual Odometry Estimation,"LS-VO: Learning Dense Optical Subspace for Robust Visual Odometry
Estimation
Gabriele Costante†,1 and Thomas A. Ciarfuglia†,1"
f11d070cdc9ee12b201757ca4a50a3682967ba0c,Spatial Language Understanding with Multimodal Graphs using Declarative Learning based Programming,"Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing, pages 33–43
Copenhagen, Denmark, September 7–11, 2017. c(cid:13)2017 Association for Computational Linguistics"
f18c34458460b9b62b51213b9165b37c057c5837,Unsupervised object discovery and co-localization by deep descriptor transformation,"Noname manuscript No.
(will be inserted by the editor)
Unsupervised Object Discovery and Co-Localization
y Deep Descriptor Transforming
Xiu-Shen Wei · Chen-Lin Zhang · Jianxin Wu · Chunhua Shen ·
Zhi-Hua Zhou
Received: date / Accepted: date"
f1aa120fb720f6cfaab13aea4b8379275e6d40a2,InverseFaceNet: Deep Single-Shot Inverse Face Rendering From A Single Image,"InverseFaceNet: Deep Single-Shot Inverse Face Rendering From A Single Image
Hyeongwoo Kim1
Justus Thies2
Max-Planck-Institute for Informatics
Michael Zollhöfer1
Christian Richardt3
University of Erlangen-Nuremberg 3 University of Bath
Christian Theobalt1
Ayush Tewari1
Figure 1. Our single-shot deep inverse face renderer InverseFaceNet obtains a high-quality geometry, reflectance and illumination estimate
from just a single input image. We jointly recover the face pose, shape, expression, reflectance and incident scene illumination. From left to
right: input photo, our estimated face model, its geometry, and the pointwise Euclidean error compared to Garrido et al. [14]."
f157ec3f4d330aac34331300b4c1fd1edcb46156,Improving landfill monitoring programs with the aid of geoelectrical-imaging techniques and geographical information systems,"Analysis and Classification of Object Poses:
Using Visual / Infrared Images and Feature Fusion
Master of Science Thesis
YIXIAO YUN
Department of Signals and Systems
Signal Processing Group
Chalmers University of Technology
Gothenburg, Sweden 2011
Report No. Ex 040/2011
Improving landfill monitoring programswith the aid of geoelectrical - imaging techniquesand geographical information systems Master’s Thesis in the Master Degree Programme, Civil Engineering KEVIN HINEDepartment of Civil and Environmental Engineering Division of GeoEngineering Engineering Geology Research GroupCHALMERS UNIVERSITY OF TECHNOLOGYGöteborg, Sweden 2005Master’s Thesis  2005:22"
f1a0010f588a41682c1efd770541c4c381949d88,VisGraB: A benchmark for vision-based grasping,"VisGraB: A Benchmark for Vision-Based Grasping
Gert Kootstra ∗1, Mila Popovi´c2, Jimmy Alison Jørgensen3, Danica Kragic1, Henrik Gordon Petersen3, and Norbert Kr¨uger2
Computer Vision and Active Perception Lab, CSC, Royal Institute of Technology (KTH), Stockholm, Sweden
Cognitive Vision Lab, The Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Campusvej 55, DK-5230 Odense, Denmark
Robotics Lab, The Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Campusvej 55, DK-5230 Odense, Denmark
August 3, 2012"
f17d6db4844f26a023f92b8771a1c33cea91b9e4,1 Million Captioned Dutch Newspaper Images,"Million Captioned Dutch Newspaper Images
Desmond Elliott∗† and Martijn Kleppe‡
ILLC, University of Amsterdam; †CWI; ‡Erasmus University Rotterdam"
f174b24860b4cacbe047d3a5650cf8866d2244d9,Monocular Depth Estimation by Learning from Heterogeneous Datasets,"Monocular Depth Estimation by Learning from Heterogeneous
Datasets
Akhil Gurram1,2, Onay Urfalioglu2, Ibrahim Halfaoui2, Fahd Bouzaraa2 and Antonio M. L´opez1"
f1a05136c8b8f9334a4b3d9de2a4b192d2c762c2,Scene Classification via Hypergraph-Based Semantic Attributes Subnetworks Identification,"Scene Classification via Hypergraph-Based
Semantic Attributes Subnetworks Identification
Sun-Wook Choi, Chong Ho Lee, and In Kyu Park
Department of Information and Communication Engineering
Inha University, Incheon 402-751, Korea"
f1052df3e311b7caa563685e741e0a1bb6b288df,A Hierarchical Fusion Strategy based Multimodal Biometric System,"The International Arab Conference on Information Technology (ACIT’2013)
A Hierarchical Fusion Strategy based Multimodal
Biometric System
Youssef Elmir, 2Zakaria Elberrichi and 2Réda Adjoudj
Faculty of Sciences and Technology, University of Adrar, Algeria
Faculty of Technology, Djillali Liabès University of Sidi Bel Abbès, Algeria"
f1d3e9335b4fe5c514c7ecd9e0d9bf3c864454ff,Driving dataset in the wild : Various driving scenes by Byung Gon,"Driving dataset in the wild: Various driving scenes
Kurt Keutzer
Byung Gon Song
John Chuang, Ed.
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2016-92
http://www.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-92.html
May 13, 2016"
f168cfb66dc40b49d2e2076907a4c6ab1bdb0085,A Compositional Object-Based Approach to Learning Physical Dynamics,"Published as a conference paper at ICLR 2017
A COMPOSITIONAL OBJECT-BASED APPROACH TO
LEARNING PHYSICAL DYNAMICS
Michael B. Chang*, Tomer Ullman**, Antonio Torralba*, and Joshua B. Tenenbaum**
*Department of Electrical Engineering and Computer Science, MIT
**Department of Brain and Cognitive Sciences, MIT"
f1d8c377093ecf64afd7f17383738e81666fe5ae,Remote Detection of Idling Cars Using Infrared Imaging and Deep Networks,"Noname manuscript No.
(will be inserted by the editor)
Remote Detection of Idling Cars Using Infrared Imaging and Deep Networks
Muhammet Bastan · Kim-Hui Yap · Lap-Pui Chau
Date: April 2018"
568727a76dc1242e3d48392f9c19678a27c63482,High Entropy Ensembles for Holistic Figure-ground Segmentation,"GALLO et al.: HEE FOR HOLISTIC FIGURE-GROUND SEGMENTATION
High Entropy Ensembles for Holistic
Figure-ground Segmentation
Ignazio Gallo
Alessandro Zamberletti
Simone Albertini
Lucia Noce
Applied Recognition Technology
Laboratory
Department of Theoretical and Applied
Science
University of Insubria
Varese, Italy"
566038a3c2867894a08125efe41ef0a40824a090,Face recognition and gender classification in personal memories,"978-1-4244-2354-5/09/$25.00 ©2009 IEEE
ICASSP 2009"
56b9c6efe0322f0087d2f82b52129cc6b41ab356,"Acquire, Augment, Segment & Enjoy: Weakly Supervised Instance Segmentation of Supermarket Products","Acquire, Augment, Segment & Enjoy:
Weakly Supervised Instance Segmentation of
Supermarket Products
Patrick Follmann+*, Bertram Drost+, and Tobias B¨ottger+*
+MVTec Software GmbH, Munich, Germany
Technical University of Munich (TUM)
July 9, 2018"
56e95fa26fb417776824e5adf6d6d511e5b30110,Object and Action Classification with Latent Window Parameters,"Int J Comput Vis
DOI 10.1007/s11263-013-0646-8
Object and Action Classification with Latent Window Parameters
Hakan Bilen · Vinay P. Namboodiri · Luc J. Van Gool
Received: 1 October 2012 / Accepted: 18 July 2013
© Springer Science+Business Media New York 2013"
56bc524d7cc1ff2fad8f27c0414cac437fc2b4f0,Protest Activity Detection and Perceived Violence Estimation from Social Media Images,"To appear in Proceedings of the 25th ACM International Conference on Multimedia 2017
Protest Activity Detection and Perceived Violence Estimation
from Social Media Images
Donghyeon Won
Zachary C. Steinert-Threlkeld
Jungseock Joo"
5665d98136cc39322d47cb782b8e49d141c5a29e,AN AGILE FRAMEWORK FOR REAL-TIME VISUAL TRACKING IN VIDEOS,"REPORT DOCUMENTATION PAGE
Form Approved OMB NO. 0704-0188
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the  data  needed,  and  completing  and  reviewing
this  collection  of"
56852a56dd830a6ee3882773c453025ddec652e2,Emotion recognition through static faces and moving bodies: a comparison between typically developed adults and individuals with high level of autistic traits,"ORIGINAL RESEARCH
published: 23 October 2015
doi: 10.3389/fpsyg.2015.01570
Emotion recognition through static
faces and moving bodies: a
omparison between typically
developed adults and individuals
with high level of autistic traits†
Rossana Actis-Grosso1,2*, Francesco Bossi1 and Paola Ricciardelli1,2
Department of Psychology, University of Milano-Bicocca, Milano, Italy, 2 Milan Centre for Neuroscience, Milano, Italy
We investigated whether the type of stimulus (pictures of static faces vs. body motion)
ontributes differently to the recognition of emotions. The performance (accuracy and
response times) of 25 Low Autistic Traits (LAT group) young adults (21 males) and 20
young adults (16 males) with either High Autistic Traits or with High Functioning Autism
Spectrum Disorder (HAT group) was compared in the recognition of four emotions
(Happiness, Anger, Fear, and Sadness) either shown in static faces or conveyed by
moving body patch-light displays (PLDs). Overall, HAT individuals were as accurate as
LAT ones in perceiving emotions both with faces and with PLDs. Moreover, they correctly
described non-emotional actions depicted by PLDs, indicating that they perceived the
motion conveyed by the PLDs per se. For LAT participants, happiness proved to be"
5615d6045301ecbc5be35e46cab711f676aadf3a,Discriminatively Learned Hierarchical Rank Pooling Networks,"Discriminatively Learned Hierarchical Rank Pooling Networks
Basura Fernando · Stephen Gould
Received: date / Accepted: date"
564555b7fdc45938d813650de7a7b1cd40005aa8,Implementation of SIFT In Various Applications,"International Journal of Engineering Research and Development
e-ISSN: 2278-067X, p-ISSN: 2278-800X, www.ijerd.com
Volume 7, Issue 4 (May 2013), PP. 59-64
Implementation of SIFT In Various Applications
,2,3Deen Bandhu Chotu Ram University of Science and Technology Murthal, Haryana, India.
Ritu Rani1, S. K. Grewal 2, Indiwar 3"
564babec16b895d385d06d38545febd66ef02f35,Robust Statistics for Feature-based Active Appearance Models,
5666ed763698295e41564efda627767ee55cc943,Relatively-Paired Space Analysis: Learning a Latent Common Space From Relatively-Paired Observations,"Manuscript
Click here to download Manuscript: template.tex
Click here to view linked References
Noname manuscript No.
(will be inserted by the editor)
Relatively-Paired Space Analysis: Learning a Latent Common
Space from Relatively-Paired Observations
Zhanghui Kuang · Kwan-Yee K. Wong
Received: date / Accepted: date"
56a0ead811a1bf15e42be8a9a007b0299636f213,Talk the Walk: Navigating New York City through Grounded Dialogue,"Talk the Walk: Navigating New York City through
Grounded Dialogue
Harm de Vries1, Kurt Shuster3, Dhruv Batra3,2, Devi Parikh3,2, Jason Weston3 & Douwe Kiela3
MILA, Université de Montréal; 2Georgia Institute of Technology; 3Facebook AI Research"
56dca23481de9119aa21f9044efd7db09f618704,Riemannian Dictionary Learning and Sparse Coding for Positive Definite Matrices,"Riemannian Dictionary Learning and Sparse
Coding for Positive Definite Matrices
Anoop Cherian
Suvrit Sra"
56c5d08103c5bf4b263a81da73135455136bbe6d,Kernel MBPLS for a Scalable and Multi-Camera Person Re-Identification System,"Kernel MBPLS for a Scalable and Multi-Camera Person
Re-Identification System
Raphael Pratesa,*, William Robson Schwartza
Smart Surveillance Interest Group, Computer Science Department, Universidade Federal de Minas Gerais, Minas
Gerais, Brazil
Person re-identification aims at establishing global identities for individuals as they move
cross a camera network.
It is a challenging task due to the drastic appearance changes that
occur between cameras as consequence of different pose and illumination conditions. Pairwise
matching models yield state-of-the-art results in most of the person re-identification datasets by
apturing nuances that are robust and discriminative for a specific pair of cameras. Nonetheless,
pairwise models are not scalable with the number of surveillance cameras. Therefore, elegant solu-
tions combining scalability with high matching rates are crucial for the person re-identification in
real-world scenarios. In this work, we tackle this problem proposing a multi-camera nonlinear re-
gression model called Kernel Multiblock Partial Least Squares (Kernel MBPLS), a single subspace
model for the entire camera network that uses all the labeled information. In this subspace, probe
nd gallery individual can be successfully matched. Experimental results in three multi-camera
person re-identification datasets (WARD, RAID and SAIVT-SoftBIO) demonstrate that the Ker-
nel MBPLS presents favorable aspects such as the scalability and robustness with respect to the
number of cameras combined with the high matching rates."
56cf859363f1b5231418b40b957a9132a78ea546,VLASE: Vehicle Localization by Aggregating Semantic Edges,"VLASE: Vehicle Localization by Aggregating Semantic Edges
Xin Yu1∗, Sagar Chaturvedi1∗, Chen Feng2, Yuichi Taguchi2, Teng-Yok Lee2, Clinton Fernandes1, Srikumar Ramalingam1"
564d4ee76c0511bc395dfc8ef8e3b3867fc34a6d,Robust group sparse representation via half-quadratic optimization for face recognition,"Robust Group Sparse Representation via Half-Quadratic Optimization
for Face Recognition
Yong Peng and Bao-Liang Lu(cid:3), Senior Member, IEEE"
560447750f45ea18cb21f202e30344c4fe12c52e,Removal Of Blurred And Illuminated Face Image With Different Poses,"International Journal of Scientific & Engineering Research, Volume 5, Issue 3, March-2014                                                                           33
ISSN 2229-5518
Removal Of Blurred And Illuminated
Face Image With Different Poses
C.Indhumathi, C.Dhanamani"
562f35a662545d839876deeb605ca2c864507a82,Revealing Variations in Perception of Mental States from Dynamic Facial Expressions: A Cautionary Note,"Revealing Variations in Perception of Mental States from
Dynamic Facial Expressions: A Cautionary Note
Elisa Back1*, Timothy R. Jordan2
Department of Psychology, Kingston University London, Kingston upon Thames, United Kingdom, 2 Department of Psychology, Zayed University, Dubai, United Arab
Emirates"
987c9a137d638f3d561c52b6dd0f987734ad5460,Efficient Dense Modules of Asymmetric Convolution for Real-Time Semantic Segmentation,"Efficient Dense Modules of Asymmetric Convolution for
Real-Time Semantic Segmentation
Shao-Yuan Lo1     Hsueh-Ming Hang1     Sheng-Wei Chan2     Jing-Jhih Lin2
National Chiao Tung University     2 Industrial Technology Research Institute
{ShengWeiChan,"
98c548a4be0d3b62971e75259d7514feab14f884,Deep generative-contrastive networks for facial expression recognition,"Deep generative-contrastive networks for facial expression recognition
Youngsung Kim†, ByungIn Yoo‡,†, Youngjun Kwak†, Changkyu Choi†, and Junmo Kim‡
Samsung Advanced Institute of Technology (SAIT), ‡KAIST
hangkyu"
9817e0d11701e9ce0e31a32338ff3ff0969621ed,Dppnet: Approximating Determinantal Point Processes with Deep Networks,"Under review as a conference paper at ICLR 2019
DPPNET: APPROXIMATING DETERMINANTAL POINT
PROCESSES WITH DEEP NETWORKS
Anonymous authors
Paper under double-blind review"
983534325c649e391fefe87025337187021b9830,Towards Automatic Generation of Question Answer Pairs from Images,"Towards Automatic Generation of Question Answer Pairs from Images
Issey Masuda Mora, Santiago Pascual de la Puente, Xavier Giro-i-Nieto
Universitat Politecnica de Catalunya (UPC)
Barcelona, Catalonia/Spain"
9820f8d1f4fd7c1e5b294a2c8fef542d2f1050b4,Modeling of image variability for recognition,"Modeling of Image Variability for Recognition
Von der Fakult¨at f¨ur
Mathematik, Informatik und Naturwissenschaften der
Rheinisch-Westf¨alischen Technischen Hochschule Aachen
zur Erlangung des akademischen Grades eines
Doktors der Naturwissenschaften genehmigte Dissertation
vorgelegt von
Diplom-Informatiker
Daniel Martin Keysers
us D¨usseldorf
Berichter: Universit¨atsprofessor Dr.-Ing. Hermann Ney
Universit¨atsprofessor Dr.-Ing. Hans Burkhardt
Tag der m¨undlichen Pr¨ufung: 14. M¨arz 2006
Diese Dissertation ist auf den Internetseiten der Hochschulbibliothek online verf¨ugbar."
98a660c15c821ea6d49a61c5061cd88e26c18c65,Face Databases for 2 D and 3 D Facial Recognition : A Survey,"IOSR Journal of Engineering (IOSRJEN)
e-ISSN: 2250-3021, p-ISSN: 2278-8719
Vol. 3, Issue 4 (April. 2013), ||V1 || PP 43-48
Face Databases for 2D and 3D Facial Recognition: A Survey
R.Senthilkumar1, Dr.R.K.Gnanamurthy2
Assistant Professor,  Department of Electronics and Communication Engineering, Institute of Road and
Professor and Dean , Department of Electronics and Communication Engineering, Odaiyappa College of
Transport Technology,Erode-638 316.
Engineering and Technology,Theni-625 531."
981c2619adb110dec12165ac9dde93f2a9d4e389,Semi-automatic Hand Annotation Making Human-human Interaction Analysis Fast and Accurate,
9853136dbd7d5f6a9c57dc66060cab44a86cd662,"Improving the Neural Network Training for Face Recognition using Adaptive Learning Rate , Resilient Back Propagation and Conjugate Gradient Algorithm","International Journal of Computer Applications (0975 – 8887)
Volume 34– No.2, November 2011
Improving the Neural Network Training for Face
Recognition using Adaptive Learning Rate, Resilient
Back Propagation and Conjugate Gradient Algorithm
Hamed Azami
M.Sc. Student
Department of Electrical
Engineering, Iran University
of Science and Technology,
Tehran, Iran
Saeid Sanei
Associate Professor
Department of Computing,
Faculty of Engineering and
Physical Sciences, University
of Surrey, UK
Karim Mohammadi
Professor
Department of Electrical"
98f66e4597fb51a1f9990d30856f2e190dc44da1,Quantum-inspired evolutionary algorithms: a survey and empirical study,"J Heuristics (2011) 17: 303–351
DOI 10.1007/s10732-010-9136-0
Quantum-inspired evolutionary algorithms: a survey
nd empirical study
Gexiang Zhang
Received: 2 December 2009 / Revised: 1 June 2010 / Accepted: 13 June 2010 /
Published online: 29 June 2010
© Springer Science+Business Media, LLC 2010"
98b98a8413f21a48ee6effd52da8c31ece6a910d,Detecting handwritten signatures in scanned documents,"9th Computer Vision Winter Workshop
Zuzana Kúkelová and Jan Heller (eds.)
Křtiny, Czech Republic, February 3–5, 2014
Detecting handwritten signatures in scanned documents
İlkhan Cüceloğlu1,2, Hasan Oğul1
Department of Computer Engineering, Başkent University, Ankara, Turkey
DAS Document Archiving and Management Systems CO., Ankara, Turkey"
981449cdd5b820268c0876477419cba50d5d1316,Learning Deep Features for One-Class Classification,"Learning Deep Features for One-Class
Classification
Pramuditha Perera, Student Member, IEEE, and Vishal M. Patel, Senior Member , IEEE"
984ecfbda7249e67eca8d9b1697e81f80e2e483d,Visual object categorization with new keypoint-based adaBoost features,"Visual object categorization with new keypoint-based
daBoost features
Taoufik Bdiri, Fabien Moutarde, Bruno Steux
To cite this version:
Taoufik Bdiri, Fabien Moutarde, Bruno Steux. Visual object categorization with new keypoint-based
daBoost features. IEEE Symposium on Intelligent Vehicles (IV’2009), Jun 2009, XiAn, China. 2009.
<hal-00422580>
HAL Id: hal-00422580
https://hal.archives-ouvertes.fr/hal-00422580
Submitted on 7 Oct 2009
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
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émanant des établissements d’enseignement et de"
98127346920bdce9773aba6a2ffc8590b9558a4a,Efficient human action recognition using histograms of motion gradients and VLAD with descriptor shape information,"Noname manuscript No.
(will be inserted by the editor)
Ef‌f‌icient Human Action Recognition using
Histograms of Motion Gradients and
VLAD with Descriptor Shape Information
Ionut C. Duta · Jasper R.R. Uijlings ·
Bogdan Ionescu · Kiyoharu Aizawa ·
Alexander G. Hauptmann · Nicu Sebe
Received: date / Accepted: date"
9889596a98824bdf7e7c59b62e732c0b2d356c69,Soft Correspondences in Multimodal Scene Parsing.,"Sarah Taghavi Namin, Mohammad Najafi, Mathieu Salzmann, and Lars Petersson"
988d1295ec32ce41d06e7cf928f14a3ee079a11e,Semantic Deep Learning,"Semantic Deep Learning
Hao Wang
September 29, 2015"
98142e84a3cee08661b31371a2c610183df82c8f,Tight Bounds for the Expected Risk of Linear Classifiers and PAC-Bayes Finite-Sample Guarantees,"Tight Bounds for the Expected Risk of Linear Classifiers and
PAC-Bayes Finite-Sample Guarantees
Jean Honorio
CSAIL, MIT
Cambridge, MA 02139, USA"
98bf42055160845e6f8f3c022298e3b8e4e55f80,Vision Meets Drones: A Challenge,"Vision Meets Drones: A Challenge
Pengfei Zhu, Longyin Wen, Xiao Bian, Haibin Ling and Qinghua Hu"
986be05b286d99d840583578c102af31c56428fd,An Efficient Algorithm for Implementing Traffic Sign Detection on Low Cost Embedded System,"International Journal of Innovative
Computing, Information and Control
Volume 14, Number 1, February 2018
ICIC International c(cid:13)2018 ISSN 1349-4198
pp. 1–14
AN EFFICIENT ALGORITHM FOR IMPLEMENTING TRAFFIC SIGN
DETECTION ON LOW COST EMBEDDED SYSTEM
Aryuanto Soetedjo and I Komang Somawirata
Department of Electrical Engineering
National Institute of Technology
Jalan Raya Karanglo KM 2 Malang 65153, Indonesia
Received May 2017; revised September 2017"
98220d35ae6a3ba745f7dea1434f000ca60c62c0,Multi-object Tracking using Particle Swarm Optimization on Target Interactions,"Multi-object Tracking using Particle Swarm
Optimization on Target Interactions
Bogdan Kwolek"
98f1613889657963b102460e4e970fe421c6ed3c,Accurate and Robust Neural Networks for Security Related Applications Exampled by Face Morphing Attacks,"Accurate and Robust Neural Networks for
Security Related Applications Exampled by Face
Morphing Attacks
Clemens Seibold1, Wojciech Samek1, Anna Hilsmann1 and Peter Eisert1,2
Fraunhofer HHI, Einsteinufer 37, 10587 Berlin, Germany
Humboldt University Berlin, Unter den Linden 6, 10099 Berlin, Germany"
9818c401e36dcf43b261e62c053bcfddd0b903ea,Visual Simultaneous Localization And Mapping ( VSLAM ) methods applied to indoor 3 D topographical and radiological mapping in real-time,"EPJ Web of Conferences 53
ICRS-13 & RPSD-2016
, 01005 (2017)
DOI: 10.1051/
epjconf/201
71530
Visual Simultaneous Localization And Mapping (VSLAM) methods applied
to indoor 3D topographical and radiological mapping in real-time
Felix Hautot1,3, Philippe Dubart2, Charles-Olivier Bacri3, Benjamin Chagneau2, Roger Abou-Khalil4
AREVA D&S, Technical Department, 1 route de la Noue 91196 Gif-sur-Yvette, France
AREVA D&S Technical Department, Marcoule, France
CSNSM (IN2P3/CNRS), Bat 104 et 108, 91405 Orsay, France
AREVA Corporate, Innovation Department, 1 place Jean Millier, 92084 Paris La Défense, France"
980cf8e3b59dd0923f7e7cf66d2bec4102d7035f,Unsupervised Learning for Physical Interaction through Video Prediction,"Unsupervised Learning for Physical Interaction
through Video Prediction
Chelsea Finn∗
UC Berkeley
Ian Goodfellow
OpenAI
Sergey Levine
Google Brain
UC Berkeley"
98c7a6210ca7bc81d2f7092ab28451f47039e920,UC Merced Proceedings of the Annual Meeting of the Cognitive Science Society Title What is the Ground ?,"UC Merced
Proceedings of the Annual Meeting of the Cognitive Science
Society
Title
What is the Ground? Continuous Maps for Symbol Grounding
Permalink
https://escholarship.org/uc/item/9p5236j4
Journal
Proceedings of the Annual Meeting of the Cognitive Science Society, 36(36)
Authors
Perera, Ian
Allen, James
Publication Date
014-01-01
Peer reviewed
eScholarship.org
Powered by the California Digital Library
University of California"
9833e347f8e19de59b931c94d50ef2685fd405fb,Statistical models for fruit detectability : spatial and temporal analyses of sweet peppers,"Statistical models for fruit detectability: spatial and
temporal analyses of sweet peppers.
Polina Kurtser, Yael Edan
Ben-Gurion University of the Negev"
98960be5ae51d30118f091f7091299a49f2f34bb,1 GLOBAL AND FEATURE BASED GENDER CLASSIFICATION OF FACES : A COMPARISON OF HUMAN PERFORMANCE AND COMPUTATIONAL MODELS,"GLOBAL AND FEATURE BASED GENDER CLASSIFICATION
OF FACES: A COMPARISON OF HUMAN PERFORMANCE
AND COMPUTATIONAL MODELS
SAMARASENA BUCHALAA   TIM M.GALEA,B   NEIL DAVEYA   RAY J.FRANKA
KERRY FOLEYB
A Department of Computer Science, University of Hertfordshire, College Lane, Hatfield,
{S.Buchala, N.Davey, T.Gale,
AL10 9AB, UK
B Department of Psychiatry, QEII Hospital, Welwyn Garden City, AL7 4HQ, UK
Most computational models for gender classification use global information (the full face
image) giving equal weight to the whole face area irrespective of the importance of the
internal features. Here, we use a global and feature based representation of face images
that  includes  both  global  and  featural  information.  We  use  dimensionality  reduction
techniques and a support vector machine classifier and show that this method performs
etter than either global or feature based representations alone.
.  Introduction
Most  computational  models  of  gender  classification  use  whole  face  images,
giving  equal  weight  to  all  areas  of  the  face,  irrespective  of  the  importance  of
internal  facial  features.  In  this paper we evaluate the  importance of global and
local  information  in  a  series  of  gender  recognition  experiments.  Global"
981847c0a3d667aae385276221834edbb8ebd11c,A generalizable approach for multi-view 3D human pose regression,"A generalizable approach for multi-view 3D human pose regression
Abdolrahim Kadkhodamohammadia,∗, Nicolas Padoya
ICube, University of Strasbourg, CNRS, IHU Strasbourg, France"
984d5ed1fa80124117fdd0aa9a5be69f269da268,[insert Cover Letter Here],[Insert cover letter here]
98582edd6029c94844f5a40d246eaa86f74d8512,Learning Visual Scene Attributes,"Learning Visual Scene Attributes
Vazheh Moussavi
A Glance at Attribute-Centric Scene Representations
Take a look around you. How would you describe your surroundings to best give an idea of what
everything looks like to someone not there? Maybe you will give a category to the scene, say,
‘bedroom’. You might try to list some of the objects around you, like ‘bed’, ‘lamp’, and ‘desk’. Or
perhaps you’ll describe it with adjectives like ‘indoors’, ‘cozy’, and ‘cluttered’. In computer vision,
(or more specifically, in scene understanding), the most effective way to describe a visual scene is
lso a major question.
Of the these three ways of describing a scene, (commonly referred to as categorization, scene pars-
ing, and attribute-based representation respectively), categories have historically been the method of
hoice. In categorization, an image (scene) is allowed to fall into exactly one of an arbitrary number
of buckets. Attribute representations, however, are typically composed of several sets of buckets
each of which will have a value associated with that scene. For instance, a simple category-based
model would place an image in one of urban/rural/room, whereas a binary attribute-based model
would have as attributes indoors and warm, each of which are marked as either present or not. In
larger models, this leads to high dimensionality for attribute-based models, which has been a large
disincentive for its use. In addition, classifying a scene’s entire attribute set non-trivially falls un-
der multi-label learning, for which there exist very few learning algorithms in popular use. Lastly,
there is scene parsing[5], which involves using object detectors, possibly in conjunction, to build"
986224bad9684c359db7fac2192b7134b855fbe3,Shopping for emotion Evaluating the usefulness of emotion recognition data from a retail perspective,"Shopping for emotion
Evaluating the usefulness of emotion recognition data from a retail perspective
Anton Forsberg
Anton Forsberg
VT 2017
Examensarbete f¨or civilingenj¨orer, 30hp
Supervisor: Lars-Erik Janlert
Examiner: Anders Broberg
Civilingenj¨orsprogammet i Interaktion & Design"
98424c79970a80f30db837db84880a4c02e76f1a,Deepagent: An Algorithm Integration Approach for Person Re-Identification,"DEEPAGENT: AN ALGORITHM INTEGRATION APPROACH FOR PERSON
RE-IDENTIFICATION
Fulong Jiao, Bir Bhanu
Center for Research in Intelligent Systems
University of California, Riverside, Riverside, CA 92521, USA"
98126d18be648640fc3cfeb7ffc640a2ec1d5f6f,Supplemental Material : Discovering Groups of People in Images,"Supplemental Material: Discovering Groups of People in
Images
Wongun Choi1, Yu-Wei Chao2, Caroline Pantofaru3 and Silvio Savarese4
. NEC Laboratories 2. University of Michigan, Ann Arbor
. Google, Inc
. Stanford University
Qualitative Examples
In Fig. 1 and 2, we show additional qualitative examples obtained using our model
with poselet [1] and ground truth (GT) detections, respectively. We show the image
onfiguration of groups on the left and corresponding 3D configuration on the right.
Different colors and different line types (solid or dashed) represent different groups,
the type of each structured group is overlayed on the bottom-left of one participant. In
D visualization, squares represent standing people, circles represent people sitting on
n object, and triangles represent people sitting on the ground. The view point of each
individual is shown with a line. The gray triangle is the camera position. The poses are
obtained by using the individual pose classification output for visualization purposes.
The figures show that our algorithm is capable of correctly associating individu-
ls into multiple different groups while estimating the type of each group. Notice that
our algorithm can successfully segment different instances of the same group type that
ppear in proximity. A distance-based clustering method would not be able to differ-"
98eba4505ae23473bb377ee1040ae24331b26247,An Anomaly Detection Algorithm of Cloud Platform Based on Self-Organizing Maps,"Publishing CorporationMathematical Problems in EngineeringVolume 2016, Article ID 3570305, 9 pageshttp://dx.doi.org/10.1155/2016/3570305"
982db27f0a092d5c8db88e959a77fae5b4f9cdf6,"A cross-cultural, multimodal, affective corpus for gesture expressivity analysis","J Multimodal User Interfaces
DOI 10.1007/s12193-012-0112-x
ORIGINAL PAPER
A cross-cultural, multimodal, affective corpus for gesture
expressivity analysis
G. Caridakis · J. Wagner · A. Raouzaiou ·
F. Lingenfelser · K. Karpouzis · E. Andre
Received: 5 March 2012 / Accepted: 15 September 2012
© OpenInterface Association 2012"
982b86f58f33fe27edc03bbde5a419e242c99998,Analysis Of Face Recognition-A Case Study On Feature Selection And Feature Normalization,"B.Vijay, A.Nagabhushana Rao / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622   www.ijera.com
Vol. 3, Issue 3, May-Jun 2013, pp.971-979
Analysis Of Face Recognition- A Case Study On Feature Selection
And Feature Normalization
B.Vijay1, A.Nagabhushana Rao2
,2Assistant Professor, Dept of CSE, AITAM ,Tekkali, Andhra Pradesh, INDIA."
980d4a2aeb7e55406e6784340e846883b2f77021,Noise Models in Feature-based Stereo Visual Odometry,"Noise Models in Feature-based Stereo Visual Odometry
Pablo F. Alcantarilla† and Oliver J. Woodford‡
the location of each feature in each image,"
988d5ad8d114f5f21a73b2ae464dca4277f5725f,Persian Viseme Classification Using Interlaced Derivative Patterns and Support Vector Machine,"Journal of Information Assurance and Security.
ISSN 1554-1010 Volume 9 (2014) pp. 148-156
© MIR Labs, www.mirlabs.net/jias/index.html
Persian Viseme Classification Using Interlaced
Derivative Patterns and Support Vector Machine
Mohammad Mahdi Dehshibi1, Jamshid Shanbehzadeh2
Digital Signal Processing Lab., Pattern Research Center,
Karaj, Iran
Department of Computer Engineering, Kharazmi University,
Tehran, Iran
is  a"
98f13ab2845cfe8513a0c05427a8b90d9c0c1b69,Pedestrian Attribute Recognition with Part-based CNN and Combined Feature Representations,
9854145f2f64d52aac23c0301f4bb6657e32e562,An Improved Face Verification Approach Based on Speedup Robust Features and Pairwise Matching,"An Improved Face Verification Approach based on
Speedup Robust Features and Pairwise Matching
Eduardo Santiago Moura, Herman Martins Gomes and Jo˜ao Marques de Carvalho
Center for Electrical Engineering and Informatics (CEEI)
Federal University of Campina Grande (UFCG)
Campina Grande, Para´ıba, Brazil
Email:"
98c5b88db35d7ab2d3cc0a63c7ff1414160d2aa6,Convolutional Neural Network-Based Finger-Vein Recognition Using NIR Image Sensors,"Article
Convolutional Neural Network-Based Finger-Vein
Recognition Using NIR Image Sensors
Hyung Gil Hong, Min Beom Lee and Kang Ryoung Park *
Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu,
Seoul 100-715, Korea; (H.G.H); (M.B.L.)
* Correspondence: Tel.: +82-10-3111-7022; Fax: +82-2-2277-8735
Academic Editor: Vittorio M. N. Passaro
Received: 11 May 2017; Accepted: 1 June 2017; Published: 6 June 2017"
98a6f2145a358cb2e54eddc99dd29911764bce0e,Learning Single-view 3D Reconstruction of Objects and Scenes,"Learning Single-view 3D Reconstruction of Objects and
Scenes
Shubham Tulsiani
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2018-93
http://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-93.html
July 26, 2018"
98519f3f615e7900578bc064a8fb4e5f429f3689,Dictionary-Based Domain Adaptation Methods for the Re-identification of Faces,"Dictionary-based Domain Adaptation Methods
for the Re-identification of Faces
Qiang Qiu, Jie Ni, and Rama Chellappa"
987dd3dd6079e5fa8a10a1c53b2580fd71e27ede,Concept-Based Video Retrieval By Cees,"Foundations and Trends R(cid:1) in
Information Retrieval
Vol. 2, No. 4 (2008) 215–322
(cid:1) 2009 C. G. M. Snoek and M. Worring
DOI: 10.1561/1500000014
Concept-Based Video Retrieval
By Cees G. M. Snoek and Marcel Worring
Contents
Introduction
How to Retrieve Video Content?
Human-Driven Labeling
.3 Machine-Driven Labeling
Aims, Scope, and Organization
Detecting Semantic Concepts in Video
Introduction
Basic Concept Detection
Feature Fusion
Classifier Fusion
.5 Modeling Relations
Best of Selection"
fe4986bbb10f3417372a02fed1218acb5162ddec,Classification model of arousal and valence mental states by EEG signals analysis and Brodmann correlations,"(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 6, No. 6, 2015
Classification model of arousal and valence mental
states by EEG signals analysis and Brodmann
orrelations
Adrian  Rodriguez  Aguin˜aga  and  Miguel  Angel  Lo´pez  Ram´ırez
Instituto  Tecnolo´gico  de  Tijuana
Calzada  del  Tecnolo´gico  S/N,  Toma´s  Aquino,  22414
Tijuana,  B.C.  Me´xico
Mar´ıa  del  Rosario  Baltazar  Flores
Instituto  Tecnolo´gico  de  Leo´n
Av.  Tecnolo´gico  S/N
Industrial  Julia´n  de  Obrego´n,  37290
Leo´n,  Gto.  Me´xico"
fe01e1099dc2ce02158de607be993f9fc8aade57,Aerial LaneNet: Lane Marking Semantic Segmentation in Aerial Imagery using Wavelet-Enhanced Cost-sensitive Symmetric Fully Convolutional Neural Networks,"Aerial LaneNet: Lane Marking Semantic
Segmentation in Aerial Imagery using
Wavelet-Enhanced Cost-sensitive Symmetric Fully
Convolutional Neural Networks
Seyed Majid Azimi, Peter Fischer, Marco Körner, and Peter Reinartz"
fec5c0100c72d7c1c823a91dc146ecd5e98e77ff,Coherence criterion for region labelling and description,"Coherence criterion for region labelling and
description
Hichem Houissa
INRIA Rocquencourt
Domaine de Voluceau
Nozha Boujemaa
INRIA Rocquencourt
Domaine de Voluceau
Email:
Email:"
fecce467b42856eadb8dd0c08674d9381f52efab,The Role of Shape in Visual Recognition,"The Role of Shape in Visual Recognition
Bj¨orn Ommer"
fe7f5c7da203c48aa1a9a2468aae55c6e0053df9,Interactive Text2Pickup Network for Natural Language based Human-Robot Collaboration,"Interactive Text2Pickup Network for Natural Language based
Human-Robot Collaboration
Hyemin Ahn, Sungjoon Choi, Nuri Kim, Geonho Cha, and Songhwai Oh"
fec6648b4154fc7e0892c74f98898f0b51036dfe,"A Generic Face Processing Framework : Technologies , Analyses and Applications","A Generic Face Processing
Framework: Technologies,
Analyses and Applications
JANG Kim-fung
A Thesis Submitted in Partial Ful(cid:12)lment
of the Requirements for the Degree of
Master of Philosophy
Computer Science and Engineering
Supervised by
Prof. Michael R. Lyu
(cid:13)The Chinese University of Hong Kong
July 2003
The Chinese University of Hong Kong holds the copyright of this thesis. Any
person(s) intending to use a part or whole of the materials in the thesis in
proposed publication must seek copyright release from the Dean of the
Graduate School."
fe9c460d5ca625402aa4d6dd308d15a40e1010fa,Neural Architecture for Temporal Emotion Classification,"Neural Architecture for Temporal Emotion
Classification
Roland Schweiger, Pierre Bayerl, and Heiko Neumann
Universit¨at Ulm, Neuroinformatik, Germany"
fe51fd153fa6dac3b7c50fe79e71123af5c5f43c,Satellite image-based localization via learned embeddings,"Satellite Image-based Localization via Learned Embeddings
Dong-Ki Kim
Matthew R. Walter"
fe35639349a87808481e64f9cbea065339063154,Understanding deep learning via backtracking and deconvolution,"Fang  J Big Data  (2017) 4:40
DOI 10.1186/s40537-017-0101-8
METHODOLOGY
Understanding deep learning
via backtracking and deconvolution
Open Access
Xing Fang*
*Correspondence:
School of Information
Technology, Illinois State
University, Normal, IL, USA"
fed9e971e042b40cc659aca6e338d79dc1d4b59c,GROUPING-BY-ID: GUARDING AGAINST ADVERSAR-,"Under review as a conference paper at ICLR 2018
GROUPING-BY-ID: GUARDING AGAINST ADVERSAR-
IAL DOMAIN SHIFTS
Anonymous authors
Paper under double-blind review"
febb6454a3bfbc76f4c7934854d377ac15666215,Improving the Accuracy of Face Annotation in Social Network,"International Journal of Computer Applications (0975 – 8887)
Volume 182 – No. 14, September 2018
Improving the Accuracy of Face Annotation in Social
Network
C. Jayaramulu
Research Scholar
individual
Dayananda Sagar University, Bangalore
photographs."
fe07ddb8dd1ba331affea713b75f68546bbaf106,"People detection, tracking and re-identification through a video camera network. (Détection, suivi et ré-identification de personnes à travers un réseau de caméra vidéo)","People detection, tracking and re-identification through
video camera network
Malik Souded
To cite this version:
Malik Souded. People detection, tracking and re-identification through a video camera network.
Other [cs.OH]. Université Nice Sophia Antipolis, 2013. English. <NNT : 2013NICE4152>. <tel-
00913072v2>
HAL Id: tel-00913072
https://tel.archives-ouvertes.fr/tel-00913072v2
Submitted on 29 Jan 2014
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
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broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
fe8b2b2a2ace6d6af28dc0f1d63400554c8c675d,Random walk distances in data clustering and applications,"Adv Data Anal Classif (2013) 7:83–108
DOI 10.1007/s11634-013-0125-7
REGULAR ARTICLE
Random walk distances in data clustering
nd applications
Sijia Liu · Anastasios Matzavinos ·
Sunder Sethuraman
Received: 28 September 2011 / Revised: 24 May 2012 / Accepted: 30 September 2012 /
Published online: 6 March 2013
© Springer-Verlag Berlin Heidelberg 2013"
fe466e84fa2e838adc3c37ee327cd68004ae08fe,MUTAN: Multimodal Tucker Fusion for Visual Question Answering,"MUTAN: Multimodal Tucker Fusion for Visual Question Answering
Hedi Ben-younes 1,2 *
R´emi Cadene 1*
Matthieu Cord 1
Nicolas Thome 3
Sorbonne Universit´es, UPMC Univ Paris 06, CNRS, LIP6 UMR 7606, 4 place Jussieu, 75005 Paris
Heuritech, 248 rue du Faubourg Saint-Antoine, 75012 Paris
Conservatoire National des Arts et M´etiers"
fe9b0accf0e7d3821bc5d7d62937499a441633c9,Learning Disentangled Representations with Reference-Based Variational Autoencoders,"Learning Disentangled Representations with
Reference-Based Variational Autoencoders
Adria Ruiz 1 Oriol Martinez 2 Xavier Binefa 2 Jakob Verbeek 1"
fea0895326b663bf72be89151a751362db8ae881,Homocentric Hypersphere Feature Embedding for Person Re-identification,"Homocentric Hypersphere Feature Embedding for
Person Re-identification
Wangmeng Xiang, Jianqiang Huang, Xianbiao Qi, Xiansheng Hua, Fellow, IEEE and Lei Zhang, Fellow, IEEE"
fe07ab1f417c96ae9851aaf0b59908925073fdd5,AN ADAPTIVE COLOUR BASED FACE DETECTOR SYSTEM FOR A SOCIAL MOBILE ROBOT,"AN ADAPTIVE COLOUR BASED FACE DETECTOR SYSTEM FOR A
SOCIAL MOBILE ROBOT
Jon Azpiazu , Tim Smithers
I˜naki Ra˜n´o
Parque Tecnol´ogico de San Sebasti´an
Depto. Inform´atica e Ingenier´ıa de Sistemas
Mikeletegi 53,
0009 San Sebasti´an (Spain)
/ Mar´ıa de Luna 1,
50018 Zaragoza (Spain)"
fec9fb202906e6f136ae92c3a3540b2a84257c4e,Automatic Facial Feature Detection for Facial Expression Recognition,"AUTOMATIC FACIAL FEATURE DETECTION FOR FACIAL
EXPRESSION RECOGNITION
Taner Danisman, Marius Bilasco, Nacim Ihaddadene and Chabane Djeraba
LIFL - UMR CNRS 8022, University of Science and Technology of Lille, Villeneuve d'Ascq, France
Keywords:
Facial Feature Detection, Emotion Recognition, Eye Detection, Mouth Corner Detection."
fe59049553ee2a6eb78a7aa1f6b660b122f312d9,Advances of Robust Subspace Face Recognition,"We are IntechOpen,
the world’s leading publisher of
Open Access books
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fe41550ed350df4cd731a5df3dca5b0ea13511db,Compact Generalized Non-local Network,"Compact Generalized Non-local Network
Kaiyu Yue1,3 Ming Sun1 Yuchen Yuan1 Feng Zhou2 Errui Ding1 Fuxin Xu3
Baidu VIS 2Baidu Research
Central South University
{yuekaiyu, sunming05, yuanyuchen02, zhoufeng09,"
fea83550a21f4b41057b031ac338170bacda8805,Learning a Metric Embedding for Face Recognition using the Multibatch Method,"Learning a Metric Embedding
for Face Recognition
using the Multibatch Method
Oren Tadmor
Yonatan Wexler
Tal Rosenwein
Shai Shalev-Shwartz
Amnon Shashua
Orcam Ltd., Jerusalem, Israel"
feb4bcd20de6ce4f9503ef01c87390e662538c15,Monocular Depth Estimation with Augmented Ordinal Depth Relationships,"Monocular Depth Estimation with Augmented
Ordinal Depth Relationships
Yuanzhouhan Cao, Tianqi Zhao, Ke Xian, Chunhua Shen, Zhiguo Cao"
fe40be62a131deaf62a5a313e2842234845ee200,Dissimilarity-based people re-identification and search for intelligent video surveillance,"Ph.D. in Electronic and Computer Engineering
Dept. of Electrical and Electronic Engineering
University of Cagliari
Dissimilarity-based people
re-identification and search for
intelligent video surveillance
Riccardo Satta
Advisor: Prof. Fabio Roli
Co-advisor: Prof. Giorgio Fumera
Curriculum: ING-INF/05 - Sistemi di Elaborazione delle Informazioni
XXV Cycle
April 2013"
feb171df36e33cfc23cc27b782c40fa49af64a58,Disentangling Propagation and Generation for Video Prediction,"Disentangling Propagation and Generation for Video Prediction
Hang Gao∗,1 Huazhe Xu∗,2 Qi-Zhi Cai3 Ruth Wang2
Columbia University1 UC Berkeley2 Nanjing University3
Fisher Yu2
Trevor Darrell2"
fe0c51fd41cb2d5afa1bc1900bbbadb38a0de139,Bayesian face recognition using 2D Gaussian-Hermite moments,"Rahman et al. EURASIP Journal on Image and Video Processing  (2015) 2015:35
DOI 10.1186/s13640-015-0090-5
RESEARCH
Open Access
Bayesian face recognition using 2D
Gaussian-Hermite moments
S. M. Mahbubur Rahman1*, Shahana Parvin Lata2 and Tamanna Howlader2"
fec295c6b6a1795d8ccb4592603040794667dfa7,LDOP: Local Directional Order Pattern for Robust Face Retrieval,"LDOP: Local Directional Order Pattern for Robust
Face Retrieval
Shiv Ram Dubey and Snehasis Mukherjee"
fe005c5036ad646051cc779aafb63534bda14f06,I The Hand Vein Pattern Used as a Biometric Feature – Annemarie Nadort The Hand Vein Pattern Used as a Biometric Feature,"The Hand Vein Pattern Used as a Biometric Feature
Master Literature Thesis
Annemarie Nadort
Amsterdam - May 2007"
fe030b87e3c985c9dedab130949e2868e3e5e7d5,Explaining Neural Networks Semantically,"Under review as a conference paper at ICLR 2019
EXPLAINING NEURAL NETWORKS SEMANTICALLY
AND QUANTITATIVELY
Anonymous authors
Paper under double-blind review"
feaedb6766f42e867aab7f1a33ba4d7ddacfc7aa,UvA-DARE ( Digital Academic Repository ) Tag-based Video Retrieval by Embedding Semantic Content in a Continuous Word,"UvA-DARE (Digital Academic Repository)
Tag-based Video Retrieval by Embedding Semantic Content in a Continuous Word
Space
Agharwal, A.; Kovvuri, R.; Nevatia, R.; Snoek, C.G.M.
Published in:
016 IEEE Winter Conference on Applications of Computer Vision: WACV 2016: Lake Placid, New York, USA,
7-10 March 2016
0.1109/WACV.2016.7477706
Link to publication
Citation for published version (APA):
Agharwal, A., Kovvuri, R., Nevatia, R., & Snoek, C. G. M. (2016). Tag-based Video Retrieval by Embedding
Semantic Content in a Continuous Word Space. In 2016 IEEE Winter Conference on Applications of Computer
Vision: WACV 2016: Lake Placid, New York, USA, 7-10 March 2016 (pp. 1354-1361). Piscataway, NJ: Institute
of Electrical and Electronic Engineers. DOI: 10.1109/WACV.2016.7477706
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fef89593599b78db7d133fc6893519b3ee8ff8d2,3 D Face recognition by ICP-based shape matching,"D Face recognition by ICP-based shape matching
Boulbaba Ben Amor1, Karima Ouji1, Mohsen Ardabilian1, Liming Chen1
LIRIS Lab, Lyon Research Center for Images and Intelligent Information Systems, UMR 5205 CNRS
Centrale Lyon, France"
346dbc7484a1d930e7cc44276c29d134ad76dc3f,Artists portray human faces with the Fourier statistics of complex natural scenes.,"This article was downloaded by:[University of Toronto]
On: 21 November 2007
Access Details: [subscription number 785020433]
Publisher: Informa Healthcare
Informa Ltd Registered in England and Wales Registered Number: 1072954
Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
Systems
Publication details, including instructions for authors and subscription information:
http://www.informaworld.com/smpp/title~content=t713663148
Artists portray human faces with the Fourier statistics of
omplex natural scenes
Christoph Redies a; Jan Hänisch b; Marko Blickhan a; Joachim Denzler b
Institute of Anatomy I, School of Medicine, Friedrich Schiller University, Germany
Department of Computer Science, Friedrich Schiller University, D-07740 Jena,
Germany
First Published on: 28 August 2007
To cite this Article: Redies, Christoph, Hänisch, Jan, Blickhan, Marko and Denzler,
Joachim (2007) 'Artists portray human faces with the Fourier statistics of complex
To link to this article: DOI: 10.1080/09548980701574496
URL: http://dx.doi.org/10.1080/09548980701574496"
34c594abba9bb7e5813cfae830e2c4db78cf138c,Transport-based single frame super resolution of very low resolution face images,"Transport-Based Single Frame Super Resolution of Very Low Resolution Face Images
Soheil Kolouri1, Gustavo K. Rohde1,2
Department of Biomedical Engineering, Carnegie Mellon University. 2Department of Electrical and Computer Engineering, Carnegie Mellon University.
We describe a single-frame super-resolution method for reconstructing high-
resolution (abbr. high-res) faces from very low-resolution (abbr. low-res)
face images (e.g. smaller than 16× 16 pixels) by learning a nonlinear La-
grangian model for the high-res face images. Our technique is based on the
mathematics of optimal transport, and hence we denote it as transport-based
SFSR (TB-SFSR). In the training phase, a nonlinear model of high-res fa-
ial images is constructed based on transport maps that morph a reference
image into the training face images. In the testing phase, the resolution of
degraded image is enhanced by finding the model parameters that best fit
the given low resolution data.
Generally speaking, most SFSR methods [2, 3, 4, 5] are based on a
linear model for the high-res images. Hence, ultimately, the majority of
SFSR models in the literature can be written as, Ih(x) = ∑i wiψi(x), where
Ih is a high-res image or a high-res image patch, w’s are weight coefficients,
nd ψ’s are high-res images (or image patches), which are learned from the
training images using a specific model. Here we propose a fundamentally
different approach toward modeling high-res images. In our approach the"
34d53d2a418051c56cad9e0c90ea793af6cbb729,Structured Multi-class Feature Selection for Effective Face Recognition,"Structured multi-class feature selection for
effective face recognition
Giovanni Fusco, Luca Zini, Nicoletta Noceti, and Francesca Odone
DIBRIS - Universit`a di Genova
via Dodecaneso, 35
6146-IT, Italy"
34128e93f4af820cea65477526645cdc82e0e59b,Decomposed Learning for Joint Object Segmentation and Categorization,"TSAI et al.: DECOMPOSED LEARNING FOR OBJECT RECOGNITION
Decomposed Learning for Joint Object
Segmentation and Categorization
Yi-Hsuan Tsai
Jimei Yang
Ming-Hsuan Yang
Electrical Engineering and Computer
Science
University of California
Merced, USA"
34f60ecedeb798397849b171e2e8bcf46c9b7ada,An Efficient Face Recognition System based on the Combination of Pose Invariant and Illumination Factors,"International Journal of Computer Applications (0975 – 8887)
Volume 50 – No.2, July 2012
An Efficient Face Recognition System based on the
Combination of Pose Invariant and Illumination Factors
S. Muruganantham
Assistant Professor, S.T.Hindu College, Nagercoil.
the  performance  of"
348035720dba98ff54f2ff8c375ace09287c89f6,3D Human Pose Estimation in RGBD Images for Robotic Task Learning,"D Human Pose Estimation in RGBD Images for Robotic Task Learning
Christian Zimmermann*, Tim Welschehold*, Christian Dornhege, Wolfram Burgard and Thomas Brox"
341ed69a6e5d7a89ff897c72c1456f50cfb23c96,"DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional Neural Network","DAGER: Deep Age, Gender and Emotion
Recognition Using Convolutional Neural
Networks
Afshin Dehghan
Enrique G. Ortiz
Guang Shu
Syed Zain Masood
{afshindehghan, egortiz, guangshu,
Computer Vision Lab, Sighthound Inc., Winter Park, FL"
34d784636e2a8078a6c517f6a9b132b31c2ab3d2,Recognition of a Person Wearing Sport Shoes or High Heels through Gait Using Two Types of Sensors,"Article
Recognition of a Person Wearing Sport Shoes or High
Heels through Gait Using Two Types of Sensors
Marcin Derlatka 1,* and Mariusz Bogdan 2
Department of Biocybernetics and Biomedical Engineering of the Faculty of Mechanical Engineering at
Bialystok University of Technology, 15-351 Bialystok, Poland
Department of Automatic Control and Robotics of the Faculty of Mechanical Engineering at Bialystok
University of Technology, 15-351 Bialystok, Poland;
* Correspondence: Tel.: +48-571-443-044
Received: 9 April 2018; Accepted: 18 May 2018; Published: 21 May 2018"
340d1a9852747b03061e5358a8d12055136599b0,Audio-Visual Recognition System Insusceptible to Illumination Variation over Internet Protocol,"Audio-Visual Recognition System Insusceptible
to Illumination Variation over Internet Protocol
Yee Wan Wong, Kah Phooi Seng, and Li-Minn Ang"
340798e6b7a9863005863f38c1bbfda5cf85d201,"Image Retrieval, Object Recognition, and Discriminative Models","Image Retrieval, Object Recognition,
nd Discriminative Models
Von der Fakult¨at f¨ur Mathematik, Informatik und Naturwissenschaften der
RWTH Aachen University zur Erlangung des akademischen Grades eines
Doktors der Naturwissenschaften genehmigte Dissertation
vorgelegt von
Diplom-Informatiker Thomas Deselaers
us Aachen
Berichter:
Universit¨atsprofessor Dr.-Ing. Hermann Ney
Universit¨atsprofessor Dr. Bernt Schiele
Tag der m¨undlichen Pr¨ufung: 2. Dezember 2008
Diese Dissertation ist auf den Internetseiten der Hochschulbibliothek online verf¨ugbar."
343d21ae54b45ef219ac4ba024265eeabf4d6edd,Where Will They Go? Predicting Fine-Grained Adversarial Multi-agent Motion Using Conditional Variational Autoencoders,"Where Will They Go? Predicting Fine-Grained
Adversarial Multi-Agent Motion using
Conditional Variational Autoencoders
Panna Felsen1,2, Patrick Lucey2, and Sujoy Ganguly2
BAIR, UC Berkeley
STATS
{plucey,"
3468740e4a9fc72a269f4f0ca8470ccd60925f92,Robustness Analysis of Visual QA Models by Basic Questions,"Robustness Analysis of Visual QA Models by Basic Questions
Jia-Hong Huang
Bernard Ghanem
Cuong Duc Dao* Modar Alfadly*
C. Huck Yang
King Abdullah University of Science and Technology ; Georgia Institute of Technology
{jiahong.huang, dao.cuong, modar.alfadly, ;"
3402b5e354eebcf443789f3c8d3c97eccd3ae55e,Multimodal Machine Learning: A Survey and Taxonomy,"Multimodal Machine Learning:
A Survey and Taxonomy
Tadas Baltruˇsaitis, Chaitanya Ahuja, and Louis-Philippe Morency"
344f647463ef160956143ebc8ce370cca144961a,Confidence-Aware Probability Hypothesis Density Filter for Visual Multi-Object Tracking,
3493b2232449635aff50fc17e03163cb4b66f1b5,Visual exploration of machine learning results using data cube analysis,"Visual Exploration of Machine Learning Results
using Data Cube Analysis
Minsuk Kahng
Georgia Tech
Atlanta, GA, USA
Dezhi Fang
Georgia Tech
Atlanta, GA, USA
Duen Horng (Polo) Chau
Georgia Tech
Atlanta, GA, USA"
34d15fea236612e9df10e4c7a25ca31c0d95edd1,Biases in spatial bisection induced by viewing male and female faces.,"Research Article
Biases in Spatial Bisection Induced
y Viewing Male and Female Faces
Zaira Cattaneo,1,2 Susanna Schiavi,1 Carlotta Lega,1 Chiara Renzi,2 Matteo
Tagliaferri,1 Jana Boehringer,4 Claus-Christian Carbon,4 and Tomaso Vecchi2,3
Department of Psychology, University of Milano-Bicocca, Milano, Italy, 2Brain Connectivity Center,
National Neurological Institute C. Mondino, Pavia, Italy, 3Department of Brain and Behavioral
Sciences, University of Pavia, Italy, 4Department of General Psychology
nd Methodology, University of Bamberg, Germany"
34ec83c8ff214128e7a4a4763059eebac59268a6,Action Anticipation By Predicting Future Dynamic Images,"Action Anticipation By Predicting Future
Dynamic Images
Cristian Rodriguez, Basura Fernando and Hongdong Li
Australian Centre for Robotic Vision, ANU, Canberra, Australia
{cristian.rodriguez, basura.fernando,"
341002fac5ae6c193b78018a164d3c7295a495e4,von Mises-Fisher Mixture Model-based Deep learning: Application to Face Verification,"von Mises-Fisher Mixture Model-based Deep
learning: Application to Face Verification
Md. Abul Hasnat, Julien Bohn´e, Jonathan Milgram, St´ephane Gentric and Liming Chen"
34cf90fcbf83025666c5c86ec30ac58b632b27b0,Learning Deep Context-Aware Features over Body and Latent Parts for Person Re-identification,"Learning Deep Context-aware Features over Body and Latent Parts
for Person Re-identification
Dangwei Li1,2, Xiaotang Chen1,2, Zhang Zhang1,2, Kaiqi Huang1,2,3
CRIPAC & NLPR, CASIA 2University of Chinese Academy of Sciences
CAS Center for Excellence in Brain Science and Intelligence Technology
{dangwei.li, xtchen, zzhang,"
3413af6c689eedb4fe3e7d6c5dc626647976307a,Horizontally scalable submodular maximization,"Horizontally Scalable Submodular Maximization
Mario Lucic1
Olivier Bachem1
Morteza Zadimoghaddam2
Andreas Krause1
Department of Computer Science, ETH Zurich, Switzerland
Google Research, New York"
3423f3dcb0edee1c5c6a5505b9e8c0bbdcffbd51,Nurses ' Reactions to Patient Weight : Effects on Clinical Decisions,"University of Wisconsin Milwaukee
UWM Digital Commons
Theses and Dissertations
May 2017
Nurses' Reactions to Patient Weight: Effects on
Clinical Decisions
Heidi M. Pfeiffer
University of Wisconsin-Milwaukee
Follow this and additional works at: http://dc.uwm.edu/etd
Part of the Psychology Commons
Recommended Citation
Pfeiffer, Heidi M., ""Nurses' Reactions to Patient Weight: Effects on Clinical Decisions"" (2017). Theses and Dissertations. 1524.
http://dc.uwm.edu/etd/1524
This Dissertation is brought to you for free and open access by UWM Digital Commons. It has been accepted for inclusion in Theses and Dissertations
y an authorized administrator of UWM Digital Commons. For more information, please contact"
3410136b86b813b075a258842450835906d58600,A facial expression image database and norm for Asian population: A preliminary report,"Image Quality and System Performance VI, edited by Susan P. Farnand, Frans Gaykema,
Proc. of SPIE-IS&T Electronic Imaging, SPIE Vol. 7242, 72421D · © 2009 SPIE-IS&T
CCC code: 0277-786X/09/$18 · doi: 10.1117/12.806130
SPIE-IS&T/ Vol. 7242  72421D-1
Downloaded from SPIE Digital Library on 07 Oct 2009 to 140.112.113.225. Terms of Use:  http://spiedl.org/terms"
344682f69dd9bec68d89a79b0b7f28a3891ab857,Perception of Social Cues of Danger in Autism Spectrum Disorders,"Perception of Social Cues of Danger in Autism Spectrum
Disorders
Nicole R. Zu¨ rcher1,2, Ophe´ lie Rogier1, Jasmine Boshyan2, Loyse Hippolyte1, Britt Russo1, Nanna Gillberg3,
Adam Helles3, Torsten Ruest1, Eric Lemonnier4, Christopher Gillberg3, Nouchine Hadjikhani1,2,3*
Brain Mind Institute, EPFL, Lausanne, Switzerland, 2 Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital,
Charlestown, Massachusetts, United States of America, 3 Gillberg Centrum, University of Gothenburg, Gothenburg, Sweden, 4 Laboratoire de Neurosciences, Universite´ de
Brest, Brest, France"
341de07abfb89bf78f3a72513c8bce40d654e0a3,Sparse and Deep Generalizations of the FRAME Model,"Annals of Mathematical Sciences and Applications
Volume 3, Number 1, 211–254, 2018
Sparse and deep generalizations of the
FRAME model
Ying Nian Wu, Jianwen Xie, Yang Lu, and Song-Chun Zhu
In the pattern theoretical framework developed by Grenander and
dvocated by Mumford for computer vision and pattern recog-
nition, different patterns are represented by statistical generative
models. The FRAME (Filters, Random fields, And Maximum En-
tropy) model is such a generative model for texture patterns. It
is a Markov random field model (or a Gibbs distribution, or an
energy-based model) of stationary spatial processes. The log prob-
bility density function of the model (or the energy function of the
Gibbs distribution) is the sum of translation-invariant potential
functions that are one-dimensional non-linear transformations of
linear filter responses. In this paper, we review two generalizations
of this model. One is a sparse FRAME model for non-stationary
patterns such as objects, where the potential functions are loca-
tion specific, and they are non-zero only at a selected collection of
locations. The other generalization is a deep FRAME model where"
3412d9f3c620155bf3eb203f5817a310000f0c63,Biomarkers in autism spectrum disorder: the old and the new,"DOI 10.1007/s00213-013-3290-7
REVIEW
Biomarkers in autism spectrum disorder: the old and the new
Barbara Ruggeri & Ugis Sarkans & Gunter Schumann &
Antonio M. Persico
Received: 15 April 2013 /Accepted: 7 September 2013
# Springer-Verlag Berlin Heidelberg 2013"
34f8086eb67eb2cd332cd2d6bca0dd8f1e8f1062,Face Recognition and Growth Prediction using a 3 D Morphable Face Model,"Saarland University
Faculty of Natural Sciences and Technology I
Department of Computer Science
Master’s Program in Computer Science
Master’s Thesis
Face Recognition and
Growth Prediction using
3D Morphable Face Model
submitted by Kristina Scherbaum
on October 30, 2007
Supervisor
Prof. Dr. Hans-Peter Seidel
Saarland University – Computer Science Department
Advisor
Prof. Dr. Volker Blanz
Universit¨at Siegen – Dekanat FB 12
Reviewers
Prof. Dr. Hans-Peter Seidel
Prof. Dr. Volker Blanz"
34c7254d2f420df6309260b2bb461a9c107dfd5a,Semi-supervised image classification based on a multi-feature image query language,"University of Huddersfield Repository
Pein, Raoul Pascal
Semi-Supervised Image Classification based on a Multi-Feature Image Query Language
Original Citation
Pein, Raoul Pascal (2010) Semi-Supervised Image Classification based on a Multi-Feature Image
Query Language. Doctoral thesis, University of Huddersfield.
This version is available at http://eprints.hud.ac.uk/9244/
The University Repository is a digital collection of the research output of the
University, available on Open Access. Copyright and Moral Rights for the items
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• The content is not changed in any way.
For more information, including our policy and submission procedure, please
ontact the Repository Team at:
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34b3b14b4b7bfd149a0bd63749f416e1f2fc0c4c,The AXES submissions at TrecVid 2013,"The AXES submissions at TrecVid 2013
Robin Aly1, Relja Arandjelovi´c3, Ken Chatfield3, Matthijs Douze6, Basura Fernando4, Zaid Harchaoui6,
Kevin McGuinness2, Noel E. O’Conner2, Dan Oneata6, Omkar M. Parkhi3, Danila Potapov6, Jérôme Revaud6,
Cordelia Schmid6, Jochen Schwenninger5, David Scott2, Tinne Tuytelaars4, Jakob Verbeek6, Heng Wang6,
Andrew Zisserman3
University of Twente 2Dublin City University 3Oxford University
KU Leuven 5Fraunhofer Sankt Augustin 6INRIA Grenoble"
34ae449ae64cd2c6bfc2f102eac82bd606cd12f7,A Unified Model with Structured Output for Fashion Images Classification,"A Unified Model with Structured Output for Fashion Images
Classification
Beatriz Quintino Ferreira
ISR, Instituto Superior Técnico, Universidade de Lisboa,
Portugal
João Faria
Farfetch"
343acba1d609c1e6f274e3b6733c9173b5a46342,Online Learning for Crowd-sensitive Path Planning,"Online Learning for Crowd-sensitive Path Planning
Anoop Aroor
of New York
Robotics Track
Susan L. Epstein
New York
Raj Korpan
of New York
The Graduate Center, City University
Hunter College, City University of
The Graduate Center, City University"
34d484b47af705e303fc6987413dc0180f5f04a9,RI : Medium : Unsupervised and Weakly-Supervised Discovery of Facial Events 1,"RI:Medium: Unsupervised and Weakly-Supervised
Discovery of Facial Events
Introduction
The face is one of the most powerful channels of nonverbal communication. Facial expression has been a
focus of emotion research for over a hundred years [11]. It is central to several leading theories of emotion
[16, 28, 44] and has been the focus of at times heated debate about issues in emotion science [17, 23, 40].
Facial expression figures prominently in research on almost every aspect of emotion, including psychophys-
iology [30], neural correlates [18], development [31], perception [4], addiction [24], social processes [26],
depression [39] and other emotion disorders [46], to name a few. In general, facial expression provides cues
bout emotional response, regulates interpersonal behavior, and communicates aspects of psychopathology.
While people have believed for centuries that facial expressions can reveal what people are thinking and
feeling, it is relatively recently that the face has been studied scientifically for what it can tell us about
internal states, social behavior, and psychopathology.
Faces possess their own language. Beginning with Darwin and his contemporaries, extensive efforts
have been made to manually describe this language. A leading approach, the Facial Action Coding System
(FACS) [19] , segments the visible effects of facial muscle activation into ”action units.” Because of its
descriptive power, FACS has become the state of the art in manual measurement of facial expression and is
widely used in studies of spontaneous facial behavior. The FACS taxonomy was develop by manually ob-
serving graylevel variation between expressions in images and to a lesser extent by recording the electrical
ctivity of underlying facial muscles [9]. Because of its importance to human social dynamics, person per-"
34e23b934794a5abff251698df09cbac5ad2dd56,Towards Engineering a Web-Scale Multimedia Service: A Case Study Using Spark,"Towards Engineering a Web-Scale Multimedia Service:
A Case Study Using Spark∗
Gylfi Þór Guðmundsson
Reykjavik University
Reykjavík, Iceland
Björn Þór Jónsson
Reykjavik University, Iceland
IT University of Copenhagen, Denmark
Laurent Amsaleg
IRISA–CNRS
Rennes, France
Michael J. Franklin
University of Chicago
Chicago, IL, USA"
34530c3a1df47c888506b836a061092df05972cc,Unpaired High-Resolution and Scalable Style Transfer Using Generative Adversarial Networks,"Unpaired High-Resolution and Scalable Style Transfer
Using Generative Adversarial Networks
Andrej Junginger, Markus Hanselmann, Thilo Strauss, Sebastian Boblest, Jens Buchner, and Holger Ulmer
Machine Learning Team at ETAS GmbH (Bosch Group), Stuttgart, Germany
Neural networks have proven their capabilities by outperforming many other approaches on regression
or classification tasks on various kinds of data. Other astonishing results have been achieved using neural
nets as data generators, especially in settings of generative adversarial networks (GANs). One special
pplication is the field of image domain translations. Here, the goal is to take an image with a certain
style (e. g. a photography) and transform it into another one (e. g. a painting). If such a task is performed
nd this leads to a high peak memory consumption during, both, training and evaluation phase. This
sets a limit to the highest processable image size. We address this issue by the idea of not processing the
whole image at once, but to train and evaluate the domain translation on the level of overlapping image
subsamples. This new approach not only enables us to translate high-resolution images that otherwise
annot be processed by the neural network at once, but also allows us to work with comparably small
neural networks and with limited hardware resources. Additionally, the number of images required for the
training process is significantly reduced. We present high-quality results on images with a total resolution
of up to over 50 megapixels and demonstrate that our method helps to preserve local image details while
it also keeps global consistency.
Introduction
Over the recent years, neural networks (NNs) have be-"
3490683560ca18d19884949dccca0ad7c98d4749,Content-Based Filtering for Video Sharing Social Networks,"Content-Based Filtering for Video Sharing Social Networks
Eduardo Valle1, Sandra Avila2, Fillipe de Souza2,
Marcelo Coelho2,3, Arnaldo de A. Araújo2
RECOD Lab — DCA / FEEC / UNICAMP, Campinas, SP, Brazil
NPDI Lab — DCC / UFMG, Belo Horizonte, MG, Brazil
Preparatory School of Air Cadets — EPCAR, Barbacena, MG, Brazil
{sandra, fdms, mcoelho,"
34cd99528d873e842083abec429457233fdb3226,Person Re-identification using group context,"Person Re-identification using group context
Yiqiang Chen, Stefan Duffner, Andrei Stoian, Jean-Yves Dufour, Atilla
Baskurt
To cite this version:
Yiqiang Chen, Stefan Duffner, Andrei Stoian, Jean-Yves Dufour, Atilla Baskurt. Person Re-
identification using group context. Advanced Concepts for Intelligent Vision systems, Sep 2018,
Poitiers, France. <hal-01895373>
HAL Id: hal-01895373
https://hal.archives-ouvertes.fr/hal-01895373
Submitted on 15 Oct 2018
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
34df09a9445089c8f23eff5b2a43a822c9713f6e,Boosting chamfer matching by learning chamfer distance normalization,"Boosting Chamfer Matching by Learning
Chamfer Distance Normalization
Tianyang Ma, Xingwei Yang, and Longin Jan Latecki
Dept. of Computer and Information Sciences,Temple Unviersity, Philadelphia.
{tianyang.ma,xingwei,latecki}.temple.edu"
88dc2b2f6d033b290ed56b844c98c3ee6efde80b,Experimental manipulation of face-evoked activity in the  fusiform gyrus of individuals with autism.,"!""#$%&’(#)*+%,&$%-.,/*.&-+-%012%34&*+%5/#6+’$#(17
8/2%9:%;+<(+=0+’%9>?>
FB0*#$""+’%F$1)"".*.G1%F’+$$
H/I.’=&%J(-%K+G#$(+’+-%#/%L/G*&/-%&/-%M&*+$%K+G#$(+’+-%NB=0+’2%?>D9COP%K+G#$(+’+-%.II#)+2%Q.’(#=+’%R.B$+S%EDT
P?%Q.’(#=+’%;(’++(S%J./-./%M?!%EURS%5V
;.)#&*%N+B’.$)#+/)+
FB0*#)&(#./%-+(&#*$S%#/)*B-#/G%#/$(’B)(#./$%I.’%&B("".’$%&/-%$B0$)’#<(#./%#/I.’=&(#./2
""((<2WW,,,X#/I.’=&,.’*-X).=W$=<<W(#(*+Y)./(+/(Z(DP?DD??PE
L[<+’#=+/(&*%=&/#<B*&(#./%.I%I&)+T+6.\+-%&)(#6#(1%#/%(""+%IB$#I.’=%G1’B$%.I
#/-#6#-B&*$%,#(""%&B(#$=
‘#’$(%<B0*#$""+-%./2%>P%Q&1%9>?>
N+B’.$)#+/)+SS%‘#’$(%<B0*#$""+-%./2%>P%Q&1%9>?>%a#‘#’$(b
5KJ2%""((<2WW-[X-.#X.’GW?>X?>d>W?DPD>C??>>E:dE?dO
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This article may be used for research, teaching and private study purposes. Any substantial or
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will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses"
8818dafda0cf230731ac2f962d8591c89a9fac09,xGEMs: Generating Examplars to Explain Black-Box Models,"xGEMs: Generating Examplars to Explain Black-Box
Models
Shalmali Joshi
UT Austin
Oluwasanmi Koyejo
Been Kim
Google Brain
Joydeep Ghosh
UT Austin"
88bbedf7f6f0dcc830640c521acece28e67be356,Robust sparse coding for face recognition,"Robust Sparse Coding for
Face Recognition
Meng Yang, Lei Zhang, Jian Yang, David Zhang
Hong Kong Polytechnic Univ.
Presenter : 江振國"
88cc7220be4ca882c129722a9a4e3ec420ece99c,Fusion of PCA and LDA Based Face Recognition System,"2012 International Conference on Software and Computer Applications (ICSCA 2012)
IPCSIT vol. 41 (2012) © (2012) IACSIT Press, Singapore
Fusion of PCA and LDA Based Face Recognition System
Jamal Ahmad Dargham1, Ali Chekima1, Ervin Gubin Moung1
School of Engineering and Information Technology, University Malaysia Sabah, Locked Bag 2073,
TelukLikas, 88999 Kota Kinabalu, Sabah, Malaysia
email: {jamalad,"
8855755a72c148dfde84bb08ae65d58c260e70d4,Robust image classification: analysis and applications,"POUR L'OBTENTION DU GRADE DE DOCTEUR ÈS SCIENCESacceptée sur proposition du jury:Prof. P. Vandergheynst, président du juryProf. P. Frossard, directeur de thèseProf. J. Bruna, rapporteurProf. N. Paragios, rapporteurDr F. Fleuret, rapporteurRobust image classification: analysis and applicationsTHÈSE NO 7258 (2016)ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNEPRÉSENTÉE LE 16 DÉCEMBRE 2016 À LA FACULTÉ DES SCIENCES ET TECHNIQUES DE L'INGÉNIEURLABORATOIRE DE TRAITEMENT DES SIGNAUX 4PROGRAMME DOCTORAL EN GÉNIE ÉLECTRIQUE Suisse2016PARAlhussein FAWZI"
8809860da2786eef69c078073df62f322ab882d1,Generating Textual Adversarial Examples for Deep Learning Models: A Survey,"JOURNAL OF XX, VOL. XX, NO. XX, JANUARY 2019
Generating Textual Adversarial Examples for
Deep Learning Models: A Survey
Wei Emma Zhang, Quan Z. Sheng, Ahoud Abdulrahmn F Alhazmi, and Chenliang Li"
8855d6161d7e5b35f6c59e15b94db9fa5bbf2912,COGNITION IN PREGNANCY AND THE POSTPARTUM PERIOD,COGNITION IN PREGNANCY AND THE POSTPARTUM PERIOD
88850b73449973a34fefe491f8836293fc208580,XBeats-An Emotion Based Music Player,"www.ijaret.org                                                                                                                                                     Vol. 2, Issue I, Jan. 2014
ISSN 2320-6802
INTERNATIONAL JOURNAL FOR ADVANCE RESEARCH IN
ENGINEERING AND TECHNOLOGY
WINGS TO YOUR THOUGHTS…..
XBeats-An Emotion Based Music Player
Sayali Chavan1, Ekta Malkan2, Dipali Bhatt3, Prakash H. Paranjape4
U.G. Student, Dept. of Computer Engineering,
D.J. Sanghvi College of Engineering,
Vile Parle (W), Mumbai-400056.
U.G. Student, Dept. of Computer Engineering,
D.J. Sanghvi College of Engineering,
Vile Parle (W), Mumbai-400056.
U.G. Student, Dept. of Computer Engineering,
D.J. Sanghvi College of Engineering,
Vile Parle (W), Mumbai-400056.
Assistant Professor, Dept. of Computer Engineering,
D.J. Sanghvi College of Engineering,
Vile Parle (W), Mumbai-400056."
88c5baffa5522ea62ff5d5c41036b92e30d7e3c9,Who is who at different cameras. People re-identification using Depth Cameras,"Document downloaded from:
This paper must be cited as:
The final publication is available at
Copyright
Additional Information
http://dx.doi.org/10.1049/iet-cvi.2011.0140http://hdl.handle.net/10251/56627Institution of Engineering and Technology (IET)Albiol Colomer, AJ.; Albiol Colomer, A.; Oliver Moll, J.; Mossi García, JM. (2012). Who iswho at different cameras: people re-identification using depth cameras. IET ComputerVision. 6(5):378-387. doi:10.1049/iet-cvi.2011.0140."
88fd4d1d0f4014f2b2e343c83d8c7e46d198cc79,Joint action recognition and summarization by sub-modular inference,"978-1-4799-9988-0/16/$31.00 ©2016 IEEE
ICASSP 2016"
88590857138505ee524f3adf6da9c57352d917f2,Random Subspace Two-Dimensional PCA for Face Recognition,"Random Subspace Two-Dimensional PCA for
Face Recognition
Nam Nguyen, Wanquan Liu and Svetha Venkatesh
Department of Computing, Curtin University of Technology, WA 6845, Australia"
8813368c6c14552539137aba2b6f8c55f561b75f,Trunk-Branch Ensemble Convolutional Neural Networks for Video-Based Face Recognition,"Trunk-Branch Ensemble Convolutional Neural
Networks for Video-based Face Recognition
Changxing Ding, Student Member, IEEE, Dacheng Tao, Fellow, IEEE"
8897dd825230695a8a669b29a4d1b284373adb31,Face Recognition using Co-occurrence Matrix of Local Average Binary Pattern ( CMLABP ),"Cyber Journals: Multidisciplinary Journals in Science and Technology, Journal of Selected Areas in Telecommunications (JSAT), April Edition, 2012
Face Recognition using Co-occurrence Matrix
of Local Average Binary Pattern (CMLABP)
A. Hazrati Bishak, Z. Ghandriz, T. Taheri"
8878871ec2763f912102eeaff4b5a2febfc22fbe,Human Action Recognition in Unconstrained Videos by Explicit Motion Modeling,"Human Action Recognition in Unconstrained
Videos by Explicit Motion Modeling
Yu-Gang Jiang, Qi Dai, Wei Liu, Xiangyang Xue, and Chong-Wah Ngo"
88c6d4b73bd36e7b5a72f3c61536c8c93f8d2320,Image patch modeling in a light field,"Image patch modeling in a light field
Zeyu Li
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2014-81
http://www.eecs.berkeley.edu/Pubs/TechRpts/2014/EECS-2014-81.html
May 15, 2014"
88132a786442ab8a5038d81164384c1c1f7231c8,Limited attentional bias for faces in toddlers with autism spectrum disorders.,"ORIGINAL ARTICLE
Limited Attentional Bias for Faces in Toddlers
With Autism Spectrum Disorders
Katarzyna Chawarska, PhD; Fred Volkmar, MD; Ami Klin, PhD
Context: Toddlers with autism spectrum disorders (ASD)
exhibit poor face recognition and atypical scanning pat-
terns in response to faces. It is not clear if face-processing
deficits are also expressed on an attentional level. Typical
individuals require more effort to shift their attention from
faces compared with other objects. This increased disen-
gagement cost is thought to reflect deeper processing of these
socially relevant stimuli.
Objective: To examine if attention disengagement from
faces is atypical in the early stages of ASD.
Design: Attention disengagement was tested in a varia-
tion of the cued attention task in which participants were
required to move their visual attention from face or non-
face central fixation stimuli and make a reactive saccade
to a peripheral target. The design involved diagnosis as
between-group factor and central fixation stimuli type"
886dfe069bd0f6bbb0a885e0bf2788007bfb737c,3-D Facial Expression Representation using B-spline Statistical Shape Model,"-D Facial Expression Representation using
B-spline Statistical Shape Model
Wei Quan, Bogdan J. Matuszewski, Lik-Kwan Shark, Djamel Ait-Boudaoud
Applied Digital Signal and Image Processing Research Centre
University of Central Lancashire
Preston PR1 2HE, UK"
8850a9748da6579b939ab9f1aa705b7886c4417b,Serving Self Loading Video Composition,"International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 1, January 2014)
Serving Self Loading Video Composition
Rajesh1, Hariharan2
PG Student, 2Assistant Professor, PSN Engineering College"
887cd2271ca5a58501786d49afa53139f48c66f3,"Visual orienting in children with autism: Hyper‐responsiveness to human eyes presented after a brief alerting audio‐signal, but hyporesponsiveness to eyes presented without sound","SHORT REPORT
Visual Orienting in Children With Autism: Hyper-Responsiveness
to Human Eyes Presented After a Brief Alerting Audio-Signal,
ut Hyporesponsiveness to Eyes Presented Without Sound
Johan Lundin Kleberg, Emilia Thorup, and Terje Falck-Ytter
Autism Spectrum Disorder (ASD) has been associated with reduced orienting to social stimuli such as eyes, but the
results are inconsistent. It is not known whether atypicalities in phasic alerting could play a role in putative altered
social orienting in ASD. Here, we show that in unisensory (visual) trials, children with ASD are slower to orient to
eyes (among distractors) than controls matched for age, sex, and nonverbal IQ. However, in another condition where
brief spatially nonpredictive sound was presented just before the visual targets, this group effect was reversed. Our
results indicate that orienting to social versus nonsocial stimuli is differently modulated by phasic alerting mecha-
nisms in young children with ASD. Autism Res 2017, 10: 246–250. VC 2016 The Authors Autism Research published
y Wiley Periodicals, Inc. on behalf of International Society for Autism Research.
Keywords: Autism; social orienting; eye tracking; phasic alerting; arousal; face perception
According to social orienting theories of Autism Spec-
trum Disorder (ASD), people with this condition orient
less or slower to socially salient stimuli than people
with typical development (TD; Dawson et al., 2004).
Further, it is assumed that reduced orienting early in
life may have cascading effects on both brain develop-"
885d589101ab3c09bda20ee9578f2c6d2f6cddfa,Learning to Guide Decoding for Image Captioning,"Learning to Guide Decoding for Image Captioning
Wenhao Jiang1 Lin Ma1 Xinpeng Chen2 Hanwang Zhang3 Wei Liu1
Tencent AI Lab, 2Wuhan University, 3Nanyang Technological University
{cswhjiang, xinpeng"
88a898592b4c1dfd707f04f09ca58ec769a257de,MobileFace: 3D Face Reconstruction with Efficient CNN Regression,"MobileFace: 3D Face Reconstruction
with Ef‌f‌icient CNN Regression
Nikolai Chinaev1, Alexander Chigorin1, and Ivan Laptev1,2
VisionLabs, Amsterdam, The Netherlands
{n.chinaev,
Inria, WILLOW, Departement d’Informatique de l’Ecole Normale Superieure, PSL
Research University, ENS/INRIA/CNRS UMR 8548, Paris, France"
887b7d34ebac80bbe3fb3792ed579dd82ff7e373,Query-driven iterated neighborhood graph search for scalable visual indexing ∗,"Query-driven iterated neighborhood graph search for scalable
visual indexing∗
Jingdong Wang† Xian-Sheng Hua‡ Shipeng Li†
Microsoft Corporation
Microsoft Research Asia
August 10, 2012"
88e3aefe454e72388bbbe7dfa0b74fcfc52032f0,Weighted Gradient Feature Extraction Based on Multiscale Sub-Blocks for 3D Facial Recognition in Bimodal Images,"Article
Weighted Gradient Feature Extraction Based on
Multiscale Sub-Blocks for 3D Facial Recognition in
Bimodal Images
Yingchun Guo *, Ruoyu Wei and Yi Liu *
School of Computer Science and Engineering, Hebei University of Technology, Tianjin 300400, China;
* Correspondence: (Y.G.); (Y.L.)
Received: 6 January 2018; Accepted: 19 February 2018; Published: 28 February 2018"
88f2952535df5859c8f60026f08b71976f8e19ec,A neural network framework for face recognition by elastic bunch graph matching,"A neural network framework for face
recognition by elastic bunch graph matching
Francisco A. Pujol López, Higinio Mora Mora*, José A. Girona Selva"
887b7676a4efde616d13f38fcbfe322a791d1413,Deep Temporal Appearance-Geometry Network for Facial Expression Recognition,"Deep Temporal Appearance-Geometry Network
for Facial Expression Recognition
Injae Lee‡ Chunghyun Ahn‡
Junmo Kim†
Heechul Jung† Sihaeng Lee† Sunjeong Park†
Korea Advanced Institute of Science and Technology†
Electronics and Telecommunications Research Institute‡
{heechul, haeng, sunny0414, {ninja,"
883006c0f76cf348a5f8339bfcb649a3e46e2690,Weakly supervised pain localization using multiple instance learning,"Weakly Supervised Pain Localization using Multiple Instance Learning
Karan Sikka, Abhinav Dhall and Marian Bartlett"
8816ee1e23983f5a4340743c7744a336fae02d60,Face Recognition Using Boosted Local Features,"MITSUBISHI ELECTRIC RESEARCH LABORATORIES
http://www.merl.com
Face Recognition Using Boosted Local
Features
Michael J. Jones and Paul Viola
TR2003-25 April 2003"
88babcb7cfa8e46f814c241e441c890285f6f9d4,"Is it Safe to Drive? An Overview of Factors, Challenges, and Datasets for Driveability Assessment in Autonomous Driving.","Is it Safe to Drive? An Overview of Factors,
Challenges, and Datasets for Driveability
Assessment in Autonomous Driving
Junyao Guo, Unmesh Kurup, Mohak Shah"
88bee9733e96958444dc9e6bef191baba4fa6efa,Extending Face Identification to Open-Set Face Recognition,"Extending Face Identification to
Open-Set Face Recognition
Cassio E. dos Santos Jr., William Robson Schwartz
Department of Computer Science
Universidade Federal de Minas Gerais
Belo Horizonte, Brazil"
50e47857b11bfd3d420f6eafb155199f4b41f6d7,Human Face Reconstruction Using a Hybrid of Photometric Stereo and Independent Component Analysis,"International Journal of Computer, Consumer and Control (IJ3C), Vol. 2, No.1 (2013)
D Human Face Reconstruction Using a Hybrid of Photometric
Stereo and Independent Component Analysis
*Cheng-Jian Lin, 2Shyi-Shiun Kuo, 1Hsueh-Yi Lin, 2Shye-Chorng Kuo and 1Cheng-Yi Yu"
50bf792c721293222248f906e95726ac2ac2fe9e,Characterising Pedestrian Detection on a Heterogeneous Platform,"Characterising Pedestrian Detection on a Heterogeneous Platform
Calum Blair1, Neil Robertson2 and Danny Hume3"
50a48fcd6176b72aea7a61233d3c7fb12a279ba4,A Computational Model of Eye Movements during Object Class Detection,"A Computational Model of Eye Movements
during Object Class Detection
Wei Zhang†
Hyejin Yang‡∗
Dimitris Samaras†
Gregory J. Zelinsky†‡
Dept. of Computer Science†
Dept. of Psychology‡
State University of New York at Stony Brook
Stony Brook, NY 11794"
50c1ab22f442470efbe3198f0b338fb699416bc5,A Commute in Data: The comma2k19 Dataset,"A Commute in Data: The comma2k19 Dataset
Harald Sch¨afer, Eder Santana, Andrew Haden, and Riccardo Biasini
omma.ai"
501eda2d04b1db717b7834800d74dacb7df58f91,Pedro Miguel Neves Marques Discriminative Sparse Representation for Expression Recognition,"Pedro Miguel Neves Marques    Discriminative Sparse Representation for Expression Recognition     Master Thesis in Electrical and Computer Engineering September, 2014"
5090e374a0d505040ca6fe957936a12026f5347a,Human Emotion Classification From Videos,"Human Emotion Classification From Videos
Maria Soledad Elli (mselli) - Dhvani Kotak (dkotak)"
50a2ba70d42f6543b26444695459d0bac38a4ab3,Development and testing of new combined visual speech parameterization,"ISCA Archive
http://www.isca-speech.org/archive
Auditory-Visual Speech Processing
007 (AVSP2007)
Hilvarenbeek, The Netherlands
August 31 -- September 3, 2007
Development and Testing of New Combined Visual Speech Parameterization
Petr Císař, Miloš Železný, Jan Zelinka, Jana Trojanová
University of West Bohemia, Faculty of Applied Sciences, Department of Cybernetics
Univerzitní 8, 306 14 Plzeň, Czech Republic"
5028c0decfc8dd623c50b102424b93a8e9f2e390,REVISITING CLASSIFIER TWO-SAMPLE TESTS,"Published as a conference paper at ICLR 2017
REVISITING CLASSIFIER TWO-SAMPLE TESTS
David Lopez-Paz1, Maxime Oquab1,2
Facebook AI Research, 2WILLOW project team, Inria / ENS / CNRS"
50984f8345a3120d0e6c0a75adc2ac1a13e37961,Impaired face processing in autism: fact or artifact?,"DOI 10.1007/s10803-005-0050-5
Published Online: February 14, 2006
Impaired Face Processing in Autism: Fact or Artifact?
Boutheina Jemel,1,3–5 Laurent Mottron,2–4 and Michelle Dawson2
Within the last 10 years, there has been an upsurge of interest in face processing abilities in
utism which has generated a proliferation of new empirical demonstrations employing a
variety of measuring techniques. Observably atypical social behaviors early in the develop-
ment of children with autism have led to the contention that autism is a condition where the
processing of social
is impaired. While several empirical
sources of evidence lend support to this hypothesis, others suggest that there are conditions
under which autistic individuals do not differ from typically developing persons. The present
paper reviews this bulk of empirical evidence, and concludes that the versatility and abilities of
face processing in persons with autism have been underestimated.
information, particularly faces,
KEY WORDS: Autism; face processing; FFA; configural; local bias.
Impaired face processing is one of the most
the social cognition
ommonly cited aspects of
deficits observed among persons with autism spec-"
5083c6be0f8c85815ead5368882b584e4dfab4d1,Automated Face Analysis for Affective Computing,"Please do not quote.  In press, Handbook of affective computing. New York, NY: Oxford
Automated Face Analysis for Affective Computing
Jeffrey F. Cohn & Fernando De la Torre"
506e2850a564b6085d8f0af4834a97ddd301d423,Visual object class recognition using local descriptions,"Alexandra Teynor
Visual Object Class Recognition
using Local Descriptions
Dissertation zur Erlangung des Doktorgrades
der Fakultät für Angewandte Wissenschaften
der Albert-Ludwigs-Universität Freiburg im Breisgau
August 2008"
5087d9bdde0ba5440eb8658be7183bf5074a2a94,Object Detection via a Multi-region and Semantic Segmentation-Aware CNN Model,"Object detection via a multi-region
semantic segmentation-aware CNN model
Spyros Gidaris, Nikos Komodakis
To cite this version:
Spyros Gidaris, Nikos Komodakis. Object detection via a multi-region
semantic segmentation-aware CNN model. ICCV 2015, Dec 2015, Santiago, Chile. ICCV 2015, 2016,
<10.1109/ICCV.2015.135>. <hal-01245664>
HAL Id: hal-01245664
https://hal.archives-ouvertes.fr/hal-01245664
Submitted on 17 Dec 2015
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
505942c5f9b5779bda2859e22e9ed0b1c0c7b54a,Towards 3D Face Recognition in the Real: A Registration-Free Approach Using Fine-Grained Matching of 3D Keypoint Descriptors,"Int J Comput Vis
DOI 10.1007/s11263-014-0785-6
Towards 3D Face Recognition in the Real: A Registration-Free
Approach Using Fine-Grained Matching of 3D Keypoint
Descriptors
Huibin Li · Di Huang · Jean-Marie Morvan ·
Yunhong Wang · Liming Chen
Received: 26 April 2013 / Accepted: 27 October 2014
© Springer Science+Business Media New York 2014"
50457c55b318dde8c9024851fdf7e5ce3a936f65,Unsupervised Learning from Continuous Video in a Scalable Predictive Recurrent Network,"Unsupervised Learning from Continuous Video in a Scalable Predictive
Recurrent Network
Filip Piekniewski∗
Patryk Laurent
Csaba Petre
Micah Richert
Dimitry Fisher
Todd L. Hylton
October 3, 2016"
50a0930cb8cc353e15a5cb4d2f41b365675b5ebf,Robust Facial Landmark Detection and Face Tracking in Thermal Infrared Images using Active Appearance Models,
50b6d2db19fb71ff5cfde8e2bfa484b10fbb39fe,Perception of Suicide Risk in Mental Health Professionals.,"RESEARCH ARTICLE
Perception of Suicide Risk in Mental Health
Professionals
Tim M. Gale1,2*, Christopher J. Hawley3, John Butler4, Adrian Morton5, Ankush Singhal6
Department of Research, Hertfordshire Partnership University NHS Foundation Trust, Hatfield, United
Kingdom, 2 Department of Psychology, University of Hertfordshire, Hatfield, United Kingdom, 3 Department
of Post-graduate Medicine, University of Hertfordshire, Hatfield, United Kingdom, 4 School of Health,
University of Central Lancaster, Preston, United Kingdom, 5 Reigate Psychology Service, Reigate, Surrey,
United Kingdom, 6 Psychological Medicine Service, The Royal Oldham Hospital, Oldham, United Kingdom
11111"
50f0c495a214b8d57892d43110728e54e413d47d,Pairwise support vector machines and their application to large scale problems,"Submitted 8/11; Revised 3/12; Published 8/12
Pairwise Support Vector Machines and their Application to Large
Scale Problems
Carl Brunner
Andreas Fischer
Institute for Numerical Mathematics
Technische Universit¨at Dresden
01062 Dresden, Germany
Klaus Luig
Thorsten Thies
Cognitec Systems GmbH
Grossenhainer Str. 101
01127 Dresden, Germany
Editor: Corinna Cortes"
501096cca4d0b3d1ef407844642e39cd2ff86b37,Illumination Invariant Face Image Representation Using Quaternions,"Illumination Invariant Face Image
Representation using Quaternions
Dayron Rizo-Rodr´ıguez, Heydi M´endez-V´azquez, and Edel Garc´ıa-Reyes
Advanced Technologies Application Center. 7a # 21812 b/ 218 and 222,
Rpto. Siboney, Playa, P.C. 12200, La Habana, Cuba."
506f744801c97f005fa04a09e4a4ae5fdabe94d7,MARCOnI—ConvNet-Based MARker-Less Motion Capture in Outdoor and Indoor Scenes,"Local Submodularization
for Binary Pairwise Energies
Lena Gorelick, Yuri Boykov, Olga Veksler, Ismail Ben Ayed, and Andrew Delong"
50d15cb17144344bb1879c0a5de7207471b9ff74,"Divide , Share , and Conquer : Multitask Attribute Learning with Selective Sharing","Divide, Share, and Conquer: Multi-task
Attribute Learning with Selective Sharing
Chao-Yeh Chen*, Dinesh Jayaraman*, Fei Sha, and Kristen Grauman"
503c16d9cb1560f13a7d6baedf8c9f889b22459d,Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation,"Encoder-Decoder with Atrous Separable
Convolution for Semantic Image Segmentation
Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and
Hartwig Adam
{lcchen, yukun, gpapan, fschroff,
Google Inc."
50ccc98d9ce06160cdf92aaf470b8f4edbd8b899,Towards robust cascaded regression for face alignment in the wild,"Towards Robust Cascaded Regression for Face Alignment in the Wild
Chengchao Qu1,2 Hua Gao3
Eduardo Monari2
J¨urgen Beyerer2,1
Jean-Philippe Thiran3
Vision and Fusion Laboratory (IES), Karlsruhe Institute of Technology (KIT)
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (Fraunhofer IOSB)
Signal Processing Laboratory (LTS5), ´Ecole Polytechnique F´ed´erale de Lausanne (EPFL)"
50814328c14b107c62c3e49bc7347059a9f590ac,Evaluating aerial vessel detector in multiple maritime surveillance scenarios,"Evaluating Aerial Vessel Detector In Multiple Maritime Surveillance Scenarios
Gonc¸alo Cruz
Alexandre Bernardino
Portuguese Air Force,
715-021 Sintra, Portugal
Institute for Systems and Robotics,
Department of Electrical and Computer Engineering,
Instituto Superior Tcnico,
049-001 Lisboa, Portugal"
50dff7d619de13076f04382690f2ef83cbb43155,Improving Face Recognition with Multispectral Fusion and Support Vector Machines,"Improving Face Recognition with Multispectral Fusion and Support Vector
Machines
Giovani Chiachia, Aparecido Nilceu Marana
High Performance Computing Laboratory
UNESP - S˜ao Paulo State University
Bauru, Brazil
Christian K¨ublbeck
Department of Electronic Imaging
Fraunhofer Institute for Integrated Circuits
Erlangen, Germany"
50fb5e2f0c2fe8679c218ff88d4906e5a0812d34,"Sketch-editing games: human-machine communication, game theory and applications","Sketch-Editing Games: Human-Machine Communication,
Game Theory and Applications
Andre Ribeiro
Takeo Igarashi
JST, Erato, Igarashi
Design Interface Project,
-28-1-7F, Koishikawa
JST, Erato, Igarashi
Design Interface Project,
-28-1-7F, Koishikawa
sketches).  We  argue"
50d9891114d281e498c4793962977d1ad3d2606f,Zero-Shot Visual Recognition Using Semantics-Preserving Adversarial Embedding Networks,"Zero-Shot Visual Recognition using Semantics-Preserving
Adversarial Embedding Networks
Long Chen1 Hanwang Zhang2
Jun Xiao1∗ Wei Liu3
Shih-Fu Chang4
Zhejiang University 2Nanyang Technological University 3Tencent AI Lab 4Columbia University
{longc, {wliu,
Figure 1: (a) Attribute variance heat maps of the 312 attributes in CUB birds [60] and the 102 attributes in SUN scenes [47]
(lighter color indicates lower variance, i.e., lower discriminability) and the t-SNE [35] visualizations of the test images
represented by all attributes (left) and only the high-variance ones (right). Some of the low-variance attributes (the lighter
part to the left of the cut-off line) discarded at training are still needed in discriminating unseen test classes. (b) Comparison
of reconstructed images using SAE [25] and our proposed SP-AEN method, which is shown to retain sufficient semantics for
photo-realistic reconstruction."
50eb2ee977f0f53ab4b39edc4be6b760a2b05f96,Emotion recognition based on texture analysis of facial expression,"Australian Journal of Basic and Applied Sciences, 11(5) April 2017, Pages: 1-11
AUSTRALIAN JOURNAL OF BASIC AND
APPLIED SCIENCES
ISSN:1991-8178        EISSN: 2309-8414
Journal home page: www.ajbasweb.com
Emotion  Recognition  Based  on  Texture  Analysis  of  Facial  Expressions
Using Wavelets Transform
Suhaila N. Mohammed and 2Loay E. George
Assistant Lecturer, Computer Science Department, College of Science, Baghdad University, Baghdad, Iraq,
Assistant Professor, Computer Science Department, College of Science, Baghdad University, Baghdad, Iraq,
Address For Correspondence:
Suhaila N. Mohammed, Baghdad University, Computer Science Department, College of Science, Baghdad, Iraq.
A R T I C L E   I N F O
Article history:
Received 18 January 2017
Accepted 28 March 2017
Available online 15 April 2017
Keywords:
Facial  Emotion,  Face  Detection,
Template  Based  Methods,  Texture"
50ad84bdc3c7cd262ed22f360a37fef457550e25,NoScope: Optimizing Neural Network Queries over Video at Scale,"NoScope: Optimizing Neural Network Queries
over Video at Scale
Daniel Kang, John Emmons, Firas Abuzaid, Peter Bailis, Matei Zaharia
Stanford InfoLab"
509abc3031dbf347c29e2a42d88650e0b8545f3d,OBJECT DETECTION WITH LARGE INTRA-CLASS VARIATION,"OBJECT DETECTION WITH LARGE INTRA-CLASS VARIATION
A Thesis presented to
the Faculty of the Graduate School
t the University of Missouri
In Partial Fulfillmen
of the Requirements for the Degree
Master of Science
GUANG CHEN
Dr. Tony X Han
DECEMBER 2011"
5020a75c45416073d0b07b1deb7382bc80de1779,Human Detection Using Learned Part Alphabet and Pose Dictionary,"Human Detection using Learned Part Alphabet
nd Pose Dictionary
Anonymous ECCV submission
Paper ID 895"
508eb5a6156b8fa1b4547b611e85969438116fa2,Perceptual Generative Adversarial Networks for Small Object Detection,"Perceptual Generative Adversarial Networks for Small Object Detection
Jianan Li Xiaodan Liang Yunchao Wei
Tingfa Xu
Jiashi Feng
Shuicheng Yan"
507c9672e3673ed419075848b4b85899623ea4b0,Multi-View Facial Expression Classification,"Faculty of Informatics
Institute for Anthropomatics
Chair Prof. Dr.-Ing. R. Stiefelhagen
Facial Image Processing and Analysis Group
Multi-View Facial Expression
Classification
DIPLOMA THESIS OF
Nikolas Hesse
ADVISORS
Dr.-Ing. Hazım Kemal Ekenel
Dipl.-Inform. Hua Gao
Dipl.-Inform. Tobias Gehrig
MARCH 2011
KIT – University of the State of Baden-Württemberg and National Laboratory of the Helmholtz Association
www.kit.edu"
5056186a5001921d0a24587e26167a7ee9d88cf9,Optimizing the Capacity of a Convolutional Neural Network for Image Segmentation and Pattern Recognition,"World Academy of Science, Engineering and Technology
International Journal of Computer and Information Engineering
Vol:12, No:10, 2018
Optimizing the Capacity of a Convolutional Neural
Network for Image Segmentation and Pattern
Recognition
Yalong Jiang, Zheru Chi"
50bc8a4e7e6ab9837c6244b29ff800f523494d65,Learning to Answer Questions From Image using Convolutional Neural Network,"Learning to Answer Questions From Image Using Convolutional Neural Network
Noah’s Ark Lab, Huawei Technologies
Lin Ma
Zhengdong Lu
Hang Li"
5080655990fe0e0446bcb038b3e0adad0218bd29,Quantum Cuts A Quantum Mechanical Spectral Graph Partitioning Method for Salient Object Detection Julkaisu,"Çağlar Aytekin
Quantum Cuts
A Quantum Mechanical Spectral Graph Partitioning Method for Salient
Object Detection
Julkaisu 1440 • Publication 1440
Tampere 2016"
5058a7ec68c32984c33f357ebaee96c59e269425,A Comparative Evaluation of Regression Learning Algorithms for Facial Age Estimation,"A Comparative Evaluation of Regression Learning
Algorithms for Facial Age Estimation
Carles Fern´andez1, Ivan Huerta2, and Andrea Prati2
Herta Security
Pau Claris 165 4-B, 08037 Barcelona, Spain
DPDCE, University IUAV
Santa Croce 1957, 30135 Venice, Italy"
50014f4f5b0bd604d65db278b22d1478beade5dc,Deep Learning for Vanishing Point Detection Using an Inverse Gnomonic Projection,
50da9965104d944a8ae648c9aaec43be8ea1c501,Improving the Correspondence Establishment Based on Interactive Homography Estimation,"Improving the Correspondence Establishment
Based on Interactive Homography Estimation*
Xavier Cortés, Carlos Moreno, and Francesc Serratosa
Universitat Rovira i Virgili, Departament d’Enginyeria Informàtica i Matemàtiques, Spain"
50c5a552c191bff34ca74e0f8dbac159e3814533,"Overview of the ImageCLEF 2015 Scalable Image Annotation, Localization and Sentence Generation task","Overview of the ImageCLEF 2015 Scalable
Image Annotation, Localization and Sentence
Generation task
Andrew Gilbert, Luca Piras, Josiah Wang, Fei Yan, Emmanuel Dellandrea,
Robert Gaizauskas, Mauricio Villegas and Krystian Mikolajczyk"
1ca9ab2c1b5e8521cba20f78dcf1895b3e1c36ac,"""Here's looking at you, kid"". Detecting people looking at each other in videos","""Here's looking at you, kid""
Citation for published version:
Marin-Jimenez, M, Zisserman, A & Ferrari, V 2011, ""Here's looking at you, kid"": Detecting people looking at
each other in videos. in Proceedings of the British Machine Vision Conference (BMVC): Dundee, September
011. BMVA Press, pp. 22.1-22.12. DOI: 10.5244/C.25.22
Digital Object Identifier (DOI):
0.5244/C.25.22
Link:
Link to publication record in Edinburgh Research Explorer
Document Version:
Peer reviewed version
Published In:
Proceedings of the British Machine Vision Conference (BMVC)
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nd / or other copyright owners and it is a condition of accessing these publications that users recognise and
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ontent complies with UK legislation. If you believe that the public display of this file breaches copyright please"
1cbc189a4484cd2b1371798bae2ff50c0442ce60,A Hybrid Loss for Multiclass and Structured Prediction,"IEEE TRANSACTIONS ON PATTERN ANALYSIS & MACHINE INTELLIGENCE, FINAL DRAFT, FEB. 2014
A Hybrid Loss for Multiclass
nd Structured Prediction
Qinfeng Shi, Mark Reid, Tiberio Caetano, Anton van den Hengel and Zhenhua Wang"
1cd66a9d9158f65f9e099141189d9a00fb82b525,Quick and energy-efficient Bayesian computing of binocular disparity using stochastic digital signals,"Quick and energy-ef‌f‌icient Bayesian computing of
inocular disparity using stochastic digital signals
Alexandre Coninxa,∗, Pierre Bessi`erea, Jacques Drouleza
ISIR CNRS/UPMC, 4 place Jussieu 75005 Paris, France"
1c1a24169be56e01b0e36e260f49025260a5c7e7,A Deep Compositional Framework for Human-like Language Acquisition in Virtual Environment,"A Deep Compositional Framework for Human-like
Language Acquisition in Virtual Environment
Haonan Yu, Haichao Zhang, and Wei Xu
Baidu Research - Institue of Deep Learning
Sunnyvale, CA 94089"
1cb95f013ec3e78acdda6ac6cfdb362ae6a5ceac,Nonnegative matrix factorization for segmentation analysis,"Nonnegative matrix factorization for
segmentation analysis
Roman Sandler
Technion - Computer Science Department - Ph.D. Thesis  PHD-2010-09 - 2010"
1c93b48abdd3ef1021599095a1a5ab5e0e020dd5,A Compositional and Dynamic Model for Face Aging,"JOURNAL OF LATEX CLASS FILES, VOL. *, NO. *, JANUARY 2009
A Compositional and Dynamic Model for Face Aging
Jinli Suo , Song-Chun Zhu , Shiguang Shan and Xilin Chen"
1cf6bc0866226c1f8e282463adc8b75d92fba9bb,"Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering","Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for
Visual Question Answering
Huijuan Xu
UMass Lowell
Kate Saenko
UMass Lowell"
1ce3a91214c94ed05f15343490981ec7cc810016,Exploring photobios,"Exploring Photobios
Ira Kemelmacher-Shlizerman1
Eli Shechtman2
Rahul Garg1,3
Steven M. Seitz1,3
University of Washington∗
Adobe Systems†
Google Inc."
1c400dcd6c3e54498d9a7bd5aa4c456079a9d236,Sketch and Validate for Big Data Clustering,"Sketch and Validate for Big Data Clustering
Panagiotis A. Traganitis, Konstantinos Slavakis, Senior Member, IEEE, and Georgios B. Giannakis, Fellow, IEEE"
1cd0bc067e66bc1f66a73b401a4a470e43e4bb9e,Houdini : Fooling Deep Structured Visual and Speech Recognition Models with Adversarial Examples,"Houdini: Fooling Deep Structured Visual and Speech
Recognition Models with Adversarial Examples
Moustapha Cisse
Facebook AI Research
Natalia Neverova*
Facebook AI Research"
1cd584f519d9cd730aeef1b1d87f7e2e82b4de59,A fully automatic face recognition system using a combined audio-visual approach ∗,"A fully automatic face recognition system using a combined
udio-visual approach ∗
Alberto Albiol†, Luis Torres†, and Edward J. Delp? †
Communications Department
Technical University of Valencia, Valencia, Spain
Department of Signal Theory & Communications
Technical University of Catalonia, Barcelona, Spain
?School of Electrical and Computer Engineering
Purdue University West Lafayette, IN 47907-1285
Corresponding Author:
Dr. Alberto Albiol
Communications Department
Technical University of Valencia, Valencia, Spain
6022 Valencia (Spain)
Telephone: +34 96 387 97 38
Fax: +34 96 387 73 09
Email:"
1c0eaf4c568664007b092225095c9c5e008c20fe,FEATURE EXTRACTION METHODS 3 . 1 Discrete Wavelet Transform ( DWT ),"International Journal of Scientific & Engineering Research Volume 3, Issue 8, August-2012                                                                                         1
ISSN 2229-5518
Particle Swarm Optimization- Best Feature Selection
method for Face Images
Ms. P.V. Shinde
M.E. 2nd Year
Dept. Of Computer Engg.
AVCOE Sangamner
Prof. B.L. Gunjal
Assistant Professor
Dept. Of Computer Engg.
AVCOE Sangamner"
1c80bc91c74d4984e6422e7b0856cf3cf28df1fb,Hierarchical Adaptive Structural SVM for Domain Adaptation,"Noname manuscript No.
(will be inserted by the editor)
Hierarchical Adaptive Structural SVM for Domain Adaptation
Jiaolong Xu · Sebastian Ramos · David V´azquez · Antonio M. L´opez
Received: date / Accepted: date"
1c3073b57000f9b6dbf1c5681c52d17c55d60fd7,Direction de thèse:,"THÈSEprésentéepourl’obtentiondutitredeDOCTEURDEL’ÉCOLENATIONALEDESPONTSETCHAUSSÉESSpécialité:InformatiqueparCharlotteGHYSAnalyse,Reconstruction3D,&AnimationduVisageAnalysis,3DReconstruction,&AnimationofFacesSoutenancele19mai2010devantlejurycomposéde:Rapporteurs:MajaPANTICDimitrisSAMARASExaminateurs:MichelBARLAUDRenaudKERIVENDirectiondethèse:NikosPARAGIOSBénédicteBASCLE"
1ca155a4b65ae19ccb73df48516e4775770a382c,Action Representations in Robotics: A Taxonomy and Systematic Classification,"Action representations in robotics: A
taxonomy and systematic classification
Journal Title
XX(X):1–32
(cid:13)The Author(s) 2016
Reprints and permission:
sagepub.co.uk/journalsPermissions.nav
DOI: 10.1177/ToBeAssigned
www.sagepub.com/
Philipp Zech, Erwan Renaudo, Simon Haller, Xiang Zhang and Justus Piater"
1c9333bcf523388d75f852e0689b0e7f5a04faa4,Person Part Segmentation based on Weak Supervision,"JIANG, CHI: PERSON PART SEGMENTATION BASED ON WEAK SUPERVISION  1
Person Part Segmentation based on Weak
Supervision
Yalong Jiang1                                                                   1Department of Electronic and Information
Engineering
Zheru Chi1                                                                             The Hong Kong Polytechnic University, HK"
1c40cb899fccbe98a2bb63088e02d6c59e87c187,An Interaction Framework for a Cooperation between Fully Automated Vehicles and External Users in Semi-stationary Urban Scenarios,
1c521ac6e68436f6c6aad3c0eb7ffa557fe25b0d,Modeling Image Patches with a Generic Dictionary of Mini-epitomes,"Modeling Image Patches with a Generic Dictionary of Mini-Epitomes
George Papandreou
TTI Chicago
Liang-Chieh Chen
UC Los Angeles
Alan L. Yuille
UC Los Angeles"
1cf29a0131211079fc73908ecf211ee78f090ad9,Regionlets for Generic Object Detection,"Regionlets for Generic Object Detection
Xiaoyu Wang Ming Yang
Shenghuo Zhu
Yuanqing Lin
NEC Laboratories America, Inc."
1cee733ee31e245dac4655a870fd9226163a52b5,Bidirectional Beam Search: Forward-Backward Inference in Neural Sequence Models for Fill-in-the-Blank Image Captioning,"Bidirectional Beam Search: Forward-Backward Inference in
Neural Sequence Models for Fill-in-the-Blank Image Captioning
Qing Sun
Virginia Tech
Stefan Lee
Virginia Tech
Dhruv Batra
Georgia Tech"
1c012e5b3ddb8a60420e8f92162d32ad135f9ba1,Self-ensembling for visual domain adaptation,"Self-ensembling for visual domain adaptation
French, G.
Mackiewicz, M.
Fisher, M.
September 25, 2018"
1c90ad1e264c29a8d180de47373257a5f1b5aa57,Generalizing Image Captions for Image-Text Parallel Corpus,"Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pages 790–796,
Sofia, Bulgaria, August 4-9 2013. c(cid:13)2013 Association for Computational Linguistics
house being pulled by a boat.” “I saw her in the light of her reading lamp and sneaked back to her door with the camera.” “Sections of the bridge sitting in the Dyer Construction yard south of Cabelas Driver.” Circumstantial information that is not visually present Visually relevant, but with overly extraneous details Visually truthful, but for an uncommon situation Figure1:Examplesofcaptionsthatarenotreadilyapplicabletoothervisuallysimilarimages.textfromtheretrievedsamplestothequeryim-age(e.g.Farhadietal.(2010),Ordonezetal.(2011),Kuznetsovaetal.(2012)).Otherwork(e.g.FengandLapata(2010a),FengandLapata(2010b))usescomputervisiontobiassummariza-tionoftextassociatedwithimagestoproducede-scriptions.Alloftheseapproachesrelyonex-istingtextthatdescribesvisualcontent,butmanytimesexistingimagedescriptionscontainsignifi-cantamountsofextraneous,non-visual,orother-wisenon-desirablecontent.Thegoalofthispaperistodeveloptechniquestoautomaticallycleanupvisuallydescriptivetexttomakeitmoredirectlyusableforapplicationsexploitingtheconnectionbetweenimagesandlanguage.Asaconcreteexample,considerthefirstimageinFigure1.Thiscaptionwaswrittenbythephotoownerandthereforecontainsinformationrelatedtothecontextofwhenandwherethephotowastaken.Objectssuchas“lamp”,“door”,“camera”arenotvisuallypresentinthephoto.Thesecondimageshowsasimilarbutsomewhatdifferentis-sue.Itscaptiondescribesvisibleobjectssuchas“bridge”and“yard”,but“CabelasDriver”areoverlyspecificandnotvisuallydetectable.The"
1cfe3533759bf95be1fce8ce1d1aa2aeb5bfb4cc,Recognition of Facial Gestures Based on Support Vector Machines,"Recognition of Facial Gestures based on Support
Vector Machines
Attila Fazekas and Istv(cid:19)an S(cid:19)anta
Faculty of Informatics, University of Debrecen, Hungary
H-4010 Debrecen P.O.Box 12."
1c0e8c3fb143eb5eb5af3026eae7257255fcf814,Weakly Supervised Deep Detection Networks,"GOALS
Goal: Learn object detectors using only image-level labels
Why weakly supervised learning?
• annotations are costly
• CNN training is data-hungry
Hypothesis: Pre-trained CNNs should contain meaningful
representations of data such as objects and object parts.
Thus we can exploit this implicit knowledge to learn localizing
objects.
Classification stream
𝑹𝟏 𝑹𝟐 𝑹𝟑 𝑹𝟒
0.52 0.47 0.04 0.93
horse
person 0.48 0.53 0.96 0.07
Normalize over classes
Detection stream
𝑹𝟏 𝑹𝟐 𝑹𝟑 𝑹𝟒
horse
0.04 0.01 0.07 0.88
person 0.02 0.03 0.91 0.04"
1cc0183d8fbef098d29b6b5f621745ff099f6c6c,Joint Discovery of Object States and Manipulation Actions,"Joint Discovery of Object States and Manipulation Actions
Jean-Baptiste Alayrac∗ †
Josef Sivic∗ † ‡
Ivan Laptev∗ †
Simon Lacoste-Julien§"
1c60a13e3d48c5425b08a775d40e0d92c9c581f8,Robust moving objects detection in lidar data exploiting visual cues,"Robust Moving Objects Detection in Lidar Data
Exploiting Visual Cues
Gheorghii Postica1 Andrea Romanoni1 Matteo Matteucci1"
1c6e22516ceb5c97c3caf07a9bd5df357988ceda,Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data,"NetworkCNNimageslabelsFakeDatasetimages24132labelsTarget NetworkCNNimageslabelsOriginalDatasetFakeDatasetFig.1:Ontheleft,thetargetnetworkistrainedwithanoriginal(confidential)datasetandisservedpubliclyasanAPI,receivingimagesasinputandprovidingclasslabelsasoutput.Ontheright,itispresentedtheprocesstogetstolenlabelsandtocreateafakedataset:randomnaturalimagesaresenttotheAPIandthelabelsareobtained.Afterthat,thecopycatnetworkistrainedusingthisfakedataset.cloud-basedservicestocustomersallowingthemtooffertheirownmodelsasanAPI.Becauseoftheresourcesandmoneyinvestedincreatingthesemodels,itisinthebestinterestofthesecompaniestoprotectthem,i.e.,toavoidthatsomeoneelsecopythem.Someworkshavealreadyinvestigatedthepossibilityofcopyingmodelsbyqueryingthemasablack-box.In[1],forexample,theauthorsshowedhowtoperformmodelextractionattackstocopyanequivalentornear-equivalentmachinelearningmodel(decisiontree,logisticregression,SVM,andmultilayerperceptron),i.e.,onethatachievescloseto100%agreementonaninputspaceofinterest.In[2],theauthorsevaluatedtheprocessofcopyingaNaiveBayesandSVMclassifierinthecontextoftextclassification.Bothworksfocusedongeneralclassifiersandnotondeepneuralnetworksthatrequirelargeamountsofdatatobetrainedleavingthequestionofwhetherdeepmodelscanbeeasilycopied.Althoughthesecondusesdeeplearningtostealtheclassifiers,itdoesnottrytouseDNNstostealfromdeepmodels.Additionally,theseworksfocusoncopyingbyqueryingwithproblemdomaindata.Inrecentyears,researchershavebeenexploringsomeintriguingpropertiesofdeepneuralnetworks[3],[4].More©2018IEEE.Personaluseofthismaterialispermitted.PermissionfromIEEEmustbeobtainedforallotheruses,inanycurrentorfuturemedia,includingreprinting/republishingthismaterialforadvertisingorpromotionalpurposes,creatingnewcollectiveworks,forresaleorredistributiontoserversorlists,orreuseofanycopyrightedcomponentofthisworkinotherworks."
1c0319d67707dd7dde76e47668e852e74692df30,Human Re-identification Through a Video Camera Network. (Ré-identification de personne dans un réseau de cameras vidéo),"Human Re-identification Through a Video Camera
Network
Slawomir Bak
To cite this version:
Slawomir Bak. Human Re-identification Through a Video Camera Network. Computer Vision and
Pattern Recognition [cs.CV]. Université Nice Sophia Antipolis, 2012. English. <tel-00763443>
HAL Id: tel-00763443
https://tel.archives-ouvertes.fr/tel-00763443
Submitted on 10 Dec 2012
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scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de
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1c26e415c7eae2f3b0f49e0519f0d985ec661c63,Intersection of Longest Paths in Graph Theory and Predicting Performance in Facial Recognition,"Georgia State University
ScholarWorks Georgia State University
Mathematics Dissertations
Department of Mathematics and Statistics
-6-2017
Intersection of Longest Paths in Graph Theory and
Predicting Performance in Facial Recognition
Amy Yates
Follow this and additional works at: http://scholarworks.gsu.edu/math_diss
Recommended Citation
Yates, Amy, ""Intersection of Longest Paths in Graph Theory and Predicting Performance in Facial Recognition."" Dissertation, Georgia
State University, 2017.
http://scholarworks.gsu.edu/math_diss/34
This Dissertation is brought to you for free and open access by the Department of Mathematics and Statistics at ScholarWorks Georgia State
University. It has been accepted for inclusion in Mathematics Dissertations by an authorized administrator of ScholarWorks Georgia State
University. For more information, please contact"
1c1e4415f0acf5d536c9579117d326471f0b678b,Temporal Model Adaptation for Person Re-Identification,"Temporal Model Adaptation for
Person Re-Identification
Niki Martinel1,3, Abir Das2,
Christian Micheloni1, and Amit K. Roy-Chowdhury3
University of Udine, 33100 Udine, Italy
University of Massatchussets Lowell, 01852 Lowell, MA, USA
University of California Riverside, 92507 Riverside, CA, USA"
1cd9dba357e05c9be0407dc5d477fd528cfeb79b,Model-driven Simulations for Deep Convolutional Neural Networks,"Model-driven Simulations for Deep Convolutional
Neural Networks
V S R Veeravasarapu1, Constantin Rothkopf2, Visvanathan Ramesh1
Center for Cognition and Computing, Goethe University, Frankfurt.
Cognitive Science Center, Technical University, Darmstadt."
1cbf3b90065e8a410668ed914e9d03a94a4d94aa,Visual-Inertial Semantic Scene Representation,"Visual-Inertial Semantic Scene Representation
UCLA TR CSD160005
Stefano Soatto
May 20, 2016"
1c51aeece7a3c30302ebd83bdcaa65df0bfc48fe,Unsupervised Video Indexing based on Audiovisual Characterization of Persons. (Indexation vidéo non-supervisée basée sur la caractérisation des personnes),"Unsupervised Video Indexing based on Audiovisual
Characterization of Persons
Elie El Khoury
To cite this version:
Elie El Khoury. Unsupervised Video Indexing based on Audiovisual Characterization of Per-
sons. Human-Computer Interaction [cs.HC]. Universit´e Paul Sabatier - Toulouse III, 2010.
English. <tel-00515424v3>
HAL Id: tel-00515424
https://tel.archives-ouvertes.fr/tel-00515424v3
Submitted on 7 Sep 2010
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rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
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broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,
´emanant des ´etablissements d’enseignement et de"
45954ed44b99edc5f0d1100a1ea33d856602d78a,Retinal Vessel Segmentation under Extreme Low Annotation: A Generative Adversarial Network Approach,"Retinal Vessel Segmentation under Extreme Low
Annotation: A Generative Adversarial Network
Approach
Avisek Lahiri*, Vineet Jain*, Arnab Mondal*, and Prabir Kumar Biswas, Senior Member, IEEE"
4583d7d1d76dfe18e86e91f7438ce1a03cdcf68f,3D Face: biometric template protection for 3d face recognition,"\3D Face"": Biometric Template Protection for
D Face Recognition
E.J.C. Kelkboom, B. G(cid:127)okberk, T.A.M. Kevenaar, A.H.M. Akkermans, and M.
van der Veen
Philips Research, High-Tech Campus 34, 5656AE, Eindhoven
femile.kelkboom, berk.gokberk, tom.kevenaar, ton.h.akkermans,"
45379046c6c1311dfa6d8e1941b3e2c7971ca2bc,An alternating direction and projection algorithm for structure-enforced matrix factorization,"Noname manuscript No.
(will be inserted by the editor)
An Alternating Direction and Projection Algorithm
for Structure-enforced Matrix Factorization
Lijun Xu · Bo Yu · Yin Zhang
Received: date / Accepted: date"
4599b9d9a379385a3d31681696d2523beeb0e9c1,LG ] 8 F eb 2 01 6 A Latent-Variable Grid Model,"A Latent-Variable Grid Model
Rajasekaran Masatran
Computer Science and Engineering, Indian Institute of Technology Madras
FREESHELL · ORG"
451bf4124ec8a55b9112cf9cc167d304fa004924,Modelling State of Interaction from Head Poses for Social Human-Robot Interaction,"Modelling State of Interaction from Head Poses
for Social Human-Robot Interaction
Andre Gaschler
fortiss GmbH
Guerickstr. 25
80805 München, Germany
Ingmar Kessler
fortiss GmbH
Guerickstr. 25
80805 München, Germany
Kerstin Huth
Universität Bielefeld
Universitätsstr. 25
3615 Bielefeld, Germany
Jan de Ruiter
Universität Bielefeld
Universitätsstr. 25
3615 Bielefeld, Germany
ielefeld.de
Manuel Giuliani"
458e44d20f7a85a0ce378b48a41febb16383c075,Tracking Interacting Objects in Image Sequences,"Tracking Interacting Objects in Image Sequences
THÈSE NO 6632 (2015)
PRÉSENTÉE LE 3 JUILLET 2015
À LA FACULTÉ INFORMATIQUE ET COMMUNICATIONS
LABORATOIRE DE VISION PAR ORDINATEUR
PROGRAMME DOCTORAL EN INFORMATIQUE ET COMMUNICATIONS
ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE
POUR L'OBTENTION DU GRADE DE DOCTEUR ÈS SCIENCES
Xinchao WANG
cceptée sur proposition du jury:
Prof. W. Gerstner, président du jury
Prof. P. Fua,   directeur de thèse
Prof. J. Sullivan, rapporteuse
Prof. P. Dillenbourg, rapporteur
Prof. S. Roth, rapporteur
Suisse"
450e9f80a273df2cdaafd9ae3a9ff149950cc834,Human Pose Estimation using Histograms of Edge Directions,"Human Pose Estimation
using Histograms of Edge Directions
Andrès Koetsier
University of Twente HMI Department"
4541f3ee510b593243ff9a66d3586ef9125c2931,Development of an Efficient Face Recognition System based on Linear and Nonlinear Algorithms,"Development of an Efficient Face Recognition System based on Linear and Nonlinear Algorithms
{tag}                                                 {/tag}
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 134
Number 7
Year of Publication: 2016
Authors:
Filani Araoluwa S., Adetunmbi Adebayo O.
10.5120/ijca2016907932
{bibtex}2016907932.bib{/bibtex}"
4560491820e0ee49736aea9b81d57c3939a69e12,Investigating the Impact of Data Volume and Domain Similarity on Transfer Learning Applications,"Investigating the Impact of Data Volume and
Domain Similarity on Transfer Learning
Applications
Michael Bernico, Yuntao Li, and Dingchao Zhang
State Farm Insurance, Bloomington IL 61710, USA,"
4526992d4de4da2c5fae7a5ceaad6b65441adf9d,System for Medical Mask Detection in the Operating Room Through Facial Attributes,"System for Medical Mask Detection
in the Operating Room Through
Facial Attributes
A. Nieto-Rodr´ıguez, M. Mucientes(B), and V.M. Brea
Center for Research in Information Technologies (CiTIUS),
University of Santiago de Compostela, Santiago de Compostela, Spain"
451ed51346fe2e6c5de2dbf29733711b31f5fd68,Weakly-Supervised Learning for Tool Localization in Laparoscopic Videos,"Weakly-Supervised Learning for Tool
Localization in Laparoscopic Videos
Armine Vardazaryan1, Didier Mutter2, Jacques Marescaux2, and
Nicolas Padoy1
ICube, University of Strasbourg, CNRS, IHU Strasbourg, France
University Hospital of Strasbourg, IRCAD, IHU Strasbourg, France"
453e311c6de1285cd5ea6d93fd78a636eac0ba82,Multi patches 3D facial representation for person authentication using AdaBoost,"Multi patches 3D facial representation for Person
Authentication using AdaBoost
Lahoucine Ballihi, Boulbaba Ben Amor, Mohamed Daoudi, Anuj Srivastava
To cite this version:
Lahoucine Ballihi, Boulbaba Ben Amor, Mohamed Daoudi, Anuj Srivastava. Multi patches 3D facial
representation for Person Authentication using AdaBoost. I/V Communications and Mobile Network
(ISVC), 2010 5th International Symposium on, Sep 2010, Rabat, Morocco. pp.1-4, 2010. <hal-
00665904>
HAL Id: hal-00665904
https://hal.archives-ouvertes.fr/hal-00665904
Submitted on 3 Feb 2012
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rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
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broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,"
4534d78f8beb8aad409f7bfcd857ec7f19247715,Transformation-Based Models of Video Sequences,"Under review as a conference paper at ICLR 2017
TRANSFORMATION-BASED MODELS OF VIDEO
SEQUENCES
Joost van Amersfoort ∗, Anitha Kannan, Marc’Aurelio Ranzato,
Arthur Szlam, Du Tran & Soumith Chintala
Facebook AI Research
{akannan, ranzato, aszlam, trandu,"
45aefa11101129862e323958b62505700bc281ae,Unsupervised Learning in Generative Models of Occlusion,"Unsupervised Learning in Generative
Models of Occlusion
Dissertation
zur Erlangung des Doktorgrades
der Naturwissenschaften
vorgelegt beim Fachbereich Physik
der Johann Wolfgang Goethe-Universität
in Frankfurt am Main
Marc Henniges
us Frankfurt am Main
Frankfurt (2012)
(D 30)"
456983805a8781d6429bed1ed66dc9f3902767af,Seeing with Humans : Gaze-Assisted Neural Image,"Seeing with Humans: Gaze-Assisted
Neural Image Captioning
Yusuke Sugano and Andreas Bulling"
451d777ee33833a3b5eb6ba5292fae162c6d265f,Exploiting Feature Correlations by Brownian Statistics for People Detection and Recognition,"TRANSACTIONS ON CYBERNETICS
Exploiting Feature Correlations by Brownian
Statistics for People Detection and Recognition
Sławomir B ˛ak1, Marco San Biagio2, Ratnesh Kumar1, Vittorio Murino2 and François Brémond1
STARS Lab, INRIA Sophia Antipolis Méditerranée, Sophia Antipolis, 06902 Valbonne, France
Pattern Analysis and Computer Vision (PAVIS), IIT IStituto Italiano di Tecnologia, 16163 Genova, Italy
Characterizing an image region by its feature inter-correlations is a modern trend in computer vision. In this paper, we introduce
new image descriptor that can be seen as a natural extension of a covariance descriptor with the advantage of capturing nonlinear
nd non-monotone dependencies. Inspired from the recent advances in mathematical statistics of Brownian motion, we can express
highly complex structural information in a compact and computationally efficient manner. We show that our Brownian covariance
descriptor can capture richer image characteristics than the covariance descriptor. Additionally, a detailed analysis of the Brownian
manifold reveals that in opposite to the classical covariance descriptor, the proposed descriptor lies in a relatively flat manifold,
which can be treated as a Euclidean. This brings significant boost in the efficiency of the descriptor. The effectiveness and the
generality of our approach is validated on two challenging vision tasks, pedestrian classification and person re-identification. The
experiments are carried out on multiple datasets achieving promising results.
Index Terms—brownian descriptor, covariance descriptor, pedestrian detection, re-identification.
I. INTRODUCTION
D ESIGNING proper image descriptors is a crucial step
in computer vision applications, including scene detec-
tion, target tracking and object recognition. A good descrip-"
45013959589013b946d98b787cfaef404f52a5b3,Linear measurements of facial morphology using automatic aproach,"ORIGINAL ARTICLE
ORIGINALNI RAD
Serbian Dental Journal, vol. 63, No 2, 2016
DOI: 10.1515/sdj-2016-0007
UDC: 572.544.087:004
Linear measurements of facial morphology using
utomatic aproach
Marijana Arapović-Savić1, Mirjana Umićević-Davidović1, Adriana Arbutina1, Mihajlo Savić2
Department of Orthodontics, Study Program Dentistry, Faculty of Medicine, University of Banja Luka, Banja Luka,
Bosnia and Herzegovina;
Faculty of Electrical Engineering, University of Banja Luka, Banja Luka, Bosnia and Herzegovina
SUMMARY
Introduction Clinical extraoral examination prior to orthodontic treatment includes face analysis (front and profile).
Development of computer technology has increased efficacy and simplified this process through automating several
steps of the analysis. The aim of this paper was to examine the possibility of automatic determining of linear measure-
ments based on the facial image of a patient.
Material and Methods Based on the set of 20 patients in NHP (Natural Head Position) position, three sets of measure-
ments were conducted. Trained orthodontist performed positioning of predefined points on the image of the patient
two times with one week apart, after which the points were automatically determined using customized computer
software. Based on the position of the points, measurements for bizygomatic distance, upper and lower facial height"
457d3ca924afc21719d19175caf285aa575d1c90,Analyzing Structured Scenarios by Tracking People and Their Limbs,
45e7ddd5248977ba8ec61be111db912a4387d62f,Adversarial Learning of Structure-Aware Fully Convolutional Networks for Landmark Localization,"CHEN ET AL.: ADVERSARIAL POSENET
Adversarial Learning of Structure-Aware Fully
Convolutional Networks for Landmark
Localization
Yu Chen1, Chunhua Shen2, Hao Chen2, Xiu-Shen Wei3, Lingqiao Liu2 and Jian Yang1"
456f00e213e03058a056069fa75c34929cf7d4e9,Detecting ground control points via convolutional neural network for stereo matching,"Noname manuscript No.
(will be inserted by the editor)
Detecting Ground Control Points via Convolutional Neural Network for
Stereo Matching
Zhun Zhong · Songzhi Su · Donglin Cao · Shaozi Li
Received: date / Accepted: date"
45c824c25e66b7bc1dd474f80cf2b0056b4fa6f8,"Selection of Location, Frequency, and Orientation Parameters of 2D Gabor Wavelets for Face Recognition","Selection of Location, Frequency and
Orientation Parameters of 2D Gabor Wavelets
for Face Recognition
Berk G¨okberk, M.O. ˙Irfano˘glu, Lale Akarun, and Ethem Alpaydın
Bo˘gazi¸ci University, Department of Computer Engineering,
{gokberk, irfanoglu, akarun,
TR-34342, Istanbul, Turkey"
45ca696076e9c073e6cf699766f808899589bc88,Aalborg Universitet Thermal Tracking of Sports Players,"Aalborg Universitet
Thermal Tracking of Sports Players
Gade, Rikke; Moeslund, Thomas B.
Published in:
Sensors
DOI (link to publication from Publisher):
0.3390/s140813679
Publication date:
Document Version
Publisher's PDF, also known as Version of record
Link to publication from Aalborg University
Citation for published version (APA):
Gade, R., & Moeslund, T. B. (2014). Thermal Tracking of Sports Players. Sensors, 14(8), 13679-13691. DOI:
0.3390/s140813679
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45dffa3cd37371c5eed78b6f170c7ab3b5cc491f,Face Recognition Using a Unified 3D Morphable Model,"Face Recognition Using a Unified 3D Morphable Model
Hu, G., Yan, F., Chan, C-H., Deng, W., Christmas, W., Kittler, J., & Robertson, N. M. (2016). Face Recognition
Using a Unified 3D Morphable Model. In Computer Vision – ECCV 2016: 14th European Conference,
Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part VIII (pp. 73-89). (Lecture Notes in
Computer Science; Vol. 9912). Springer Verlag. DOI: 10.1007/978-3-319-46484-8_5
Published in:
Computer Vision – ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14,
016, Proceedings, Part VIII
Document Version:
Peer reviewed version
Queen's University Belfast - Research Portal:
Link to publication record in Queen's University Belfast Research Portal
Publisher rights
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-46484-8_5
General rights
Copyright for the publications made accessible via the Queen's University Belfast Research Portal is retained by the author(s) and / or other
opyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated
with these rights.
Take down policy
The Research Portal is Queen's institutional repository that provides access to Queen's research output. Every effort has been made to"
45a6333fc701d14aab19f9e2efd59fe7b0e89fec,Dataset Creation for Gesture Recognition,"HAND POSTURE DATASET CREATION FOR GESTURE
RECOGNITION
Luis Anton-Canalis
Instituto de Sistemas Inteligentes y Aplicaciones Numericas en Ingenieria
Campus Universitario de Tafira, 35017 Gran Canaria, Spain
Elena Sanchez-Nielsen
Departamento de E.I.O. y Computacion
8271 Universidad de La Laguna, Spain
Keywords:
Image understanding, Gesture recognition, Hand dataset."
4568063b7efb66801e67856b3f572069e774ad33,Correspondence driven adaptation for human profile recognition,"Correspondence Driven Adaptation for Human Profile Recognition
Ming Yang1, Shenghuo Zhu1, Fengjun Lv2, Kai Yu1
NEC Laboratories America, Inc.
Huawei Technologies (USA)
Cupertino, CA 95014
Santa Clara, CA 95050"
458677de7910a5455283a2be99f776a834449f61,Face Image Retrieval Using Facial Attributes By K-Means,"Face Image Retrieval Using Facial Attributes By
K-Means
[1]I.Sudha,  [2]V.Saradha, [3]M.Tamilselvi, [4]D.Vennila
[1]AP, Department of CSE ,[2][3][4] B.Tech(CSE)
Achariya college of Engineering Technology-
Puducherry"
45e81d04d01ef1db78a04ef7a9472fd4cd6de84c,Variational learning of finite Beta-Liouville mixture models using component splitting,"Variational Learning of Finite Beta-Liouville Mixture Models Using
Component Splitting
Wentao Fan and Nizar Bouguila"
45ede580b1e402aae6832256586211a47c53afe3,BIOMETRIC APPLICATION : TEXTURE AND SHAPE BASED 3 D FACE RECOGNITION,"BIOMETRIC APPLICATION: TEXTURE AND SHAPE BASED 3D FACE
RECOGNITION
P.Manju Bala1
Senior Assistant professor,
A.Kalaiselvi2
Assistant Professor,
Department of Computer Science and Engineering,
Department of Computer Science and Engineering,
IFET College of Engineering,
Villupuram."
45c340c8e79077a5340387cfff8ed7615efa20fd,Assessment of the Emotional States of Students during e-Learning,
4572725e98f3e1b6f258c03643d74b69982aa39a,Semantic Cluster Unary Loss for Efficient Deep Hashing,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
Semantic Cluster Unary Loss for Efficient Deep
Hashing
Shifeng Zhang, Jianmin Li, and Bo Zhang
hashing [15], [22], [27], [32], [38], [54] and semi-supervised
hashing [43]. Experiments convey that hashcodes learned by
(semi-)supervised hashing methods contain more semantic
information than those learned by the unsupervised ones."
4508afd43a616e62627a5f5c6089bb3b3629518f,Dismantling Complicated Query Attributes with Crowd,0.5441/002/edbt.2015.36
4563cbfbdba1779fc598081071ae40be021cb81d,Adversarial Attacks on Variational Autoencoders,"Adversarial Attacks on Variational Autoencoders
George Gondim-Ribeiro, Pedro Tabacof, and Eduardo Valle
RECOD Lab. — DCA / School of Electrical and Computer Engineering (FEEC)
University of Campinas (Unicamp)
Campinas, SP, Brazil
{gribeiro, tabacof,"
45e459462a80af03e1bb51a178648c10c4250925,LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning,"LCrowdV: Generating Labeled Videos for
Simulation-based Crowd Behavior Learning
Ernest Cheung1, Tsan Kwong Wong1, Aniket Bera1, Xiaogang Wang2, and
Dinesh Manocha1
The University of North Carolina at Chapel Hill"
456ccc8bbb538037ff00fabf25afb2aceb39149e,Computational Aspects of the Hausdorff Distance in Unbounded Dimension,"Journal of Computational Geometry
COMPUTATIONAL ASPECTS OF THE HAUSDORFF DISTANCE
IN UNBOUNDED DIMENSION
Stefan K¨onig∗"
45f884c4c3bcdabdca46ee0e3794ce1631b9c558,Vision-based assessment of parkinsonism and levodopa-induced dyskinesia with pose estimation,"Vision-Based Assessment of Parkinsonism and
Levodopa-Induced Dyskinesia with Deep
Learning Pose Estimation
Michael H. Li, Tiago A. Mestre, Susan H. Fox, Babak Taati*"
458713d5c1dd8ff95865302e51f0f8df22204d91,ON FACE RECOGNITION USING DIFFERENT PRE-PROCESSING METHODS IN IMAGES CAPTURED UNDER VARIOUS ILLUMINATION AND POSING CONDITIONS,
45ae4c0cdc2df02c278995623b2e25ae5cc4c91f,Visual Search for Musical Performances and Endoscopic Videos,"Visual Search
for Musical Performances
nd Endoscopic Videos
Degree’s Final Project Dissertation
Telecommunications Engineering
Author:
Advisors: Mathias Lux and Xavier Gir´o-i-Nieto
Jennifer Rold´an Carlos
Alpen-Adria University of Klagenfurt (AAU Klagenfurt)
Universitat Polit`ecnica de Catalunya (UPC))
014 - 2015"
2b0ff4b82bac85c4f980c40b3dc4fde05d3cc23f,An Effective Approach for Facial Expression Recognition with Local Binary Pattern and Support Vector Machine,"An Effective Approach for Facial Expression Recognition with Local Binary
Pattern and Support Vector Machine
Cao Thi Nhan, 2Ton That Hoa An, 3Hyung Il Choi
*1School of Media, Soongsil University,
School of Media, Soongsil University,
School of Media, Soongsil University,"
2badc21fc72730f3ae540ba2d20051d31c4a62bc,The audio-video australian English speech data corpus AVOZES,"The Audio-Video Australian English Speech Data Corpus AVOZES
Roland Goecke1,3 and J Bruce Millar2,3
Fraunhofer IGD-R, Rostock, Germany, 2Australian National University, Canberra, Australia,
National ICT Australia
, Canberra Laboratory
Corresponding author:"
2b0a6aea501a8a29b5a6b757e89a8ea502e654cd,Depth-Adaptive Deep Neural Network for Semantic Segmentation,"Depth Adaptive Deep Neural Network
for Semantic Segmentation
Byeongkeun Kang, Yeejin Lee, and Truong Q. Nguyen, Fellow, IEEE"
2b8dfbd7cae8f412c6c943ab48c795514d53c4a7,Polynomial based texture representation for facial expression recognition,"014 IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP)
978-1-4799-2893-4/14/$31.00 ©2014 IEEE
e-mail:
e-mail:
RECOGNITION
. INTRODUCTION
(d1,d2)∈[0;d]2
d1+d2≤d"
2bae810500388dd595f4ebe992c36e1443b048d2,Analysis of Facial Expression Recognition by Event-related Potentials,"International Journal of Bioelectromagnetism
Vol. 18, No. 1, pp. 13 - 18, 2016
www.ijbem.org
Analysis of Facial Expression Recognition
y Event-related Potentials
Taichi Hayasaka and Ayumi Miyachi
Department of Information and Computer Engineering,
National Institute of Technology, Toyota College, Japan
Correspondence: Taichi Hayasaka, Department of Information and Computer Engineering, National Institute of Technology,
Toyota College, 2-1 Eisei, Toyota-shi, Aichi, 471-8525 Japan,
E-mail: phone +81 565 36 5861, fax +81 565 36 5926"
2bdc0c79b26fed51bc2af1af16117879ee3f571e,Augmented Multitouch Interaction upon a 2-DOF Rotating Disk,"Augmented Multitouch Interaction
upon a 2-DOF Rotating Disk
Xenophon Zabulis, Panagiotis Koutlemanis, and Dimitris Grammenos
Institute of Computer Science, Foundation for Research and Technology - Hellas,
Herakleion, Crete, Greece"
2bac4161a928eb33e6be700ed8ea4d823494b22c,MergeNet: A Deep Net Architecture for Small Obstacle Discovery,"MergeNet: A Deep Net Architecture for Small Obstacle Discovery
Krishnam Gupta1, Syed Ashar Javed2, Vineet Gandhi2 and K. Madhava Krishna2
evidences is more likely to perform the task better. Recent
efforts [3] on multi modal fusion also suggests likewise."
2bfb43cb0e72aaa7aff71007bb420df2c9ae4375,Deep Attentional Structured Representation Learning for Visual Recognition,": DEEP ATTENTIONAL STRUCTURED REPRESENTATION LEARNING
Deep Attentional Structured Representation
Learning for Visual Recognition
Krishna Kanth Nakka
Mathieu Salzmann
Computer Vision Lab, EPFL
Lausanne, Switzerland
Computer Vision Lab, EPFL
Lausanne, Switzerland"
2b1327a51412646fcf96aa16329f6f74b42aba89,Improving performance of recurrent neural network with relu nonlinearity,"Under review as a conference paper at ICLR 2016
IMPROVING PERFORMANCE OF RECURRENT NEURAL
NETWORK WITH RELU NONLINEARITY
Sachin S. Talathi & Aniket Vartak
Qualcomm Research
San Diego, CA 92121, USA"
2baea24cc71793ba40cf738b7ad1914f0e549863,Attribute Augmented Convolutional Neural Network for Face Hallucination,"Attribute Augmented Convolutional Neural Network for Face Hallucination
Cheng-Han Lee1 Kaipeng Zhang1 Hu-Cheng Lee1 Chia-Wen Cheng2 Winston Hsu1
National Taiwan University 2The University of Texas at Austin
{r05922077, r05944047, r05922174,"
2b35c76d511e9b9168152ebecd92284a4762b65f,A method of limiting performance loss of CNNs in noisy environments,"A method of limiting performance loss of CNNs in noisy environments
James R. Geraci
Samsung Electronics Co,Ltd.
Seoul, South Korea
Parichay Kapoor
Samsung Electronics Co,Ltd.
Seoul, South Korea"
2b4b0795358d0264f846e8b3c19ec3180da301cc,Active MAP Inference in CRFs for Efficient Semantic Segmentation,"Active MAP Inference in CRFs for Efficient Semantic Segmentation
Roderick de Nijs2
Gemma Roig1 ∗
Sebastian Ramos3
Xavier Boix1 ∗
Kolja K¨uhnlenz2
Luc Van Gool1,4
ETH Z¨urich, Switzerland 2TU Munchen, Germany 3CVC Barcelona, Spain 4KU Leuven, Belgium
Both first authors contributed equally."
2bf571fd8020a68f513ba4ce690083aa7dcdad6e,Visual Speech and Speaker Recognition,"VisualSpeechAndSpeaker
Recognition
JuergenLuettin
DepartmentofComputerScience
UniversityofShe(cid:14)eld
DissertationsubmittedtotheUniversityofShe(cid:14)eld
forthedegreeofDoctorofPhilosophy
(cid:13)JuergenLuettin
		.
May
		
Allrightsreserved.Thisworkmaynotbereproducedinwholeorinpartwithoutpriorwritten
permissionbytheauthor."
2b50f8e4568ecd84e2f9d6357254272d8db4bbd4,Hierarchical Gaussian Descriptor for Person Re-identification,"Hierarchical Gaussian Descriptor for Person Re-Identification
Tetsu Matsukawa1, Takahiro Okabe2, Einoshin Suzuki1, Yoichi Sato3
Kyushu University 2 Kyushu Institute of Technology 3 The University of Tokyo
{matsukawa,"
2b1358efbceda12de2f36398cdbdb3c7bccc70d4,Unified Detection and Tracking of Instruments during Retinal Microsurgery,"This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.
JOURNAL OF LATEX CLASS FILES, VOL. 6, NO. 1, JANUARY 2007
Unified detection and tracking of instruments
during retinal microsurgery
Raphael Sznitman, Rogerio Richa, Russell H. Taylor Fellow, IEEE, Bruno Jedynak
nd Gregory D. Hager, Fellow, IEEE"
2ba64deeb3e170e4776e2d2704771019cf9c8639,Differences between Old and Young Adults’ Ability to Recognize Human Faces Underlie Processing of Horizontal Information,"AGING NEUROSCIENCE
ORIGINAL RESEARCH ARTICLE
published: 23 April 2012
doi: 10.3389/fnagi.2012.00003
Differences between old and young adults’ ability to
recognize human faces underlie processing of
horizontal information
Sven Obermeyer *,Thorsten Kolling, Andreas Schaich and Monika Knopf
Department of Psychology, Institute for Psychology, Goethe-University Frankfurt am Main, Frankfurt am Main, Germany
Edited by:
Hari S. Sharma, Uppsala University,
Sweden
Reviewed by:
Luis Francisco Gonzalez-Cuyar,
University of Washington School of
Medicine, USA
Gregory F. Oxenkrug, Tufts University,
*Correspondence:
Sven Obermeyer , Department of
Psychology, Goethe-University"
2b285e5eaeb7a2aa7e37c5ae6762b838d3742b4e,Video event recognition using concept attributes,"Video Event Recognition Using Concept Attributes
Jingen Liu, Qian Yu, Omar Javed, Saad Ali, Amir Tamrakar, Ajay Divakaran, Hui Cheng, Harpreet Sawhney
SRI International Sarnoff
Princeton, NJ, USA 08540"
2beb9777bf452d02f9bec5275c100f4a736def10,Near Duplicate Image Discovery on One Billion Images,"Near Duplicate Image Discovery on One Billion Images
Saehoon Kim ∗
Department of Computer Science,
POSTECH, Korea
Xin-Jing Wang
Web Search and Mining Group
Microsoft Research Asia, Beijing
Lei Zhang
Web Search and Mining Group
Microsoft Research Asia, Beijing
Seungjin Choi
Department of Computer Science,
POSTECH, Korea"
2baf54199b4b0047f3610ba691fb0a718dbce97e,Development of an Efficient Face Recognition System based on Linear and Nonlinear Algorithms,"International Journal of Computer Applications (0975 – 8887)
Volume 134 – No.7, January 2016
Development of an Efficient Face Recognition System
ased on Linear and Nonlinear Algorithms
Filani Araoluwa S.
Department of Computer Science,
The Federal University of Technology,
P.M.B.704, Akure, Ondo State, Nigeria."
2bd49bdfc61788c8ac5621fe7f08a06dd2152fb9,Pose Invariant Face Recognition Using Neuro-Biologically Inspired Features,"International Journal of Future Computer and Communication, Vol. 1, No. 3, October 2012
Pose Invariant Face Recognition Using
Neuro-Biologically Inspired Features
Pramod Kumar Pisharady and Martin Saerbeck"
2ba5e4c421b1413139e4bc5d935d6d48cc753757,Vantage Feature Frames for Fine-Grained Categorization,"Vantage Feature Frames For Fine-Grained Categorization
Asma Rejeb Sfar
INRIA Saclay
Palaiseau, France
Nozha Boujemaa
INRIA Saclay
Palaiseau, France
Donald Geman
Johns Hopkins University
Baltimore, MD, USA
sma.rejeb"
2b212a9416027dc63273f9e29d93230d837abacf,Fast Optical Flow using Dense Inverse Search,"Fast Optical Flow using Dense Inverse Search
Till Kroeger1
Radu Timofte1
Dengxin Dai1
Luc Van Gool1,2
Computer Vision Laboratory, D-ITET, ETH Zurich
VISICS / iMinds, ESAT, K.U. Leuven
{kroegert, timofter, dai,"
2befea9b289f22547f8911aa56672d6373c1ac64,Recognizing activities with cluster-trees of tracklets,"GAIDON et al.: RECOGNIZING ACTIVITIES WITH CLUSTER-TREES OF TRACKLETS
Recognizing activities with cluster-trees of
tracklets
Adrien Gaidon
http://lear.inrialpes.fr/people/gaidon
Zaid Harchaoui
http://lear.inrialpes.fr/people/harchaoui
Cordelia Schmid
http://lear.inrialpes.fr/people/schmid
LEAR - INRIA Grenoble, LJK
655, avenue de l’Europe
8330 Montbonnot, France"
2b3fe9a0356eaf50f1340dda3f3d14f6904905ec,Taking advantage of sensor modality specific properties in Automated Driving Extended Abstract,"Taking advantage of sensor modality specific properties in
Automated Driving"
2badc4c87a7751dd5ae1797bc4091d10d1acf442,Multimodal Retrieval with Asymmetrically Weighted Regularized Canonical Correla- Tion Analysis,"Under review as a conference paper at ICLR 2016
MULTIMODAL RETRIEVAL WITH ASYMMETRICALLY
WEIGHTED REGULARIZED CANONICAL CORRELA-
TION ANALYSIS
Youssef Mroueh, Etienne Marcheret, Vaibhava Goel
Multimodal Algorithms and Engines Group
IBM T.J Watson Research Center, USA"
2b8667df1a0332386d8d799fbac0327496ce02c9,Stranger danger : Parenthood increases the envisioned bodily formidability of menacing men ☆,"Evolution and Human Behavior 35 (2014) 109–117
Contents lists available at ScienceDirect
Evolution and Human Behavior
j o u r n a l h o m e p a g e : w w w . e h b o n l i n e . o r g
Original Article
Stranger danger: Parenthood increases the envisioned bodily formidability
of menacing men☆
Daniel M.T. Fessler a,b,⁎, Colin Holbrook a,b, Jeremy S. Pollack b, Jennifer Hahn-Holbrook b,c
Department of Anthropology, University of California, Los Angeles, Los Angeles, CA 90095, USA
Center for Behavior, Evolution, and Culture, University of California, Los Angeles, Los Angeles, CA 90095, USA
Department of Psychology, University of California, Los Angeles, Los Angeles, CA 90095, USA
r t i c l e
i n f o
b s t r a c t
Article history:
Initial receipt 6 April 2013
Final revision received 1 November 2013
Keywords:
Parenthood
Relative formidability"
2bf41bf420c8d86dd1bffbacd28c70fa8b12b6dd,Counting the uncountable: deep semantic density estimation from Space,"Counting the uncountable: Deep semantic
density estimation from space
Andres C. Rodriguez and Jan D. Wegner
ETH Zurich, Stefano-franscini-platz 5 8093 Zurich, Switzerland
Accepted at GCPR 2018"
2b507f659b341ed0f23106446de8e4322f4a3f7e,Deep Identity-aware Transfer of Facial Attributes,"Deep Identity-aware Transfer of Facial Attributes
Mu Li1, Wangmeng Zuo2, David Zhang1
The Hong Kong Polytechnic University 2Harbin Institute of Technology"
2b537d826718b7578ea7c5d0164873d376824e6d,Gradient-based Camera Exposure Control for Outdoor Mobile Platforms,"Auto Manual Manual Ours Ours Auto Manual Ours Fig.1:Imagescapturedunderdifferentilluminationcondi-tions.Fromlefttorighttheimagesarefromcameraswithabuilt-inauto-exposuremethod,amanuallytunedfixedexpo-suresetting,andourmethod.Boththebuilt-inauto-exposuremethodandthemanualsettingfailtocapturewell-exposedimages,whileourmethodcapturesimagesthataresuitableforprocessingwithcomputervisionalgorithms.ascenehasasignificantilluminationgapbetweendynamicrangesoftheregionofinterestandthebackground.Thiscommonconditiondegradestheperformanceofthesubse-quentcomputervisionalgorithms.Therefore,overcomingtheproblemofdiverseandchallengingilluminationconditionsattheimagecapturestageisanessentialprerequisitefordevelopingrobustvisionsystems.Morespecifically,Figure1showssomecomparisonsofimagesresultingfromthestandardbuilt-inauto-exposureandthefixed-exposureapproachesinanoutdoorenvironment.Whenthedynamicrangeofthesceneisrelativelynarrow,bothmethodscapturewell-exposedimages.Consequently,underanarrowdynamicrange,asinglerepresentativepa-rametercaneasilybedetermined.Incontrast,bothmethodsresultinundesirablyexposedimagesunderabruptlyvaryingilluminationconditions.Therationalesbehindtheseresultscanbecharacterizedasfollows:1)theauto-exposurecontrolalgorithmshavelimitedadaptability(i.e.,prediction),2)donotconsiderthelimiteddynamicrangeofthecamera,and3)useweakcriteriatoassesstheexposurestatus.Weaddressthefirstandthesecondissuesusingasimulation-basedapproachandthethirdissueusingagradient-basedmetric.Inthispaper,wepresentanewmethodtoautomaticallyadjustcameraexposureusingthegradientinformation.Tohandlesevereilluminationchangesandawidedynamicrangeofsceneradiance,wesimulatetheproperexposureofthesceneinthegradientdomain;thisprocessisfollowedbyafeedbackmechanismtoperformauto-exposure.Becausethegradientdomainisrobustagainstilluminationchangesandhasbeenleveragedbymanycomputervisionalgorithms,theproposedmethodissuitableforcapturingwell-exposedimageswithenrichedimagefeaturesthatarebeneficialforcomputer"
2b8a61184b6423e3d5285803eb1908ff955db1a8,Processing and analysis of 2 . 5 D face models for non-rigid mapping based face recognition using differential geometry tools,"Processing and analysis of 2.5D face models for
non-rigid mapping based face recognition using
differential geometry tools
Przemyslaw Szeptycki
To cite this version:
Przemyslaw Szeptycki. Processing and analysis of 2.5D face models for non-rigid mapping
ased face recognition using differential geometry tools. Other. Ecole Centrale de Lyon, 2011.
English. <NNT : 2011ECDL0020>. <tel-00675988>
HAL Id: tel-00675988
https://tel.archives-ouvertes.fr/tel-00675988
Submitted on 2 Mar 2012
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,"
2b632f090c09435d089ff76220fd31fd314838ae,Early Adaptation of Deep Priors in Age Prediction from Face Images,"Early Adaptation of Deep Priors in Age Prediction from Face Images
Mahdi Hajibabaei
Computer Vision Lab
D-ITET, ETH Zurich
Anna Volokitin
Computer Vision Lab
D-ITET, ETH Zurich
Radu Timofte
CVL, D-ITET, ETH Zurich
Merantix GmbH"
4684c487758df6b6bf4b69f3fe22e1aad874378a,A Discriminative Voting Scheme for Object Detection using Hough Forests,"VIJAY KUMAR B G, IOANNIS PATRAS:
A Discriminative Voting Scheme for Object
Detection using Hough Forests
Vijay Kumar.B.G
Dr Ioannis Patras
Multimedia Vision Research Group
Queen Mary, UoL
London, UK"
46a01565e6afe7c074affb752e7069ee3bf2e4ef,Local Descriptors Encoded by Fisher Vectors for Person Re-identification,"Local Descriptors Encoded by Fisher Vectors for Person
Re-identification
Bingpeng Ma, Yu Su, Fr´ed´eric Jurie
To cite this version:
Bingpeng Ma, Yu Su, Fr´ed´eric Jurie. Local Descriptors Encoded by Fisher Vectors for Person
Re-identification. 12th European Conference on Computer Vision (ECCV) Workshops, 2012,
Italy. pp.413-422, 2012, <10.1007/978-3-642-33863-2 41>. <hal-00806066>
HAL Id: hal-00806066
https://hal.archives-ouvertes.fr/hal-00806066
Submitted on 29 Mar 2013
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,
´emanant des ´etablissements d’enseignement et de"
46c82ea7aa2812e5adee6c4804d15cd5ecb96041,An affine view and illumination invariant iterative image matching approach for face recognition,"International Journal of Engineering & Technology, 7 (2.8) (2018) 42-46
International Journal of Engineering & Technology
Website: www.sciencepubco.com/index.php/IJET
Research Paper
An affine view and illumination invariant iterative image
matching approach for face recognition
D. Rajasekhar 1 *, T. Jayachandra Prasad 2, K. Soundararajan 3
Research Scholar, Department of ECE, JNTUA, Ananthapuramu, Andhra Pradesh-515002
Professor and Principal, RGMCET, Nandyal, Andhra Pradesh, India-518501
Professor and Dean of R&D, TKREC, Hyderabad, Telangana-500097
*Corresponding author E-mail:"
46994b489f7c673d031f6ef644e84ebe5d843d93,A learning-based visual saliency prediction model for stereoscopic 3D video (LBVS-3D),"A Learning-Based Visual Saliency Prediction
Model for Stereoscopic 3D Video (LBVS-3D)
Amin Banitalebi-Dehkordi, Mahsa T. Pourazad, and Panos Nasiopoulos"
4686bdcee01520ed6a769943f112b2471e436208,Fast search based on generalized similarity measure,"Utsumi et al. IPSJ Transactions on Computer Vision and
Applications  (2017) 9:11
DOI 10.1186/s41074-017-0024-5
IPSJ Transactions on Computer
Vision and Applications
EXPRESS PAPER
Open Access
Fast search based on generalized
similarity measure
Yuzuko Utsumi*†, Tomoya Mizuno†, Masakazu Iwamura and Koichi Kise"
466212a84d5b60f4517e8ab3e4473c3c9e081897,Thermal-Visible Registration of Human Silhouettes : a Similarity Measure Performance Evaluation,"Thermal-Visible Registration of Human Silhouettes: a
Similarity Measure Performance Evaluation
Guillaume-Alexandre Bilodeaua,∗, Atousa Torabib, Pierre-Luc St-Charlesa,
Dorra Riahia
LITIV Lab., Department of Computer and Software Engineering,
´EcolePolytechnique de Montr´eal,
P.O. Box 6079, Station Centre-ville, Montr´eal
Qu´ebec, Canada, H3C 3A7
LISA, Dept. IRO,
Universit´e de Montr´eal,
Montr´eal, Qu´ebec, Canada, H2C 3J7"
4634bf44a0c994e2bed89686225f8cef601a0224,NLM at ImageCLEF 2018 Visual Question Answering in the Medical Domain,"NLM at ImageCLEF 2018 Visual Question
Answering in the Medical Domain
Asma Ben Abacha, Soumya Gayen, Jason J Lau, Sivaramakrishnan
Rajaraman, and Dina Demner-Fushman
Lister Hill National Center for Biomedical Communications,
National Library of Medicine, Bethesda, MD, USA."
46df854f57b6553b4b3238779e46bf2a3a3fffcf,3 D Face Recognition using ICP and Geodesic Computation Coupled Approach,"D Face Recognition using ICP and Geodesic
Computation Coupled Approach
Karima Ouji‡, Boulbaba Ben Amor§, Mohsen Ardabilian§,
Faouzi Ghorbel‡, and Liming Chen§
§LIRIS, Laboratoire d’InfoRmatique en Image et Systmes d’information,
6, av. Guy de Collongue, 69134 Ecully, France.
GRIFT, Groupe de Recherche en Images et Formes de Tunisie,
Ecole Nationale des Sciences de l’Informatique, Tunisie.
Key words: 3D face recognition, Iterative Closest Point, Geodesics computa-
tion, biometric evaluation"
469fa274bbef1e8c7b5b4b1948963abdffaf4e1c,Socially Aware Kalman Neural Networks for Trajectory Prediction,"Socially Aware Kalman Neural Networks for Trajectory Prediction
Ce Ju ∗ ‡
Zheng Wang† ‡
Xiaoyu Zhang∗
In spite of the challenges above, we exploit the following"
4669b079c3ca15aba08130c36ead597014f7341a,GrabCut-Based Human Segmentation in Video Sequences,"Sensors 2012, 12, 15376-15393; doi:10.3390/s121115376
OPEN ACCESS
sensors
ISSN 1424-8220
www.mdpi.com/journal/sensors
Article
GrabCut-Based Human Segmentation in Video Sequences
Antonio Hern´andez-Vela 1,2,⋆, Miguel Reyes 1,2, V´ıctor Ponce 1,2 and Sergio Escalera 1,2
Departamento MAIA, Universitat de Barcelona, Gran Via 585, 08007 Barcelona, Spain;
E-Mails: (M.R.); (V.P.); (S.E.)
Centre de Visi´o per Computador, Campus UAB, Edifici O, 08193 Bellaterra, Barcelona, Spain
* Author to whom correspondence should be addressed; E-Mail:
Tel.: +34-93-402-1897; Fax: +34-93-402-1601.
Received: 4 September 2012; in revised form: 1 November 2012 / Accepted: 6 November 2012 /
Published: 9 November 2012"
46c3e8c2b2042b193659c0a613adc72100a2f301,Vision for Robotics By Danica Kragic and,"Foundations and Trends R(cid:1) in
Robotics
Vol. 1, No. 1 (2010) 1–78
(cid:1) 2009 D. Kragic and M. Vincze
DOI: 10.1561/2300000001
Vision for Robotics
By Danica Kragic and Markus Vincze
Contents
Introduction
.1 Scope and Outline
Historical Perspective
.1 Early Start and Industrial Applications
.2 Biological Influences and Affordances
.3 Vision Systems
What Works
.1 Object Tracking and Pose Estimation
.2 Visual Servoing–Arms and Platforms
.3 Reconstruction, Localization, Navigation, and
Visual SLAM
.4 Object Recognition"
46f3b113838e4680caa5fc8bda6e9ae0d35a038c,Automated Dermoscopy Image Analysis of Pigmented Skin Lesions,"Cancers 2010, 2, 262-273; doi:10.3390/cancers2020262
OPEN ACCESS
ancers
ISSN 2072-6694
www.mdpi.com/journal/cancers
Review
Automated Dermoscopy Image Analysis of Pigmented Skin
Lesions
Alfonso Baldi 1,2,*, Marco Quartulli 3, Raffaele Murace 2, Emanuele Dragonetti 2,
Mario Manganaro 3, Oscar Guerra 3 and Stefano Bizzi 3
Department of Biochemistry, Section of Pathology, Second University of Naples, Via L. Armanni
5, 80138 Naples, Italy
Futura-onlus, Via Pordenone 2, 00182 Rome, Italy; E-Mail:
ACS, Advanced Computer Systems, Via della Bufalotta 378, 00139 Rome, Italy
*  Author to whom correspondence should be addressed; E-Mail:
Fax: +390815569693.
Received: 23 February 2010; in revised form: 15 March 2010 / Accepted: 25 March 2010 /
Published: 26 March 2010"
4640dfc0bfe7923c08d0c762a9c33b52b9029409,Head Movement and Facial Expression Transfer from 2 D Video to a 3 D Model,"Head Movement and Facial Expression Transfer
from 2D Video to a 3D Model
Mairead Grogan
A dissertation submitted to the University of Dublin, Trinity College,
in partial fulfilment of the requirements for the degree of
Master of Science in Computer Science (Interactive Entertainment Technology)
University of Dublin, Trinity College"
46971fb6caa61c606b046da855be4e196a830ccf,Identification of Scene Text by Character Descriptor in Smart Mobile Devices,"International Journal of Engineering Research and General Science Volume 3, Issue 3, Part-2 , May-June, 2015
ISSN 2091-2730
Identification of Scene Text by Character Descriptor in Smart Mobile Devices
Devdas¹, Bhavana.S², Dr. Shubhangi D.C.³
Student, Department of computer science and engineering, VTU RO Kalaburagi, India¹
Assistant professor, Department of computer science and engineering, VTU RO Kalaburagi, India²
Head of Department, Department of computer science and engineering, VTU RO Kalaburagi, India³
Contact no: 8951781387"
46386d4aa6a2b96106ab1d18658103622b24f9d8,Google Street View images support the development of vision-based driver assistance systems,"Google Street View Images Support the Development of
Vision-Based Driver Assistance Systems
Jan Salmen∗, Sebastian Houben∗, and Marc Schlipsing∗"
46c52f92e10fd2f2dddda162ad7995a1658e1245,Finding Socio-Textual Associations Among Locations,"Series ISSN: 2367-2005
0.5441/002/edbt.2017.12"
46471a285b1d13530f1885622d4551b48c19fc67,Generating Artificial Data for Private Deep Learning,"Generating Artificial Data for Private Deep Learning
Ecole Polytechnique Fédérale de Lausanne
Ecole Polytechnique Fédérale de Lausanne
Aleksei Triastcyn
Artificial Intelligence Laboratory
Lausanne, Switzerland
Boi Faltings
Artificial Intelligence Laboratory
Lausanne, Switzerland"
46a4551a6d53a3cd10474ef3945f546f45ef76ee,Robust and continuous estimation of driver gaze zone by dynamic analysis of multiple face videos,"014 IEEE Intelligent Vehicles Symposium (IV)
June 8-11, 2014. Dearborn, Michigan, USA
978-1-4799-3637-3/14/$31.00 ©2014 IEEE"
460845e06ca99f292fa2265beb4e535d20ba16f8,Object Detection for Comics using Manga109 Annotations,"Object Detection for Comics using Manga109
Annotations
Toru Ogawa · Atsushi Otsubo · Rei
Narita · Yusuke Matsui · Toshihiko
Yamasaki · Kiyoharu Aizawa"
4679f4a7da1cf45323c1c458b30d95dbed9c8896,Recognizing Facial Expressions Using Model-Based Image Interpretation,"We are IntechOpen,
the world’s leading publisher of
Open Access books
Built by scientists, for scientists
,800
16,000
Open access books available
International  authors and editors
Downloads
Our authors are among the
Countries delivered to
TOP 1%
2.2%
most cited scientists
Contributors from top 500 universities
Selection of our books indexed in the Book Citation Index
in Web of Science™ Core Collection (BKCI)
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Contact
Numbers displayed above are based on latest data collected."
465b75fa4b84948e19d8bf2ebf4fe4459c3c87ae,A deformation model to reduce the effect of expressions in 3D face recognition,"Vis Comput (2011) 27: 333–345
DOI 10.1007/s00371-010-0530-2
O R I G I NA L A RT I C L E
A deformation model to reduce the effect of expressions in 3D face
recognition
Yueming Wang · Gang Pan · Jianzhuang Liu
Published online: 5 November 2010
© Springer-Verlag 2010"
463bfb0b55c085cda77c2c6e1583abb64baa5d0a,Learning Arbitrary Pairwise Potentials in CRFs for Semantic Segmentation,"Learning Arbitrary Potentials in CRFs with Gradient Descent
M˚ans Larsson1
Fredrik Kahl1,2
Chalmers Univ. of Technology 2Lund Univ.
Shuai Zheng3 Anurag Arnab3
Oxford Univ.
Philip Torr3 Richard Hartley4
Australian National Univ."
46b031a3e368f25dd1e42f70f21165fef7b16de2,"Faces in the mirror, from the neuroscience of mimicry to the emergence of mentalizing.","doi 10.4436/jass.94037
Vol. 94 (2016), pp. 113-126
Faces in the mirror, from the neuroscience of mimicry
to the emergence of mentalizing
Antonella Tramacere & Pier Francesco Ferrari
University of Parma, Dep. of Neuroscience, via Volturno 39, 43100, Parma, Italy
e-mail:
Summary - In the current opinion paper, we provide a comparative perspective on specific aspects
of primate empathic abilities, with particular emphasis on the mirror neuron system associated with
mouth/face actions and expression. Mouth and faces can be very salient communicative classes of stimuli
that allow an observer access to the emotional and physiological content of other individuals. We thus
describe patterns of activations of neural populations related to observation and execution of specific
mouth actions and emotional facial expressions in some species of monkeys and in humans. Particular
ttention is given to dynamics of face-to-face interactions in the early phases of development and to
the differences in the anatomy of facial muscles among different species of primates. We hypothesize
that increased complexity in social environments and patterns of social development have promoted
specializations of facial musculature, behavioral repertoires related to production and recognition of
facial emotional expression, and their neural correlates. In several primates, mirror circuits involving
parietal-frontal regions, insular regions, cingulate cortices, and amygdala seem to support automatic
forms of embodied empathy, which probably contribute to facial mimicry and behavioural synchrony."
4602bbec65b0c718d5887fdf2381fb7cee77a64d,Explicit Occlusion Modeling for 3D Object Class Representations,"Explicit Occlusion Modeling for 3D Object Class Representations
M. Zeeshan Zia1, Michael Stark2, and Konrad Schindler1
Photogrammetry and Remote Sensing, ETH Z¨urich, Switzerland
Stanford University and Max Planck Institute for Informatics"
46fbf807f1c0c863aa35d3d8acb40870182d3b28,Multi-Instance Dynamic Ordinal Random Fields for Weakly Supervised Facial Behavior Analysis,"Multi-Instance Dynamic Ordinal Random Fields for
Weakly-supervised Facial Behavior Analysis
Adria Ruiz∗, Ognjen (Oggi) Rudovic†, Xavier Binefa∗ and Maja Pantic(cid:5)"
46299c9db8a4570d060ee8fc1616c4a148056365,IJCSI Publicity Board 2011,"IJCSI
IJCSI
International Journal of
Computer Science Issues
© IJCSI PUBLICATION
www.IJCSI.org
Volume 7, Issue 5, September 2010
ISSN (Online): 1694-0814"
46106d9f9d9b90401b7984794536e2f45fff1dbe,Learning Distance Functions for Automatic Annotation of Images,"Learning Distance Functions for
Automatic Annotation of Images
Josip Krapac and Fr´ed´eric Jurie
INRIA Rhˆone-Alpes, 655, Avenue de l’Europe, 38334 Saint Ismier Cedex, France"
468aaa87ccdba65f3115bd0864f7772b6706c00e,A Survey on Heterogeneous Face Matching : NIR Images to VIS Images,"International Journal of Computer Applications (0975 – 8887)
Emerging Trends In Computing 2016
Heterogeneous Face Matching: NIR images to VIS
Images
Sandhya  R.Waddhavane
M.E Student
Department of Computer Engineering
KKWIEER, Nashik, India.
Savitribai Phule Pune University,Pune
S.M.Kamalapur, PhD
Associate Professor
Department of Computer Engineering
KKWIEER, Nashik, India.
Savitribai Phule Pune University,Pune"
4682fee7dc045aea7177d7f3bfe344aabf153bd5,Tabula rasa: Model transfer for object category detection,"Tabula Rasa: Model Transfer for
Object Category Detection
Yusuf Aytar & Andrew Zisserman,
Department of Engineering Science
Oxford
(Presented by Elad Liebman)"
46312c80e0583e956ac351615d73e11c21749c4b,Chapter 5 Multimodal Affect Recognition : Current Approaches and Challenges,"We are IntechOpen,
the world’s leading publisher of
Open Access books
Built by scientists, for scientists
,800
16,000
Open access books available
International  authors and editors
Downloads
Our authors are among the
Countries delivered to
TOP 1%
2.2%
most cited scientists
Contributors from top 500 universities
Selection of our books indexed in the Book Citation Index
in Web of Science™ Core Collection (BKCI)
Interested in publishing with us?
Contact
Numbers displayed above are based on latest data collected."
46888c67aa5351730ce5022cc800239f6557f254,Online Multi-target Visual Tracking using a HISP Filter,
463a1ca5f819af35e71ae47ea0e57293691507d3,Soft Biometrics Classification Using Denoising Convolutional Autoencoders and Support Vector Machines,"Soft Biometrics Classification Using Denoising
Convolutional Autoencoders and Support Vector
Machines
Nelson Marcelo Romero Aquino1, Matheus Gutoski2
Leandro Takeshi Hattori3 and Heitor Silv´erio Lopes4
Federal University of Technology - Paran´a
Av. Sete de Setembro, 3165 - Rebou¸cas CEP 80230-901"
462e4d0b35bf571bfc35dcd8e9bd589dca07a464,"Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation","Inverted Residuals and Linear Bottlenecks: Mobile Networks for
Classification, Detection and Segmentation
Mark Sandler Andrew Howard Menglong Zhu Andrey Zhmoginov Liang-Chieh Chen
{sandler, howarda, menglong, azhmogin,
Google Inc."
46282f10271875647219b641dac2cc01c7dc8ab2,Psychopathic traits are associated with reduced fixations to the eye region of fearful faces.,"018, Vol. 127, No. 1, 43–50
0021-843X/18/$12.00
© 2018 American Psychological Association
http://dx.doi.org/10.1037/abn0000322
Psychopathic Traits Are Associated With Reduced Fixations to the Eye
Region of Fearful Faces
Monika Dargis, Richard C. Wolf, and Michael Koenigs
University of Wisconsin–Madison
Impairments in processing fearful faces have been documented in both children and adults with
psychopathic traits, suggesting a potential mechanism by which psychopathic individuals develop callous
nd manipulative interpersonal and affective traits. Recently, research has demonstrated that psycho-
pathic traits are associated with reduced fixations to the eye regions of faces in samples of children and
ommunity-dwelling adults, however this relationship has not yet been established in an offender sample
with high levels of psychopathy. In the current study, we employed eye-tracking with paradigms
involving the identification and passive viewing of facial expressions of emotion, respectively, in a
sample of adult male criminal offenders (n ⫽ 108) to elucidate the relationship between visual processing
of fearful facial expressions and interpersonal and affective psychopathic traits. We found that the
interpersonal-affective traits of psychopathy were significantly related to fewer fixations to the eyes of
fear faces during the emotion recognition task. This association was driven particularly by the interper-
sonal psychopathic traits (e.g., egocentricity, deceitfulness), whereas fear recognition accuracy was"
46d728356b5090bc28461b30cb21a08c3a690195,"Deep Multi-patch Aggregation Network for Image Style, Aesthetics, and Quality Estimation","Deep Multi-Patch Aggregation Network
for Image Style, Aesthetics, and Quality Estimation
Xin Lu(cid:63)
James Z. Wang(cid:63)
(cid:63)The Pennsylvania State University, University Park, Pennsylvania
Zhe Lin† Xiaohui Shen† Radom´ır Mˇech†
Adobe Research, San Jose, California
{xinlu, {zlin, xshen,"
46f2611dc4a9302e0ac00a79456fa162461a8c80,Spatio-Temporal Channel Correlation Networks for Action Classification,"for Action Classification
Ali Diba1,4,(cid:63), Mohsen Fayyaz3,(cid:63), Vivek Sharma2, M.Mahdi Arzani4, Rahman
Yousefzadeh4, Juergen Gall3, Luc Van Gool1,4
ESAT-PSI, KU Leuven, 2CV:HCI, KIT, Karlsruhe, 3University of Bonn, 4Sensifai"
3a4f522fa9d2c37aeaed232b39fcbe1b64495134,Face Recognition and Retrieval Using Cross-Age Reference Coding With Cross-Age Celebrity Dataset,"ISSN (Online) 2321 – 2004
ISSN (Print) 2321 – 5526
INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN ELECTRICAL, ELECTRONICS, INSTRUMENTATION AND CONTROL ENGINEERING
Vol. 4, Issue 5, May 2016
IJIREEICE
Face Recognition and Retrieval Using Cross
Age Reference Coding
Sricharan H S1, Srinidhi K S1, Rajath D N1, Tejas J N1, Chandrakala B M2
BE, DSCE, Bangalore1
Assistant Professor, DSCE, Bangalore2"
3a4ecdf7d73b0fb392763048aa834a537a495537,Contour-based object detection,"SCHLECHT, OMMER: CONTOUR-BASED OBJECT DETECTION
Contour-based Object Detection
Joseph Schlecht
Björn Ommer
Interdisciplinary Center for
Scientific Computing
University of Heidelberg
Germany"
3a8f16d8f7adae8bd0cdc5cc5114dac0b388a9f6,Interpreting Deep Neural Network: Fast Object Localization via Sensitivity Analysis,"Under review as a conference paper at ICLR 2019
INTERPRETING DEEP NEURAL NETWORK:
FAST OBJECT LOCALIZATION VIA SENSITIVITY
ANALYSIS
Anonymous authors
Paper under double-blind review"
3a165f7e22f0667b401cba1b2615048193781b4c,Patch-Based Object Recognition,"Diplomarbeit im Fach Informatik
Rheinisch-Westf¨alische Technische Hochschule Aachen
Lehrstuhl f¨ur Informatik 6
Prof. Dr.-Ing. H. Ney
Patch-Based Object Recognition
vorgelegt von:
Andre Hegerath
Matrikelnummer 228760
Gutachter:
Prof. Dr.-Ing. H. Ney
Prof. Dr. T. Seidl
Betreuer:
Dipl.-Inform. T. Deselaers"
3a2fc58222870d8bed62442c00341e8c0a39ec87,Probabilistic Local Variation Segmentation,"Probabilistic Local Variation
Segmentation
Michael Baltaxe
Technion - Computer Science Department - M.Sc. Thesis  MSC-2014-02 - 2014"
3a8846ca16df5dfb2daadc189ed40c13d2ddc0c5,Validation loss for landmark detection,"Validation loss for landmark detection
Wolfgang Fuhl
Eberhard Karls Universit¨at T¨ubingen
Institution1 address
Rene Alexander Lotz
Daimler AG
Inselstrae 140, 70546 Stuttgart, Germany
Wolfgang Rosenstiel
Eberhard Karls Universit¨at T¨ubingen
Auf der Morgenstelle 8, 72076 Tbingen, Germany
Thomas K¨ubler
Eberhard Karls Universit¨at T¨ubingen
Sand 14, 72076 Tbingen, Germany
Gjergji Kasneci
Eberhard Karls Universit¨at T¨ubingen
Sand 14, 72076 Tbingen, Germany
Enkelejda Kasneci
Eberhard Karls Universit¨at T¨ubingen
Sand 14, 72076 Tbingen, Germany"
3a89236bb9fb3198a45089fb4a99ddba070d0cba,Image Area Reduction for Efficient Medical Image Retrieval,"Image Area Reduction for Ef‌f‌icient
Medical Image Retrieval
Zehra Camlica
A thesis
presented to the University of Waterloo
in fulfillment of the
thesis requirement for the degree of
Master of Applied Science
Systems Design Engineering
Waterloo, Ontario, Canada, 2015
(cid:13) Zehra Camlica 2015"
3af0400c011700f3958062edfdfed001e592391c,The Intense World Theory – A Unifying Theory of the Neurobiology of Autism,"HUMAN NEUROSCIENCE
The Intense World Theory – a unifying theory of the
neurobiology of autism
Review ARticle
published: 21 December 2010
doi: 10.3389/fnhum.2010.00224
Kamila Markram
* and
Henry Markram
Laboratory of Neural Microcircuits, Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
Edited by:
Silvia A. Bunge, University of California
Berkeley, USA
Reviewed by:
Matthew K. Belmonte, Cornell
University, USA; University of
Cambridge, UK
Egidio D’Angelo, University of Pavia,
Italy
*Correspondence:"
3aa9c8c65ce63eb41580ba27d47babb1100df8a3,Differentiating Duchenne from non-Duchenne smiles using active appearance models,"Annals of the
University of North Carolina Wilmington
Master of Science in
Computer Science and Information Systems"
3acfbc2aee9b2ed246a640930ebc2e350621f990,Progressive Boosting for Class Imbalance,"Progressive Boosting for Class Imbalance
Roghayeh Soleymania,∗, Eric Grangera, Giorgio Fumerab
Laboratoire d’imagerie, de vision et d’intelligence artificielle, ´Ecole de technologie sup´erieure
Pattern Recognition and Applications Group, Dept. of Electrical and Electronic Engineering
Universit´e du Qu´ebec, Montreal, Canada
University of Cagliari, Cagliari, Italy"
3ab13f3ee6d66186c33766ac115d57f8b381468f,Stream Clustering with Dynamic Estimation of Emerging Local Densities,"Stream Clustering with Dynamic Estimation of
Emerging Local Densities
Ziyin Wang
Gavriil Tsechpenakis
Department of Computer and Information Science
Indiana University-Purdue University Indianapolis
Department of Computer and Information Science
Indiana University-Purdue University Indianapolis
Indianapolis, IN 46202, USA
Email:
Indianapolis, IN 46202, USA
Email:"
3aa66f2829ef440842c71a52cdaff30398a90ccb,Pointly-Supervised Action Localization,"International Journal of Computer Vision manuscript No.
(will be inserted by the editor)
Pointly-Supervised Action Localization
Pascal Mettes · Cees G. M. Snoek
Received: date / Accepted: date"
3abb51739b90c8bfd665e045b0eeadc87e065b63,Intrinsic 3D Dynamic Surface Tracking based on Dynamic Ricci Flow and Teichmüller Map,"Intrinsic 3D Dynamic Surface Tracking based on Dynamic Ricci Flow and
Teichm ¨uller Map
Xiaokang Yu
Dept of Comp Sci
Qingdao Univ
Na Lei
Dept of Soft and Tech
Dalian Univ of Tech
Qingdao, PR China
Dalian,PR China
Yalin Wang
Comp.Sci.& Engin
Arizona State Univ
Arizona, USA
Xianfeng Gu
Dept of Comp Sci
Stony Brook Univ
Stony Brook, USA"
3a591a9b5c6d4c62963d7374d58c1ae79e3a4039,Driver Cell Phone Usage Detection from HOV/HOT NIR Images,"Driver Cell Phone Usage Detection From HOV/HOT NIR Images
Yusuf Artan, Orhan Bulan, Robert P. Loce, and Peter Paul
Xerox Research Center Webster
800 Phillips Rd. Webster NY 14580"
3aef744dad3982a7ae1ad97b4f126b6772fc3d07,Scene-Centric Joint Parsing of Cross-View Videos,"Scene-centric Joint Parsing of Cross-view Videos
Hang Qi1∗, Yuanlu Xu1∗, Tao Yuan1∗, Tianfu Wu2, Song-Chun Zhu1
Dept. Computer Science and Statistics, University of California, Los Angeles (UCLA)
{hangqi, tianfu
Dept. Electrical and Computer Engineering, NC State University"
3abfab8740ffc66c0c191ce32ce1240062620bea,Continuous Facial Affect Recognition from Videos,"N. Garay, J. Abascal (Eds.): Actas del XII Congreso Internacional Interacción 2011, Lisboa
Continuous Facial Affect Recognition from Videos
Sergio Ballano1, Isabelle Hupont1, Eva Cerezo2 and Sandra Baldassarri2
Aragon Institute of Technology, Department of R&D and Technology Services,
Zaragoza. 5018, María de Luna 7-8, Spain
University of Zaragoza, Computer Science and Systems Engineering Department,
Zaragoza. 50018, María de Luna 3, Spain
{sballano, {ecerezo,"
3a9681e2e07be7b40b59c32a49a6ff4c40c962a2,"Comparing treatment means : overlapping standard errors , overlapping confidence intervals , and tests of hypothesis","Biometrics & Biostatistics International Journal
Comparing treatment means: overlapping standard
errors, overlapping confidence intervals, and tests of
hypothesis"
3af0a26ef9a4084703b310eb997ca630d0bae237,Automatic conversion of monoscopic image / video to stereo for 3 D visualization,"________________________________________________________________________________________________
International Journal of Recent Advances in Engineering & Technology (IJRAET)
Automatic conversion of monoscopic image/ video to stereo for 3D
visualization
R.C.Gokul Nanda Kumar, 2Vijaykumar T
4th sem, M.Tech (Digital Electronics), SJBIT, Bangalore
Assoc Prof, Dept. of ECE, SJBIT, Bangalore
Email:
into  a"
3a7f9b4badc7407273325650763e887ad7b5cc9e,Anthropometric Comparison of Cross-Sectional External Ear between Monozygotic Twin,"Annals of Forensic Research and Analysis
*Corresponding author
Rumiza Abd Rashid, Institute of Forensic Sciences,
Universiti Teknologi MARA, 40450 Shah Alam, Selangor,
Malaysia; Tel: +60196943080; Fax: +603-55444562 ;
Email:
Submitted: 19 November 2014
Accepted: 20 November 2014
Published: 22 November 2014
Copyright
© 2014 Rashid et al.
OPEN ACCESS
Keywords
•	External ear
•	Monozygotic twin
•	Anthropometric measurement
•	Forensic anthropology
•	Identification
Research Article
Anthropometric Comparison"
3a772ed83fdc90e10def9d38f59153aee49cd47b,A Camera Network Tracking (CamNeT) Dataset and Performance Baseline,"A Camera Network Tracking (CamNeT) Dataset and Performance Baseline
Shu Zhang1, Elliot Staudt1, Tim Faltemier2, and Amit K. Roy-Chowdhury1
Department of Electrical and Computer Engineering, University of California, Riverside
Progeny Systems Corporation"
3a192e0391c357124cd2ec2287b1706f523ecdfd,An Introduction to the 3rd Workshop on Egocentric (First-Person) Vision,"An Introduction to the 3rd Workshop on Egocentric (First-person) Vision
Steve Mann, Kris M. Kitani, Yong Jae Lee, M. S. Ryoo, Alireza Fathi"
3affe6f9c2244f4b32c1c0f7d7f1d24770d40efe,Evaluating the Resilience of Face Recognition Systems against Malicious Attacks,"OMAR L., IVRISSIMTZIS I.: RESILIENCE OF FACE RECOGNITION SYSTEMS
Evaluating the Resilience of Face
Recognition Systems against Malicious
Attacks
Luma Omar1
Ioannis Ivrissimtzis1
School of Engineering and
Computing Sciences
Durham University
Durham, UK"
3a28fe49e7a856ddd60d134696a891ed7bca5962,Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation,"Small-scale Pedestrian Detection Based on
Somatic Topology Localization and Temporal
Feature Aggregation
Tao Song, Leiyu Sun, Di Xie, Haiming Sun, Shiliang Pu
Hikvision Research Institute"
3a846704ef4792dd329a5c7a2cb8b330ab6b8b4e,FACE-GRAB: Face recognition with General Region Assigned to Binary operator,"in  any  current  or
future  media,
for  all  other  uses,
© 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be
obtained
including
reprinting/republishing  this  material  for  advertising  or  promotional  purposes,  creating
new  collective  works,  for  resale  or  redistribution  to  servers  or  lists,  or  reuse  of  any
opyrighted component of this work in other works.
Pre-print of article that appeared at the IEEE Computer Society Workshop on Biometrics
010.
The published article can be accessed from:
http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5544597"
3a13c964cc7adc5f010164ccb91d150457685a78,LIMO: Lidar-Monocular Visual Odometry,"LIMO: Lidar-Monocular Visual Odometry
Johannes Graeter1, Alexander Wilczynski1 and Martin Lauer1"
3ab7f06cf8e7e7ca34427f81b766b823647ac117,To care or not to care: Analyzing the caregiver in a computational gaze following framework,"Proceedings of the 2004 International
Conference on Development and Learning
Editors: Jochen Triesch and Tony Jebara
Publisher: UCSD Institute for Neural Computation
Location: The Salk Institute for Biological Studies
La Jolla California, USA
ISBN: 0-615-12704-5"
3a37f57a9b94fff82ffea4e77803ebe5ebf6401b,ER7ST-algorithm for extracting facial expressions,"068                                                                 The International Arab Journal of Information Technology Vol. 13, No. 6B, 2016
ER7ST-Algorithm for Extracting Facial Expressions
Ahmad Tayyar1, Shadi Al-Shehabi2, and Majida AlBakoor3
Department of Computer Science, Jerash University, Jordan
Department of C omputer Engineeringm, Türk Hava Kurumu Üniversitesi, Turkey
Department of Mathematics, Aleppo University, Syria"
3a280773e130abd44f44eb3181ea050e4e5b64f0,PRNU Variance Analysis for Morphed Face Image Detection,"⃝ IEEE. Personal use of this material is permitted. However, permission to reprint/republish
this material for advertising or promotional purposes or for creating new collective works for
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y each author’s copyright. In most cases, these works may not be reposted without the explicit
permission of the copyright holder."
3a8023d206613c930cee8e9166fcbbfd743e6634,Enhancing Person Re-identification in a Self-Trained Subspace,"Enhancing Person Re-identification in a Self-trained
Subspace
Xun Yang, Meng Wang, Richang Hong, Qi Tian, Yong Rui"
3abd07b770c8cc0e3dc781611cab5e4e4aeb162c,"Show, Control and Tell: A Framework for Generating Controllable and Grounded Captions","A Framework for Generating Controllable and Grounded Captions
Show, Control and Tell:
Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
University of Modena and Reggio Emilia"
3abbb484e93e87ae661a1a5c26990c61908398f7,Urban scene segmentation with laser-constrained CRFs,"Urban Scene Segmentation with Laser-Constrained CRFs
Charika De Alvis
Lionel Ott
Fabio Ramos
typically possess"
3a7f3d38157bf90cfb429c57bdf51933a6d5aabc,Shape Constraint Strategies: Novel Approaches and Comparative Robustness,"CERROLAZA,VILLANUEVA,CABEZA:SHAPECONSTRAINTSTRATEGIES
Shape Constraint Strategies: Novel
Approaches and Comparative Robustness
Juan J. Cerrolaza
Arantxa Villanueva
Rafael Cabeza
Biomedical Engineering Group.
Department of Electrical and
Electronics Engineering.
Public University of Navarra.
Navarra, SPAIN"
3abc833f4d689f37cc8a28f47fb42e32deaa4b17,Large Scale Retrieval and Generation of Image Descriptions,"Noname manuscript No.
(will be inserted by the editor)
Large Scale Retrieval and Generation of Image Descriptions
Vicente Ordonez · Xufeng Han · Polina Kuznetsova · Girish Kulkarni ·
Margaret Mitchell · Kota Yamaguchi · Karl Stratos · Amit Goyal ·
Jesse Dodge · Alyssa Mensch · Hal Daum´e III · Alexander C. Berg ·
Yejin Choi · Tamara L. Berg
Received: date / Accepted: date"
3a962138ede25d81a6d5aa42aa1abba649481f10,Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation,"Flow Fields: Dense Correspondence Fields for Highly Accurate Large
Displacement Optical Flow Estimation
Christian Bailer1
Bertram Taetz1,2
Didier Stricker1,2
German Research Center for Artificial Intelligence (DFKI), 2University of Kaiserslautern"
3a032433fc93acd5a482e4194a49ee7f0fd86afd,Deposited in DRO : 28 April 2016 Version of attached le : Published Version Peer-review status of attached le : Peer-reviewed Citation for published item,"Durham Research Online
Deposited in DRO:
8 April 2016
Version of attached le:
Published Version
Peer-review status of attached le:
Peer-reviewed
Citation for published item:
Omar, Luma and Ivrissimtzis, Ioannis (2015) 'Evaluating the resilience of face recognition systems against
malicious attacks.', in Proceedings of the 7th UK Computer Vision Student Workshop (BMVW). , 5.1-5.9.
Further information on publisher's website:
http://dx.doi.org/10.5244/C.29.BMVW.5
Publisher's copyright statement:
Additional information:
Use policy
The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for
personal research or study, educational, or not-for-prot purposes provided that:
• a full bibliographic reference is made to the original source
• a link is made to the metadata record in DRO
• the full-text is not changed in any way"
3a24c276368fa63473078723ce4bc99c9ea36019,Stability comparison of dimensionality reduction techniques attending to data and parameter variations,"Eurographics Conference on Visualization (EuroVis) (2013)
M. Hlawitschka and T. Weinkauf (Editors)
Short Papers
Stability comparison of dimensionality reduction techniques
ttending to data and parameter variations
Francisco J. García-Fernández1,2, Michel Verleysen2, John A. Lee2 and Ignacio Díaz1
University of Oviedo, Spain
Université Catholique de Louvain, Belgium"
3aad63c3c049eedb1c6da4871faa90e797b933e8,Highway Networks for Visual Question Answering,"Highway Networks for Visual Question Answering
Aaditya Prakash and James Storer
Brandeis University"
3a0cceb1a10697e3e17738579d27708c9c3303a8,Data-Intensive Multimedia Semantic Concept Modeling using Robust Subspace Bagging and MapReduce,"Data-Intensive Multimedia Semantic Concept Modeling
using Robust Subspace Bagging and MapReduce"
3abf8e5f1f5778b99890b193de59a3a9031e3691,Revisiting Linear Discriminant Techniques in Gender Recognition,"Revisiting Linear Discriminant Techniques
in Gender Recognition
Juan Bekios-Calfa, Jose´ M. Buenaposada, and
Luis Baumela"
3a35154f765dcba4e3789a38346bf54bce69e336,Object Hallucination in Image Captioning,"Object Hallucination in Image Captioning
Anna Rohrbach∗1, Lisa Anne Hendricks∗1,
Kaylee Burns1 , Trevor Darrell1, Kate Saenko2
UC Berkeley, 2 Boston University"
3acb6b3e3f09f528c88d5dd765fee6131de931ea,Novel representation for driver emotion recognition in motor vehicle videos,"(cid:49)(cid:50)(cid:57)(cid:40)(cid:47)(cid:3)(cid:53)(cid:40)(cid:51)(cid:53)(cid:40)(cid:54)(cid:40)(cid:49)(cid:55)(cid:36)(cid:55)(cid:44)(cid:50)(cid:49)(cid:3)(cid:41)(cid:50)(cid:53)(cid:3)(cid:39)(cid:53)(cid:44)(cid:57)(cid:40)(cid:53)(cid:3)(cid:40)(cid:48)(cid:50)(cid:55)(cid:44)(cid:50)(cid:49)(cid:3)(cid:53)(cid:40)(cid:38)(cid:50)(cid:42)(cid:49)(cid:44)(cid:55)(cid:44)(cid:50)(cid:49)(cid:3)(cid:3)
(cid:44)(cid:49)(cid:3)(cid:48)(cid:50)(cid:55)(cid:50)(cid:53)(cid:3)(cid:57)(cid:40)(cid:43)(cid:44)(cid:38)(cid:47)(cid:40)(cid:3)(cid:57)(cid:44)(cid:39)(cid:40)(cid:50)(cid:54)(cid:3)
(cid:53)(cid:68)(cid:77)(cid:78)(cid:88)(cid:80)(cid:68)(cid:85)(cid:3)(cid:55)(cid:75)(cid:72)(cid:68)(cid:74)(cid:68)(cid:85)(cid:68)(cid:77)(cid:68)(cid:81)(cid:13)(cid:15)(cid:3)(cid:37)(cid:76)(cid:85)(cid:3)(cid:37)(cid:75)(cid:68)(cid:81)(cid:88)(cid:13)(cid:15)(cid:3)(cid:36)(cid:79)(cid:69)(cid:72)(cid:85)(cid:87)(cid:3)(cid:38)(cid:85)(cid:88)(cid:93)(cid:130)(cid:15)(cid:3)(cid:37)(cid:72)(cid:79)(cid:76)(cid:81)(cid:71)(cid:68)(cid:3)(cid:47)(cid:72)(cid:13)(cid:15)(cid:3)(cid:36)(cid:86)(cid:82)(cid:81)(cid:74)(cid:88)(cid:3)(cid:55)(cid:68)(cid:80)(cid:69)(cid:82)(cid:13)(cid:3)
(cid:3)
*Center for Research in Intelligent Systems, University of California, Riverside, CA 92521, USA
(cid:130) Computer Perception Lab, California State University, Bakersfield, CA 93311, USA
(cid:36)(cid:37)(cid:54)(cid:55)(cid:53)(cid:36)(cid:38)(cid:55)(cid:3)
the  background
(cid:3)
A  novel  feature  representation  of  human  facial  expressions
for  emotion  recognition  is  developed.  The  representation
leveraged
texture  removal  ability  of
Anisotropic  Inhibited  Gabor  Filtering  (AIGF)  with  the
ompact  representation  of  spatiotemporal
local  binary
patterns. The  emotion recognition  system incorporated face
detection  and registration  followed  by the proposed  feature
representation:  Local  Anisotropic  Inhibited  Binary  Patterns
in  Three  Orthogonal"
3a0673199699cd51abe0f104ebe080f63d1b6d37,Sparse shape registration for occluded facial feature localization,"Sparse Shape Registration for Occluded Facial Feature Localization
Fei Yang, Junzhou Huang and Dimitris Metaxas"
3ab036b680e8408ec74f78a918f3ffbf6c906d70,Saying What You're Looking For: Linguistics Meets Video Search,"Saying What You’re Looking For:
Linguistics Meets Video Search
Andrei Barbu∗
N. Siddharth∗
Jeffrey Mark Siskind∗"
3acdccd33e518f22dcfe36ee29c332a644afdb25,Automatic Detection of Facial Midline And Its Contributions To Facial Feature Extraction,"Electronic Letters on Computer Vision and Image Analysis 6(3):55-66, 2008
Automatic Detection of Facial Midline
And Its Contributions To Facial Feature Extraction
Nozomi NAKAO, Wataru OHYAMA, Tetsushi WAKABAYASHI and Fumitaka KIMURA
Graduate School of Engineering, Mie University, 1577 Kurimamachiya–cho, Tsu–shi, Mie 514–8507, Japan
Received 17 April 2007; revised 17 June 2007; accepted 17 September 2007"
3af28e9e9e883c235b6418a68bda519b08f9ae26,Implications of Adult Facial Aging on Biometrics,"We are IntechOpen,
the world’s leading publisher of
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b516e9f933a573b957f21a8b9a617c8ebeaf1fea,Jawad Fusing Local Binary Patterns with Wavelet Features for Ethnicity Identification,"World Academy of Science, Engineering and Technology
International Journal of Computer and Information Engineering
Vol:7, No:7, 2013
Fusing Local Binary Patterns with Wavelet
Features for Ethnicity Identification
S. Hma Salah, H. Du, and N. Al-Jawad"
b5c5a57f5ecd8e11cd47814d584daba53aa14d3c,SOSVR Team Description Paper Robocup 2017 Rescue Virtual Robot League,"SOSVR Team Description Paper
Robocup 2017 Rescue Virtual Robot League
Mahdi Taherahmadi, Sajjad Azami, MohammadHossein GohariNejad, Mostafa
Ahmadi, and Saeed Shiry Ghidary
Cognitive Robotics Lab, Amirkabir University of Technology (Tehran Polytechnic),
No. 424, Hafez Ave., Tehran, Iran. P. O. Box"
b503f481120e69b62e076dcccf334ee50559451e,Recognition of Facial Action Units with Action Unit Classifiers and an Association Network,"Recognition of Facial Action Units with Action
Unit Classifiers and An Association Network
Junkai Chen1, Zenghai Chen1, Zheru Chi1 and Hong Fu1,2
Department of Electronic and Information Engineering, The Hong Kong Polytechnic
University, Hong Kong
Department of Computer Science, Chu Hai College of Higher Education, Hong Kong"
b5160e95192340c848370f5092602cad8a4050cd,Video Classification With CNNs: Using the Codec as a Spatio-Temporal Activity Sensor,"Video Classification With CNNs: Using The Codec
As A Spatio-Temporal Activity Sensor
Aaron Chadha, Alhabib Abbas and Yiannis Andreopoulos, Senior Member, IEEE"
b5f9d5be7561bb6eacee9012275b17c75696c388,A Teacher Student Network for Faster Video Classification,"Under review as a conference paper at ICLR 2019
A TEACHER STUDENT NETWORK FOR FASTER VIDEO
CLASSIFICATION
Anonymous authors
Paper under double-blind review"
b58417561ea400b60bd976104e43b1361e1314ba,Target Tracking In Real Time Surveillance Cameras and Videos,"Target Tracking In Real Time Surveillance
Cameras and Videos
Nayyab Naseem                                             Mehreen Sirshar
Department of Software Engineering             Department of Software Engineering
Fatima Jinnah Women University                   Fatima Jinnah Women University"
b506aa23949b6d1f0c868ad03aaaeb5e5f7f6b57,Modeling Social and Temporal Context for Video Analysis,"UNIVERSITY OF CALIFORNIA
RIVERSIDE
Modeling Social and Temporal Context for Video Analysis
A Dissertation submitted in partial satisfaction
of the requirements for the degree of
Doctor of Philosophy
Computer Science
Zhen Qin
June 2015
Dissertation Committee:
Dr. Christian R. Shelton, Chairperson
Dr. Tao Jiang
Dr. Stefano Lonardi
Dr. Amit Roy-Chowdhury"
b573a57b3da678631bd78f25ecdeac7cd36fa617,A Multi-view RGB-D Approach for Human Pose Estimation in Operating Rooms,"A Multi-view RGB-D Approach for Human Pose Estimation in Operating Rooms
Abdolrahim Kadkhodamohammadi1, Afshin Gangi1,2, Michel de Mathelin1, Nicolas Padoy1
ICube, University of Strasbourg, CNRS, IHU Strasbourg, France
Radiology Department, University Hospital of Strasbourg, France
{kadkhodamohammad, gangi, demathelin,"
b5f7b17b0feb3a1f3af60dce61fd9a9c6b067368,The Benefits of Dense Stereo for Pedestrian Detection,"The Benefits of Dense Stereo
for Pedestrian Detection
Christoph G. Keller, Markus Enzweiler, Marcus Rohrbach, David Fernández Llorca,
Christoph Schnörr, and Dariu M. Gavrila"
b510d66bc70772f89924863a8555d815aacf3bee,Modeling Marginal Distributions of Gabor Coefficients: Application to Biometric Template Reduction,"Modeling Marginal Distributions of Gabor
Coef‌f‌icients: Application to Biometric Template
Reduction
Daniel Gonz´alez-Jim´enez and Jos´e Luis Alba-Castro(cid:2)
Signal Theory and Communications Department
University of Vigo, Spain"
b5f5781cba3c3da807359a6f600aa19c666a3f81,Comparing Attention to Socially-Relevant Stimuli in Autism Spectrum Disorder and Developmental Coordination Disorder,"Journal of Abnormal Child Psychology
https://doi.org/10.1007/s10802-017-0393-3
Comparing Attention to Socially-Relevant Stimuli in Autism
Spectrum Disorder and Developmental Coordination Disorder
Emma Sumner 1
& Hayley C. Leonard 2 & Elisabeth L. Hill 3
# The Author(s) 2018. This article is an open access publication"
b5d1a7bc9f89ba9e8d99f6b151f83a4ff9220bb9,"A Framework for the Segmentation and Classification of 3D Point Clouds using Temporal, Spatial and Semantic Information",
b5857b5bd6cb72508a166304f909ddc94afe53e3,SSIG and IRISA at Multimodal Person Discovery,"SSIG and IRISA at Multimodal Person Discovery
Cassio E. dos Santos Jr1, Guillaume Gravier2, William Robson Schwartz1
Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
IRISA & Inria Rennes , CNRS, Rennes, France"
b51e3d59d1bcbc023f39cec233f38510819a2cf9,"Can a biologically-plausible hierarchy effectively replace face detection, alignment, and recognition pipelines?","CBMM Memo No. 003
March 27, 2014
Can a biologically-plausible hierarchy effectively
replace face detection, alignment, and
recognition pipelines?
Qianli Liao1, Joel Z Leibo1, Youssef Mroueh1, Tomaso Poggio1"
b5fffbc0e590ce67d485f1602c8158befcef9fa8,The use of hidden Markov models to verify the identity based on facial asymmetry,"Kubanek and Bobulski EURASIP Journal on Image and Video
Processing  (2017) 2017:45
DOI 10.1186/s13640-017-0193-2
EURASIP Journal on Image
nd Video Processing
RESEARCH
Open Access
The use of hidden Markov models to
verify the identity based on facial asymmetry
Mariusz Kubanek and Janusz Bobulski*"
b58672881dd8112cd3e6dedebcf8367ce2c9d78b,Mechanistic Analytical Modeling of Superscalar In-Order Processor Performance,"Mechanistic Analytical Modeling of Superscalar In-Order
Processor Performance
MAXIMILIEN B. BREUGHE, STIJN EYERMAN, and LIEVEN EECKHOUT,
Ghent University, Belgium
Superscalar in-order processors form an interesting alternative to out-of-order processors because of their
energy efficiency and lower design complexity. However, despite the reduced design complexity, it is nontrivial
to get performance estimates or insight in the application–microarchitecture interaction without running
slow, detailed cycle-level simulations, because performance highly depends on the order of instructions within
the application’s dynamic instruction stream, as in-order processors stall on interinstruction dependences
nd functional unit contention. To limit the number of detailed cycle-level simulations needed during design
space exploration, we propose a mechanistic analytical performance model that is built from understanding
the internal mechanisms of the processor.
The mechanistic performance model for superscalar in-order processors is shown to be accurate with an
verage performance prediction error of 3.2% compared to detailed cycle-accurate simulation using gem5. We
lso validate the model against hardware, using the ARM Cortex-A8 processor and show that it is accurate
within 10% on average. We further demonstrate the usefulness of the model through three case studies:
(1) design space exploration, identifying the optimum number of functional units for achieving a given
performance target; (2) program–machine interactions, providing insight into microarchitecture bottlenecks;
nd (3) compiler–architecture interactions, visualizing the impact of compiler optimizations on performance.
Categories and Subject Descriptors: C.0 [Computer Systems Organization]: General—Modeling of com-"
b5cf931cf0bd606575bc793c0c8ec6d913d08bc6,"Geometric primitive feature extraction - concepts, algorithms, and applications","GEOMETRIC PRIMITIVE FEATURE EXTRACTION –
CONCEPTS, ALGORITHMS, AND APPLICATIONS
DILIP KUMAR PRASAD
School of Computer Engineering
A Thesis submitted to the Nanyang Technological University
in fulfillment of the requirement for the degree of
Doctor of Philosophy"
b55d0c9a022874fb78653a0004998a66f8242cad,Hybrid Facial Representations for Emotion Recognition,"Hybrid Facial Representations
for Emotion Recognition
Woo-han Yun, DoHyung Kim, Chankyu Park, and Jaehong Kim
Automatic  facial  expression  recognition  is  a  widely
studied  problem  in  computer  vision  and  human-robot
interaction.  There  has  been  a  range  of  studies  for
representing  facial  descriptors  for  facial  expression
recognition. Some prominent descriptors were presented
in  the  first  facial  expression  recognition  and  analysis
hallenge  (FERA2011).  In  that  competition,  the  Local
Gabor  Binary  Pattern  Histogram  Sequence  descriptor
showed the most powerful description capability. In this
paper, we introduce hybrid facial representations for facial
expression  recognition,  which  have  more  powerful
description  capability  with  lower  dimensionality.  Our
descriptors consist of a block-based descriptor and a pixel-
ased  descriptor.  The  block-based  descriptor  represents
the  micro-orientation  and  micro-geometric  structure
information. The pixel-based descriptor represents texture
information.  We  validate  our  descriptors  on  two  public"
b569f22ce779d221ec008c0baa354796d71e3d80,Image Classification for Arabic: Assessing the Accuracy of Direct English to Arabic Translations,"Image Classification for Arabic: Assessing the Accuracy of
Direct English to Arabic Translations
Information Systems Department, Prince Sattam Bin Abdulaziz university, Al Kharj, Saudi Arabia
Abdulkareem Alsudais"
b558be7e182809f5404ea0fcf8a1d1d9498dc01a,Bottom-up and top-down reasoning with convolutional latent-variable models,"Bottom-up and top-down reasoning with convolutional latent-variable models
Peiyun Hu
UC Irvine
Deva Ramanan
UC Irvine"
b5fc4f9ad751c3784eaf740880a1db14843a85ba,Significance of image representation for face verification,"SIViP (2007) 1:225–237
DOI 10.1007/s11760-007-0016-5
ORIGINAL PAPER
Significance of image representation for face verification
Anil Kumar Sao · B. Yegnanarayana ·
B. V. K. Vijaya Kumar
Received: 29 August 2006 / Revised: 28 March 2007 / Accepted: 28 March 2007 / Published online: 1 May 2007
© Springer-Verlag London Limited 2007"
b599f323ee17f12bf251aba928b19a09bfbb13bb,Autonomous Quadcopter Videographer,"AUTONOMOUS QUADCOPTER VIDEOGRAPHER
REY R. COAGUILA
B.S. Universidad Peruana de Ciencias Aplicadas, 2009
A thesis submitted in partial fulfillment of the requirements
for the degree of Master of Science in Computer Science
in the Department of Electrical Engineering and Computer Science
in the College of Engineering and Computer Science
t the University of Central Florida
Orlando, Florida
Spring Term
Major Professor: Gita R. Sukthankar"
b55853483873d3947e8c962f1152128059369d93,DoShiCo challenge: Domain shift in control prediction,"DoShiCo challenge:
Domain Shift in Control prediction
Klaas Kelchtermans∗ and Tinne Tuytelaars∗"
b525a863eab597055e02351acfeab64754d22690,Pictorial Structures Revisited : Multiple Human Pose Estimation,"IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
D Pictorial Structures Revisited:
Multiple Human Pose Estimation
Vasileios Belagiannis, Sikandar Amin, Mykhaylo Andriluka,
Bernt Schiele, Nassir Navab, and Slobodan Ilic"
b5ba0c50cfe2559f4197bb35cf50441118b768c8,audEERING's approach to the One-Minute-Gradual Emotion Challenge,"udEERING’s approach to the One-Minute-Gradual Emotion Challenge
Andreas Triantafyllopoulos, Hesam Sagha, Florian Eyben, Bj¨orn Schuller
udEERING GmbH, Gilching, Germany"
b5476afccf97fc498f51170e65ac9cd9665fd2ce,Wide Range Face Pose Estimation by Modelling the 3D Arrangement of Robustly Detectable Sub-parts,"Wide Range Face Pose Estimation
y Modelling the 3D Arrangement
of Robustly Detectable Sub-Parts
Thiemo Wiedemeyer1, Martin Stommel2 and Otthein Herzog3
TZI Center for Computing and Communication Technologies,
University Bremen, Am Fallturm 1, 28359 Bremen, Germany"
b5790f1bc586a77ff2cbea002b7ad2646e32af6b,Person Re-Identification Ranking Optimisation by Discriminant Context Information Analysis,"Person Re-Identification Ranking Optimisation by
Discriminant Context Information Analysis
Jorge Garc´ıa1, Niki Martinel2, Christian Micheloni2 and Alfredo Gardel1
Department of Electronics, University of Alcala, Alcal´a de Henares, Spain
Department of Mathematics and Computer Science, University of Udine, Udine, Italy"
b5f9c5af707f55d96b1d3d65d970270d35a60987,Comparison of face Recognition Algorithms on Dummy Faces,"The International Journal of Multimedia & Its Applications (IJMA) Vol.4, No.4, August 2012
Comparison of face Recognition Algorithms on
Dummy Faces
Aruni Singh, Sanjay Kumar Singh, Shrikant Tiwari
Department of Computer Engineering, IT-BHU, Varanasi-India"
b5d14fee9658877abbbfa760dd9765db0af86ba6,Swarm intelligence and evolutionary computation approaches for 2 D face recognition : a systematic review,"Revista Brasileira de Computação Aplicada, July, 2018
DOI: 10.5335/rbca.v10i2.8046
Vol. 10, No 2, pp. 2–17
Homepage: seer.upf.br/index.php/rbca/index
TUTORIAL
Swarm intelligence and evolutionary computation
pproaches for 2D face recognition: a systematic review
Guilherme Felippe Plichoski1, Chidambaram Chidambaram2 and Rafael
Stubs Parpinelli1
Graduate Program in Applied Computing - State University of Santa Catarina, Joinville - SC - Brazil and
Department of Production Engineering - State University of Santa Catarina, Joinville - SC - Brazil
Received: 13/03/2018. Revised: 21/06/2018. Accepted: 23/06/2018."
b5af4b9d68f1b9b2c2999a726f6d2fbb2a49a3bf,Modulating early visual processing by language,"Modulating early visual processing by language
Harm de Vries∗
University of Montreal
Florian Strub∗
Univ. Lille, CNRS, Centrale Lille,
Jérémie Mary†
Univ. Lille, CNRS, Centrale Lille,
Inria, UMR 9189 CRIStAL
Inria, UMR 9189 CRIStAL
Hugo Larochelle
Google Brain
Olivier Pietquin
DeepMind
Aaron Courville
University of Montreal, CIFAR Fellow"
b58e71a3336193bed5785b2818a4fec85dd5f5ff,Object Detection and Tracking for Autonomous Navigation in Dynamic Environments,"Object detection and tracking for autonomous navigation
in dynamic environments
Andreas Ess · Konrad Schindler · Bastian Leibe · Luc Van Gool"
b52886610eda6265a2c1aaf04ce209c047432b6d,Microexpression Identification and Categorization Using a Facial Dynamics Map,"Microexpression Identification and Categorization
using a Facial Dynamics Map
Feng Xu, Junping Zhang, James Z. Wang"
b5dd744df6de73bd072b18d9108b79431a28c539,Gait Recognition With Shifted Energy Image and Structural Feature Extraction,"Gait Recognition with Shifted Energy Image
nd Structural Feature Extraction
Xiaxi Huang and Nikolaos V. Boulgouris, Senior Member, IEEE"
d7eae9f76dcfa978b99eef430feb9420eac702eb,A Multi-Layer K-means Approach for Multi-Sensor Data Pattern Recognition in Multi-Target Localization,"A Multi-Layer K-means Approach for Multi-Sensor Data Pattern
Recognition in Multi-Target Localization
Samuel Silva, Rengan Suresh, Feng Tao, Johnathan Votion, Yongcan Cao"
d7d09323bf8226f9cc06402dc3026fd4f1e75859,BDPCA plus LDA: a novel fast feature extraction technique for face recognition,"IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 36, NO. 4, AUGUST 2006
BDPCA Plus LDA: A Novel Fast Feature Extraction
Technique for Face Recognition
Wangmeng Zuo, David Zhang, Senior Member, IEEE,
Jian Yang, and Kuanquan Wang"
d7e8672caecc7e4b17e8d9d3cbd673d402c7e7af,Robust Stereo-Based Person Detection and Tracking for a Person Following Robot,"Robust Stereo-Based Person Detection and Tracking
for a Person Following Robot
Junji Satake and Jun Miura
Department of Information and Computer Sciences
Toyohashi University of Technology"
d7f19812ee77e508b314d0ac6ab49d05ac81e0d1,Active Visual-Based Detection and Tracking of Moving Objects from Clustering and Classification Methods,"Active Visual-based Detection and Tracking of Moving
Objects from Clustering and Classification methods
David Márquez-Gámez Michel Devy
CNRS; LAAS; Université de Toulouse
7 avenue du Colonel Roche, F-31077 Toulouse Cedex, France"
d7f3836f2d28adf15fc809bd4f90afb1f61ba8e0,Segment-before-Detect: Vehicle Detection and Classification through Semantic Segmentation of Aerial Images,"Article
Segment-before-Detect: Vehicle Detection and
Classification through Semantic Segmentation of
Aerial Images
Nicolas Audebert 1,2,*, Bertrand Le Saux 1 and Sébastien Lefèvre 2
ONERA, The French Aerospace Lab, F-91761 Palaiseau, France;
Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), University Bretagne Sud, UMR 6074,
F-56000 Vannes, France;
* Correspondence:
Academic Editors: Norman Kerle, Markus Gerke and Prasad S. Thenkabail
Received: 28 December 2016; Accepted: 7 April 2017; Published: 13 April 2017"
d745eaeb096fbf61ac0694e447acd2081a08b084,Ðáñáêïëïýèçóç øõ÷ïóùìáôéêÞò êáôÜóôáóçò ïäçãïý ìå,"Ðáñáêïëïýèçóç øõ÷ïóùìáôéêÞò êáôÜóôáóçò ïäçãïý ìå
÷ñÞóç âéïóçìÜôùí
Ç ÄÉÄÁÊÔÏÑÉÊÇ ÄÉÁÔÑÉÂÇ
õðïâÜëëåôáé óôçí
ïñéóèåßóá áðü ôçí ÃåíéêÞ ÓõíÝëåõóç ÅéäéêÞò Óýíèåóçò
ôïõ ÔìÞìáôïò ÐëçñïöïñéêÞò ÅîåôáóôéêÞ ÅðéôñïðÞ
áðü ôïí
Ãåþñãéï ÑÞãá
ùò ìÝñïò ôùí Õðï÷ñåþóåùí ãéá ôç ëÞøç ôïõ
ÄÉÄÁÊÔÏÑÉÊÏÕ ÄÉÐËÙÌÁÔÏÓ ÓÔÇÍ ÐËÇÑÏÖÏÑÉÊÇ
ÄåêÝìâñéïò 2009"
d7da0f595d135474cc2193d382b22458b313cdbf,Multi-View Constraint Propagation with Consensus Prior Knowledge.,Multi-View Constraint Propagation with Consensus Prior Knowledge
d7c659ce0442bf1047e7d2e942837b18105f6f47,Depth-Adaptive Deep Neural Network for Semantic Segmentation,"Depth Adaptive Deep Neural Network
for Semantic Segmentation
Byeongkeun Kang, Yeejin Lee, and Truong Q. Nguyen, Fellow, IEEE"
d7ed878c08c90186e3bf607c20ff943834ad0d68,Semantic Data Integration,"Semantic Data Integration
Michelle Cheatham and Catia Pesquita"
d7144bc7d91841963b037f210f9356d28f76e70e,A comparison of features for regression-based driver head pose estimation under varying illumination conditions,"A COMPARISON OF FEATURES FOR REGRESSION-BASED DRIVER HEAD POSE
ESTIMATION UNDER VARYING ILLUMINATION CONDITIONS
Dimitri J. Walger1, Toby P. Breckon2, Anna Gaszczak3, Thomas Popham3
Cranfield University, Bedfordshire, UK 2Durham University, Durham, UK
Jaguar Land Rover, Warwickshire, UK"
d767c97af96461eac76fb897a0eec35804f0398d,Learn to See by Events: RGB Frame Synthesis from Event Cameras,"Learn to See by Events:
RGB Frame Synthesis from Event Cameras
Stefano Pini, Guido Borghi, Roberto Vezzani, Rita Cucchiara
University of Modena and Reggio Emilia
Figure 1: Sample frames synthesized by the proposed framework. Given an initial RGB frame at time t0 and a set of following
event frames at time t1, ..., tn as input, the proposed framework accordingly synthesizes a RGB frame for each time step."
d7f7eb0fbe3339d13f5a6a23df0fd27fdb357d48,Intention-Aware Multi-Human Tracking for Human-Robot Interaction via Particle Filtering over Sets,"Intention-Aware Multi-Human Tracking for
Human-Robot Interaction via Particle Filtering over Sets
Aijun Bai
Univ. of Sci. & Tech. of China
Reid Simmons
Carnegie Mellon Univ.
Manuela Veloso
Carnegie Mellon Univ.
The Approach
The ability for an autonomous robot to track and identify
multiple humans and understand their intentions is crucial
for socialized human-robot interactions in dynamic envi-
ronments (Michalowski and Simmons 2006). Take CoBot
(Rosenthal, Biswas, and Veloso 2010) trying to enter an ele-
vator as an example. When the elevator door opens, suppose
there are multiple humans occupied, CoBot needs to track
each human’s state and intention in terms of whether he/she
is going to exit the elevator or not. For the purposes of safely
nd friendly interacting with humans, CoBot can only make
the decision to enter the elevator when any human who in-"
d79f9ada35e4410cd255db39d7cc557017f8111a,Evaluation of accurate eye corner detection methods for gaze estimation,"Journal of Eye Movement Research
7(3):3, 1-8
Evaluation of accurate eye corner detection methods for gaze
estimation
Jose Javier Bengoechea
Public University of Navarra, Spain
Juan J. Cerrolaza
Childrens National Medical Center, USA
Arantxa Villanueva
Public University of Navarra, Spain
Rafael Cabeza
Public University of Navarra, Spain
Accurate detection of iris center and eye corners appears to be a promising
pproach for low cost gaze estimation.
In this paper we propose novel eye
inner corner detection methods. Appearance and feature based segmentation
pproaches are suggested. All these methods are exhaustively tested on a realistic
dataset containing images of subjects gazing at different points on a screen.
We have demonstrated that a method based on a neural network presents the
est performance even in light changing scenarios."
d7d9fa9a5a57f9f3da7ab2c87ca58127665774cc,Improving Shadow Suppression for Illumination Robust Face Recognition,"Improving Shadow Suppression for Illumination
Robust Face Recognition
Wuming Zhang, Xi Zhao, Jean-Marie Morvan and Liming Chen, Senior Member, IEEE"
d74c6e6fbd8952cbad96013e227374c903797162,With Great Training Comes Great Vulnerability: Practical Attacks against Transfer Learning,"With Great Training Comes Great Vulnerability:
Practical Attacks against Transfer Learning
Bolun Wang
Yuanshun Yao
Bimal Viswanath
Haitao Zheng
UC Santa Barbara
University of Chicago
Virginia Tech
University of Chicago
Ben Y. Zhao
University of Chicago"
d75d074c11a62780b836376249391da39660cad6,Task Scheduling Frameworks for Heterogeneous Computing Toward Exascale,"(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 9, No. 10, 2018
Task Scheduling Frameworks for Heterogeneous
Computing Toward Exascale
Suhelah Sandokji1, Fathy Eassa2
Faculty of Computing and Information Technology, KAU
Jeddah ,Saudi Arabia
studies  consider  partitioning"
d745cf8c51032996b5fee6b19e1b5321c14797eb,Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features,"Viewpoint Invariant Pedestrian Recognition
with an Ensemble of Localized Features
Douglas Gray and Hai Tao
University of California, Santa Cruz
{dgray,
http://vision.soe.ucsc.edu/"
d7d2a1d42f0e3182d538cf8fb4d55f3e9d7ce779,"Setting an attention region for convolutional neural networks using region selective features, for recognition of materials within glass vessels","Setting an attention region for convolutional neural
networks using region selective features, for
recognition of materials within glass vessels
Sagi Eppel1"
d7d6200e41d574e2f3ddd9ded299613683519c7c,Accurate Iris Recognition at a Distance Using Stabilized Iris Encoding and Zernike Moments Phase Features,"IEEE Trans. Image Processing, 2014
Accurate Iris Recognition at a Distance Using
Stabilized Iris Encoding and Zernike Moments Phase Features
Chun-Wei Tan, Ajay Kumar"
d7612e01c10f351a3e2ff1a57465c3d17ddbb193,Rain Streaks Removal in an Image by using Image Decomposition,"International Journal of Science and Research (IJSR)
ISSN (Online): 2319-7064
Index Copernicus Value (2013): 6.14 | Impact Factor (2015): 6.391
Rain Streaks Removal in an Image by using Image
Decomposition
Priyanka A. Chougule1, J. A. Shaikh2
Research Student, Electronics Dept., PVPIT, Budhgaon
Associate Professor, Electronics Dept. PVPIT, Budhgaon"
d76f68c2d0a45ab224065d57836bf3da360c82f2,Learning to Segment Human by Watching YouTube,"Learning to Segment Human by Watching
YouTube
Xiaodan Liang, Yunchao Wei, Liang Lin, Yunpeng Chen, Xiaohui Shen, Jianchao Yang,
Shuicheng Yan"
d78dde04ac4215ed0ed6f2bd5d85094b389d7f5e,A Warping Window Approach to Real-time Vision-based Pedestrian Detection in a Truck's Blind Spot Zone,"A warping window approach to real-time vision-based pedestrian
detection in a truck’s blind spot zone
Kristof Van Beeck1, Toon Goedem´e1;2 and Tinne Tuytelaars2
IIW/EAVISE, Lessius Mechelen - Campus De Nayer, J. De Nayerlaan 5, 2860, Sint-Katelijne-Waver, Belgium
ESAT/PSI-VISICS, KU Leuven, IBBT, Kasteelpark Arenberg 10, 3100, Heverlee, Belgium
fkristof.vanbeeck,
Keywords:
Computer vision: Pedestrian tracking: Real-time: Active safety systems"
d7b6bbb94ac20f5e75893f140ef7e207db7cd483,griffith . edu . au Face Recognition across Pose : A Review,"Griffith Research Online
https://research-repository.griffith.edu.au
Face Recognition across Pose: A
Review
Author
Zhang, Paul, Gao, Yongsheng
Published
Journal Title
Pattern Recognition
https://doi.org/10.1016/j.patcog.2009.04.017
Copyright Statement
Copyright 2009 Elsevier. This is the author-manuscript version of this paper. Reproduced in accordance
with the copyright policy of the publisher. Please refer to the journal's website for access to the
definitive, published version.
Downloaded from
http://hdl.handle.net/10072/30193"
d7a0f9ab321e728b981e12775b4906f55d3aab15,3 D Object Reconstruction using Computer Vision : Reconstruction and Characterization Applications for External Human Anatomical Structures,"D Object Reconstruction using
Computer Vision: Reconstruction
nd Characterization Applications for
External Human Anatomical Structures
Teresa Cristina de Sousa Azevedo
BSc in Electrical and Computer Engineering by
Faculdade de Engenharia da Universidade do Porto (2002)
MSc in Biomedical Engineering by
Faculdade de Engenharia da Universidade do Porto (2007)
Thesis submitted for the fulfilment of the requirements for the
PhD degree in Informatics Engineering by
Faculdade de Engenharia da Universidade do Porto
Supervisor:
João Manuel R. S. Tavares
Associate Professor of the Department of Mechanical Engineering
Faculdade de Engenharia da Universidade do Porto
Co-supervisor:
Mário A. P. Vaz
Associate Professor of the Department of Mechanical Engineering
Faculdade de Engenharia da Universidade do Porto"
d78b190f98f9630cab261eabc399733af052f05c,Unsupervised Deep Domain Adaptation for Pedestrian Detection,
d7d166aee5369b79ea2d71a6edd73b7599597aaa,Fast Subspace Clustering Based on the Kronecker Product,"Fast Subspace Clustering Based on the
Kronecker Product
Lei Zhou1, Xiao Bai1, Xianglong Liu1, Jun Zhou2, and Hancock Edwin3
Beihang University 2Grif‌f‌ith University 3University of York, UK"
d708ce7103a992634b1b4e87612815f03ba3ab24,FCVID : Fudan-Columbia Video Dataset,"FCVID: Fudan-Columbia Video Dataset
Yu-Gang Jiang, Zuxuan Wu, Jun Wang, Xiangyang Xue, Shih-Fu Chang
Available at: http://bigvid.fudan.edu.cn/FCVID/
OVERVIEW
Recognizing visual contents in unconstrained videos
has become a very important problem for many ap-
plications, such as Web video search and recommen-
dation, smart content-aware advertising, robotics, etc.
Existing datasets for video content recognition are
either small or do not have reliable manual labels.
In this work, we construct and release a new Inter-
net video dataset called Fudan-Columbia Video Dataset
(FCVID), containing 91,223 Web videos (total duration
,232 hours) annotated manually according to 239
ategories. We believe that the release of FCVID can
stimulate innovative research on this challenging and
important problem.
COLLECTION AND ANNOTATION
The categories in FCVID cover a wide range of topics
like social events (e.g., “tailgate party”), procedural"
d7c6e4348542fd2b5e64a73d9c1fd0172e2b1774,Grounding language acquisition by training semantic parsers using captioned videos,"Grounding language acquisition by training semantic parsers
using captioned videos
Candace Ross
CSAIL, MIT
Andrei Barbu
CSAIL, MIT
Yevgeni Berzak
BCS, MIT
Battushig Myanganbayar
CSAIL, MIT"
d73b16b1a4f96eda9abcecaf6425b17fd02c631f,A Survey Paper on Retrieval of Deterministic Data with Ranking Strategy,"International Journal of Science and Research (IJSR)
ISSN (Online): 2319-7064
Index Copernicus Value (2013): 6.14 | Impact Factor (2015): 6.391
A Survey Paper on Retrieval of Deterministic Data
with Ranking Strategy
Ashish S Mutrak1, Kishor Shedge2
Student, Master of Engineering, Department of Computer Engineering, Sir Visvesvaraya Institute of Technology, Chincholi, Sinner
Assistant Professor, Department of Computer Engineering, Sir Visvesvaraya Institute of Technology, Chincholi, Sinner"
d7f153112c51923c8e78036fc694220c9d4bf4bc,The 2018 DAVIS Challenge on Video Object Segmentation,"The 2018 DAVIS Challenge on
Video Object Segmentation
Sergi Caelles, Alberto Montes, Kevis-Kokitsi Maninis, Yuhua Chen,
Luc Van Gool, Federico Perazzi, and Jordi Pont-Tuset"
d7b850537ccf33cabc2f0b231553aad79ad43aa8,Grid Map based Free Space Estimation using Stereo Vision,"Grid Map based Free Space Estimation using Stereo Vision
Hannes Harms1, Eike Rehder1 and Martin Lauer1"
d7e8c6da1a95f41d8097b7b713890ccde13ef1d8,Development of an Efficient Face Recognition System Based on Linear and Nonlinear Algorithms,"Development of an Efficient Face Recognition System based on Linear and Nonlinear Algorithms
{tag}                                                 {/tag}
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 134
Number 7
Year of Publication: 2016
Authors:
Filani Araoluwa S., Adetunmbi Adebayo O.
10.5120/ijca2016907932
{bibtex}2016907932.bib{/bibtex}"
2284ab73ac9c913328349de54f9892082f16dd3b,"Biometrics at the Frontiers : Assessing the Impact on Society For the European Parliament Committee on Citizens ' Freedoms and Rights , Justice and Home Affairs ( LIBE ) February","Biometrics at the Frontiers: Assessing the impact on Society
Institute for
Prospective Technological Studies
Directorate General Joint Research Centre
European Commission
Biometrics at the Frontiers:
Assessing the Impact on Society
For the European Parliament
Committee on Citizens' Freedoms and Rights,
Justice and Home Affairs (LIBE)
February 2005
IPTS, Edificio Expo-WTC,
C/ Inca Garcilaso, s/n, E-41092, Seville, Spain
Tel: +34 954488281, Fax: +34 954488208
EC-DG JRC-IPTS
Page 1 of 166"
229bce6384ae16a388881e766bfa5a672b61dc9b,Application of Video Scene Semantic Recognition Technology in Smart Video,"ISSN 1330-3651 (Print), ISSN 1848-6339 (Online)                                                                                                                       https://doi.org/10.17559/TV-20180620082101
Original scientific paper
Application of Video Scene Semantic Recognition Technology in Smart Video
Lele QIN, Lihua KANG"
224d4cf75e8baf32a795f38ee8ccfdf82e4c5a70,Identifying Exceptional Descriptions of People Using Topic Modeling and Subgroup Discovery,"Identifying Exceptional Descriptions of People
using Topic Modeling and Subgroup Discovery
Andrew T. Hendrickson, Jason Wang, and Martin Atzmueller
Tilburg University, 5037AB, the Netherlands
{a.hendrickson, y.w.wang,"
22043cbd2b70cb8195d8d0500460ddc00ddb1a62,Separability-Oriented Subclass Discriminant Analysis,"Separability-Oriented Subclass Discriminant
Analysis
Huan Wan, Hui Wang, Gongde Guo, Xin Wei"
227b18fab568472bf14f9665cedfb95ed33e5fce,Compositional Dictionaries for Domain Adaptive Face Recognition,"Compositional Dictionaries for Domain Adaptive
Face Recognition
Qiang Qiu, and Rama Chellappa, Fellow, IEEE."
22264e60f1dfbc7d0b52549d1de560993dd96e46,UnitBox: An Advanced Object Detection Network,"UnitBox: An Advanced Object Detection Network
Jiahui Yu1,2
Yuning Jiang2
Zhangyang Wang1
Zhimin Cao2
Thomas Huang1
University of Illinois at Urbana−Champaign
Megvii Inc
{jyu79, zwang119, {jyn,"
220b815229ac5557b3360f96b3afd9453635088d,Artificial Intelligence with Stereo Vision Algorithms and its Methods,"International Conference on Recent Trends in Information Technology and Computer Science (IRCTITCS) 2011
Proceedings published in International Journal of Computer Applications® (IJCA)
Artificial Intelligence with Stereo Vision Algorithms and its
Methods
Sahil S.Thakare#1, Rupesh P. Arbal#2, Makarand R. Shahade*3
#1,2Student, Department of IT, Jawaharlal Darda Institute of Engineering & Technology, Yavatmal
*Asst. Professor, Department of IT, Jawaharlal Darda Institute of Engineering & Technology, Yavatmal
(MS) INDIA
*Third Author
(MS) INDIA"
2251a88fbccb0228d6d846b60ac3eeabe468e0f1,Matrix-Based Kernel Subspace Methods,"Matrix-Based Kernel Subspace Methods
S. Kevin Zhou
Integrated Data Systems Department
Siemens Corporate Research
755 College Road East, Princeton, NJ 08540
Email:"
22fb836a593267d9ff09a4d12aa5b4a6fd52c81e,Brief report: Visual processing of faces in individuals with fragile X syndrome: an eye tracking study.,"J Autism Dev Disord (2009) 39:946–952
DOI 10.1007/s10803-009-0744-1
B R I E F R E P O R T
Brief Report: Visual Processing of Faces in Individuals
with Fragile X Syndrome: An Eye Tracking Study
Faraz Farzin Æ Susan M. Rivera Æ David Hessl
Published online: 28 April 2009
Ó The Author(s) 2009. This article is published with open access at Springerlink.com"
229e105fd4d34815e476702dd5ca4362943c475d,WildDash - Creating Hazard-Aware Benchmarks,"WildDash - Creating Hazard-Aware Benchmarks
Oliver Zendel, Katrin Honauer, Markus Murschitz, Daniel Steininger, and
Gustavo Fern´andez Dom´ınguez
AIT, Austrian Institute of Technology, Giefinggasse 4, 1210, Vienna, Austria
{oliver.zendel, katrin.honauer.fl, markus.murschitz, daniel.steininger,"
22c01d758a4941c01239fa8facdb3407559132ed,Segmentation and Restoration of Images on Surfaces by Parametric Active Contours with Topology Changes,"Segmentation and Restoration of Images on Surfaces by Parametric
Active Contours with Topology Changes
Heike Benninghoff∗ and Harald Garcke†"
22ee43dbd2bdefbc8945d453c6cd453f49ab5eb7,Urban Traffic Surveillance in Smart Cities Using Radar Images,"Urban Traffic Surveillance in Smart Cities
Using Radar Images
J. S´anchez-Oro, David Fern´andez-L´opez, R. Cabido,
Antonio S. Montemayor, and Juan Jos´e Pantrigo
Dept. Ciencias de la Computaci´on
Universidad Rey Juan Carlos
Spain"
22532c6e38ded690dc1420f05c18e23f6f24804d,Chapter 5 Genetic & Evolutionary Biometrics,"We are IntechOpen,
the world’s leading publisher of
Open Access books
Built by scientists, for scientists
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in Web of Science™ Core Collection (BKCI)
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Contact"
2279cae83716e2a00181593a7b10966020dd11d1,Real-time head pose estimation and facial feature localization using a depth sensor and triangular surface patch features,"MITSUBISHI ELECTRIC RESEARCH LABORATORIES
http://www.merl.com
Real-time head pose estimation and facial feature localization
using a depth sensor and triangular surface patch features
Papazov, C.; Marks, T.K.; Jones, M.J.
TR2015-069
June 2015"
227a312324edd41892eb2c1dbc4bf8d94984a326,Deep Learning Based Vehicle Make-Model Classification,"Deep Learning Based Vehicle Make-Model
Classification
Burak Satar1 and Ahmet Emir Dirik2(cid:63)
Uludag University, Bursa, Turkey
Department of Electrical-Electronics Engineering
Uludag University, Bursa, Turkey
Department of Computer Engineering"
22dabd4f092e7f3bdaf352edd925ecc59821e168,Exploiting side information in locality preserving projection,"Deakin Research Online
This is the published version:
An, Senjian, Liu, Wanquan and Venkatesh, Svetha 2008, Exploiting side information in
locality preserving projection, in CVPR 2008 : Proceedings of the 26th IEEE Conference on
Computer Vision and Pattern Recognition, IEEE, Washington, D. C., pp. 1-8.
Available from Deakin Research Online:
http://hdl.handle.net/10536/DRO/DU:30044576
Reproduced with the kind permissions of the copyright owner.
Personal use of this material is permitted. However, permission to reprint/republish this
material for advertising or promotional purposes or for creating new collective works for
resale or redistribution to servers or lists, or to reuse any copyrighted component of this work
in other works must be obtained from the IEEE.
Copyright : 2008, IEEE"
22f8148e43c50341bad686d7fccb425b0682e667,Facial ethnicity classification based on boosted local texture and shape descriptions,"Facial Ethnicity Classification based on Boosted Local Texture and
Shape Descriptions
Huaxiong Ding, Di Huang, IEEE Member, Yunhong Wang, IEEE Member, Liming Chen, IEEE Member,"
22029beb936c9871757813758c5ae3e5820260c9,Proximity Distribution Kernels for Geometric Context in Category Recognition,"Proximity Distribution Kernels for Geometric Context in Category Recognition
Haibin Ling∗
Stefano Soatto
Integrated Data Systems Department
Computer Science Department
Siemens Corporate Research, Princeton, NJ
University of California, Los Angeles, CA
haibin.ling siemens.com
soatto cs.ucla.edu"
227094e85ae30794d03f3cee426f40877ac2b11b,Performance Improvements in Face Classification using Random Forest,"Vatsal Vishwakarma, Abhishek Kumar Srivastava / International Journal of Engineering Research and
Applications (IJERA)      ISSN: 2248-9622   www.ijera.com
Vol. 2, Issue 3, May-Jun 2012, pp.2384-2388
Performance Improvements in Face Classification using Random Forest
Vatsal Vishwakarma*, Abhishek Kumar Srivastava **
*(Department of Electronics and Communication, Lovely Professional University, Jalandhar , India.)
** (Department of Electronics and Communication, Lovely Professional University, Jalandhar , India.)"
2270c94d3f9d9451b3d337aa5ba2d5681cb98497,Evaluation of GIST descriptors for web-scale image search,"Evaluation of GIST descriptors for web-scale image
search
Matthijs Douze, Hervé Jégou, Sandhawalia Harsimrat, Laurent Amsaleg,
Cordelia Schmid
To cite this version:
Matthijs Douze, Hervé Jégou, Sandhawalia Harsimrat, Laurent Amsaleg, Cordelia Schmid. Evaluation
of GIST descriptors for web-scale image search. CIVR 2009 - International Conference on Image and
Video Retrieval, Jul 2009, Santorini, Greece. ACM, pp.19:1-8, 2009, <10.1145/1646396.1646421>.
<inria-00394212>
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https://hal.inria.fr/inria-00394212
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HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
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2230848e506553159e0edfc20472b8cd6084be17,Vision Based Hand Puppet,"ENTERFACE’10, JULY 12TH - AUGUST 6TH, AMSTERDAM, THE NETHERLANDS.
Vision Based Hand Puppet
Cem Keskin, ˙Ismail Arı, Tolga Eren, Furkan Kırac¸, Lukas Rybok, Hazım Ekenel, Rainer Stiefelhagen, Lale Akarun"
22e4e64c1172c90ba23f634d850931ee5f9a972f,Robust Bayesian fitting of 3D morphable model,"Robust Bayesian Fitting of 3D Morphable
Model
Claudia Arellano and Rozenn Dahyot
School of Computer Science and Statistics
Trinity College Dublin, Ireland
7th November 2013"
22c951d542f4d03c718bd58f8ec1b40239af6974,Neural Learning Methods for Visual Object Detection I Foundations 13,"Neural learning methods for
visual object detection
(Neuronale Lernverfahren zur
visuellen Objekterkennung)
Dissertation
zur Erlangung des Grades
”Doktor der Naturwissenschaften”
n der Fakult¨at f¨ur Physik und Astronomie
der Ruhr-Universit¨at Bochum
vorgelegt von
Alexander Rainer Tassilo Gepperth
m 19.April 2006"
22086b3c772ba638e7d50b10bcf544abd93c9305,Face Localization based on Skin Color,"International Journal of Computer Applications (0975 – 8887)
Volume 109 – No. 12, January 2015
Face Localization based on Skin Color
M. Mahadevi
Research Scholar, M.S. University
S.D.N.B. Vaishnav College for Women
Chrompet,Chennai-44"
22aa426aeffb77339646cc03da8e94de22396efc,S HAKES HAKE REGULARIZATION OF 3-BRANCH RESIDUAL NETWORKS,"Workshop track - ICLR 2017
SHAKE-SHAKE
RESIDUAL NETWORKS
REGULARIZATION OF
-BRANCH
Xavier Gastaldi"
22137ce9c01a8fdebf92ef35407a5a5d18730dde,Recognition of Faces from single and Multi-View Videos,
227b1a09b942eaf130d1d84cdcabf98921780a22,Multi-feature shape regression for face alignment,"Yang et al. EURASIP Journal on Advances in Signal Processing  (2018) 2018:51
https://doi.org/10.1186/s13634-018-0572-6
EURASIP Journal on Advances
in Signal Processing
R ES EAR CH
Multi-feature shape regression for face
lignment
Wei-Jong Yang, Yi-Chen Chen, Pau-Choo Chung and Jar-Ferr Yang*
Open Access"
22fdd8d65463f520f054bf4f6d2d216b54fc5677,Efficient Small and Capital Handwritten Character Recognition with Noise Reduction,"International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 8, August 2013)
Efficient Small and Capital Handwritten Character
Recognition with Noise Reduction
Beerendra Kumar Pal, Prof. Shailendra Tiwari, Prof. Sandeep Kumar
Department of Computer Science Engg., IES College of Technology, Bhopal"
22e189a813529a8f43ad76b318207d9a4b6de71a,What will Happen Next? Forecasting Player Moves in Sports Videos,"What will Happen Next?
Forecasting Player Moves in Sports Videos
Panna Felsen
UC Berkeley, STATS
Pulkit Agrawal
UC Berkeley
Jitendra Malik
UC Berkeley"
22d656a6395e22473b8764eddff759ea03f48032,Nonrigid Image Registration Using Higher-Order MRF Model with Dense Local Descriptor,"Nonrigid Image Registration Using Higher-Order MRF Model
with Dense Local Descriptor
Dongjin Kwon1, Kyong Joon Lee1, Il Dong Yun2, and Sang Uk Lee1
School of EECS, ASRI, Seoul Nat’l Univ., Seoul, 151-742, Korea
School of EIE, Hankuk Univ. of F. S., Yongin, 449-791, Korea
fdjk,"
22634b09c3c83f4a959f4f732b03ec3c92808094,DeepMatching: Hierarchical Deformable Dense Matching,"Noname manuscript No.
(will be inserted by the editor)
DeepMatching: Hierarchical Deformable Dense Matching
Philippe Weinzaepfel
Jerome Revaud
Cordelia Schmid
INRIA
Zaid Harchaoui
the date of receipt and acceptance should be inserted later"
2258e01865367018ed6f4262c880df85b94959f8,Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics,"Hindawi Publishing Corporation
EURASIP Journal on Image and Video Processing
Volume 2008, Article ID 246309, 10 pages
doi:10.1155/2008/246309
Research Article
Evaluating Multiple Object Tracking Performance:
The CLEAR MOT Metrics
Keni Bernardin and Rainer Stiefelhagen
Interactive Systems Lab, Institut f¨ur Theoretische Informatik, Universit¨at Karlsruhe, 76131 Karlsruhe, Germany
Correspondence should be addressed to Keni Bernardin,
Received 2 November 2007; Accepted 23 April 2008
Recommended by Carlo Regazzoni
Simultaneous tracking of multiple persons in real-world environments is an active research field and several approaches have
een proposed, based on a variety of features and algorithms. Recently, there has been a growing interest in organizing systematic
evaluations to compare the various techniques. Unfortunately, the lack of common metrics for measuring the performance of
multiple object trackers still makes it hard to compare their results. In this work, we introduce two intuitive and general metrics to
llow for objective comparison of tracker characteristics, focusing on their precision in estimating object locations, their accuracy
in recognizing object configurations and their ability to consistently label objects over time. These metrics have been extensively
used in two large-scale international evaluations, the 2006 and 2007 CLEAR evaluations, to measure and compare the performance
of multiple object trackers for a wide variety of tracking tasks. Selected performance results are presented and the advantages and"
22cf367d14e646914cc959bbcd402df0c20cd0dc,Towards Automated Melanoma Screening: Proper Computer Vision & Reliable Results,"Towards Automated Melanoma Screening:
Proper Computer Vision & Reliable Results
Michel Fornaciali, Micael Carvalho, Fl´avia Vasques Bittencourt, Sandra Avila, Eduardo Valle"
220f8088f2fc1ddd9df1a0b583d3d01cb929ee8d,ROML: A Robust Feature Correspondence Approach for Matching Objects in A Set of Images,"Noname manuscript No.
(will be inserted by the editor)
ROML: A Robust Feature Correspondence Approach for
Matching Objects in A Set of Images
Kui Jia · Tsung-Han Chan · Zinan Zeng · Shenghua Gao
Gang Wang · Tianzhu Zhang · Yi Ma"
223ec77652c268b98c298327d42aacea8f3ce23f,TR-CS-1102 Acted Facial Expressions In The Wild Database,"TR-CS-11-02
Acted Facial Expressions In The Wild
Database
Abhinav Dhall, Roland Goecke, Simon
Lucey, Tom Gedeon
September 2011
ANU Computer Science Technical Report Series"
22c89775cb5309eae5ac1f9ce9d1c2d569439492,Face recognition based on extended separable lattice 2-D HMMS,"978-1-4673-0046-9/12/$26.00 ©2012 IEEE
ICASSP 2012"
224ffad672f7e6c7995780eb9bd3c8a141cb25cd,Understanding pedestrian behaviors from stationary crowd groups,"Understanding Pedestrian Behaviors from Stationary Crowd Groups
Shuai Yi1, Hongsheng Li1,2, Xiaogang Wang1
Department of Electronic Engineering, The Chinese University of Hong Kong.
School of Electronic Engineering, University of Electronic Science and Technology of China.
Pedestrian behavior modeling and analysis is important for crowd scene un-
derstanding and has various applications in video surveillance. Stationary
rowd groups are a key factor influencing pedestrian walking patterns but
was largely ignored in literature. As shown in Figure 1 (d), the walking
path of a pedestrian (black curve) is affected by a stationary crowd group.
Without modeling the stationary crowd group, it is difficult to explain why
the pedestrian detours when approaching the destination (Figure 1 (f)). Sta-
tionary crowd groups can serve as multiple roles (Figure 1 (e)) for different
pedestrians, such as source, destination, or obstacle. Moreover, the spatial
distribution of stationary crowd groups might change over time (Figure 1
(a)-(d)), which leads to the dynamic variations of traffic patterns. In our
work, the factor of stationary crowd groups is introduced for the first time
to model pedestrian behaviors.
The Proposed Pedestrian Behavior Model
A general energy map M is proposed to model the traveling difficulty of
every location of the scene. It can be modeled with three channels calculated"
22355c1b0a8af6b9e1cc2ef3fd5dce08edc48dd5,Learning to Detect Vehicles by Clustering Appearance Patterns,"(cid:13)2015IEEE.Personaluseofthismaterialispermitted.PermissionfromIEEEmustbeobtainedforallotheruses,inanycurrentorfuturemedia,includingreprinting/republishingthismaterialforadvertisingorpromotionalpurposes,creatingnewcollectiveworks,forresaleorredistributiontoserversorlists,orreuseofanycopyrightedcomponentofthisworkinotherworks."
225f09fd8103626c486ea9bfcd3770858dcf1906,Strangeness Based Feature Selection for Part Based Recognition,"Strangeness Based Feature Selection for Part Based Recognition
Fayin Li and Jana Koˇseck´a and Harry Wechsler
George Mason Univerity
400 University Dr. Fairfax, VA 22030 USA"
222d86787abed673600f1054796367f439c2eec1,Etworks via a Ttention T Ransfer,"Published as a conference paper at ICLR 2017
PAYING MORE ATTENTION TO ATTENTION:
IMPROVING THE PERFORMANCE OF CONVOLUTIONAL
NEURAL NETWORKS VIA ATTENTION TRANSFER
Sergey Zagoruyko, Nikos Komodakis
Universit´e Paris-Est, ´Ecole des Ponts ParisTech
Paris, France"
2251a1efad0cef802fd64fc79cc1b7007b64f425,Estimating 3D Pose via Stochastic Search and Expectation Maximization,"-IJE=JEC !, 2IA LE= 5J?D=IJE? 5A=H?D
-NFA?J=JE =NEE=JE
*A ,=K>AO :E=CDK= :EA
,AF=HJAJ B +FKJAH 5?EA?A 5M=IA= 7ELAHIEJO
5)  &22
*,=K>AO::EA(IM=IA==?K
)>IJH=?J 1 JDEI F=FAH = =FFH=?D EI J AIJE=JA !, FIA
KIEC = F=HJ IJ?D=IJE? ) HAFHAIAJ=JE B JDA
DK= EI LAH EJI JD=J AFOI BK
A=HJ >AJMAA EJI 6DEI HAFHAIAJ=JE EI
=C=EIJ = FFK=H =JAH=JELA LAH F=HJI KIEC E>
J EI IDM JD=J KIEC BK E> HAIKJI E =
JD=J EI B=H HA HAFHAIAJ=JELA B JDA HECE= JH=EEC .KH
JDAHHA EJ EI JD=J -NFA?J=JE =NEE=JE EI IKEJ=>A
BH AIJE=JEC !, FIA >AJJAH ?LAHCA?A EI MDA KIEC BK
E> 6 JDA A?=?O B JDA EJ
EI J JDA B !, FIA AIJE=JE KIEC = IECA ?K=H
E=CA 3K=JEJ=JELA HAIKJI =HA KIEC JDA 0K=-L=
MDE?D ?
H JD=J JDA KJFAHBHI JD=J B JDA ?
FAJEC F=HJ 1 JDEI MH KIJ = IECA EI A=HJ J"
225fbfd99465033e993460a1bc838a87fbf42346,Gaussian-Bernoulli deep Boltzmann machine,"Gaussian-Bernoulli Deep Boltzmann Machine
KyungHyun Cho, Tapani Raiko and Alexander Ilin
Department of Information and Computer Science,
Aalto University School of Science
Email:"
221debbd7878ed303eaa4666f8df04a48e4c5070,Making Computer Vision Computationally Efficient,"Making computer vision computationally efficient
Narayanan Sundaram
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2012-106
http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-106.html
May 11, 2012"
228558a2a38a6937e3c7b1775144fea290d65d6c,Nonparametric Context Modeling of Local Appearance for Pose- and Expression-Robust Facial Landmark Localization,"Nonparametric Context Modeling of Local Appearance
for Pose- and Expression-Robust Facial Landmark Localization
Brandon M. Smith1
Jonathan Brandt2
University of Wisconsin–Madison
Zhe Lin2
Adobe Research
Li Zhang1
http://www.cs.wisc.edu/~lizhang/projects/face-landmark-localization/"
7c8d57ca9cbefd1c2b3f4d45ab6791adba2d6bb4,Two-Stage Hashing for Fast Document Retrieval,"Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Short Papers), pages 495–500,
Baltimore, Maryland, USA, June 23-25 2014. c(cid:13)2014 Association for Computational Linguistics"
7c2f2080473d25db73c53869337afb79d2135731,"Remondino , Gerke 75 Oblique Aerial Imagery – A Review","Remondino, Gerke
Oblique Aerial Imagery – A Review
Fabio Remondino, Trento
Markus Gerke, Twente"
7c349932a3d083466da58ab1674129600b12b81c,Leveraging Multiple Features for Image Retrieval and Matching,
7c0ffae3acb0fd0a14ff66b6d474229aa16c53ab,Covariance Descriptor Multiple Object Tracking and Re-identification with Colorspace Evaluation,"Covariance Descriptor Multiple Object Tracking
nd Re-Identification with Colorspace
Evaluation
Andr´es Romero, Mich`ele Gouiff´es and Lionel Lacassagne
Institut d’´El´ectronique Fondamentale, UMR 8622, Universit´e Paris-Sud XI, Bˆatiment
660, rue Noetzlin, Plateau du Moulon, 91400 Orsay"
7c7b0550ec41e97fcfc635feffe2e53624471c59,"Head, Eye, and Hand Patterns for Driver Activity Recognition","051-4651/14 $31.00 © 2014 IEEE
DOI 10.1109/ICPR.2014.124"
7c8adb2fa156b119a1f576652c39fb06e4e19675,Ordinal Regression using Noisy Pairwise Comparisons for Body Mass Index Range Estimation,"Ordinal Regression using Noisy Pairwise Comparisons for Body Mass Index
Range Estimation
Luisa F. Polan´ıa
Dongning Wang
Glenn M. Fung
American Family Insurance, Strategic Data & Analytics, Madison, WI
{lpolania, dwang1,"
7c26559e7269679ef52a85d02c6ff7000c2387d2,Towards a Development of a Learners’ Ratified Acceptance of Multi-biometrics Intentions Model (RAMIM): Initial Empirical Results,"Yair Levy, Michelle M. Ramim
Towards a Development of a Learners’ Ratified
Acceptance of Multi-biometrics Intentions Model
(RAMIM): Initial Empirical Results
Graduate School of Computer and Information
H. Wayne Huizenga School of Business and
Nova Southeastern University, USA
Nova Southeastern University, USA
Yair Levy
Sciences
Michelle M. Ramim
Entrepreneurship
implemented  as"
7c03a0ad5202a6a31ad3b78b11f6b45ecd840616,Scale-Invariant Feature Learning using Deconvolutional Neural Networks for Weakly-Supervised Semantic Segmentation,"Scale-Invariant Feature Learning using
Deconvolutional Neural Networks for
Weakly-Supervised Semantic Segmentation
Hyo-Eun Kim and Sangheum Hwang
Lunit Inc., Seoul, South Korea
{hekim,"
7c739dddc905c967e0b432dc7515cb1f4b82e580,Social Attention: Modeling Attention in Human Crowds,"Social Attention: Modeling Attention in Human Crowds
Anirudh Vemula, Katharina Muelling and Jean Oh"
7c4864065f4e107cb5be49a8dba8cf7d94b8340f,Multi-target Tracking by Lagrangian Relaxation to Min-cost Network Flow,"Multi-target Tracking by Lagrangian Relaxation to Min-Cost Network Flow
Asad A. Butt and Robert T. Collins
The Pennsylvania State University, University Park, PA. 16802, USA"
7c7af300c4780ad01e7db4d60fbf89771672585b,Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning,"Detection and Segmentation of Manufacturing Defects with
Convolutional Neural Networks and Transfer Learning
Max Ferguson1 Ronay Ak2 Yung-Tsun Tina Lee2 and Kincho. H. Law1"
7cda4fc187f446a52cc5c9ac0e6a0752c1f0d5e9,Domain-Specific Approximation for Object Detection,"© 2018 IEEE. DOI: 10.1109/MM.2018.112130335
© 2018 IEEE. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other uses, in
ny current or future media, including
reprinting/republishing this material for advertising or
promotional purposes, creating new collective works, for
resale or redistribution to servers or lists, or reuse of any
opyrighted component of this work in other works. DOI:
0.1109/MM.2018.112130335"
7c98c27f4be40a7675ba9c85179ce72d12593a7a,Training Bit Fully Convolutional Network for Fast Semantic Segmentation,"Training Bit Fully Convolutional Network for Fast Semantic Segmentation
He Wen and Shuchang Zhou and Zhe Liang and Yuxiang Zhang and Dieqiao Feng and Xinyu Zhou and Cong Yao
{wenhe, zsc, liangzhe, zyx, fdq, zxy,
Megvii Inc."
7cf579088e0456d04b531da385002825ca6314e2,Emotion Detection on TV Show Transcripts with Sequence-based Convolutional Neural Networks,"Emotion Detection on TV Show Transcripts with
Sequence-based Convolutional Neural Networks
Sayyed M. Zahiri
Jinho D. Choi
Mathematics and Computer Science
Mathematics and Computer Science
Emory University
Atlanta, GA 30322, USA
Emory University
Atlanta, GA 30322, USA"
7c3e09e0bd992d3f4670ffacb4ec3a911141c51f,Transferring Object-Scene Convolutional Neural Networks for Event Recognition in Still Images,"Noname manuscript No.
(will be inserted by the editor)
Transferring Object-Scene Convolutional Neural Networks for
Event Recognition in Still Images
Limin Wang · Zhe Wang · Yu Qiao · Luc Van Gool
Received: date / Accepted: date"
7c18965f5573020f32b151a08178ee4906b5bf4c,Recursive Coarse-to-Fine Localization for Fast Object Detection,"Recursive Coarse-to-Fine Localization
for fast Object Detection
Marco Pedersoli, Jordi Gonz`alez, Andrew D. Bagdanov, and Juan J. Villanueva
Dept. Ci`encies de la Computaci´o & Centre de Visi´o per Computador,
Edifici O, Campus UAB 08193 Bellaterra (Cerdanyola) Barcelona, Spain"
7c7158273f0f833329ad86f7f642aedeb161a73c,A video database of human faces under near Infra-Red illumination for human computer interaction applications,"A Video Database of Human Faces under Near Infra-Red
Illumination for Human Computer Interaction Aplications
S L Happy, Anirban Dasgupta, Anjith George, and Aurobinda Routray
Department of Electrical Engineering
Indian Institute of Technology Kharagpur"
7c25a4b2eaa7bf0bc4e0bd239f05d6c0d4cb3431,Fast Appearance-based Person Re-identification and Retrieval Using Dissimilarity Representations,"Fast Appearance-based Person Re-identification
nd Retrieval Using Dissimilarity
Representations
Riccardo Satta, Giorgio Fumera, and Fabio Roli
Dept. of Electrical and Electronic Engineering, University of Cagliari
Piazza d’Armi, 09123 Cagliari, Italy
e-mail: {satta, fumera,
WWW: http://prag.diee.unica.it"
7c95449a5712aac7e8c9a66d131f83a038bb7caa,Facial first impressions from another angle: How social judgements are influenced by changeable and invariant facial properties.,"This is an author produced version of Facial first impressions from another angle: How
social judgements are influenced by changeable and invariant facial properties.
White Rose Research Online URL for this paper:
http://eprints.whiterose.ac.uk/102935/
Article:
Sutherland, Clare, Young, Andrew William orcid.org/0000-0002-1202-6297 and Gillian,
Rhodes (2017) Facial first impressions from another angle: How social judgements are
97-415. ISSN 0007-1269
https://doi.org/10.1111/bjop.12206
promoting access to
White Rose research papers
http://eprints.whiterose.ac.uk/"
7ca37d66cbedf61d30e18e0608078c7b1d7cbf58,Photometric Normalization Techniques for Illumination Invariance,"Photometric Normalization Techniques for
Illumination Invariance
AVTOR: Vitomir Štruc
INTERNAL REPORT: LUKS"
7ca600523495b3d6c9addf26cd89d3bd23ce4cf3,ReDMark : Framework for Residual Diffusion Watermarking based on Deep Networks,"Copyright may be transferred without notice, after which this version may no longer be accessible.
This work has been submitted to the IEEE for possible publication.
ReDMark: Framework for Residual Diffusion
Watermarking based on Deep Networks
Mahdi Ahmadi, Alireza Norouzi, S.M.Reza Soroushmehr, Nader Karimi,
Kayvan Najarian, Shadrokh Samavi and Ali Emami"
7c25ed788da1f5f61d8d1da23dd319dfb4e5ac2d,Human-In-The-Loop Person Re-Identification,"Human-In-The-Loop Person Re-Identification
Hanxiao Wang, Shaogang Gong, Xiatian Zhu, and Tao Xiang"
7c45b5824645ba6d96beec17ca8ecfb22dfcdd7f,News Image Annotation on a Large Parallel Text-image Corpus.,"News image annotation on a large parallel text-image corpus
Pierre Tirilly, Vincent Claveau, Patrick Gros
Universit´e de Rennes 1/IRISA, CNRS/IRISA, INRIA Rennes-Bretagne Atlantique
Campus de Beaulieu
5042 Rennes Cedex, France"
7c1802d8d43dfe783650a03f03d41609fa5ae91e,Discriminability Objective for Training Descriptive Captions,"Discriminability objective for training descriptive captions
Ruotian Luo
TTI-Chicago
Brian Price
Adobe Research
Scott Cohen
Adobe Research
Gregory Shakhnarovich
TTI-Chicago"
7c9a65f18f7feb473e993077d087d4806578214e,SpringerLink - Zeitschriftenbeitrag,"SpringerLink - Zeitschriftenbeitrag
http://www.springerlink.com/content/93hr862660nl1164/?p=abe5352...
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| Text"
7cbf3ff040ce3d68d530fcddccf56788fa9b7a53,An algorithm to minimize within-class scatter and to reduce common matrix dimension for image recognition,"Turk J Elec Eng & Comp Sci, Vol.19, No.6, 2011, c(cid:2) T ¨UB˙ITAK
doi:10.3906/elk-1003-403
An algorithm to minimize within-class scatter and to
reduce common matrix dimension for image recognition
¨Umit C¸ i˘gdem TURHAL1,∗, Alpaslan DUYSAK2
Department of Electrical and Electronics Engineering, Bilecik University, Bilecik-TURKEY
Department of Computer Engineering, Dumlupınar University, K¨utahya-TURKEY
e-mail:
Received: 12.03.2010"
7caca02d3c61271d22c43580677acb6d52b23503,What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?,"IJCV VISI manuscript No.
(will be inserted by the editor)
What Makes Good Synthetic Training Data for Learning
Disparity and Optical Flow Estimation?
Nikolaus Mayer · Eddy Ilg · Philipp Fischer · Caner Hazirbas · Daniel
Cremers · Alexey Dosovitskiy · Thomas Brox
Received: date / Accepted: date"
7cb4d30b3bfb0d4b02499c15c7c7a9dfddda8049,Object Tracking using L 1 / L 2 Sparse Coding and Multi Scale Max Pooling,"________________________________________________________________________________________________
International Journal of Electrical, Electronics and Computer Systems (IJEECS)
Online Object Tracking using L1/L2 Sparse Coding and Multi Scale
Max  Pooling
V.S.R Kumari, 2K.Srinivasarao
Professor & HOD, Dept. of ECE, Sri Mittapalli College of Engineering, Guntur, A.P, India.
PG Student (M. Tech), Dept. of ECE, Sri Mittapalli College of Engineering, Guntur, A.P, India.
Email :
In  order"
7c4022dd2525882a0f7ba0d60db1c6290d5a9aa8,CSRNCVA: A MODEL OF CROSS-MEDIA SEMANTIC RETRIEVAL BASED ON NEURAL COMPUTING OF VISUAL AND AUDITORY SENSATIONS,"CSRNCVA: A MODEL OF CROSS-MEDIA
SEMANTIC RETRIEVAL BASED ON NEURAL
COMPUTING OF VISUAL AND AUDITORY
SENSATIONS
Y. Liu∗, K. Cai†, C. Liu‡, F. Zheng§"
513b11920f15a55ff4e3dd1a063c386b863d6679,Real-time CPU-based large-scale 3D mesh reconstruction,"Real-time CPU-based large-scale 3D mesh reconstruction
Enrico Piazza1 Andrea Romanoni1 Matteo Matteucci1"
5122a5d4bdf58b4f413d4de1fb250d4ab5e0608a,Gender Classification from Pose-Based GEIs,"Gender Classification from Pose-Based GEIs(cid:2)
Ra´ul Mart´ın-F´elez, Ram´on A. Mollineda, and J. Salvador S´anchez
Institute of New Imaging Technologies (INIT)
Universitat Jaume I. Av. Sos Baynat s/n, 12071, Castell´o de la Plana, Spain"
51bfc693d170b4171f5bd9f9aed51f1fe8b5304d,Zero-shot Recognition via Direct Classifier Learning with Transferred Samples and Pseudo Labels AAAI Anonymous Submission 182,"Zero-shot Recognition via Direct Classifier Learning
with Transferred Samples and Pseudo Labels
AAAI Anonymous Submission 182"
5121f42de7cb9e41f93646e087df82b573b23311,Classifying Online Dating Profiles on Tinder using FaceNet Facial Embeddings,"CLASSIFYING ONLINE DATING PROFILES ON TINDER USING FACENET FACIAL
EMBEDDINGS
Charles F. Jekel and Raphael T. Haftka
Department of Mechanical & Aerospace Engineering - University of Florida - Gainesville, FL 32611"
51319bb12c67fb5b11cbf2012a7e2059718b52eb,Local Fisher Discriminant Analysis for Pedestrian Re-identification,"Local Fisher Discriminant Analysis for Pedestrian Re-identification
Sateesh Pedagadi, James Orwell
Kingston University London
Sergio Velastin
Universidad de Santiago de Chile
Boghos Boghossian
Ipsotek Ltd, UK"
5120fb7db8eadb26118847d0553fca1c22ed6f07,DEEP EXTREME TRACKER BASED ON BOOTSTRAP PARTICLE FILTER,"Journal of Theoretical and Applied Information Technology
31st August 2014. Vol. 66 No.3
© 2005 - 2014 JATIT & LLS. All rights reserved.
ISSN: 1992-8645                                                       www.jatit.org                                                          E-ISSN: 1817-3195
DEEP EXTREME TRACKER BASED ON
BOOTSTRAP PARTICLE FILTER
ALEXANDER A S GUNAWAN,
2 MOHAMAD IVAN FANANY,
WISNU JATMIKO
Bina Nusantara University, Mathematics Department, School of Computer Science, Jakarta, Indonesia
, 3 Universitas Indonesia, Faculty of Computer Science, Depok, Indonesia
E-mail:  1 2 3"
511dda02d39dc8107ac385ea8a572970e2eb9b7b,"Face recognition using distributed, mobile computing","014 IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP)
978-1-4799-2893-4/14/$31.00 ©2014 IEEE
Klipsch School of Electrical and Computer Engineering
Gregorio Hinojos and Phillip L. De Leon
Las Cruces, New Mexico, U.S.A.
New Mexico State University
. INTRODUCTION"
51b7a57a2dbc2df0b0353cfcb4331c8f9c621e56,Bayesian learning for weakly supervised object classification,"Bayesian learning for weakly supervised object classification
Peter Carbonetto, Gyuri Dork´o and Cordelia Schmid
INRIA Rhˆone-Alpes, Grenoble, France
August 5, 2004"
518439ba2895c84ba686db5b83674c440e637c0b,The Price of Fair PCA: One Extra Dimension,"The Price of Fair PCA: One Extra Dimension
Samira Samadi
Georgia Tech
Uthaipon Tantipongpipat
Georgia Tech
Jamie Morgenstern
Georgia Tech
Mohit Singh
Georgia Tech
Santosh Vempala
Georgia Tech"
516a014f4654c90a22ae3d363b6e80bda68a084d,Adaptive human-centered representation for activity recognition of multiple individuals from 3D point cloud sequences,"Adaptive Human-Centered Representation for Activity Recognition of
Multiple Individuals from 3D Point Cloud Sequences
Hao Zhang1, Christopher Reardon2, Chi Zhang2, and Lynne E. Parker2"
51c3050fb509ca685de3d9ac2e965f0de1fb21cc,Fantope Regularization in Metric Learning,"Fantope Regularization in Metric Learning
Marc T. Law
Nicolas Thome
Matthieu Cord
Sorbonne Universit´es, UPMC Univ Paris 06, UMR 7606, LIP6, F-75005, Paris, France"
5193328862366e114781cb6b196ae958c1553357,Incremental Learning in Person Re-Identification,"Incremental Learning in Person Re-Identification
Prajjwal Bhargava
SRM University
Chennai"
51d97f4e4385a3da78bf9277a5426216198698c3,Improving the Accuracy of Face Detection for Damaged Video and Distant Targets,"Improving the Accuracy of Face Detection for Damaged Video and
Distant Targets
Department of Communication Engineering, Oriental Institute of Technology, New Taiepi City, Taiwan
Jun-Horng Chen
Keywords:
Error Concealment, Face Detection, Super-resolution."
5194a8acc87dd05a92a21f94fea966a2815f9b38,Noise aware analysis operator learning for approximately cosparse signals,"978-1-4673-0046-9/12/$26.00 ©2012 IEEE
ICASSP 2012"
51d1a6e15936727e8dd487ac7b7fd39bd2baf5ee,"A Fast and Accurate System for Face Detection, Identification, and Verification","JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
A Fast and Accurate System for Face Detection,
Identification, and Verification
Rajeev Ranjan, Ankan Bansal, Jingxiao Zheng, Hongyu Xu, Joshua Gleason, Boyu Lu, Anirudh Nanduri,
Jun-Cheng Chen, Carlos D. Castillo, Rama Chellappa"
51a81a17328ad36f1bbc15e240076b68d3271c0c,Laplacian object: One-shot object detection by locality preserving projection,"LAPLACIAN OBJECT: ONE-SHOT OBJECT DETECTION BY LOCALITY PRESERVING
PROJECTION
Sujoy Kumar Biswas and Peyman Milanfar
Electrical Engineering Department
University of California, Santa Cruz
156 High Street, Santa Cruz, CA, 95064"
51a162f6d21e48c3731aec8f676ba7c18c65bd26,From trajectories to behaviors : an algorithm to track and describe dancing birds,"2nd Computer Vision Winter Workshop
Nicole M. Artner, Ines Janusch, Walter G. Kropatsch (eds.)
Retz, Austria, February 6–8, 2017
From trajectories to behaviors:
n algorithm to track and describe dancing birds
Leonardo Oliva1,2, Alessia Saggese2, Nicole M. Artner1, Walter G. Kropatsch1, and Mario Vento2
Pattern Recognition and Image Processing Group (PRIP), TU Wien, Austria
Dept. of Information Eng., Electrical Eng. and Applied Mathematics (DIEM)
Faculty of Engineering, University of Salerno, Italy"
5171157c2c09a85ad6558c5c03da6b75b0cf5fe6,Dynamic Coattention Networks For Question Answering,"Published as a conference paper at ICLR 2017
DYNAMIC COATTENTION NETWORKS
FOR QUESTION ANSWERING
Caiming Xiong∗, Victor Zhong∗, Richard Socher
Salesforce Research
Palo Alto, CA 94301, USA
{cxiong, vzhong,"
511662e02373433c8c9e27d1425707069e3695b7,Effects of image compression on ear biometrics,"Engineering and Technology Copyright. The copy of record is available at IET Digital Library.
Research Article
Effects of image compression on ear
iometrics
ISSN 2047-4938
Received on 23rd October 2015
Revised on 27th January 2016
Accepted on 15th February 2016
doi: 10.1049/iet-bmt.2015.0098
www.ietdl.org
Christian Rathgeb1 ✉, Anika Pflug2, Johannes Wagner1, Christoph Busch1
da/sec – Biometrics and Internet Security Research Group, Hochschule Darmstadt, Germany
Media Security and IT Forensics – Fraunhofer Institute for Secure Information Technology, Germany
✉ E-mail:"
51cf3fa26b7c31c10427317fb5d72a6712023279,What Shape Is Your Conjugate? A Survey of Computational Convex Analysis and Its Applications,"A SURVEY OF COMPUTATIONAL CONVEX ANALYSIS AND ITS APPLICATIONS
WHAT SHAPE IS YOUR CONJUGATE?
YVES LUCET"
51a9f9dcffad494cb88b949b7e98e7e11240a015,A Hybrid Face Recognition Approach Using GPUMLib,"A Hybrid Face Recognition Approach Using
GPUMLib
Noel Lopes1,2 and Bernardete Ribeiro1
CISUC - Center for Informatics and Systems of University of Coimbra, Portugal
UDI/IPG - Research Unit, Polytechnic Institute of Guarda, Portugal"
51b70582fb0d536d4a235f91bf6ad382f29e2601,Detection of emotions from video in non-controlled environment. (Détection des émotions à partir de vidéos dans un environnement non contrôlé),"Detection of emotions from video in non-controlled
environment
Rizwan Ahmed Khan
To cite this version:
Rizwan Ahmed Khan. Detection of emotions from video in non-controlled environment. Image
Processing. Universit´e Claude Bernard - Lyon I, 2013. English. <NNT : 2013LYO10227>.
<tel-01166539v2>
HAL Id: tel-01166539
https://tel.archives-ouvertes.fr/tel-01166539v2
Submitted on 23 Jun 2015
HAL is a multi-disciplinary open access
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´emanant des ´etablissements d’enseignement et de"
51e43578ad761c7c4d58cb159eee0f8e6cf0f7a4,Incremental indexing and distributed image search using shared randomized vocabularies,"Introduction
Method
Results
Incremental Indexing and Distributed Image Search
using Shared Randomized Vocabularies
Rapha¨el Mar´ee, Philippe Denis, Louis Wehenkel, Pierre Geurts
GIGA Bioinformatics
GIGA Research ; Dept. EE & CS (Montefiore Institute)
University of Li`ege, Belgium
MIR 2010
March 29–31, 2010
Philadelphia, Pennsylvania, USA
Mar´ee et al.
Shared Randomized Vocabularies
(1 / 44)"
5146832515ba8b4ad48372967d9fb7dcdea61869,CUNI System for WMT16 Automatic Post-Editing and Multimodal Translation Tasks,"Proceedings of the First Conference on Machine Translation, Volume 2: Shared Task Papers, pages 646–654,
Berlin, Germany, August 11-12, 2016. c(cid:13)2016 Association for Computational Linguistics"
51ab92566306c2f26e8415b451f3dd1f83f59008,The USC CreativeIT database of multimodal dyadic interactions: from speech and full body motion capture to continuous emotional annotations,"The USC CreativeIT Database of Multimodal Dyadic
Interactions: From Speech and Full Body Motion
Capture to Continuous Emotional Annotations
Angeliki Metallinou · Zhaojun Yang ·
Chi-chun Lee · Carlos Busso · Sharon
Carnicke · Shrikanth Narayanan
March 23, 2015"
51cc78bc719d7ff2956b645e2fb61bab59843d2b,Face and Facial Expression Recognition with an Embedded System for Human-Robot Interaction,"Face and Facial Expression Recognition with an
Embedded System for Human-Robot Interaction
Yang-Bok Lee1, Seung-Bin Moon1, and Yong-Guk Kim 1*
School of Computer Engineering, Sejong University, Seoul, Korea"
51c7c5dfda47647aef2797ac3103cf0e108fdfb4,Cs 395t: Celebrity Look-alikes *,"CS 395T: Celebrity Look-Alikes ∗
Adrian Quark"
51c7236feaa2ae23cef78c7bca75c69d7081e24a,Deep multi-frame face super-resolution,"Deep multi-frame face super-resolution
Evgeniya Ustinova, Victor Lempitsky
October 17, 2017"
ddbfea5302fcb5cbc2ca4c498a592ddb063b9eff,L Ow Supervision Visual Learning through Cooperative Agents,"Low-supervision visual learning through cooperative agents
Ashish Bora
Abhishek Sinha"
ddfdc4bf9fe440926e5e80909d444316fb7bc694,UvA-DARE ( Digital Academic Repository ) Selective Search for Object Recognition,"UvA-DARE (Digital Academic Repository)
Selective Search for Object Recognition
Uijlings, J.R.; van de Sande, K.E.A.; Gevers, T.; Smeulders, A.W.M.
Published in:
International Journal of Computer Vision
0.1007/s11263-013-0620-5
Link to publication
Citation for published version (APA):
Uijlings, J. R., van de Sande, K. E. A., Gevers, T., & Smeulders, A. W. M. (2013). Selective Search for Object
Recognition. International Journal of Computer Vision, 104(2), 154-171. DOI: 10.1007/s11263-013-0620-5
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UvA-DARE is a service provided by the library of the University of Amsterdam (http://dare.uva.nl)"
ddbd24a73ba3d74028596f393bb07a6b87a469c0,Multi-region Two-Stream R-CNN for Action Detection,"Multi-region two-stream R-CNN
for action detection
Xiaojiang Peng, Cordelia Schmid
Inria(cid:63)"
dde24967490f58c8d10b2a00f12bf9103bd9b4a6,EVALUATION OF SHAPE FEATURES FOR EFFICIENT CLASSIFICATION BASED ON ROTATIONAL INVARIANT USING TEXTON MODEL,"Dr. P Chandra Sekhar Reddy, International Journal of Computer Science and Mobile Computing, Vol.5 Issue.8, August- 2016, pg. 282-295
Available Online at www.ijcsmc.com
International Journal of Computer Science and Mobile Computing
A Monthly Journal of Computer Science and Information Technology
ISSN 2320–088X
IMPACT FACTOR: 5.258
IJCSMC, Vol. 5, Issue. 8, August 2016, pg.282 – 295
EVALUATION OF SHAPE FEATURES FOR
EFFICIENT CLASSIFICATION BASED ON
ROTATIONAL INVARIANT USING TEXTON MODEL
Dr. P Chandra Sekhar Reddy
Professor, CSE Dept.
Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad"
dd54255065cf93895661c40073cdd031af7dd7e8,"GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose","GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose
Zhichao Yin and Jianping Shi
SenseTime Research
{yinzhichao,"
ddfde5d6f4e720aeb770a20e4197db3a0c279958,Learning Convolutional Text Representations for Visual Question Answering,"Learning Convolutional Text Representations for Visual Question Answering
Zhengyang Wang∗
Shuiwang Ji†"
dda7bb490171a1d3364928fb8143bbe021146c5f,Local Shape Spectrum Analysis for 3D Facial Expression Recognition,"Local Shape Spectrum Analysis for 3D Facial Expression Recognition
Department of Information and Communication Technologies, Pompeu Fabra University, Barcelona, Spain
Dmytro Derkach and Federico M. Sukno"
dd80f3d19a4e3de9e9ef6dc3d23d52852a2ec23c,Audio-Visual Arabic Speech ( AVAS ) Database for Human-Computer Interaction Applications,"Volume 3, Issue 10, October 2013                                    ISSN: 2277 128X
International Journal of Advanced Research in
Computer Science and Software Engineering
Research Paper
Available online at: www.ijarcsse.com
Audio-Visual Arabic Speech (AVAS) Database for Human-
Computer Interaction Applications
Samar Antar Alaa Sagheer
Center for Artificial Intelligence and Robotics (CAIRO),
Department of Mathematics, Aswan University, Aswan, Egypt"
dd7ed20a65d811dcf863f796d6dcbe873f57e7c4,Object Detection Via Structural Feature Selection and Shape Model,"Object Detection via Structural Feature
Selection and Shape Model
Huigang Zhang, Xiao Bai, Jun Zhou, Senior Member, IEEE, Jian Cheng and
Huijie Zhao"
ddcb77d09e4e9e2a948f9ffe7eaa5554dceb8ce3,Revisiting Cross Modal Retrieval,
ddeb017c0452f14690ce240c90128d979289ab5f,A Comprehensive Survey on Human Skin Detection,"I.J. Image, Graphics and Signal Processing, 2016, 5, 1-35
Published Online May 2016 in MECS (http://www.mecs-press.org/)
DOI: 10.5815/ijigsp.2016.05.01
A Comprehensive Survey on Human Skin
Detection
Mohammad Reza Mahmoodi
Department of Electrical and Computer Engineering, Isfahan University of Technology, 8415683111 Isfahan, Iran
E-mail addresses:
Sayed Masoud Sayedi
Department of Electrical and Computer Engineering, Isfahan University of Technology, 8415683111 Isfahan, Iran
E-mail addresses:"
dd0a334b767e0065c730873a95312a89ef7d1c03,Eigenexpressions: Emotion Recognition Using Multiple Eigenspaces,"Eigenexpressions: Emotion Recognition using Multiple
Eigenspaces
Luis Marco-Gim´enez1, Miguel Arevalillo-Herr´aez1, and Cristina Cuhna-P´erez2
University of Valencia. Computing Department,
Burjassot. Valencia 46100, Spain,
Universidad Cat´olica San Vicente M´artir de Valencia (UCV),
Burjassot. Valencia. Spain"
ddc8f480898a846c2a6ba0dddd7d733ce35f0e19,Dense Pose Transfer,"Dense Pose Transfer
Natalia Neverova1, Rıza Alp G¨uler2, and Iasonas Kokkinos1
Facebook AI Research, Paris, France, {nneverova,
INRIA-CentraleSup´elec, Paris, France,"
ddf55fc9cf57dabf4eccbf9daab52108df5b69aa,Methodology and Performance Analysis of 3-D Facial Expression Recognition Using Statistical Shape Representation,"International Journal of Grid and Distributed Computing
Vol. 4, No. 3, September, 2011
Methodology and Performance Analysis of 3-D Facial Expression
Recognition Using Statistical Shape Representation
Wei Quan, Bogdan J. Matuszewski, Lik-Kwan Shark
ADSIP Research Centre, University of Central Lancashire
{WQuan, BMatuszewski1,
Charlie Frowd
School of Psychology, University of Central Lancashire"
ddea3c352f5041fb34433b635399711a90fde0e8,FACIAL EXPRESSION CLASSIFICATION USING VISUAL CUES AND LANGUAGE,"Facial Expression Classification using Visual Cues and Language
Abhishek Kar
Advisor: Dr. Amitabha Mukerjee
Department of Computer Science and Engineering, IIT Kanpur"
ddefb92908e6174cf48136ae139efbb4bd198896,Feature-wise Bias Amplification,"Under review as a conference paper at ICLR 2019
FEATURE-WISE BIAS AMPLIFICATION
Anonymous authors
Paper under double-blind review"
dde5125baefa1141f1ed50479a3fd67c528a965f,Synthesizing Normalized Faces from Facial Identity Features,"Synthesizing Normalized Faces from Facial Identity Features
Forrester Cole1 David Belanger1,2 Dilip Krishnan1 Aaron Sarna1 Inbar Mosseri1 William T. Freeman1,3
Google, Inc. 2University of Massachusetts Amherst 3MIT CSAIL
{fcole, dbelanger, dilipkay, sarna, inbarm,"
596b2ad91ba21884960c67fad21bc2ac62800200,RelCom: Relational combinatorics features for rapid object detection,"MITSUBISHI ELECTRIC RESEARCH LABORATORIES
http://www.merl.com
RelCom: Relational Combinatorics Features
for Rapid Object Detection
Vijay Venkataraman, Fatih Porikli
TR2010-036
July 2010"
5911dcef05ffec02cc1dd88ec6feb1f1e0e8bdcb,Happy Companion: A System of Multimodal Human-Computer Affective Interaction,"Happy Companion: A System of Multimodal Human-Computer
Affective Interaction
Jia Jia1,2,3, Lianhong Cai1,2,3, Sirui Wang4, Xiaolan Fu4
State Key Laboratory on Intelligent Technology and Systems"
59f65b2a3a50b64193ee09dac29137cdd8dc6688,Learning Similarity Metrics by Factorising Adjacency Matrices,"Learning Similarity Metrics by Factorising Adjacency Matrices
Henry Gouk†
Bernhard Pfahringer†
Michael Cree‡
Department of Computer Science, University of Waikato, Hamilton, New Zealand
School of Engineering, University of Waikato, Hamilton, New Zealand"
59d0d7ccec2db66cad20cac5721ce54a8a058294,Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference,"Quantization and Training of Neural Networks for Efficient
Integer-Arithmetic-Only Inference
Benoit Jacob
Skirmantas Kligys
Matthew Tang
Andrew Howard
Bo Chen
Hartwig Adam
Menglong Zhu
Dmitry Kalenichenko
{benoitjacob,skligys,bochen,menglong,
Google Inc."
59b71e19819c1c6aee98020b34bf92e605f33819,Max-min convolutional neural networks for image classification,"MAX-MIN CONVOLUTIONAL NEURAL NETWORKS FOR IMAGE CLASSIFICATION
Michael Blot, Matthieu Cord, Nicolas Thome
Sorbonne Universit´es, UPMC Univ Paris 06, CNRS, LIP6 UMR 7606, 4 place Jussieu 75005 Paris"
59ee0f67bcf2d8ea0bbbfcbc71159725fc3a7059,Object Detection with Appearance-based Mixture Models Anonymous CVPR submission,"CVPR 2011 Submission #885. CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE.
Object Detection with Appearance-based Mixture Models
Anonymous CVPR submission
Paper ID 885"
59ee327192c270fc727c5f6d2ef90058ed072b14,Motion Models for People Tracking,"Motion Models for People Tracking
David J. Fleet"
59f325e63f21b95d2b4e2700c461f0136aecc171,Kernel sparse representation with local patterns for face recognition,"978-1-4577-1302-6/11/$26.00 ©2011 IEEE
FOR FACE RECOGNITION
. INTRODUCTION"
59b11427853b7892a9f0d8ab6683d96ce59c2ff2,A Multi-Face Challenging Dataset for Robust Face Recognition,"A Multi-Face Challenging Dataset for Robust Face Recognition
Shiv Ram Dubey and Snehasis Mukherjee"
593d8c2230cda76c83385ab90677a024c3b04a90,A Canonical Image Set for Examining and Comparing Image Processing Algorithms,"A Canonical Image Set for Examining and
Comparing Image Processing Algorithms
Jeffrey Uhlmann
Dept. of Electrical Engineering & Computer Science
University of Missouri-Columbia"
59031a35b0727925f8c47c3b2194224323489d68,Sparse Variation Dictionary Learning for Face Recognition with a Single Training Sample per Person,"Sparse Variation Dictionary Learning for Face Recognition with A Single
Training Sample Per Person
Meng Yang, Luc Van Gool
ETH Zurich
Switzerland"
59945763707557baace208253c029265b4b6e0a9,FACE RECOGNITION UNDER PARTIAL OCCLUSION AND SMALL DENSE NOISE,"FACE RECOGNITION UNDER PARTIAL
OCCLUSION AND SMALL DENSE NOISE
A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE
REQUIREMENTS FOR THE DEGREE OF
MASTER OF TECHNOLOGY
ELECTRONIC SYSTEMS AND COMMUNICATIONS
ROHIT KUMAR
ROLL NO. -212EE1210
Department of Electrical Engineering
National Institute of Technology, Rourkela-769008
| P a g e"
59efb1ac77c59abc8613830787d767100387c680,DIF : Dataset of Intoxicated Faces for Drunk Person Identification,"DIF : Dataset of Intoxicated Faces for Drunk Person
Identification
Devendra Pratap Yadav
Indian Institute of Technology Ropar
Abhinav Dhall
Indian Institute of Technology Ropar"
59bdd317abe8d87fb525eb4e3197a9311e2766e7,AND THE COMMITTEE ON GRADUATE STUDIES OF STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY,"DEMYSTIFYING UNSUPERVISED FEATURE LEARNING
A DISSERTATION
SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE
AND THE COMMITTEE ON GRADUATE STUDIES
OF STANFORD UNIVERSITY
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
Adam Coates
September 2012"
590a52702bdf7f9522cff02f477de1fa98fc2ff3,"Visual tracking of hands, faces and facial features of multiple persons","DOI 10.1007/s00138-012-0409-5
ORIGINAL PAPER
Visual tracking of hands, faces and facial features
of multiple persons
Haris Baltzakis · Maria Pateraki · Panos Trahanias
Received: 17 November 2010 / Revised: 9 December 2011 / Accepted: 18 January 2012
© Springer-Verlag 2012"
59fc69b3bc4759eef1347161e1248e886702f8f7,Final Report of Final Year Project HKU-Face : A Large Scale Dataset for Deep Face Recognition,"Final Report of Final Year Project
HKU-Face: A Large Scale Dataset for
Deep Face Recognition
Haoyu Li
035141841
COMP4801 Final Year Project
Project Code: 17007"
5984c4a65c8fbd02be6054c30d929e76b9b9110a,Discovery of Sets of Mutually Orthogonal Vanishing Points in Videos,"Discovery of Sets of Mutually Orthogonal Vanishing Points in Videos
Till Kroeger1
Dengxin Dai1
Radu Timofte1
Luc Van Gool1,2
Computer Vision Laboratory, D-ITET, ETH Zurich
VISICS / iMinds, ESAT, K.U. Leuven
{kroegert, dai, timofter,"
5950512e21114236208b9eaeebc9a09735e367a6,Master research Internship Internship report Segmentation and recognition of symbols for printed and handwritten music scores,"Master research Internship
Internship report
Segmentation and recognition of symbols for printed and
handwritten music scores
Domain: Document and Text Processing, Computer Vision and Pattern Recognition
Author:
Kwon-Young Choi
Supervisors:
Bertrand Coüasnon
Yann Ricquebourg
Intuidoc, Irisa, France
Richard Zanibbi
RIT, USA"
59ec9bef0d331444db7d763960095213eecb3b20,INVARIANT FACE RECOGNITION IN A NETWORK OF CORTICAL COLUMNS,"INVARIANT FACE RECOGNITION
IN A NETWORK OF CORTICAL COLUMNS
Frankfurt Institute for Advanced Studies, JWG University, Ruth-Moufang-Str. 1, Frankfurt am Main, Germany
Philipp Wolfrum
J¤org L¤ucke
Gatsby Unit, UCL, London, United Kingdom
Christoph von der Malsburg
Frankfurt Institute for Advanced Studies, JWG University, Ruth-Moufang-Str. 1, Frankfurt am Main, Germany
Keywords:"
59c9d416f7b3d33141cc94567925a447d0662d80,Matrix factorization over max-times algebra for data mining,"Universität des Saarlandes
Max-Planck-Institut für Informatik
Matrix factorization over max-times
lgebra for data mining
Masterarbeit im Fach Informatik
Master’s Thesis in Computer Science
von / by
Sanjar Karaev
ngefertigt unter der Leitung von / supervised by
Dr. Pauli Miettinen
egutachtet von / reviewers
Dr. Pauli Miettinen
Prof. Gerhard Weikum
November 2013
UNIVERSITASSARAVIENSIS"
59b21f61ac46e1f982cbd9f49cb855ba5fcd3c45,CCNY at TRECVID 2014 : Surveillance Event Detection,"CCNY at TRECVID 2014: Surveillance Event Detection
Yang Xian, Xuejian Rong, Xiaodong Yang, and Yingli Tian
Graduate Center and City College
City University of New York
{xrong, xyang02,"
59bfeac0635d3f1f4891106ae0262b81841b06e4,Face Verification Using the LARK Face Representation,"Face Verification Using the LARK Face
Representation
Hae Jong Seo, Student Member, IEEE, Peyman Milanfar, Fellow, IEEE,"
59d225486161b43b7bf6919b4a4b4113eb50f039,Complex Event Recognition from Images with Few Training Examples,"Complex Event Recognition from Images with Few Training Examples
Unaiza Ahsan∗
Chen Sun∗∗
James Hays∗
Irfan Essa∗
*Georgia Institute of Technology
**University of Southern California1"
598ccf73ba504a31d65b50c7ede8982c3b1d9192,Learning a Family of Detectors,"LEARNING A FAMILY OF DETECTORS
QUAN YUAN
Dissertation submitted in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
BOSTON
UNIVERSITY"
595d0fe1c259c02069075d8c687210211908c3ed,A Survey on Learning to Hash,"A Survey on Learning to Hash
Jingdong Wang, Ting Zhang, Jingkuan Song, Nicu Sebe, and Heng Tao Shen"
591bd78a06814e75cae7cdef50ad91cf22e66c23,3D face recognition based on evolution of iso-geodesic distance curves,"978-1-4244-4296-6/10/$25.00 ©2010 IEEE
ICASSP 2010"
5925a25dfe107c49c636eccb8f9fd1aeef7b438c,Temporal Shift Module for Efficient Video Understanding,"Temporal Shift Module for Efficient Video Understanding
Ji Lin
Chuang Gan
MIT-IBM Watson AI Lab
Song Han"
59da714d643757871bf3a48757a5919b9b577e89,A Statistical Quadtree Decomposition to Improve Face Analysis,
599b7e1b4460c8ad77def2330ec76a2e0dfedb84,Robust Subspace Clustering via Smoothed Rank Approximation,"Robust Subspace Clustering via Smoothed Rank
Approximation
Zhao Kang, Chong Peng, and Qiang Cheng∗"
590c277e8ca10f2c2d7e32eb4a9dc61078a67b96,Statistical Approaches to Face Recognition a Qualifying Examination Report,"StatisticalApproachesTo
FaceRecognition
AQualifyingExaminationReport
AraV.Ne(cid:12)an
PresentedtotheQualifyingExaminationCommittee
InPartialFul(cid:12)llmentoftheRequirementsforthe
DegreeofDoctorofPhilosophyinElectricalEngineering
Dr.AlbinJ.Gasiewski
Dr.Je(cid:11)Geronimo
Dr.MonsonH.HayesIII
Dr.RussellM.Mersereau
Dr.RonaldW.Schafer
GeorgiaInstituteofTechnology
SchoolofElectricalEngineering
December,
		"
59f8d0e79eb02c30a5f872038129c4b5dd9bc73a,Design of a Face Recognition System for Security Control,"International Conference on African Development Issues (CU-ICADI) 2015: Information and Communication Teclmology Track
Design of a Face Recognition System for Security
Control
Ambrose A. Azeta, Nicholas A.  Omoregbe, Adewole Adewumi, Dolapo Oguntade
Department of Computer and Information Sciences,
Covenant University,
Ota, Ogun-State, Nigeria"
59d25dfa200e099662ec34eec620726ebcf02ea8,Information fusion and evidential grammars for object class segmentation,"Information fusion and evidential grammars for object class
segmentation
Jean-Baptiste Bordes1
Philippe Xu1,2
Franck Davoine2 Huijing Zhao2
Thierry Denœux1"
59ef1efb9239a101c2782fab8adc09b7af07d336,Cross-Domain Image Matching with Deep Feature Maps,"Cross-Domain Image Matching with Deep Feature Maps
Bailey Kong · James Supan˘ci˘c, III · Deva Ramanan · Charless C.
Fowlkes
Received: date / Accepted: date"
590630990cf014f8c30296bc7a622d9dccc43163,Recognition of expression variant faces using masked log-Gabor features and Principal Component Analysis,"Recognition of expression variant faces using
masked log-Gabor features and Principal
Component Analysis
Vytautas Perlibakas
Image Processing and Analysis Laboratory, Computational Technologies Centre,
Kaunas University of Technology, Studentu st. 56-305, LT-51424 Kaunas,
Lithuania"
598f330fc061852162f2aaaf59ea9a3a55d3f6f7,A new strategy based on spatiogram similarity association for multi-pedestrian tracking,"A NEW STRATEGY BASED ON SPATIOGRAM
SIMILARITY ASSOCIATION FOR
MULTI-PEDESTRIAN TRACKING
Nabila MANSOURI1 5, Yousra BEN JEMAA2, Cina MOTAMED 3, Antonio PINTI 4 and Eric WATELAIN1 6
University of Lille North of France, UVHC, LAMIH laboratory
e-mail:
University of Sfax-Tunisie, U2S laboratory
e-mail:
University of Lille North of France, ULCO, LISIC laboratory
e-mail:
University of Orleans -France, I3MTO laboratory
e-mail:
5 University of Sfax-Tunisie, ReDCAD laboratory
6 University of south Toulon-Var, HandiBio laboratory"
5945464d47549e8dcaec37ad41471aa70001907f,Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos,"Noname manuscript No.
(will be inserted by the editor)
Every Moment Counts: Dense Detailed Labeling of Actions in Complex
Videos
Serena Yeung · Olga Russakovsky · Ning Jin · Mykhaylo Andriluka · Greg Mori ·
Li Fei-Fei
Received: date / Accepted: date"
59c194bc84d604baf09241238dc47806a998df70,Building a post-compression region-of-interest encryption framework for existing video surveillance systems,"(will be inserted by the editor)
Building a Post-Compression Region-of-Interest Encryption Framework
for Existing Video Surveillance Systems
Challenges, obstacles and practical concerns
Andreas Unterweger · Kevin Van Ryckegem · Dominik Engel · Andreas Uhl
Received: November 13, 2014 / Accepted: (will be entered by the editor)"
59e266adc3525b4325156f0cc0052c1d76b1c9ae,Contextual Spatial Analysis and Processing for Visual Surveillance Applications,"Contextual Spatial Analysis and Processing
for Visual Surveillance Applications
Vikas Reddy
A thesis submitted for the degree of Doctor of Philosophy at
The University of Queensland in September 2011
(revised in March 2012)
School of Information Technology and Electrical Engineering"
590065c40574dc797e5aeb380d6e6dab79fad6e5,Face detection using boosted Jaccard distance-based regression,"FACE DETECTION USING BOOSTED
JACCARD DISTANCE-BASED REGRESSION
Cosmin Atanasoaei        Chris McCool
Sébastien Marcel
Idiap-RR-02-2012
JANUARY 2012
Centre du Parc, Rue Marconi 19, P.O. Box 592, CH - 1920 Martigny
T +41 27 721 77 11  F +41 27 721 77 12   www.idiap.ch"
595ff304aac871abcccaffec7c1d33c3800ddf36,Robust Matching of Occupancy Maps for Odometry in Autonomous Vehicles,
5921d9a8e143b6d82a2722d9ee27bafa363475f0,Driving Policy Transfer via Modularity and Abstraction,
259bd09bc382763f864986498e46ab0178714f58,Lifelong Machine Learning,"Lifelong Machine Learning
November, 2016
Zhiyuan Chen and Bing Liu
Draft : This is mainly an early draft of the book.
We also updated a few places after the publication, highlighted in yellow.
Zhiyuan Chen and Bing Liu. Lifelong Machine Learning.
Morgan & Claypool Publishers, Nov 2016.
LifelongMachineLearningZhiyuan ChenBing Liu"
2504b7bddd1892bc905fc5df6b5afc0b109ef40e,Function Norms and Regularization in Deep Networks,"Function Norms and Regularization in Deep
Networks
Amal Rannen Triki∗
KU Leuven, ESAT-PSI, imec, Belgium
Maxim Berman
KU Leuven, ESAT-PSI, imec, Belgium
Matthew B. Blaschko
KU Leuven, ESAT-PSI, imec, Belgium"
2551721b91069d8eff0816da87a29bb133de8351,A Hybrid Method Using Temporal and Spatial Information for 3D Lidar Data Segmentation,
25a5f7179b794ab2bb7283c8337480fccee51944,Two novel motion-based algorithms for surveillance video analysis on embedded platforms,"Julien A. Vijverberg, Marijn J.H. Loomans, Cornelis J. Koeleman and Peter H.N. de With, ”Two novel
motion-based algorithms for surveillance video analysis on embedded platforms,” Real-Time Image and Video
Processing, Nasser Kehtarnavaz and Matthias F. Carlsohn, Editors, Proc. SPIE 7724, 77240I(2010).
Copyright 2010 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be
made for personal use only. Systematic electronic or print reproduction and distribution, duplication of any
material in this paper for a fee or for commercial purposes, or modification of the content of the paper are
prohibited.
http://dx.doi.org/10.1117/12.851371"
2581bfa070095f60786452dec3df006e240283b0,Older Adults' Trait Impressions of Faces Are Sensitive to Subtle Resemblance to Emotions.,"J Nonverbal Behav (2013) 37:139–151
DOI 10.1007/s10919-013-0150-4
O R I G I N A L P A P E R
Older Adults’ Trait Impressions of Faces Are Sensitive
to Subtle Resemblance to Emotions
Robert G. Franklin Jr. • Leslie A. Zebrowitz
Published online: 9 April 2013
Ó Springer Science+Business Media New York 2013"
2528022c14428ad5912c323f6a356009457c985b,Automatic 3D facial expression recognition using geometric and textured feature fusion,"Automatic 3D Facial Expression Recognition using Geometric and
Textured Feature Fusion
Department of Electronic and Computer Engineering, Brunel University London, UK
Asim Jan and Hongying Meng"
25337690fed69033ef1ce6944e5b78c4f06ffb81,STRATEGIC ENGAGEMENT REGULATION: AN INTEGRATION OF SELF-ENHANCEMENT AND ENGAGEMENT,"STRATEGIC ENGAGEMENT REGULATION:
AN INTEGRATION OF SELF-ENHANCEMENT AND ENGAGEMENT
Jordan B. Leitner
A dissertation submitted to the Faculty of the University of Delaware in partial
fulfillment of the requirements for the degree of Doctor of Philosophy in Psychology
Spring 2014
© 2014 Jordan B. Leitner
All Rights Reserved"
251da2569036cebc2ea109972f412c5b1a9db20f,Appearance modeling for person re-identification using Weighted Brightness Transfer Functions,"1st International Conference on Pattern Recognition (ICPR 2012)
November 11-15, 2012. Tsukuba, Japan
978-4-9906441-1-6 ©2012 IAPR"
25e62096a44e3fe2f641b492379e7c4babce7ee6,Investigating Gaze of Children with ASD in Naturalistic Settings,"Investigating Gaze of Children with ASD in Naturalistic
Settings
Basilio Noris1*, Jacqueline Nadel2, Mandy Barker3, Nouchine Hadjikhani4, Aude Billard1
Learning Algorithms and Systems Laboratory, Ecole Polyte´chnique Fe´de´rale de Lausanne, Lausanne, Switzerland, 2 Emotion Centre, Hoˆ pital de La Salpe´trie`re, Paris,
France, 3 Lausanne University Department of Child and Adolescent Psychiatry, University Hospital of Canton de Vaud, Lausanne, Switzerland, 4 Brain and Mind Institute,
Ecole Polyte´chnique Fe´de´rale de Lausanne, Lausanne, Switzerland & Martinos Center for Biomedical Imaging Massachusetts General Hospital/Healthcare Management
Systems/HST, Boston, Massachusetts, United States of America"
25291c10213b6bec098e1611d145facfa5d2b398,Personalized Recommendation of Travel Itineraries based on Tourist Interests and Preferences,"Personalized Recommendation of Travel Itineraries based
on Tourist Interests and Preferences
*Department of Computing and Information Systems, The University of Melbourne, Australia
Victoria Research Laboratory, National ICT Australia, Australia
Kwan Hui Lim*†"
25ae83767c926898047bbc50971b5b11de34e12a,Detection and Tracking of Occluded People,"Noname manuscript No.
(will be inserted by the editor)
Detection and Tracking of Occluded People
Siyu Tang · Mykhaylo Andriluka · Bernt Schiele
Received: date / Accepted: date"
252ea831fbc092b95ae464bce2476619aaf02d01,Acoustic-labial Speaker Verification Pattern Recognition Letters Acoustic-labial Speaker Verification,"IDIAP
Martigny - Valais - Suisse
Acoustic(cid:0)Labial Speaker
Verification
Pierre Jourlin 
 
Dominique Genoud 

Juergen Luettin 

Hubert Wassner 

IDIAP(cid:0)RR 	(cid:3)

to appear in
Pattern Recognition Letters
D a l l e M o l l e
I n s t i t u t e
f o r P e r c e p t i v e A r t i f i c i a l
Intelligence (cid:0) P(cid:0)O(cid:0)Box 	 (cid:0)
Martigny (cid:0) Valais (cid:0) Switzerland
phone (cid:0)
 (cid:1)  (cid:1) 
  


(cid:0)
 (cid:1)  (cid:1) 
  

e(cid:4)mail secretariat(cid:0)idiap(cid:1)ch
internet http(cid:2)(cid:3)(cid:3)www(cid:1)idiap(cid:1)ch"
254f7ef73629c18ff9ba13af59b2d78df3fda59d,Deep Object-Centric Representations for Generalizable Robot Learning,"Deep Object-Centric Representations for Generalizable Robot Learning
Coline Devin1, Pieter Abbeel1,2, Trevor Darrell1, Sergey Levine1"
253cedd3022e25a79bcaffe74e3405db65c6d2ce,Deep Hashing for Scalable Image Search,"Deep Hashing for Scalable Image Search
Jiwen Lu, Senior Member, IEEE, Venice Erin Liong, and Jie Zhou, Senior Member, IEEE"
258a2dad71cb47c71f408fa0611a4864532f5eba,Discriminative Optimization of Local Features for Face Recognition,"Discriminative Optimization
of Local Features for Face Recognition
H O S S E I N   A Z I Z P O U R
Master of Science Thesis
Stockholm, Sweden 2011"
25b9ef5c78dbf17c71e6fd94054dd55d66c39264,Multimedia Semantic Integrity Assessment Using Joint Embedding Of Images And Text,"Multimedia Semantic Integrity Assessment Using Joint
Embedding Of Images And Text
Ayush Jaiswal∗
USC Information Sciences Institute
Marina del Rey, CA, USA
Ekraam Sabir∗
USC Information Sciences Institute
Marina del Rey, CA, USA
Wael AbdAlmageed
USC Information Sciences Institute
Marina del Rey, CA, USA
Premkumar Natarajan
USC Information Sciences Institute
Marina del Rey, CA, USA"
259f0699d7e4066966a38860ad3227fe123d1660,Convolutional Neural Networks for joint object detection and pose estimation: A comparative study.,"Under review as a conference paper at ICLR 2015
CONVOLUTIONAL NEURAL NETWORKS FOR JOINT
OBJECT DETECTION AND POSE ESTIMATION:
A COMPARATIVE STUDY
Francisco Massa, Mathieu Aubry, Renaud Marlet
Universit´e Paris-Est, LIGM (UMR CNRS 8049), ENPC
F-77455 Marne-la-Vall´ee, France"
2547607a98eff30654994902f518e30caf2f8271,Synthesizing manipulation sequences for under-specified tasks using unrolled Markov Random Fields,"Synthesizing Manipulation Sequences for Under-Specified Tasks
using Unrolled Markov Random Fields
Jaeyong Sung, Bart Selman and Ashutosh Saxena"
25c19d8c85462b3b0926820ee5a92fc55b81c35a,Pose-Invariant Facial Expression Recognition Using Variable-Intensity Templates,"Noname manuscript No.
(will be inserted by the editor)
Pose-Invariant Facial Expression Recognition
Using Variable-Intensity Templates
Shiro Kumano · Kazuhiro Otsuka · Junji Yamato ·
Eisaku Maeda · Yoichi Sato
Received: date / Accepted: date"
25b83cffddff334d78c55db4d67c65b1d8999b2f,Optimization of Person Re-Identification through Visual Descriptors,
25e3fd1074968896fca45be20ca1d678438081fc,Group Invariant Deep Representations for Image Instance Retrieval,
25bb4212af72d64ec20cac533f58f7af1472e057,Person Re-Identification by Camera Correlation Aware Feature Augmentation,"Person Re-Identification by Camera
Correlation Aware Feature Augmentation
Ying-Cong Chen, Xiatian Zhu, Wei-Shi Zheng, Jian-Huang Lai
Code is available at the project page:
http://isee.sysu.edu.cn/%7ezhwshi/project/CRAFT.html
For reference of this work, please cite:
Ying-Cong Chen, Xiatian Zhu,Wei-Shi Zheng, and Jian-Huang Lai. Per-
son Re-Identification by Camera Correlation Aware Feature Augmenta-
0.1109/TPAMI.2017.2666805)
title={Person Re-Identification by Camera Correlation Aware Feature Aug-
mentation},
uthor={Chen, Ying-Cong and Zhu, Xiatian and Zheng, Wei-Shi and Lai,
Jian-Huang},
(DOI: 10.1109/TPAMI.2017.2666805)}"
25982e2bef817ebde7be5bb80b22a9864b979fb0,Facial Feature Tracking Under Varying Facial Expressions and Face Poses Based on Restricted Boltzmann Machines,"(a)26facialfeaturepointsthatwetrack(b)oneexamplesequenceFigure1.Facialfeaturepointtrackingunderexpressionvariationandocclusion.Inrecentyears,thesemodelshavebeenusedexplicitlytohandletheshapevariations[17][5].Thenonlinearityem-beddedinRBManditsvariantsmakesthemmoreeffectiveandefficienttorepresentthenonrigiddeformationsofob-jectscomparedtothelinearmethods.Theirlargenumberofhiddennodesanddeeparchitecturesalsocanimposesuffi-cientconstraintsaswellasenoughdegreesoffreedomsintotherepresentationsofthetargetobjects.Inthispaper,wepresentaworkthatcaneffectivelytrackfacialfeaturepointsusingfaceshapepriormodelsthatareconstructedbasedonRBM.Thefacialfeaturetrackercantrack26facialfeaturepoints(Fig.1(a))eveniffaceshavedifferentfacialexpressions,varyingposes,orocclu-sion(Fig.1(b)).Unlikethepreviousworksthattrackfacialfeaturepointsindependentlyorbuildashapemodeltocap-turethevariationsoffaceshapeorappearanceregardlessofthefacialexpressionsandfaceposes,theproposedmodelcouldcapturethedistinctionsaswellasthevariationsoffaceshapesduetofacialexpressionandposechangeinaunifiedframework.Specifically,wefirstconstructamodel1"
25894be540936562953f37fbbcff69e5ac17a494,Semantic Image Retrieval via Active Grounding of Visual Situations,"Semantic Image Retrieval
via Active Grounding of Visual Situations
Max H. Quinn1, Erik Conser1, Jordan M. Witte1, and Melanie Mitchell1,2
Portland State University 2Santa Fe Institute
Email:"
2598c02e537b02ce181eea1aa49a698080a391a8,Improving Recognition Performance for Duplicate Facial Images,"ImprovingRecognitionPerformancefor
DuplicateFacialImages
KazunoriOkada
UniversityofSouthernCalifornia,Dept.ofComputerscienceandSectionforNeu-
robiology,LosAngeles,CA		-,USA
Summary.Previousworkinourgrouphasdescribedafacerecognitiontechnology
asedonGaborwaveletsbasedrepresentationandElasticGraphMatching.[]We
heredescribeasummaryofoure(cid:11)ortstowardstheFaceRecognitionTechnology
(FERET)PhaseIIItestheldinFebruary
		.Oneofourgoalsinthistestwas
toimproverecognitionperformanceforduplicatefacialimages.Weproposeda
modelofcuestoexplainbehavioroffailuresandanalyzedvariousfailurecases
ndcauses.Utilizingtheselearnedknowledgeofthefailuresasageneralstrategy,
threeadditionalfunctions,histogramequalization,facesizenormalizationbykernel
rescalingandjettransformationforrotationindepth[][]wereevaluatedinour
systemaspostprocessesafterafacemodelgraphcreation.Theperformanceofthe
systemisimprovedforrecognitionofduplicatefacialimages.

.Introduction
Thispapersummarizesoure(cid:11)ortstopreparefortheFaceRecognitionTechnology
(FERET)PhaseIIItestwhichwassuccessfullyoperatedinFebruary
		.Thistest
isthethirdofaseriesoftestsconductedbyUSArmyResearchLaboratory(ARL)."
257eb6d5ca49eb4ea90658a8668d1853d9c38af7,"Wide-Area Video Understanding: Tracking, Video Summarization and Algorithm-Platform Co-Design","UNIVERSITY OF CALIFORNIA
RIVERSIDE
Wide-Area Video Understanding: Tracking, Video Summarization and
Algorithm-Platform Co-Design
A Dissertation submitted in partial satisfaction
of the requirements for the degree of
Doctor of Philosophy
Electrical Engineering
Shu Zhang
December 2015
Dissertation Committee:
Dr. Amit K. Roy-Chowdhury, Chairperson
Dr. Qi Zhu
Dr. Ertem Tuncel"
25f1f195c0efd84c221b62d1256a8625cb4b450c,Experiments with Facial Expression Recognition using Spatiotemporal Local Binary Patterns,"-4244-1017-7/07/$25.00 ©2007 IEEE
ICME 2007"
25695abfe51209798f3b68fb42cfad7a96356f1f,AN INVESTIGATION INTO COMBINING BOTH FACIAL DETECTION AND LANDMARK LOCALISATION INTO A UNIFIED PROCEDURE USING GPU COMPUTING,"AN INVESTIGATION INTO COMBINING
BOTH FACIAL DETECTION AND
LANDMARK LOCALISATION INTO A
UNIFIED PROCEDURE USING GPU
COMPUTING
J M McDonagh
MSc by Research"
25aa935217a52d83bc1637687a78017984fcb731,The Continuous N-tuple Classiier and Its Application to Face Recognition,"Thecontinuousn-tupleclassi(cid:12)eranditsapplicationto
facerecognition
S.M.Lucas
DepartmentofElectronicSystemsEngineering
UniversityofEssex
ColchesterCOSQ,UK"
25127c2d9f14d36f03d200a65de8446f6a0e3bd6,EVALUATING THE PERFORMANCE OF DEEP SUPERVISED AUTO ENCODER IN SINGLE SAMPLE FACE RECOGNITION PROBLEM USING KULLBACK-LEIBLER DIVERGENCE SPARSITY REGULARIZER,"Journal of Theoretical and Applied Information Technology
20th May 2016. Vol.87. No.2
© 2005 - 2016 JATIT & LLS. All rights reserved.
ISSN: 1992-8645                                                       www.jatit.org                                                          E-ISSN: 1817-3195
EVALUATING THE PERFORMANCE OF DEEP SUPERVISED
AUTO ENCODER IN SINGLE SAMPLE FACE RECOGNITION
PROBLEM USING KULLBACK-LEIBLER DIVERGENCE
SPARSITY REGULARIZER
OTNIEL Y. VIKTORISA, 2ITO WASITO, 2ARIDA F. SYAFIANDINI
Faculty of Computer  of Computer Science, Universitas Indonesia, Kampus UI Depok, Indonesia
E-mail:  ,"
25474c21613607f6bb7687a281d5f9d4ffa1f9f3,Recognizing disguised faces,"This article was downloaded by: [Carnegie Mellon University]
On: 03 May 2012, At: 06:22
Publisher: Psychology Press
Informa Ltd Registered in England and Wales Registered Number: 1072954
Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH,
Visual Cognition
Publication details, including instructions for authors
nd subscription information:
http://www.tandfonline.com/loi/pvis20
Recognizing disguised faces
Giulia Righi a , Jessie J. Peissig b & Michael J. Tarr c
Children's Hospital Boston, Harvard Medical School,
Boston, MA, USA
Department of Psychology, California State University
Fullerton, Fullerton, CA, USA
Department of Psychology, Carnegie Mellon
University, Pittsburgh, PA, USA
Available online: 13 Feb 2012
To cite this article: Giulia Righi, Jessie J. Peissig & Michael J. Tarr (2012): Recognizing
disguised faces, Visual Cognition, 20:2, 143-169"
2562d6ec0044eee9d604fe3a351f80d4d10d4a3d,Conditional Image-Text Embedding Networks,"Conditional Image-Text Embedding Networks
Bryan A. Plummer†, Paige Kordas†, M. Hadi Kiapour‡, Shuai Zheng‡,
Robinson Piramuthu‡, and Svetlana Lazebnik†
University of Illinois at Urbana-Champaign†
Ebay Inc.‡"
258a8c6710a9b0c2dc3818333ec035730062b1a5,Benelearn 2005 Annual Machine Learning Conference of Belgium and the Netherlands,"Benelearn 2005
Annual Machine Learning Conference of
Belgium and the Netherlands
CTIT PROCEEDINGS OF THE FOURTEENTH
ANNUAL MACHINE LEARNING CONFERENCE
OF BELGIUM AND THE NETHERLANDS
Martijn van Otterlo, Mannes Poel and Anton Nijholt (eds.)"
25d48ab3b05bf299fe61ed6580674e893f08380b,"Pedestrian Detection : A Survey of Methodologies , Techniques and Current Advancements","International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882
Volume 4, Issue 1, January 2015
Pedestrian Detection: A Survey of Methodologies, Techniques and Current
Advancements
Tanmay Bhadra1, Joydeep Sonar2 , Arup Sarmah3 ,Chandan Jyoti Kumar4
Dept. of CSE & IT, School of Technology
Assam Don Bosco University"
25d3e122fec578a14226dc7c007fb1f05ddf97f7,The first facial expression recognition and analysis challenge,"The First Facial Expression Recognition and Analysis Challenge
Michel F. Valstar, Bihan Jiang, Marc Mehu, Maja Pantic, and Klaus Scherer"
25811285a1c1fd514b315d6a05adc2cb4abe9618,"DynaSLAM: Tracking, Mapping, and Inpainting in Dynamic Scenes","DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes
Berta Bescos, Jos´e M. F´acil, Javier Civera and Jos´e Neira"
250ebcd1a8da31f0071d07954eea4426bb80644c,DenseBox: Unifying Landmark Localization with End to End Object Detection,"DenseBox: Unifying Landmark Localization with
End to End Object Detection
Lichao Huang1
Yi Yang2
Yafeng Deng2
Institute of Deep Learning
Baidu Research
Yinan Yu3"
250449a9827e125d6354f019fc7bc6205c5fd549,Adversarial Reconstruction Loss,"PAIRWISE AUGMENTED GANS WITH
ADVERSARIAL RECONSTRUCTION LOSS
Aibek Alanov1,2,3∗, Max Kochurov1,2∗, Daniil Yashkov5, Dmitry Vetrov1,3,4
Samsung AI Center in Moscow
Skolkovo Institute of Science and Technology
National Research University Higher School of Economics
Joint Samsung-HSE lab
5Federal Research Center ""Informatics and Management"" of the Russian Academy of Sciences"
25afa24d85e693351bad795ee1c3e801d10c4a15,"Anisotropic Gaussian Filters for Face Class Modeling August 31 , 2006","Anisotropic Gaussian Filters for Face Class Modeling
August 31, 2006"
258972e9df3cdf0b8babbf607eaef7cce689226a,Multimodal Affect Recognition: Current Approaches and Challenges,"We are IntechOpen,
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Numbers displayed above are based on latest data collected."
25c57d1916c926bea3d92548f1c9836cffc23fe5,Automatic Ground Truths: Projected Image Annotations for Omnidirectional Vision,"Automatic Ground Truths: Projected Image
Annotations for Omnidirectional Vision
Victor Stamatescu∗, Peter Barsznica∗, Manjung Kim ∗, Kin K. Liu∗, Mark McKenzie†,
Will Meakin∗, Gwilyn Saunders∗, Sebastien C. Wong† and Russell S. A. Brinkworth∗
University of South Australia, Mawson Lakes, SA, Australia
Defence Science and Technology Group, Edinburgh, SA, Australia
Email:"
259706f1fd85e2e900e757d2656ca289363e74aa,Improving People Search Using Query Expansion : How Friends Help To Find People,"Improving People Search Using Query Expansions
How Friends Help To Find People
Thomas Mensink and Jakob Verbeek
LEAR - INRIA Rhˆone Alpes - Grenoble, France"
253325f09f07c2f7a05191f76e4977f473f4bac5,Filtering and Optimization Strategies for Markerless Human Motion Capture with Skeleton-based Shape Models,"FILTERING AND OPTIMIZATION
STRATEGIES FOR MARKERLESS
HUMAN MOTION CAPTURE WITH
SKELETON-BASED SHAPE MODELS.
DISSERTATION
ZUR ERLANGUNG DES GRADES DES
DOKTORS DER INGENIEURWISSENSCHAFTEN (DR.-ING.)
DER NATURWISSENSCHAFTLICH-TECHNISCHEN FAKULT ¨ATEN
DER UNIVERSIT ¨AT DES SAARLANDES
VORGELEGT VON
JUERGEN GALL
SAARBR ¨UCKEN"
257e61e6b38ae23b7ddce9907c05b0e78be4d79d,The LORACs prior for VAEs: Letting the Trees Speak for the Data,"The LORACs prior for VAEs: Letting the Trees Speak for the Data
Sharad Vikram
U.C. San Diego1
Matthew D. Hoffman
Matthew J. Johnson
Google AI
Google Brain"
25885e9292957feb89dcb4a30e77218ffe7b9868,Analyzing the Affect of a Group of People Using Multi-modal Framework,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2016
Analyzing the Affect of a Group of People Using
Multi-modal Framework
Xiaohua Huang, Abhinav Dhall, Xin Liu, Guoying Zhao, Jingang Shi, Roland Goecke and Matti Pietik¨ainen"
551fa37e8d6d03b89d195a5c00c74cc52ff1c67a,GeThR-Net: A Generalized Temporally Hybrid Recurrent Neural Network for Multimodal Information Fusion,"GeThR-Net: A Generalized Temporally Hybrid
Recurrent Neural Network for Multimodal
Information Fusion
Ankit Gandhi1 ∗, Arjun Sharma1 ∗ , Arijit Biswas2, and Om Deshmukh1
Xerox Research Centre India; 2 Amazon Development Center India
(*-equal contribution)"
558613d96d7c125c00eae0c58c56ee6983208fd5,Identification of Unmodeled Objects from Symbolic Descriptions,"Identification of Unmodeled Objects from Symbolic Descriptions*
Andrea Baisero, Stefan Otte, Peter Englert and Marc Toussaint"
5523c9ed32d2e5acd09cccf4f0370f9dd3d9a6c4,Improving the Egomotion Estimation by Correcting the Calibration Bias,"Improving the Egomotion Estimation by Correcting the Calibration Bias
University of Zagreb Faculty of Electrical Engineering and Computing, Unska 3, 10000 Zagreb, Croatia
Ivan Kreˇso and Siniˇsa ˇSegvi´c
fivan.kreso,
Keywords:
stereo vision, camera motion estimation, visual odometry, feature tracking, camera calibration, camera model
ias, deformation field"
55eaece6db38d90704081ddcef06339cc5af1016,Representing 3 D models for alignment and recognition,"Representing 3D models for alignment and recognition
Mathieu Aubry
To cite this version:
Mathieu Aubry. Representing 3D models for alignment and recognition. Computer Vision and
Pattern Recognition [cs.CV]. ENS, 2015. English. <tel-01160300>
HAL Id: tel-01160300
https://tel.archives-ouvertes.fr/tel-01160300
Submitted on 9 Jun 2015
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
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broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,
´emanant des ´etablissements d’enseignement et de
recherche fran¸cais ou ´etrangers, des laboratoires
publics ou priv´es."
5582bebed97947a41e3ddd9bd1f284b73f1648c2,Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization,"Visual Explanations from Deep Networks via Gradient-based Localization
Grad-CAM: Why did you say that?
Ramprasaath R. Selvaraju
Abhishek Das
Devi Parikh
Ramakrishna Vedantam
Dhruv Batra
Virginia Tech
Michael Cogswell
{ram21, abhshkdz, vrama91, cogswell, parikh,
(a) Original Image
(b) Guided Backprop ‘Cat’
(c) Grad-CAM for ‘Cat’
(d) Guided Grad-CAM ‘Cat’
(e) Occlusion Map ‘Cat’
(f) ResNet Grad-CAM ‘Cat’
(g) Original Image
(h) Guided Backprop ‘Dog’
(i) Grad-CAM for ‘Dog’
(l) ResNet Grad-CAM ‘Dog’"
5522073ebd53a6502cec9d716a77bb2c18aca593,Multi-view Body Part Recognition with Random Forests,"KAZEMI, BURENIUS, AZIZPOUR, SULLIVAN: MULTI-VIEW BODY PART RECOGNITION 1
Multi-view Body Part Recognition with
Random Forests
CVAP / KTH
The Royal Institute of Technology
Stockholm, Sweden
Vahid Kazemi
Magnus Burenius
Hossein Azizpour
Josephine Sullivan"
55956278efa78ccb59660a48c4ce9ad3e7d88e70,Video Based Group Tracking and Management,"Video Based Group Tracking and Management
Am(cid:19)erico Pereira1;2, Alexandra Familiar1;2, Bruno Moreira1;2,
Teresa Terroso1;4, Pedro Carvalho1;3, and Lu(cid:19)(cid:16)s C^orte-Real1;2
INESC TEC, Portugal
Faculty of Engineering of the University of Porto, Porto, Portugal
School of Engineering, Polytechnic Institute of Porto, Porto, Portugal
The School of Management and Industrial Studies, Polytechnic Institute of Porto,
Vila do Conde, Portugal"
551fedfeaf55e3f7a7cf19d2b21f1a56f8cbe9f6,Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems,"Egocentric Vision-based Future Vehicle Localization
for Intelligent Driving Assistance Systems
Yu Yao1∗, Mingze Xu2∗, Chiho Choi3, David J. Crandall2, Ella M. Atkins1, and Behzad Dariush3"
5582aafd943f2b67805cdb4aba9e2f288dfe0ca8,"Human Object Sketches: Datasets, Descriptors, Computational Recognition and 3d Shape Retrieval","Human Object Sketches:
Datasets, Descriptors, Computational
Recognition and 3d Shape Retrieval
vorgelegt von
Mathias Eitz, Dipl.-Inf., M.Eng.
us Friedrichshafen
von der Fakultät IV - Elektrotechnik und Informatik
der Technischen Universität Berlin
zur Erlangung des akademischen Grades
Doktor der Ingenieurwissenschaften
– Dr.-Ing. –
genehmigte Dissertation
Promotionsausschuss:
Vorsitzender: Prof. Dr. Oliver Brock
Gutachter: Prof. Dr. Marc Alexa
Gutachter: Prof. Tamy Boubekeur, PhD
Tag der wissenschaftlichen Aussprache: 07.12.2012
Berlin 2012"
55dcaee65936583846e8c4fa36589df066ebadfa,Learning to Relate Literal and Sentimental Descriptions of Visual Properties,"Atlanta, Georgia, 9–14 June 2013. c(cid:13)2013 Association for Computational Linguistics
Proceedings of NAACL-HLT 2013, pages 416–425,"
55c40cbcf49a0225e72d911d762c27bb1c2d14aa,Indian Face Age Database : A Database for Face Recognition with Age Variation,"Indian Face Age Database: A Database for Face Recognition with Age Variation
{tag}                                                                  {/tag}
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 126
Number 5
Year of Publication: 2015
Authors:
Reecha Sharma, M.S. Patterh
10.5120/ijca2015906055
{bibtex}2015906055.bib{/bibtex}"
554b53f6e5e37d0f8c8eade1a962b39ce591f6ae,"COCO-CN for Cross-Lingual Image Tagging, Captioning and Retrieval","COCO-CN for Cross-Lingual Image Tagging, Captioning and
Retrieval
Xirong Li, Xiaoxu Wang, Chaoxi Xu, Weiyu Lan, Qijie Wei, Gang Yang, Jieping Xu
Key Lab of Data Engineering and Knowledge Engineering, Renmin University of China
Multimedia Computing Lab, Renmin University of China"
5502dfe47ac26e60e0fb25fc0f810cae6f5173c0,Affordance Prediction via Learned Object Attributes,"Affordance Prediction via Learned Object Attributes
Tucker Hermans
James M. Rehg
Aaron Bobick"
558719ec858120908ef40b27a5d32904a68f6dd9,Mini Cooper Mini Driggs Idaho Black cat Cat Felix Bombay Posing Windows Bay Beach Boxing,"Towards an Automatic Evaluation of Retrieval Performance
with Large Scale Image Collections
Adrian Popescu1, Eleftherios Spyromitros-Xioufis2, Symeon Papadopoulos2, Hervé Le
Borgne1, Ioannis Kompatsiaris2
CEA, LIST, 91190 Gif-sur-Yvette, France,
CERTH-ITI, Thermi-Thessaloniki, Greece,"
55cad1f4943018459b761f89afd9292d347610f2,Self-supervised Multi-level Face Model Learning for Monocular Reconstruction at over 250 Hz,
55079a93b7d1eb789193d7fcdcf614e6829fad0f,Efficient and Robust Inverse Lighting of a Single Face Image Using Compressive Sensing,"Efficient and Robust Inverse Lighting of a Single Face Image using Compressive
Sensing
Miguel Heredia Conde†, Davoud Shahlaei#, Volker Blanz# and Otmar Loffeld†
Center for Sensor Systems† (ZESS) and Institute for Vision and Graphics#, University of Siegen
57076 Siegen, Germany"
554b9478fd285f2317214396e0ccd81309963efd,Spatio-Temporal Action Localization For Human Action Recognition in Large Dataset,"Spatio-Temporal Action Localization For Human Action
Recognition in Large Dataset
Sameh MEGRHI1, Marwa JMAL 2, Azeddine BEGHDADI1 and Wided Mseddi1,2
L2TI, Institut Galil´ee, Universit´e Paris 13, France;
SERCOM, Ecole Polytechnique de Tunisie"
5556234869c36195ffdcd29349e5dcdf695023e9,Minimum Distance between Pattern Transformation Manifolds: Algorithm and Applications,"JULY 2009
Minimum Distance between
Pattern Transformation Manifolds:
Algorithm and Applications
Effrosyni Kokiopoulou, Student Member, IEEE, and Pascal Frossard, Senior Member, IEEE"
55c22f9c8f76b40793a8473248873f726abd8ce9,Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks,"Unpaired Image-to-Image Translation
using Cycle-Consistent Adversarial Networks
Jun-Yan Zhu∗
Taesung Park∗
Berkeley AI Research (BAIR) laboratory, UC Berkeley
Phillip Isola
Alexei A. Efros
Figure 1: Given any two unordered image collections X and Y , our algorithm learns to automatically “translate” an image
from one into the other and vice versa: (left) Monet paintings and landscape photos from Flickr; (center) zebras and horses
from ImageNet; (right) summer and winter Yosemite photos from Flickr. Example application (bottom): using a collection
of paintings of famous artists, our method learns to render natural photographs into the respective styles."
555488f1da920bb1a06b4d19ff687805993eb7fb,Finding Speaker Face Region by Audiovisual Correlation,"Author manuscript, published in ""Workshop on Multi-camera and Multi-modal Sensor Fusion Algorithms and Applications - M2SFA2
008, Marseille : France (2008)"""
55ba5e4c07f6ecf827bfee04e96de35a170f7485,MODELING THE HUMAN FACE THROUGH MULTIPLE VIEW THREE-DIMENSIONAL STEREOPSIS : A SURVEY AND COMPARATIVE ANALYSIS OF FACIAL RECOGNITION OVER MULTIPLE MODALITIES,"This Dissertation
entitled
MODELING THE HUMAN FACE THROUGH MULTIPLE
VIEW THREE-DIMENSIONAL STEREOPSIS: A SURVEY AND
COMPARATIVE ANALYSIS OF FACIAL RECOGNITION
OVER MULTIPLE MODALITIES
typeset with nddiss2"" v1.0 (2004/06/15) on July 26, 2006 for
Xin Chen
This LATEX 2"" class(cid:12)le conforms to the University of Notre Dame style guide-
lines established in Spring 2004. However it is still possible to generate a non-
onformant document if the published instructions are not followed! Be sure to re-
fer to the published Graduate School guidelines at http://graduateschool.nd.edu
s well.
It is YOUR resposnsibility to ensure that the Chapter titles and Table caption
titles are put in CAPS LETTERS. This class(cid:12)le does NOT do that! This way,
you have total control over how you want the symbols and sub-/superscripts in
titles and captions look like.
This summary page can be disabled by specifying the nosummary option to the class
invocation. (i.e., ndocumentclass[...,nosummary,...]fnddiss2eg)
THIS PAGE IS NOT PART OF THE THESIS, BUT"
558fd79d8f0d7b05c3db32b8efa0cce4bd5d9970,"Biometrics at the frontiers , assessing the impact on Society Technical impact of Biometrics","Biometrics at the frontiers, assessing the impact on Society
Technical impact of Biometrics
Bernadette Dorizzi
Background paper for the Institute of Prospective Technological
Studies, DG JRC – Sevilla, European Commission
January 2005
Legal notice
Neither the European Commission nor any person acting on behalf of the Commission is
responsible for the use which might be made of the following information.
Disclaimer
The author of this report is solely responsible for the content, style, language and editing.
The views expressed do not necessarily reflect those of the European Commission.
Reproduction is authorised provided the source is acknowledged
© European Communities, 2005"
555b332252522fce0f31b0c0b7630cf4f36ba0a5,Face processing in Williams syndrome and Autism,"Face processing in Williams syndrome and Autism
Deborah Michelle Riby
Department of Psychology,
University of Stirling"
55a158f4e7c38fe281d06ae45eb456e05516af50,Simile Classifiers for Face Classification,"The 22nd International Conference on Computer Graphics and Vision
GraphiCon’2012"
5543224d6f8e22e7eaabfcbc4bed9e8a9451e3f8,Automatische Bildfolgenanalyse mit statistischen Mustererkennungsverfahren,"Automatische Bildfolgenanalyse
mit statistischen
Mustererkennungsverfahren
Vom Fachbereich Elektrotechnik
der Gerhard-Mercator-Universit¨at Duisburg
zur Erlangung des akademischen Grades eines
Doktors der Ingenieurwissenschaften
genehmigte Dissertation
Dipl.-Ing. Stefan Eickeler
us Duisburg
Referent: Prof. Dr. Gerhard Rigoll
Korreferent: Prof. Dr. Martin Reiser
Tag der m¨undlichen Pr¨ufung: 5. November 2001"
550c369cc3080c03b89d738d82f1ed50145c5aa7,"Information, Technology, and Information Worker Productivity","Information, Technology and Information Worker Productivity
NYU Stern School of Business & MIT, 44 West 4th Street Room: 8-81, New York, NY 10012
MIT Sloan School of Management, Room: E53-313, 50 Memorial Drive, Cambridge, MA 02142
Sinan Aral
Erik Brynjolfsson
Marshall Van Alstyne
Boston University & MIT, 595 Commonwealth Avenue, Boston, MA 02215
We study the fine-grained relationships among information flows, IT use, and individual information-worker produc-
tivity, by analyzing work at a midsize executive recruiting firm.  We analyze both project-level and individual-level
performance using: (1) direct observation of over 125,000 e-mail messages over a period of 10 months by individual
workers (2) detailed accounting data on revenues, compensation, project completion rates, and team membership for
over 1300 projects spanning 5 years, and (3) survey data on a matched set of the same workers’ IT skills, IT use and in-
formation sharing. These detailed data permit us to econometrically evaluate a multistage model of production and in-
teraction activities at the firm, and to analyze the relationships among communications flows, key technologies, work
practices, and output. We find that (a) the structure and size of workers’ communication networks are highly correlated
with their performance; (b) IT use is strongly correlated with productivity but mainly by allowing multitasking rather
than by speeding up work;  (c) productivity is greatest for small amounts of multitasking but beyond an optimum, mul-
titasking is associated with declining project completion rates and revenue generation; and (d) asynchronous informa-
tion seeking such as email and database use promotes multitasking while synchronous information seeking over the
phone shows a negative correlation. Overall, these data show statistically significant relationships among social net-"
55ea0c775b25d9d04b5886e322db852e86a556cd,DOCK: Detecting Objects by transferring Common-sense Knowledge,"DOCK: Detecting Objects
y transferring Common-sense Knowledge
Santosh Divvala2,3[0000−0003−4042−5874], Ali Farhadi2,3[0000−0001−7249−2380], and
Krishna Kumar Singh1,3[0000−0002−8066−6835],
Yong Jae Lee1[0000−0001−9863−1270]
University of California, Davis 2University of Washington 3Allen Institute for AI
https://dock-project.github.io"
5592574c82eec9367e9173b7820ff329a27b6c21,Image Enhancement and Automated Target Recognition Techniques for Underwater Electro-Optic Imagery,"Image Enhancement and Automated Target Recognition
Techniques for Underwater Electro-Optic Imagery
Thomas Giddings (PI), Cetin Savkli and Joseph Shirron
Metron, Inc.
1911 Freedom Dr., Suite 800
Reston, VA 20190
phone: (703) 437-2428   fax: (703) 787-3518    email:
Contract Number N00014-07-C-0351
http:www.metsci.com
LONG TERM GOALS
The long-term goal of this project is to provide a flexible, accurate and extensible automated target
recognition (ATR) system for use with a variety of imaging and non-imaging sensors.  Such an ATR
system, once it achieves a high level of performance, can relieve human operators from the tedious
usiness of pouring over vast quantities of mostly mundane data, calling the operator in only when the
omputer assessment involves an unacceptable level of  ambiguity. The ATR system will provide most
leading edge algorithms for detection, segmentation, and classification while incorporating many novel
lgorithms that we are developing at Metron.  To address one of the most critical challenges in ATR
technology, the system will also provide powerful feature extraction routines designed for specific
pplications of current interest.
OBJECTIVES"
55c68c1237166679d2cb65f266f496d1ecd4bec6,Learning to score the figure skating sports videos,"Learning to Score Figure Skating Sport Videos
Chengming Xu, Yanwei Fu, Zitian Chen,Bing Zhang, Yu-Gang Jiang, Xiangyang Xue"
550edcdc27aff4e7ea8807356a265a0031434a49,Fine-Grained Recognition with Automatic and Efficient Part Attention,"Fine-Grained Recognition with Automatic and Efficient Part Attention
Xiao Liu, Tian Xia, Jiang Wang, Yi Yang, Feng Zhou and Yuanqing Lin
Baidu Research
{liuxiao12,xiatian,wangjiang03, yangyi05, zhoufeng09,"
5520fdb531a27bebe7df8062dd5450344dea107c,DepthComp: Real-time Depth Image Completion Based on Prior Semantic Scene Segmentation,"ATAPOUR-ABARGHOUEI, BRECKON: REAL-TIME DEPTH IMAGE COMPLETION
DepthComp: Real-time Depth Image
Completion Based on Prior Semantic Scene
Segmentation
Amir Atapour-Abarghouei
Toby P. Breckon
Engineering and Computer Science
Durham University
Durham, UK"
55202f10bb1d7640b0b279a4cdc8e9925cd9ef81,ICM: An Intuitive Model Independent and Accurate Certainty Measure for Machine Learning,
559295770dc2e2e3a1348df31ac5c3f3e66f1764,Generating Multiple Hypotheses for Human 3D Pose Consistent with 2D Joint Detections,"Generating Multiple Hypotheses for Human 3D Pose Consistent with 2D Joint Detections
Johns Hopkins University
Johns Hopkins University
Alan L. Yuille
Baltimore, USA
Ehsan Jahangiri
Baltimore, USA"
558c587373e2ea44898f70de7858da71aa217b8d,Cross-Lingual Image Caption Generation,"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pages 1780–1790,
Berlin, Germany, August 7-12, 2016. c(cid:13)2016 Association for Computational Linguistics"
55e8cfd4a96bdc77d10459c0aa73991ff098c60e,Nonnegative Discriminant Matrix Factorization,"This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TCSVT.2016.2539779, IEEE
Transactions on Circuits and Systems for Video Technology
Copyright (c) 2016 IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes
must be obtained from the IEEE by sending an email to
Nonnegative Discriminant Matrix Factorization
Yuwu Lu, Zhihui Lai, Yong Xu, Senior Member, IEEE, Xuelong Li, Fellow, IEEE, David Zhang,
Fellow, IEEE and Chun Yuan"
55ef8c3c28e2afda486d8471205204927127c605,Multiview Alignment Hashing for Efficient Image Search,"Multiview Alignment Hashing for Efficient Image
Search
Li Liu, Mengyang Yu, Student Member, IEEE, and Ling Shao, Senior Member, IEEE"
555222f2ad6dae447eef04f96fa40c1b8a397150,CaloriNet: From silhouettes to calorie estimation in private environments,"CaloriNet: From silhouettes to calorie estimation in private
environments
Alessandro Masullo∗
Tilo Burghardt
Victor Ponce-López
Dima Damen
Majid Mirmehdi
Sion Hannuna
June 22, 2018"
4e25cd4e40494aa5073fcfbef7506336b84152f4,"Independent Component Analysis, Principal Component Analysis and Rough Sets in Face Recognition","Independent Component Analysis, Principal
Component Analysis and Rough Sets in Face
Recognition
Roman W. ´Swiniarski1 and Andrzej Skowron2
Department of Mathematical and Computer Sciences
San Diego State University
5500 Campanile Drive San Diego, CA 92182, USA
Institute of Computer Science, Polish Academy of Sciences
Ordona 21, 01-237 Warsaw, Poland
Institute of Mathematics, Warsaw University
Banacha 2, 02-097 Warsaw, Poland"
4e4b41b8d9f27e262e4a853082e690c32c490954,Towards MultiView Object Class Detection,"Author manuscript, published in ""IEEE Conference on Computer Vision & Pattern Recognition (CPRV '06) 2 (2006) 1589""
DOI : 10.1109/CVPR.2006.311"
4ecaa651722a98c2847377f3ae1c70294b4791ce,Few-Example Object Detection with Model Communication.,"Few-Example Object Detection
with Model Communication
Xuanyi Dong, Liang Zheng, Fan Ma, Yi Yang, Deyu Meng"
4e8206dd2e163c6a139bfd0ec3adf410e7b78c4a,A Multi-scale Boosted Detector for Efficient and Robust Gesture Recognition,"A Multi-scale Boosted Detector for Ef‌f‌icient and
Robust Gesture Recognition
Camille Monnier, Stan German, Andrey Ost
Charles River Analytics
Cambridge, MA, USA"
4e23628370d3ca9695c8eb1eee488a9eea4d5eec,Contributions to Statistical Signal Processing with Applications in Biomedical Engineering. (Contributions au traitement statistique du signal avec des applications biomédicales),"N° d’ordre : 2012telb0244   Sous le sceau de lSous le sceau de l’’UUniversitniversitéé  européenne de Beuropéenne de Bretagneretagne     Télécom Bretagne  En habilitation conjointe avec l’Université de Bretagne Occidentale   Ecole Doctorale – SICMA     CONTRIBUTIONS TO STATISTICAL SIGNAL PROCESSING WITH APPLICATIONS IN BIOMEDICAL ENGINEERING     Thèse de Doctorat  Mention : STIC – Science et Technologies Information Communication    Présentée par Quang-Thang NGUYEN  Département : Signal et Communications  Laboratoire : Lab-STICC          Pôle: CID   Directeur de thèse : Dominique PASTOR   Soutenue le 23 Novembre 2012   Jury :   M. Lionel Fillatre – Professeur, I3S (Rapporteur) M. Alfredo Hernandez – Chargé de recherche (HDR), LTSI INSERM U642 (Rapporteur) M. Dominique Pastor – Professeur, TELECOM Bretagne (Directeur de thèse) M. Erwan L’Her – Professeur, LaTIM INSERM U1101 (Examinateur) M. Lotfi Senhadji – Professeur, LTSI INSERM U642 (Examinateur) M. Emanuel Radoi – Professeur, UBO/Lab-STICC CNRS UMR 6285 (Examinateur) M. Ronan Fablet – Maître de conférence (HDR), TELECOM Bretagne (Examinateur) M. François Lellouche – Professeur, Université Laval (Québec –Canada) (Invité)"
4e608c77043f56b0abfb2760fb2fd2516b5412b0,Spectral Face Recognition Using Orthogonal Subspace Bases,
4ecd459aa4b4590bdc552e07b6d0bbe132fb1fcf,Learning of Graph Compressed Dictionaries for Sparse Representation Classification,"Learning of Graph Compressed Dictionaries for Sparse
Representation Classification
Farshad Nourbakhsh and Eric Granger
Laboratoire d’imagerie de vision et d’intelligence artificielle
´Ecole de technologie sup´erieure, Universit´e du Qu´ebec, Montr´eal, Canada
Keywords:
Matrix Factorization, Graph Compression, Dictionary Learning, Sparse Representation Classification,
Clustering, Face Recognition, Video Surveillance"
4e27fec1703408d524d6b7ed805cdb6cba6ca132,SSD-Sface: Single shot multibox detector for small faces,"SSD-Sface: Single shot multibox detector for small faces
C. Thuis"
4e1029a43324eccae1f25a342cd615f57c47a740,Circle-based eye center localization (CECL),"This is the non-final version of the paper.    The final version is published in the 14th IAPR International Conference on Machine Vision Applications (18-22
May 2015, http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7153202). Content may change prior to final publication.
Circle-based Eye Center Localization (CECL)
Yustinus Eko Soelistio1,2, Eric Postma2, Alfons Maes2
Information System Department1, Tilburg center for Cognition and Communication2
Universitas Multimedia Nusantara1, Tilburg University2
Tangerang, Indonesia1, Tilburg, The Netherlands2"
4e5698894946680e4d6e766346355b2dc1959819,Cross-pose Facial Expression Recognition,Cross-pose Facial Expression Recognition
4ed0be0b5d67cff63461ba79f2a7928d652cf310,Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey,"JOURNAL OF LATEX CLASS FILES, VOL. PP, AUGUST 2017
Threat of Adversarial Attacks on Deep Learning
in Computer Vision: A Survey
ACKNOWLEDGEMENTS: The authors thank Nicholas Carlini (UC Berkeley) and Dimitris Tsipras (MIT) for feedback to improve the survey
quality. We also acknowledge X. Huang (Uni. Liverpool), K. R. Reddy (IISC), E. Valle (UNICAMP), Y. Yoo (CLAIR) and others for providing pointers
to make the survey more comprehensive. This research was supported by ARC grant DP160101458.
Naveed Akhtar and Ajmal Mian"
4ec3c7fa51d823a43b3808c7c6baa2e153104bdf,Neuron Pruning for Compressing Deep Networks using Maxout Architectures,"Neuron Pruning for Compressing Deep
Networks using Maxout Architectures
Fernando Moya Rueda, Rene Grzeszick, Gernot A. Fink
TU Dortmund University
Department of Computer Science"
4ee380e444063f9b948a2fd82e5c11b97a570ad1,Operating system support to an online hardware-software co-design scheduler for heterogeneous multicore architectures,"Universidade de São Paulo
Biblioteca Digital da Produção Intelectual - BDPI
Departamento de Sistemas de Computação - ICMC/SSC
Comunicações em Eventos - ICMC/SSC
014-08-20
Operating system support to an online
hardware-software co-design scheduler for
heterogeneous multicore architectures
IEEE International Conference on Embedded and Real-Time Computing Systems and Applications,
0th, 2014, Chongqing.
http://www.producao.usp.br/handle/BDPI/48567
Downloaded from: Biblioteca Digital da Produção Intelectual - BDPI, Universidade de São Paulo"
4e12080616da4b540c8f79db2dd1b654cd8345ce,Pose-Driven Deep Models for Person Re-Identification.,"Pose-Driven Deep Models for Person
Re-Identification
Masters thesis of
Andreas Eberle
At the faculty of Computer Science
Institute for Anthropomatics and Robotics
Reviewer:
Second reviewer:
Advisors:
Prof. Dr.-Ing. Rainer Stiefelhagen
Prof. Dr.-Ing. Jürgen Beyerer
Dr.-Ing. Saquib Sarfraz
Dipl.-Inform. Arne Schumann
Duration: 31. August 2017 –
8. February 2018
KIT – University of the State of Baden-Wuerttemberg and National Laboratory of the Helmholtz Association
www.kit.edu"
4e613c9342d6e90f7af5fd3f246c6d82a33fe98d,Estimating Human Pose in Images,"Estimating Human Pose in Images
Navraj Singh
December 11, 2009
Introduction
This project attempts to improve the performance of an existing method of estimating the pose of humans in still images.
Tasks such as object detection and classification have received much attention already in the literature. However, sometimes we are
interested in more detailed aspects of objects like pose. This is a challenging task due to the large variety of poses an object can
take in a variety of settings. For human pose estimation, aspects such as clothing, occlusion of body parts, etc. make the task even
harder.
The approaches taken up in the literature to solve this problem focus on either a top-down approach, bottom-up approach,
or a hybrid of the two. The top-down approach involves comparing test images with stored examples of humans in various poses
using some similarity measure. This approach might require a very large set of examples of human poses. The bottom-up approach,
on the other hand, uses low level human body part detectors and in some manner assembles the information to predict the entire
ody pose. This project attempts to build upon a mostly bottom-up approach, called LOOPS (Localizing Object Outlines using
Probabilistic Shape), that was developed in [1] by G. Heitz, et al. in Prof. Daphne Koller's group. Specifically, we investigate the
onstruction and incorporation of a skin detector into the LOOPS pipeline, and a couple of pairwise features in the appearance
model.  The   overall   improvement   in   the   localization   is   negligible,   with   some   improvement   in   head   localization.  Since   the
improvements considered are within the framework of LOOPS, a brief overview of the LOOPS method is discussed next.
Brief Overview of the LOOPS method as applied to humans
The main random variables defined in the LOOPS method, described in detail in [1], are the locations of a set of key"
4ee87ed965e78adb1035a5322350afac9ca901f5,Multi-target tracking of time-varying spatial patterns,"Multi-Target Tracking of Time-varying Spatial Patterns
Jingchen Liu1
Yanxi Liu1,2
Department of Computer Science and Engineering
Department of Electrical Engineering
The Pennsylvania State University
University Park, PA 16802, USA
{jingchen,"
4eda4c1c63d96c2764577fed9a2bb3e10937e551,Robust Facial Feature Extraction Using Embedded Hidden Markov Model for Face Recognition under Large Pose Variation,"MVA2007 IAPR Conference on Machine Vision Applications, May 16-18, 2007, Tokyo, JAPAN
Robust Facial Feature Extraction Using Embedded Hidden Markov Model
for Face Recognition under Large Pose Variation
, Ming-Hsuan Yang3 and Yi-Ping Hung1
Ping-Han Lee1
Dept. of Computer Science and Information Engineering, Nation Taiwan University
, Yun-Wen Wang1
, Jison Hsu2
PENPOWER Technology Ltd., Taiwan
Honda Research Institute
ontact email:"
4e97b53926d997f451139f74ec1601bbef125599,Discriminative Regularization for Generative Models,"Discriminative Regularization for Generative Models
Alex Lamb, Vincent Dumoulin and Aaron Courville
Montreal Institute for Learning Algorithms, Universit´e de Montr´eal"
4ebf84c6389e842e90c39850f0152671ba7fa0dc,Adversarial Attribute-Image Person Re-identification,"Adversarial Attribute-Image Person Re-identification
Zhou Yin, Wei-Shi Zheng, Ancong Wu, Hong-Xing Yu, Hai Wan, Xiaowei Guo, Feiyue
Huang, Jianhuang Lai
For reference of this work, please cite:
Adversarial Attribute-Image Person Re-identification
Zhou Yin, Wei-Shi Zheng, Ancong Wu, Hong-Xing Yu, Hai Wan, Xiaowei Guo, Feiyue Huang, Jianhuang
Lai, IJCAI, 2018
title={Adversarial Attribute-Image Person Re-identification},
uthor={Zhou Yin, Wei-Shi Zheng, Ancong Wu, Hong-Xing Yu, Hai Wan, Xiaowei Guo, Feiyue Huang,
Jianhuang Lai},
journal={ International Joint Conference on Artificial Intelligence},
year={2018}"
4e6ff8ff80a1610bb841b669bb7667413ed2982f,Dependence Characteristics of Face Recognition Algorithms,"Dependence Characteristics of Face Recognition Algorithms
Patrick Grother, P. Jonathon Phillips,
Stefan Leighy, Alan Heckerty,
NIST, Gaithersburg, MD
Elaine Newton
Rand Corporation
Pittsburgh, PA
Andrew Rukhin
University of
Maryland Baltimore County
Baltimore, MD"
4ec4392246a7760d189cd6ea48a81664cd2fe4bf,GPU Accelerated ACF Detector,
4edd41dcd724df28302cc5a7bc0bee348fc81456,Large Scale Correlation Clustering Optimization,"Shai Bagon Meirav Galun
Dept. of Computer Science and Applied Mathmatics
The Weizmann Institute of Scince
http://www.wisdom.weizmann.ac.il/∼{bagon, meirav}
Rehovot 76100, Israel"
4ec4e9a682cab979e90c5029d8455e852abedf26,A New Approach for Face Recognition Using Power Method Algorithm,"Proc. Int. Conf. on Advances in Computing, Control, and Telecommunication Technologies, ACT
A New Approach for Face Recognition Using Power
Method Algorithm
Shivam Gupta1,1, Vilas H. Gaidhane1 and  Vijander Singh1
ICE Division Netaji Subhas Institute of Technology, University of Delhi, Sector-3,
Dwarka, New Delhi, 110078 India"
4e32fbb58154e878dd2fd4b06398f85636fd0cf4,A Hierarchical Matcher using Local Classifier Chains,"A Hierarchical Matcher using Local Classifier Chains
L. Zhang and I.A. Kakadiaris
Computational Biomedicine Lab, 4849 Calhoun Rd, Rm 373, Houston, TX 77204"
4efb08fcd652c60764b6fd278cee132b71c612a1,Pixel Deconvolutional Networks,"PIXEL DECONVOLUTIONAL NETWORKS
Hongyang Gao
Washington State University
Hao Yuan
Washington State University
Zhengyang Wang
Washington State University
Shuiwang Ji
Washington State University"
4ea53e76246afae94758c1528002808374b75cfa,A Review of Scholastic Examination and Models for Face Recognition and Retrieval in Video,"Lasbela, U. J.Sci. Techl., vol.IV , pp. 57-70, 2015
Review ARTICLE
A Review of Scholastic  Examination and  Models for Face Recognition
ISSN 2306-8256
nd Retrieval in Video
Varsha Sachdeva1, Junaid Baber2, Maheen Bakhtyar2, Muzamil Bokhari3, Imran Ali4
Department of Computer Science, SBK Women’s University, Quetta, Balochistan
Department of CS and IT, University of Balochistan, Quetta
Department of Physics, University of Balochistan, Quetta
Institute of Biochemistry, University of Balochistan, Quetta"
4e7f0dfc390f88623c825fe702da45b994342011,Photorealistic Face De-Identification by Aggregating Donors' Face Components,"Photorealistic Face de-Identification by Aggregating
Donors’ Face Components
Saleh Mosaddegh, Lo¨ıc Simon, Fr´ed´eric Jurie
To cite this version:
Saleh Mosaddegh, Lo¨ıc Simon, Fr´ed´eric Jurie. Photorealistic Face de-Identification by Aggre-
gating Donors’ Face Components. Asian Conference on Computer Vision, Nov 2014, Singapore.
pp.1-16, 2014. <hal-01070658>
HAL Id: hal-01070658
https://hal.archives-ouvertes.fr/hal-01070658
Submitted on 2 Oct 2014
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,
´emanant des ´etablissements d’enseignement et de"
4eb600aa4071b9a73da49e5374d6e22ca46eaba6,Understanding bag-of-words model: a statistical framework,"Noname manuscript No.
(will be inserted by the editor)
Understanding Bag-of-Words Model: A Statistical Framework
Yin Zhang ⋅ Rong Jin ⋅ Zhi-Hua Zhou
Received: date / Accepted: date"
4eca3e3c4876fc7ec81224d4ec2f159c9e7c72c3,Facial recognition using new LBP representations,
4e33798e364826af1241d28d57977bec9a579709,Active learning with version spaces for object detection,"Active learning with version spaces for object detection 1
Soumya Roy 2
Vinay P. Namboodiri 2
Arijit Biswas 3"
4e6c9be0b646d60390fe3f72ce5aeb0136222a10,Long-Term Temporal Convolutions for Action Recognition,"Long-term Temporal Convolutions
for Action Recognition
G¨ul Varol, Ivan Laptev, and Cordelia Schmid, Fellow, IEEE"
4e0e49c280acbff8ae394b2443fcff1afb9bdce6,Automatic Learning of Gait Signatures for People Identification,"Automatic learning of gait signatures for people identification
F.M. Castro
Univ. of Malaga
fcastro<at>uma.es
M.J. Mar´ın-Jim´enez
Univ. of Cordoba
mjmarin<at>uco.es
N. Guil
Univ. of Malaga
nguil<at>uma.es
N. P´erez de la Blanca
Univ. of Granada
nicolas<at>ugr.es"
4eb22856671b9340e5ae532a021be62b9d31c9bc,THE MINORITY GLASS CEILING HYPOTHESIS : EXPLORING REASONS AND REMEDIES FOR THE UNDERREPRESENTATION OF RACIAL-ETHNIC MINORITIES IN LEADERSHIP POSITIONS,"THE MINORITY GLASS CEILING HYPOTHESIS:
EXPLORING REASONS AND REMEDIES FOR THE
UNDERREPRESENTATION OF RACIAL-ETHNIC MINORITIES IN
LEADERSHIP POSITIONS
Seval Gündemir"
4e3c07283334a9b90dac011033fa2403bcf3c473,A novel feature selection method and its application,"J Intell Inf Syst (2013) 41:235–268
DOI 10.1007/s10844-013-0243-x
A novel feature selection method and its application
Bing Li· Tommy W. S. Chow· Di Huang
Received: 11 April 2012 / Revised: 8 March 2013 / Accepted: 11 March 2013 /
Published online: 4 April 2013
© Springer Science+Business Media New York 2013"
4e61f3dc6aa7994613a3708e823aadd478c73f5f,Generating Discriminative Object Proposals via Submodular Ranking,"Generating Discriminative Object Proposals via Submodular Ranking
Yangmuzi Zhang∗, Zhuolin Jiang†, Xi Chen∗, and Larry S. Davis∗
University of Maryland at College Park, MD
Raytheon BBN Technologies, USA
Email:"
4e165914c800d5569fd22cd69ca2ca7d92ffe705,Graph based over-segmentation methods for 3D point clouds,"Graph Based Over-Segmentation Methods for 3D Point
Clouds
Yizhak Ben-Shabat · Tamar Avraham · Michael Lindenbaum ·
Anath Fischer
Received: date / Accepted: date"
4e19917a786c611ffdecd171fae37183ad55ad49,A survey of practical adversarial example attacks,"Sun et al. Cybersecurity  (2018) 1:9
https://doi.org/10.1186/s42400-018-0012-9
SURVEY
Cybersecurity
Open Access
A survey of practical adversarial example
ttacks
Lu Sun, Mingtian Tan and Zhe Zhou*"
4edc7f27d4512b69be54abfc6b9876e5b00725ab,Facial Expression Recognition using Convolutional Neural Networks: State of the Art,"Facial Expression Recognition using
Convolutional Neural Networks: State of the Art
Christopher Pramerdorfer, Martin Kampel
Computer Vision Lab, TU Wien
Vienna, Austria
Email:"
4e82908e6482d973c280deb79c254631a60f1631,Improving Efficiency and Scalability in Visual Surveillance Applications,
4eaaefc53fd61d27b9ce310c188fe76003a341bd,Assessing Generative Models via Precision and Recall,"Assessing Generative Models via Precision and Recall
Mehdi S. M. Sajjadi∗
MPI for Intelligent Systems,
Max Planck ETH Center
for Learning Systems
Olivier Bachem
Google Brain
Mario Lucic
Google Brain
Olivier Bousquet
Google Brain
Sylvain Gelly
Google Brain"
4e71e03d4122aad182ad51ab187d4b55b41fc957,Clustering-Based Discriminant Analysis for Eye Detection,"Clustering-Based Discriminant Analysis
for Eye Detection
Shuo Chen and Chengjun Liu
paper
three
proposes"
4e83b9cfd19b7963e2044916821d7a09bbd1574d,LINGYU ZHU TEACHING DEVELOPMENT PROJECT: GENE EXPRESSION PREDICTION WITH DEEP LEARNING,"LINGYU ZHU
TEACHING DEVELOPMENT PROJECT: GENE EXPRESSION
PREDICTION WITH DEEP LEARNING
Master of Science thesis
Examiner: University lecturer Heikki
Huttunen
Examiner and topic approved by the
Faculty Council of the Faculty of
Computing and Electrical Engineering
on 1 February 2017"
eacb95e81156c48f4ff7470567ba205225170fa7,Learning Aerial Image Segmentation From Online Maps,"Learning Aerial Image Segmentation
from Online Maps
Pascal Kaiser, Jan Dirk Wegner, Aur´elien Lucchi, Martin Jaggi, Thomas Hofmann, and Konrad Schindler"
ea94d834f912f092618d030f080de8395fe39b3f,Joint autoencoders : a flexible meta-learning framework,"Under review as a conference paper at ICLR 2018
JOINT AUTOENCODERS: A FLEXIBLE META-LEARNING
FRAMEWORK
Anonymous authors
Paper under double-blind review"
ea3e3f62be20b9b11994a6308c79a286725db116,DCAR: A Discriminative and Compact Audio Representation to Improve Event Detection,"DCAR: A Discriminative and Compact Audio Representation to
Improve Event Detection
Liping Jing ∗
Bo Liu∗
Michael W. Mahoney §
Jaeyoung Choi †
Adam Janin ‡
Gerald Friedland §
Julia Bernd‡"
ea099ee1183145131e29009f2af0e4b13ac583f0,Effects of exposure to facial expression variation in face learning and recognition,"Psychological Research (2015) 79:1042–1053
DOI 10.1007/s00426-014-0627-8
O R I G I N A L A R T I C L E
Effects of exposure to facial expression variation in face learning
nd recognition
Chang Hong Liu • Wenfeng Chen • James Ward
Received: 25 July 2014 / Accepted: 6 November 2014 / Published online: 15 November 2014
Ó The Author(s) 2014. This article is published with open access at Springerlink.com"
eaaf411826d129c2a31d997dc3f5f708a8186656,SDALF: Modeling Human Appearance with Symmetry-Driven Accumulation of Local Features,"SDALF: Modeling Human Appearance with
Symmetry-Driven Accumulation of Local
Features
Loris Bazzani and Marco Cristani and Vittorio Murino"
eabbf37742b79147c3bcf42d376dbceaae869a01,Recurrent Multimodal Interaction for Referring Image Segmentation,"Recurrent Multimodal Interaction for Referring Image Segmentation
Chenxi Liu1
Zhe Lin2 Xiaohui Shen2
Jimei Yang2 Xin Lu2 Alan Yuille1
Johns Hopkins University1 Adobe Research2
{cxliu,
{zlin, xshen, jimyang,"
ea638559b6dd6b5520f9abe2674b92c07873a157,Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks,"Semantic Segmentation of Earth Observation
Data Using Multimodal and Multi-scale Deep
Networks
Nicolas Audebert1,2, Bertrand Le Saux1, S´ebastien Lef`evre2
ONERA, The French Aerospace Lab, F-91761 Palaiseau, France -
{nicolas.audebert,bertrand.le
Univ. Bretagne-Sud, UMR 6074, IRISA, F-56000 Vannes, France -"
ea2d43aa2490331cd1406e1432ce706c53139323,Tracked Instance Search,"TRACKED INSTANCE SEARCH
Andreu Girbau†
Ryota Hinami(cid:63)
Shin’ichi Satoh(cid:63)
Universitat Polit`ecnica de Catalunya, Barcelona
(cid:63) National Institute of Informatics, Tokyo"
eabdefeb685dd71a39417bf40247d206af4f9b9e,"Of Kith and Kin: Perceptual Enrichment, Expectancy, and Reciprocity in Face Perception.","657250 PSRXXX10.1177/1088868316657250Personality and Social Psychology ReviewCorrell et al.
research-article2016
Article
Of Kith and Kin: Perceptual Enrichment,
Expectancy, and Reciprocity in Face
Perception
Personality and Social Psychology Review
1 –25
© 2016 by the Society for Personality
nd Social Psychology, Inc.
Reprints and permissions:
sagepub.com/journalsPermissions.nav
DOI: 10.1177/1088868316657250
pspr.sagepub.com
Joshua Correll1, Sean M. Hudson1, Steffanie Guillermo1,
nd Holly A. Earls1"
eac1b644492c10546a50f3e125a1f790ec46365f,"Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for Action Classification and Detection","Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for
Action Classification and Detection
Mohammadreza Zolfaghari , Gabriel L. Oliveira, Nima Sedaghat, and Thomas Brox
University of Freiburg
Freiburg im Breisgau, Germany"
ea572991a75acfc8a8791955f670d2c48db49023,Arbitrary-Shape object localization using adaptive image grids,"Arbitrary-Shape Object Localization using
Adaptive Image Grids
Chunluan Zhou and Junsong Yuan
School of EEE, Nanyang Technology University, Singapore"
ead2701e883174028a1b1b25472bc83bedc330aa,"Face Recognition Methods Based on Feedforward Neural Networks, Principal Component Analysis and Self-Organizing Map","RADIOENGINEERING, VOL. 16, NO. 1, APRIL 2007
Face Recognition Methods Based on Feedforward
Neural Networks, Principal Component Analysis
nd Self-Organizing Map
Miloš ORAVEC, Jarmila PAVLOVIČOVÁ
Dept. of Telecommunications, Faculty of Electrical Engineering and Information Technology, Slovak University of
Technology, Ilkovičova 3, 812 19 Bratislava, Slovak Republic"
ea8cb4a79b211fb288f747bdd64b3fc36e11c0fc,Chapter 10 Automatic Facial Action Unit Recognition by Modeling Their Semantic And Dynamic Relationships,"Chapter 10
Automatic Facial Action Unit Recognition
y Modeling Their Semantic And Dynamic
Relationships
Yan Tong, Wenhui Liao, and Qiang Ji"
ea8b306eb10ea4de4c0253d63750b29467b581e1,A Survey of the Recent Architectures of Deep Convolutional Neural Networks,"A Survey of the Recent Architectures of Deep Convolutional Neural Networks
Asifullah Khan1, 2*, Anabia Sohail1
, Umme Zahoora1, and Aqsa Saeed Qureshi1
Pattern Recognition Lab, DCIS, PIEAS, Nilore, Islamabad 45650, Pakistan
Deep Learning Lab, Center for Mathematical Sciences, PIEAS, Nilore, Islamabad 45650, Pakistan"
ea0785c2d4ac8f8d6415cffdb83547bfc4e7adba,Spontaneous Facial Expression Recognition using Sparse Representation,"Spontaneous Facial Expression Recognition using Sparse Representation
Univ. Grenoble Alpes, GIPSA-Lab, F-38000 Grenoble, France CNRS, GIPSA-Lab, F-38000 Grenoble, France
Dawood Al Chanti1 and Alice Caplier1
Keywords:
Dictionary learning, Random projection, Spontaneous facial expression, Sparse representation."
eafda8a94e410f1ad53b3e193ec124e80d57d095,Observer-Based Measurement of Facial Expression With the Facial Action Coding System,"Jeffrey F. Cohn
Zara Ambadar
Paul Ekman
Observer-Based Measurement of Facial Expression
With the Facial Action Coding System
Facial expression has been a focus of emotion research for over
hundred years (Darwin, 1872/1998). It is central to several
leading theories of emotion (Ekman, 1992; Izard, 1977;
Tomkins, 1962) and has been the focus of at times heated
debate about issues in emotion science (Ekman, 1973, 1993;
Fridlund, 1992; Russell, 1994). Facial expression figures
prominently in research on almost every aspect of emotion,
including psychophysiology (Levenson, Ekman, & Friesen,
990), neural bases (Calder et al., 1996; Davidson, Ekman,
Saron, Senulis, & Friesen, 1990), development (Malatesta,
Culver, Tesman, & Shephard, 1989; Matias & Cohn, 1993),
perception (Ambadar, Schooler, & Cohn, 2005), social pro-
esses (Hatfield, Cacioppo, & Rapson, 1992; Hess & Kirouac,
000), and emotion disorder (Kaiser, 2002; Sloan, Straussa,
Quirka, & Sajatovic, 1997), to name a few."
ea8abe31f3cac058cf757f16e1eefa11295322bc,Ensemble of Deep Learned Features for Melanoma Classification,"Ensemble of Deep Learned Features for Melanoma
Classification
Loris Nanni1*, Alessandra Lumini2, Stefano Ghidoni1
Department  of  Information  Engineering,  University  of  Padua,  via  Gradenigo  6/B,  35131
Padova, Italy.
Department  of  Computer  Science  and  Engineering,  University  of  Bologna,  via  Sacchi  3,
7521, Cesena (FC), Italy."
ea2c44e4792c0d35af737f46a2dc2b78e6dedc8d,Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction,"Deep Stacked Bidirectional and Unidirectional
LSTM Recurrent Neural Network for
Network-wide Traffic Speed Prediction
Zhiyong Cui, Student Member, IEEE, Ruimin Ke, Student Member, IEEE, and Yinhai Wang*"
ea9857a5e5c72d435054a5a73e50dafb755a2597,Comparative study of histogram distance measures for re-identification,"Comparative study of histogram distance measures for re-identification
Pedro A. Mar´ın-Reyes, Javier Lorenzo-Navarro, Modesto Castrill´on-Santana
Instituto Universitario SIANI
Universidad de Las Palmas de Gran Canaria"
eaaec63bb86ee87d56f5844951143485ce84a4ea,GANtruth – an unpaired image-to-image translation method for driving scenarios,"GANtruth – an unpaired image-to-image translation
method for driving scenarios
Anonymous Author(s)
Affiliation
Address
email"
ea5eaaadb8bc928fb7543d6fa24f9f4a229ff979,Mirror Neuron Forum.,"Perspectives on Psychological
Science
http://pps.sagepub.com/
Vittorio Gallese, Morton Ann Gernsbacher, Cecilia Heyes, Gregory Hickok and Marco Iacoboni
Mirror Neuron Forum
Perspectives on Psychological Science
DOI: 10.1177/1745691611413392
2011 6: 369
The online version of this article can be found at:
http://pps.sagepub.com/content/6/4/369
Perspectives on Psychological Science
can be found at:
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ea3503e9dc74b30b4c98a89843fe2ea0dc9221ab,Human Action Recognition Using LBP-TOP as Sparse Spatio-Temporal Feature Descriptor,"Human Action Recognition Using LBP-TOP as Sparse
Spatio-Temporal Feature Descriptor
Riccardo Mattivi and Ling Shao
Philips Research, Eindhoven, The Netherlands"
eadf6cb8f16c507e4a73db33da201cde3d9b2f5a,PAD-Net: Multi-Tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing,"PAD-Net: Multi-Tasks Guided Prediction-and-Distillation Network
for Simultaneous Depth Estimation and Scene Parsing
Dan Xu1, Wanli Ouyang2, Xiaogang Wang3, Nicu Sebe1
The University of Trento, 2The University of Sydney, 3The Chinese University of Hong Kong
{dan.xu,"
eaa334c28bd53d2cc37c1973cd9f5f4a5be1012b,SPADE: Scalar Product Accelerator by Integer Decomposition for Object Detection,"SPADE: Scalar Product Accelerator by Integer
Decomposition for Object Detection
Mitsuru Ambai and Ikuro Sato
Denso IT Laboratory, Inc."
ea482bf1e2b5b44c520fc77eab288caf8b3f367a,Flexible Orthogonal Neighborhood Preserving Embedding,Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
ea2ee5c53747878f30f6d9c576fd09d388ab0e2b,Viola-Jones based detectors: how much affects the training set?,"Viola-Jones based Detectors: How much affects
the Training Set?
Modesto Castrill´on-Santana, Daniel Hern´andez-Sosa, Javier Lorenzo-Navarro
SIANI
Edif. Central del Parque Cient´ıfico Tecnol´ogico
Universidad de Las Palmas de Gran Canaria
5017 - Spain"
eace134548f9be17c243b06f133bfac76a797676,ADNet: A Deep Network for Detecting Adverts,"ADNet: A Deep Network for Detecting Adverts
Murhaf Hossari(cid:63)1, Soumyabrata Dev(cid:63)1, Matthew Nicholson1, Killian McCabe1,
Atul Nautiyal1, Clare Conran1, Jian Tang3, Wei Xu3, and Fran¸cois Piti´e1,2
The ADAPT SFI Research Centre, Trinity College Dublin
Department of Electronic & Electrical Engineering, Trinity College Dublin
Huawei Ireland Research Center, Dublin"
ea251fc90da36fdbaf7be76f449a9e0dac1d42ef,Brain mechanisms for processing direct and averted gaze in individuals with autism.,"J Autism Dev Disord
DOI 10.1007/s10803-011-1197-x
O R I G I N A L P A P E R
Brain Mechanisms for Processing Direct and Averted Gaze
in Individuals with Autism
Naomi B. Pitskel • Danielle Z. Bolling • Caitlin M. Hudac •
Stephen D. Lantz • Nancy J. Minshew • Brent C. Vander Wyk •
Kevin A. Pelphrey
Ó Springer Science+Business Media, LLC 2011"
ea5dd7125c73756d7d81e49fa9826198f533cff7,Appearance tracking by transduction in surveillance scenarios,"8th IEEE International Conference on Advanced Video and Signal-Based Surveillance, 2011
978-1-4577-0845-9/11/$26.00 c(cid:13)2011 IEEE"
ea9cecb5b619cfa4afef6c70e193c2303696a4f9,Integration of Probabilistic Pose Estimates from Multiple Views,"Integration of Probabilistic Pose Estimates From
Multiple Views
¨Ozg¨ur Erkent, Dadhichi Shukla and Justus Piater
Institute of Computer Science,
University of Innsbruck"
eaa0433abe4601aefe865a82119b4491e3618a61,Global fitting of a facial model to facial features for model – based video coding,"Global fitting of a facial model to facial features for model–based video coding
P M Hillman
J M Hannah
P M Grant
University of Edinburgh School of Engineering and Electronics
Sanderson Building, King’s Buildings, Mayfield Road, Edinburgh EH9 3JL, UK"
ea4a61299b3b19adb02d4246aa33cf8e8469ce98,A Novel Technique for Face Recognition through Gabor Ordinal,"A Novel Technique for Face Recognition through Gabor Ordinal
H.Swarnalatha1, A.Valli Bhasha2
PG Student. Dept. of Electronics and Communication Engineering, KSRM College of Engineering, Kadapa
Email:
Assistant Professor, Dept. of Electronics and Communication Engineering, KSRM College of Engineering, Kadapa"
ead587db6b2b76726e98b17cb1fbf973a34ddf31,Development of an Efficient Face Recognition System Based on Linear and Nonlinear Algorithms,"Development of an Efficient Face Recognition System based on Linear and Nonlinear Algorithms
{tag}                                                 {/tag}
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 134
Number 7
Year of Publication: 2016
Authors:
Filani Araoluwa S., Adetunmbi Adebayo O.
10.5120/ijca2016907932
{bibtex}2016907932.bib{/bibtex}"
ea6f5c8e12513dbaca6bbdff495ef2975b8001bd,Applying a Set of Gabor Filter to 2 D-Retinal Fundus Image to Detect the Optic Nerve Head ( ONH ),"Applying a Set of Gabor Filter to 2D-Retinal Fundus Image
to Detect the Optic Nerve Head (ONH)
Rached Belgacem1,2*, Hédi Trabelsi2, Ines Malek3, Imed Jabri1
Higher National School of engineering of Tunis, ENSIT, Laboratory LATICE (Information Technology and Communication and
Electrical Engineering LR11ESO4), University of Tunis EL Manar. Adress: ENSIT 5, Avenue Taha Hussein, B. P. : 56, Bab
Menara, 1008 Tunis; 2University of Tunis El-Manar, Tunis with expertise in Mechanic, Optics, Biophysics, Conference Master
ISTMT, Laboratory of Research in Biophysics and Medical Technologies LRBTM Higher Institute of Medical Technologies of Tunis
ISTMT, University of Tunis El Manar Address: 9, Rue Docteur Zouheïr Safi – 1006; 3Faculty of Medicine of Tunis; Address: 15
Rue Djebel Lakhdhar. La Rabta. 1007, Tunis - Tunisia
Corresponding author:
Rached Belgacem,
High Institute of Medical Technologies
of Tunis, ISTMT, and High National
School Engineering of Tunis,
Information Technology and
Communication Technology and
Electrical Engineering, University of
Tunis El-Manar, ENSIT 5, Avenue Taha
Hussein, B. P.: 56, Bab Menara, 1008
Tunis, Tunisia,"
ea939d72d55c095e57fedaaf2aa49f596002c196,A Part based Modeling Approach for Invoice Parsing,
eaf8c104ab14600ecc5e9cce739b55280eef7ad4,Abstractive Compression of Captions with Attentive Recurrent Neural Networks,"Proceedings of The 9th International Natural Language Generation conference, pages 41–50,
Edinburgh, UK, September 5-8 2016. c(cid:13)2016 Association for Computational Linguistics"
ea079334121a0ba89452036e5d7f8e18f6851519,Unsupervised incremental learning of deep descriptors from video streams,"UNSUPERVISED INCREMENTAL LEARNING OF DEEP DESCRIPTORS
FROM VIDEO STREAMS
Federico Pernici and Alberto Del Bimbo
MICC – University of Florence"
ea533fac61db537fe1e1f351c98ae28db7272705,Theoretical Informatics and Applications Eye Localization for Face Recognition *,"Theoretical Informatics and Applications
Informatique Th´eorique et Applications
Will be set by the publisher
EYE LOCALIZATION FOR FACE RECOGNITION ∗
PAOLA CAMPADELLI, RAFFAELLA LANZAROTTI, GIUSEPPE LIPORI 1"
eae625274767cb695fa2121ccdcb30828ffc9b66,Social Context Modulates Facial Imitation of Children’s Emotional Expressions,"RESEARCH ARTICLE
Social Context Modulates Facial Imitation of
Children’s Emotional Expressions
Peter A. Bos*, Nadine Jap-Tjong, Hannah Spencer, Dennis Hofman
Department of Experimental Psychology, Utrecht University, Utrecht, The Netherlands"
27e97b67a8401def58eb41b4b00d3dfb0e4ad1a8,Knowledge Based Face Detection Using Fusion Features,"International Journal of Computer Engineering and Applications, ICCSTAR-2016, Special Issue,
May.16
Knowledge Based Face Detection Using Fusion Features.
Savitri Kulkarni
Assistant Professor,Department of CSE
City Engineering College,
2Annapurna N S
UG Student (B.E) Department of CSE
City Engineering College,"
2725a68be6bc677bd435c19664569ecd45c52d7a,DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers,"DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers
Amir Ghodrati1∗, Ali Diba1∗, Marco Pedersoli2†‡, Tinne Tuytelaars1, Luc Van Gool1,3
KU Leuven, ESAT-PSI, iMinds
Inria
CVL, ETH Zurich"
2792e5d569b94406ca28f86c9999f569a3d60c6d,Illumination Multiplexing within Fundamental Limits,"Illumination Multiplexing within Fundamental Limits
Netanel Ratner
Yoav Y. Schechner
Department of Electrical Engineering
Technion - Israel Institute of Technology
Haifa 32000, ISRAEL"
27ee8482c376ef282d5eb2e673ab042f5ded99d7,Scale Normalization for the Distance Maps AAM,"Scale Normalization for the Distance Maps AAM.
Denis GIRI, Maxime ROSENWALD, Benjamin VILLENEUVE, Sylvain LE GALLOU and Renaud S ´EGUIER
Email: {denis.giri, maxime.rosenwald, benjamin.villeneuve, sylvain.legallou,
Avenue de la boulaie, BP 81127,
5 511 Cesson-S´evign´e, France
Sup´elec, IETR-SCEE Team"
2757ff9bba677e7bceaa4802d85cc6f872618583,From basis components to complex structural patterns,"FROM BASIS COMPONENTS TO COMPLEX STRUCTURAL PATTERNS
Anh Huy Phan‡, Andrzej Cichocki‡∗, Petr Tichavsk´y•†, Rafal Zdunek§ and Sidney Lehky‡⋆
Brain Science Institute, RIKEN, Wakoshi, Japan
•Institute of Information Theory and Automation, Prague, Czech Republic
§Wroclaw University of Technology, Poland
⋆Computational Neurobiology Lab, The Salk Institute, USA"
277cadfadc4550fc781be7df8cb4ec89e54b793e,Autonomous Real-time Vehicle Detection from a Medium-Level UAV,"Autonomous Real-time Vehicle Detection from a
Medium-Level UAV
Toby P. Breckon, Stuart E. Barnes, Marcin L. Eichner and Ken Wahren"
2704959c75a2e6741867ae18f11fa822fa544c74,Hierarchical Convex NMF for Clustering Massive Data,"JMLR: Workshop and Conference Proceedings 13: 253-268
nd Asian Conference on Machine Learning (ACML2010), Tokyo, Japan, Nov. 8–10, 2010.
Hierarchical Convex NMF for Clustering Massive Data
Kristian Kersting
Mirwaes Wahabzada
Knowledge Discovery Department
Fraunhofer IAIS, Schloss Birlinghoven
53754 Sankt Augustin, Germany
Christian Thurau
Christian Bauckhage
Vision and Social Media Group
Fraunhofer IAIS, Schloss Birlinghoven
53754 Sankt Augustin, Germany
Editor: Masashi Sugiyama and Qiang Yang"
27f8b01e628f20ebfcb58d14ea40573d351bbaad,Events based Multimedia Indexing and Retrieval,"DEPARTMENT OF INFORMATION ENGINEERING AND COMPUTER SCIENCE
ICT International Doctoral School
Events based Multimedia Indexing
nd Retrieval
Kashif Ahmad
SUBMITTED TO THE DEPARTMENT OF
INFORMATION ENGINEERING AND COMPUTER SCIENCE (DISI)
IN THE PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE
DOCTOR OF PHILOSOPHY
Advisor:
Examiners: Prof. Marco Carli, Universit`a degli Studi di Roma Tre, Italy
Prof. Nicola Conci, Universit`a degli Studi di Trento, Italy
Prof. Pietro Zanuttigh, Universit`a degli Studi di Padova, Italy
Prof. Giulia Boato, Universit`a degli Studi di Trento, Italy
December 2017"
273d6c307cae64e3f3813a1a70299205f519e8a7,Regularised Energy Model for Robust Monocular Ego-motion Estimation,
2799d53ca80d67f104bef207a667fa12b4c59d62,Multiple-Person Tracking for a Mobile Robot Using Stereo,"MVA2009 IAPR Conference on Machine Vision Applications, May 20-22, 2009, Yokohama, JAPAN
Multiple-Person Tracking for a Mobile Robot using Stereo
Junji Satake
Jun Miura
Toyohashi University of Technology
-1 Hibarigaoka, Tempaku-cho, Toyohashi, Aichi 441-8580, Japan
{satake,"
27fda2c61f3fe1f74e18bd11555df7751d178bca,Real-time 3D head pose and facial landmark estimation from depth images using triangular surface patch features,"Real-time 3D Head Pose and Facial Landmark Estimation from Depth Images
Using Triangular Surface Patch Features
Chavdar Papazov
Tim K. Marks
Michael Jones
Mitsubishi Electric Research Laboratories (MERL)
01 Broadway, Cambridge, MA 02139"
2713423d87d011c0a5aae99bef57523769121a1d,A Codebook Design Method for Robust VQ-Based Face Recognition Algorithm,"J. Software Engineering & Applications, 2010, 3: 119-124
A Codebook Design Method for Robust VQ-Based
Face Recognition Algorithm
Qiu Chen1, Koji Kotani2, Feifei Lee1, Tadahiro Ohmi1
New Industry Creation Hatchery Center, Tohoku University; 2Department of Electronics, Graduate School of Engineering, Tohoku Univer-
sity, Japan..
Email:
Received September 5th, 2009; revised November 2nd, 2009; accepted November 6th, 2009."
27c978bdb9de3a5135349976fdbc514ff547dcab,Multi-Objective Stochastic Optimization by Co-Direct Sequential Simulation for History Matching of Oil Reservoirs,"Multi-Objective Stochastic Optimization by Co-Direct Sequential
Simulation for History Matching of Oil Reservoirs
Jo˜ao Daniel Trigo Pereira Carneiro∗
under the supervision of Am´ılcar de Oliveira Soares†
Dep. Mines, IST, Lisbon, Portugal
December 2010"
27448716366bed56515c1b32579daf224165861e,Deep Multi-camera People Detection,"Deep Multi-Camera People Detection
Tatjana Chavdarova and Franc¸ois Fleuret
Idiap Research Institute and
´Ecole Polytechnique F´ed´erale de Lausanne
Email:"
27ae95d9ad6492511296360ba0618f5d0565cf9e,Person re-Identification over distributed spaces and time,"Person re-Identification over distributed spaces and time
Prosser, Bryan James
For additional information about this publication click this link.
http://qmro.qmul.ac.uk/jspui/handle/123456789/2513
Information about this research object was correct at the time of download; we occasionally
make corrections to records, please therefore check the published record when citing. For
more information contact"
2785c5769489825671a6138fdf0537fcd444038a,A Deep Cascade Network for Unaligned Face Attribute Classification,"A Deep Cascade Network for Unaligned Face Attribute Classification
Hui Ding,1 Hao Zhou,2 Shaohua Kevin Zhou,3 Rama Chellappa4
,2,4University of Maryland, College Park
Siemens Healthineers, New Jersey"
2734b3a6345396499b2b7c6cc1b43fc7e9b375ee,Full-System Simulation of big.LITTLE Multicore Architecture for Performance and Energy Exploration,"Full-System Simulation of big.LITTLE Multicore
Architecture for Performance and Energy
Exploration
Anastasiia Butko, Florent Bruguier, Abdoulaye Gamati´e,
Gilles Sassatelli, David Novo, Lionel Torres and Michel Robert
LIRMM (CNRS and University of Montpellier)
Montpellier, France
Email:"
27f1fd71538ba420c63aa4c74704718a0633b22a,Multimodal News Article Analysis,Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
275416b209906988a73125e8ee0615774895c869,Use of Sparse Representation for Pedestrian Detection in Thermal Images,"Use of Sparse Representation for Pedestrian Detection in Thermal Images
Bin Qi1,2, Vijay John1,2, Zheng Liu1,2, and Seiichi Mita2
Intelligent Information Processing Laboratory, Toyota Technological Institute, Nagoya, Japan,
Research Centre for Smart Vehicles, Toyota Technological Institute, Nagoya, Japan,"
27b87bdee46964757b83b5afb4184e438cad6b1b,Sequence searching with deep-learnt depth for condition- and viewpoint-invariant route-based place recognition,"Sequence Searching with Deep-learnt Depth for Condition- and Viewpoint-
invariant Route-based Place Recognition
Michael Milford, Stephanie Lowry, Niko
Sunderhauf, Sareh Shirazi, Edward Pepperell,
Ben Upcroft
Queensland University of Technology Australia
Australian Centre for Robotic Vision"
276d35fef150f61adf53270eb6e50625022d4e7f,The ACRV picking benchmark: A robotic shelf picking benchmark to foster reproducible research,"A Robotic Shelf Picking Benchmark to Foster Reproducible Research
The ACRV Picking Benchmark:
J¨urgen Leitner1,2, Adam W. Tow1,2, Niko S¨underhauf1,2, Jake E. Dean2, Joseph W. Durham3, Matthew
Cooper2, Markus Eich1,2, Christopher Lehnert2, Ruben Mangels2, Christopher McCool2, Peter T. Kujala1,2,
Lachlan Nicholson2, Trung Pham1,4, James Sergeant1,2, Fangyi Zhang1,2, Ben Upcroft1,2, and Peter Corke1,2."
270567401251cad629f6d569febe95fe446a895c,A Pose Invariant Face Recognition system using Subspace Techniques,"A Pose Invariant Face Recognition system using
Subspace Techniques
Mohammed Aleemuddin
A Thesis Presented to the
DEANSHIP OF GRADUATE STUDIES
In Partial Fulfillment of the Requirements
for the Degree
MASTER OF SCIENCE
Telecommunication Engineering
KING FAHD UNIVERSITY
OF PETROLEUM AND MINERALS
Dhahran, Saudi Arabia
November 2004"
27962faaafbf01092da03130550a70e097e1dd9f,Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields,"Learning Depth from Single Monocular Images
Using Deep Convolutional Neural Fields
Fayao Liu, Chunhua Shen, Guosheng Lin, Ian Reid"
27f9b43737e234cefb3c5cd72324a36cbe61ee3c,Sparse Manifold Clustering and Embedding,"Sparse Manifold Clustering and Embedding
Ehsan Elhamifar
Center for Imaging Science
Johns Hopkins University
Ren´e Vidal
Center for Imaging Science
Johns Hopkins University"
2770b095613d4395045942dc60e6c560e882f887,GridFace: Face Rectification via Learning Local Homography Transformations,"GridFace: Face Rectification via Learning Local
Homography Transformations
Erjin Zhou, Zhimin Cao, and Jian Sun
Face++, Megvii Inc."
2783efc96a0d59473e4236ccf1db6ed7e958839e,An Overview of Multi-Task Learning in Deep Neural Networks,"An Overview of Multi-Task Learning
in Deep Neural Networks∗
Sebastian Ruder
Insight Centre for Data Analytics, NUI Galway
Aylien Ltd., Dublin"
273b973092a4491974d173cc5258c74aede692cc,Monocular Long-Term Target Following on UAVs,"Monocular Long-term Target Following on UAVs
Rui Li ∗
Minjian Pang†
Cong Zhao ‡
Guyue Zhou ‡
Lu Fang †§"
27421586a04584d38dd961b37d0ca85408acfe59,Large brains in autism: the challenge of pervasive abnormality.,"Large Brains in Autism:
The Challenge of Pervasive Abnormality
MARTHA R. HERBERT
Pediatric Neurology, Center for Morphometric Analysis
Massachusetts General Hospital
REVIEW I
The most replicated finding in autism neuroanatomy—a tendency to unusually large brains—has seemed
paradoxical in relation to the specificity of the abnormalities in three behavioral domains that define autism.
We now know a range of things about this phenomenon, including that brains in autism have a growth spurt
shortly  after  birth  and  then  slow  in  growth  a  few  short  years  afterward,  that  only  younger  but  not  older
rains are larger in autism than in controls, that white matter contributes disproportionately to this volume
increase and in a nonuniform pattern suggesting postnatal pathology, that functional connectivity among
regions of autistic brains is diminished, and that neuroinflammation (including microgliosis and astrogliosis)
ppears to be present in autistic brain tissue from childhood through adulthood. Alongside these pervasive
rain  tissue  and  functional  abnormalities,  there  have  arisen  theories  of  pervasive  or  widespread  neural
information  processing  or  signal  coordination  abnormalities  (such  as  weak  central  coherence,  impaired
omplex processing, and underconnectivity), which are argued to underlie the specific observable behav-
ioral features of autism. This convergence of findings and models suggests that a systems- and chronic
disease–based  reformulation  of  function  and  pathophysiology  in  autism  needs  to  be  considered,  and
it  opens  the  possibility  for  new  treatment  targets.  NEUROSCIENTIST  11(5):417–440;  2005.  DOI:"
276dbb667a66c23545534caa80be483222db7769,An Introduction to Image-based 3 D Surface Reconstruction and a Survey of Photometric Stereo Methods,"D Res. 2, 03(2011)4
0.1007/3DRes.03(2011)4
DR REVIEW                                                            w
An  Introduction  to  Image-based  3D  Surface  Reconstruction  and  a
Survey of Photometric Stereo Methods
Steffen Herbort • Christian Wöhler
introduction
image-based  3D
techniques.  Then  we  describe
Received: 21Feburary 2011 / Revised: 20 March 2011 / Accepted: 11 May 2011
© 3D Research Center, Kwangwoon University and Springer 2011"
27d2e977356915c63c4562fe41df9e9ed0290f15,A Hierarchical Compositional Model for Face Representation and Sketching,"A Hierarchical Compositional Model for Face
Representation and Sketching
Zijian Xu1, Hong Chen1, Song-Chun Zhu1, Jiebo Luo2
Department of Statistics, University of California at Los Angeles, Los Angeles, CA 90095
Kodak Research Laboratories, Eastman Kodak Company, Rochester, NY 14650-1816"
27326ae43ec7a6a31ecd257171b8a338053946cd,Boundary-enhanced supervoxel segmentation for sparse outdoor LiDAR data,"Boundary-enhanced supervoxel
segmentation for sparse outdoor LiDAR data
Soohwan Song, Honggu Lee and Sungho Jo
supervoxel
Voxelisation methods are extensively employed for efficiently proces-
sing large point clouds. However, it is possible to lose geometric in-
formation and extract inaccurate features through these voxelisation
methods. A novel, flexibly shaped ‘supervoxel’ algorithm, called
oundary-enhanced
omplex outdoor light detection and ranging (LiDAR) data is pro-
posed. The algorithm consists of two key components: (i) detecting
oundaries by analysing consecutive points and (ii) clustering the
points by first excluding the boundary points. The generated super-
voxels include spatial and geometric properties and maintain the
shape of the object’s boundary. The proposed algorithm is tested
using sparse LiDAR data obtained from outdoor urban environments.
segmentation,
sparse
Introduction: Correctly perceiving a three-dimensional (3D) outdoor
environment is still a challenging task for autonomous vehicles. For"
272ac22c670fd0c7c3f1b4ca02e925ff22dd4b27,Articulated part-based model for joint object detection and pose estimation,"Articulated Part-based Model for Joint Object Detection and Pose Estimation
Dept. of Electrical and Computer Engineering, University of Michigan at Ann Arbor, USA
Min Sun
Silvio Savarese
COARSE
LEVEL"
27187d4c36f71d08898a53dfda0e81df11b25f21,Worst Case Linear Discriminant Analysis as Scalable Semidefinite Feasibility Problems,"MANUSCRIPT
Worst-Case Linear Discriminant Analysis as
Scalable Semidefinite Feasibility Problems
Hui Li, Chunhua Shen, Anton van den Hengel, Qinfeng Shi"
277096c5e536784da9856ac083a972715ce9f9c3,Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction,"Article
Gender Recognition from Human-Body Images
Using Visible-Light and Thermal Camera Videos
Based on a Convolutional Neural Network for
Image Feature Extraction
Dat Tien Nguyen, Ki Wan Kim, Hyung Gil Hong, Ja Hyung Koo, Min Cheol Kim and
Kang Ryoung Park *
Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu,
Seoul 100-715, Korea; (D.T.N.); (K.W.K.);
(H.G.H.); (J.H.K.); (M.C.K.)
* Correspondence: Tel.: +82-10-3111-7022; Fax: +82-2-2277-8735
Academic Editor: Joonki Paik
Received: 31 January 2017; Accepted: 18 March 2017; Published: 20 March 2017"
275ad26b7e4d7847f7ad4eedda65f327007a9452,Query-by-Example Image Retrieval using Visual Dependency Representations,"Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers,
pages 109–120, Dublin, Ireland, August 23-29 2014."
27173d0b9bb5ce3a75d05e4dbd8f063375f24bb5,Effect of Different Occlusion on Facial Expressions Recognition,"Ankita Vyas  Int. Journal of Engineering Research and Applications                               www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 10( Part - 3), October 2014, pp.40-44
RESEARCH ARTICLE
OPEN ACCESS
Effect of Different Occlusion on Facial Expressions Recognition
Ankita Vyas*, Ramchand Hablani**
*(Department of Computer Science, RGPV University, Indore)
** (Department of Computer Science, RGPV University, Indore)"
27a4bbd7bc90ad118f15c61bb30079d6e6bff78e,3D Deformable Super-Resolution for Multi-Camera 3D Face Scanning,"J Math Imaging Vis
DOI 10.1007/s10851-012-0399-y
D Deformable Super-Resolution for Multi-Camera 3D Face
Scanning
Karima Ouji · Mohsen Ardabilian · Liming Chen ·
Faouzi Ghorbel
© Springer Science+Business Media New York 2012"
27a0a7837f9114143717fc63294a6500565294c2,Face Recognition in Unconstrained Environments : A Comparative Study,"Face Recognition in Unconstrained Environments: A
Comparative Study
Rodrigo Verschae, Javier Ruiz-Del-Solar, Mauricio Correa
To cite this version:
Rodrigo Verschae, Javier Ruiz-Del-Solar, Mauricio Correa. Face Recognition in Unconstrained
Environments: A Comparative Study: . Workshop on Faces in ’Real-Life’ Images: Detection,
Alignment, and Recognition, Oct 2008, Marseille, France. 2008. <inria-00326730>
HAL Id: inria-00326730
https://hal.inria.fr/inria-00326730
Submitted on 5 Oct 2008
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rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
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destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,
´emanant des ´etablissements d’enseignement et de"
27183d23f50884a0e06b978acf9ad77dbcbfb112,Autonomous indoor helicopter flight using a single onboard camera,"The 2009 IEEE/RSJ International Conference on
Intelligent Robots and Systems
October 11-15, 2009 St. Louis, USA
978-1-4244-3804-4/09/$25.00 ©2009 IEEE"
27dafaa0478bc70e3af9c5a45f278bcee44a920c,Learnability and semantic universals,"Learnability and semantic universals*
Shane Steinert-Threlkeld
Institute for Logic, Language and
Jakub Szymanik
Institute for Logic, Language and
Computation
Computation
Universiteit van Amsterdam
Universiteit van Amsterdam
Forthcoming in Semantics & Pragmatics."
274046ccc3f6641f29e404f4c731e5d6b771de26,A New Approach to Object-Related Image Retrieval,"(cid:74)(cid:111)(cid:117)(cid:114)(cid:110)(cid:97)(cid:108) (cid:111)(cid:102) (cid:86)(cid:105)(cid:115)(cid:117)(cid:97)(cid:108) (cid:76)(cid:97)(cid:110)(cid:103)(cid:117)(cid:97)(cid:103)(cid:101)(cid:115) (cid:97)(cid:110)(cid:100) (cid:67)(cid:111)(cid:109)(cid:112)(cid:117)(cid:116)(cid:105)(cid:110)(cid:103) (cid:40)(cid:50)(cid:48)(cid:48)(cid:48)(cid:41) 11, (cid:51)(cid:52)(cid:53)(cid:177)(cid:51)(cid:54)(cid:51)
(cid:100)(cid:111)(cid:105)(cid:58)(cid:49)(cid:48)(cid:46)(cid:49)(cid:48)(cid:48)(cid:54)/(cid:106)(cid:118)(cid:108)(cid:99)(cid:46)(cid:50)(cid:48)(cid:48)(cid:48)(cid:46)(cid:48)(cid:49)(cid:54)(cid:50)(cid:44) (cid:97)(cid:118)(cid:97)(cid:105)(cid:108)(cid:97)(cid:98)(cid:108)(cid:101) (cid:111)(cid:110)(cid:108)(cid:105)(cid:110)(cid:101) (cid:97)(cid:116) (cid:104)(cid:116)(cid:116)(cid:112)(cid:58)//(cid:119)(cid:119)(cid:119)(cid:46)(cid:105)(cid:100)(cid:101)(cid:97)(cid:108)(cid:105)(cid:98)(cid:114)(cid:97)(cid:114)(cid:121)(cid:46)(cid:99)(cid:111)(cid:109) (cid:111)(cid:110)
A New Approach to Object-Related Image Retrieval
(cid:65)(cid:76)(cid:69)(cid:73)(cid:88) (cid:77)(cid:46) (cid:77)(cid:65)(cid:82)(cid:84)(cid:73)D (cid:78)(cid:69)(cid:90)* (cid:65)(cid:78)(cid:68) (cid:74)(cid:85)(cid:65)(cid:78) (cid:82)(cid:46) (cid:83)(cid:69)(cid:82)(cid:82)(cid:65)s
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(cid:119)(cid:104)(cid:105)(cid:99)(cid:104) (cid:174)(cid:110)(cid:97)(cid:108)(cid:108)(cid:121) (cid:97)(cid:108)(cid:108)(cid:111)(cid:119)(cid:115) (cid:116)(cid:104)(cid:101) (cid:117)(cid:115)(cid:101) (cid:111)(cid:102) (cid:115)(cid:101)(cid:109)(cid:97)(cid:110)(cid:116)(cid:105)(cid:99)(cid:115) (cid:100)(cid:114)(cid:105)(cid:118)(cid:101)(cid:110) (cid:98)(cid:121) (cid:116)(cid:104)(cid:101)(cid:115)(cid:101) (cid:111)(cid:98)(cid:106)(cid:101)(cid:99)(cid:116) (cid:110)(cid:97)(cid:109)(cid:101)(cid:115)(cid:44) (cid:101)(cid:46)(cid:103)(cid:46) (cid:114)(cid:101)(cid:116)(cid:114)(cid:105)(cid:101)(cid:118)(cid:101) (cid:96)(cid:97)(cid:108)(cid:108)
(cid:116)(cid:104)(cid:111)(cid:115)(cid:101) (cid:105)(cid:109)(cid:97)(cid:103)(cid:101)(cid:115) (cid:116)(cid:104)(cid:97)(cid:116) (cid:104)(cid:97)(cid:118)(cid:101) (cid:97) (cid:98)(cid:117)(cid:108)(cid:108) (cid:97)(cid:110)(cid:100) (cid:77)(cid:101)(cid:108)(cid:105)(cid:115)(cid:115)(cid:97)(cid:39)(cid:115) (cid:102)(cid:97)(cid:99)(cid:101)(cid:39)(cid:46)"
d69e644016042d1032995bc9f51e2d72a1c1cd93,Beyond trees: adopting MITI to learn rules and ensemble classifiers for multi-instance data,"Beyond Trees: Adopting MITI to Learn Rules
nd Ensemble Classifiers for Multi-instance Data
Luke Bjerring and Eibe Frank
Department of Computer Science, University of Waikato"
d6f49b63e4e285ff2bb3ba92e1e10287d407d6c0,Tasks determine what is learned in visual statistical learning.,"Psychon Bull Rev
https://doi.org/10.3758/s13423-017-1405-6
BRIEF REPORT
Tasks determine what is learned in visual statistical learning
Timothy J. Vickery 1 & Su Hyoun Park 1 & Jayesh Gupta 1 & Marian E. Berryhill 2
# Psychonomic Society, Inc. 2017"
d6255a0db6f8f157c5c901d758c7a5f36416ab51,Face Recognition Using Gabor Wavelet Transform,"FACE RECOGNITION USING GABOR WAVELET TRANSFORM
A THESIS SUBMITTED TO
THE GRADUATE SCHOOL OF NATURAL SCIENCES
THE MIDDLE EAST TECHNICAL UNIVERSITY
BURCU KEPENEKCI
IN PARTIAL FULLFILMENT OF THE REQUIREMENTS FOR THE DEGREE
MASTER OF SCIENCE
THE DEPARTMENT OF ELECTRICAL AND ELECTRONICS ENGINEERING
SEPTEMBER 2001"
d6adb54f5d25dda71d157b5d574c70c732fdd722,Feature Map Filtering: Improving Visual Place Recognition with Convolutional Calibration,"Pre-print of article that will appear in Proceedings of the Australasian Conference on Robotics and Automation
018.
Please cite this paper as:
Stephen Hausler, Adam Jacobson, and Michael Milford. Feature Map Filtering: Improving Visual Place Recognition
with Convolutional Calibration. Proceedings of Australasian Conference on Robotics and Automation, 2018.
ibtex:
uthor = {Hausler, Stephen and Jacobson, Adam and Milford, Michael},
title = {Feature Map Filtering: Improving Visual Place Recognition with Convolutional Calibration},
ooktitle = {Proceedings of Australasian Conference on Robotics and Automation (ACRA)},
year = {2018},"
d68f24e2c8e753d4d1e62f2231f6f33370de24de,NATIONAL BIOMETRIC TEST CENTER COLLECTED WORKS 1997-2000,"NATIONAL BIOMETRIC TEST CENTER
COLLECTED WORKS
997-2000
Edited by:
James L. Wayman, Director
Version 1.2
August, 2000
Prepared under DoD Contract MDA904-97-C-03
nd FAA Award DTFA0300P10092"
d6dfe23018172d29c36746d24f73bf86e1aaa0a6,Searching Scenes by Abstracting Things.,
d6c7092111a8619ed7a6b01b00c5f75949f137bf,A Novel Feature Extraction Technique for Facial Expression Recognition *,"A Novel Feature Extraction Technique for Facial Expression
Recognition
*Mohammad Shahidul Islam1,  Surapong Auwatanamongkol2
1 Department of Computer Science, School of Applied Statistics,
National Institute of Development Administration,
Bangkok, 10240, Thailand
Department of Computer Science, School of Applied Statistics,
National Institute of Development Administration,
Bangkok, 10240, Thailand"
d65b82b862cf1dbba3dee6541358f69849004f30,Elastic graph matching,"Computer Vision and Image Understanding 115 (2011) 1062–1072
Contents lists available at ScienceDirect
Computer Vision and Image Understanding
j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / c v i u
.5D Elastic graph matching
Stefanos Zafeiriou
, Maria Petrou
Imperial College, Department of Electrical and Electronic Engineering, London, UK
r t i c l e
i n f o
b s t r a c t
Article history:
Received 29 November 2009
Accepted 1 December 2010
Available online 17 March 2011
Keywords:
Elastic graph matching
D face recognition
Multiscale mathematical morphology
Geodesic distances"
d6b514a68abff3ab14af9fc0152cd5b28bd0192c,Instance Segmentation by Deep Coloring,"JULY 2018
Instance Segmentation by Deep Coloring
Victor Kulikov, Victor Yurchenko, and Victor Lempitsky"
d671a210990f67eba9b2d3dda8c2cb91575b4a7a,Social Environment Description from Data Collected with a Wearable Device,"Journal of Machine Learning Research ()
Submitted ; Published
Social Environment Description from Data Collected with a
Wearable Device
Pierluigi Casale
Computer Vision Center
Autonomous University of Barcelona
Barcelona, Spain
Editor: Radeva Petia, Pujol Oriol"
d687fa99586a9ad229284229f20a157ba2d41aea,Face Recognition Based on Wavelet Packet Coefficients and Radial Basis Function Neural Networks,"Journal of Intelligent Learning Systems and Applications, 2013, 5, 115-122
http://dx.doi.org/10.4236/jilsa.2013.52013 Published Online May 2013 (http://www.scirp.org/journal/jilsa)
Face Recognition Based on Wavelet Packet Coefficients
nd Radial Basis Function Neural Networks
Thangairulappan Kathirvalavakumar1*, Jeyasingh Jebakumari Beulah Vasanthi2
Department of Computer Science, Virudhunagar Hindu Nadars’ Senthikumara Nadar College, Virudhunagar, India; 2Department of
Computer Applications, Ayya Nadar Janaki Ammal College, Sivakasi, India.
Email:
Received December 12th, 2012; revised April 19th, 2013; accepted April 26th, 2013
Copyright © 2013 Thangairulappan Kathirvalavakumar, Jeyasingh Jebakumari Beulah Vasanthi. This is an open access article dis-
tributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any me-
dium, provided the original work is properly cited."
d6a9ea9b40a7377c91c705f4c7f206a669a9eea2,Visual Representations for Fine-grained Categorization,"Visual Representations for Fine-grained
Categorization
Ning Zhang
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2015-244
http://www.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-244.html
December 17, 2015"
d6683c74c17d4fcc48ce3d9df9df6aea38fd4923,Learning Instance Weights in Multi-Instance Learning,"Learning Instance Weights in
Multi-Instance Learning
James Foulds
This thesis is submitted in partial fulfillment of
the requirements for the degree of
Master of Science
t the
University of Waikato.
Department of Computer Science
Hamilton, New Zealand
February 2007 - February 2008
(cid:13) 2008 James Foulds"
d6dab84451254d7fbb5b9e1d40a7d2a92dec13b3,Enhanced Local Binary Patterns for Automatic Face Recognition,"ENHANCED LOCAL BINARY PATTERNS FOR AUTOMATIC FACE RECOGNITION
Pavel Kr´al1
, Anton´ın Vrba1
Dept. of Computer Science & Engineering 2New Technologies for the Information Society
Faculty of Applied Sciences
University of West Bohemia
Plzeˇn, Czech Republic
Faculty of Applied Sciences
University of West Bohemia
Plzeˇn, Czech Republic"
d65f11b44180d9997ad5ba6e6970fe4874891f4f,Unobtrusive emotion sensing and interpretation in smart environment,"Journal of Ambient Intelligence and Smart Environments 7 (2015) 59–83
DOI 10.3233/AIS-140298
IOS Press
Unobtrusive emotion sensing and
interpretation in smart environment
Oleg Starostenko *, Ximena Cortés, J. Afredo Sánchez and Vicente Alarcon-Aquino
Department of Computing, Electronics and Mechatronics, Universidad de las Americas Puebla, Cholula,
Pue. 72810, Mexico"
d69df51cff3d6b9b0625acdcbea27cd2bbf4b9c0,Robust Remote Heart Rate Determination for E-Rehabilitation - A Method that Overcomes Motion and Intensity Artefacts,
d689cdb4e535be040316722229e6362de6617f9e,GEOMETRIC DEEP PARTICLE FILTER FOR MOTORCYCLE TRACKING : DEVELOPMENT OF INTELLIGENT TRAFFIC SYSTEM IN JAKARTA,"INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS VOL. 8, NO. 1, MARCH 2015
GEOMETRIC DEEP PARTICLE FILTER FOR MOTORCYCLE
TRACKING: DEVELOPMENT OF INTELLIGENT TRAFFIC
SYSTEM IN JAKARTA
Alexander A S Gunawan1, Wisnu Jatmiko2
Bina Nusantara University, Mathematics Department,
School of Computer Science, Jakarta, Indonesia
Faculty of Computer Science,Universitas Indonesia, Depok, Indonesia
Submitted: Oct. 4, 2014      Accepted: Jan. 20, 2015        Published: Mar. 1, 2015"
d6bfa9026a563ca109d088bdb0252ccf33b76bc6,Unsupervised Temporal Segmentation of Facial Behaviour,"Unsupervised Temporal Segmentation of Facial Behaviour
Abhishek Kar
Advisors: Dr. Amitabha Mukerjee & Dr. Prithwijit Guha
Department of Computer Science and Engineering, IIT Kanpur"
d69ef8b5658fabd0ac092fb2bfd0c9c109574dcc,Neural Class-Specific Regression for face verification,"Neural Class-Specific Regression for face
verification
Guanqun Cao, Alexandros Iosifidis, Moncef Gabbouj"
d65bcbcddec932480c434f0ffa778e429cdd4ee7,Periocular biometrics: When iris recognition fails,"Periocular Biometrics: When Iris Recognition Fails
Samarth Bharadwaj, Himanshu S. Bhatt, Mayank Vatsa and Richa Singh"
d660abfbe5f84c1c49f1e7174eb166b8b23e53c4,"AMIGOS: A dataset for Mood, personality and affect research on Individuals and GrOupS","AMIGOS: A dataset for Mood, personality and
ffect research on Individuals and GrOupS
Nicu Sebe, Senior Member, IEEE, and Ioannis Patras, Senior Member, IEEE"
d6102a7ddb19a185019fd2112d2f29d9258f6dec,Fashion Style Generator,"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
GeneratorPatch……Global+…lstyle(2)lstyle(1)lcontent(1)lcontent(2)φθϕsϕcDiscriminatorDGXX(1)X(2)(a) Framework of the training stage(b) Examples of fashion style generationFigure1:Fashionstylegeneratorframeworkoverview.TheinputXconsistsofasetofclothingpatchesX(1)andfullclothingimagesX(2).Thesystemconsistsoftwocomponents:animagetransfor-mationnetworkGservedasfashionstylegenerator,andadiscrimi-natornetworkDcalculatesbothglobalandpatchbasedcontentandstylelosses.Gisaconvolutionalencoderdecodernetworkparam-eterizedbyweights(cid:18).Sixgeneratedshirtswithdifferentstylesbyourmethodareshownasexamples.(Wehighlyrecommendtozoominallthefigureswithcolorversionformoredetails.)recentneuralstyletransferworks[Gatysetal.,2015].Tak-ingVanGogh’s“StarryNight”astheexamplestyleimage,styleisbetweenthelow-levelcolor/texture(e.g.,blueandyellowcolor,roughorsmoothertexture)andthehigh-levelobjects(e.g.,houseandmountain).“Style”isarelativelyab-stractconcept.Fashionstylegenerationhasatleasttwoprac-ticalusages.Designerscouldquicklyseehowtheclothinglookslikeinagivenstyletofacilitatethedesignprocessing.Shopperscouldsynthesizetheclothingimagewiththeidealstyleandapplyclothingretrievaltools[Jiangetal.,2016b]tosearchthesimilaritems.Fashionstylegenerationisrelatedtoexistingneuralstyletransferworks[Gatysetal.,2015;LiandWand,2016a;EfrosandFreeman,2001],buthasitsownchallenges.Infashionstylegeneration,thesyntheticclothingimageshould"
d623428f02e80a689eb58d022237daeae2ae7b9c,Guided depth upsampling for precise mapping of urban environments,"Guided Depth Upsampling for Precise Mapping of Urban Environments
Sascha Wirges1, Bj¨orn Roxin2 , Eike Rehder2, Tilman K¨uhner1 and Martin Lauer2"
d65ad3e9293bd1dca1b137f8b81f18e201e76c3a,Supplementary Material for Hierarchical Gaussian Descriptor for Person Re-Identification,"Supplementary Material for
Hierarchical Gaussian Descriptor for Person Re-Identification
Tetsu Matsukawa1, Takahiro Okabe2, Einoshin Suzuki1, Yoichi Sato3
Kyushu University 2 Kyushu Institute of Technology 3 The University of Tokyo
fmatsukawa,
. Details of the baseline descriptors
In section 4.2 of the paper, we compared the distribu-
tion modeling of GOG to other distributions. Below, we
describe the details of the compared methods.
The Mean, Cov and Gauss are global distribution de-
scriptors of pixel features within each region. The Cov-of-
Cov, Cov-of-Gauss and GOG are hierarchical distribution
descriptors. The Cov-of-Cov uses covariance matrix in both
patch and region modeling. The Cov-of-Gauss uses Gaus-
sian for patch modeling and covariance matrix for region
modeling.
For a fair comparison to GOG which is incorporated with
patch weights, we adopted the weighted pooling for all de-
scriptors. Formally,
Mean: (cid:22)"
d69b542b3714b5e90c384d39b5ab0c4bf9dd5375,Activity Report 2012 Project-Team EMOTION Geometry and Probability for Motion and Action,"IN PARTNERSHIP WITH:
Institut polytechnique de
Grenoble
Université Pierre Mendes-France
(Grenoble)
Université Joseph Fourier
(Grenoble)
Activity Report 2012
Project-Team E-MOTION
Geometry and Probability for Motion and
Action
IN COLLABORATION WITH: Laboratoire d’Informatique de Grenoble (LIG)
RESEARCH CENTER
Grenoble - Rhône-Alpes
THEME
Robotics"
d6eda0c16d226976506396653d14044c185eaf3e,Toward Multimodal Image-to-Image Translation,"Toward Multimodal Image-to-Image Translation
Jun-Yan Zhu
UC Berkeley
Richard Zhang
UC Berkeley
Deepak Pathak
UC Berkeley
Trevor Darrell
UC Berkeley
Alexei A. Efros
UC Berkeley
Oliver Wang
Adobe Research
Eli Shechtman
Adobe Research"
d6efd1b7b39d91b067488e0c4bf800ce3e3704d8,Visual Analysis of Pedestrian Motion,"Visual Analysis of Pedestrian Motion
PRS Transfer Report
Supervised by Dr Ian Reid
David Ellis
St John’s College
Robotics Research Group
Department of Engineering Science
Michaelmas 2009"
d6ceebb0cde7fb0fbe916472d7b613a2d7d2e1e6,Do faces capture the attention of individuals with Williams syndrome or autism? Evidence from tracking eye movements.,"Do faces capture the attention of individuals with Williams syndrome
or Autism? Evidence from tracking eye movements
Deborah M Riby & Peter J B Hancock
http://dx.doi.org/10.1007/s10803-008-0641-z"
d665213b59f2460faf171d3b03ecd9c96d606883,A MULTIMODAL NONVERBAL HUMAN-ROBOT COMMUNICATION SYSTEM,"VI International Conference on Computational Bioengineering
ICCB 2015
M. Cerrolaza and S.Oller (Eds)
A MULTIMODAL NONVERBAL HUMAN-ROBOT COMMUNICATION
SYSTEM
S. SALEH†*, M. SAHU†, Z. ZAFAR† AND K. BERNS†
Robotics Research Lab. - Dept. of Computer Science
University of Kaiserslautern
Kaiserslautern, Germany
web page: http://agrosy.cs.uni-kl.de
e-mail: {saleh, sahu, zafar,
* Dept. of Computer Science, University of Basrah
Basrah, Iraq
Key words: HRI, Facial Expression Recognition, Nonverbal Communication"
d64b24e9b01f4681d92fc29f36e46d94db7b8bb0,Avoiding Extraverts : Pathogen Concern Downregulates Preferences for Extraverted Faces,"See	discussions,	stats,	and	author	profiles	for	this	publication	at:	https://www.researchgate.net/publication/305793723
Avoiding	Extraverts:	Pathogen	Concern
Downregulates	Preferences	for	Extraverted
Faces
Article	·	August	2016
DOI:	10.1007/s40806-016-0064-6
CITATIONS
authors,	including:
Mitch	Brown
University	of	Southern	Mississippi
6	PUBLICATIONS			5	CITATIONS
SEE	PROFILE
READS
Some	of	the	authors	of	this	publication	are	also	working	on	these	related	projects:
Limbal	Rings	View	project
Morality	and	Mate	Preferences	View	project
All	content	following	this	page	was	uploaded	by	Mitch	Brown	on	06	December	2016.
The	user	has	requested	enhancement	of	the	downloaded	file.	All	in-text	references	underlined	in	blue	are	added	to	the	original	document
nd	are	linked	to	publications	on	ResearchGate,	letting	you	access	and	read	them	immediately."
807913b776bc5039cd3f195841419e55979ec7c7,Recreation of spontaneous non-verbal behavior on a synthetic agent,"Roboti c.s. d.o.o, 2Faculty of Electrical Engineering and Computer Science, University of Maribor
IZIDOR MLAKAR, 2MATEJ ROJC
Recreation of spontaneous non-verbal behavior on a synthetic agent
Tržaška cesta 23, 2Smetanova ulica 17
SLOVENIA
systematic
sequencing"
80135ed7e34ac1dcc7f858f880edc699a920bf53,EFFICIENT ACTION AND EVENT RECOGNITION IN VIDEOS USING EXTREME LEARNING MACHINES,"EFFICIENT ACTION AND EVENT RECOGNITION IN VIDEOS USING
EXTREME LEARNING MACHINES
G¨ul Varol
B.S., Computer Engineering, Bo˘gazi¸ci University, 2013
Submitted to the Institute for Graduate Studies in
Science and Engineering in partial fulfillment of
the requirements for the degree of
Master of Science
Graduate Program in Computer Engineering
Bo˘gazi¸ci University"
80d9d4b5d7af67721212c0e9a89efb7f69671a5c,People detection and tracking using a network of low-cost depth cameras,"Tommi Tikkanen
People detection and tracking using a
network of low-cost depth cameras
School of Electrical Engineering
Thesis submitted for examination for the degree of Master of
Science in Technology.
Espoo 20.1.2014
Thesis supervisor:
Thesis advisor:
Prof. Arto Visala
M.Sc. (Tech.) Otto Korkalo"
801a80f7a18fccb2e8068996a73aee2cf04ae460,Optimal transport maps for distribution preserving operations on latent spaces of Generative Models,"OPTIMAL TRANSPORT MAPS FOR DISTRIBUTION PRE-
SERVING OPERATIONS ON LATENT SPACES OF GENER-
ATIVE MODELS
Eirikur Agustsson
D-ITET, ETH Zurich
Switzerland
Alexander Sage
D-ITET, ETH Zurich
Switzerland
Radu Timofte
D-ITET, ETH Zurich
Merantix GmbH
Luc Van Gool
D-ITET, ETH Zurich
ESAT, KU Leuven"
808b03e28bb45bd446ee7e82f767e48db354fefd,Fast Optical Flow using Dense Inverse Search Supplementary Material,"Fast Optical Flow using Dense Inverse Search
Supplementary Material
Till Kroeger1
Radu Timofte1
Dengxin Dai1
Luc Van Gool1,2
Computer Vision Laboratory, D-ITET, ETH Zurich
VISICS / iMinds, ESAT, KU Leuven
{kroegert, timofter, dai,
A Derivation of the fast inverse search in § 2.1 of the
paper
We adopt the terminology of [1,2] and closely follow their derivation. We consider
W(x; u) a warp, parametrized by u = (u, v)T , on pixel x such that W(x; u) =
(x + u, y + v). The following derivation holds for other warps as well: See [2]
for a discussion on the limits of its applicability. The objective function for the
inverse search, eq. (1) in the paper, then becomes
(cid:88)
[It+1(W(x; u)) − T (x)]2 .
The warp parameter u is found by iteratively minimizing
[It+1(W(x; u + ∆u)) − T (x)]2"
809e25da311366bfd684228e16184737d948eef6,Supplementary material for : Learning Finer-class Networks for Universal Representations,"GIRARD ET AL.: SUPPLEMENTARY FOR FINER-CLASS NETWORKS
Supplementary material for: Learning
Finer-class Networks for Universal
Representations
Julien Girard12
Youssef Tamaazousti123
Hervé Le Borgne2
Céline Hudelot3
Both authors contributed equally.
CEA LIST
Vision Laboratory,
Gif-sur-Yvette, France.
CentraleSupélec,
MICS Laboratory,
Châtenay-Malabry, France."
802ecaabffbece0dc2c31d44b693967c683fc5ff,Faster RER-CNN: application to the detection of vehicles in aerial images,"Faster RER-CNN: application to the detection of
vehicles in aerial images
Jean Ogier du Terrail(1,2), Fr´ed´eric Jurie(1)
(1)Normandie Univ, UNICAEN, ENSICAEN, CNRS
(2)Safran Electronics and Defense
September 21, 2018"
8093b784be493efc1d833af7e99c5de72eb5afe9,Understanding object descriptions in robotics by open-vocabulary object retrieval and detection,"Understanding Object Descriptions in
Robotics by Open-vocabulary Object
Retrieval and Detection
The International Journal of Robotics
Research
000(00):1–20
(cid:13)The Author(s) 2010
Reprints and permission:
sagepub.co.uk/journalsPermissions.nav
DOI:doi number
http://mms.sagepub.com
Sergio Guadarrama∗1, Erik Rodner2, Kate Saenko3 and Trevor Darrell1
EECS Department, University of California at Berkeley, USA
Computer Vision Group, Friedrich Schiller University of Jena, Germany
CS Department, University of Massachussetts Lowell, USA"
8064d7a28c763ec37a840450d729f23428ad8f8b,Development of an Efficient Face Recognition System based on Linear and Nonlinear Algorithms,"Development of an Efficient Face Recognition System based on Linear and Nonlinear Algorithms
{tag}                                                 {/tag}
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 134
Number 7
Year of Publication: 2016
Authors:
Filani Araoluwa S., Adetunmbi Adebayo O.
10.5120/ijca2016907932
{bibtex}2016907932.bib{/bibtex}"
80c8d143e7f61761f39baec5b6dfb8faeb814be9,Local Directional Pattern based Fuzzy Co-occurrence Matrix Features for Face recognition,"Local Directional Pattern based Fuzzy Co-
occurrence Matrix Features for Face recognition
Dr. P Chandra Sekhar Reddy
Professor, CSE Dept.
Gokaraju Rangaraju Institute of Engineering and Technology, Hyd."
80510c47d7fad872b18d865f3957568dc512780c,Occlusion Invariant 3 D Face Recognition with UMB – DB and BOSPHORUS Databases,"International Journal of Computer Applications (0975 – 8887)
National Conference on Advances in Computing (NCAC 2015)
Occlusion Invariant 3D Face Recognition with UMB – DB
nd BOSPHORUS Databases
G.E.S. R.H. Sapat College of Engineering, Nashik
G.E.S. R.H. Sapat College of Engineering, Nashik
H. Y. Patil, PhD
Assistant Professor (Dept. of E&TC),
Maharashtra
Charushila R. Singh
M.E. student (Dept. of E&TC),
Maharashtra"
804b4c1b553d9d7bae70d55bf8767c603c1a09e3,CLUSTERING WITH A LEARNED DIMENSIONALITY REDUCTION PROJECTION,"978-1-4799-9988-0/16/$31.00 ©2016 IEEE
ICASSP 2016"
80fdf9757c0e4b62dcfff03941f1951304ba002c,Geometry of face space,"Geometry of face space
Lawrence Sirovich and Marsha Meytlis
Laboratory of Applied Mathematics
Mount Sinai School of Medicine
Gustave L. Levy Place
New York, NY 10029"
80345fbb6bb6bcc5ab1a7adcc7979a0262b8a923,Soft Biometrics for a Socially Assistive Robotic Platform,"Research Article
Pierluigi Carcagnì*, Dario Cazzato, Marco Del Coco, Pier Luigi Mazzeo, Marco Leo, and
Cosimo Distante
Soft Biometrics for a Socially Assistive Robotic
Platform
Open Access"
800cbbe16be0f7cb921842d54967c9a94eaa2a65,MULTIMODAL RECOGNITION OF EMOTIONS,"MULTIMODAL RECOGNITION OF
EMOTIONS"
8010636454316faf1a09202542af040ffd04fefa,"Performance Parameter Analysis of Face Recognition Based On Fuzzy C-Means Clustering , Shape and Corner Detection","Minj Salen Kujur et al Int. Journal of Engineering Research and Applications              www.ijera.com
ISSN : 2248-9622, Vol. 3, Issue 6, Nov-Dec 2013, pp.515-520
RESEARCH ARTICLE                                                                               OPEN ACCESS
Performance Parameter Analysis of Face Recognition Based On
Fuzzy C-Means Clustering, Shape and Corner Detection
Minj Salen Kujur1, Prof. Prashant Jain2
Department of Electronics & Communication Engineering college Jabalpur"
80265d7c9fe6a948dd8c975bd4d696fb7ba099c9,Face Recognition Based on Human Visual Perception Theories and Unsupervised ANN,"Face Recognition Based on
Human Visual Perception Theories and
Unsupervised ANN
Mario I. Chacon M. and Pablo Rivas P.
Chihuahua Institute of Technology
Mexico
. Introduction
The  face  recognition  problem  has  been  faced  for  more  than  30  years.  Although  a  lot  of
research has been done, much more research is and will be required in order to end up with
robust  face  recognition  system  with  a  potential  close  to  human  performance.  Currently
face  recognition  systems,  FRS,  report  high  performance  levels,  however  achievement  of
00% of correct recognition is still a challenge. Even more, if the FRS must work on non-
ooperative  environment  its  performance  may  decrease  dramatically.  Non-cooperative
environments  are  characterized  by  changes  on;  pose,  illumination,  facial  expression.
Therefore  FRS  for  non-cooperative  environment  represents  an  attractive  challenge  to
researchers working on the face recognition area.
Most  of  the  work  presented  in  the  literature  dealing  with  the  face  recognition  problem
follows an engineering approach that in some cases do not incorporate information from a
psychological or  neuroscience  perspective. It  is  our  interest  in  this  material,  to  show  how
information from the psychological and neuroscience areas may contribute in the solution of"
80242615f2370f494432633adcd620e04dbecbc1,Fast and Accurate Semantic Mapping through Geometric-based Incremental Segmentation,"Fast and Accurate Semantic Mapping through Geometric-based
Incremental Segmentation
Yoshikatsu Nakajima1, Keisuke Tateno2, Federico Tombari2 and Hideo Saito1"
80c4f5bc43f21041343c6d7a61cdc281cb36be07,"Information routing, correspondence finding, and object recognition in the brain","Information Routing, Correspondence
Finding, and Object Recognition
in the Brain
DISSERTATION
Erlangung des Grades
„Doktor der Naturwissenschaften“
vorgelegt beim Fachbereich Informatik und Mathematik
der Goethe-Universität Frankfurt am Main
Philipp Wolfrum
Heilbronn
Frankfurt (2008)"
803c92a3f0815dbf97e30c4ee9450fd005586e1a,Max-Mahalanobis Linear Discriminant Analysis Networks,"Max-Mahalanobis Linear Discriminant Analysis Networks
Tianyu Pang 1 Chao Du 1 Jun Zhu 1"
805c77bd351fc98d6acbee68b73af915c5cb6776,Overview of the ImageCLEF 2012 Scalable Web Image Annotation Task,"Overview of the ImageCLEF 2012 Scalable Web
Image Annotation Task
Mauricio Villegas and Roberto Paredes
Institut Tecnol`ogic d’Inform`atica
Universitat Polit`ecnica de Val`encia
Cam´ı de Vera s/n, 46022 Val`encia, Spain"
80a6bb337b8fdc17bffb8038f3b1467d01204375,Subspace LDA Methods for Solving the Small Sample Size Problem in Face Recognition,"Proceedings of the International Conference on Computer and Information Science and Technology
Ottawa, Ontario, Canada, May 11 – 12, 2015
Paper No. 126
Subspace LDA Methods for Solving the Small Sample Size
Problem in Face Recognition
Ching-Ting Huang, Chaur-Chin Chen
Department of Computer Science/National Tsing Hua University
01 KwanFu Rd., Sec. 2, Hsinchu, Taiwan"
801b0ae343a11a15fd7abc5720831afea6f0a61d,Similarity Learning with Listwise Ranking for Person Re-Identification,"SIMILARITY LEARNING WITH LISTWISE
RANKING FOR PERSON RE-IDENTIFICATION
Yiqiang Chen, Stefan Duffner, Andrei Stoian, Jean-Yves Dufour, Atilla
Baskurt
To cite this version:
Yiqiang Chen, Stefan Duffner, Andrei Stoian, Jean-Yves Dufour, Atilla Baskurt. SIMILARITY
LEARNING WITH LISTWISE RANKING FOR PERSON RE-IDENTIFICATION. International
onference on image processing, Oct 2018, Athenes, Greece. <hal-01895355>
HAL Id: hal-01895355
https://hal.archives-ouvertes.fr/hal-01895355
Submitted on 15 Oct 2018
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,"
8031dd2c6583d8681fdd85bdae4371c7c745713f,Generative adversarial models for people attribute recognition in surveillance,"Generative Adversarial Models for People Attribute Recognition in Surveillance
Matteo Fabbri
Simone Calderara
Rita Cucchiara
University of Modena and Reggio Emilia
via Vivarelli 10 Modena 41125 Italy"
80097a879fceff2a9a955bf7613b0d3bfa68dc23,Active Self-Paced Learning for Cost-Effective and Progressive Face Identification,"Active Self-Paced Learning for Cost-Effective and
Progressive Face Identification
Liang Lin, Keze Wang, Deyu Meng, Wangmeng Zuo, and Lei Zhang"
80c8f02c945c1dbbec31983164c1e4e0b742c44a,Cohort of LSTM and lexicon verification for handwriting recognition with gigantic lexicon,"Cohort of LSTM and lexicon verification for
handwriting recognition with gigantic lexicon
Bruno STUNERa,∗, Cl´ement CHATELAINa, Thierry PAQUETa
Normandie Univ, UNIROUEN, UNIHAVRE, INSA Rouen, LITIS, 76000 Rouen, France"
8097841cc4f3559e32c32db97624255808bacf22,Biometrie symmetry: Implications on template protection,"Biometric Symmetry:
Implications on Template Protection
M. Gomez-Barrero∗, C. Rathgeb∗, K. B. Raja†, R. Raghavendra†, C. Busch∗
da/sec - Biometrics and Internet Security Research Group, Hochschule Darmstadt, Germany
Email:
Norwegian Biometrics Laboratory, NTNU, Gjøvik, Norway
Email:"
8047586d2223f3076a1fc028197f54d0997bccfc,Pelee: A Real-Time Object Detection System on Mobile Devices,"2nd Conference on Neural Information Processing Systems (NeurIPS 2018)
Pelee: A Real-Time Object Detection System on Mobile
Devices
Robert J. Wang, Xiang Li & Charles X. Ling
Department of Computer Science
University of Western Ontario
London, Ontario, Canada, N6A 3K7"
8096279890779bdcce4bfa8e1f753389e8eb8fda,A Real-Time Pedestrian Detector using Deep Learning for Human-Aware Navigation,"A Real-Time Pedestrian Detector using
Deep Learning for Human-Aware Navigation
David Ribeiro, Andr´e Mateus, Jacinto C. Nascimento, and Pedro Miraldo"
57165586f65f25edd9d14f0173c4c35dab8c2e66,Aligning plot synopses to videos for story-based retrieval,"Noname manuscript No.
(will be inserted by the editor)
Aligning Plot Synopses to Videos for Story-based Retrieval
Makarand Tapaswi · Martin B¨auml · Rainer Stiefelhagen
Received: date / Accepted: date"
573b687ad970e1931debbf366004c0983de28718,A Corpus for Investigating the Multimodal Nature of Multi-Speaker Spontaneous Conversations – EVA Corpus,"A Corpus for Investigating the Multimodal Nature of Multi-Speaker
Spontaneous Conversations – EVA Corpus
IZIDOR MLAKAR, ZDRAVKO KAČIČ, MATEJ ROJC
Faculty of Electrical Engineering and Computer Science, University of Maribor
SLOVENIA"
578d4ad74818086bb64f182f72e2c8bd31e3d426,"The MR2: A multi-racial, mega-resolution database of facial stimuli.","Behav Res
DOI 10.3758/s13428-015-0641-9
The MR2: A multi-racial, mega-resolution database of facial
stimuli
Nina Strohminger1,6 · Kurt Gray2 · Vladimir Chituc3 · Joseph Heffner4 ·
Chelsea Schein2 · Titus Brooks Heagins5
© Psychonomic Society, Inc. 2015"
57235f22abcd6bb928007287b17e235dbef83347,Exemplar Guided Unsupervised Image-to-Image Translation with Semantic Consistency,"EXEMPLAR GUIDED UNSUPERVISED
IMAGE-TO-
IMAGE TRANSLATION WITH SEMANTIC CONSISTENCY
Liqian Ma1 Xu Jia2
KU-Leuven/PSI, TRACE (Toyota Res in Europe)
{liqian.ma, xu.jia, tinne.tuytelaars,
{georgous,
Stamatios Georgoulis1,3 Tinne Tuytelaars2 Luc Van Gool1,3
KU-Leuven/PSI, IMEC 3ETH Zurich"
5725c06b406b5291915a6bef8b5c3d20b2873aa0,Face Recognition Using Principal Component Analysis Based Feature Space By Incorporating With Probabilistic Neural Network,"International Journal of Computer Science Trends and Technology (IJCST) – Volume 4 Issue 2, Mar - Apr 2016
RESEARCH  ARTICLE
OPEN  ACCESS
Face Recognition Using Principal Component Analysis
Based Feature Space By Incorporating With Probabilistic
Muhammad  Tahir, Shahid Akbar, Shahzad, Maqsood Hayat, Nazia Azim
Neural Network
Department of Computer Science
Abdul Wali  Khan University
Mardan - Pakistan"
57e8e226e605fe6491111c5dc9461527c5fce56c,Articulated Object Detection,"Articulated Object Detection
Maciej Halber
MEng Computer Science
Submission Date: 26th April 2013
Supervisors
Niloy J. Mitra
Simon Julier
This report is submitted as part requirement for the MEng Degree in Computer
Science at UCL. It is substantially the result of my own work except where ex-
plicitly indicated in the text. The report may be freely copied and distributed
provided the source is explicitly acknowledged."
579bf3ac200b6262458b054e3866f76a80d4b6d8,Recognition and Detection of Occluded Faces by a Neural Network Classifier with Recursive Data Reconstruction,"RECOGNITION AND DETECTION OF OCCLUDED FACES BY A NEURAL NETWORK
CLASSIFIER WITH RECURSIVE DATA RECONSTRUCTION
T. Kurita, M. Pic
T. Takahashi
Neuroscience Research Institute, AIST
 takio-kurita,mickael.pic
"
576372383bfd6ce6944d885e60b19151efdffc99,Can we unify monocular detectors for autonomous driving by using the pixel-wise semantic segmentation of CNNs?,"Can we unify monocular detectors for autonomous driving
y using the pixel-wise semantic segmentation of CNNs?
Eduardo Romera, Luis M. Bergasa, Roberto Arroyo"
57d37ad025b5796457eee7392d2038910988655a,Aeaeêêìáîî Áåèääååaeììáçae Çç Àááêêêàáááä Aeçîîäìì Ììììçê,"GEERAT
VE
EETAT
F

ERARC
CAVETYDETECTR
DagaEha
UdeheS	eviif
f.DahaWeiha
ATheiS	b
iediaiaF	(cid:28)
efhe
Re	ie
efheDegeef
aefSciece
TheSchfC
	eScieceadEgieeig
ebewUiveiyfe	ae

ae91904
Dece
be2009"
57a14a65e8ae15176c9afae874854e8b0f23dca7,Seeing Mixed Emotions: The Specificity of Emotion Perception From Static and Dynamic Facial Expressions Across Cultures,"UvA-DARE (Digital Academic Repository)
Seeing mixed emotions: The specificity of emotion perception from static and dynamic
facial expressions across cultures
Fang, X.; Sauter, D.A.; van Kleef, G.A.
Published in:
Journal of Cross-Cultural Psychology
0.1177/0022022117736270
Link to publication
Citation for published version (APA):
Fang, X., Sauter, D. A., & van Kleef, G. A. (2018). Seeing mixed emotions: The specificity of emotion perception
from static and dynamic facial expressions across cultures. Journal of Cross-Cultural Psychology, 49(1), 130-
48. DOI: 10.1177/0022022117736270
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the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam,
The Netherlands. You will be contacted as soon as possible."
5712cfc11c561c453da6a31d515f4340dacc91a4,3D Facial Expression Reconstruction using Cascaded Regression,"SUBMITTED TO PATTERN RECOGNITION LETTERS
Cascaded Regression using Landmark
Displacement for 3D Face Reconstruction
Fanzi Wu, Songnan Li, Tianhao Zhao, and King Ngi Ngan,Lv Sheng"
57f8e1f461ab25614f5fe51a83601710142f8e88,Region Selection for Robust Face Verification using UMACE Filters,"Region Selection for Robust Face Verification using UMACE Filters
Salina Abdul Samad*, Dzati Athiar Ramli, Aini Hussain
Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering,
Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia.
In  this  paper,  we  investigate  the  verification  performances  of  four  subdivided  face  images  with  varying  expressions.  The
objective of this study is to evaluate which part of the face image is more tolerant to facial expression and still retains its personal
haracteristics due to the variations of the image. The Unconstrained Minimum Average Correlation Energy (UMACE) filter is
implemented to perform the verification process because of its advantages such as shift–invariance, ability to trade-off between
discrimination and distortion tolerance, e.g. variations in pose, illumination and facial expression. The database obtained from the
facial expression database of Advanced Multimedia Processing (AMP) Lab at CMU is used in this study. Four equal
sizes of face regions i.e. bottom, top, left and right halves are used for the purpose of this study. The results show that the bottom
half of the face region gives the best performance in terms of the PSR values with zero false accepted rate (FAR) and zero false
rejection rate (FRR) compared to the other three regions.
. Introduction
Face  recognition  is  a  well  established  field  of  research,
nd a large number of algorithms have been proposed in the
literature. Various classifiers have been explored to improve
the accuracy of face classification. The basic approach is to
use distance-base methods which measure Euclidean distance
etween any two vectors and then compare it with the preset"
57a1466c5985fe7594a91d46588d969007210581,A taxonomy of face-models for system evaluation,"A Taxonomy of Face-models for System Evaluation
Vijay N. Iyer, Shane. R. Kirkbride, Brian C. Parks, Walter J. Scheirer and Terrance. E. Boult
Motivation and Data Types
Synthetic Data Types
Unverified – Have no underlying physical or
statistical basis
Physics -Based – Based on structure and
materials combined with the properties
formally modeled in physics.
Statistical  – Use statistics from real
data/experiments to estimate/learn model
parameters. Generally have measurements
of accuracy
Guided Synthetic – Individual models based
on individual people. No attempt to capture
properties of large groups, a unique model
per person. For faces, guided models are
omposed of 3D structure models and skin
textures,  capturing many artifacts  not
easily  parameterized. Can be combined with"
57680f0d53392178bb3c431e03bcd8626c12f620,SEMANTIC IMAGE SEGMENTATION,"Workshop track - ICLR 2017
ADVERSARIAL EXAMPLES FOR
SEMANTIC IMAGE SEGMENTATION
Volker Fischer1, Mummadi Chaithanya Kumar2, Jan Hendrik Metzen1 & Thomas Brox2
Bosch Center for Artificial Intelligence, Robert Bosch GmbH
University of Freiburg
{volker.fischer,"
57e9b0d3ab6295e914d5a30cfaa3b2c81189abc1,Self-Learning Scene-Specific Pedestrian Detectors Using a Progressive Latent Model,"Self-learning Scene-specific Pedestrian Detectors
using a Progressive Latent Model
Qixiang Ye1,4, Tianliang Zhang 1, Qiang Qiu4, Baochang Zhang2, Jie Chen3, and Guillermo Sapiro4
EECE, University of Chinese Academy of Sciences.
ASEE, Beihang University. 3CMV, Oulu University. 4ECE, Duke University."
5720784b7e45693109b867992e3f93e4c747e536,Sparse Methods for Robust and Efficient Visual Recognition,
57b55a7a1adc8ec06285ebaf93995d67cf80c719,External Data Overcomplete Dictionary Similarity Graph ≈ + Probeimage Gallery Compressed Dictionary With Coefficient Design Phase : Operational Phase : CD Compressed Dictionary,
57e562cf99b3dfbb6baa5bbf665aa6fd97ffe8ca,Expression-Compensated 3D Face Recognition with Geodesically Aligned Bilinear Models,"Expression-Compensated 3D Face Recognition with Geodesically
Aligned Bilinear Models
Iordanis Mpiperis1,2,Sotiris Malassiotis1 and Michael G. Strintzis1,2"
57126589b3fe62c35a36a2646dac3045d095ecf5,Adversarial Defense based on Structure-to-Signal Autoencoders,"Adversarial Defense based on
Structure-to-Signal Autoencoders
Joachim Folz(cid:63), Sebastian Palacio(cid:63), Joern Hees, Damian Borth, and Andreas
Dengel
German Research Center for Artificial Intelligence (DFKI)
TU Kaiserslautern"
5700291077b509b11fb227f84ee9fc2de8f2df99,Line search and trust region strategies for canonical decomposition of semi-nonnegative semi-symmetric 3 rd order tensors,"Line search and trust region strategies for canonical
decomposition of semi-nonnegative semi-symmetric 3rd
Julie Coloigner, Ahmad Karfoul, Laurent Albera, Pierre Comon
order tensors
To cite this version:
Julie Coloigner, Ahmad Karfoul, Laurent Albera, Pierre Comon. Line search and trust region
strategies for canonical decomposition of semi-nonnegative semi-symmetric 3rd order tensors.
Linear Algebra and Applications, Elsevier - Academic Press, 2014, 450, pp.334-374.
HAL Id: hal-00945606
https://hal.archives-ouvertes.fr/hal-00945606
Submitted on 12 Feb 2014
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,"
57ff1222a78a230c46fc81f22daa57981b0fa306,Face recognition in multi-camera surveillance videos using Dynamic Bayesian Network,"Face Recognition
in Multi-Camera
Surveillance
Videos using Dynamic Bayesian Network
Center for Research
Le An, Mehran Kafai,  Bir
Bhanu
in Intelligent
Systems,
University
of California,
Riverside
.edu, mkafai bhanu"
57246142814d7010d3592e3a39a1ed819dd01f3b,Verification of Very Low-Resolution Faces Using An Identity-Preserving Deep Face Super-resolution Network,"MITSUBISHI ELECTRIC RESEARCH LABORATORIES
http://www.merl.com
Verification of Very Low-Resolution Faces Using An
Identity-Preserving Deep Face Super-resolution Network
Ataer-Cansizoglu, E.; Jones, M.J.; Zhang, Z.; Sullivan, A.
TR2018-116 August 24, 2018"
57db5825a8eb2927735fb7c18c3ee4fb18d27d47,Max-Mahalanobis Linear Discriminant Analysis Networks,"Max-Mahalanobis Linear Discriminant Analysis Networks
Tianyu Pang 1 Chao Du 1 Jun Zhu 1"
57c59011614c43f51a509e10717e47505c776389,Unsupervised Human Action Detection by Action Matching,"Unsupervised Human Action Detection by Action Matching
Basura Fernando∗ Sareh Shirazi† Stephen Gould∗
The Australian National University †Queensland University of Technology"
5782d17ad87262739d69dcbe76cadfa881179a91,Data Analysis Project : What Makes Paris Look like Paris ?,"Data Analysis Project: What Makes Paris Look like
Paris?
Machine Learning Department
Carnegie-Mellon University
Pittsburgh, PA 15213
Carl Doersch⇤"
57fd8bafa4526b9a56fe43fac22dd62b2ab94563,BEYOND SHARED HIERARCHIES: DEEP MULTITASK LEARNING THROUGH SOFT LAYER ORDERING,"Under review as a conference paper at ICLR 2018
BEYOND SHARED HIERARCHIES: DEEP MULTITASK
LEARNING THROUGH SOFT LAYER ORDERING
Anonymous authors
Paper under double-blind review"
5740a5f9cbfe790afc0ba9a425cfb71197927470,Supplementary Material for Superpixel Sampling Networks,"Supplementary Material for
Superpixel Sampling Networks
Varun Jampani1, Deqing Sun1, Ming-Yu Liu1,
Ming-Hsuan Yang1,2, Jan Kautz1
NVIDIA
UC Merced
In Section 1, we formally define the Acheivable Segmentation Accuracy (ASA)
used for evaluating superpixels. Then, in Section 2, we report F-measure and
Compactness scores with more visual results on different datasets. We also in-
lude a supplementary video1 that gives an overview of Superpixel Sampling
Networks (SSN) with a glimpse of experimental results.
Evaluation Metrics
Here, we formally define the Achievable Segmentation Accuracy (ASA) met-
ric that is used in the main paper. Given an image I with n pixels, let H ∈
{0, 1,··· , m}n×1 denotes the superpixel segmentation with m superpixels. H is
j=1 H j, where jth segment is repre-
sented as H j. Similarly, let G ∈ {0, 1,··· , w}n×1 denotes ground-truth (GT)
l=1 Gl, where Gl denotes lth GT segment.
ASA Score. The ASA score between a given superpixel segmentation H and
the GT segmentation G is defined as"
57fd229097e4822292d19329a17ceb013b2cb648,Fast Structural Binary Coding,"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16)
Fast Structural Binary Coding
⇤Department of Electrical and Computer Engineering,University of California, San Diego
Dongjin Song⇤, Wei Liu], and David A. Meyer†
La Jolla, USA, 92093-0409. Email:
] Didi Research, Didi Kuaidi, Beijing, China. Email:
Department of Mathematics,University of California, San Diego
La Jolla, USA, 92093-0112. Email:"
9c4365a56fb3cf41b15712657b15f7422ca0dab2,A Hybrid Supervised-Unsupervised Vocabulary Generation Algorithm for Visual Concept Recognition,"A Hybrid Supervised-Unsupervised Vocabulary
Generation Algorithm for Visual Concept
Recognition
Alexander Binder1, Wojciech Wojcikiewicz1,2, Christina M¨uller1,2, and
Motoaki Kawanabe1,2
Berlin Institute of Technology, Machine Learning Group, Franklinstr. 28/29, 10587
Berlin, Germany
Fraunhofer Institute FIRST, Kekul´estr. 7, 12489 Berlin, Germany"
9c2739256937fbe66c5b5ce2a23d2d47b48aa4aa,On Optimising Local Feature Face Recognition for Mobile Devices !,"Huesca • 2 y 3 de Septiembre de 2010
V Jornadas de Reconocimiento Biom´etrico de Personas
On Optimising Local Feature Face Recognition
for Mobile Devices!
Mauricio Villegas and Roberto Paredes
Instituto Tecnol´ogico de Inform´atica
Universidad Polit´ecnica de Valencia
Camino de Vera s/n, Edif. 8G Acc. B 46022 Valencia (Spain)"
9cc4abd2ec10e5fa94ff846c5ee27377caf17cf0,Improved Techniques for GAN based Facial Inpainting,"Improved Techniques for GAN based Facial
Inpainting
Avisek Lahiri*, Arnav Jain*, Divyasri Nadendla and Prabir Kumar Biswas, Senior Member, IEEE"
9c8da385750db215dc0728dc310251b320d319af,- CL-TR-899 ISSN 1476-2986 Deep embodiment : grounding semantics in perceptual modalities,"Technical Report
UCAM-CL-TR-899
ISSN 1476-2986
Number 899
Computer Laboratory
Deep embodiment:
grounding semantics
in perceptual modalities
Douwe Kiela
February 2017
5 JJ Thomson Avenue
Cambridge CB3 0FD
United Kingdom
phone +44 1223 763500
http://www.cl.cam.ac.uk/"
9c63c2210b5dde771ec8751cebc4281e74034fb0,CNN for IMU assisted odometry estimation using velodyne LiDAR,"CNN for IMU Assisted Odometry Estimation using Velodyne LiDAR
Martin Velas, Michal Spanel, Michal Hradis, and Adam Herout"
9ca82f5936723a773fb44336cd66c315f2024d34,Latent-Class Hough Forests for 3D Object Detection and Pose Estimation,"Latent-Class Hough Forests for 3D Object Detection
nd Pose Estimation
Alykhan Tejani, Danhang Tang, Rigas Kouskouridas, and Tae-Kyun Kim
Imperial Collge London"
9cdb83ed96f5aa74bc4e2e9edacfbb5263e8fc37,Learning Mutual Visibility Relationship for Pedestrian Detection with a Deep Model,"Manuscript
Click here to download Manuscript: Mutual-DBN-J2.pdf
Click here to view linked References
Noname manuscript No.
(will be inserted by the editor)
Learning Mutual Visibility Relationship for Pedestrian Detection with a
Deep Model
Wanli Ouyang · Xingyu Zeng · Xiaogang Wang
Received: date / Accepted: date"
9cb152758ee57f2abcc0b59348752e528a2ed2f7,Full Video Processing for Mobile Audio-Visual Identity Verification,
9c1860de6d6e991a45325c997bf9651c8a9d716f,3D reconstruction and face recognition using kernel-based ICA and neural networks,"D Reconstruction and Face Recognition Using Kernel-Based
ICA and Neural Networks
Cheng-Jian Lin                  Ya-Tzu Huang
Chi-Yung Lee
Dept. of Electrical                Dept. of CSIE                    Dept. of CSIE
Engineering              Chaoyang University              Nankai Institute of
National University            of Technology                        Technology
of Kaohsiung"
9ce0d64125fbaf625c466d86221505ad2aced7b1,Recognizing expressions of children in real life scenarios View project PhD ( Doctor of Philosophy ) View project,"Saliency Based Framework for Facial Expression
Recognition
Rizwan Ahmed Khan, Alexandre Meyer, Hubert Konik, Saïda Bouakaz
To cite this version:
Rizwan Ahmed Khan, Alexandre Meyer, Hubert Konik, Saïda Bouakaz. Saliency Based Framework for
Facial Expression Recognition. Frontiers of Computer Science, 2017, <10.1007/s11704-017-6114-9>.
<hal-01546192>
HAL Id: hal-01546192
https://hal.archives-ouvertes.fr/hal-01546192
Submitted on 23 Jun 2017
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
9caa7f125d3e861450bc3685699fceeaebea04d8,Designing Video Surveillance Systems as Services,"Designing Video Surveillance Systems as
Services
R. Cucchiara and A. Prati and R. Vezzani"
9c781f7fd5d8168ddae1ce5bb4a77e3ca12b40b6,Attribute Based Face Classification Using Support Vector Machine Brindha,"International Research Journal of Engineering and Technology (IRJET)      e-ISSN: 2395 -0056
Volume: 03 Issue: 07 | July-2016                       www.irjet.net                                                               p-ISSN: 2395-0072
Attribute Based Face Classification Using Support Vector Machine
Brindha.M1, Amsaveni.R2
Research Scholar, Dept. of Computer Science, PSGR Krishnammal College for Women, Coimbatore
Assistant Professor, Dept. of Information Technology, PSGR Krishnammal College for Women, Coimbatore."
9cd3ea5cbbe0716fe19ff750940222cdedb22fc8,Learning to Attend On Essential Terms: An Enhanced Retriever-Reader Model for Scientific Question Answering,"Learning to Attend On Essential Terms: An Enhanced Retriever-Reader
Model for Scientific Question Answering
Jianmo Ni1,2∗, Chenguang Zhu1, Weizhu Chen1, Julian McAuley2
Microsoft Business Applications Group AI Research
Department of Computer Science, UC San Diego"
9cc3172efb42d2f9fa1b9ae7b7eef9cc349cdef9,Imbalanced Deep Learning by Minority Class Incremental Rectification,"Imbalanced Deep Learning by Minority Class
Incremental Rectification
Qi Dong, Shaogang Gong, and Xiatian Zhu"
9cf07922cf91c4aea66c8d72606ca444f4607cc6,Distinct neural activation patterns underlie economic decisions in high and low psychopathy scorers.,"doi:10.1093/scan/nst093
SCAN (2014) 9,1099^1107
Distinct neural activation patterns underlie economic
decisions in high and low psychopathy scorers
Joana B. Vieira,1,2,3 Pedro R. Almeida,1,4 Fernando Ferreira-Santos,1 Fernando Barbosa,1 Joa˜o Marques-Teixeira,1
nd Abigail A. Marsh3
Laboratory of Neuropsychophysiology, Faculty of Psychology and Educational Sciences, 2Faculty of Medicine, University of Porto, 4200-135
Porto, Portugal, 3Department of Psychology, Georgetown University, Washington, DC 20057, USA, and 4School of Criminology, Faculty of Law,
University of Porto, 4200-135 Porto, Portugal
Psychopathic traits affect social functioning and the ability to make adaptive decisions in social interactions. This study investigated how psychopathy
ffects the neural mechanisms that are recruited to make decisions in the ultimatum game. Thirty-five adult participants recruited from the community
underwent functional magnetic resonance imaging scanning while they performed the ultimatum game under high and low cognitive load. Across load
onditions, high psychopathy scorers rejected unfair offers in the same proportion as low scorers, but perceived them as less unfair. Among low
scorers, the perceived fairness of offers predicted acceptance rates, whereas in high scorers no association was found. Imaging results revealed
that responses in each group were associated with distinct patterns of brain activation, indicating divergent decision mechanisms. Acceptance of
unfair offers was associated with dorsolateral prefrontal cortex activity in low scorers and ventromedial prefrontal cortex activity in high scorers. Overall,
our findings point to distinct motivations for rejecting unfair offers in individuals who vary in psychopathic traits, with rejections in high psychopathy
scorers being probably induced by frustration. Implications of these results for models of ventromedial prefrontal cortex dysfunction in psychopathy
re discussed.
Keywords: psychopathy; functional magnetic resonance imaging; ultimatum game; ventromedial prefrontal cortex"
9c2039d036c01e421176d33c1436633d03be4678,Review of Person Re-identification Techniques,"Received on 21st February 2013
Revised on 14th November 2013
Accepted on 18th December 2013
doi: 10.1049/iet-cvi.2013.0180
www.ietdl.org
ISSN 1751-9632
Review of person re-identification techniques
Mohammad Ali Saghafi1, Aini Hussain1, Halimah Badioze Zaman2,
Mohamad Hanif Md. Saad1
Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia
Institute of Visual Informatics, Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia
E-mail:"
9cd8e1ccc5a410c7f31c7e404588597c0bb1952b,What ’ s your type ? Personalized Prediction of Facial Attractiveness 
,"Whats Your Type? Personalized Prediction of
Facial Attractiveness
Sam Crognale, Computer Science, Danish Shabbir Electrical Engineering
INTRODUCTION
Attempts to obtain a universal model of facial beauty by
the  way  of  symmetry,  golden  ratios,  and  measured
placement of various facial features fall short in explaining
the varied attraction that is actually witnessed in the world.
In this investigation, we devise an application to give a user
some insight about their ‘type’ as users swipe yes or no on a
large dataset of images
There  is  a  wealth  of  interesting  literature  attempting  to
map the psychophysics of attraction. For example, Johnston
nd Franklin (1993) use a genetic algorithm which evolves a
“most  beautiful”  female  face  according  to  interactive  user
selections. They sought to  mimic the way humans filter for
features they find the most attractive.
Our approach builds on Kagian et. al (2007), where it was
shown that feature selection and training procedure with the
original geometric features instead of the eigenfeatures fails"
9cb916aa3672a8071d2d77931ed221f4f98138f2,Composition-Aided Face Photo-Sketch Synthesis,"JOURNAL OF LATEX CLASS FILES, VOL. X, NO. X, XX 2018
Composition-Aided Face Photo-Sketch Synthesis
Jun Yu, Senior Member, IEEE,, Shengjie Shi, Fei Gao, Dacheng Tao, Fellow, IEEE,
nd Qingming Huang, Fellow, IEEE"
9c6d92f3d796242332ebf419a4f9b584864cfa15,Genetic Model Optimization for Hausdorff Distance-Based Face Localization,"(cid:176) In Proc. International ECCV 2002 Workshop on Biometric Authentication,
Springer, Lecture Notes in Computer Science, LNCS-2359, pp. 103{111,
Copenhagen, Denmark, June 2002.
Genetic Model Optimization
for Hausdorfi Distance-Based Face Localization
Klaus J. Kirchberg, Oliver Jesorsky, and Robert W. Frischholz
BioID AG, Germany
WWW home page: http://www.bioid.com"
9c2f3e9c223153b70f37ee84224d67b5a577bd58,Towards unlocking web video: Automatic people tracking and clustering,"Towards Unlocking Web Video: Automatic People Tracking and Clustering
Alex Holub*, Pierre Moreels*, Atiq Islam*, Andrei Makhanov*, Rui Yang*
Ooyala Inc, 800 W. El Camino Real, Suite 350, Mountain View, CA 94040
*All authors contributed equally to this work"
9c93512df188d7dbab63ebe47586a930559e6279,Development of an Efficient Face Recognition System based on Linear and Nonlinear Algorithms,"Development of an Efficient Face Recognition System based on Linear and Nonlinear Algorithms
{tag}                                                 {/tag}
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 134
Number 7
Year of Publication: 2016
Authors:
Filani Araoluwa S., Adetunmbi Adebayo O.
10.5120/ijca2016907932
{bibtex}2016907932.bib{/bibtex}"
9cf6d66a0b4e5a3347466a60caea411d67c4b5b7,Joint transfer component analysis and metric learning for person re-identification,"Joint transfer component analysis and
metric learning for person re-identification
Yixiu Liu, Yunzhou Zhang✉, Sonya Coleman and
Jianning Chi
nd efficient metric
A novel
learning strategy for person
re-identification is proposed. Person re-identification is formulated as
multi-domain learning problem. The assumption that the feature dis-
tributions from different camera views are the same is overthrown in
this Letter. ID-based transfer component analysis (IDB-TCA) is pro-
posed to learn a shared subspace, in which the differences in the
feature distribution between source domain and target domain are sig-
nificantly reduced. Experimental evaluation on the CUHK01 dataset
demonstrates that metric learning with IDB-TCA embedded outper-
forms state-of-art metric methods for person re-identification.
Introduction: Person re-identification, aiming to finding the images that
match the target person in a large-scale image library, greatly reduces the
time cost of human search. Due to its great significance to visual super-
vision, it has rapidly become a research hotspot in the field of computer"
9ca2dfe8a6265c4f6ea12bae0e7ff6ffc9128226,Dialog-based Interactive Image Retrieval,"Dialog-based Interactive Image Retrieval
Xiaoxiao Guo†
IBM Research AI
Hui Wu†
IBM Research AI
Steven Rennie
Fusemachines Inc.
Gerald Tesauro
IBM Research AI"
9c3b9dee9da817134325357afbebbd1a0d67cab2,Deep Learning for Saliency Prediction in Natural Video,"Deep Learning for Saliency Prediction in Natural Video
Souad CHAABOUNIa,b, Jenny BENOIS-PINEAUa, Ofer HADARc, Chokri
BEN AMARb
Universit´e de Bordeaux, Laboratoire Bordelais de Recherche en Informatique, Bˆatiment
Sfax university, Research Groups in Intelligent Machines, National Engineering School of
A30, F-33405 Talence cedex, France
Communication Systems Engineering department, Ben Gurion University of the Nagev
Sfax (ENIS), Tunisia"
9ca7899338129f4ba6744f801e722d53a44e4622,Deep neural networks regularization for structured output prediction,"Deep Neural Networks Regularization for Structured
Output Prediction
Soufiane Belharbi∗
INSA Rouen, LITIS
76000 Rouen, France
Clément Chatelain
INSA Rouen, LITIS
76000 Rouen, France
Romain Hérault
INSA Rouen, LITIS
76000 Rouen, France
Sébastien Adam
INSA Rouen, LITIS
76000 Rouen, France
Normandie Univ, UNIROUEN, UNIHAVRE,
Normandie Univ, UNIROUEN, UNIHAVRE,
Normandie Univ, UNIROUEN, UNIHAVRE,
Normandie Univ, UNIROUEN, UNIHAVRE,"
9cabbb686883635d8755706ee4f1349d812d7ccb,Detection and Tracking of General Movable Objects in Large 3D Maps,"Detection and Tracking of General
Movable Objects in Large 3D Maps
Nils Bore, Johan Ekekrantz, Patric Jensfelt and John Folkesson
Robotics, Perception and Learning Lab
Royal Institute of Technology (KTH)
Stockholm, SE-100 44, Sweden
Email: {nbore, ekz, patric,"
9cfb3a68fb10a59ec2a6de1b24799bf9154a8fd1,Semi-supervised learning in Spectral Dimensionality Reduction,"Semi-supervised learning in
Spectral Dimensionality Reduction
Maryam Mehdizadeh
This thesis is presented for the degree of
Masters by Research
of The University of Western Australia
Department of Computer Science & Software Engineering.
June 20, 2016"
9cf69de9e06e39f7f7ce643b3327bf69be8b9678,SHREC ’ 18 track : Recognition of geometric patterns over 3 D models,"SHREC’18 track: Recognition of geometric patterns
over 3D models
S Biasotti, E. Moscoso Thompson, L Bathe, S Berretti, A. Giachetti, T
Lejemble, N Mellado, K Moustakas, Iason Manolas, Dimitrios Dimou, et al.
To cite this version:
S Biasotti, E. Moscoso Thompson, L Bathe, S Berretti, A. Giachetti, et al.. SHREC’18 track: Recog-
nition of geometric patterns over 3D models. Eurographics Workshop on 3D Object Retrieval, 2018.
<hal-01774423>
https://hal-mines-paristech.archives-ouvertes.fr/hal-01774423
HAL Id: hal-01774423
Submitted on 30 Apr 2018
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,"
9cd7487e0eed11dabc94dd867178204c53eb2270,Self-Organizing Traffic Lights : A Pedestrian Oriented Approach,"Self-Organizing Traffic Lights: A Pedestrian
Oriented Approach
Jessica S. Souza1, Cesar A. M. Ferreira2, Cassio E. dos Santos Jr3, Victor H. C. Melo4, William Robson Schwartz4
Computer Science Department, Federal University of Minas Gerais, Belo Horizonte, Brazil
the vehicular and pedestrian traffic. One of"
9c341221e19fac7a5e38b9fe5c62361f780a7f08,Productivity Effects of Information Diffusion in Networks Paper 234,"A research and education initiative at the MIT
Sloan School of Management
Productivity Effects of Information
Diffusion in Networks
Paper 234
July 2007
Sinan Aral
Erik Brynjolfsson
Marshall Van Alstyne
For more information,
please visit our website at  http://digital.mit.edu
or contact the Center directly at
or 617-253-7054"
9c49e4ba8ad0ba4634fe9306fb612695ed2b8cae,Satellite Imagery Feature Detection using Deep Convolutional Neural Network: A Kaggle Competition,"Satellite Imagery Feature Detection using
Deep Convolutional Neural Network: A Kaggle Competition
Vladimir Iglovikov
True Accord
Sergey Mushinskiy
Open Data Science
Vladimir Osin
AeroState"
9c065dfb26ce280610a492c887b7f6beccf27319,Learning from Video and Text via Large-Scale Discriminative Clustering,"Learning from Video and Text via Large-Scale Discriminative Clustering
Antoine Miech1,2
Jean-Baptiste Alayrac1,2
Piotr Bojanowski2
Ivan Laptev 1,2
Josef Sivic1,2,3
´Ecole Normale Sup´erieure
Inria
CIIRC"
9c59304a619b7d503be95bd560f90be976a5309a,DenseASPP for Semantic Segmentation in Street Scenes,"DenseASPP for Semantic Segmentation in Street Scenes
Maoke Yang
Kun Yu
Chi Zhang
DeepMotion
Zhiwei Li
Kuiyuan Yang
{maokeyang, kunyu, chizhang, zhiweili,"
9294bba3ea887a16ce6332cedf40eb389b8aeb73,DISOCCLUSION OF 3 D LIDAR POINT CLOUDS USING RANGE IMAGES,"DISOCCLUSION OF 3D LIDAR POINT CLOUDS USING RANGE IMAGES
P. Biasuttia, b, J-F. Aujola, M. Br´edifc, A. Bugeaub
Universit´e de Bordeaux, IMB, CNRS UMR 5251, INP, 33400 Talence, France.
Universit´e de Bordeaux, LaBRI, CNRS UMR 5800, 33400 Talence, France.
Universit´e Paris-Est, LASTIG MATIS, IGN, ENSG, F-94160 Saint-Mand´e, France.
{pierre.biasutti,
KEY WORDS: LiDAR, MMS, Range Image, Disocclusion, Inpainting, Variational, Segmentation, Point Cloud"
92c2dd6b3ac9227fce0a960093ca30678bceb364,On Color Texture Normalization for Active Appearance Models,"Provided by the author(s) and NUI Galway in accordance with publisher policies. Please cite the published
version when available.
Title
On color texture normalization for active appearance models
Author(s)
Ionita, Mircea C.; Corcoran, Peter M.; Buzuloiu, Vasile
Publication
009-05-12
Publication
Information
Ionita, M. C., Corcoran, P., & Buzuloiu, V. (2009). On Color
Texture Normalization for Active Appearance Models. Image
Processing, IEEE Transactions on, 18(6), 1372-1378.
Publisher
Link to
publisher's
version
http://dx.doi.org/10.1109/TIP.2009.2017163
Item record
http://hdl.handle.net/10379/1350"
921aaac9b33ec6a417bfc8bb0e21e11e743342c2,Image enhancement for improving face detection under non-uniform lighting conditions,"978-1-4244-1764-3/08/$25.00 ©2008 IEEE
ICIP 2008"
923412acb90ed2acbb29290147a567f39d2dfc95,FACS-BASED EMOTIONAL FACIAL EXPRESSIONS FACSGen : A Tool to Synthesize Emotional Facial Expressions through Systematic Manipulation of Facial Action Units,"J Nonverbal Behav
DOI 10.1007/s10919-010-0095-9
O R I G I N A L P A P E R
FACSGen: A Tool to Synthesize Emotional Facial
Expressions Through Systematic Manipulation of Facial
Action Units
Etienne B. Roesch • Lucas Tamarit •
Lionel Reveret • Didier Grandjean •
David Sander • Klaus R. Scherer
Ó Springer Science+Business Media, LLC 2010"
92f57973d84404505fdaac530d0009b7bafdae68,Two-Dimensional-Oriented Linear Discriminant Analysis for Face Recognition,"Two-Dimensional-Oriented Linear Discriminant Analysis
for Face Recognition
Muriel Visani, Christophe Garcia, Jean-Michel Jolion
To cite this version:
Muriel Visani, Christophe Garcia, Jean-Michel Jolion. Two-Dimensional-Oriented Linear Discriminant
Analysis for Face Recognition. Springer. International Conference on Computer Vision and Graphics
(ICCVG 04), Sep 2004, Warsaw, Poland. pp.1008-1017, 2004, Computational Imaging and Vision.
<hal-00452438>
HAL Id: hal-00452438
https://hal.archives-ouvertes.fr/hal-00452438
Submitted on 9 Sep 2013
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
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broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,"
927ad0dceacce2bb482b96f42f2fe2ad1873f37a,Interest-Point based Face Recognition System,"Interest-Point based Face Recognition System
Interest-Point based Face Recognition System
Cesar Fernandez and Maria Asuncion Vicente
Miguel Hernandez University
Spain
. Introduction
Among  all  applications  of  face  recognition  systems,  surveillance  is  one  of  the  most
hallenging ones. In such an application, the goal is to detect known criminals in crowded
environments, like airports or train stations. Some attempts have been made, like those of
Tokio (Engadget, 2006) or Mainz (Deutsche Welle, 2006), with limited success.
The first task to be carried out in an automatic surveillance system involves the detection of
ll the faces in the images taken by the video cameras. Current face detection algorithms are
highly reliable and thus, they will not be the focus of our work. Some of the best performing
examples are the Viola-Jones algorithm (Viola & Jones, 2004) or the Schneiderman-Kanade
lgorithm (Schneiderman & Kanade, 2000).
The second task to be carried out involves the comparison of all detected faces among the
database of known criminals. The ideal behaviour of an automatic system performing this
task  would  be  to  get  a  100%  correct  identification  rate,  but  this  behaviour  is  far  from  the
apabilities  of  current  face  recognition  algorithms.  Assuming  that  there  will  be  false
identifications,  supervised  surveillance  systems  seem  to  be  the  most  realistic  option:  the"
92b748f2629b3227a9c56bc9e580f45eb5bdfba5,Novel Adaptive Eye Detection and Tracking for Challenging Lighting Conditions,"Version
This is the Accepted Manuscript version. This version is defined in the NISO
recommended practice RP-8-2008 http://www.niso.org/publications/rp/
Suggested Reference
Rezaei, M., & Klette, R. (2013). Novel Adaptive Eye Detection and Tracking for
Challenging Lighting Conditions. In Lecture Notes in Computer Science Vol. 7729
(pp. 427-440). Daejeon, Korea: Springer Berlin Heidelberg.
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-
642-37484-5_35
Copyright
Items in ResearchSpace are protected by copyright, with all rights reserved, unless
otherwise indicated. Previously published items are made available in accordance
with the copyright policy of the publisher.
http://www.sherpa.ac.uk/romeo/issn/0302-9743/
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm"
92a044df6c37571aac25756252dda27676492bb5,IMPLEMENTATION OF REAL-TIME SYSTEM ON FPGA BOARD FOR HUMAN ' S FACE DETECTION AND TRACKING AUTHOR,"IMPLEMENTATION OF REAL-TIME SYSTEM ON FPGA BOARD FOR HUMAN'S
FACE DETECTION AND TRACKING AUTHOR
MOHD NORHAFIZ HASHIM
A project report submitted in partial
Fulfillment of the requirement for the award of the
Degree of Master Electrical Engineering
Fakulti Kejuruteraan Elektrik dan Elektronik
Universiti Tun Hussein Onn Malaysia
JANUARY 2014"
92d614b732b89cbdfc0e726f9ac057de5a17d997,A Taught-Obesrve-Ask (TOA) Method for Object Detection with Critical Supervision,"A Taught-Obesrve-Ask (TOA) Method for Object
Detection with Critical Supervision
Chi-Hao Wu, Qin Huang, Siyang Li, and C.-C. Jay Kuo, Fellow, IEEE"
9282239846d79a29392aa71fc24880651826af72,Classification of extreme facial events in sign language videos,"Antonakos et al. EURASIP Journal on Image and Video Processing 2014, 2014:14
http://jivp.eurasipjournals.com/content/2014/1/14
RESEARCH
Open Access
Classification of extreme facial events in sign
language videos
Epameinondas Antonakos1,2*, Vassilis Pitsikalis1 and Petros Maragos1"
921c33d3036818d4e2d5f879c667eaa669729adb,Object Recognition using Geometric Properties and a variant of Boosting 1 ),"Object Recognition using Geometric Properties and a
variant of Boosting1)
Martin Antenreiter and Peter Auer
Chair of Information Technology (CiT), University of Leoben,
Austria
{martin.antenreiter,
http://www.unileoben.ac.at/˜infotech/"
9207671d9e2b668c065e06d9f58f597601039e5e,Face Detection Using a 3D Model on Face Keypoints,"Face Detection Using a 3D Model on
Face Keypoints
Adrian Barbu, Gary Gramajo"
923e9b437a55853120f1778f55fcd956d81260f8,Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection,"Noname manuscript No.
(will be inserted by the editor)
Zoom Out-and-In Network with Map Attention Decision
for Region Proposal and Object Detection
Hongyang Li · Yu Liu · Wanli Ouyang · Xiaogang Wang
Received: date / Accepted: date"
927ba64123bd4a8a31163956b3d1765eb61e4426,Customer satisfaction measuring based on the most significant facial emotion,"Customer satisfaction measuring based on the most
significant facial emotion
Mariem Slim, Rostom Kachouri, Ahmed Atitallah
To cite this version:
Mariem Slim, Rostom Kachouri, Ahmed Atitallah. Customer satisfaction measuring based on the
most significant facial emotion. 15th IEEE International Multi-Conference on Systems, Signals
Devices (SSD 2018), Mar 2018, Hammamet, Tunisia. <hal-01790317>
HAL Id: hal-01790317
https://hal-upec-upem.archives-ouvertes.fr/hal-01790317
Submitted on 11 May 2018
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
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broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
927ac98da38db528b780f14996bb02b05009c9cc,Hand pose estimation through semi-supervised and weakly-supervised learning,"Hand Pose Estimation through Semi-Supervised and Weakly-Supervised Learning
Natalia Neverovaa,∗, Christian Wolfa, Florian Neboutb, Graham W. Taylorc
Universit´e de Lyon, INSA-Lyon, CNRS, LIRIS, F-69621, France
Awabot SAS, France
School of Engineering, University of Guelph, Canada"
92a5af98c47bce7208d043c7c418633cd537701c,Improving Image Captioning by Leveraging Knowledge Graphs,"Improving Image Captioning by Leveraging Knowledge Graphs
Yimin Zhou, Yiwei Sun, Vasant Honavar
Artificial Intelligent Research Laboratory
The Pennsylvania State University"
92f0e02c9f4e95098452d0fd78ba46cd6e7b1f6d,Dynamic machine learning for supervised and unsupervised classification. (Apprentissage automatique dynamique pour la classification supervisée et non supervisée),"Dynamic machine learning for supervised and
unsupervised classification
Adela-Maria Sîrbu
To cite this version:
Adela-Maria Sîrbu. Dynamic machine learning for supervised and unsupervised classification. Machine
Learning [cs.LG]. INSA de Rouen, 2016. English. <NNT : 2016ISAM0002>. <tel-01402052>
HAL Id: tel-01402052
https://tel.archives-ouvertes.fr/tel-01402052
Submitted on 24 Nov 2016
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de
recherche français ou étrangers, des laboratoires"
929bd1d11d4f9cbc638779fbaf958f0efb82e603,"Improving the Performance of Facial Expression Recognition Using Dynamic, Subtle and Regional Features","This is the author’s version of a work that was submitted/accepted for pub-
lication in the following source:
Zhang, Ligang & Tjondronegoro, Dian W. (2010) Improving the perfor-
mance of facial expression recognition using dynamic, subtle and regional
features.
In Kok, WaiWong, B. Sumudu, U. Mendis, & Abdesselam ,
Bouzerdoum (Eds.) Neural Information Processing. Models and Applica-
tions, Lecture Notes in Computer Science, Sydney, N.S.W, pp. 582-589.
This file was downloaded from: http://eprints.qut.edu.au/43788/
(cid:13) Copyright 2010 Springer-Verlag
Conference proceedings published, by Springer Verlag, will be available
via Lecture Notes in Computer Science http://www.springer.de/comp/lncs/
Notice: Changes introduced as a result of publishing processes such as
opy-editing and formatting may not be reflected in this document. For a
definitive version of this work, please refer to the published source:
http://dx.doi.org/10.1007/978-3-642-17534-3_72"
920a92900fbff22fdaaef4b128ca3ca8e8d54c3e,LEARNING PATTERN TRANSFORMATION MANIFOLDS WITH PARAMETRIC ATOM SELECTION,"LEARNING PATTERN TRANSFORMATION MANIFOLDS WITH PARAMETRIC ATOM
SELECTION
Elif Vural and Pascal Frossard
Ecole Polytechnique F´ed´erale de Lausanne (EPFL)
Signal Processing Laboratory (LTS4)
Switzerland-1015 Lausanne"
923ede53b0842619831e94c7150e0fc4104e62f7,Masked correlation filters for partially occluded face recognition,"978-1-4799-9988-0/16/$31.00 ©2016 IEEE
ICASSP 2016"
92679c8cff92442f39de3405c21c8028162fe56a,Temporal 3 D ConvNets using Temporal Transition Layer,"Temporal 3D ConvNets using Temporal Transition Layer
Ali Diba1
, Mohsen Fayyaz2, Vivek Sharma3, A.Hossein Karami4, M.Mahdi Arzani4,
Rahman Yousefzadeh4, Luc Van Gool1
ESAT-PSI, KU Leuven, 2University of Bonn, 3CV:HCI, KIT, Karlsruhe, 4Sensifai"
92b61b09d2eed4937058d0f9494d9efeddc39002,BoxCars: Improving Vehicle Fine-Grained Recognition using 3D Bounding Boxes in Traffic Surveillance,"Under review in IJCV manuscript No.
(will be inserted by the editor)
BoxCars: Improving Vehicle Fine-Grained Recognition using
D Bounding Boxes in Traf‌f‌ic Surveillance
Jakub Sochor · Jakub ˇSpaˇnhel · Adam Herout
Received: date / Accepted: date"
926ca7ce14332f9f848c28565d0f2f9a2d1e35a8,Impaired facial and vocal emotion decoding in schizophrenia is underpinned by basic perceptivo-motor deficits,"Cognitive Neuropsychiatry
ISSN: 1354-6805 (Print) 1464-0619 (Online) Journal homepage: http://www.tandfonline.com/loi/pcnp20
Impaired facial and vocal emotion decoding in
schizophrenia is underpinned by basic perceptivo-
motor deficits
C. Mangelinckx, J. B. Belge, P. Maurage & E. Constant
To cite this article: C. Mangelinckx, J. B. Belge, P. Maurage & E. Constant (2017): Impaired facial
nd vocal emotion decoding in schizophrenia is underpinned by basic perceptivo-motor deficits,
Cognitive Neuropsychiatry, DOI: 10.1080/13546805.2017.1382342
To link to this article:  http://dx.doi.org/10.1080/13546805.2017.1382342
Published online: 03 Oct 2017.
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9263ca6211ec39469f0daa8790ccaecbd5898423,Exploring Models and Data for Remote Sensing Image Caption Generation,"Exploring Models and Data for
Remote Sensing Image Caption Generation
Xiaoqiang Lu, Senior Member, IEEE, Binqiang Wang, Xiangtao Zheng, and Xuelong Li, Fellow, IEEE"
92bbb5364f65b1b0fccc27032b688e1ff0dafa00,RED-Net: A Recurrent Encoder–Decoder Network for Video-Based Face Alignment,"IJCV special issue (Best papers of ECCV 2016) manuscript No.
(will be inserted by the editor)
RED-Net:
A Recurrent Encoder-Decoder Network for Video-based Face Alignment
Xi Peng · Rogerio S. Feris · Xiaoyu Wang · Dimitris N. Metaxas
Submitted: April 19 2017 / Revised: December 12 2017"
92373095869f1b9e93823f0bd16bb8527c1665dc,How face blurring affects body language processing of static gestures in women and men,"Social Cognitive and Affective Neuroscience, 2018, 590–603
doi: 10.1093/scan/nsy033
Advance Access Publication Date: 14 May 2018
Original article
How face blurring affects body language processing
of static gestures in women and men
Alice Mado Proverbio, Laura Ornaghi, and Veronica Gabaro
Department of Psychology, Neuro-MI Center for Neuroscience, University of Milano-Bicocca, Milano, Italy
Correspondence should be addressed to Alice Mado Proverbio, Department of Psychology, University of Milano-Bicocca, piazza dell’Ateneo Nuovo 1, U6
Building, Milano, Italy. E-mail:"
a3f1db123ce1818971a57330d82901683d7c2b67,Poselets and their applications in high-level computer vision,"Poselets and Their Applications in High-Level
Computer Vision
Lubomir Bourdev
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2012-52
http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-52.html
May 1, 2012"
a32fc1edb2d23d117f47d86f79ac88c9dc3a45b1,Evaluation of LDA based face verification with respect to available computational resources,"Evaluation of LDA-based Face Veriflcation with
respect to Available Computational Resources
Jacek Czyz and Luc Vandendorpe
Telecommunication Laboratory
Universit¶e catholique de Louvain
Place du Levant, 1348 Louvain-la-Neuve, Belgium"
a3fdba7975494c34552b33cf839f21d62734e6f0,Excavate Condition-invariant Space by Intrinsic Encoder,"Excavate Condition-invariant Space by Intrinsic Encoder
Jian Xu, Chunheng Wang, Cunzhao Shi, and Baihua Xiao
Institute of Automation, Chinese Academy of Sciences (CASIA)"
a35d85c2efd1fb090267980ebb3fd7b6381e3b74,Very Low Resolution Image Classification,"Very Low Resolution Image Classification
Adam Vest1
Muhammadabdullah Jamal2
Boqing Gong2
University of Louisville 2 University of Central Florida"
a3ccf7fa5c130c8bcd20cbcd356ad7a47cdd4296,SymNMF: nonnegative low-rank approximation of a similarity matrix for graph clustering,"Journal of Global Optimization manuscript No.
(will be inserted by the editor)
SymNMF: Nonnegative Low-Rank Approximation of
Similarity Matrix for Graph Clustering
Da Kuang · Sangwoon Yun · Haesun Park
The final publication is available at Springer via http://dx.doi.org/10.1007/s10898-014-0247-2."
a3f78cc944ac189632f25925ba807a0e0678c4d5,Action Recognition in Realistic Sports Videos,"Action Recognition in Realistic Sports Videos
Khurram Soomro and Amir Roshan Zamir"
a3a34c1b876002e0393038fcf2bcb00821737105,Face Identification across Different Poses and Illuminations with a 3D Morphable Model,"Face Identification across Different Poses and Illuminations
with a 3D Morphable Model
V. Blanz, S. Romdhani, and T. Vetter
University of Freiburg
Georges-K¨ohler-Allee 52, 79110 Freiburg, Germany
fvolker, romdhani,"
a3a97bb5131e7e67316b649bbc2432aaa1a6556e,Role of the hippocampus and orbitofrontal cortex during the disambiguation of social cues in working memory.,"Cogn Affect Behav Neurosci
DOI 10.3758/s13415-013-0170-x
Role of the hippocampus and orbitofrontal cortex
during the disambiguation of social cues in working memory
Robert S. Ross & Matthew L. LoPresti & Karin Schon &
Chantal E. Stern
# Psychonomic Society, Inc. 2013"
a38b4eaa536dd2b709eb725bf2a2192b162cbf06,Multi-modal Deep Learning Approach for Flood Detection,"Multi-modal Deep Learning Approach for Flood Detection
Laura Lopez-Fuentes1,2,3, Joost van de Weijer2, Marc Bolaños 4, Harald Skinnemoen3
University of the Balearic Islands, Palma, Spain
Autonomous University of Barcelona, Barcelona, Spain
AnsuR Technologies, Oslo, Norway, 4Universitat de Barcelona, Barcelona, Spain"
a3fe284b029269ad5f071dd37bb137593c67dfc2,Feature Learning for the Image Retrieval Task,"Feature Learning for the Image Retrieval Task
Aakanksha Rana, Joaquin Zepeda, Patrick Perez
Technicolor R&I, 975 avenue des Champs Blancs, CS 17616, 35576 Cesson Sevigne, France"
a378fa57d6638fe89772a4a4ac12d07087e81a6d,Automatic Person Verification Using Speech and Face Information,"Automatic Person Verification
Using Speech and Face Information
A Dissertation
Presented to
The School of Microelectronic Engineering
Faculty of Engineering and Information Technology
Grif‌f‌ith University
Submitted in Fulfillment
of the Requirements of the Degree of
Doctor of Philosophy
Conrad Sanderson, BEng (Hons)
August 2002
[revised February 2003]"
a32ebfa79097fdf5c9d44d2f74e33b7c8343425c,A Deeper Look at Dataset Bias,"Chapter 2
A Deeper Look at Dataset Bias
Tatiana Tommasi, Novi Patricia, Barbara Caputo and Tinne Tuytelaars"
a3ad32249fcc85ef9dfb2ea575b0c636edcb2da9,Local Appearance-based 3D Face Recognition,"Universit¨at Karlsruhe
Fakult¨at f¨ur Informatik
Institut f¨ur Theoretische Informatik (ITI)
Prof. Dr. A. Waibel
WS 2006/07
Studienarbeit
Local Appearance-based 3D Face
Recognition
Hua Gao
November 2006
Betreuer: M.Sc. H. K. Ekenel
Dr.-Ing. R. Stiefelhagen
Prof. Dr. A. Waibel"
a357bc79b1ac6f2474ff6b9f001419745a8bc21c,Toward More Realistic Face Recognition Evaluation Protocols for the YouTube Faces Database,"Toward More Realistic Face Recognition Evaluation Protocols
for the YouTube Faces Database
Yoanna Mart´ınez-D´ıaz, Heydi M´endez-V´azquez, Leyanis L´opez-Avila
Advanced Technologies Application Center (CENATAV)
7A ♯21406 Siboney, Playa, P.C. 12200, Havana, Cuba
Leonardo Chang
L. Enrique Sucar
Massimo Tistarelli
Tecnol´ogico de Monterrey,
Estado de Mexico, Mexico
INAOE,
University of Sassari,
Puebla, Mexico
Sassari, Italy"
a3a6e3cadfed3c0a520e4417fc27da561324fbc6,Facing the challenge of teaching emotions to individuals with low- and high-functioning autism using a new Serious game: a pilot study,"Serret et al. Molecular Autism 2014, 5:37
http://www.molecularautism.com/content/5/1/37
R ES EAR CH
Facing the challenge of teaching emotions to
individuals with low- and high-functioning autism
using a new Serious game: a pilot study
Sylvie Serret1*, Stephanie Hun1, Galina Iakimova2, Jose Lozada3, Margarita Anastassova3, Andreia Santos1,
Stephanie Vesperini1 and Florence Askenazy4
Open Access"
a3431ac9b157f7f510b855988107b065ce1deef8,Launch These Manhunts! Shaping the Synergy Maps for Multi-camera Detection,"Launch These Manhunts! Shaping the Synergy Maps for Multi-camera
Detection
Muhammad Owais Mehmood1, Sebastien Ambellouis1 and Catherine Achard2
LEOST, French Institute of Science and Technology for Transport, Spatial Planning, Development and Networks,
Sorbonne Universites, UPMC Univ. Paris 06 and CNRS UMR 7222, ISIR, F-75005, Paris, France
Villeneuve D’Ascq, France
Keywords:
People Localization, Ghost Pruning, Multi-camera Surveillance, Shape Representations, Pattern Recognition."
a3d8887625040d3c07f779ac5353452fd48058e4,A Study of Activity Recognition and Questionable Observer Detection,"International Journal of Computer Applications (0975 – 8887)
Volume 182 – No. 15, September 2018
A Study of Activity Recognition and Questionable
Observer Detection
D. M. Anisuzzaman
Department of Computer Science and Engineering,
Ahsanullah University of Science and Technology,
Dhaka, Bangladesh"
a3eab933e1b3db1a7377a119573ff38e780ea6a3,Sparse Representation for accurate classification of corrupted and occluded facial expressions,"978-1-4244-4296-6/10/$25.00 ©2010 IEEE
ICASSP 2010"
a32c5138c6a0b3d3aff69bcab1015d8b043c91fb,Video redaction: a survey and comparison of enabling technologies,"Downloaded From: https://www.spiedigitallibrary.org/journals/Journal-of-Electronic-Imaging on 9/19/2018
Terms of Use: https://www.spiedigitallibrary.org/terms-of-use
Videoredaction:asurveyandcomparisonofenablingtechnologiesShaganSahAmeyaShringiRaymondPtuchaAaronBurryRobertLoceShaganSah,AmeyaShringi,RaymondPtucha,AaronBurry,RobertLoce,“Videoredaction:asurveyandcomparisonofenablingtechnologies,”J.Electron.Imaging26(5),051406(2017),doi:10.1117/1.JEI.26.5.051406."
a3fcf3d32a5a4fcc83027e3d367ecc0df3ec4f64,Iris Recognition: On the Segmentation of Degraded Images Acquired in the Visible Wavelength,"Iris Recognition: On the Segmentation
of Degraded Images Acquired
in the Visible Wavelength
Hugo Proenc¸ a"
a348c7d55b97a3dc77eb8890b63f2c228bf94504,A Symmetry Based Face Detection Technique,"A Symmetry Based Face Detection Technique
Sriparna Saha, Student Member, IEEE and Sanghamitra Bandyopadhyay, Senior Member, IEEE
Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India-700108
Email: fsriparna r,"
a3bf7248e38ed6f9456f0f309b36470c5c0dabd0,Predicting the Driver's Focus of Attention: the DR(eye)VE Project,"Predicting the Driver’s Focus of Attention:
the DR(eye)VE Project
Andrea Palazzi∗, Davide Abati∗, Simone Calderara, Francesco Solera, and Rita Cucchiara"
a30e987e9909a4e307c35809275cf80431211f22,Automatic Sapstain Detection in Processed Timber Through Image Feature Analysis,"Automatic Sapstain Detection in Processed
Timber Through Image Feature Analysis
Jeremiah Deng
The Information Science
Discussion Paper Series
Number 2009/04
April 2009
ISSN 1177-455X"
a34d75da87525d1192bda240b7675349ee85c123,Naive-Deep Face Recognition: Touching the Limit of LFW Benchmark or Not?,"Naive-Deep Face Recognition: Touching the Limit of LFW Benchmark or Not?
Erjin Zhou
Face++, Megvii Inc.
Zhimin Cao
Face++, Megvii Inc.
Qi Yin
Face++, Megvii Inc."
a32dadf343f811e6837b8ac5bab873674fa626b3,Moving Object Detection and Tracking in Forward Looking Infra-Red Aerial Imagery,"Moving Object Detection and Tracking
in Forward Looking Infra-Red Aerial Imagery
Subhabrata Bhattacharya, Haroon Idrees, Imran Saleemi, Saad Ali
nd Mubarak Shah"
a3b87364aa68b371ca9831d333b934402fbc3713,Which neural mechanisms mediate the effects of a parenting intervention program on parenting behavior: design of a randomized controlled trial,"Kolijn et al. BMC Psychology  (2017) 5:9
DOI 10.1186/s40359-017-0177-0
Open Access
ST UD Y P R O T O C O L
Which neural mechanisms mediate the
effects of a parenting intervention program
on parenting behavior: design of a
randomized controlled trial
Laura Kolijn1,2,3, Saskia Euser1,2,3, Bianca G. van den Bulk1,2,3, Renske Huffmeijer1,2,3,
Marinus H. van IJzendoorn1,2,3 and Marian J. Bakermans-Kranenburg1,2,3*"
a3ed080262f130051d2a02e846f5d227a440b294,ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time,"ContextNet: Exploring Context and Detail
for Semantic Segmentation in Real-time
Rudra P K Poudel, Ujwal Bonde, Stephan Liwicki, and Christopher Zach
Toshiba Research, Cambridge, UK"
a30869c5d4052ed1da8675128651e17f97b87918,Fine-Grained Comparisons with Attributes,"Fine-Grained Comparisons with Attributes
Aron Yu and Kristen Grauman"
a3017bb14a507abcf8446b56243cfddd6cdb542b,Face Localization and Recognition in Varied Expressions and Illumination,"Face Localization and Recognition in Varied
Expressions and Illumination
Hui-Yu Huang, Shih-Hang Hsu"
a3ebacd8bcbc7ddbd5753935496e22a0f74dcf7b,"First International Workshop on Adaptive Shot Learning for Gesture Understanding and Production ASL4GUP 2017 Held in conjunction with IEEE FG 2017, in May 30, 2017, Washington DC, USA","First International Workshop on Adaptive Shot Learning
for Gesture Understanding and Production
ASL4GUP 2017
Held in conjunction with IEEE FG 2017, in May 30, 2017,
Washington DC, USA"
a3a6a6a2eb1d32b4dead9e702824375ee76e3ce7,Multiple Local Curvature Gabor Binary Patterns for Facial Action Recognition,"Multiple Local Curvature Gabor Binary
Patterns for Facial Action Recognition
Anıl Y¨uce, Nuri Murat Arar and Jean-Philippe Thiran
Signal Processing Laboratory (LTS5),
´Ecole Polytechnique F´ed´erale de Lausanne, Switzerland"
a3c8c7da177cd08978b2ad613c1d5cb89e0de741,A Spatio-temporal Approach for Multiple Object Detection in Videos Using Graphs and Probability Maps,"A Spatio-temporal Approach for Multiple
Object Detection in Videos Using Graphs
nd Probability Maps
Henrique Morimitsu1(B), Roberto M. Cesar Jr.1, and Isabelle Bloch2
University of S˜ao Paulo, S˜ao Paulo, Brazil
Institut Mines T´el´ecom, T´el´ecom ParisTech, CNRS LTCI, Paris, France"
a3fd234763844663f72a8fa22a076eeadce7245c,DelugeNets: Deep Networks with Efficient and Flexible Cross-Layer Information Inflows,"DelugeNets: Deep Networks with Efficient and Flexible Cross-layer Information
Inflows
Jason Kuen1
Xiangfei Kong1
Gang Wang2
Yap-Peng Tan1
Nanyang Technological University1 Alibaba Group2"
a3d071d2a5c11329aa324b2cae6b7b6ca7800213,C-VQA: A Compositional Split of the Visual Question Answering (VQA) v1.0 Dataset,"C-VQA: A Compositional Split of the
Visual Question Answering (VQA) v1.0 Dataset
Aishwarya Agrawal∗, Aniruddha Kembhavi†, Dhruv Batra‡, Devi Parikh‡
Virginia Tech, †Allen Institute for Artificial Intelligence, ‡Georgia Institute of Technology
{dbatra,"
a32f693e98ae35da5508c8eee245a876b6e130a1,Small Sample Scene Categorization from Perceptual Relations Ilan Kadar and,"Small Sample Scene Categorization from Perceptual Relations
Ilan Kadar and Ohad Ben-Shahar
Dept. of Computer Science, Ben-Gurion University
Beer-Sheva, Israel"
a38045ed82d6800cbc7a4feb498e694740568258,African American and Caucasian males ' evaluation of racialized female facial averages,"UNLV Theses, Dissertations, Professional Papers, and Capstones
5-2010
African American and Caucasian males' evaluation
of racialized female facial averages
Rhea M. Watson
University of Nevada Las Vegas
Follow this and additional works at: http://digitalscholarship.unlv.edu/thesesdissertations
Part of the Cognition and Perception Commons, Race and Ethnicity Commons, and the Social
Psychology Commons
Repository Citation
Watson, Rhea M., ""African American and Caucasian males' evaluation of racialized female facial averages"" (2010). UNLV Theses,
Dissertations, Professional Papers, and Capstones. 366.
http://digitalscholarship.unlv.edu/thesesdissertations/366
This Thesis is brought to you for free and open access by Digital It has been accepted for inclusion in UNLV Theses, Dissertations,
Professional Papers, and Capstones by an authorized administrator of Digital For more information, please contact"
a3f69a073dcfb6da8038607a9f14eb28b5dab2db,3D-Aided Deep Pose-Invariant Face Recognition,Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18)
a3d836f601c9c462cddaf1c7175246617dd8f3af,MyIdea - Sensors Specifications and Acquisition Protocol,"MyIdea - Sensors Specifications and
Acquisition Protocol
DIUF-RR 05-12
By alphabetical order :
Bruno Dumas1
Jean Hennebert2
Andreas Humm3
Rolf Ingold4
Dijana Petrovska5
Catherine Pugin6
Didier Von Rotz7
Initial: January 2005 - Revision 1.2.5 as of June 2005
Computer Science Department Research Report
D´epartement d’Informatique - Departement f¨ur Informatik • Universit´e de Fribourg -
Universit¨at Freiburg • Chemin du mus´ee 3 • 1700 Fribourg • Switzerland
phone +41 (26) 300 84 65
fax +41 (26) 300 97 31
http://diuf.unifr.ch
DIUF, University of Fribourg, Ch. du Mus´ee 3, 1700 Fribourg, Switzerland,
DIUF, University of Fribourg, Ch. du Mus´ee 3, 1700 Fribourg, Switzerland,"
793e896c2f66fb66bfc6c834f2678cf349af4e20,Incorporating Computation Time Measures During Heterogeneous Features Selection in a Boosted Cascade People Detector,"Incorporating Computation Time Measures during
Heterogeneous Features Selection in a Boosted Cascade
People Detector
Alhayat Ali Mekonnen, Frédéric Lerasle, Ariane Herbulot, Cyril Briand
To cite this version:
Alhayat Ali Mekonnen, Frédéric Lerasle, Ariane Herbulot, Cyril Briand. Incorporating Computation
Time Measures during Heterogeneous Features Selection in a Boosted Cascade People Detector. Inter-
national Journal of Pattern Recognition and Artificial Intelligence, World Scientific Publishing, 2016,
0 (8), pp.1655022. <10.1142/S0218001416550223>. <hal-01300472>
HAL Id: hal-01300472
https://hal.archives-ouvertes.fr/hal-01300472
Submitted on 11 Apr 2016
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
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broad, or from public or private research centers.
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destinée au dépôt et à la diffusion de documents"
79b669abf65c2ca323098cf3f19fa7bdd837ff31,Efficient tensor based face recognition,"Deakin Research Online
This is the published version:
Rana, Santu, Liu, Wanquan, Lazarescu, Mihai and Venkatesh, Svetha 2008, Efficient tensor
ased face recognition, in ICPR 2008 : Proceedings of the 19th International Conference on
Pattern Recognition, IEEE, Washington, D. C., pp. 1-4.
Available from Deakin Research Online:
http://hdl.handle.net/10536/DRO/DU:30044585
Reproduced with the kind permissions of the copyright owner.
Personal use of this material is permitted. However, permission to reprint/republish this
material for advertising or promotional purposes or for creating new collective works for
resale or redistribution to servers or lists, or to reuse any copyrighted component of this work
in other works must be obtained from the IEEE.
Copyright : 2008, IEEE"
79335495e54446541a3655d145911beba7c29d7d,The face inversion effect in opponent-stimulus rivalry,"ORIGINAL RESEARCH ARTICLE
published: 15 May 2014
doi: 10.3389/fnhum.2014.00295
The face inversion effect in opponent-stimulus rivalry
Malte Persike*, Bozana Meinhardt-Injac and Günter Meinhardt
Research Methods and Statistics, Department of Psychology, Institute of Psychology, Johannes Gutenberg University Mainz, Mainz, Germany
Edited by:
Davide Rivolta, University of East
London, UK
Reviewed by:
Guillaume A. Rousselet, University
of Glasgow, UK
Timo Stein, Charité
Universitätsmedizin Berlin, Germany
*Correspondence:
Malte Persike, Research Methods
nd Statistics, Department of
Psychology, Institute of Psychology,
Johannes Gutenberg University
Mainz, Mainz, Rheinland-Pfalz,"
79e7f1e13e8aafee6558729804cf1284134815b3,Deep Representation Learning for Domain Adaptation of Semantic Image Segmentation,"BENBIHI, GEIST, PRADALIER: DEEP REPRESENTATION LEARNING
Deep Representation Learning for Domain
Adaptation of Semantic Image Segmentation
Assia Benbihi1
Matthieu Geist2
Cedric Pradalier1
UMI 2958 GT-CNRS – GeorgiaTech
Lorraine
Metz, France
Université de Lorraine
CNRS LIEC UNR 7360,
Metz, France"
790aa543151312aef3f7102d64ea699a1d15cb29,Confidence-Weighted Local Expression Predictions for Occlusion Handling in Expression Recognition and Action Unit Detection,"Confidence-Weighted Local Expression Predictions for
Occlusion Handling in Expression Recognition and Action
Unit detection
Arnaud Dapogny1
Kevin Bailly1
Séverine Dubuisson1
Sorbonne Universités, UPMC Univ Paris 06, CNRS, ISIR UMR 7222
place Jussieu 75005 Paris"
7918698ffa86cdd6123bc2f1f613be1ab38c0d2f,Learning to Recognize Faces in Realistic Conditions,"Learning to Recognize Faces in Realistic Conditions
Anonymous Author(s)
Affiliation
Address
email"
794cf037dac115755cd15295d8c5fc1c00242548,The City Infant Faces Database: A validated set of infant facial expressions,"Behav Res (2018) 50:151–159
DOI 10.3758/s13428-017-0859-9
The City Infant Faces Database: A validated set of infant
facial expressions
Rebecca Webb 1 & Susan Ayers 1 & Ansgar Endress 2
Published online: 15 February 2017
# The Author(s) 2017. This article is published with open access at Springerlink.com"
79d13b74952449667c769be76dac9065db1acc22,STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY,"FINE-GRAINED RECOGNITION:
DATA, RECOGNITION, AND APPLICATION
A DISSERTATION
SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE
AND THE COMMITTEE ON GRADUATE STUDIES
OF STANFORD UNIVERSITY
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
Jonathan Krause
October 2016"
791eb376d4db96376eba3ef804657c5f0ba7229a,SAFE: Secure authentication with Face and Eyes,"SAFE: Secure Authentication with Face and Eyes
Arman Boehm(cid:91), Dongqu Chen§, Mario Frank(cid:91), Ling Huang†,
Cynthia Kuo(cid:93), Tihomir Lolic(cid:91), Ivan Martinovic(cid:63), Dawn Song(cid:91)
(cid:91) University of California, Berkeley; † Intel Labs; (cid:93) Nokia Research; (cid:63) Oxford University; § Yale University"
7960336aed2aa701c147ccfe36d153046f1500bc,Occlusion Reasoning for Multiple Object Visual Tracking Dissertation Occlusion Reasoning for Multiple Object Visual Tracking Occlusion Reasoning for Multiple Object Visual Tracking,"OCCLUSION REASONING
FOR MULTIPLE OBJECT VISUAL TRACKING
ZHENG WU
Dissertation submitted in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
BOSTON
UNIVERSITY"
795cea6b95af22238600aa129b1975e83c531858,Sentence Directed Video Object Codetection.,"IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Sentence Directed Video Object Codetection
Haonan Yu, Student Member, IEEE and Jeffrey Mark Siskind, Senior Member, IEEE"
79fc892abaf44a84a758268efd4d1b9e6b64ecf5,Leveraging Random Label Memorization for Unsupervised Pre-Training,"Leveraging Random Label Memorization for Unsupervised Pre-Training
Vinaychandran Pondenkandath * 1 Michele Alberti * 1 Sammer Puran 1 Rolf Ingold 1 Marcus Liwicki 1 2"
79d3e7321e50be745bef92ba1405b486bd1f133d,Emotion Recognition in Simulated Social Interactions,"This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TAFFC.2018.2799593, IEEE
> TAFFC-2017-04-0117.R1 <
Transactions on Affective Computing
Emotion Recognition in Simulated Social
Interactions
C. Mumenthaler, D. Sander, and A. S. R. Manstead"
79dd787b2877cf9ce08762d702589543bda373be,Face detection using SURF cascade,"Face Detection Using SURF Cascade
Jianguo Li, Tao Wang, Yimin Zhang
Intel Labs China"
7954a1bd6e693da8f2ae69ad01233e937d600e9b,The Lov\'asz-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks,"Accepted as a conference paper at CVPR 2018
The Lov´asz-Softmax loss: A tractable surrogate for the optimization of the
intersection-over-union measure in neural networks
Maxim Berman Amal Rannen Triki Matthew B. Blaschko
Dept. ESAT, Center for Processing Speech and Images
KU Leuven, Belgium"
7902309d3c5ab2e1e3a1f08503dc39108e1639dc,Look into Person: Joint Body Parsing & Pose Estimation Network and A New Benchmark,"Look into Person: Joint Body Parsing & Pose
Estimation Network and A New Benchmark
Xiaodan Liang, Ke Gong, Xiaohui Shen, and Liang Lin"
796e333796024acf662fe76c4761607eaaa98a5d,Nested multi-instance image classification,"Nested multi-instance image classification
Anonymous Authors"
79fc3c10ce0d0f48b25c8cf460048087c97e2e90,Variational Bi-domain Triplet Autoencoder,"Variational learning across domains with triplet
information
Rita Kuznetsova1,2, Oleg Bakhteev1,2 and Alexandr Ogaltsov2,3
Moscow Institute of Physics and Technology
National Research University Higher School of Economics
{rita.kuznetsova,
Antiplagiat Company"
7985ac55e170273dd0ffa6bd756e588bab301d57,Mind's eye: A recurrent visual representation for image caption generation,"Mind’s Eye: A Recurrent Visual Representation for Image Caption Generation
Xinlei Chen1, C. Lawrence Zitnick2
Carnegie Mellon University. 2Microsoft Research Redmond.
A good image description is often said to “paint a picture in your mind’s
eye.” The creation of a mental image may play a significant role in sentence
omprehension in humans [3]. In fact, it is often this mental image that is
remembered long after the exact sentence is forgotten [5, 7]. As an illus-
trative example, Figure 1 shows how a mental image may vary and increase
in richness as a description is read. Could computer vision algorithms that
omprehend and generate image captions take advantage of similar evolving
visual representations?
Recently, several papers have explored learning joint feature spaces for
images and their descriptions [2, 4, 9]. These approaches project image
features and sentence features into a common space, which may be used
for image search or for ranking image captions. Various approaches were
used to learn the projection, including Kernel Canonical Correlation Anal-
ysis (KCCA) [2], recursive neural networks [9], or deep neural networks
[4]. While these approaches project both semantics and visual features to
common embedding, they are not able to perform the inverse projection.
That is, they cannot generate novel sentences or visual depictions from the"
79f6a8f777a11fd626185ab549079236629431ac,Pradeep RavikumarDiscriminative Object Categorization with External Semantic Knowledge,"Copyright
Sung Ju Hwang"
794dbf68bae49bb571d1b2461289a6bb629de875,The Lovász Hinge: A Convex Surrogate for Submodular Losses,"The Lov´asz Hinge: A Convex Surrogate for Submodular
Losses
Jiaqian Yu∗1 and Matthew B. Blaschko†2
Center for Visual Computing, CentraleSup´elec, Inria, Universit´e Paris-Saclay,
Grande Voie des Vignes, 92295 Chˆatenay-Malabry, France
Center for Processing Speech and Images, Dept. Elektrotechniek, KU Leuven,
Kasteelpark Arenberg 10, 3001 Leuven, Belgium"
7903bccf6f98436f4916419e5450d1bb890876ea,Analysis of Spatiotemporal Ensemble Data Using Machine Learning,"Institut für Visualisierung und Interaktive Systeme
Universität Stuttgart
Universitätsstraße 38
D - 70569 Stuttgart
Masterarbeit
Analysis of
Spatiotemporal Ensemble Data
Using Machine Learning
Stefan Scheller
Studiengang:
Informatik
Prüfer:
Betreuer:
Prof. Dr. Thomas Ertl
Dr. Steffen Frey
Gleb Tkachev, M. Sc.
Dipl.-Phys. Oliver Fernandes
Beginn am:
Beendet am:
. November 2017"
790bce6cbe30ef9bc4431c988d0d747da1c6bb1d,Salient Object Detection Using Window Mask Transferring with Multi-layer Background Contrast,"Salient Object Detection using Window Mask
Transferring with Multi-layer Background
Contrast
Quan Zhou1, Shu Cai1, Shaojun Zhu2, and Baoyu Zheng1
College of Telecom & Inf Eng, Nanjing Univ of Posts & Telecom, P.R. China
Dept. of Comput & Inf Sci, University of Pennsylvania Philadelphia, PA, USA"
79ab59b0fafda4c3f369dbb7fae61c620dabcd10,Identifying Human Face Profiles with Semi-Local Integral Invariants,"ROBUST GEOMETRICALLY INVARIANT FEATURES FOR 2D SHAPE MATCHING
AND 3D FACE RECOGNITION
Wei-Yang Lin
A dissertation submitted in partial fulfillment of
the requirements for the degree of
Doctor of Philosophy
(Electrical Engineering)
t the
UNIVERSITY OF WISCONSIN–MADISON"
792e656d2297d3b00da73c7a606eb6f539311c25,Force from Motion: Decoding Control Force of Activity in a First Person Video.,"Force from Motion: Decoding Control Force of
Activity in a First Person Video
Hyun Soo Park and Jianbo Shi"
7910d3a86e03f4c41fbbe8029fab115547be151b,Taming Adversarial Domain Transfer with Structural Constraints for Image Enhancement,"Taming Adversarial Domain Transfer
with Structural Constraints for Image Enhancement
Elias Vansteenkiste and Patrick Kern
Brighter.AI
Torstrasse 177, Berlin
{elias,
Figure 1: Our domain transfer techniques applied to the night-to-day, removing rain and removing fog applications"
79e39f3d0577b9c5a47b93eb6d75bec04d14c07a,Person tracking and following with 2D laser scanners,"Person Tracking and Following with 2D Laser Scanners
Angus Leigh1, Joelle Pineau1, Nicolas Olmedo2, and Hong Zhang2"
7950d67f7104e9bd82d957f0ed80f11982802397,Coupled Action Recognition and Pose Estimation from Multiple Views,"Noname manuscript No.
(will be inserted by the editor)
Coupled Action Recognition and Pose Estimation from
Multiple Views
Angela Yao (cid:1) Juergen Gall (cid:1) Luc Van Gool
Received: date / Accepted: date"
79f12f28b060221f3b80ea1b7b16779ef9362ca8,Investigations of face expertise in the social developmental disorders.,"Jason J.S. Barton,
MD, PhD, FRCPC
Rebecca L. Hefter, BSc
Mariya V.
Cherkasova, BSc
Dara S. Manoach,
Address correspondence and
reprint requests to Dr. Jason
J.S. Barton, Neuro-
ophthalmology Section D, VGH
Eye Care Center, 2550 Willow
Street, Vancouver, BC Canada
V5Z 3N9
Investigations of face expertise in the
social developmental disorders"
7917a7549f00306db8775d2d559460fc93dbde5a,DaP 2018 Proceedings of the Workshop on Dialogue and Perception,"DaP 2018
Proceedings of the Workshop on
Dialogue and Perception
Christine Howes, Simon Dobnik and Ellen Breitholtz (eds.)
Gothenburg, 14–15 June 2018"
79b50cd468fcdba8f3c841c9d28d84ff66fd97fd,What do Deep Networks Like to See?,"What do Deep Networks Like to See?
Sebastian Palacio∗
Federico Raue Damian Borth Andreas Dengel
Joachim Folz∗
German Research Center for Artificial Intelligence (DFKI)
J¨orn Hees
TU Kaiserslautern"
79f02a006c77f2d7fece8302bf54d851269a515a,A Study of Deep CNN-Based Classification of Open and Closed Eyes Using a Visible Light Camera Sensor,"Article
A Study of Deep CNN-Based Classification of Open
nd Closed Eyes Using a Visible Light Camera Sensor
Ki Wan Kim, Hyung Gil Hong, Gi Pyo Nam and Kang Ryoung Park *
Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu,
Seoul 100-715, Korea; (K.W.K.); (H.G.H.); (G.P.N.)
* Correspondence: Tel.: +82-10-3111-7022; Fax: +82-2-2277-8735
Received: 2 June 2017; Accepted: 28 June 2017; Published: 30 June 2017"
79ade61f677dcadfc2b46444d2e0275d25ca1f06,Nonnegative Tucker decomposition with alpha-divergence,"NONNEGATIVE TUCKER DECOMPOSITION WITH ALPHA-DIVERGENCE
Yong-Deok Kim §, Andrzej Cichocki †, Seungjin Choi §
§ Department of Computer Science, POSTECH, Korea
Brain Science Institute, RIKEN, Japan"
79815f31f42708fd59da345f8fa79f635a070730,Autoregressive Quantile Networks for Generative Modeling,"Autoregressive Quantile Networks for Generative Modeling
Georg Ostrovski * 1 Will Dabney * 1 R´emi Munos 1"
795bd86fc22ec544e7cd9b3d3c2ccabe72de54ec,Max Margin AND / OR Graph Learning for Efficient Articulated Object Parsing Long,"Noname manuscript No.
(will be inserted by the editor)
Max Margin AND/OR Graph Learning for Efficient Articulated Object
Parsing
Long (Leo) Zhu · Yuanhao Chen · Chenxi Lin · Alan Yuille
the date of receipt and acceptance should be inserted later"
796d5d1f6052cd600e183471a2354751883d8d5d,Feature Extraction Techniques Implementation Review and Case Study,"ISSN: 2278 – 909X
International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE)
Volume 4, Issue 12, December 2015
Feature Extraction Techniques
Implementation Review and Case Study
Uma Bhati
Department of Computer Science & Engineering
JSS Academy of Technical Education
Noida-201301
Krishna Nand Chaturvedi
Department of Computer Science & Engineering
JSS Academy of Technical Education
Noida-201301
utilizing
recognition"
794fd0fb684f90704e108677edb40d3ff6a85f8c,"EyeLad: Remote Eye Tracking Image Labeling Tool - Supportive Eye, Eyelid and Pupil Labeling Tool for Remote Eye Tracking Videos","EyeLad:Remote Eye Tracking Image Labeling Tool
Supportive eye, eyelid and pupil labeling tool for remote eye tracking videos.
Wolfgang Fuhl1, Thiago Santini1, David Geisler1, Thomas K¨ubler1, and Enkelejda Kasneci1
{wolfgang.fuhl, thiago.santini, david.geisler, thomas.kuebler,
Perception Engineering, University of Tbingen, Tbingen, Germany
Keywords:
data labeling, image processing, feature tracking, object detection, eye tracking data, remote eye tracking"
1debc9cd258a8c66045f01bbb50b6c9d15883256,Agent-Centric Risk Assessment: Accident Anticipation and Risky Region Localization,"Agent-Centric Risk Assessment:
Accident Anticipation and Risky Region Localization
Kuo-Hao Zeng∗† Shih-Han Chou∗ Fu-Hsiang Chan∗ Juan Carlos Niebles† Min Sun∗
Stanford University ∗National Tsing Hua University
{khzeng, {happy810705,"
1d3004953fd521adc8be457765ec978f0df1ac60,Exploiting Spatio-Temporal Scene Structure for Wide-Area Activity Analysis in Unconstrained Environments,"Exploiting Spatio-Temporal Scene Structure for
Wide-Area Activity Analysis in Unconstrained
Environments
Nandita M. Nayak, Yingying Zhu, and Amit K. Roy-Chowdhury"
1df314a1e4dce42fd9fab094b79a0f2a10ad0b03,People Detection in Fish-eye Top-views,
1d9497450f60b874eb6ecbf82e3d0808a6fe236c,Nonconvex proximal splitting with computational errors ∗,"Nonconvex proximal splitting with computational errors∗
Suvrit Sra
Max Planck Institute, T¨ubingen, Germany
Introduction
We study in this chapter large-scale nonconvex optimization problems with composite objective functions
that are composed of a differentiable possibly nonconvex cost and a nonsmooth but convex regularizer.
More precisely, we consider optimization problems of the form
minimize Φ(x) := f (x) + r(x),
where X ⊂ Rn is a compact convex set, f : Rn → R is a differentiable cost function and r : Rn → R is a
losed convex function. Further, we assume that the gradient ∇ f is Lipschitz continuous on X (denoted
f ∈ C1
L(X )), i.e.,
x ∈ X ,
∃L > 0 s.t. (cid:107)∇ f (x) − ∇ f (y)(cid:107) ≤ L(cid:107)x − y(cid:107)
for all
x, y ∈ X .
Throughout this chapter, (cid:107)·(cid:107) denotes the standard Euclidean norm.
Problem (1) generalizes the more thoroughly studied class of composite convex optimization prob-
lems [30], a class that has witnessed huge interest in machine learning, signal processing, statistics,
nd other related areas. We refer the interested reader to [2, 3, 21, 37] for several convex examples"
1d58d83ee4f57351b6f3624ac7e727c944c0eb8d,Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions,"Enhanced Local Texture
Feature Sets for Face
Recognition under Difficult
Lighting Conditions
Xiaoyang Tan and Bill Triggs
INRIA & Laboratoire Jean
Kuntzmann,
655 avenue de l'Europe, Montbonnot 38330, France"
1dd3a58ab363cb396bf36223fadc8d2341bfdb83,Picture: A probabilistic programming language for scene perception,"Picture: a probabilistic programming language for scene perception
Tejas D Kulkarni1, Pushmeet Kohli2, Joshua B Tenenbaum1, Vikash Mansinghka1
Brain and Cognitive Science, Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology. 2Microsoft Research Cambridge.
Probabilistic scene understanding systems aim to produce high-probability
descriptions of scenes conditioned on observed images or videos, typically ei-
ther via discriminatively trained models or generative models in an “analysis
y synthesis” framework. Discriminative approaches lend themselves to fast,
ottom-up inference methods and relatively knowledge-free, data-intensive
training regimes, and have been remarkably successful on many recognition
problems. Generative approaches hold out the promise of analyzing complex
scenes more richly and flexibly, but have been less widely embraced for two
main reasons: Inference typically depends on slower forms of approximate
inference, and both model-building and inference can involve considerable
problem-specific engineering to obtain robust and reliable results. These
factors make it difficult to develop simple variations on state-of-the-art mod-
els, to thoroughly explore the many possible combinations of modeling,
representation, and inference strategies, or to richly integrate complemen-
tary discriminative and generative modeling approaches to the same problem.
More generally, to handle increasingly realistic scenes, generative approaches
will have to scale not just with respect to data size but also with respect to"
1d59ffad091a5bffa5fe935b79f5bfc08d2e802d,A ug 2 01 7 1 Intensity Video Guided 4 D Fusion for Improved Highly Dynamic 3 D Reconstruction,"Intensity Video Guided 4D Fusion for
Improved Highly Dynamic 3D Reconstruction
Jie Zhang, Christos Maniatis, Luis Horna and Robert B. Fisher"
1d585d2a5a57549e734f1b6f77ebcf4377730aab,DATABASES FOR SPEAKER RECOGNITION : ACTIVITIES IN COST 250 WORKING GROUP 2,"DATABASES FOR SPEAKER RECOGNITION:
ACTIVITIES IN COST250 WORKING GROUP 2
Håkan Melin
KTH, Centre for Speech Technology (CTT),
Drottning Kristinas väg 31, SE-100 44 Stockholm, Sweden
Email:"
1d5fe82303712a70c1d231ead2ee03f042d8ad70,ImageNet pre-trained models with batch normalization,"ImageNet pre-trained models with batch normalization
Marcel Simon, Erik Rodner, Joachim Denzler
Computer Vision Group
Friedrich-Schiller-Universit¨at Jena, Germany
{marcel.simon, erik.rodner,"
1dd3faf5488751c9de10977528ab96be24616138,Detecting Anomalous Faces with 'No Peeking' Autoencoders,"Detecting Anomalous Faces with ‘No Peeking’ Autoencoders
Anand Bhattad 1 Jason Rock 1 David Forsyth 1"
1d7df7000a3e8fafa21679db4efe2ffedcfe0335,And the Committee on Graduate Studies of Stanford University in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy,"SEMANTIC IMAGE UNDERSTANDING: FROM THE WEB, IN
LARGE SCALE, WITH REAL-WORLD CHALLENGING DATA
A DISSERTATION
SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE
AND THE COMMITTEE ON GRADUATE STUDIES
OF STANFORD UNIVERSITY
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
Jia Li
November 2011"
1dce2617c751230b51d9264af99b8c651a2494c0,Multi-shot Pedestrian Re-identification via Sequential Decision Making,"Multi-shot Pedestrian Re-identification via Sequential Decision Making
Jianfu Zhang1, Naiyan Wang2 and Liqing Zhang1
Shanghai Jiao Tong University∗, 2TuSimple"
1dc45403839d6aefe65c6e7f2179d5ea697dfeac,DCT-based features for categorisation of social media in compressed domain,"DCT-based Features for Categorisation of Social
Media in Compressed Domain
Sebastian Schmiedeke, Pascal Kelm, Thomas Sikora
Communication Systems Group
Technische Universit¨at Berlin
Germany"
1d455f918062f66e86ed53cf258284abd6abd8fc,SMSnet: Semantic motion segmentation using deep convolutional neural networks,"SMSnet: Semantic Motion Segmentation
using Deep Convolutional Neural Networks
Johan Vertens∗
Abhinav Valada∗
Wolfram Burgard"
1dc07322715e093c560b30fdf1e168e58e9a9409,DRBF and IRBF Based Face Recognition and Extraction of Facial Expressions from the Blur Image,"Australian Journal of Basic and Applied Sciences, 8(3) March 2014, Pages: 61-68
AENSI Journals
Australian Journal of Basic and Applied Sciences
ISSN:1991-8178
Journal home page: www.ajbasweb.com
DRBF and IRBF Based Face Recognition and Extraction of Facial Expressions from the
Blur Image
M. Jayashree, 2Dr. D. Deepa, 3M. Rubhashree
PG Scholar, Department of Information Technology, Bannari Amman Institute of Technology, Sathyamangalam, TamilNadu, India.
2Associate  Professor,  Department  of  Information  Technology,  Bannari  Amman  Institute  of  Technology,  Sathyamangalam,  TamilNadu,
India.
Assistant  Professor,  Department  of  Computer  Science  and  Engineering,  Bannari  Amman  Institute  of  Technology,  Sathyamangalam,
TamilNadu, India.
A R T I C L E   I N F O
Article history:
Received  12  January  2014
Received in revised form 22
March 2014
Accepted 27 March  2014
Available online 2 April 2014"
1daaeae28270b06962eb6fcf812a368892b5dff4,Modeling Visual Context Is Key to Augmenting Object Detection Datasets,"Modeling Visual Context is Key to
Augmenting Object Detection Datasets
Nikita Dvornik, Julien Mairal, Cordelia Schmid
Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP(cid:63), LJK, 38000 Grenoble, France"
1d4f56a9bb093c52569917537a93c7671db28e6f,Real-time Tracking of Player Identities in Team Sports,"Real-time Tracking of Player
Identities in Team Sports
Dissertation
Nicolai Baron von Hoyningen-Huene"
1d03698a46ff12fdfaf4811528b3e7961dfd2fe6,Fast Exact Max-Kernel Search,"Fast Exact Max-kernel Search
Ryan R. Curtin
Parikshit Ram
Alexander G. Gray"
1d53aebe67d0e088e2da587fd6b08c8e8ed7f45c,A Selection Module for Large-Scale Face Recognition Systems,"A Selection module for large-scale face
recognition systems
Giuliano Grossi, Raffaella Lanzarotti, and Jianyi Lin
Dipartimento di Informatica, Universit`a degli Studi di Milano
Via Comelico 39/41, Milano, Italy"
1d729693a888a460ee855040f62bdde39ae273af,Photorealistic Face De-Identification by Aggregating Donors' Face Components,"Photorealistic Face de-Identification by Aggregating
Donors’ Face Components
Saleh Mosaddegh, Lo¨ıc Simon, Fr´ed´eric Jurie
To cite this version:
Saleh Mosaddegh, Lo¨ıc Simon, Fr´ed´eric Jurie. Photorealistic Face de-Identification by Aggre-
gating Donors’ Face Components. Asian Conference on Computer Vision, Nov 2014, Singapore.
pp.1-16, 2014. <hal-01070658>
HAL Id: hal-01070658
https://hal.archives-ouvertes.fr/hal-01070658
Submitted on 2 Oct 2014
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,
´emanant des ´etablissements d’enseignement et de"
1d6905e88f64ac826344d89c51ad8daea3b95e0e,Monocular Object Orientation Estimation using Riemannian Regression and Classification Networks,"Noname manuscript No.
(will be inserted by the editor)
Monocular Object Orientation Estimation using
Riemannian Regression and Classification Networks
Siddharth Mahendran · Ming Yang Lu · Haider Ali · Ren´e Vidal
the date of receipt and acceptance should be inserted later"
1d679b371c9dfd833cee0925de483562d2bc7d88,Face Recognition using 3D Summation Invariant Features,"­4244­0367­7/06/$20.00 ©2006 IEEE
ICME 2006"
1d93a1af770040cb8a64e96215884ee363a8f53a,Improved face recognition at a distance using light field camera & super resolution schemes,"Improved Face Recognition At A Distance Using Light
Field Camera & Super Resolution Schemes
R. Raghavendra* Kiran B. Raja*† Bian Yang* Christoph Busch*†
{raghavendra.ramachandra, kiran.raja, bian.yang,
*Norwegian Biometrics Laboratory
Hochschule Darmstadt - CASED
Gjøvik University College
802 Gjøvik, Norway
Haardtring 100,
64295 Darmstadt, Germany"
1d9306ea0f0239c88aecbcf0a48a11c964a0fcd4,3D facial expression recognition using maximum relevance minimum redundancy geometrical features,"Rabiu et al. EURASIP Journal on Advances in Signal Processing 2012, 2012:213
http://asp.eurasipjournals.com/content/2012/1/213
RESEARCH
Open Access
D facial expression recognition using
maximum relevance minimum redundancy
geometrical features
Habibu Rabiu*, M. Iqbal Saripan, Syamsiah Mashohor and Mohd Hamiruce Marhaban"
1de8f38c35f14a27831130060810cf9471a62b45,A Branch-and-Bound Framework for Unsupervised Common Event Discovery,"Int J Comput Vis
DOI 10.1007/s11263-017-0989-7
A Branch-and-Bound Framework for Unsupervised Common
Event Discovery
Wen-Sheng Chu1
Jeffrey F. Cohn1,2 · Daniel S. Messinger3
· Fernando De la Torre1 ·
Received: 3 June 2016 / Accepted: 12 January 2017
© Springer Science+Business Media New York 2017"
1da57510321fb8b25dc4d21844fb9afa4e40571e,Activity representation with motion hierarchies,"Int J Comput Vis
DOI 10.1007/s11263-013-0677-1
Activity representation with motion hierarchies
Adrien Gaidon · Zaid Harchaoui · Cordelia Schmid
Received: 17 May 2013 / Accepted: 20 November 2013
© Springer Science+Business Media New York 2013"
1d35a0955d2c406d7399d54117af58e2f434fa59,Efficient Learning of Relational Object Class Models,"Efficient Learning of Relational Object Class Models
Aharon Bar Hillel
Tomer Hertz
Daphna Weinshall
School of Computer Science and Engineering and the Center for Neural Computation
Hebrew university of Jerusalem, Israel 91904
{ aharonbh, tomboy,"
1d0a6759de0d55d15439b0367f0aa49c1e248c5c,"Networking in Autism: Leveraging Genetic, Biomarker and Model System Findings in the Search for New Treatments","...............................................................................................................................................................
REVIEW
Networking in Autism: Leveraging Genetic, Biomarker
nd Model System Findings in the Search for New
Treatments
Jeremy Veenstra-VanderWeele1,2,3,4 and Randy D Blakely*,1,3,4
Department of Psychiatry, Vanderbilt University School of Medicine, Nashville, TN, USA; 2Department of Pediatrics,
Vanderbilt University School of Medicine, Nashville, TN, USA; 3Department of Pharmacology, Vanderbilt University School of
Medicine, Nashville, TN, USA; 4Center for Molecular Neuroscience, Vanderbilt University School of Medicine, Nashville,
TN, USA
Autism Spectrum Disorder (ASD) is a common neurodevelopmental disorder affecting approximately 1% of children. ASD is
defined by core symptoms in two domains: negative symptoms of impairment in social and communication function, and
positive symptoms of restricted and repetitive behaviors. Available treatments are inadequate for treating both core
symptoms and associated conditions. Twin studies indicate that ASD susceptibility has a large heritable component. Genetic
studies have identified promising leads, with converging insights emerging from single-gene disorders that bear ASD
features, with particular interest in mammalian target of rapamycin (mTOR)-linked synaptic plasticity mechanisms. Mouse
models of these disorders are revealing not only opportunities to model behavioral perturbations across species, but also
evidence of postnatal rescue of brain and behavioral phenotypes. An intense search for ASD biomarkers has consistently
pointed to elevated platelet serotonin (5-HT) levels and a surge in brain growth in the first 2 years of life. Following a review of
the diversity of ASD phenotypes and its genetic origins and biomarkers, we discuss opportunities for translation of these"
1d1cc4936d72fd78d8001a20d4c1981b8f6c1ce9,Continuous Audio-visual Speech Recognition Continuous Audio-visual Speech Recognition,"IDIAP
Martigny - Valais - Suisse
Continuous Audio(cid:0)Visual
Speech Recognition
Juergen Luettin 

St(cid:0)ephane Dupont (cid:5)

IDIAP(cid:0)RR 	(cid:3)
th European Conference on Computer Vision(cid:1) Freiburg(cid:1) 
		
published in
D a l l e M o l l e
I n s t i t u t e
f o r P e r c e p t u a l A r t i f i c i a l
Intelligence (cid:0) P(cid:0)O(cid:0)Box 	 (cid:0)
Martigny (cid:0) Valais (cid:0) Switzerland
phone (cid:0)
 (cid:1)  (cid:1) 
  


(cid:0)
 (cid:1)  (cid:1) 
  

e(cid:4)mail secretariat(cid:0)idiap(cid:1)ch
internet http(cid:2)(cid:3)(cid:3)www(cid:1)idiap(cid:1)ch

 IDIAP(cid:5) email(cid:6) luettin(cid:7)idiap(cid:8)ch
 Facult(cid:9)e Polytechnique de Mons (cid:10) TCTS 
(cid:5) Bld(cid:8) Dolez(cid:5) B(cid:12) Mons(cid:5) Belgium(cid:5)"
1d0cbbe466647286bd73d41032a418b0e2265e7c,Fusion of face and gait for human recognition,"FUSION OF FACE AND GAIT FOR HUMAN RECOGNITION
RABIA JAFRI
(Under the Direction of Hamid R. Arabnia)"
1d5caa4128fc169bf961830048fe493ed0da0e98,Image Recognition with Deep Learning Techniques and TensorFlow,"Universitat Politècnica de Catalunya (UPC) - BarcelonaTech
Facultat d’Informàtica de Barcelona (FIB)
Image Recognition with Deep Learning
Techniques and TensorFlow
Maurici Yagües Gomà
Master in Innovation and Research in Informatics
Data Mining and Business Intelligence
Advisor: Jordi Torres Viñals
Universitat Politècnica de Catalunya (UPC)
Department of Computer Architecture (DAC)
Co-Advisor: Ruben Tous Liesa
Universitat Politècnica de Catalunya (UPC)
Department of Computer Architecture (DAC)
October 20, 2016"
1d776bfe627f1a051099997114ba04678c45f0f5,Deployment of Customized Deep Learning based Video Analytics On Surveillance Cameras,"Deployment of Customized Deep Learning based
Video Analytics On Surveillance Cameras
Pratik Dubal(cid:63), Rohan Mahadev(cid:63), Suraj Kothawade(cid:63),
Kunal Dargan, and Rishabh Iyer
AitoeLabs (www.aitoelabs.com)"
1d1e78bb93590a86ecfd2516f4e5789cc05d76f5,Generative Models,"FACE AUTHENTICATION BASED ON
LOCAL FEATURES AND
GENERATIVE MODELS
Fabien Cardinaux (a)
IDIAP–RR 05-85
JANUARY 2006
ESEARCHREPRORTIDIAPRue du Simplon 4IDIAP Research Institute1920 Martigny − Switzerlandwww.idiap.chTel: +41 27 721 77 11Email: Box 592Fax: +41 27 721 77 12"
1d524c57214384ad6a003c54b1918130744b69d2,Identifying Human-Object Interactions in Motionless Images by Modeling the Mutual Context of Objects and Human Poses,"International Journal of Science and Research (IJSR)
ISSN (Online): 2319-7064
Impact Factor (2012): 3.358
Identifying Human-Object Interactions in
Motionless Images by Modeling the Mutual Context
of Objects and Human Poses
A. N. Bhagat1, N. B. Pokale2
Department of Computer Engineering, TSSM,s Bhivrabai Sawant College Of Engineering and Research, Narhe, Pune, Maharashtra, India.
Associate Professor, Department of Computer Engineering, TSSM,s Bhivrabai Sawant College Of Engineering and Research, Narhe, Pune,
Maharashtra, India."
1d4c2dd3996cb3d87da6c35d72572637d3175ea5,Toward Storytelling From Visual Lifelogging: An Overview,"JOURNAL OF TRANSACTIONS ON HUMAN-MACHINE SYSTEMS JULY 2015
Towards Storytelling from
Visual Lifelogging: An Overview
Marc Bola˜nos∗, Mariella Dimiccoli∗, and Petia Radeva"
1da8178bfca7c76cae53ec34364d86c7d5713fdd,Pairwise Relational Networks using Local Appearance Features for Face Recognition,"Pairwise Relational Networks using Local
Appearance Features for Face Recognition
Bong-Nam Kang
Yonghyun Kim, Daijin Kim
Department of Creative IT Engineering
Department of Computer Science and Engineering
POSTECH, Korea
POSTECH, Korea"
1d7ecdcb63b20efb68bcc6fd99b1c24aa6508de9,The Hidden Sides of Names—Face Modeling with First Name Attributes,"The Hidden Sides of Names—Face Modeling
with First Name Attributes
Huizhong Chen, Student Member, IEEE, Andrew C. Gallagher, Senior Member, IEEE, and
Bernd Girod, Fellow, IEEE"
1d9bd24e65345258259ee24332141e371c6e4868,Learning Image Descriptors with Boosting,"Learning Image Descriptors with Boosting
Tomasz Trzcinski, Mario Christoudias, and Vincent Lepetit"
1d5d68bee741d81771e9224fe53806e85ed469aa,RATM: Recurrent Attentive Tracking Model,"RATM: Recurrent Attentive Tracking Model
Samira Ebrahimi Kahou, Vincent Michalski, and Roland Memisevic"
1dc94886ca1d4893208d38b18cb7ad1541a74b82,Weakly Supervised Training of Speaker Identification Models,"Weakly Supervised Training of Speaker Identification Models
Martin Karu, Tanel Alum¨ae
Department of Software Science
Tallinn University of Technology, Estonia"
1d6e0399ccf832585dcb3541f1b3ca8358f0c462,Data-Efficient Decentralized Visual SLAM,"Data-Efficient Decentralized Visual SLAM
Titus Cieslewski1, Siddharth Choudhary2 and Davide Scaramuzza1"
1dca96fdcab180133644442df4ad78eeec1aa00b,Learning from Synthetic Humans,"Learning from Synthetic Humans
G¨ul Varol∗†
Javier Romero‡
Xavier Martin†
Naureen Mahmood‡
Michael Black‡
Ivan Laptev∗
Cordelia Schmid†"
1df554e992baf60f2d0b7c1b563250ba19b8f8ff,3D Face Recognition Based on 3D Ridge Lines in Range Data,"-4244-1437-7/07/$20.00 ©2007 IEEE
I - 137
ICIP 2007"
1daf18f2b1bed861a9483de129223755260193fa,Near-Eye Display Gaze Tracking via Convolutional Neural Networks,"Near-Eye Display Gaze Tracking via Convolutional Neural Networks
Robert Konrad
Shikhar Shrestha
Paroma Varma"
1d4e1b4f37caf40dc70d211c6b2745195dfa6c3f,Facial Expression Recognition Using Interpolation Features,"Facial Expression Recognition Using Interpolation
Features
Jesús García-Ramírez, Ivan Olmos-Pineda, J. Arturo Olvera-López, and
Manuel Martín-Ortíz
Benemérita Universidad Autónoma de Puebla, Faculty of Computer Science, Puebla, México"
1d1f83023686d43fd4e8805c8e517dffb02d118c,Compiler Enhanced Scheduling for OpenMP for Heterogeneous Multiprocessors,"Compiler Enhanced Scheduling for OpenMP for
Heterogeneous Multiprocessors
Jyothi Krishna V S
IIT Madras"
1d692f37c2594ddb30518da27bfc0f5044690d09,Learning Depth From Single Images With Deep Neural Network Embedding Focal Length,"Learning Depth from Single Images with Deep
Neural Network Embedding Focal Length
Lei He, Guanghui Wang (Senior Member, IEEE) and Zhanyi Hu"
1d81293bc17a135cfd35912146c538cd81830381,Single camera multi-person tracking based on crowd simulation,"1st International Conference on Pattern Recognition (ICPR 2012)
November 11-15, 2012. Tsukuba, Japan
978-4-9906441-1-6 ©2012 IAPR"
1dd481c6158c6f6acb99ccbd4b64018b873f7dce,Multi-Modal Target Detection for Autonomous Wide Area Search and Surveillance,"Multi-Modal Target Detection for Autonomous Wide Area
Search and Surveillance
Toby P. Breckon, Anna Gaszczak, Jiwan Han, Marcin L. Eichner, Stuart E. Barnes
School of Engineering, Cranfield University, Bedfordshire, UK"
1db316f850ccd3600ce3526da4a611f8078ec33c,Estimating Vehicle Ego-Motion and Piecewise Planar Scene Structure from Optical Flow in a Continuous Framework,"Estimating Vehicle Ego-Motion and Piecewise Planar
Scene Structure from Optical Flow in a Continuous
Framework
Andreas Neufeld, Johannes Berger, Florian Becker,
Frank Lenzen, and Christoph Schn¨orr
IPA & HCI, University of Heidelberg, Germany"
1d2af64416882b2ae8fe4de51b85fdd7d561cfee,Headgear Accessories Classification Using an Overhead Depth Sensor,"Article
Headgear Accessories Classification Using an
Overhead Depth Sensor
Carlos A. Luna, Javier Macias-Guarasa ID , Cristina Losada-Gutierrez * ID , Marta Marron-Romera,
Manuel Mazo, Sara Luengo-Sanchez and Roberto Macho-Pedroso
Department of Electronics, University of Alcala, Ctra. Madrid-Barcelona, km.33,600, 28805 Alcalá de Henares,
Spain; (C.A.L.); (J.M.-G.); (M.M.-R.);
(M.M.); (S.L.-S.); (R.M.-P.)
* Correspondence: Tel.: +34-918-856-906; Fax: +34-918-856-591
Received: 22 June 2017; Accepted: 8 August 2017; Published: 10 August 2017"
1db44d94f6a4eaa3780c251446fa0fba14dfae44,Rapid prefrontal cortex activation towards aversively paired faces and enhanced contingency detection are observed in highly trait-anxious women under challenging conditions,"ORIGINAL RESEARCH
published: 10 June 2015
doi: 10.3389/fnbeh.2015.00155
Rapid prefrontal cortex activation
towards aversively paired faces and
enhanced contingency detection are
observed in highly trait-anxious
women under challenging conditions
Maimu Alissa Rehbein 1,2*, Ida Wessing 3, Pienie Zwitserlood 2,4, Christian Steinberg 1,2,
Annuschka Salima Eden 1,2, Christian Dobel 1,2 and Markus Junghöfer 1,2
Institute for Biomagnetism and Biosignalanalysis, University Hospital Münster, Münster, Germany, 2 Otto Creutzfeldt Center
for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany, 3 Department of Child and Adolescent
Psychiatry, University Hospital Münster, Münster, Germany, 4 Institute of Psychology, University of Münster, Münster,
Germany
Relative to healthy controls, anxiety-disorder patients show anomalies in classical
onditioning that may either result from, or provide a risk factor for, clinically relevant
nxiety. Here, we investigated whether healthy participants with enhanced anxiety
vulnerability show abnormalities in a challenging affective-conditioning paradigm, in
which many stimulus-reinforcer associations had to be acquired with only few learning
trials. Forty-seven high and low trait-anxious females underwent MultiCS conditioning,"
1d99282d00f7cf3e4d912428313848add8de8220,Comparing Attribute Classifiers for Interactive Language Grounding,"Proceedings of the 2015 Workshop on Vision and Language (VL’15), pages 60–69,
Lisbon, Portugal, 18 September 2015. c(cid:13)2015 Association for Computational Linguistics."
d341ff4e93ff4407251d00417c9a756a68b6f5be,Recognition of identical twins using fusion of various facial feature extractors,"Afaneh et al. EURASIP Journal on Image and Video
Processing  (2017) 2017:81
DOI 10.1186/s13640-017-0231-0
EURASIP Journal on Image
nd Video Processing
RESEARCH
Open Access
Recognition of identical twins using
fusion of various facial feature extractors
Ayman Afaneh1, Fatemeh Noroozi2 and Önsen Toygar1*"
d3b73e06d19da6b457924269bb208878160059da,IMPLEMENTATION OF AN AUTOMATED SMART HOME CONTROL FOR DETECTING HUMAN EMOTIONS VIA FACIAL DETECTION,"Proceedings of the 5th International Conference on Computing and Informatics, ICOCI 2015
1-13 August, 2015 Istanbul, Turkey. Universiti Utara Malaysia (http://www.uum.edu.my )
Paper No.
IMPLEMENTATION OF AN AUTOMATED SMART HOME
CONTROL FOR DETECTING HUMAN EMOTIONS VIA FACIAL
DETECTION
Lim Teck Boon1, Mohd Heikal Husin2, Zarul Fitri Zaaba3 and Mohd Azam
Osman4
Universiti Sains Malaysia, Malaysia,
Universiti Sains Malaysia, Malaysia,
Universiti Sains Malaysia, Malaysia,
Universiti Sains Malaysia, Malaysia,"
d3797366259182070c598e95cef8fff1ddb21f65,Distance-based Camera Network Topology Inference for Person Re-identification,"Distance-based Camera Network Topology Inference for Person Re-identification
Yeong-Jun Cho and Kuk-Jin Yoon
Computer Vision Laboratory, GIST, South Korea
{yjcho,"
d3b898fbd3e6d788020f07c8514ecbbcebde8b9b,A Comparison of People Counting Techniques via Video Scene Analysis,
d3565af8028c0ca486b452e55d0c577c34efb5a6,Face Recognition with VG-RAM Weightless Neural Networks,"Face Recognition with
VG-RAM Weightless Neural Networks
Alberto F. De Souza1, Claudine Badue1, Felipe Pedroni1, Elias Oliveira2,
Stiven Schwanz Dias1, Hallysson Oliveira1, and Soterio Ferreira de Souza
Departamento de Inform´atica
Departamento de Ciˆencia da Informa¸c˜ao
Universidade Federal do Esp´ırito Santo
Av. Fernando Ferrari, 514, 29075-910 - Vit´oria-ES, Brazil"
d331099a0e527bfe5f74c74d13c65b45fabcf88e,Dynamic 3D Scene Reconstruction and Enhancement,"Dynamic 3D Scene Reconstruction and Enhancement
Cansen Jiang, Yohan Fougerolle, David Fofi, Cédric Demonceaux
To cite this version:
Cansen Jiang, Yohan Fougerolle, David Fofi, Cédric Demonceaux. Dynamic 3D Scene Reconstruction
nd Enhancement. IAPR 19th International Conference in Image Analysis and Processing (ICIAP17),
Sep 2017, Catania, Italy. 2017. <hal-01569314>
HAL Id: hal-01569314
https://hal.archives-ouvertes.fr/hal-01569314
Submitted on 26 Jul 2017
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de
recherche français ou étrangers, des laboratoires"
d3b18ba0d9b247bfa2fb95543d172ef888dfff95,Learning and Using the Arrow of Time,"Learning and Using the Arrow of Time
Donglai Wei1, Joseph Lim2, Andrew Zisserman3 and William T. Freeman4,5
Harvard University 2University of Southern California
University of Oxford 4Massachusetts Institute of Technology 5Google Research
Figure 1: Seeing these ordered frames from videos, can you tell whether each video is playing forward or backward? (answer
elow1). Depending on the video, solving the task may require (a) low-level understanding (e.g. physics), (b) high-level
reasoning (e.g. semantics), or (c) familiarity with very subtle effects or with (d) camera conventions. In this work, we learn
nd exploit several types of knowledge to predict the arrow of time automatically with neural network models trained on
large-scale video datasets."
d360968cbcca774bf0b70bb0f3fc870aea121924,Deep Neural Ranking for Crowdsourced Geopolitical Event Forecasting,"Deep Neural Ranking for Crowdsourced
Geopolitical Event Forecasting
Giuseppe Nebbione1, Derek Doran2, Srikanth Nadella3, and Brandon Minnery3
Dept. of Electrical & Computer Engineering, University of Pavia, Italy
Dept. of Computer Science & Engineering,
Wright State University, Dayton, OH, USA
Wright State Research Institute, Dayton, OH, USA
{derek.doran, srikanth.nadella,"
d3c1612ae08241dadf6abd650663f4f9351abaf9,Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network,"Early Start Intention Detection of Cyclists Using Motion History
Images and a Deep Residual Network
Stefan Zernetsch, Viktor Kress, Bernhard Sick and Konrad Doll"
d33f75bc05bcce1779fce534da86cb039d11ed26,Occupancy Grid Mapping using Stereo Vision by Alwyn,"Occupancy Grid Mapping using Stereo Vision
Alwyn Johannes Burger
Thesis presented in partial fulfilment of the requirements for the degree of
Master of Engineering
t Stellenbosch University
Supervisors:
Dr C.E. van Daalen
Electrical and Electronic Engineering
Dr W.H. Brink
Mathematical Sciences
March 2015"
d33beb4f1477374fbcffd8e9df74ca2547eb77ee,Feature Selection for Tracker-Less Human Activity Recognition,"Feature Selection for tracker-less human activity
recognition(cid:63)
Plinio Moreno, Pedro Ribeiro, and Jos´e Santos-Victor
Instituto de Sistemas e Rob´otica & Instituto Superior T´ecnico
Portugal"
d318f3ca49f7f2159b9fc0face08eb284d5442dc,"Scene Text Detection via Holistic, Multi-Channel Prediction","Scene Text Detection via Holistic, Multi-Channel
Prediction
Cong Yao1,2, Xiang Bai1, Nong Sang1, Xinyu Zhou2, Shuchang Zhou2, Zhimin Cao2
Huazhong University of Science and Technology (HUST). Wuhan, China.
Email: {xbai, {zxy, zsc,
Megvii Inc. Beijing, China.
its great"
d3d71a110f26872c69cf25df70043f7615edcf92,Learning Compact Feature Descriptor and Adaptive Matching Framework for Face Recognition,"Learning Compact Feature Descriptor and Adaptive
Matching Framework for Face Recognition
Zhifeng Li, Senior Member, IEEE, Dihong Gong, Xuelong Li, Fellow, IEEE, and Dacheng Tao, Fellow, IEEE
improvements"
d3c99ec84197320443127d2f2f2a8f42c878b310,Final Report : GBS : Guidance By Semantics-Using High-Level Visual Inference to Improve Vision-based Mobile Robot Localization Report,"REPORT DOCUMENTATION PAGE
Form Approved OMB NO. 0704-0188
The  public  reporting  burden  for  this  collection  of  information  is  estimated  to  average  1  hour  per  response,  including  the  time  for  reviewing  instructions,
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PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS.
. REPORT DATE (DD-MM-YYYY)
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.  TITLE AND SUBTITLE
Final Report: GBS: Guidance By Semantics-Using High-Level
Visual Inference to Improve Vision-based Mobile Robot
Localization
5a.  CONTRACT NUMBER
W911NF-11-1-0090
5b.  GRANT NUMBER
. REPORT TYPE
Final Report"
d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9,STAIR Actions: A Video Dataset of Everyday Home Actions,
d31d4bb58f5dd67016e77352ac7600e2ba71e38f,Deep Learning Object Detection Methods for Ecological Camera Trap Data,"Deep Learning Object Detection Methods for
Ecological Camera Trap Data
Stefan Schneider∗, Graham W. Taylor†, Stefan C. Kremer∗
School of Computer Science, University of Guelph
{sschne01,
School of Engineering, University of Guelph
Vector Institute for Artificial Intelligence
Canadian Institute for Advanced Research"
d31e47f45041736c93ec23ba1dbaef6c311e76d6,TÜB İ TAK UZAY at TRECVID 2009 : High-Level Feature Extraction and Content-Based Copy Detection,"TÜBİTAK UZAY at TRECVID 2009: High-Level Feature Extraction and
Content-Based Copy Detection
Ahmet Saracoğlu1,2, Ersin Esen1,2, Medeni Soysal1,2, Tuğrul K. Ateş1,2, Berker Loğoğlu1, Mashar Tekin1,
Talha Karadeniz1, Müge Sevinç1, Hakan Sevimli1, Banu Oskay Acar1, Ünal Zubari1, Ezgi C. Ozan1,2,
Egemen Özalp1, Duygu Oskay Onur1, Sezin Selçuk1,
A. Aydın Alatan2, Tolga Çiloğlu2
TÜBİTAK Space Technologies Research Institute
Department of Electrical and Electronics Engineering, M.E.T.U.
{ahmet.saracoglu, ersin.esen, medeni.soysal, tugrul.ates, berker.logoglu, mashar.tekin,
talha.karadeniz, muge.sevinc, hakan.sevimli, banu.oskay, unal.zubari, ezgican.ozan, duygu.oskay,
sezin.selcuk,"
d3e9c5a63215a9c46bc61ec04df5285ac355e42c,Integration of visual and depth information for vehicle detection,pport (cid:13)(cid:13)de recherche(cid:13)ISSN0249-6399ISRNINRIA/RR--7703--FR+ENGRoboticsINSTITUTNATIONALDERECHERCHEENINFORMATIQUEETENAUTOMATIQUEIntegrationofvisualanddepthinformationforvehicledetectionAlexandrosMakris—MathiasPerrollaz—IgorParomtchik—ChristianLaugierN°7703July2011
d3d4700181179ed24b7afc5510ab1ea1cb8cfdc2,Development of an Efficient Face Recognition System Based on Linear and Nonlinear Algorithms,"IAES International Journal of Artificial Intelligence (IJ-AI)
Vol. 5, No. 2, June 2016, pp. 80~88
ISSN: 2252-8938
Development of an Efficient Face Recognition System Based on
Linear and Nonlinear Algorithms
Araoluwa Simileolu Filani, Adebayo Olusola Adetunmbi
Federal University of Technology, Akure, Ondo State, Nigeria"
d3612bcc772761b611365fe21c42eafb181338ef,Face and Street Detection with Asymmetric Haar Features,"Face and Street Detection with Asymmetric Haar Features
Geovany A. Ramirez
University of Texas at El Paso
500 W University Ave - El Paso TX 79968
500 W University Ave - El Paso TX 79968
Olac Fuentes
University of Texas at El Paso"
d3e51c0cfd6ae3d3082c2aa27fa1c73fa9662fdf,Isometry-invariant Surface Matching : Numerical Algorithms and Applications,"ISOMETRY-INVARIANT SURFACE
MATCHING: NUMERICAL ALGORITHMS
AND APPLICATIONS
MICHAEL M. BRONSTEIN
Technion - Computer Science Department - Ph.D. Thesis  PHD-2007-04 - 2007"
d309e414f0d6e56e7ba45736d28ee58ae2bad478,Efficient Two-Stream Motion and Appearance 3 D CNNs for Video Classification,"Efficient Two-Stream Motion and Appearance 3D CNNs for
Video Classification
Ali Diba
ESAT-KU Leuven
Ali Pazandeh
Sharif UTech
Luc Van Gool
ESAT-KU Leuven, ETH Zurich"
d3761354b7df1228eabf46032fd01a4109229d43,Selection of optimal narrowband multispectral images for face recognition. (Sélection des bandes spectrales optimales pour la reconnaissance des visages),"UNIVERSITY OF BURGUANDY
SPIM doctoral school
PhD from the University of Burgundy in
Computer Science
Presented by:
Hamdi Bouchech
Defense Date: January 26, 2015
Selection of optimal narrowband multispectral images for face
recognition
Thesis supervisor:
Dr. Sebti Foufou
Jury:
Frederic Morain-Nicolier, Professeur a I’IUT de Troyes, Rapporteur.
Pierre BONTON, Professeur à  l’ Université Blaise Pascal, retraité ,  Rapporteur.
Saida Bouakaz, Professeur à  l’ Université Claude Bernard Lyon 1, Examinatrice.
Pierre Gouton, Professeur à  l’ Université de Bourgogne, Examinateur.
Yassine Ruichek, Professeur à  l’ Université de Technologie de Belfort-Montbéliard,
Examinateur.
Sebti Foufou, Professeur à  l’ Université de Bourgogne, directeur de thèse."
d33c9fe66bad7a90e34e8bc1332b73147a30d202,Trace alignment algorithms for offline workload analysis of heterogeneous architectures,"Trace Alignment Algorithms for Offline Workload Analysis
of Heterogeneous Architectures
Muhammet Mustafa Ozdal
Intel Corporation
Hillsboro, OR 97124
Aamer Jaleel
Intel Corporation
Hudson, MA
Paolo Narvaez
Intel Corporation
Hudson, MA
Steven Burns
Intel Corporation
Hillsboro, OR
Ganapati Srinivasa
Intel Corporation
Hillsboro, OR"
d348197e47a8e081bd3f12a22bc52b055ecd8302,Unified Framework for Automated Person Re-identification and Camera Network Topology Inference in Camera Networks,"Unified Framework for Automated Person Re-identification and
Camera Network Topology Inference in Camera Networks
Yeong-Jun Cho, Jae-Han Park*, Su-A Kim*, Kyuewang Lee and Kuk-Jin Yoon
Computer Vision Laboratory, GIST, South Korea
{yjcho, qkrwogks, suakim, kyuewang,"
8ff3c7b46ab36f1d01e96681baf512859cc80a4d,Dynamics of alpha oscillations elucidate facial affect recognition in schizophrenia.,"Dynamics of alpha oscillations elucidate facial affect
recognition in schizophrenia
Tzvetan G. Popov & Brigitte S. Rockstroh & Petia Popova &
Almut M. Carolus & Gregory A. Miller"
8fc21217ee89c505930b540b716b11bab89d3bcd,Memory Efficient Nonuniform Quantization for Deep Convolutional Neural Network,"Memory Efficient Nonuniform Quantization for
Deep Convolutional Neural Network
Fangxuan Sun and Jun Lin"
8f6d05b8f9860c33c7b1a5d704694ed628db66c7,Non-linear dimensionality reduction and sparse representation models for facial analysis. (Réduction de la dimension non-linéaire et modèles de la représentations parcimonieuse pour l'analyse du visage),"Non-linear dimensionality reduction and sparse
representation models for facial analysis
Yuyao Zhang
To cite this version:
Yuyao Zhang. Non-linear dimensionality reduction and sparse representation models for facial analysis.
Medical Imaging. INSA de Lyon, 2014. English. <NNT : 2014ISAL0019>. <tel-01127217>
HAL Id: tel-01127217
https://tel.archives-ouvertes.fr/tel-01127217
Submitted on 7 Mar 2015
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
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émanant des établissements d’enseignement et de
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8f2e594f55ca1b1675d8bfef25922c97109cb599,An evil face? Verbal evaluative multi-CS conditioning enhances face-evoked mid-latency magnetoencephalographic responses,"Social Cognitive and Affective Neuroscience, 2017, 695–705
doi: 10.1093/scan/nsw179
Advance Access Publication Date: 22 December 2016
Original article
An evil face? Verbal evaluative multi-CS conditioning
enhances face-evoked mid-latency magnetoencephalo-
graphic responses
Markus Jungho¨ fer,1,2 Maimu Alissa Rehbein,1,2 Julius Maitzen,1
Sebastian Schindler,3,4 and Johanna Kissler3,4
Institute for Biomagnetism and Biosignalanalysis, University Hospital Mu¨ nster, Mu¨ nster D-48149, Germany,
Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Mu¨ nster, Mu¨ nster D-48151,
Germany, 3Department of Psychology, Affective Neuropsychology Unit and 4Center of Excellence Cognitive
Interaction Technology (CITEC), University of Bielefeld, Bielefeld D-33501, Germany
Correspondence should be addressed to Johanna Kissler, Department of Psychology, Affective Neuropsychology Unit, University of Bielefeld, Bielefeld
D-33501, Germany. E-mail:"
8fe43144c0ff36ffefca869eec0a63e71ca02049,D correlation filter based class-dependence feature analysis for face recognition,"This article appeared in a journal published by Elsevier. The attached
opy is furnished to the author for internal non-commercial research
nd education use, including for instruction at the authors institution
nd sharing with colleagues.
Other uses, including reproduction and distribution, or selling or
licensing copies, or posting to personal, institutional or third party
websites are prohibited.
In most cases authors are permitted to post their version of the
rticle (e.g. in Word or Tex form) to their personal website or
institutional repository. Authors requiring further information
regarding Elsevier’s archiving and manuscript policies are
encouraged to visit:
http://www.elsevier.com/copyright"
8f0c11a3332c434af11c01ee11ff7c492c7968da,Domain Adaptive Faster R-CNN for Object Detection in the Wild,"Domain Adaptive Faster R-CNN for Object Detection in the Wild
Yuhua Chen1 Wen Li1 Christos Sakaridis1 Dengxin Dai1
Luc Van Gool1,2
Computer Vision Lab, ETH Zurich
VISICS, ESAT/PSI, KU Leuven"
8fc730d22f33d08be927e5449f359dc15b5c3503,Measuring and modeling the perception of natural and unconstrained gaze in humans and machines,"CBMM Memo No. 059
November 28, 2016
Measuring and modeling the perception of natural
nd unconstrained gaze in humans and machines
Daniel Harari*, Tao Gao*, Nancy Kanwisher, Joshua Tenenbaum, Shimon
Ullman"
8f7babdf96ac2ceab69ee5101a0a2eda5a73f775,Using KL-divergence to focus Deep Visual Explanation,"Using KL-divergence to focus Deep Visual
Explanation
Housam Khalifa Bashier Babiker and Randy Goebel
Alberta Machine Intelligence Institute
Department of Computing Science University of Alberta
Edmonton, Alberta Canada T6G 2E8"
8f8c0243816f16a21dea1c20b5c81bc223088594,LOCAL DIRECTIONAL NUMBER BASED CLASSIFICATION AND RECOGNITION OF EXPRESSIONS USING SUBSPACE METHODS,
8f98e1e041e7d3e27397c268e85e815065329d2d,Hierarchical feed forward models for robust object recognition,"Hierarchical Feed-Forward Models for
Robust Object Recognition
Ingo Bax
Der Technischen Fakult¨at der Universit¨at Bielefeld vorgelegt zur Erlangung
des akademischen Grades Doktor der Ingenieurwissenschaften"
8f2e83f6d70b9e161ad714fee79ed6d23ae2a93f,Image Intelligent Detection Based on the Gabor Wavelet and the Neural Network,"Article
Image Intelligent Detection Based on the Gabor
Wavelet and the Neural Network
Yajun Xu 1, Fengmei Liang 1,*, Gang Zhang 1 and Huifang Xu 2
College of Information Engineering, Taiyuan University of Technology, Taiyuan 030024, China;
(Y.X.); (G.Z.)
Daqin Railway Co. Ltd., Taiyuan Railway Administration, Taiyuan 030013, China;
* Correspondence: Tel.: +86-186-0341-0966
Academic Editor: Angel Garrido
Received: 21 September 2016; Accepted: 11 November 2016; Published: 15 November 2016"
8fdfd4c5039cf7d70470a2a3ac52bfd229bcd4e2,Pushing the Limits of Radiology with Joint Modeling of Visual and Textual Information,"Pushing the Limits of Radiology with Joint Modeling of Visual and
Textual Information
Department of Computing, Macquarie University1
Sonit Singh1,2
DATA61, CSIRO2
Sydney, Australia"
8f2b348673e1b59a821a3d0ff6276acdc16a1a7f,Differential training in physical prevention and rehabilitation programmes for health and exercise,"This is the author’s version of a work that was submitted/accepted for pub-
lication in the following source:
Schollhorn, Wolfgang, Beckmann, Hendrik, & Davids, Keith W. (2010) Ex-
ploiting system fluctuations. Differential training in physical prevention and
rehabilitation programs for health and exercise. Medicina (Kaunas), 46(6),
pp. 365-373.
This file was downloaded from: http://eprints.qut.edu.au/41038/
(cid:13) Copyright 2010 Kauno Medicinos Universitetas
Notice: Changes introduced as a result of publishing processes such as
opy-editing and formatting may not be reflected in this document. For a
definitive version of this work, please refer to the published source:"
8fe7354a92b4c74c22dc0a253dfe7320487d22ab,LITERATURE SURVEY ON SPARSE REPRESENTATION FOR NEURAL NETWORK BASED FACE DETECTION AND RECOGNITION,"Circuits and Systems: An International Journal (CSIJ), Vol. 1, No.2, April 2014
LITERATURE SURVEY ON SPARSE
REPRESENTATION FOR NEURAL
NETWORK BASED FACE DETECTION AND
RECOGNITION
Raviraj Mane,Poorva Agrawal,
Nisha Auti CS Department SIT, Pune"
8fe9cd45280696574a6afc10e5a06eb1888d82ee,Illumination Invariant Face Recognition Using Thermal Infrared Imagery,"Illumination Invariant Face Recognition Using Thermal Infrared Imagery
Diego A. Socolinsky†
Christopher K. Eveland‡
Lawrence B. Wolff‡
Equinox Corporation
9 West 57th Street
Joshua D. Neuheisel†
Equinox Corporation
07 East Redwood Street
New York, NY 10019
Baltimore, MD 21202"
8f077eeeb9678a31e77a17a5c28c36699cf13f83,Gender classification of faces using adaboost,"Gender Classification of Faces Using Adaboost*
Rodrigo Verschae1,2,3, Javier Ruiz-del-Solar1,2, and Mauricio Correa1,2
Department of Electrical Engineering, Universidad de Chile
Center for Web Research, Department of Computer Science, Universidad de Chile
CMLA, ENS Cachan, France"
8f9c37f351a91ed416baa8b6cdb4022b231b9085,Generative Adversarial Style Transfer Networks for Face Aging,"Generative Adversarial Style Transfer Networks for Face Aging
Sveinn Palsson
D-ITET, ETH Zurich
Eirikur Agustsson
D-ITET, ETH Zurich"
8f5566fa00f8c79f4720e14084489e784688ab0b,The role of the amygdala in atypical gaze on emotional faces in autism spectrum disorders.,"The Journal of Neuroscience, July 11, 2012 • 32(28):9469 –9476 • 9469
Behavioral/Systems/Cognitive
The Role of the Amygdala in Atypical Gaze on Emotional
Faces in Autism Spectrum Disorders
Dorit Kliemann,1,2,3,4 Isabel Dziobek,2,3 Alexander Hatri,1,2,3 Ju¨rgen Baudewig,2,3 and Hauke R. Heekeren1,2,3,4
Department of Education and Psychology, 2Cluster of Excellence “Languages of Emotion,” and 3Dahlem Institute for Neuroimaging of Emotion (D.I.N.E),
Freie Universita¨t Berlin, 14195 Berlin, Germany, and 4Max Planck Institute for Human Development, 14195 Berlin, Germany
Reduced focus toward the eyes is a characteristic of atypical gaze on emotional faces in autism spectrum disorders (ASD). Along with the
typical gaze, aberrant amygdala activity during face processing compared with neurotypically developed (NT) participants has been
repeatedly reported in ASD. It remains unclear whether the previously reported dysfunctional amygdalar response patterns in ASD
support an active avoidance of direct eye contact or rather a lack of social attention. Using a recently introduced emotion classification
task, we investigated eye movements and changes in blood oxygen level-dependent (BOLD) signal in the amygdala with a 3T MRI scanner
in 16 autistic and 17 control adult human participants. By modulating the initial fixation position on faces, we investigated changes
triggered by the eyes compared with the mouth. Between-group interaction effects revealed different patterns of gaze and amygdalar
BOLD changes in ASD and NT: Individuals with ASD gazed more often away from than toward the eyes, compared with the NT group,
which showed the reversed tendency. An interaction contrast of group and initial fixation position further yielded a significant cluster of
mygdala activity. Extracted parameter estimates showed greater response to eyes fixation in ASD, whereas the NT group showed an
increase for mouth fixation.
The differing patterns of amygdala activity in combination with differing patterns of gaze behavior between groups triggered by direct
eye contact and mouth fixation, suggest a dysfunctional profile of the amygdala in ASD involving an interplay of both eye-avoidance"
8fbfdd249ebf5a83ed3c43f185d143375382cea4,Design and realisation of an audiovisual speech activity detector,"Technical Note PR-TN 2006/00169
Issued: 02/2006
Design and realisation of an audiovisual
speech activity detector
K.C. van Bree
Philips Research Europe
Unclassified
© Koninklijke Philips Electronics N.V. 2006"
8f9fa03690428cde478f1a27d4773f78d857b88f,Visual Recognition using Embedded Feature Selection for Curvature Self-Similarity,"Visual Recognition using Embedded Feature
Selection for Curvature Self-Similarity
Angela Eigenstetter
HCI & IWR, University of Heidelberg
Bj¨orn Ommer
HCI & IWR, University of Heidelberg"
8fd9c22b00bd8c0bcdbd182e17694046f245335f,Recognizing Facial Expressions in Videos,"Recognizing Facial Expressions in Videos
Lin Su, Matthew Balazsi"
8fb849fe51fbf4b56393cfef26397caef2a22fb0,Public Document Agreed Plans for Open Source Reference Software Document Evolution Executive Summary,"Project N° IST-2002-507634 - BioSecure
D2.2.1 – Revision: b3
2 March 2005
Contract Number :
Project Acronym :
Project Title :
Instrument :
Start Date of Project :
Duration :
Deliverable Number :
Title of Deliverable :
Contractual Due Date :
Actual Date of Completion :
IST-2002-507634
BioSecure
Biometrics for Secure Authentication
Network of Excellence
01 June, 2004
6 months
D2.2.1"
8fa3478aaf8e1f94e849d7ffbd12146946badaba,Attributes for classifier feedback,"Attributes for Classifier Feedback
Amar Parkash1 and Devi Parikh2
Indraprastha Institute of Information Technology (Delhi, India)
Toyota Technological Institute (Chicago, US)"
8fc60a7489b76641ceee5da9180a3ca76b18560d,AI Fairness for People with Disabilities: Point of View,"isabilities: Point of View
AI  Fairness for People with D
Shari Trewin, IBM Accessibility Research,"
8f772d9ce324b2ef5857d6e0b2a420bc93961196,Facial Landmark Point Localization using Coarse-to-Fine Deep Recurrent Neural Network,"MAHPOD et al.: CFDRNN
Facial Landmark Point Localization using
Coarse-to-Fine Deep Recurrent Neural Network
Shahar Mahpod, Rig Das, Emanuele Maiorana, Yosi Keller, and Patrizio Campisi,"
8fda2f6b85c7e34d3e23927e501a4b4f7fc15b2a,Feature Selection with Annealing for Big Data Learning,"Feature Selection with Annealing for Big Data
Learning
Adrian Barbu, Yiyuan She, Liangjing Ding, Gary Gramajo"
8f05c4c1b3c1ad31ec95ccb87bca24a884b5ad4c,Overhead Detection: Beyond 8-bits and RGB,"Overhead Detection: Beyond 8-bits and RGB
Eliza Mace1
Keith Manville1
Monica Barbu-McInnis1
Michael Laielli2
Matthew Klaric2
Samuel Dooley2
MITRE,
NGA,"
8f44e8e3a5b233642f53c50919422425146cc443,I Know How You Feel: Emotion Recognition with Facial Landmarks,"I Know How You Feel: Emotion Recognition with Facial Landmarks
Tooploox 2Polish-Japanese Academy of Information Technology 3Warsaw University of Technology
Ivona Tautkute1,2, Tomasz Trzcinski1,3 and Adam Bielski1"
8fcdeda0c2f4e265e2180eb5ed39f6548ae3ba99,A Generic Middle Layer for Image Understanding,"UNIVERSIT ¨AT HAMBURG
A Generic Middle Layer for Image
Understanding
Kasim Terzi´c
Doktorarbeit
Fakult¨at f¨ur Mathematik, Informatik und Naturwissenschaften
Fachbereich Informatik"
91edca64a666c46b0cbca18c3e4938e557eeb21a,Guiding InfoGAN with Semi-Supervision,"Guiding InfoGAN with Semi-Supervision
Adrian Spurr, Emre Aksan, and Otmar Hilliges
Advanced Interactive Technologies, ETH Zurich
{adrian.spurr, emre.aksan,"
9143742eac54dfc1025134b8bc10f12795916ba5,Getting Robots Unfrozen and Unlost in Dense Pedestrian Crowds,"Getting Robots Unfrozen and Unlost in Dense Pedestrian Crowds
Tingxiang Fan∗, Xinjing Cheng∗, Jia Pan†, Pinxin Long, Wenxi Liu, Ruigang Yang and Dinesh Manocha"
91aff7996ea9a7257517819f8079880b6f35c92b,The non-contact biometric identified bio signal measurement sensor and algorithms,"Technology and Health Care 26 (2018) S215–S228
DOI 10.3233/THC-174569
IOS Press
The non-contact biometric identified bio
signal measurement sensor and algorithms
Chan-Il Kim and Jong-Ha Lee∗
Department of Biomedical Engineering, School of Medicine, Keimyung University, Korea"
912f6a6ac8703e095d21e2049da4871cc6d4d23b,Partitioning Networks with Node Attributes by Compressing Information Flow,"Partitioning Networks with Node Attributes by
Compressing Information Flow
Laura M. Smith
Department of Mathematics
California State University
Fullerton, CA
Kristina Lerman
Information Sciences Institute
U. of Southern California
Marina del Rey, CA 90292
Linhong Zhu
Information Sciences Institute
U. of Southern California
Marina del Rey, CA 90292
Allon G. Percus
Claremont Graduate U.
Claremont, CA 91711"
91d83d20cc22bde6b4b06afc87f76a1b0140d4e2,Image Classification Based on Quantum KNN Algorithm,"Noname manuscript No.
(will be inserted by the editor)
Image Classification Based on Quantum KNN
Algorithm
Yijie Dang · Nan Jiang · Hao Hu ·
Zhuoxiao Ji · Wenyin Zhang
Received: date / Accepted: date"
91eae81dbba3013261292296bb929a18d73b447f,Utilization of Interest Point Detectors in Content Based Image Retrieval,"Ročník 2011
Číslo II
Utilization of Interest Point Detectors in Content Based Image Retrieval
M. Zukal 1, P. Číka1
Department of Telecommunications, Faculty of Electrical Engineering, BUT, Brno,
E-mail :
Purkyňova 118, Brno"
91b3aeca88e910be86f23a5bb6c1a2351eb23fae,Fast Low-Rank Shared Dictionary Learning for Image Classification,"TRANSACTIONS ON IMAGE PROCESSING, VOL. , NO. , JULY 2017
Fast Low-rank Shared Dictionary Learning
for Image Classification
Tiep Huu Vu, Student Member, IEEE, Vishal Monga, Senior Member, IEEE"
919e827c449ca77bcff4ce5f2ccbccdab8399ac6,Purple Triangle Orange Circle FC Layers,"Under review as a conference paper at ICLR 2018
GENERATIVE ENTITY NETWORKS: DISENTANGLING ENTI-
TIES AND ATTRIBUTES IN VISUAL SCENES USING PARTIAL
NATURAL LANGUAGE DESCRIPTIONS
Anonymous authors
Paper under double-blind review"
919d3067bce76009ce07b070a13728f549ebba49,Time Based Re-ranking for Web Image Search Ms .,"International Journal of Scientific and Research Publications, Volume 4, Issue 6, June 2014
ISSN 2250-3153
Time Based Re-ranking for Web Image Search
Ms. A.Udhayabharadhi *, Mr. R.Ramachandran **
* MCA Student, Sri Manakula Vinayagar Engineering College, Pondicherry-605106
** Assistant Professor dept of MCA, Sri Manakula Vinayagar Engineering College, Pondicherry-605106"
91ddac7d1d63c52cbe30fe27674b9c1e54bc584c,Development of EyeBlink and Face Corpora for Research in Human Computer Interaction,"(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 6, No. 5, 2015
Development of Eye-Blink and Face Corpora for
Research in Human Computer Interaction
Emmanuel Jadesola Adejoke.
Dept. of Computer science
Bingham University
Nassarawa, Nigeria
Ibiyemi Tunji Samuel
Dept. of Electrical Engineering
University of Ilorin
Ilorin, Nigeria
oded
voluntary
eye-blink  based
language  communication  depends"
91e57667b6fad7a996b24367119f4b22b6892eca,Probabilistic Corner Detection for Facial Feature Extraction,"Probabilistic  Corner  Detection  for  Facial  Feature
Extraction
Article
Accepted version
E. Ardizzone, M. La Cascia, M. Morana
In Lecture Notes in Computer Science Volume 5716, 2009
It  is  advisable  to  refer  to  the  publisher's  version  if  you  intend  to  cite
from the work.
Publisher: Springer
http://link.springer.com/content/pdf/10.1007%2F978-3-
642-04146-4_50.pdf"
91f820e2cb6fb5a8adc83e6065cbdf071aca84bd,What makes Federer look so elegant ?,"What makes Federer look so elegant?
Kuldeep Kulkarni and Vinay Venkataraman"
917611cfc0fee3e834d1a6cc13ad5bc18ae428f3,Geometric models with co-occurrence groups,"Geometric Models with Co-occurrence Groups
Joan Bruna1
and St´ephane Mallat2
8/16 rue Paul Vaillant Couturier, 92240, Malakoff - France
- Zoran France
- Ecole Polytechnique - CMAP
Route de Saclay, 91128 Palaiseau - France"
91a7816609f991c1ac45b791c1cd3c6117194bb0,I Know How You Feel: Emotion Recognition with Facial Landmarks,"I Know How You Feel: Emotion Recognition with Facial Landmarks
Tooploox 2Polish-Japanese Academy of Information Technology 3Warsaw University of Technology
Ivona Tautkute1,2, Tomasz Trzcinski1,3 and Adam Bielski1"
914902618e7cc864393ad508521eb582a5af5b87,The differential effects of emotional salience on direct associative and relational memory during a nap.,"Cogn Affect Behav Neurosci (2016) 16:1150–1163
DOI 10.3758/s13415-016-0460-1
The differential effects of emotional salience on direct associative
nd relational memory during a nap
Sara E. Alger 1,2 & Jessica D. Payne 1
Published online: 26 September 2016
# Psychonomic Society, Inc. 2016"
9175b123837ecf55a9aae6c40ba245ddacbc37d5,Various Fusion Schemes to Recognize Simulated and Spontaneous Emotions,"Various Fusion Schemes to Recognize Simulated and Spontaneous
Emotions
Sonia Gharsalli1, H´el`ene Laurent2, Bruno Emile1 and Xavier Desquesnes1
Univ. Orl´eans, INSA CVL,
PRISME EA 4229, Bourges, France
on secondment from INSA CVL, Univ. Orl´eans,
PRISME EA 4229, Bourges, France
to the Rector of the Academy of Strasbourg, Strasbourg, France
Keywords:
Facial Emotion Recognition, Posed Expression, Spontaneous Expression, Early Fusion, Late Fusion, SVM,
FEEDTUM Database, CK+ Database."
91811203c2511e919b047ebc86edad87d985a4fa,Expression Subspace Projection for Face Recognition from Single Sample per Person,"Expression Subspace Projection for Face
Recognition from Single Sample per Person
Hoda Mohammadzade, Student Member, IEEE, and Dimitrios Hatzinakos, Senior Member, IEEE"
91d513af1f667f64c9afc55ea1f45b0be7ba08d4,Automatic Face Image Quality Prediction,"Automatic Face Image Quality Prediction
Lacey Best-Rowden, Student Member, IEEE, and Anil K. Jain, Life Fellow, IEEE"
91e58c39608c6eb97b314b0c581ddaf7daac075e,Pixel-wise Ear Detection with Convolutional Encoder-Decoder Networks,"Pixel-wise Ear Detection with Convolutional
Encoder-Decoder Networks
ˇZiga Emerˇsiˇc 1, Luka Lan Gabriel 2, Vitomir ˇStruc 3 and Peter Peer 1"
9120d59f2ca86954b45d254cae1409cb0806d9c7,DenseFuse: A Fusion Approach to Infrared and Visible Images,"DenseFuse: A Fusion Approach to Infrared and
Visible Images
Hui Li and Xiao-Jun Wu"
916c816f16e4934e41f09a3ff81a10e5fc4bb459,Multicalibration: Calibration for the (Computationally-Identifiable) Masses,"Multicalibration: Calibration for the (Computationally-Identifiable) Masses
´Ursula H´ebert-Johnson 1 Michael P. Kim 1 Omer Reingold 1 Guy N. Rothblum 2"
91dda4183c6118de8195e07a623962dbd22cc34e,Representing local binary descriptors with BossaNova for visual recognition,"Representing Local Binary Descriptors with
BossaNova for Visual Recognition
Carlos Caetano†, Sandra Avila†, Silvio Guimarães‡, Arnaldo de A. Araújo†
Federal University of Minas Gerais, NPDI Lab — DCC/UFMG, Minas Gerais, Brazil
Pontifical Catholic University of Minas Gerais, VIPLAB — ICEI/PUC Minas, Minas Gerais, Brazil
{carlos.caetano,"
911505a4242da555c6828509d1b47ba7854abb7a,Improved Active Shape Model for Facial Feature Localization,"IMPROVED ACTIVE SHAPE MODEL FOR FACIAL FEATURE LOCALIZATION
Hui-Yu Huang and Shih-Hang Hsu
National Formosa University, Taiwan
Email:"
9117fd5695582961a456bd72b157d4386ca6a174,Facial Expression,"Facial Expression
n Recognition Using Dee
ep Neural
Networks
Junnan Li and Edmund Y. Lam
Departm
ment of Electrical and Electronic Engineering
he University of Hong Kong, Pokfulam,
Hong Kong"
9168b36568b8abffab5b9de029be5941f673dca2,Improving 3 D Facial Action Unit Detection with Intrinsic Normalization,"YUDIN, ET AL.: IMPROVING 3D AU DETECTION WITH INTRINSIC NORMALIZATION
Improving 3D Facial Action Unit Detection
with Intrinsic Normalization
Geometric Image Processing Lab
Technion - Israel Institute of Technology
Technion City, Haifa, Israel
Eric Yudin
Aaron Wetzler
Matan Sela
Ron Kimmel"
910da5e0afef96c8acca3c6a4314a9ab5121b1e4,Détection d'obstacles multi-capteurs supervisée par stéréovision. (Multi-sensor road obstacle deetection controled by stereovision),"Détection d’obstacles multi-capteurs supervisée par
stéréovision
Mathias Perrollaz
To cite this version:
Mathias Perrollaz. Détection d’obstacles multi-capteurs supervisée par stéréovision. Vision par ordi-
nateur et reconnaissance de formes [cs.CV]. Université Pierre et Marie Curie - Paris VI, 2008. Français.
<tel-00656864>
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https://tel.archives-ouvertes.fr/tel-00656864
Submitted on 5 Jan 2012
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
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émanant des établissements d’enseignement et de"
916ca7000c022fbd97ea15cc0094f0e53c408b56,Spontaneous and Non-Spontaneous 3D Facial Expression Recognition Using a Statistical Model with Global and Local Constraints,"SPONTANEOUS AND NON-SPONTANEOUS 3D FACIAL EXPRESSION RECOGNITION
USING A STATISTICAL MODEL WITH GLOBAL AND LOCAL CONSTRAINTS"
91ead35d1d2ff2ea7cf35d15b14996471404f68d,Combining and Steganography of 3D Face Textures,"Combining and Steganography of 3D Face Textures
Mohsen Moradi and Mohammad-Reza Rafsanjani-Sadeghi"
91f67f69597a52b905c748a15db427c61f352073,Scale-Aware Pixelwise Object Proposal Networks,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
Scale-aware Pixel-wise Object Proposal Networks
Zequn Jie, Xiaodan Liang, Jiashi Feng, Wen Feng Lu, Eng Hock Francis Tay, Shuicheng Yan
essential
proposal"
91b0081a348d182d616f74a0c9fb80d56acf4198,Exploiting photographic style for category-level image classification by generalizing the spatial pyramid,"Exploiting Photographic Style for Category-Level Image
Classification by Generalizing the Spatial Pyramid
Jan C. van Gemert
Puzzual
Oudeschans 18
011LA, Amsterdam, The Netherlands"
91b1a59b9e0e7f4db0828bf36654b84ba53b0557,Simultaneous Hallucination and Recognition of Low-Resolution Faces Based on Singular Value Decomposition,"This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
> REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) <
Simultaneous Hallucination and Recognition of
Low-Resolution Faces Based on Singular Value
Decomposition
Muwei Jian,  Kin-Man Lam*, Senior Member, IEEE
(SVD)
for  performing  both"
91a5897565818631a32ce4edae5548d2baf99d77,APPROACH TO RECOGNIZING FACES UNDER VARYING POSE GIVEN A SINGLE-VIEW,"The Pennsylvania State University
The Graduate School
College of Engineering
A PATCH CORRESPONDENCE APPROACH TO RECOGNIZING FACES
UNDER VARYING POSE GIVEN A SINGLE-VIEW GALLERY
A Thesis in
Electrical Engineering
Michael Charles Ferster II
© 2012 Michael Charles Ferster II
Submitted in Partial Fulfillment
of the Requirements
for the Degree of
Master of Science
August 2012"
91bdc706ad1d7b246e457870a7eb8caff87ec05a,Face Recognition Using Holistic Based Approach,"International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 7, July 2014)
Face Recognition Using Holistic Based Approach
1Research Scholar, 2Professor, Department of Information Science and Engineering, SDM CET, Dharwad
Vandana S. Bhat1, Dr. Jagadeesh D. Pujari2"
2057837e059a1dde8c6c4c0587e652b79c04780a,Learning to Recognize Novel Objects in One Shot through Human-Robot Interactions in Natural Language Dialogues,"Learning to Recognize Novel Objects in One Shot through Human-Robot
Interactions in Natural Language Dialogues
Thomas Williams
Matthias Scheutz
Evan Krause
HRI Laboratory
Tufts University
00 Boston Ave
Medford, MA 02155, USA
Michael Zillich
Inst. for Automation and Control
Technical University Vienna
Gusshausstr 27-29/E376
040 Vienna, Austria
HRI Laboratory
Tufts University
00 Boston Ave
HRI Laboratory
Tufts University
00 Boston Ave"
20717f1cb12ab208458c0f2505b237d8f061f97a,Learning Classifiers from Synthetic Data Using a Multichannel Autoencoder,"Learning Classifiers from Synthetic Data Using a
Multichannel Autoencoder
Xi Zhang, Yanwei Fu, Andi Zang, Leonid Sigal, Gady Agam"
204db062f4952ce446cbb28fbc40d4a7f4424b03,Systematic evaluation of super-resolution using classification,"SYSTEMATIC EVALUATION OF
SUPER-RESOLUTION USING CLASSIFICATION
Vinay P. Namboodiri1, Vincent De Smet1 and Luc Van Gool1,2
ESAT-PSI/IBBT, K.U.Leuven, Belgium
Computer Vision Laboratory, BIWI/ETH Z¨urich, Switzerland"
20c71ee8275969a7df881de69b8d8baf88f1d120,A Variational Observation Model of 3D Object for Probabilistic Semantic SLAM,"A Variational Observation Model of 3D Object for Probabilistic
Semantic SLAM
H. W. Yu and B. H. Lee"
20a0b23741824a17c577376fdd0cf40101af5880,Learning to Track for Spatio-Temporal Action Localization,"Learning to track for spatio-temporal action localization
Philippe Weinzaepfela
Zaid Harchaouia,b
NYU
Inria∗
Cordelia Schmida"
20f9a09defe5b02b98c464ca6df36b3b6358f60b,The State-of-the-Art in Visual Object Tracking,Volume 36 Number 3 September 2012
2049ca79ce94ddfe0cc3d39bf770f580a740f3ac,Activity analysis : finding explanations for sets of events,ActivityAnalysis:FindingExplanationsforSetsofEventsbyDimaJamalAlDamenSubmittedinaccordancewiththerequirementsforthedegreeofDoctorofPhilosophy.TheUniversityofLeedsSchoolofComputingSeptember2009Thecandidateconfirmsthattheworksubmittedisherownandthattheappropriatecredithasbeengivenwherereferencehasbeenmadetotheworkofothers.Thiscopyhasbeensuppliedontheunderstandingthatitiscopyrightmaterialandthatnoquotationfromthethesismaybepublishedwithoutproperacknowledgement.
20928315086a49e0cdea0ec66f2e78e9c564f794,Person Detection for Indoor Videosurveillance Using Spatio-temporal Integral Features,"Person Detection for Indoor Videosurveillance
using Spatio-Temporal Integral Features
Adrien Descamps1, Cyril Carincotte2, and Bernard Gosselin1
TCTS Lab, University of Mons, Mons, Belgium
Multitel ASBL, 2 Rue Pierre et Marie Curie, Mons, Belgium"
20111924fbf616a13d37823cd8712a9c6b458cd6,Linear Regression Line based Partial Face Recognition,"International Journal of Computer Applications (0975 – 8887)
Volume 130 – No.11, November2015
Linear Regression Line based Partial Face Recognition
Naveena M.
Department of Studies in
Computer Science,
Manasagagothri,
Mysore.
G. Hemantha Kumar
Department of Studies in
Computer Science,
Manasagagothri,
Mysore.
P. Nagabhushan
Department of Studies in
Computer Science,
Manasagagothri,
Mysore.
images.  In"
20500887735f3fb87ae3ec351e2115e89c0f663e,Multiview random forest of local experts combining RGB and LIDAR data for pedestrian detection,"Multiview Random Forest of Local Experts Combining RGB and
LIDAR data for Pedestrian Detection
Alejandro Gonz´alez1,2, Gabriel Villalonga1,2, Jiaolong Xu2, David V´azquez1,2, Jaume Amores2, Antonio M. L´opez1,2
{agalzate,gvillalonga,jiaolong,dvazquez,jaume,antonio}
Autonomous University of Barcelona, 2Computer Vision Center"
20a6de85d7d5f445dfaba90ab2e33879142023fc,Autonomous Vehicles that Interact with Pedestrians: A Survey of Theory and Practice,"THIS WORK HAS BEEN SUBMITTED TO THE IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS.
Autonomous Vehicles that Interact with Pedestrians:
A Survey of Theory and Practice
Amir Rasouli and John K. Tsotsos"
20b8a76e988e796f0f225876a69842f6839e4c98,Real-time Gender Recognition for Uncontrolled Environment of Real-life Images,"REAL-TIME GENDER RECOGNITION FOR UNCONTROLLED
ENVIRONMENT OF REAL-LIFE IMAGES
Duan-Yu Chen and Kuan-Yi Lin
Department of Electrical Engineering, Yuan-Ze University, Taiwan
Keywords:
Gender recognition, Uncontrolled environment, Real-life images."
20e903faf8e2e656a89d983541b15f2e0d614eeb,Image to Image Translation for Domain Adaptation,"Image to Image Translation for Domain Adaptation
Zak Murez1,2
Soheil Kolouri2 David Kriegman1 Ravi Ramamoorthi1 Kyungnam Kim2
University of California, San Diego; 2 HRL Laboratories, LLC;"
209324c152fa8fab9f3553ccb62b693b5b10fb4d,CROWDSOURCED VISUAL KNOWLEDGE REPRESENTATIONS A THESIS SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE AND THE COMMITTEE ON GRADUATE STUDIES OF STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTERS OF SCIENCE,"CROWDSOURCED VISUAL KNOWLEDGE REPRESENTATIONS
VISUAL GENOME
A THESIS
SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE
AND THE COMMITTEE ON GRADUATE STUDIES
OF STANFORD UNIVERSITY
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
MASTERS OF SCIENCE
Ranjay Krishna
March 2016"
20e504782951e0c2979d9aec88c76334f7505393,Robust LSTM-Autoencoders for Face De-Occlusion in the Wild,"Robust LSTM-Autoencoders for Face De-Occlusion
in the Wild
Fang Zhao, Jiashi Feng, Jian Zhao, Wenhan Yang, Shuicheng Yan"
203abfcc3df8de6606cf34fa32cf225627f52d00,Learning Robot Vision for Assisted Living,"Robotic Vision:
Technologies for Machine
Learning and Vision Applications
José García-Rodríguez
University of Alicante, Spain
Miguel Cazorla
University of Alicante, Spain"
20823c6b9798094048bf4d59b26f5b92723c9b71,Color Face Recognition Using Quaternion PCA,"Color Face Recognition Using Quaternion PCA
Emad S. Jaha1 and Lahouari Ghouti2
King Abdulaziz University. Jeddah. Saudi Arabia.1
King Fahd University of Petroleum and Minerals. Dhahran 31261. Saudi Arabia.
Keywords: Biometric Systems, Face Recognition, Color
Face Recognition, Principal Component Analysis (PCA), Hy-
percomplex PCA."
205672cd9986044a03483058d9462f52c2cfc543,A Practical Guide to CNNs and Fisher Vectors for Image Instance Retrieval,"A practical guide to CNNs and Fisher Vectors
for image instance retrieval
Vijay Chandrasekhar∗,1, Jie Lin∗,1, Olivier Mor`ere∗,1,2
Hanlin Goh1, Antoine Veillard2
Institute for Infocomm Research (A*STAR), 1 Fusionolopis Way, #21-01, 138632, Singapore
Universit´e Pierre et Marie Curie, 4 place Jussieu, 75252, Paris, France"
20a432a065a06f088d96965f43d0055675f0a6c1,The E ff ects of Regularization on Learning Facial Expressions with Convolutional Neural Networks,"In: Proc. of the 25th Int. Conference on Artificial Neural Networks (ICANN)
Part II, LNCS 9887, pp. 80-87, Barcelona, Spain, September 2016
The final publication is available at Springer via
http://dx.doi.org//10.1007/978-3-319-44781-0_10
The Effects of Regularization on Learning Facial
Expressions with Convolutional Neural Networks
Tobias Hinz, Pablo Barros, and Stefan Wermter
University of Hamburg Department of Computer Science,
Vogt-Koelln-Strasse 30, 22527 Hamburg, Germany
http://www.informatik.uni-hamburg.de/WTM"
208e903211ddc62b997afb5a1bd3c2c43e0e69ee,Real-Time Action Detection in Video Surveillance using Sub-Action Descriptor with Multi-CNN,"Real-Time Action Detection in Video Surveillance using Sub-Action
Descriptor with Multi-CNN
Cheng-Bin Jin*, Shengzhe Li†, and Hakil Kim*
*Inha University, Incheon, Korea
Visionin Inc., Incheon, Korea"
202d8d93b7b747cdbd6e24e5a919640f8d16298a,Face Classification via Sparse Approximation,"Face Classification via Sparse Approximation
Elena Battini S˝onmez1, Bulent Sankur2 and Songul Albayrak3
Computer Science Department, Bilgi University, Dolapdere, Istanbul, TR
Electric and Electronic Engineering Department, Bo¯gazici University, Istanbul, TR
Computer Engineering Department, Yıldız Teknik University, Istanbul, TR"
205b34b6035aa7b23d89f1aed2850b1d3780de35,Log-domain polynomial filters for illumination-robust face recognition,"014 IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP)
978-1-4799-2893-4/14/$31.00 ©2014 IEEE
Shenzhen Key Lab. of Information Sci&Tech,
♯Nagaoka University of Technology, Japan
RECOGNITION
. INTRODUCTION"
20cdf98173bb99bb59c5a1d387f9a45d3f7755ae,CNN-based thermal infrared person detection by domain adaptation,"CNN-based thermal infrared person detection by domain adaptation
Christian Herrmanna,b, Miriam Rufa, and J¨urgen Beyerera,b
Vision and Fusion Lab, Karlsruhe Institute of Technology KIT, Karlsruhe, Germany
Fraunhofer IOSB, Karlsruhe, Germany"
2069f85e9efcd429a3d68c918ebd8c13aefb46cb,Measuring External Face Appearance for Face Classification,"We are IntechOpen,
the world’s leading publisher of
Open Access books
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20a3ce81e7ddc1a121f4b13e439c4cbfb01adfba,Sparse-MVRVMs Tree for Fast and Accurate Head Pose Estimation in the Wild,"Sparse-MVRVMs Tree for Fast and Accurate
Head Pose Estimation in the Wild
Mohamed Selim, Alain Pagani, and Didier Stricker
Augmented Vision Research Group,
German Research Center for Artificial Intelligence (DFKI),
Tripstaddterstr. 122, 67663 Kaiserslautern, Germany
Technical University of Kaiserslautern
http://www.av.dfki.de"
200f1a55c5974c4cac243bed3131ac5a9338840d,Human Computation for Object Detection,"May 09, 2013
TR Number: UCSC-SOE-15-03
Human Computation for Object Detection
Rajan Vaish1, Sascha T. Ishikawa1, Sheng Lundquist2, Reid Porter2, James Davis1
University of California at Santa Cruz1, Los Alamos National Laboratory2
{rvaish, stishika,  {slundquist,"
20e783a2df0486cd1c8b6b59fc76220f5718b304,Stereo-based Pedestrian Detection Using Two-stage Classifiers,"4-26
MVA2011 IAPR Conference on Machine Vision Applications, June 13-15, 2011, Nara, JAPAN
Stereo-based Pedestrian Detection Using Two-stage Classifiers
Manabu Nishiyama, Akihito Seki, Tomoki Watanabe
Corporate Research and Development Center, Toshiba Corporation
, Komukai-Toshiba-cho, Saiwai-ku, Kawasaki, 212-8582, Japan"
203fcd66c043e44fefd783b8f54105f0a577fc25,Analyzing Content and Customer Engagement in Social Media with Deep Learning,"Analyzing Content and Customer Engagement in
Social Media with Deep Learning
(The bulk of this work was done by a student.)"
2056ba48e687d619c0ce69d0be323d48c5b90701,Similarity Mapping with Enhanced Siamese Network for Multi-Object Tracking.,"Similarity Mapping with Enhanced Siamese Network
for Multi-Object Tracking
Minyoung Kim
Cupertino, CA
Stefano Alletto
Modena, MO
Panasonic Silicon Valley Laboratory
University of Modena and Reggio Emilia
Panasonic Silicon Valley Laboratory
Luca Rigazio
Cupertino, CA"
20260d36506911e04ad1efed1e60b06bfc178d52,Deep 3D face identification,"Deep 3D Face Identification
Donghyun Kim
Matthias Hernandez
Jongmoo Choi
G´erard Medioni
USC Institute for Robotics and Intelligent Systems (IRIS)
Unversity of Southern California
{kim207, mthernan, jongmooc,"
2067ab35379381f05acaa7406a30d0ee02c0b8cc,Directional Statistics-based Deep Metric Learning for Image Classification and Retrieval,"Directional Statistics-based Deep Metric Learning
for Image Classification and Retrieval
Xuefei Zhe, Shifeng Chen, and Hong Yan, Fellow, IEEE"
20100323ec5c32ae91add8e866d891a78f1a2bbe,Unsupervised Object Discovery and Tracking in Video Collections,"Unsupervised Object Discovery and Tracking in Video Collections
Suha Kwak1,∗
Minsu Cho1,∗
Ivan Laptev1,∗
Jean Ponce2,∗
Cordelia Schmid1,†
Inria
´Ecole Normale Sup´erieure / PSL Research University"
202cbc83c22a9c7b3d878cc1bed1c5cf152eb6fb,Learning Embeddings for Product Visual Search with Triplet Loss and Online Sampling,"Learning Embeddings for Product Visual Search with
Triplet Loss and Online Sampling
Eric Dodds, Huy Nguyen, Simao Herdade, Jack Culpepper, Andrew Kae, Pierre Garrigues
{eric.mcvoy.dodds, huyng, sherdade, jackcul, andrewkae,
Yahoo Research"
202a923504ea81e94c06a81581539b893b461ee5,YELP : Masking Sound-based Opportunistic A acks in Zero-E ort,"YELP: Masking Sound-based Opportunistic A(cid:130)acks in
Zero-E(cid:128)ort Deauthentication
University of Alabama at Birmingham
University of Alabama at Birmingham
University of Alabama at Birmingham
Prakash Shrestha
S Abhishek Anand
Nitesh Saxena"
20f272f4bdf562aa8b4dae84b67cfafa34a00738,Periocular biometrics: An emerging technology for unconstrained scenarios,"Periocular Biometrics:
An Emerging Technology for Unconstrained
Scenarios
Gil Santos and Hugo Proenc¸a
IT - Instituto de Telecomunicac¸ ˜oes
Universidade da Beira Interior
Covilh˜a, Portugal
Email:"
20e64f44ce2977a4dc5099fce6f73842613f0865,"Ridge Regression, Hubness, and Zero-Shot Learning","Ridge Regression, Hubness, and Zero-Shot Learning(cid:63)
Yutaro Shigeto1, Ikumi Suzuki2, Kazuo Hara3, Masashi Shimbo1, and
Yuji Matsumoto1
Nara Institute of Science and Technology, Ikoma, Nara, Japan
The Institute of Statistical Mathematics, Tachikawa, Tokyo, Japan
National Institute of Genetics, Mishima, Shizuoka, Japan"
20e210bb6b1d3e637e2b2674aeead3fad8c2c70e,Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer,"Published as a conference paper at ICLR 2017
PAYING MORE ATTENTION TO ATTENTION:
IMPROVING THE PERFORMANCE OF CONVOLUTIONAL
NEURAL NETWORKS VIA ATTENTION TRANSFER
Sergey Zagoruyko, Nikos Komodakis
Universit´e Paris-Est, ´Ecole des Ponts ParisTech
Paris, France"
20eaa3ebe2b6e1aff7c4585733c9fb0cfc941919,Image similarity using Deep CNN and Curriculum Learning,"Image similarity using Deep CNN and Curriculum Learning
Srikar Appalaraju
Vineet Chaoji
Amazon Development Centre (India) Pvt. Ltd.
Image similarity involves fetching similar looking images given a reference image. Our solution called SimNet, is a deep
Siamese network which is trained on pairs of positive and negative images using a novel online pair mining strategy inspired
y Curriculum learning. We also created a multi-scale CNN, where the final image embedding is a joint representation of
top as well as lower layer embedding’s. We go on to show that this multi-scale Siamese network is better at capturing fine
grained image similarities than traditional CNN’s.
Keywords — Multi-scale CNN, Siamese network, Curriculum learning, Transfer learning.
I.  INTRODUCTION
The  ability  to  find  a  similar  set  of  images  for  a  given
image  has  multiple  uses-cases  from  visual  search  to
duplicate  product  detection  to  domain  specific  image
lustering. Our approach called SimNet, tries to identify
similar images for a new image using multi-scale Siamese
network. Fig. 1 shows examples of image samples from
CIFAR10 [39] on which SimNet is trained on.
Fig. 1 examples of CIFAR 10 images. Task is - given a new image
ut belonging to one of the 10 categories, find similar set of images."
20c59a55795eaa4f2629cc83fb556dc8c5bcfc1f,Modeling and visual recognition of human actions and interactions,"Modeling and visual recognition of human actions and
interactions
Ivan Laptev
To cite this version:
Ivan Laptev. Modeling and visual recognition of human actions and interactions. Computer Vision and
Pattern Recognition [cs.CV]. Ecole Normale Supérieure de Paris - ENS Paris, 2013. <tel-01064540>
HAL Id: tel-01064540
https://tel.archives-ouvertes.fr/tel-01064540
Submitted on 16 Sep 2014
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rchive for the deposit and dissemination of sci-
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scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de
recherche français ou étrangers, des laboratoires"
2084e54505cfe4fd81005167b1b11d10b5f837d1,Person Re-Identification by Discriminative Selection in Video,"Person Re-Identification by Discriminative Selection in Video Ranking
Wang, T; Gong, S; Zhu, X; Wang, S
•(cid:9)“The final publication is available at http://link.springer.com/chapter/10.1007%2F978-3-319-
0593-2_45”
For additional information about this publication click this link.
http://qmro.qmul.ac.uk/xmlui/handle/123456789/11432
Information about this research object was correct at the time of download; we occasionally
make corrections to records, please therefore check the published record when citing. For
more information contact"
200f68f899f0bf72dd2c49ba2b4a5027e0291531,Efficient Activity Detection in Untrimmed Video with Max-Subgraph Search,"Efficient Activity Detection in Untrimmed Video
with Max-Subgraph Search
Chao Yeh Chen and Kristen Grauman"
205af28b4fcd6b569d0241bb6b255edb325965a4,Facial expression recognition and tracking for intelligent human-robot interaction,"Intel Serv Robotics (2008) 1:143–157
DOI 10.1007/s11370-007-0014-z
SPECIAL ISSUE
Facial expression recognition and tracking for intelligent human-robot
interaction
Y. Yang · S. S. Ge · T. H. Lee · C. Wang
Received: 27 June 2007 / Accepted: 6 December 2007 / Published online: 23 January 2008
© Springer-Verlag 2008"
2059d2fecfa61ddc648be61c0cbc9bc1ad8a9f5b,Co-Localization of Audio Sources in Images Using Binaural Features and Locally-Linear Regression,"TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 23, NO. 4, APRIL 2015
Co-Localization of Audio Sources in Images Using
Binaural Features and Locally-Linear Regression
Antoine Deleforge∗ Radu Horaud∗ Yoav Y. Schechner‡ Laurent Girin∗†
INRIA Grenoble Rhˆone-Alpes, Montbonnot Saint-Martin, France
Univ. Grenoble Alpes, GIPSA-Lab, France
Dept. Electrical Eng., Technion-Israel Inst. of Technology, Haifa, Israel"
c4dcf41506c23aa45c33a0a5e51b5b9f8990e8ad,Understanding Activity : Learning the Language of Action,"Understanding Activity: Learning the Language of Action
Randal Nelson and Yiannis Aloimonos
Univ. of Rochester and Maryland
.1 Overview
Understanding observed activity is an important
problem, both from the standpoint of practical applications,
nd as a central issue in attempting to describe the
phenomenon of intelligence. On the practical side, there are a
large number of applications that would benefit from
improved machine ability to analyze activity. The most
prominent are various surveillance scenarios. The current
emphasis on homeland security has brought this issue to the
forefront, and resulted in considerable work on mostly low-
level detection schemes. There are also applications in
medical diagnosis and household assistants that, in the long
run, may be even more important. In addition, there are
numerous scientific projects, ranging from monitoring of
weather conditions to observation of animal behavior that
would be facilitated by automatic understanding of activity.
From a scientific standpoint, understanding activity"
c46ae522b8cedb68339dbb8fd9c1fa3b2d676f8e,Kinship Verification,"Kinship Verification
Kanchan Pardeshi , Vrushali Pawar, Snehal Sonawane , Kavita Wagh
KKWIEER,
Nashik
images"
c46d44143d1e0cbab34f120d65c3a869101c7622,DecomposeMe: Simplifying ConvNets for End-to-End Learning,"DecomposeMe: Simplifying ConvNets for End-to-End
Learning
Jose M. Alvarez and Lars Petersson
NICTA / Data61, Canberra, Australia"
c4a024d73902462275879fa6133bff22134fcc7e,When crowds hold privileges: Bayesian unsupervised representation learning with oracle constraints,"When crowds hold privileges: Bayesian unsupervised
representation learning with oracle constraints
Theofanis Karaletsos
Computational Biology Program, Sloan Kettering Institute
275 York Avenue, New York, USA
Serge Belongie
Cornell Tech
11 Eighth Avenue #302, New York, USA
Gunnar R¨atsch
Computational Biology Program, Sloan Kettering Institute
275 York Avenue, New York, USA"
c45183ec95f89aff793a2629a0520006b4153d6a,Entropy-based template analysis in face biometric identification systems,"SIViP (2013) 7:493–505
DOI 10.1007/s11760-013-0451-4
ORIGINAL PAPER
Entropy-based template analysis in face biometric identification
systems
Maria De Marsico · Michele Nappi · Daniel Riccio ·
Genoveffa Tortora
Received: 19 December 2011 / Revised: 7 June 2012 / Accepted: 10 October 2012 / Published online: 17 March 2013
© Springer-Verlag London 2013"
c4f1fcd0a5cdaad8b920ee8188a8557b6086c1a4,The Ignorant Led by the Blind: A Hybrid Human–Machine Vision System for Fine-Grained Categorization,"Int J Comput Vis (2014) 108:3–29
DOI 10.1007/s11263-014-0698-4
The Ignorant Led by the Blind: A Hybrid Human–Machine Vision
System for Fine-Grained Categorization
Steve Branson · Grant Van Horn · Catherine Wah ·
Pietro Perona · Serge Belongie
Received: 7 March 2013 / Accepted: 8 January 2014 / Published online: 20 February 2014
© Springer Science+Business Media New York 2014"
c4baa3d2fe702d3e96c500274f7fd9e63f8b3d6d,Pedestrian Detection Optimization Based on Random Filtering,"Pedestrian Detection Optimization Based on
Random Filtering
Victor Hugo Cunha de Melo, Samir Le˜ao, William Robson Schwartz
Universidade Federal de Minas Gerais
Department of Computer Science
Belo Horizonte, Minas Gerais, Brazil
Email: {victorhcmelo, samirleao,"
c4b3a1cf8842da8c64f7abf4a352583d5fd9762c,Gait recognition using sub-vector quantisation technique,"Int. J. Machine Intelligence and Sensory Signal Processing, Vol. 1, No. 1, 2013
Gait recognition using sub-vector quantisation
technique
Neel K. Pandey*
Department of Electrical Engineering and Trades,
Faculty of Engineering and Trades,
Manukau Institute of Technology,
Private Bag 94006, Manukau 2241, Auckland, New Zealand
E-mail:
*Corresponding author
Waleed H. Abdulla and Zoran Salcic
Department of Electrical and Computer Engineering,
The University of Auckland,
Private Bag 92019, Auckland Mail Centre,
Auckland 1142, New Zealand
E-mail:
E-mail:"
c4d3033356066ef8133f03f4060bb8cad842918f,Inference of quantized neural networks on heterogeneous all-programmable devices,"Inference of Quantized Neural Networks
on Heterogeneous All-Programmable Devices
Thomas B. Preußer
Marie Skłodowska-Curie Fellow
Xilinx Research Labs
Giulio Gambardella
Xilinx Research Labs
Dublin, Ireland
Nicholas Fraser
Xilinx Research Labs
Dublin, Ireland
Michaela Blott
Xilinx Research Labs
Dublin, Ireland
Dublin, Ireland"
c4827fe8002ea61a2748b78369afe3a0747d1a0c,Towards Optimal Naive Bayes Nearest Neighbor,"Towards Optimal Naive Bayes Nearest Neighbor
R´egis Behmo1, Paul Marcombes1,2, Arnak Dalalyan2, and V´eronique Prinet1
NLPR / LIAMA, Institute of Automation, Chinese Academy of Sciences(cid:2)
IMAGINE, LIGM, Universit´e Paris-Est"
c43490eb0a3ce18fb2326ef1d0828664b60e73e2,Is This Car Looking at You? How Anthropomorphism Predicts Fusiform Face Area Activation when Seeing Cars,"RESEARCH ARTICLE
Is This Car Looking at You? How
Anthropomorphism Predicts Fusiform Face
Area Activation when Seeing Cars
Simone Ku¨ hn1*, Timothy R. Brick1, Barbara C. N. Mu¨ ller2,3, Ju¨ rgen Gallinat4,5
. Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195,
Berlin, Germany, 2. Behavioural Science Institute, Radboud University of Nijmegen, P. O. Box 9104, 6500 HE,
Nijmegen, Netherlands, 3. Department of Psychology, Ludwig-Maximilian University, Leopoldstrasse 13,
80802, Mu¨ nchen, Germany, 4. Clinic for Psychiatry and Psychotherapy, Charite´ University Medicine, St.
Hedwig-Krankenhaus, Große Hamburger Straße 5–11, 10115, Berlin, Germany, 5. Clinic and Policlinic for
Psychiatry and Psychotherapy, University Clinic Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg,
Germany"
c48ec3d14a223346bb50002176e9a04bfb385cc7,Fuzzy Modelling for Human Dynamics Based on Online Social Networks,"Article
Fuzzy Modelling for Human Dynamics Based on
Online Social Networks
Jesus Cuenca-Jara , Fernando Terroso-Saenz * ID , Mercedes Valdes-Vela and Antonio F. Skarmeta
Department of Communications and Information Engineering, University of Murcia, Murcia 30100, Spain;
(J.C.-J.); (M.V.-V.); (A.F.S.)
* Correspondence: Tel.: +34-868-884-644
Received: 30 June 2017; Accepted: 21 August 2017; Published: 24 August 2017"
c48c452f26e54f37faaf025ca3c76b33ce3e40f6,Incremental learning of latent structural SVM for weakly supervised image classification,"INCREMENTAL LEARNING OF LATENT STRUCTURAL SVM FOR WEAKLY SUPERVISED
IMAGE CLASSIFICATION
Thibaut Durand (1)
Nicolas Thome (1)
Matthieu Cord (1)
David Picard (2)
(1) Sorbonne Universit´es, UPMC Univ Paris 06, UMR 7606, LIP6, F-75005, Paris, France
(2) ETIS/ENSEA, University of Cergy-Pontoise, CNRS, UMR 8051, France"
c4f3375dab1886f37f542d998e61d8c30a927682,BEYOND SHARED HIERARCHIES: DEEP MULTITASK LEARNING THROUGH SOFT LAYER ORDERING,"Under review as a conference paper at ICLR 2018
BEYOND SHARED HIERARCHIES: DEEP MULTITASK
LEARNING THROUGH SOFT LAYER ORDERING
Anonymous authors
Paper under double-blind review"
c44e2fa02f0b578a2cc92795fe6a4c578f65dc97,A Method for Copyright Protection of Line Drawings,"A Method for Copyright Protection of Line Drawings
Weihan Sun*, Koichi Kise*
* Graduate School of Engineering, Osaka Prefecture University, Osaka
E-mail:"
c4c4e5ff454584ae6a68d25b36bfc860e9a893a0,"Real-Time Facial Recognition System—Design, Implementation and Validation","Journal of Signal Processing Theory and Applications
(2013) 1: 1-18
doi:10.7726/jspta.2013.1001
Research Article
Real-Time Facial Recognition System—Design,
Implementation and Validation
M. Meenakshi*
Received 29 August 2012; Published online November 10, 2012
© The author(s) 2012. Published with open access at uscip.org"
c48bde5b9ff17b708ab3e4f7c62a31a46c77f2f1,Nested sparse quantization for efficient feature coding,"Nested Sparse Quantization
for Ef‌f‌icient Feature Coding
Xavier Boix1(cid:63), Gemma Roig1(cid:63), and Luc Van Gool1,2 (cid:63)(cid:63)
Computer Vision Lab, ETH Zurich, Switzerland,
KU Leuven, Belgium"
c4fed8f23bc9ff1ffc27edb12970963ecf2dead9,Statistical Models and Optimization Algorithms for High-Dimensional Computer Vision Problems,
c4f632a1b6faa43c217e63c58a4764511104c303,Extracting Pathlets FromWeak Tracking Data,"Extracting Pathlets From Weak Tracking Data∗
Kevin Streib
James W. Davis
Dept. of Computer Science and Engineering
Ohio State University, Columbus, OH 43210"
c42a8969cd76e9f54d43f7f4dd8f9b08da566c5f,0 Towards Unconstrained Face Recognition Using 3 D Face Model,"Towards Unconstrained Face Recognition
Using 3D Face Model
Zahid Riaz1, M. Saquib Sarfraz2 and Michael Beetz1
Intelligent Autonomous Systems (IAS), Technical University of Munich, Garching
Computer Vision Research Group, COMSATS Institute of Information
Technology, Lahore
Germany
Pakistan
. Introduction
Over the last couple of decades, many commercial systems are available to identify human
faces. However, face recognition is still an outstanding challenge against different kinds of
real world variations especially facial poses, non-uniform lightings and facial expressions.
Meanwhile the face recognition technology has extended its role from biometrics and security
pplications to human robot interaction (HRI). Person identity is one of the key tasks while
interacting with intelligent machines/robots, exploiting the non intrusive system security
nd authentication of the human interacting with the system. This capability further helps
machines to learn person dependent traits and interaction behavior to utilize this knowledge
for tasks manipulation. In such scenarios acquired face images contain large variations which
demands an unconstrained face recognition system.
Fig. 1. Biometric analysis of past few years has been shown in figure showing the"
8ec7194952ee9e7cf383b1a1b0aeccaed5b7daaa,Constrained multi-target tracking for team sports activities,"Gade and Moeslund IPSJ Transactions on Computer Vision and
Applications  (2018) 10:2
DOI 10.1186/s41074-017-0038-z
IPSJ Transactions on Computer
Vision and Applications
SYSTEMS PAPER
Open Access
Constrained multi-target tracking for
team sports activities
Rikke Gade*
nd Thomas B. Moeslund"
8edb2219370a86c4277549813d36a6c139503fb4,Facial feature units ’ localization using horizontal information of most significant bit planes,"Journal of Engineering and Technology Research Vol. 3(14), pp. 381-387, 22 December, 2011
Available online at http:// www.academicjournals.org/JETR
DOI: 10.5897/JETR11.068
ISSN 2006-9790 ©2011 Academic Journals
Full Length Research Paper
Facial feature units’ localization using horizontal
information of most significant bit planes
Asif Khan1*, Khalilullah1, Ihtesham-Ul-Islam1 and Mohammad A. U. Khan2
FAST National University of Computer and Emerging Sciences, Peshawar, Pakistan.
Effat University, Jeddah, Saudi Arabia.
Accepted 8 November, 2011
We  present  here  an  approach  to  find  the  exact  position  of  some  feature  units  related  to  human  face
images.  We  use  the horizontal  information  in  most  significant  bit  planes  of images  to  accomplish  the
task.  Finding  location  of  facial  feature  units  is  of  importance  as  most  human  face  recognition
pproaches take it as initial point. The prominent feature units in a face are eyes, nostrils and lips which
re  usually  oriented  in  horizontal  direction  and  visually  significant  in  face  image.  The  majority  of  the
visually significant data in image can be extracted using higher order bits of that image. Our four step
method  consists  of  bit  planes  processing,  separating  horizontal  information  using  wavelet  transform
(WT), binary thresholding and appropriate combination of Dilation and Erosion. The proposed method
shows  high  accuracy  in  the  presence  of  all  real  world  situations  like  various  gestures,  illumination"
8eed02b44383abf697b39721f369d1ff38386901,Coin-Tracking - Double-Sided Tracking of Flat Objects,"Master Thesis
Czech
Technical
University
in Prague
Faculty of Electrical Engineering
Department of Cybernetics
Coin-Tracking - Double-Sided Tracking of
Flat Objects
Jonáš Šerých
Supervisor: Prof. Ing. Jiří Matas, Ph.D.
Field of study: Computer Vision and Image Processing
January 2018"
8e36100cb144685c26e46ad034c524b830b8b2f2,Modeling Facial Geometry using Compositional,"Modeling Facial Geometry using Compositional VAEs
Timur Bagautdinov∗1, Chenglei Wu2, Jason Saragih2, Pascal Fua1, Yaser Sheikh2
´Ecole Polytechnique F´ed´erale de Lausanne
Facebook Reality Labs, Pittsburgh"
8ef484990214b80dd0e02de09d8d65906f4daf6a,Face Authentication Using One-Class Support Vector Machines,"Face Authentication
Using One-Class Support Vector Machines
Manuele Bicego1,(cid:1), Enrico Grosso1, and Massimo Tistarelli2
DEIR, University of Sassari, via Torre Tonda 34, 07100 Sassari, Italy
Phone +39 079 2017321
DAP, University of Sassari, piazza Duomo 6, 07041 Alghero (SS), Italy"
8e112ad656ff90720ae609841bd0fcb2caa90d65,"""Show Me the Cup"": Reference with Continuous Representations",[cs.CL]  28 Jun 2016
8eabd39cf43596e56f377aa26f985ef20f0aeb4e,On the Recent Use of Local Binary Patterns for Face Authentication,"INTERNATIONAL JOURNAL OF IMAGE AND VIDEO PROCESSING, SPECIAL ISSUE ON FACIAL IMAGE PROCESSING
On the Recent Use of Local Binary Patterns for
Face Authentication
S´ebastien Marcel, Yann Rodriguez and Guillaume Heusch"
8e33183a0ed7141aa4fa9d87ef3be334727c76c0,Robustness of Face Recognition to Image Manipulations,"– COS429 Written Report, Fall 2017 –
Robustness of Face Recognition to Image Manipulations
Cathy Chen (cc27), Zachary Liu (zsliu), and Lindy Zeng (lindy)
. Motivation
We can often recognize pictures of people we know even if the image has low resolution or obscures
part of the face, if the camera angle resulted in a distorted image of the subject’s face, or if the
subject has aged or put on makeup since we last saw them. Although this is a simple recognition task
for a human, when we think about how we accomplish this task, it seems non-trivial for computer
lgorithms to recognize faces despite visual changes.
Computer facial recognition is relied upon for many application where accuracy is important.
Facial recognition systems have applications ranging from airport security and suspect identification
to personal device authentication and face tagging [7]. In these real-world applications, the system
must continue to recognize images of a person who looks slightly different due to the passage of
time, a change in environment, or a difference in clothing.
Therefore, we are interested in investigating face recognition algorithms and their robustness to
image changes resulting from realistically plausible manipulations. Furthermore, we are curious
bout whether the impact of image manipulations on computer algorithms’ face recognition ability
mirrors related insights from neuroscience about humans’ face recognition abilities.
. Goal
In this project, we implement both face recognition algorithms and image manipulations. We then"
8e579a8a43f6af1d66e927a48b89e8296eba63f7,Learning to hash faces using large feature vectors,"Learning to Hash Faces Using Large Feature Vectors
Cassio E. dos Santos Jr.∗, Ewa Kijak†, Guillaume Gravier†, William Robson Schwartz∗
Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
IRISA & Inria Rennes (CNRS, Univ. Rennes 1), Campus de Beaulieu, Rennes, France"
8ec7fff88b2e5b49154e6654e5e27f6678ddb7f0,On identification from periocular region utilizing SIFT and SURF,"ON IDENTIFICATION FROM PERIOCULAR REGION UTILIZING SIFT AND SURF
Şamil Karahan, 1Adil Karaöz, 1Ömer Faruk Özdemir, 1Ahmet Gökhan Gül, 2Umut Uludağ
Department of Computer Engineering, Gebze Institute of Technology, 41400, Gebze, Kocaeli, Turkey
TUBITAK BILGEM, 41470, Gebze, Kocaeli, Turkey
{ samilkarahan, adilkaraoz, farukozdemir24, ahmetgokhangul"
8e378ef01171b33c59c17ff5798f30293fe30686,A system for automatic face analysis based on statistical shape and texture models,"Lehrstuhl f¨ur Mensch-Maschine-Kommunikation
der Technischen Universit¨at M¨unchen
A System for Automatic Face Analysis
Based on
Statistical Shape and Texture Models
Ronald M¨uller
Vollst¨andiger Abdruck der von der Fakult¨at
f¨ur Elektrotechnik und Informationstechnik
der Technischen Universit¨at M¨unchen
zur Erlangung des akademischen Grades eines
Doktor-Ingenieurs
genehmigten Dissertation
Vorsitzender: Prof. Dr. rer. nat. Bernhard Wolf
Pr¨ufer der Dissertation:
. Prof. Dr.-Ing. habil. Gerhard Rigoll
. Prof. Dr.-Ing. habil. Alexander W. Koch
Die Dissertation wurde am 28.02.2008 bei der Technischen Universit¨at M¨unchen
eingereicht und durch die Fakult¨at f¨ur Elektrotechnik und Informationstechnik
m 18.09.2008 angenommen."
8e6957334ab60111fd7e2ae59b008a745223aabe,An incremental learning face recognition system for single sample per person,"An Incremental Learning Face Recognition System
for Single Sample Per Person
Tao Zhu, Furao Shen and Jinxi Zhao
recognition system. In nowadays, most of the existed in-
remental
learning systems are designed to update the
eigenspace of face data as new images arrive [8]. To our
knowledge, few of them can automatically decide when to
learn new information from an input image. In other words,
they need an external observer to tell them how to prevent
learning distorted information from a misclassified or non-
ideal image. Moreover, few of these methods can be applied
in the scenario of single sample per person.
In this paper, we mainly focus on the issue of robust incre-
mental face recognition under the condition of one training
sample per person. Inspired by the Single Image subspace
(SIS) approach [9], we propose an incremental learning face
recognition system. The goals of the proposed system are:
(1) self-adaptively updating and adjusting training samples
during learning process; (2) keeping learning new knowledge"
8e7749f635b161558efa3e98a324e88c73e2b18f,Neuroimaging Findings in Au ti sm : A Brief Review,"Türk Psikiyatri Dergisi 2009;
Turkish Journal of Psychiatry
Neuroimaging Findings in Auti sm: A Brief Review
Halime Tuna ULAY1, Aygün ERTUĞRUL2"
8e9ff8224753b22e3b1f8bbe271382d6fdb8ddfa,Scale optimization for full-image-CNN vehicle detection,"Scale Optimization for Full-Image-CNN Vehicle Detection
Yang Gao, Shouyan Guo, Kaimin Huang, Jiaxin Chen, Qian Gong, Yang Zou, Tong Bai and Gary Overetta"
8e6526b46a52a18028336a8d026e9d466aa12edf,Moving Poselets: A Discriminative and Interpretable Skeletal Motion Representation for Action Recognition,"Moving Poselets: A Discriminative and Interpretable Skeletal Motion
Representation for Action Recognition
Lingling Tao and Ren´e Vidal
Center for Imaging Science, Johns Hopkins University
ltao4,"
8e8c511ebc12a093d3f73a4717ec71c32e4dbd49,The use of visual information in the recognition of posed and spontaneous facial expressions.,"The use of visual information in the recognition of posed and
spontaneous facial expressions
Camille Saumure
Marie-Pier Plouffe-Demers
Amanda Est ´ephan
Daniel Fiset
Caroline Blais
Department of Psychoeducation and Psychology,
Universit ´e du Qu ´ebec en Outaouais,
Gatineau, Qu ´ebec, Canada
Department of Psychoeducation and Psychology,
Universit ´e du Qu ´ebec en Outaouais,
Gatineau, Qu ´ebec, Canada
Department of Psychoeducation and Psychology,
Universit ´e du Qu ´ebec en Outaouais,
Gatineau, Qu ´ebec, Canada
Department of Psychoeducation and Psychology,
Universit ´e du Qu ´ebec en Outaouais,
Gatineau, Qu ´ebec, Canada
Department of Psychoeducation and Psychology,"
8ee50fd3e19729a487f7196b682ccaa2d17aa0df,Improving head and body pose estimation through semi-supervised manifold alignment,"IMPROVING HEAD AND BODY POSE ESTIMATION
THROUGH SEMI-SUPERVISED MANIFOLD ALIGNMENT
Alexandre Heili(cid:63), Jagannadan Varadarajan†, Bernard Ghanem‡, Narendra Ahuja(cid:63)†, Jean-Marc Odobez(cid:63)
(cid:63) Idiap Research Institute, ´Ecole Polytechnique F´ed´erale de Lausanne, Switzerland
Advanced Digital Sciences Center, Singapore, (cid:63)† University of Illinois at Urbana-Champaign
King Abdullah University of Science and Technology, Saudi Arabia"
8ee62f7d59aa949b4a943453824e03f4ce19e500,Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear Regressions,"Robust Head-Pose Estimation Based on
Partially-Latent Mixture of Linear Regression
Vincent Drouard∗, Radu Horaud∗, Antoine Deleforge†, Sil`eye Ba∗ and Georgios Evangelidis∗
INRIA Grenoble Rhˆone-Alpes, Montbonnot Saint-Martin, France
INRIA Rennes Bretagne Atlantique, Rennes, France"
8e36cc33db5aa581cd826e6ba5f830d40d674712,Using Biologically Inspired Features for Face Processing,"Int J Comput Vis (2008) 76: 93–104
DOI 10.1007/s11263-007-0058-8
S H O RT PA P E R
Using Biologically Inspired Features for Face Processing
Ethan Meyers · Lior Wolf
Received: 4 March 2006 / Accepted: 2 April 2007 / Published online: 12 July 2007
© Springer Science+Business Media, LLC 2007"
8ec76d7d4a9abd09f088fb3f7a3351a7fda1fde0,Generative Adversarial Networks to Synthetically Augment Data for Deep Learning based Image Segmentation *,"Proceedings of the OAGM Workshop 2018
DOI: 10.3217/978-3-85125-603-1-07"
8e0becfc5fe3ecdd2ac93fabe34634827b21ef2b,Learning from Longitudinal Face Demonstration - Where Tractable Deep Modeling Meets Inverse Reinforcement Learning,"International Journal of Computer Vision manuscript No.
(will be inserted by the editor)
Learning from Longitudinal Face Demonstration -
Where Tractable Deep Modeling Meets Inverse Reinforcement Learning
Chi Nhan Duong · Kha Gia Quach · Khoa Luu · T. Hoang Ngan Le · Marios
Savvides · Tien D. Bui
Received: date / Accepted: date"
8eeab0aeb3170b1ef6497745d2a9bf78c001331d,Machine Vision Techniques for the Evaluation of Animal Behaviour by Dr,"Machine Vision Techniques for the
Evaluation of Animal Behaviour
Dr Derek Robert Magee
Submitted in accordance with the requirements
for the degree of Doctor of Philosophy
SI T Y O
The University of Leeds
School of Computing
October 2000
The candidate confirms that the work submitted is his own and that appropriate credit has been
given where reference has been made to the work of others."
8e7493bdabddc2ec99cfa2b9b862343f70c1701a,Pseudo-positive regularization for deep person re-identification,"Noname manuscript No.
(will be inserted by the editor)
Pseudo-positive regularization for deep person re-identification
Fuqing Zhu · Xiangwei Kong · Haiyan Fu · Qi Tian
Received: date / Accepted: date"
8ea30ade85880b94b74b56a9bac013585cb4c34b,From turbo hidden Markov models to turbo state-space models [face recognition applications],"FROM TURBO HIDDEN MARKOV MODELS TO TURBO STATE-SPACE MODELS
Florent Perronnin and Jean-Luc Dugelay
Institut Eur´ecom
Multimedia Communications Department
BP 193, 06904 Sophia Antipolis Cedex, France
fflorent.perronnin,"
8e963c09144cab961bc90a3c807dc9b92c6aa916,Support Vector Number Reduction: Survey and Experimental Evaluations,"Support Vector Number Reduction: Survey and
Experimental Evaluations
Ho Gi Jung, Senior Member, IEEE, and Gahyun Kim"
8e6a403943d00b31aa0241c36b00234353507124,Learn to Detect and Recognize Humans using Small Data Sets,"Learn to Detect and Recognize Humans
using Small Data Sets
Shichao Ou
Laboratory for Perceptual
Robotics
Computer Science
Department
University of Masschusetts
Amherst
Amherst, Massachusetts
Rachel Lee
Computer Science
Department
Swarthmore College, PA
Rod Grupen
Laboratory for Perceptual
Robotics
Computer Science
Department
University of Masschusetts"
8e6f67ba883169d6103795d7366a3821843ac758,A Novel Face Recognition Algorithm with Support Vector Machine Classifier,"INTERNATIONAL JOURNAL OF MATHEMATICS AND SCIENTIFIC COMPUTING, VOL. 1, NO. 1, 2011
A Novel Face Recognition Algorithm with
Support Vector Machine Classifier
Latha Parthiban
Phase-based
Texture  Representation"
8e9f973e9d01fdd275af6c1460e5307d2ff3d2bc,"OF KITH AND KIN 1 Of kith and kin : Perceptual enrichment , expectancy and reciprocal processing in face perception","OF	KITH	AND	KIN
Of kith and kin:
Perceptual enrichment, expectancy and reciprocal processing in face perception
Joshua Correll     Sean M. Hudson     Steffanie Guillermo     Holly A. Earls
University of Colorado Boulder
Author Note
Joshua Correll, Sean M. Hudson, Steffanie Guillermo, Holly A. Earls, Department of
Psychology & Neuroscience, University of Colorado Boulder.
We dedicate this paper to the memory of Sean Hudson, a wonderful scientist and a true
friend. We thank Jasmin Cloutier, Tim Correll, Tim Curran, Tiffany Ito, Sarah Lamer,
Debbie Ma, Max Weisbuch, and Bernd Wittenbrink for their thoughtful comments on
previous drafts.
Correspondence should be addressed to Joshua Correll, Department of Psychology &
Neuroscience, UCB 345, Boulder, Colorado, 80309-0345;"
8e723e8a3a5a9ea258591d384232e0251f842a1c,Twin-GAN - Unpaired Cross-Domain Image Translation with Weight-Sharing GANs,"Twin-GAN – Unpaired Cross-Domain Image
Translation with Weight-Sharing GANs
Jerry Li
Google
600 Amphitheatre Parkway, Mountain View, CA 94040"
8ed051be31309a71b75e584bc812b71a0344a019,Class-Based Feature Matching Across Unrestricted Transformations,"Class-based feature matching across unrestricted
transformations
Evgeniy Bart and Shimon Ullman"
8e8e3f2e66494b9b6782fb9e3f52aeb8e1b0d125,"Detecting and classifying scars, marks, and tattoos found in the wild","in  any  current  or
future  media,
for  all  other  uses,
 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be
obtained
including
reprinting/republishing  this  material  for  advertising  or  promotional  purposes,  creating
new  collective  works,  for  resale  or  redistribution  to  servers  or  lists,  or  reuse  of  any
opyrighted component of this work in other works.
Pre-print of article that will appear at BTAS 2012.!!"
8e3d0b401dec8818cd0245c540c6bc032f169a1d,McGan: Mean and Covariance Feature Matching GAN,"McGan: Mean and Covariance Feature Matching GAN
Youssef Mroueh * 1 2 Tom Sercu * 1 2 Vaibhava Goel 2"
8ed32c8fad924736ebc6d99c5c319312ba1fa80b,Centralized Gradient Pattern for Face Recognition,"IEICE TRANS. INF. & SYST., VOL.E96–D, NO.3 MARCH 2013
PAPER SpecialSectiononFacePerceptionandRecognition
Centralized Gradient Pattern for Face Recognition
Dong-Ju KIM†a), Sang-Heon LEE†, and Myoung-Kyu SHON†, Members
SUMMARY
This paper proposes a novel face recognition approach
using a centralized gradient pattern image and image covariance-based fa-
ial feature extraction algorithms, i.e. a two-dimensional principal compo-
nent analysis and an alternative two-dimensional principal component anal-
ysis. The centralized gradient pattern image is obtained by AND operation
of a modified center-symmetric local binary pattern image and a modified
local directional pattern image, and it is then utilized as input image for the
facial feature extraction based on image covariance. To verify the proposed
face recognition method, the performance evaluation was carried out using
various recognition algorithms on the Yale B, the extended Yale B and the
CMU-PIE illumination databases. From the experimental results, the pro-
posed method showed the best recognition accuracy compared to different
pproaches, and we confirmed that the proposed approach is robust to illu-
mination variation.
key words: centralized gradient pattern, local binary pattern, local direc-"
8e0cc47c194ef7daf15aaef14d61e493879ae137,Deep Network Flow for Multi-object Tracking,"Deep Network Flow for Multi-Object Tracking
Samuel Schulter
Paul Vernaza Wongun Choi Manmohan Chandraker
NEC Laboratories America, Media Analytics Department
Cupertino, CA, USA"
8e92168860d8c6591a0c088573629e4d167f5947,"Look at the Driver, Look at the Road: No Distraction! No Accident!","Look at the Driver, Look at the Road: No Distraction! No Accident!
Mahdi Rezaei and Reinhard Klette
The University of Auckland
Private Bag 92019, Auckland, New Zealand"
8e1d84e08109b5c692f7eff5cbc1816e5bdb00a3,Adversarial Face Recognition and Phishing Detection Using Multi-Layer Data Fusion,"Mason Archival Repository Service
http://mars.gmu.edu
etd Mason (Electronic Theses and Dissertations)
The Volgenau School of Engineering
Adversarial Face Recognition and
Phishing Detection Using Multi-Layer
Data Fusion
Ramanathan, Venkatesh
http://hdl.handle.net/1920/8075
service of Mason Publishing"
8eb2e7c9017b4a110978a1bb504accbc7b9ba211,Marching into battle: synchronized walking diminishes the conceptualized formidability of an antagonist in men.,"Downloaded from
http://rsbl.royalsocietypublishing.org/
on June 9, 2015
rsbl.royalsocietypublishing.org
Research
Cite this article: Fessler DMT, Holbrook C.
014 Marching into battle: synchronized
walking diminishes the conceptualized
formidability of an antagonist in men. Biol.
Lett. 10: 20140592.
http://dx.doi.org/10.1098/rsbl.2014.0592
Received: 25 July 2014
Accepted: 6 August 2014
Subject Areas:
ehaviour
Keywords:
synchrony, alliance, fighting capacity
Author for correspondence:
Daniel M. T. Fessler
e-mail:"
8e6adaf03ac84acc16e428869b66a5a1e94ed753,Privacy-preserving classifiers recognize shared mobility behaviours from WiFi network imperfect data,"Privacy-preserving classifiers recognize shared
mobility behaviours from WiFi network imperfect
Orestes Manzanilla-Salazar∗ and Brunilde Sansò†
Email:"
8ee02b8d375b21fe37b837e1a9288624f47c38d3,The Lifecycle of Geotagged Data,"The Lifecycle of Geotagged Data
Rossano Schifanella
University of Turin
Turin, Italy
Bart Thomee
Google
San Bruno, CA, USA
David A. Shamma
Centrum Wiskunde & Informatica
Amsterdam, Netherlands"
8ec368bcc138736efedec4ce4fb5eac2c7d7585f,Testosterone Modulates Altered Prefrontal Control of Emotional Actions in Psychopathic Offenders123,"New Research
Cognition and Behavior
Testosterone Modulates Altered Prefrontal
Control of Emotional Actions in Psychopathic
Offenders1,2,3
Inge Volman,1,2,3 Anna Katinka Louise von Borries2,3,4,5, Berend Hendrik Bulten,5 Robbert Jan
Verkes,3,4,5
Ivan Toni,3 and Karin Roelofs2,3
DOI:http://dx.doi.org/10.1523/ENEURO.0107-15.2016
Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, University College
London, London WC1N 3BG, United Kingdom, 2Behavioural Science Institute, Radboud University Nijmegen, 6525
HR, Nijmegen, The Netherlands, 3Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen,
6525 EN, Nijmegen, The Netherlands, 4Department of Psychiatry, UMC Sint Radboud, 6525 GA, Nijmegen, The
Netherlands, and 5Pompestichting, 6532 CN, Nijmegen, The Netherlands"
8ea9093542075bd8cc4928a4c671a95f363c61ef,Sliced-Wasserstein Autoencoder : An Embarrassingly Simple Generative Model,"Sliced-Wasserstein Autoencoder: An
Embarrassingly Simple Generative Model"
8e42568c2b3feaafd1e442e1e861ec50a4ac144f,An Evaluation of Deep CNN Baselines for Scene-Independent Person Re-identification,"An Evaluation of Deep CNN Baselines for
Scene-Independent Person Re-Identification
Paul Marchwica, Michael Jamieson, Parthipan Siva
Senstar Corporation
Waterloo, Canada
{Paul.Marchwica, Mike.Jamieson,
the art"
8e94ed0d7606408a0833e69c3185d6dcbe22bbbe,For your eyes only,"© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE
must  be  obtained  for  all  other  uses,  in  any  current  or  future  media,  including
reprinting/republishing  this  material  for  advertising  or  promotional  purposes,
reating  new  collective  works,  for  resale  or  redistribution  to  servers  or  lists,  or
reuse of any copyrighted component of this work in other works.
Pre-print of article that will appear at WACV 2012."
a1132e2638a8abd08bdf7fc4884804dd6654fa63,Real-Time Video Face Recognition for Embedded Devices,"Real-Time Video Face Recognition
for Embedded Devices
Gabriel Costache, Sathish Mangapuram, Alexandru
Drimbarean, Petronel Bigioi and Peter Corcoran
Tessera, Galway,
Ireland
. Introduction
This  chapter  will  address  the  challenges  of  real-time  video  face  recognition  systems
implemented  in  embedded  devices.  Topics  to  be  covered  include:  the  importance  and
hallenges of video face recognition in real life scenarios, describing a general architecture of
generic  video  face  recognition  system  and  a  working  solution  suitable  for  recognizing
faces  in  real-time  using  low  complexity  devices.  Each  component  of  the  system  will  be
described  together  with  the  system’s  performance  on  a  database  of  video  samples  that
resembles real life conditions.
. Video face recognition
Face recognition remains a very active topic in computer vision and receives attention from
large  community  of  researchers  in  that  discipline.  Many  reasons  feed  this  interest;  the
main being  the wide range of commercial, law enforcement and security applications that
require  authentication.  The  progress  made  in  recent  years  on  the  methods  and  algorithms
for data processing as well as the availability of new technologies makes it easier to study"
a11600deb182677f4fe586fcea59f10d032a6c6f,Active Appearance Models with Rotation Invariant Kernels,"Active Appearance Models with Rotation Invariant Kernels
Onur C. Hamsici and Aleix M. Martinez
Department of Electrical and Computer Engineering
Ohio State University, Columbus, OH 43210"
a1aac8e95cd262f974b26374ec8fe35c0f000185,Transferrable Feature and Projection Learning with Class Hierarchy for Zero-Shot Learning,"IJCV manuscript No.
(will be inserted by the editor)
Transferrable Feature and Projection Learning with Class Hierarchy for
Zero-Shot Learning
Aoxue Li · Zhiwu Lu · Jiechao Guan · Tao Xiang · Liwei Wang · Ji-Rong Wen
Received: date / Accepted: date"
a11a63e00c0e587adf4efc1425c0651c242263b7,Two More Strategies to Speed Up Connected Components Labeling Algorithms,"Two More Strategies to Speed Up Connected
Components Labeling Algorithms
Federico Bolelli, Michele Cancilla, Costantino Grana
Dipartimento di Ingegneria “Enzo Ferrari”
Universit`a degli Studi di Modena e Reggio Emilia
Via Vivarelli 10, Modena MO 41125, Italy"
a13dac9255dd738932f463a8f462c11419f072db,Use of Generative Adversarial Network for Cross-Domain Change Detection,"Use of Generative Adversarial Network for
Cross-Domain Change Detection
Yamaguchi Kousuke
Tanaka Kanji
Sugimoto Takuma
Graduate School of Engineering, University of Fukui
-9-1, bunkyo, fukui, fukui
Email:"
a1669fa7d3d8f0c0cafe770c79007949cd32b245,Deep Metric Learning with BIER: Boosting Independent Embeddings Robustly,"TPAMI SUBMISSION
Deep Metric Learning with BIER:
Boosting Independent Embeddings Robustly
Michael Opitz, Georg Waltner, Horst Possegger, and Horst Bischof"
a13a4e4cc8f4744b40668fe7cca660ae0e88537d,Explorer Multi 30 K : Multilingual English-German Image Descriptions,"Multi30K: Multilingual English-German Image Descriptions
Citation for published version:
Elliott, D, Frank, S, Sima'an, K & Specia, L 2016, Multi30K: Multilingual English-German Image
Descriptions. in Proceedings of the 5th Workshop on Vision and Language, hosted by the 54th Annual
Meeting of the Association for Computational Linguistics, 2016, August 12, Berlin, Germany.
Association for Computational Linguistics (ACL), pp. 70-74.
Link:
Link to publication record in Edinburgh Research Explorer
Document Version:
Publisher's PDF, also known as Version of record
Published In:
Proceedings of the 5th Workshop on Vision and Language, hosted by the 54th Annual Meeting of the
Association for Computational Linguistics, 2016, August 12, Berlin, Germany
General rights
Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s)
nd / or other copyright owners and it is a condition of accessing these publications that users recognise and
bide by the legal requirements associated with these rights.
Take down policy
The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer
ontent complies with UK legislation. If you believe that the public display of this file breaches copyright please"
a172653d4559ca848b9a85f9ef5230bc794b3c3c,Averaging Representation of Standard Face Images and Recognition by KPCA and GFMT,"Jagankumar K, Geetha K ; International Journal of Advance Research, Ideas and Innovations in Technology.
ISSN: 2454-132X
Impact factor: 4.295
(Volume3, Issue3)
Available online at www.ijariit.com
Averaging Representation of Standard Face Images and
Recognition by KPCA and GFMT
Jagan Kumar
Garuda Aerospace Private Limited
Geetha
Plintron Global technology solutions Pvt Ltd"
a15663e0c0a2427ac4da5161e4ed75d331a5a2be,Streaming spectral clustering,"Streaming Spectral Clustering
Shinjae Yoo
Computational Science Center
Brookhaven National Laboratory
Upton, New York 11973-5000
Email:
Hao Huang
Machine Learning Laboratory
General Electric Global Research
San Ramon, CA 94583
Email:
Shiva Prasad Kasiviswanathan
Samsung Research America
Mountain View, CA 94043
Email:"
a120cac99c85548d0749dd83b0450520949e6474,Unsupervised Eye Pupil Localization through Differential Geometry and Local Self-Similarity Matching,"Unsupervised Eye Pupil Localization through Differential
Geometry and Local Self-Similarity Matching
Marco Leo1*, Dario Cazzato1,2, Tommaso De Marco1, Cosimo Distante1
National Research Council of Italy, Institute of Optics, Arnesano, Lecce, Italy, 2 Faculty of Engineering, University of Salento, Lecce, Italy"
a19de85fa1533a1a1929b98b5fc3b1fb618dc668,Towards Improving Abstractive Summarization via Entailment Generation,
a125bc46fee1bd170a0654b8856d3b78d62e9d29,Learning weighted sparse representation of encoded facial normal information for expression-robust 3D face recognition,"Learning Weighted Sparse Representation of Encoded Facial Normal
Information for Expression-Robust 3D Face Recognition
Huibin Li1,2, Di Huang1,2, Jean-Marie Morvan1,3,4, Liming Chen1,2
Universit´e de Lyon, CNRS, 2Ecole Centrale de Lyon, LIRIS UMR5205, F-69134, Lyon, France
Universit´e Lyon 1, Institut Camille Jordan, 43 blvd. du 11 Nov. 1918, F-69622 Villeurbanne - Cedex, France
King Abdullah University of Science and Technology, GMSV Research Center, Bldg 1, Thuwal 23955-6900, Saudi Arabia"
a147cec1434753777b3651101bdbda1489b09fd4,Individual differences in shifting decision criterion: a recognition memory study.,"Mem Cogn (2012) 40:1016–1030
DOI 10.3758/s13421-012-0204-6
Individual differences in shifting decision criterion:
A recognition memory study
Elissa M. Aminoff & David Clewett & Scott Freeman &
Amy Frithsen & Christine Tipper & Arianne Johnson &
Scott T. Grafton & Michael B. Miller
Published online: 4 May 2012
# Psychonomic Society, Inc. 2012"
a18c8f76f2599d6d61f26cb1d4025ea386919dfe,Video Event Detection: From Subvolume Localization To Spatio-Temporal Path Search.,"This document is downloaded from DR-NTU, Nanyang Technological
University Library, Singapore.
Title
Video event detection : from subvolume localization to
spatio-temporal path search
Author(s)
Tran, Du; Yuan, Junsong; Forsyth, David
Citation
Tran, D., Yuan, J., & Forsyth, D. (2014). Video Event
Detection: From Subvolume Localization to
Spatiotemporal Path Search. IEEE Transactions on
Pattern Analysis and Machine Intelligence, 36(2), 404-
http://hdl.handle.net/10220/19322
Rights
© 2014 IEEE. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other
uses, in any current or future media, including
reprinting/republishing this material for advertising or
promotional purposes, creating new collective works, for
resale or redistribution to servers or lists, or reuse of any"
a1af05502eac70296ee22e5ab7e066420f5fe447,A Probabilistic Approach for Breast Boundary Extraction in Mammograms,"Hindawi Publishing Corporation
Computational and Mathematical Methods in Medicine
Volume 2013, Article ID 408595, 19 pages
http://dx.doi.org/10.1155/2013/408595
Research Article
A Probabilistic Approach for Breast Boundary
Extraction in Mammograms
Hamed Habibi Aghdam, Domenec Puig, and Agusti Solanas
Department of Computer Engineering and Mathematics, Rovira i Virgili University, 43007 Tarragona, Spain
Correspondence should be addressed to Domenec Puig;
Received 31 May 2013; Revised 21 August 2013; Accepted 16 September 2013
Academic Editor: Reinoud Maex
Copyright © 2013 Hamed Habibi Aghdam et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The extraction of the breast boundary is crucial to perform further analysis of mammogram. Methods to extract the breast boundary
an be classified into two categories: methods based on image processing techniques and those based on models. The former use
image transformation techniques such as thresholding, morphological operations, and region growing. In the second category, the
oundary is extracted using more advanced techniques, such as the active contour model. The problem with thresholding methods
is that it is a hard to automatically find the optimal threshold value by using histogram information. On the other hand, active
ontour models require defining a starting point close to the actual boundary to be able to successfully extract the boundary. In this"
a102edaa9fd458316637ce51a0b7aba2ee651637,Learning Human Poses from Actions,"ADITYA, JAWAHAR, PAWAN: LEARNING HUMAN POSES FROM ACTIONS
Learning Human Poses from Actions
IIIT Hyderabad
University of Oxford &
The Alan Turing Institute
Aditya Arun1
C.V. Jawahar1
M. Pawan Kumar2"
a14ae81609d09fed217aa12a4df9466553db4859,Face Identification Using Large Feature Sets,"REVISED VERSION, JUNE 2011
Face Identification Using Large Feature Sets
William Robson Schwartz, Huimin Guo, Jonghyun Choi, and Larry S. Davis, Fellow, IEEE"
a19f08d7b1ce8b451df67ec125dd9254b5a05d95,3D Face Recognition Using Multiview Keypoint Matching,"009 Advanced Video and Signal Based Surveillance
D Face Recognition Using Multiview Keypoint Matching
Michael Mayo, Edmond Zhang
Department of Computer Science, University of Waikato, New Zealand
{mmayo,"
a14879e4326105502892fee66606a8998b1baad6,"- 1-Age-related differences in brain electrical activity during extended continuous face recognition in younger children , older children , and adults","Age-related differences in brain electrical activity during extended continuous
face recognition in younger children, older children, and adults
Jan W. Van Strien1
Johanna C. Glimmerveen2
Ingmar H.A. Franken1
Vanessa E.G. Martens3
Eveline A. de Bruin3
Institute of Psychology, Faculty of Social Sciences, Erasmus University Rotterdam, The
Netherlands
School of Social and Behavioral Sciences, Tilburg University, The Netherlands
Sensation, Perception and Behaviour, Unilever R&D Vlaardingen, The Netherlands"
a139c62c27a884cf447ad020cb7b154e63477681,A Data-driven Model for Interaction-Aware Pedestrian Motion Prediction in Object Cluttered Environments,"A Data-driven Model for Interaction-aware Pedestrian Motion Prediction in
Object Cluttered Environments
Mark Pfeiffer, Giuseppe Paolo, Hannes Sommer, Juan Nieto, Roland Siegwart, and Cesar Cadena"
a133374b9630bbe6eb2b7de8c3204aa57e75c582,A Deep Network Solution for Attention and Aesthetics Aware Photo Cropping.,"A Deep Network Solution for Attention and
Aesthetics Aware Photo Cropping
Wenguan Wang, Jianbing Shen, Senior Member, IEEE, and Haibin Ling"
a1030e6e0e6995768dbcafedc712a59db090d2b4,Bayesian Sparsification of Recurrent Neural Networks,"Bayesian Sparsification of Recurrent Neural Networks
Ekaterina Lobacheva * 1 2 Nadezhda Chirkova * 1 3 Dmitry Vetrov 1 4"
a1f1120653bb1bd8bd4bc9616f85fdc97f8ce892,Latent Embeddings for Zero-Shot Classification,"Latent Embeddings for Zero-shot Classification
Yongqin Xian1, Zeynep Akata1, Gaurav Sharma1,2,∗, Quynh Nguyen3, Matthias Hein3 and Bernt Schiele1
MPI for Informatics
IIT Kanpur
Saarland University"
a157ebc849d57ccff00a52a68b24e4ac8eba9536,The Contextual Loss for Image Transformation with Non-aligned Data,"The Contextual Loss for Image Transformation
with Non-Aligned Data
Roey Mechrez(cid:63) , Itamar Talmi(cid:63), Lihi Zelnik-Manor
Technion - Israel Institute of Technology
Fig. 1. Our Contextual loss is effective for many image transformation tasks: It can
make a Trump cartoon imitate Ray Kurzweil, give Obama some of Hillary’s features,
nd, turn women more masculine or men more feminine. Mutual to these tasks is the
bsence of ground-truth targets that can be compared pixel-to-pixel to the generated
images. The Contextual loss provides a simple solution to all of these tasks."
a1c6f88330762cc97f26585c124c6b3ac791eb89,Confidence Sets for Fine-Grained Categorization and Plant Species Identification,"Int J Comput Vis
DOI 10.1007/s11263-014-0743-3
Confidence Sets for Fine-Grained Categorization and Plant
Species Identification
Asma Rejeb Sfar · Nozha Boujemaa · Donald Geman
Received: 1 January 2014 / Accepted: 20 June 2014
© Springer Science+Business Media New York 2014"
a1e1bd4dacddc703a236681e987a09601ee1016d,Embedding Visual Hierarchy With Deep Networks for Large-Scale Visual Recognition,"Embedding Visual Hierarchy with Deep Networks
for Large-Scale Visual Recognition
Tianyi Zhao, Baopeng Zhang, Wei Zhang, Ning Zhou, Jun Yu, Jianping Fan"
a1ff747cf512c8156620d9c17cb6ed8d21a76ad6,KonIQ-10k: Towards an ecologically valid and large-scale IQA database,"KonIQ-10K: TOWARDS AN ECOLOGICALLY VALID AND LARGE-SCALE IQA DATABASE
Hanhe Lin*, Vlad Hosu* and Dietmar Saupe
Department of Computer and Information Science, University of Konstanz, Germany
Email: {hanhe.lin, vlad.hosu,"
a158c1e2993ac90a90326881dd5cb0996c20d4f3,Symmetry as an Intrinsically Dynamic Feature,"OPEN ACCESS
ISSN 2073-8994
Article
Vito Di Gesu 1,2,†, Marco E. Tabacchi 1,3,* and Bertrand Zavidovique 4
DMA, Università degli Studi di Palermo, via Archirafi 34, 90123 Palermo, Italy
CITC, Università degli Studi di Palermo, via Archirafi 34, 90123 Palermo, Itlay
Istituto Nazionale di Ricerche Demopolis, via Col. Romey 7, 91100 Trapani, Italy
IEF, Université Paris IX–Orsay, Paris, France; E-Mail: (B.Z.)
Deceased on 15 March 2009.
* Author to whom correspondence should be addressed; E-Mail:
Received: 4 March 2010; in revised form: 23 March 2010 / Accepted: 29 March 2010 /
Published: 1 April 2010"
a11f5e74b13a6353d14e024d06a902b9afa728b3,Yum-me: Personalized Healthy Meal Recommender System,"Yum-me: Personalized Healthy Meal Recommender System
Longqi Yang
Cornell Tech
Nicola Dell
Cornell Tech
Cheng-Kang Hsieh
Serge Belongie
Cornell Tech
Hongjian Yang
Cornell Tech
Deborah Estrin
Cornell Tech"
a1b7b23bd8f2b2ef37a9113e6b8499f0069aac85,Performance assessment of face recognition using super-resolution,"Performance Assessment of Face Recognition Using
Super-Resolution
Shuowen Hu
Robert Maschal
S. Susan Young
U.S. Army Research Laboratory
U.S. Army Research Laboratory
U.S. Army Research Laboratory
800 Powder Mill Rd.
Adelphi, MD 20783
(301)394-2526
800 Powder Mill Rd.
Adelphi, MD 20783
(301)394-0437
800 Powder Mill Rd.
Adelphi, MD 20783
(301)394-0230
Tsai Hong Hong
Jonathon P. Phillips
National Institute of Standards and"
a1e198454bd0868b4da9bca7a35218dd235cfdda,3d‐facial Expression Synthesis and Its Application to Face Recognition Systems,"D‐Facial Expression Synthesis and its Application to Face Recognition Systems
Leonel Ramírez‐Valdez1, Rogelio Hasimoto‐Beltran2
,2Centro de Investigación en Matemáticas(CIMAT)
Jalisco s/n, Col. Mineral de Valenciana, Guanajuato, Gto., México 36240"
a15f4e3adb56dbbdd6f922489efef48fc5efa003,Grounding Semantic Roles in Images,"Grounding Semantic Roles in Images
Carina Silberer†♣
Manfred Pinkal†
Department of Computational Linguistics
Saarland University, Saarbr¨ucken, Germany
♣Universitat Pompeu Fabra
Barcelona, Spain"
a10f734e30d8dcb8506c9ea5b1074e6c668904e2,Learning Features and Parts for Fine-Grained Recognition,"Learning Features and Parts for Fine-Grained
Recognition
(Invited Paper)
Jonathan Krause∗, Timnit Gebru∗, Jia Deng †, Li-Jia Li ‡, Li Fei-Fei∗
Stanford University: {jkrause, tgebru,
University of Michigan:
Yahoo! Research:"
a15d9d2ed035f21e13b688a78412cb7b5a04c469,Object detection using strongly-supervised deformable part models,"Object Detection Using
Strongly-Supervised Deformable Part Models
Hossein Azizpour1 and Ivan Laptev2
Computer Vision and Active Perception Laboratory (CVAP), KTH, Sweden
INRIA, WILLOW, Laboratoire d’Informatique de l’Ecole Normale Superieure"
a14260cd8c607afc6a9bd0c4df2ee22162e6d8c0,Discriminative Dictionary Learning With Ranking Metric Embedded for Person Re-Identification,Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
d1082eff91e8009bf2ce933ac87649c686205195,Pruning of Error Correcting Output Codes by optimization of accuracy–diversity trade off,"(will be inserted by the editor)
Pruning of Error Correcting Output Codes by
Optimization of Accuracy-Diversity Trade off
S¨ureyya ¨Oz¨o˘g¨ur Aky¨uz · Terry
Windeatt · Raymond Smith
Received: date / Accepted: date"
d1f58798db460996501f224fff6cceada08f59f9,Transferrable Representations for Visual Recognition,"Transferrable Representations for Visual Recognition
Jeffrey Donahue
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2017-106
http://www2.eecs.berkeley.edu/Pubs/TechRpts/2017/EECS-2017-106.html
May 14, 2017"
d19df82c5ea644937bf182fabdc0e36e78ea6867,Emotional Facial Expression Recognition from Two Different Feature Domains,"EMOTIONAL FACIAL EXPRESSION RECOGNITION FROM TWO
DIFFERENT FEATURE DOMAINS
Jonghwa Kim and Frank Jung
Institute of Computer Science, University of Augsburg, Germany
Keywords:"
d1a0425f764ce8847d20d278e4a4267c8258c4dc,3D Human Pose Estimation with Siamese Equivariant Embedding,"D Human Pose Estimation with Siamese Equivariant
Embedding
M´arton V´egesa,∗, Viktor Vargaa, Andr´as L˝orincza
E¨otv¨os Lor´and University, Budapest, Hungary"
d170adb2c508edaedb731ada8cb995172a839a1f,Cascade of Boolean detector combinations,"Mahkonen et al. EURASIP Journal on Image and Video
Processing  (2018) 2018:61
https://doi.org/10.1186/s13640-018-0303-9
EURASIP Journal on Image
nd Video Processing
RESEARCH
Open Access
Cascade of Boolean detector
ombinations
Katariina Mahkonen*
, Tuomas Virtanen and Joni Kämäräinen"
d1ade6a8c3a4c929efb70810a171c62a39e6f195,Review on Latest Approaches used in Natural Language Processing for Generation of Image Captioning,"SSRG International Journal of Computer Science and Engineering (SSRG-IJCSE) – volume 4 Issue 6 – June 2017
Review on Latest Approaches used in Natural
Language Processing for Generation of Image
Captioning
M. A. Bhalekar  ,  Dr. M. V. Bedekar
Department of Computer Engineering, MAEER’S Maharashtra Institute of Technology,
Pune, Maharashtra, India."
d1a43737ca8be02d65684cf64ab2331f66947207,IJB – S : IARPA Janus Surveillance Video Benchmark ∗,"IJB–S: IARPA Janus Surveillance Video Benchmark (cid:3)
Nathan D. Kalka y
Stephen Elliott z
Brianna Maze y
Kaleb Hebert y
James A. Duncan y
Julia Bryan z
Kevin O’Connor z
Anil K. Jain x"
d16c8ac2d194a6e862be0d1c4edf1ca2cdf5dc18,Robust Subspace Approaches to Visual Learning and recognition,"Univerza v Ljubljani
Fakulteta za raˇcunalniˇstvo in informatiko
Danijel Skoˇcaj
Robustni pristopi k vizualnemu uˇcenju
in razpoznavanju na osnovi podprostorov
DOKTORSKA DISERTACIJA
Ljubljana, 2003
Mentor: prof. dr. Aleˇs Leonardis"
d198b5bc5eae22f7a788729c0ea15b6b60b62f36,Transfer Learning for Estimating Causal Effects using Neural Networks,"Transfer Learning for Estimating Causal Effects
using Neural Networks
Sören R. Künzel∗
UC Berkeley
Varsha Ramakrishnan
UC Berkeley
Bradly C. Stadie∗
UC Berkeley
Nikita Vemuri
UC Berkeley
Jasjeet S. Sekhon
UC Berkeley
Pieter Abbeel
UC Berkeley"
d111faa1990f80e3351ea1eef0e5fc177d4e44b4,Iteratively Training Look-Up Tables for Network Quantization,"Iteratively Training Look-Up Tables
for Network Quantization
Fabien Cardinaux∗
Sony Europe Ltd.†
Stefan Uhlich∗
Sony Europe Ltd.†
Kazuki Yoshiyama∗
Sony Corporation‡
Javier Alonso García
Sony Europe Ltd.†
Stephen Tiedemann
Sony Europe Ltd.†
Thomas Kemp
Sony Europe Ltd.†
Akira Nakamura
Sony Corporation‡"
d1c103c63d930d3ae7397618f486117a48e35f16,Does gaze direction modulate facial expression processing in children with autism spectrum disorder?,"BIROn - Birkbeck Institutional Research Online
Enabling open access to Birkbeck’s published research output
Does gaze direction modulate facial expression
processing in children with autism spectrum disorder?
Journal Article
http://eprints.bbk.ac.uk/2561
Version: Accepted (Refereed)
Citation:
© 2009 Wiley Blackwell
Publisher version
______________________________________________________________
All articles available through Birkbeck ePrints are protected by intellectual property law, including
opyright law. Any use made of the contents should comply with the relevant law.
______________________________________________________________
Akechi, H.; Senju, A.; Kikuchi, Y.; Tojo, Y.; Osanai, H.; Hasegawa, T.
(2009)
Does gaze direction modulate facial expression processing in children
with autism spectrum disorder?
Deposit Guide
Contact:"
d1e66107eb084ea0ef5a97f3363f8787b8df91ed,Max-Margin Regularization for Reducing Accidentalness in Chamfer Matching,"Max-margin Regularization for Reducing
Accidentalness in Chamfer Matching
Angela Eigenstetter*, Pradeep Yarlagadda* and Bj¨orn Ommer
Interdisciplinary Center for Scientific Computing, University of Heidelberg, Germany"
d1295a93346411bb833305acc0e092c9e3b2eff1,The eMPaThy iMBalance hyPoThesis oF aUTisM : a TheoReTical aPPRoach To cogniTiVe and eMoTional eMPaThy in aUTisTic deVeloPMenT,"the Psychological record, 2009, 59, 489-510
The eMPaThy iMBalance hyPoThesis oF aUTisM:
TheoReTical aPPRoach To cogniTiVe and
eMoTional eMPaThy in aUTisTic deVeloPMenT
Adam Smith
Dundee, Scotland
There has been a widely held belief that people with autism spectrum disorders
lack empathy. This article examines the empathy imbalance hypothesis (EIH) of
utism. According to this account, people with autism have a deficit of cognitive
empathy but a surfeit of emotional empathy. The behavioral characteristics of
utism might be generated by this imbalance and a susceptibility to empathic
overarousal.  The  EIH  builds  on  the  theory  of  mind  account  and  provides  an
lternative to the extreme-male-brain theory of autism. Empathy surfeit is a re-
urrent theme in autistic narratives, and empirical evidence for the EIH is grow-
ing. A modification of the pictorial emotional Stroop paradigm could facilitate
n experimental test of the EIH.
Autism is a pervasive developmental disorder that continues to fascinate
researchers,  challenge  clinicians,  and  distress  affected  families.  empathy
is  a  set  of  processes  and  outcomes  at  the  heart  of  human  social  behavior.
Fascination with autism is often interwoven with the study of empathy because"
d1a9ce4a250ede36ff1bfae090a905e7795f0e26,Chapter 3 – Available Technologies 3.1 Speech Processing 3.1.1 Speech Coding,"Chapter 3 – AVAILABLE TECHNOLOGIES
Speech  technology  is  traditionally  divided  into  speech  processing  and  natural  (i.e.  written)  language
processing. This chapter presents these and their combination, followed by some related technologies.
.1  SPEECH PROCESSING
Modern  speech  technology  is  based  on  digital  signal  processing,  probabilistic  theory  and  search
lgorithms.  These  techniques  make  it  possible  to  perform  significant  data  reduction  for  coding  and
transmission of speech signals, speech synthesis and automatic recognition of speech, speaker or language.
In this section the state-of-the-art is presented and related to realistic military applications.
.1.1  Speech Coding
When digital systems became available, it was obvious that the transmission of digital signals was more
efficient  than  the  transmission  of  analogue  signals.  If  analogue  signals  are  transmitted  under  adverse
onditions,  it  is  not  easy  to  reconstruct  the  received  signal,  because  the  possible  signal  values  are  not
known  in  advance.  For  digital  signals  discrete  levels  are  used.  This  allows,  within  certain  limits,
the  reconstruction  of  distorted  signals.  The  first  digital  transmission  systems  were  based  on  coding  the
waveform  of  the  speech  signal.  This  results  in  bit  rates  between  8000  to  64000  Bps  (bits  per  second).
The higher the bit rate the better the quality. Later, more advanced coding systems were used where basic
properties  of  the  speech  were  determined  and  encoded,  resulting  in  a  more  efficient  coding  (bit  rates
etween 300 and 4800 Bps) but also in reduced intelligibility. These methods are discussed in this section.
The  first  technique  used  to  convert  an  analogue  signal  into  a  digital  signal  was  based  on  the  work  of
Shannon.  He  converted  the  instantaneous  signal  value  at  discrete  moments  into  a  binary  number"
d1dc5a8b4d13d2c51eec7bcb29d08f471d3b65dc,Adversarially Occluded Samples for Person Re-identification ( Supplementary Material ) 1 . Improvement of Ranking Results,"Adversarially Occluded Samples for Person Re-identification
Houjing Huang 1
Dangwei Li 1
Zhang Zhang 1
Xiaotang Chen 1
Kaiqi Huang 1
CRIPAC & NLPR, CASIA 2 University of Chinese Academy of Sciences
CAS Center for Excellence in Brain Science and Intelligence Technology
{houjing.huang, dangwei.li, zzhang, xtchen,"
d10b03ddae7348ea8de079f6175d6833c885a991,"A Graph Based People Silhouette Segmentation Using Combined Probabilities Extracted from Appearance, Shape Template Prior, and Color Distributions","A Graph Based People Silhouette Segmentation
Using Combined Probabilities Extracted
from Appearance, Shape Template Prior,
nd Color Distributions
Christophe Coniglio1,2(B), Cyril Meurie1,2, Olivier L´ezoray3,
nd Marion Berbineau1,2
Univ Lille Nord de France, 59000 Lille, France
IFSTTAR, COSYS, LEOST, 59650 Villeneuve d’Ascq, France
Normandie Univ., UNICAEN, ENSICAEN, GREYC UMR CNRS 6072,
Caen, France"
d1dfdc107fa5f2c4820570e369cda10ab1661b87,Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation,"Super SloMo: High Quality Estimation of Multiple Intermediate Frames
for Video Interpolation
Huaizu Jiang1
Deqing Sun2
Varun Jampani2
Ming-Hsuan Yang3,2
Erik Learned-Miller1
Jan Kautz2
UMass Amherst
NVIDIA 3UC Merced"
d1a9f71e5563a1bb2f956b9b805cfc6aafe4a6e6,Robust Methods for Visual Tracking and Model Alignment,
d1443d18d40c323d5ad01277675ee00c8decdb99,EXPERIMENTAL EVALUATION OF TEXT-INDEPENDENT SPEAKER VERIFICATION ON LABORATORY AND FIELD TEST DATABASES IN THE M2VTS PROJECT,"EXPERIMENTAL EVALUATION OF TEXT-INDEPENDENT
SPEAKER VERIFICATION ON LABORATORY AND FIELD
TEST DATABASES IN THE M2VTS PROJECT
L. Besacie
, J. Luett
, G. Maîtr
, E. Meurv
(1) IMT, Neuchâtel (CH) -
(2) IDIAP, Martigny (CH) -
(3) now at EIV, Sion (CH) -
(4) now at EPFL, Lausanne (CH) -"
d1d4c49e764a200bc90113b0ba9c34664d0f9462,"Memo No . 082 May 10 , 2018 Scene Graph Parsing as Dependency Parsing","CBMM Memo No. 082
May 10, 2018
Scene Graph Parsing as Dependency Parsing
Yu-Siang Wang1, Chenxi Liu2, Xiaohui Zeng3, Alan Yuille2
: National Taiwan University
: Johns Hopkins University
: Hong Kong University of Science and Technology"
d1c0592f4f9f0ff2e14e0591d87539e5141b7361,Mobile Emotion Recognition Engine,"Mobile Emotion Recognition Engine
Alberto Scicali1"
d122d66c51606a8157a461b9d7eb8b6af3d819b0,AUTOMATED RECOGNITION OF FACIAL EXPRESSIONS,"Vol-3 Issue-4 2017
IJARIIE-ISSN(O)-2395-4396
AUTOMATED RECOGNITION OF FACIAL
EXPRESSIONS
Pavan S. Ahire, PG Student, Dept. of Computer Engineering, METs Institute of Engineering,
Prof. R. P. Dahake, Dept. of Computer Engineering, METs Institute of  Engineering,
Adgoan,Nashik,Maharashtra.
Adgoan, Nashik, Maharashtra."
d102f18d319d9545588075010f5d10b1ff77f967,Effects of Degradations on Deep Neural Network Architectures,"Effects of Degradations on Deep Neural Network
Architectures
Prasun Roy∗, Subhankar Ghosh∗, Saumik Bhattacharya∗ and Umapada Pal
Indian Statistical Institute Kolkata, India - 700108"
d142e74c6a7457e77237cf2a3ded4e20f8894e1a,EEG AND F ACE U SING S TATISTICAL F EATURES AND SVM,"HUMAN EMOTION ESTIMATION FROM
EEG AND FACE USING STATISTICAL
FEATURES AND SVM
Strahil Sokolov1, Yuliyan Velchev2, Svetla Radeva3 and Dimitar Radev4
,3Department of Information Technologies,
University of telecommunications and post, Sofia, Bulgaria
2,4Department of Telecommunications,
University of telecommunications and post, Sofia, Bulgaria"
d1c091bf9402f1caf13892a3fae39326507401be,Speeding up Semantic Segmentation for Autonomous Driving,"Speeding up Semantic Segmentation for Autonomous
Driving
Michael Treml ∗1, José Arjona-Medina∗1, Thomas Unterthiner∗1,
Rupesh Durgesh2, Felix Friedmann2, Peter Schuberth2,
Andreas Mayr1, Martin Heusel1, Markus Hofmarcher1, Michael Widrich1,
Bernhard Nessler1, Sepp Hochreiter1
Institute of Bioinformatics, Johannes Kepler University Linz, Austria
Audi Electronics Venture GmbH, Germany
{treml, arjona, unterthiner, nessler,
{rupesh.durgesh, felix.friedmann,"
d1d6f1d64a04af9c2e1bdd74e72bd3ffac329576,Neural Face Editing with Intrinsic Image Disentangling,"Neural Face Editing with Intrinsic Image Disentangling
Zhixin Shu1 Ersin Yumer2 Sunil Hadap2 Kalyan Sunkavalli2 Eli Shechtman 2 Dimitris Samaras1,3
Stony Brook University 2Adobe Research 3 CentraleSup´elec, Universit´e Paris-Saclay"
6f3391fda6b25796b5e051f822f91243f69276cb,Performance Comparison of Various Face Detection Techniques,"International Journal of Scientific Research Engineering & Technology (IJSRET)
Volume 2 Issue1   pp 019-0027 April 2013
ISSN 2278 - 0882
www.ijsret.org
Performance Comparison of Various Face Detection Techniques
Mohammed Javed, 2Bhaskar Gupta
M.Tech. Student, Jamia Hamdard, New Delhi
Associate Professor,ECE,BBDIT,Ghaziabad,UP
Corresponding Author"
6f089f9959cc711e16f1ebe0c6251aaf8a65959a,Improvement in object detection using Super Pixels,"International Journal of Engineering Research in Electronic and Communication
Engineering (IJERECE) Vol 3, Issue 5, May 2016
Improvement in object detection using Super Pixels
[1] Shruti D Kadam [2] H.Mallika
Dept. of Electronics and communication
M. S. Ramaiah Institute of Technology, Bangalore, Karnataka
[1] [2]"
6fe2efbcb860767f6bb271edbb48640adbd806c3,Soft Biometrics; Human Identification Using Comparative Descriptions,"SOFT BIOMETRICS: HUMAN IDENTIFICATION USING COMPARATIVE DESCRIPTIONS
Soft Biometrics; Human Identification using
Comparative Descriptions
Daniel A. Reid, Mark S. Nixon, Sarah V. Stevenage"
6fe8386b88d8a2162250b899b73bf1e72eb545f9,Cascade Learning by Optimally Partitioning,"Cascade Learning by Optimally Partitioning
Yanwei Pang, Senior Member, IEEE, Jiale Cao, and Xuelong Li, Fellow, IEEE,"
6f5d57460e0e156497c4667a875cc5fa83154e3a,Retinal Verification Using a Feature Points-Based Biometric Pattern,"Hindawi Publishing Corporation
EURASIP Journal on Advances in Signal Processing
Volume 2009, Article ID 235746, 13 pages
doi:10.1155/2009/235746
Research Article
Retinal Verification Using a Feature Points-Based
Biometric Pattern
M. Ortega,1 M. G. Penedo,1 J. Rouco,1 N. Barreira,1 and M. J. Carreira2
VARPA Group, Faculty of Informatics, Department of Computer Science, University of Coru˜na, 15071 A Coru˜na, Spain
Department of Electronics and Computer Science, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
Correspondence should be addressed to M. Ortega,
Received 14 October 2008; Accepted 12 February 2009
Recommended by Natalia A. Schmid
Biometrics refer to identity verification of individuals based on some physiologic or behavioural characteristics. The typical
uthentication process of a person consists in extracting a biometric pattern of him/her and matching it with the stored pattern
for the authorised user obtaining a similarity value between patterns. In this work an ef‌f‌icient method for persons authentication
is showed. The biometric pattern of the system is a set of feature points representing landmarks in the retinal vessel tree. The
pattern extraction and matching is described. Also, a deep analysis of similarity metrics performance is presented for the biometric
system. A database with samples of retina images from users on different moments of time is used, thus simulating a hard and real
environment of verification. Even in this scenario, the system allows to establish a wide confidence band for the metric threshold"
6f9873e2a7bc279c4f0a45c1a6e831ef3ba78ae7,Improving GAN Training via Binarized Representation Entropy (BRE) Regularization,"Published as a conference paper at ICLR 2018
IMPROVING GAN TRAINING VIA
BINARIZED REPRESENTATION ENTROPY (BRE)
REGULARIZATION
Yanshuai Cao, Gavin Weiguang Ding, Kry Yik-Chau Lui, Ruitong Huang
Borealis AI
Canada"
6f79c4b82f9ccdee918659a8f7091b8ab99fe889,Mono-Camera 3D Multi-Object Tracking Using Deep Learning Detections and PMBM Filtering,"Mono-Camera 3D Multi-Object Tracking Using
Deep Learning Detections and PMBM Filtering
Samuel Scheidegger∗†, Joachim Benjaminsson∗†, Emil Rosenberg†, Amrit Krishnan∗, Karl Granstr¨om†
Zenuity, †Department of Electrical Engineering, Chalmers University of Technology"
6fd3bafa25bf6d376bc9d1cc1311eb260d10d024,Facial Recognition Utilizing Patch Based Game Theory,"International Journal of Machine Learning and Computing, Vol. 5, No. 4, August 2015
Facial Recognition Utilizing Patch Based Game Theory
Foysal Ahmad, Kaushik Roy, Brian O‟Connor, Joseph Shelton, Pablo Arias, Albert Esterline, and Gerry
Dozier
theory.  Texture  based"
6f1be86c77492af422e936028858c9180b52b698,Indoor Scene Understanding in 2.5/3D: A Survey,"JOURNAL OF LATEX CLASS FILES, VOL. 6, NO. 1, JULY 2015
Indoor Scene Understanding in 2.5/3D: A Survey
Muzammal Naseer, Salman H Khan, Fatih Porikli"
6f7ce89aa3e01045fcd7f1c1635af7a09811a1fe,A novel rank order LoG filter for interest point detection,"978-1-4673-0046-9/12/$26.00 ©2012 IEEE
ICASSP 2012"
6f5151c7446552fd6a611bf6263f14e729805ec7,Facial Action Unit Recognition Using Filtered Local Binary Pattern Features with Bootstrapped and Weighted ECOC Classifiers,".=?E= )?JE 7EJ 4A?CEJE KIEC
?= *E=HO 2=JJAH .A=JKHAI MEJD
-++ +=IIE
AHI
55EJD
+AJHA BH 8EIE 5FAA?D 5EC= 2H?AIIEC 7ELAHIEJO B 5KHHAO
5KHHAO /7  %:0 7
)>IJH=?J 9EJDE JDA ?JANJ B=?A ANFHAIIE ?=IIE
?=JE KIEC JDA
B=?E= =?JE IOIJA .)+5 MA JDA FH>A B
EC B=?E= =?JE KEJI )7I 6DA EI J JH=E = IECA
AHHH?HHA?JEC KJFKJ -++ KJE?=II ?=IIE
AH J AIJE=JA JDA
FH>=>EEJEAI JD=J A=?D A B IALAH= ?O ??KHHEC )7 CHKFI EI
FHAIAJ E JDA FH>A E=CA 2=JJ I?=EC EI J ?=E>H=JA JDA -++
KJFKJI J FH>=>EEJEAI =FFHFHE=JA IKI B JDAIA FH>=>EEJEAI =HA
J=A J >J=E = IAF=H=JA FH>=>EEJO BH A=?D )7 .A=JKHA
ANJH=?JE EI >O CAAH=JEC = =HCA K>AH B ?= >E=HO F=J
JAH *2 BA=JKHAI JDA IAA?JEC BH JDAIA KIEC B=IJ ?HHA=JE

JAHEC .+*. 6DA >E=I L=HE=?A FHFAHJEAI B JDA ?=IIE
AH
=HA MA IDM JD=J >JD JDAIA IKH?AI B AHHH ?= >A HA
>O AD=?EC -++ JDHKCD JDA =FFE?=JE B >JIJH=FFEC
?=IIIAF=H=>EEJO MAECDJEC"
6f5a3c34360caad4644aea897b8fe7dd72076d0f,Self-calibrating Marker Tracking in 3D with Event-Based Vision Sensors,"Self-Calibrating Marker Tracking in 3D
with Event-Based Vision Sensors
Georg R. Müller, Jörg Conradt
Technische Universität München, Arcisstr. 21,
80290 München, Germany"
6f7d06ced04ead3b9a5da86b37e7c27bfcedbbdd,Multi-Scale Fully Convolutional Network for Fast Face Detection,"Pages 51.1-51.12
DOI: https://dx.doi.org/10.5244/C.30.51"
6f35b6e2fa54a3e7aaff8eaf37019244a2d39ed3,Learning probabilistic classifiers for human–computer interaction applications,"DOI 10.1007/s00530-005-0177-4
R E G U L A R PA P E R
Nicu Sebe · Ira Cohen · Fabio G. Cozman ·
Theo Gevers · Thomas S. Huang
Learning probabilistic classifiers for human–computer
interaction applications
Published online: 10 May 2005
(cid:1) Springer-Verlag 2005
intelligent
interaction,"
6f8fc12004fa068c424369793fd39426e772b07d,Demystifying Core Ranking in Pinterest Image Search,"Demystifying Core Ranking in Pinterest Image Search
Linhong Zhu
Pinterest & USC/ISI"
6fc129d384431d17eb7aa22afd6ab68f1084f038,Development of an Efficient Face Recognition System based on Linear and Nonlinear Algorithms,"Development of an Efficient Face Recognition System based on Linear and Nonlinear Algorithms
{tag}                                                 {/tag}
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 134
Number 7
Year of Publication: 2016
Authors:
Filani Araoluwa S., Adetunmbi Adebayo O.
10.5120/ijca2016907932
{bibtex}2016907932.bib{/bibtex}"
6fdc0bc13f2517061eaa1364dcf853f36e1ea5ae,DAISEE: Dataset for Affective States in E-Learning Environments,"DAISEE: Dataset for Affective States in
E-Learning Environments
Abhay Gupta1, Richik Jaiswal2, Sagar Adhikari2, Vineeth Balasubramanian2
Microsoft India R&D Pvt. Ltd.
Department of Computer Science, IIT Hyderabad
{cs12b1032, cs12b1034,"
6fa9bae381274518d3972294d81e460f0c63900b,Personalized Recommendations in Police Photo Lineup Assembling Task,"S. Krajˇci (ed.): ITAT 2018 Proceedings, pp. 157–160
CEUR Workshop Proceedings Vol. 2203, ISSN 1613-0073, c(cid:13) 2018 Ladislav Peška and Hana Trojanová"
6f8ea33c29de7ef94f674c4c847185a127c6ea2f,Cue Integration by Similarity Rank List Coding - Application to Invariant Object Recognition,"nd IEEE International Workshops on Foundations and Applications of Self* Systems
nd IEEE International Workshops on Foundations and Applications of Self* Systems
Cue Integration by Similarity Rank List Coding —
Application to Invariant Object Recognition
Raul Grieben and Rolf P. W¨urtz
Institut f¨ur Neuroinformatik, Ruhr-Universit¨at Bochum,44780 Bochum, Germany"
6fa3857faba887ed048a9e355b3b8642c6aab1d8,Face Recognition in Challenging Environments: An Experimental and Reproducible Research Survey,"Face Recognition in Challenging Environments:
An Experimental and Reproducible Research
Survey
Manuel G¨unther and Laurent El Shafey and S´ebastien Marcel"
6f77ff9990973a6cdad6b5b6022323bff9d03965,Action Recognition in Still Images Using Word Embeddings from Natural Language Descriptions,"017 IEEE Winter Conference on Applications of Computer Vision Workshops
Action Recognition in Still Images Using Word Embeddings from Natural
Language Descriptions
Karan Sharma
Arun CS Kumar
Department of Computer Science, The University of Georgia, Athens, GA 30602-7404, USA
E-mails:
Suchendra M. Bhandarkar"
6f206b46c26b70b3be0b1e89b1d4b6518b601005,Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights,"Published as a conference paper at ICLR 2017
INCREMENTAL NETWORK QUANTIZATION: TOWARDS
LOSSLESS CNNS WITH LOW-PRECISION WEIGHTS
Aojun Zhou∗, Anbang Yao, Yiwen Guo, Lin Xu, and Yurong Chen
Intel Labs China
{aojun.zhou, anbang.yao, yiwen.guo, lin.x.xu,"
6feb0d42232c31eecee5d90290287afe803e88a5,Recognizing Challenging Handwritten Annotations with Fully Convolutional Networks,"Recognizing Challenging Handwritten Annotations
with Fully Convolutional Networks
Andreas K¨olsch∗†, Ashutosh Mishra∗, Saurabh Varshneya∗†, Muhammad Zeshan Afzal∗†, Marcus Liwicki∗†‡§
{a koelsch12, a ashutosh16, s
MindGarage, University of Kaiserslautern, Germany
Insiders Technologies GmbH, Kaiserslautern, Germany
University of Fribourg, Switzerland
§Lule˚a, University of Technology, Sweden"
6f5ce5570dc2960b8b0e4a0a50eab84b7f6af5cb,Low Resolution Face Recognition Using a Two-Branch Deep Convolutional Neural Network Architecture,"Low Resolution Face Recognition Using a
Two-Branch Deep Convolutional Neural Network
Architecture
Erfan Zangeneh, Mohammad Rahmati, and Yalda Mohsenzadeh"
6f1a784ebb8df0689361afe26a2e5f7a1f4c66ca,A unified probabilistic framework for measuring the intensity of spontaneous facial action units,"A Unified Probabilistic Framework For Measuring The Intensity of
Spontaneous Facial Action Units
Yongqiang Li1, S. Mohammad Mavadati2, Mohammad H. Mahoor and Qiang Ji
(AU),"
6f8fa219ea82ded79757de59250b7213f9f5a104,OriNet: A Fully Convolutional Network for 3D Human Pose Estimation,"Chenxu Luo1
Xiao Chu2
Alan Yuille1
Department of Computer Science
The Johns Hopkins University
Baltimore, MD 21218, USA
Baidu Research (USA)
Sunnyvale, CA 94089, USA
LUO ET AL.: ORINET: A FULLY CONVOLUTIONAL NETWORK FOR 3D HUMAN POSE
OriNet: A Fully Convolutional Network for 3D
Human Pose Estimation"
6f08885b980049be95a991f6213ee49bbf05c48d,Author ' s personal copy Multi-Kernel Appearance Model ☆,"This article appeared in a journal published by Elsevier. The attached
opy is furnished to the author for internal non-commercial research
nd education use, including for instruction at the authors institution
nd sharing with colleagues.
Other uses, including reproduction and distribution, or selling or
licensing copies, or posting to personal, institutional or third party
websites are prohibited.
In most cases authors are permitted to post their version of the
rticle (e.g. in Word or Tex form) to their personal website or
institutional repository. Authors requiring further information
regarding Elsevier’s archiving and manuscript policies are
encouraged to visit:
http://www.elsevier.com/authorsrights"
6f41e2ba877ec690bd1c9e5e8742c4088f95c346,Video Frames Segmentation time Modular Network Clock Fires Executed,"Clockwork Convnets for Video Semantic Segmentation
Evan Shelhamer(cid:63)
Kate Rakelly(cid:63)
Judy Hoffman(cid:63)
Trevor Darrell
UC Berkeley"
6f3054f182c34ace890a32fdf1656b583fbc7445,Age Estimation Robust to Optical and Motion Blurring by Deep Residual CNN,"Article
Age Estimation Robust to Optical and Motion
Blurring by Deep Residual CNN
Jeon Seong Kang, Chan Sik Kim, Young Won Lee, Se Woon Cho and Kang Ryoung Park *
Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu,
Seoul 100-715, Korea; (J.S.K.); (C.S.K.);
(Y.W.L.); (S.W.C.)
* Correspondence: Tel.: +82-10-3111-7022; Fax: +82-2-2277-8735
Received: 9 March 2018; Accepted: 10 April 2018; Published: 13 April 2018"
6f2819172d270ceb568bf7586d812b298266bcbf,Edge Fields for Robust Object Proposal,"Edge Fields for Robust Object Proposal
Junseok Kwon, Andrii Grygoriev, Yusun Lim, Youngki Hong, and
Hansung Lee(cid:63)
SW R&D Center, Samsung Electronics, Co. Ltd., Suwon, Rep. of Korea"
6f613ae524066802efb1b46a8673e62f9fc63321,An Energy-Efficient Hardware Implementation of HOG-Based Object Detection at 1080HD 60 fps with Multi-Scale Support,"(will be inserted by the editor)
An Energy-ef‌f‌icient Hardware Implementation of HOG-based
Object Detection at 1080HD 60 fps with Multi-scale Support
Amr Suleiman · Vivienne Sze
Received: date / Accepted: date"
6f42cb23262066b4034aba99bf674783ed6cac8b,An Empirical Evaluation of various Deep Learning Architectures for Bi-Sequence Classification Tasks,"Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers,
pages 2762–2773, Osaka, Japan, December 11-17 2016."
6f6b4e2885ea1d9bea1bb2ed388b099a5a6d9b81,"Structured Output SVM Prediction of Apparent Age, Gender and Smile from Deep Features","Structured Output SVM Prediction of Apparent Age,
Gender and Smile From Deep Features
Michal Uˇriˇc´aˇr
CMP, Dept. of Cybernetics
FEE, CTU in Prague
Radu Timofte
Computer Vision Lab
D-ITET, ETH Zurich
Rasmus Rothe
Computer Vision Lab
D-ITET, ETH Zurich
Luc Van Gool
PSI, ESAT, KU Leuven
CVL, D-ITET, ETH Zurich
Jiˇr´ı Matas
CMP, Dept. of Cybernetics
FEE, CTU in Prague"
6fd4048bfe3123e94c2648e53a56bc6bf8ff4cdd,Micro-facial movement detection using spatio-temporal features,"Micro-Facial Movement Detection
Using Spatio-Temporal Features
Adrian Keith Davison
A thesis submitted in partial fulfillment for the
degree of Doctor of Philosophy
Supervised by Dr. Moi Hoon Yap, Mr. Cliff Lansley, Dr. Nicholas
Costen and Dr. Kevin Tan
Faculty of Science and Engineering
School of Computing, Mathematics and Digital Technology
MANCHESTER METROPOLITAN UNIVERSITY
February 2016"
6f3a8528841ea323d965d558195710fd8f916ffd,Knowledge Factorization,"Knowledge Factorization
Anubhav Ashok
Khushi Gupta
Nishant Agrawal"
4893ce89df7afde71534af9b9fd5becb947f112e,Instance-level Sketch-based Retrieval by Deep Triplet Classification Siamese Network,"Noname manuscript No.
(will be inserted by the editor)
Instance-level Sketch-based Retrieval by Deep Triplet Classification
Siamese Network
Peng Lu∗ · Hangyu Lin∗ · Yanwei Fu† · Shaogang Gong · Yu-Gang Jiang ·
Xiangyang Xue
the date of receipt and acceptance should be inserted later"
486f08c875e88b3f1f157e7de1ae2cf5176f5431,STRUCTURE-FROM-MOTION FOR CALIBRATION OF A VEHICLE CAMERA SYSTEM WITH NON-OVERLAPPING FIELDS-OF-VIEW IN AN URBAN ENVIRONMENT,"STRUCTURE-FROM-MOTION FOR CALIBRATION OF A VEHICLE CAMERA SYSTEM
WITH NON-OVERLAPPING FIELDS-OF-VIEW IN AN URBAN ENVIRONMENT
Photogrammetry & Remote Sensing, Technische Universitaet Muenchen, Germany - (alexander.hanel,
A. Hanela, U. Stillaa
Commission I, WG 9
KEY WORDS: vehicle cameras, camera calibration, structure from motion, bundle adjustment"
485eb41be3ce1600e9934167808b0319a6c3ec2f,A Novel Structural-Description Approach for Image Retrieval.,"A Novel Structural-Description Approach For
Image Retrieval
Christoph Rasche, Constantin Vertan
Laboratorul de Analiza si Prelucrarea Imaginilor
Universitatea Politehnica din Bucuresti
Bucuresti 061071, RO"
48fb35946641351f7480a5b88567aae59e526d82,Generating faces for affect analysis,"Noname manuscript No.
(will be inserted by the editor)
Generating faces for affect analysis
Dimitrios Kollias (cid:63) · Shiyang Cheng † · Evangelos Ververas ∗ · Irene
Kotsia1 · Stefanos Zafeiriou2
Received: Sept 30th 2018 / Accepted: date"
48a42303559ea518ba06f54a8cfce4226bb0e77e,Urban tribes: Analyzing group photos from a social perspective,"Urban Tribes: Analyzing Group Photos from a Social Perspective
Ana C. Murillo†,
Iljung S. Kwak‡, Lubomir Bourdev§∗, David Kriegman‡, Serge Belongie‡
DIIS - Instituto de Ingenier´ıa de Arag´on. Universidad de Zaragoza, Spain
§Facebook. 1601 Willow Road, Menlo Park, CA 94025, USA
Computer Science and Engineering Department. University of California, San Diego, USA"
4850e40b0e69e30723cb027fdc4a38ee1322589b,Detecç̃ao de Landmarks Faciais Usando SVM,"XXIX SIMP ´OSIO BRASILEIRO DE TELECOMUNICAC¸ ˜OES - SBrT’11, 02-05 DE OUTUBRO DE 2011, CURITIBA, PR
Detecc¸ ˜ao de Landmarks Faciais Usando SVM
Gabriel M. Ara´ujo, Waldir S. S. J´unior, Eduardo A. B. da Silva, Siome K. Goldenstein
Resumo— Este artigo aborda o problema de detecc¸ ˜ao de
landmarks faciais. Neste contexto, n´os apresentamos um sistema
de detecc¸ ˜ao de landmarks baseado em SVM (Support Vectors
Machine) com kernel gaussiano. O m´etodo proposto ´e comparado
om outros encontrados na literatura, sendo a avaliac¸ ˜ao feita
em duas bases de dados, a BioID e a Color FERET. Os
experimentos indicam que o m´etodo proposto supera os demais
em precis˜ao e taxa de acerto. Como o sistema proposto possui
uma complexidade computacional maior que os demais m´etodos,
podemos utiliz´a-lo em aplicac¸ ˜oes off-line.
Palavras-Chave— Reconhecimento de Padr˜oes, landmarks faci-
is, M´aquina de Vetor Suporte."
48ac5466c5d0c90fa2c6c38c51c22627f966d687,Real Time People Detection Combining Appearance and Depth Image Spaces Using Boosted Random Ferns,"Real Time People Detection Combining
Appearance and Depth Image Spaces using
Boosted Random Ferns
Victor Vaquero, Michael Villamizar, and Alberto Sanfeliu
Institut de Robotica i Informatica Industrial - CSIC-UPC
http://www.iri.upc.edu"
48705017d91a157949cfaaeb19b826014899a36b,PROBABILISTIC MULTI-PERSON TRACKING USING DYNAMIC BAYES NETWORKS,"PROBABILISTIC MULTI-PERSON TRACKING USING DYNAMIC BAYES NETWORKS
T. Klinger, F. Rottensteiner, C. Heipke
Institute of Photogrammetry and GeoInformation, Leibniz Universit¨at Hannover, Germany -
(klinger, rottensteiner,
KEY WORDS: Bayes network, Classification, Pedestrians, Tracking, Online, Video"
48c0059feb14ca3deedfa7e3b53fbc34bd6d8efb,Facial Expression Retrieval Using 3-Dimensional Mesh Sequences,"Facial Expression Retrieval Using
-Dimensional Mesh Sequences
Danelakis E. Antonios*
National and Kapodistrian University of Athens
Department of Informatics and Telecommunications"
48b7b474af1e86ee6e9db66972155c10cbbdace6,A new benchmark for vision-based cyclist detection,"Gothenburg, Sweden, June 19-22, 2016
978-1-5090-1820-8/16/$31.00 ©2016 IEEE"
483ca50670c5f7d33f7c722dd71105327a30ea60,Improving object classification using semantic attributes,"SU, ALLAN, JURIE: SEMANTIC ATTRIBUTES
Improving object classification
using semantic attributes
Yu Su
http://users.info.unicaen.fr/~ysu/
Moray Allan
http://users.info.unicaen.fr/~mallan/
Frédéric Jurie
http://users.info.unicaen.fr/~jurie/
GREYC
Université de Caen
4032 Caen Cedex
France"
489decd84645b77d31001d17a66abb92bb96c731,Learning View-Specific Deep Networks for Person Re-Identification,"Learning View-Specific Deep Networks for Person
Re-Identification
Zhanxiang Feng, Jianhuang Lai, and Xiaohua Xie"
48b38d157272f03f6b44c0df61130534d11d8569,Natural Language Guided Visual Relationship Detection,"oard)(person-behind-kid)(skate board-on-street)(person-sit on-street)...ImageVisual relationshipsFigure1:Visualrelationshipsrepresenttheinteractionsbe-tweenobservedobjects.Eachrelationshiphasthreeele-ments:subject,predicateandobject.HereisanexampleimagefromVisualGenome[17].Ourproposedmethodisabletoeffectivelydetectnumerouskindsofdifferentrela-tionshipsfromsuchimage.objectsinimages.Therelationshipscanberepresentedinatripletformofhsubject-predicate-objecti,e.g.,hkid-on-skateboardi,asshowninFig.1.Anaturalapproachforthistaskistotreatitasaclassificationproblem:eachkindofrelationships/phraseisarelationcategory[32],asshowninFig.2.Totrainsuchreliableandrobustmodel,suffi-cienttrainingsamplesforeachpossiblehsubject-predicate-objecticombinationareessential.ConsidertheVisualRe-lationshipDataset(VRD)[24],withN=100objectcate-goriesandK=70predicates,thenthereareN2K=700kcombinationsintotal.However,itcontainsonly38kre-lationships,whichmeansthateachcombinationhaslessthan1sampleonaverage.Thepreviousclassification-basedworkscanonlydetectthemostcommonrelationships,e.g.,[32]studiedonly13frequentrelationships.Anotherpopularstrategyistodetecttherelationshippredicatesandtheobjectcategoriesindependently.Al-thoughthenumberofcategoriesdecreasesdramatically,thesemanticrelationshipbetweentheobjectsandthepredi-catesareignored.Consequently,thephrasewhichhasthesamepredicatebutdifferentagentsisconsideredasthesametypeofrelationship.Forinstance,the”clock-on-1"
483f85e1ebef9d10a951b3c01751892aca92a2c2,Adaptive Classification for Person Re-identification Driven by Change Detection,"Adaptive Classification for Person Re-Identification Driven by Change
Detection
C. Pagano1, E. Granger1, R. Sabourin1, G. L. Marcialis2 and F. Roli2
Lab. d’imagerie, de vision et d’intelligence artificielle,
´Ecole de technologie sup´erieure, Universit´e du Qu´ebec, Montreal, Canada
Pattern Recognition and Applications Group, Dept. of Electrical and Electronic Engineering,
{eric.granger,
University of Cagliari, Cagliari, Italy
Keywords:
Multi-Classifier Systems, Incremental Learning, Adaptive Biometrics, Change Detection, Face Recognition,
Video Surveillance."
488676e61fcf7b79d83c25fb103c8d8a854d8987,Leveraging Convolutional Pose Machines for Fast and Accurate Head Pose Estimation,"Leveraging Convolutional Pose Machines
for Fast and Accurate Head Pose Estimation
Yuanzhouhan Cao1, Olivier Can´evet 1 and Jean-Marc Odobez1,2"
48394fa271cd182372c6fb82342d7080554f735c,Multimodal Interaction-aware Motion Prediction for Autonomous Street Crossing,"Multimodal Interaction-aware Motion
Prediction for Autonomous Street
Crossing
Noha Radwan, Abhinav Valada and Wolfram Burgard
Journal Title
XX(X):1–21
(cid:13)The Author(s) 2018
Reprints and permission:
sagepub.co.uk/journalsPermissions.nav
DOI: 10.1177/ToBeAssigned
www.sagepub.com/"
48b4f49ec708677fc9f70edc74fd0f92ef986406,CS 168 : The Modern Algorithmic Toolbox Lecture # 6 : Stochastic Gradient Descent and Regularization,"CS168: The Modern Algorithmic Toolbox
Lecture #6: Stochastic Gradient Descent and
Regularization
Tim Roughgarden & Gregory Valiant∗
April 13, 2016
Context
Last lecture we covered the basics of gradient descent, with an emphasis on the intuition
ehind and geometry underlying the method, plus a concrete instantiation of it for the
problem of linear regression (fitting the best hyperplane to a set of data points). This basic
method is already interesting and useful in its own right (see Homework #3).
This lecture we’ll cover two extensions that, while simple, will bring your knowledge a step
loser to the state-of-the-art in modern machine learning. The two extensions have different
haracters. The first concerns how to actually solve (computationally) a given unconstrained
minimization problem, and gives a modification of basic gradient descent — “stochastic
gradient descent” — that scales to much larger data sets. The second extension concerns
problem formulation rather than implementation, namely the choice of the unconstrained
optimization problem to solve (i.e., the objective function f ). Here, we introduce the idea
of “regularization,” with the goal of avoiding overfitting the function learned to the data set
t hand, even for very high-dimensional data.
Recap"
48d299fe3303c80f840816fc76971a42b4a8b624,Predicting Important Objects for Egocentric Video Summarization,"http://dx.doi.org/10.1007/s11263-014-0794-5
Predicting Important Objects for Egocentric Video Summarization
Yong Jae Lee · Kristen Grauman
Received: date / Accepted: date"
4875bed500321dec353959a556541715da5c9d18,A Domain Agnostic Normalization Layer for Unsupervised Adversarial Domain Adaptation,"A Domain Agnostic Normalization Layer
for Unsupervised Adversarial Domain Adaptation
R. Romijnders
Eindhoven, University of Technology
P. Meletis
G. Dubbelman"
48f45accce6a4a22e4ead41fe292a915f3531f5b,Active Learning for Visual Question Answering: An Empirical Study,"Active Learning for Visual Question Answering:
An Empirical Study
Xiao Lin
Virginia Tech
Devi Parikh
Georgia Tech"
485e0d178bafa959ac956aa8de6556a2439c6663,Learning from Examples to Generalize over Pose and Illumination,"Learning from Examples to Generalize over Pose
nd Illumination
Marco K. M¨uller and Rolf P. W¨urtz
Institute f¨ur Neural Computation, Ruhr-University, 44780 Bochum, Germany"
48499deeaa1e31ac22c901d115b8b9867f89f952,Interim Report of Final Year Project HKU-Face : A Large Scale Dataset for Deep Face Recognition,"Interim Report of Final Year Project
HKU-Face: A Large Scale Dataset for
Deep Face Recognition
Haicheng Wang
035140108
Haoyu Li
035141841
COMP4801 Final Year Project
Project Code: 17007"
4871300f1e5a58ce920e6b5be14e89c5da4aa4c4,Manifold Learning for Video-to-Video Face Recognition,"Manifold Learning for Video-to-Video Face
Recognition"
48be9300acdc484100436f32bd409a89a7dc1ef7,Chapter 4 FACE RECOGNITION AND ITS APPLICATIONS,"Chapter 4
FACE RECOGNITION AND ITS APPLICATIONS
{aws,
Andrew W. Senior and Ruud M. Bolle
IBM T.J.Watson Research Center,
P.O. Box 704,
Yorktown Heights,
NY 10598, USA."
484c4eec34e985d8ca0c20bf83efc56881180709,Efficient semantic image segmentation with superpixel pooling,"Ef‌f‌icient semantic image segmentation with superpixel pooling
Mathijs Schuurmans Maxim Berman Matthew B. Blaschko
Dept. ESAT, Center for Processing Speech and Images
KU Leuven, Belgium
{maxim.berman,
June 8, 2018"
48103763fa317cf769e783966f02af9a18030765,YOLO 4 D : A Spatio-temporal Approach for Real-time Multi-object Detection and Classification from LiDAR Point Clouds,"YOLO4D: A Spatio-temporal Approach for Real-time
Multi-object Detection and Classification from
LiDAR Point Clouds
Anonymous Author(s)
Affiliation
Address
email"
484c2617471fd742c4806f9281e5add45c6831a7,LSTM Self-Supervision for Detailed Behavior Analysis,"LSTM Self-Supervision for Detailed Behavior Analysis
Biagio Brattoli1∗, Uta B¨uchler1∗, Anna-Sophia Wahl2, Martin E. Schwab2, Bj¨orn Ommer1
HCI / IWR, Heidelberg University, Germany
Department of HST, ETH Zurich, Switzerland"
488493dc29c844b36660395266d8d347c7cfa9ce,Towards Flexible Classification : Cost-Aware Online Query of Cascades and Operating Points,"Towards Flexible Classification: Cost-Aware
Online Query of Cascades and Operating Points
Brandyn White, Andrew Miller, Tom Yeh, and Larry S. Davis
University of Maryland: College Park"
48186494fc7c0cc664edec16ce582b3fcb5249c0,P-CNN: Pose-Based CNN Features for Action Recognition,"P-CNN: Pose-based CNN Features for Action Recognition
Guilhem Ch´eron∗ †
Ivan Laptev∗
INRIA
Cordelia Schmid†"
48c474ab86920c70d0a404a80d68096a8fa9723f,Multiband Curvelet-Based Technique for Audio Visual Recognition over Internet Protocol,"Multiband Curvelet-Based Technique
for Audio Visual Recognition over Internet Protocol
Sue Inn Ch’ng1, KahPhooi Seng2, Fong Tien Ong1, and Li-Minn Ang1
University of Nottingham Malaysia Campus
JalanBroga, 43500 Semenyih, Selangor, Malaysia
Sunway University
No. 5, JalanUniversiti, Bandar Sunway, 46150 PetalingJaya, Selangor, Malaysia"
4874daed0f6a42d03011ed86e5ab46f231b02c13,GridFace: Face Rectification via Learning Local Homography Transformations,"GridFace: Face Rectification via Learning Local
Homography Transformations
Erjin Zhou, Zhimin Cao, and Jian Sun
Face++, Megvii Inc."
4850af6b54391fc33c8028a0b7fafe05855a96ff,Discovering useful parts for pose estimation in sparsely annotated datasets,"Discovering Useful Parts for Pose Estimation in Sparsely Annotated Datasets
Mikhail Breslav1, Tyson L. Hedrick2, Stan Sclaroff1, and Margrit Betke1
Department of Computer Science and 2Department of Biology
Boston University and 2University of North Carolina"
480810001ed845ec04a20b00461a8a82fcffbb52,Autistic Traits and Brain Activation during Face-to-Face Conversations in Typically Developed Adults,"Autistic Traits and Brain Activation during Face-to-Face
Conversations in Typically Developed Adults
Masashi Suda, Yuichi Takei, Yoshiyuki Aoyama, Kosuke Narita, Noriko Sakurai, Masato Fukuda*,
Masahiko Mikuni
Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Gunma, Japan"
48bc87ccc6b6e2d318f91d5f1886432806fec553,Multiaccuracy: Black-Box Post-Processing for Fairness in Classification,"Multiaccuracy: Black-Box Post-Processing for Fairness in
Michael P. Kim∗†
Classification
Amirata Ghorbani∗
James Zou"
486a0044b9c86c6f648f153f3d3f2e534342b754,Trajectories and Maneuvers of Surrounding Vehicles With Panoramic Camera Arrays,"Trajectories and Maneuvers of Surrounding Vehicles
with Panoramic Camera Arrays
Jacob V. Dueholm, Miklas S. Kristoffersen, Ravi K. Satzoda, Thomas B. Moeslund, and Mohan M. Trivedi"
48cfc5789c246c6ad88ff841701204fc9d6577ed,Age Invariant Face Recognition Based on DCT Feature Extraction and Kernel Fisher Analysis,"J Inf Process Syst, Vol.12, No.3, pp.392~409, September 2016
ISSN 1976-913X (Print)
ISSN 2092-805X (Electronic)
Age Invariant Face Recognition Based on DCT
Feature Extraction and Kernel Fisher Analysis
Leila Boussaad*, Mohamed Benmohammed**, and Redha Benzid***"
f4373f5631329f77d85182ec2df6730cbd4686a9,Recognizing Gender from Human Facial Regions using Genetic Algorithm,"Soft Computing manuscript No.
(will be inserted by the editor)
Recognizing Gender from Human Facial Regions using
Genetic Algorithm
Avirup Bhattacharyya · Rajkumar Saini ·
Partha Pratim Roy · Debi Prosad Dogra ·
Samarjit Kar
Received: date / Accepted: date"
f439f9a0bd535eab00cbb93c1fa7083615a08d1a,Procedural Modeling and Physically Based Rendering for Synthetic Data Generation in Automotive Applications,"Procedural Modeling and Physically Based Rendering for Synthetic Data
Generation in Automotive Applications
Apostolia Tsirikoglou1,∗ Joel Kronander1 Magnus Wrenninge2,† Jonas Unger1,‡
Link¨oping University, Sweden
7D Labs
Figure 1: Example images produced using our method for synthetic data generation."
f4ebbeb77249d1136c355f5bae30f02961b9a359,Human Computation for Attribute and Attribute Value Acquisition,"Human Computation for Attribute and Attribute Value Acquisition
Edith Law, Burr Settles, Aaron Snook, Harshit Surana, Luis von Ahn, Tom Mitchell
School of Computer Science
Carnegie Melon University"
f47404424270f6a20ba1ba8c2211adfba032f405,Identification of Face Age range Group using Neural Network,"International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, Volume 2, Issue 5, May 2012)
Identification of Face Age range Group using Neural
Network
Sneha Thakur1, Ligendra Verma2
1M.Tech scholar, CSE, RITEE Raipur
2 Reader, MCA dept, RITEE Raipur"
f47518fcd69cdbb43dc88fe5259f4f4c61921313,A Compact Embedding for Facial Expression Similarity,"A Compact Embedding for Facial Expression Similarity
Raviteja Vemulapalli
Google AI
Aseem Agarwala
Google AI"
f4dce4266a4249596d4454d73c1f0fd629c7fcd6,Distributed Compressive Sensing based Near Infrared and Visible Images Fusion for Face Recognition,"International Journal of Signal Processing, Image Processing and Pattern Recognition
Vol.9, No.4 (2016), pp.281-292
http://dx.doi.org/10.14257/ijsip.2016.9.4.26
Distributed Compressive Sensing based Near Infrared and Visible
Images Fusion for Face Recognition
Dan Wei
Shanghai University of Engineering Science"
f4f6fc473effb063b7a29aa221c65f64a791d7f4,Facial expression recognition in the wild based on multimodal texture features,"Downloaded From: https://www.spiedigitallibrary.org/journals/Journal-of-Electronic-Imaging on 4/20/2018 Terms of Use: https://www.spiedigitallibrary.org/terms-of-use
FacialexpressionrecognitioninthewildbasedonmultimodaltexturefeaturesBoSunLiandongLiGuoyanZhouJunHeBoSun,LiandongLi,GuoyanZhou,JunHe,“Facialexpressionrecognitioninthewildbasedonmultimodaltexturefeatures,”J.Electron.Imaging25(6),061407(2016),doi:10.1117/1.JEI.25.6.061407."
f442a2f2749f921849e22f37e0480ac04a3c3fec,Critical Features for Face Recognition in Humans and Machines,"Critical Features for Face Recognition in Humans and Machines  Naphtali Abudarham1, Lior Shkiller1, Galit Yovel1,2 1School of Psychological Sciences, 2Sagol School of Neuroscience Tel Aviv University, Tel Aviv, Israel   Correspondence regarding this manuscript should be addressed to: Galit Yovel  School of Psychological Sciences & Sagol School of Neuroscience Tel Aviv University Tel Aviv, 69978, Israel Email:"
f423e2072441925a16d95e7092005abf602b7145,Survey on 2D and 3D Human Pose Recovery.,"Survey on 2D and 3D Human Pose
Recovery
Xavier Perez-Sala, Email: a;c,
Sergio Escalera, Email: b;c and
Cecilio Angulo, Email: a
CETpD-UPC Technical Research Center for Dependency Care and
Autonomous Living, Universitat Polit(cid:18)ecnica de Catalunya, Ne(cid:18)apolis, Rambla de
l’Exposici(cid:19)o, 59-69, 08800 Vilanova i la Geltru, Spain
Dept. Mathematics, Universitat de Barcelona, Gran Via de les Corts Catalanes
Computer Vision Center, Campus UAB, Edi(cid:12)ci 0, 08193, Bellaterra, Spain
585, 08007, Barcelona, Spain"
f4ce7c36586c27783a1b0e737c2834f39f9d029d,Advanced non linear dimensionality reduction methods for multidimensional time series : applications to human motion analysis,"Advanced Nonlinear
Dimensionality Reduction
Methods for Multidimensional
Time Series: Application to
Human Motion Analysis
Michał Lewandowski
Submitted in partial fulfilment of the requirements of
Kingston University for the degree of
Doctor of Philosophy
June, 2011"
f412d9d7bc7534e7daafa43f8f5eab811e7e4148,Running Head : Anxiety and Emotional Faces in WS 2,"Durham Research Online
Deposited in DRO:
6 December 2014
Version of attached le:
Accepted Version
Peer-review status of attached le:
Peer-reviewed
Citation for published item:
Kirk, H. E. and Hocking, D. R. and Riby, D. M. and Cornish, K. M. (2013) 'Linking social behaviour and
nxiety to attention to emotional faces in Williams syndrome.', Research in developmental disabilities., 34
(12). pp. 4608-4616.
Further information on publisher's website:
http://dx.doi.org/10.1016/j.ridd.2013.09.042
Publisher's copyright statement:
NOTICE: this is the author's version of a work that was accepted for publication in Research in Developmental
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Additional information:"
f4e65ab81a0f4ffa50d0c9bc308d7365e012cc75,Deep Active Learning for Video-based Person Re-identification,"Deep Active Learning for Video-based Person Re-identification
Menglin Wang1, Baisheng Lai2, Zhongming Jin2, Xiaojin Gong1, Jianqiang Huang2, Xiansheng Hua2
Zhejiang University; 2 Alibaba Group
{menglinwang,
{baisheng.lbs, zhongming.jinzm, jianqiang.hjq,"
f43327075c17e71ee713ad727aa473230a432a90,Geometry meets semantics for semi-supervised monocular depth estimation,"Geometry meets semantics for semi-supervised
monocular depth estimation
Pierluigi Zama Ramirez, Matteo Poggi, Fabio Tosi,
Stefano Mattoccia, and Luigi Di Stefano
University of Bologna,
Viale del Risorgimento 2, Bologna, Italy"
f42dca4a4426e5873a981712102aa961be34539a,Next-Flow: Hybrid Multi-Tasking with Next-Frame Prediction to Boost Optical-Flow Estimation in the Wild,"Next-Flow: Hybrid Multi-Tasking with Next-Frame Prediction to Boost
Optical-Flow Estimation in the Wild
Nima Sedaghat
University of Freiburg
Germany"
f43b60a33c585827bfa354d3d49fb148a1c26c3f,Identifying Well-formed Natural Language Questions,"Identifying Well-formed Natural Language Questions
Manaal Faruqui Dipanjan Das
Google AI Language"
f41ae7b47391f10f30368c519d8fe3e904e3a35f,A Random Extension for Discriminative Dimensionality Reduction and Metric Learning,"A Random Extension for Discriminative
Dimensionality Reduction and Metric Learning
Adrian Perez-Suay, Francesc J. Ferri, and Jes´us V. Albert
Dept. Inform`atica, Universitat de Val`encia. Spain"
f445493badf53febbaeab340a4fca98d9e4ab7f7,Do CIFAR-10 Classifiers Generalize to CIFAR-10?,"Do CIFAR-10 Classifiers Generalize to CIFAR-10?
Benjamin Recht
UC Berkeley
Rebecca Roelofs
UC Berkeley
Ludwig Schmidt
Vaishaal Shankar
UC Berkeley
June 4, 2018"
f4808e78bc648f9e1829c83a68a3e8ed4e7cf325,Development of an Efficient Face Recognition System based on Linear and Nonlinear Algorithms,"Development of an Efficient Face Recognition System based on Linear and Nonlinear Algorithms
{tag}                                                 {/tag}
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 134
Number 7
Year of Publication: 2016
Authors:
Filani Araoluwa S., Adetunmbi Adebayo O.
10.5120/ijca2016907932
{bibtex}2016907932.bib{/bibtex}"
f4b40b3dc27897fdc40f419a42d64fd1ff80cc9d,A Dual-Source Approach for 3D Human Pose Estimation from a Single Image,"SUBMITTED TO COMPUTER VISION AND IMAGE UNDERSTANDING.
A Dual-Source Approach for 3D Human Pose
Estimation from a Single Image
Umar Iqbal*, Andreas Doering*, Hashim Yasin, Björn Krüger, Andreas Weber, and Juergen Gall"
f44af3b10a67fe62fd26eb82dd228a3cdeb980e1,"Understand, Compose and Respond - Answering Visual Questions by a Composition of Abstract Procedures","Understand, Compose and Respond
Understand, Compose and Respond - Answering Visual"
f49f1028052baa1588376a78a9dc64812748555e,Feature Fusion using Extended Jaccard Graph and Stochastic Gradient Descent for Robot,"JOURNAL OF LATEX CLASS FILES
Feature Fusion using Extended Jaccard Graph and
Stochastic Gradient Descent for Robot
Shenglan Liu, Muxin Sun, Wei Wang, Feilong Wang"
0ad4a9fad873e9c4914fd2464404b211f295d7b6,New insights into Laplacian similarity search,"New Insights into Laplacian Similarity Search
Xiao-Ming Wu1, Zhenguo Li2, Shih-Fu Chang1
Department of Electrical Engineering, Columbia University. 2Huawei Noah’s Ark Lab, Hong Kong.
(a) Λ = I, AP = 0.14
(b) Λ = D, AP = 0.67
(c) Λ = H, AP = 0.67
(a) Λ = I, AP = 0.27
(b) Λ = D, AP = 0.17
(c) Λ = H, AP = 0.27
Figure 1: Top 40 retrieved images on extended YaleB, with false images
highlighted in blue box (query on top left comes from the sparsest cluster).
Figure 2: Top 40 retrieved images on CIFAR-10, with positive images high-
lighted in magenta box (query on top left comes from the densest cluster).
Similarity metrics are important building blocks of many visual applica-
tions such as image retrieval, image segmentation, and manifold learning.
Well-known similarity metrics include personalized PageRank, hitting and
ommute times, and the pseudo-inverse of graph Laplacian. Despite their
popularity, the understanding of their behaviors is far from complete, and
their use in practice is mostly guided by empirical trials and error analy-
sis. This paper bridges this gap by investigating the fundamental design of"
0ae3182836b1b962902d664ddd524e8554b742cf,Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model,"Integrating Context and Occlusion for Car
Detection by Hierarchical And-Or Model
Bo Li1,2, Tianfu Wu2,(cid:2), and Song-Chun Zhu2
Beijing Lab of Intelligent Information Technology, Beijing Institute of Technology
Department of Statistics, University of California, Los Angeles"
0a481d2472958ca243a79161af97544adc67f4fe,Facial Image Processing,"EURASIP Journal on Image and Video Processing
Facial Image Processing
Guest Editors: Christophe Garcia, Jörn Ostermann, and Tim Cootes"
0a2d2b79ba39e2140c93543b8ce873f106c08e3d,Semi-Supervised Sparse Representation Based Classification for Face Recognition With Insufficient Labeled Samples,"Semi-Supervised Sparse Representation Based
Classification for Face Recognition with Insufficient
Labeled Samples
Yuan Gao, Jiayi Ma, and Alan L. Yuille Fellow, IEEE"
0a60e76e6983e1647469172a50907023913b0c9f,Longitudinal study of amygdala volume and joint attention in 2- to 4-year-old children with autism.,"ORIGINAL ARTICLE
Longitudinal Study of Amygdala Volume and Joint
Attention in 2- to 4-Year-Old Children With Autism
Matthew W. Mosconi, PhD; Heather Cody-Hazlett, PhD; Michele D. Poe, PhD;
Guido Gerig, PhD; Rachel Gimpel-Smith, BA; Joseph Piven, MD
Context: Cerebral cortical volume enlargement has been
reported in 2- to 4-year-olds with autism. Little is known
bout the volume of subregions during this period of de-
velopment. The amygdala is hypothesized to be abnormal
in volume and related to core clinical features in autism.
Objectives: To examine amygdala volume at 2 years with
follow-up at 4 years of age in children with autism and
to explore the relationship between amygdala volume and
selected behavioral features of autism.
Design: Longitudinal magnetic resonance imaging study.
Setting: University medical setting.
Participants: Fifty autistic and 33 control (11 devel-
opmentally delayed, 22 typically developing) children be-
tween 18 and 35 months (2 years) of age followed up at
2 to 59 months (4 years) of age."
0a3051c8dde80975640d42dca21fac17ed60f987,A Hierarchical Switching Linear Dynamical System Applied to the Detection of Sepsis in Neonatal Condition Monitoring,
0a6a25ee84fc0bf7284f41eaa6fefaa58b5b329a,Neural Networks Regularization Through Representation Learning,"THÈSEPour obtenir le diplôme de doctorat Spécialité Informatique Préparée au sein de « l'INSA Rouen Normandie » Présentée et soutenue parSoufiane BELHARBIThèse dirigée par Sébastien ADAM, laboratoire LITIS Neural Networks Regularization Through Representation LearningThèse soutenue publiquement le 06 Juillet 2018 devant le jury composé deSébastien ADAMProfesseur à l'Université de Rouen NormandieDirecteur de thèseClément CHATELAINMaître de conférence à l'INSA  Rouen NormandieEncadrant de thèseRomain HÉRAULTMaître de conférence à l'INSA  Rouen NormandieEncadrant de thèseElisa FROMONTProfesseur à l'Université de Rennes 1Rapporteur de thèseThierry ARTIÈRESProfesseur à l'École Centrale MarseilleRapporteur de thèseJohn LEEProfesseur à l'Université Catholique de LouvainExaminateur de thèseDavid PICARDMaître de conférences à l'École Nationale Supérieure de l'Électronique et de ses ApplicationsExaminateur de thèseFrédéric JURIEProfesseur à l' Université de Caen NormandieInvité"
0a1e3d271fefd506b3a601bd1c812a9842385829,Face Recognition Using 3D Directional Corner Points,"Face Recognition using 3D Directional Corner Points
Author
Yu, Xun, Gao, Yongsheng, Zhou, Jun
Published
Conference Title
Pattern Recognition (ICPR), 2014 22nd International Conference on
https://doi.org/10.1109/ICPR.2014.483
Copyright Statement
© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be
obtained for all other uses, in any current or future media, including reprinting/republishing this
material for advertising or promotional purposes, creating new collective works, for resale or
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works.
Downloaded from
http://hdl.handle.net/10072/66408
Link to published version
http://www.icpr2014.org/index.htm
Griffith Research Online
https://research-repository.griffith.edu.au"
0aa9872daf2876db8d8e5d6197c1ce0f8efee4b7,Timing is everything : a spatio-temporal approach to the analysis of facial actions,"Imperial College of Science, Technology and Medicine
Department of Computing
Timing is everything
A spatio-temporal approach to the analysis of facial
ctions
Michel Fran¸cois Valstar
Submitted in part fulfilment of the requirements for the degree of
Doctor of Philosophy in Computing of Imperial College, February 2008"
0afb2e3db5b8ffefe6a52fdfc5ee813c25353382,Semi-supervised Learning on Real-time Pedestrian Detection System,"3rd ITS World Congress, Melbourne, Australia, 10–14 October 2016
Paper number ITS-0236
Semi-supervised Learning on Real-time
Pedestrian Detection System
Kuo-Ching Chang1*, Zhen-Wei Zhu1, Han-Wen Huang1, Chuan-Ren Lee1
. Automotive Research and Testing Center, Taiwan
* No.6, Lugong S. 7th Rd., Lukang, Changhua County, +886-4-7811222 #2323,"
0ad0a1293f80c838c843726eeddf5a97f33f0c89,Understanding image virality,"Understanding Image Virality
Arturo Deza
UC Santa Barbara
Devi Parikh
Virginia Tech"
0ae07f24251946b2086fb992031c298ada2805de,Exemplar-AMMs: Recognizing Crowd Movements From Pedestrian Trajectories,"JOURNAL OF LATEX CLASS FILES, VOL. 13, NO. 9, SEPTEMBER 2014
Exemplar-AMMs: Recognizing Crowd Movements
from Pedestrian Trajectories
Wenxi Liu, Rynson W.H. Lau, Xiaogang Wang, Dinesh Manocha"
0a4639e09fc051c726da7feb2b3af51ffb278e3a,Endogenous Testosterone Modulates Prefrontal–Amygdala Connectivity during Social Emotional Behavior,"Cerebral Cortex October 2011;21:2282--2290
doi:10.1093/cercor/bhr001
Advance Access publication February 21, 2011
Endogenous Testosterone Modulates Prefrontal--Amygdala Connectivity during Social
Emotional Behavior
Inge Volman1,2, Ivan Toni1, Lennart Verhagen1,3 and Karin Roelofs2,4,1
Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, 6500 HB Nijmegen, the Netherlands, 2Leiden
University, Institute of Psychology & Leiden Institute for Brain and Cognition (LIBC), 2300 RB Leiden the Netherlands,
Experimental Psychology, Helmholtz Institute, Utrecht University, 3508 TC, Utrecht, the Netherlands and 4Behavioral Science
Institute (BSI), Radboud University Nijmegen, 6500 HE Nijmegen, the Netherlands
Address correspondence to Inge Volman, Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, PO Box 9101,
6500 HB Nijmegen, the Netherlands. Email:
respectively. Affect-congruent
It is clear that the steroid hormone testosterone plays an important
role in the regulation of social emotional behavior, but it remains
unknown which neural circuits mediate these hormonal influences
in humans. We investigated the modulatory effects of endogenous
testosterone on the control of social emotional behavior by applying
functional magnetic resonance imaging while healthy male
participants performed a social approach--avoidance task. This"
0a5bcd1c9e88ec6b2fcf4699a8a0a93547bd07b2,Courteous Autonomous Cars,"Courteous Autonomous Cars
Liting Sun1, Wei Zhan1, Masayoshi Tomizuka1, and Anca D. Dragan2"
0a23bdc55fb0d04acdac4d3ea0a9994623133562,Large-scale Bisample Learning on ID vs. Spot Face Recognition,"Noname manuscript No.
(will be inserted by the editor)
Large-scale Bisample Learning on ID vs. Spot Face Recognition
Xiangyu Zhu∗ · Hao Liu∗ · Zhen Lei · Hailin Shi · Fan Yang · Dong
Yi · Stan Z. Li
Received: date / Accepted: date"
0a773ed20a5920897788dd6f0d63c20defca8ab0,ConceptLearner: Discovering visual concepts from weakly labeled image collections,"ConceptLearner: Discovering Visual Concepts from Weakly Labeled Image
Collections
Bolei Zhou†, Vignesh Jagadeesh‡, Robinson Piramuthu‡
MIT ‡eBay Research Labs"
0a6d344112b5af7d1abbd712f83c0d70105211d0,Constrained Local Neural Fields for Robust Facial Landmark Detection in the Wild,"Constrained Local Neural Fields for robust facial landmark detection in the wild
Tadas Baltruˇsaitis
Peter Robinson
University of Cambridge Computer Laboratory
USC Institute for Creative Technologies
5 JJ Thomson Avenue
Louis-Philippe Morency
2015 Waterfront Drive"
0ab1fa904323c440ec6e185e2d607ccc45225df6,Paper on Face Recognition Techniques,"ISSN: 2278 – 1323
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 1, Issue 8, October 2012
A Review Paper on Face Recognition Techniques
Sujata G. Bhele1 and V. H. Mankar2
in  real
interesting  area"
0a8ab703839ae585c2f27099616c40974cbeeda2,"Fast, Exact and Multi-Scale Inference for Semantic Image Segmentation with Deep Gaussian CRFs","Fast, Exact and Multi-Scale Inference for Semantic
Image Segmentation with Deep Gaussian CRFs
Siddhartha Chandra
Iasonas Kokkinos
INRIA GALEN & Centrale Sup´elec, Paris, France"
0a85bdff552615643dd74646ac881862a7c7072d,Beyond frontal faces: Improving Person Recognition using multiple cues,"Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues
Ning Zhang1,2, Manohar Paluri2, Yaniv Taigman2, Rob Fergus2, Lubomir Bourdev2
{mano, yaniv, robfergus,
UC Berkeley
Facebook AI Research"
0a66015112da542b9b6687e4b3c9ff73565d0844,A k-NN Approach for Scalable Image Annotation Using General Web Data,"A k-NN Approach for Scalable Image Annotation
Using General Web Data
Mauricio Villegas and Roberto Paredes
Institut Tecnol`ogic d’Inform`atica
Universitat Polit`ecnica de Val`encia
Cam´ı de Vera s/n, 46022 Val`encia, Spain"
0a40415bdfe4bc9ef7e019e4f1442a9fb61f58b2,Automatic Discovery and Geotagging of Objects from Street View Imagery,"Automatic Discovery and Geotagging of Objects from Street View Imagery
Vladimir A. Krylov
Eamonn Kenny
Rozenn Dahyot
ADAPT Centre, School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland"
0a6a173a1d1d36285bae97f98f4b901067d40097,Similarity learning on an explicit polynomial kernel feature map for person re-identification,"Similarity Learning on an Explicit Polynomial Kernel Feature Map for Person
Re-Identification
Dapeng Chen y, Zejian Yuan y, Gang Huaz, Nanning Zhengy, Jingdong Wang x
y Xi’an Jiaotong University
zStevens Institute of Technology
xMicrosoft Research"
0aaa66501298c3df27293eca7906e93d8013b729,Fast HOG based person detection devoted to a mobile robot with a spherical camera,"Fast HOG based Person Detection devoted to a Mobile Robot with a
Spherical Camera
A. A. Mekonnen1, C. Briand1, F. Lerasle1, A. Herbulot1"
0ad17977977a5ed3219e763696c6b4267b36c1f4,PERSISTENT OBJECT TRACKING WITH RANDOMIZED FORESTS,"PERSISTENT OBJECT TRACKING WITH RANDOMIZED FORESTS
Tobias Klinger and Daniel Muhle
Institute of Photogrammetry and GeoInformation
Nienburger Strasse 1, 30167 Hannover, Germany
Leibniz Universitaet Hannover
http://www.ipi.uni-hannover.de/
KEY WORDS: Learning, Detection, Decision Support, Tracking, Real-time, Video
Commission III/5"
0a55e4191c90ec1edb8d872237a2dacd5f6eda90,"Intentional Minds: A Philosophical Analysis of Intention Tested through fMRI Experiments Involving People with Schizophrenia, People with Autism, and Healthy Individuals","HUMAN NEUROSCIENCE
Intentional minds: a philosophical analysis of intention tested
through fMRI experiments involving people with
schizophrenia, people with autism, and healthy individuals
Review ARticle
published: 02 February 2011
doi: 10.3389/fnhum.2011.00007
Bruno G. Bara1,2*, Angela Ciaramidaro1, Henrik Walter 3 and Mauro Adenzato1,2
Department of Psychology, Center for Cognitive Science, University of Turin, Turin, Italy
Neuroscience Institute of Turin, University of Turin, Turin, Italy
Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Berlin, Germany
Edited by:
Ivan Toni, Radboud University,
Netherlands
Reviewed by:
Ivan Toni, Radboud University,
Netherlands
Roel M. Willems, University of
California Berkeley, USA
*Correspondence:"
0ae9cc6a06cfd03d95eee4eca9ed77b818b59cb7,"Multi-task, multi-label and multi-domain learning with residual convolutional networks for emotion recognition","Noname manuscript No.
(will be inserted by the editor)
Multi-task, multi-label and multi-domain learning with
residual convolutional networks for emotion recognition
Gerard Pons · David Masip
Received: date / Accepted: date"
0a20e2fbe52efdb794b7566ce5233c41f4c5efc9,Monocular Visual Scene Understanding from Mobile Platforms,"Monocular Visual Scene
Understanding
from Mobile Platforms
A dissertation for the degree of
Doktor-Ingenieur (Dr.-Ing.)
pproved by
TECHNISCHE UNIVERSITÄT DARMSTADT
Fachbereich Informatik
presented by
CHRISTIAN ALEXANDER WOJEK
Dipl.-Inform.
orn in Schillingsfürst, Germany
Examiner:
Prof. Dr. Bernt Schiele
Co-examiner: Prof. Dr. Luc Van Gool
Date of Submission: 14th of May, 2010
0th of June, 2010
Date of Defense:
Darmstadt, 2010"
0a811063cfd674275f91006d28cb8620c781e817,Image recognition based on hidden Markov eigen-image models using variational Bayesian method,"IMAGE RECOGNITION BASED ON
HIDDEN MARKOV EIGEN-IMAGE MODELS
USING VARIATIONAL BAYESIAN METHOD
Kei Sawada, Kei Hashimoto,
Yoshihiko Nankaku, Keiichi Tokuda
Nagoya Institute of Technology
APSIPA ASC 10/30/2013"
0a572c16e635312f118d1a53f0ff6446402d3c32,Learning with proxy supervision for end-to-end visual learning,"Learning with Proxy Supervision for End-To-End Visual Learning
Jiˇr´ı ˇCerm´ak1∗ Anelia Angelova2"
0adc3ad7c40c475d5878f9bdfeb3c8b59e482c17,Learning local embedding deep features for person re-identification in camera networks,"Zhang and Huang EURASIP Journal on Wireless Communications and
Networking  (2018) 2018:85
https://doi.org/10.1186/s13638-018-1101-x
RESEARCH
Open Access
Learning local embedding deep features
for person re-identification in camera networks
Zhong Zhang1,2* and Meiyan Huang1,2"
0a8c6b40d6ca75bc1995083825e362137b130624,Nonparametric Method for Data-driven Image Captioning,"Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Short Papers), pages 592–598,
Baltimore, Maryland, USA, June 23-25 2014. c(cid:13)2014 Association for Computational Linguistics"
0a87d781fe2ae2e700237ddd00314dbc10b1429c,"Distribution Statement "" A "" ( Approved for public release , Distribution Unlimited ) Working Paper An Artificial Intelligence / Machine Learning Perspective on Social Simulation New Data and New Challenges","Distribution Statement A:  Approved for public release; distribution unlimited.
Multi-scale HOG Prescreening Algorithm for Detection of Buried
Explosive Hazards in FL-IR and FL-GPR Data
*University of Missouri, Electrical and Computer Engineering Department, Columbia, MO
K. Stone*, J. M. Keller*, D. Shaw*"
0ad545407966e1630b5e51ad8f7dbd2780de966e,Multimodal Authentication Using Asynchronous HMMs,"Multimodal Authentication using Asynchronous
Samy Bengio
Dalle Molle Institute for Perceptual Artificial Intelligence (IDIAP),
CP 592, rue du Simplon 4, 1920 Martigny, Switzerland
http://www.idiap.ch/~bengio"
0af65df112db18248ed24a1c0fb5fe8524015336,Contour Segment Analysis for Human Silhouette Pre-segmentation,"Author manuscript, published in ""5th International Conference on Computer Vision Theory and Applications (VISAPP 2010),
Angers : France (2010)"""
0adffd02029363c204a561092e1e0cc05cacfee7,A New Method for Static Video Summarization Using Local Descriptors and Video Temporal Segmentation,"A New Method for Static Video Summarization
Using Local Descriptors and Video Temporal
Segmentation
Edward J. Y. Cayllahua Cahuina
Computer Research Center
San Pablo Catholic University
Arequipa, Peru
Email:
Guillermo Camara Chavez
Department of Computer Science
Federal university of Ouro Preto
Ouro Preto, Brazil
Email:"
0a7a7b3f05918fb4fc33f04cb7e31232fa197f76,Fitting a Morphable Model to 3D Scans of Faces,"Fitting a Morphable Model to 3D Scans of Faces
Volker Blanz
Universit¤at Siegen,
Siegen, Germany
Kristina Scherbaum
MPI Informatik,
Saarbr¤ucken, Germany
Hans-Peter Seidel
MPI Informatik,
Saarbr¤ucken, Germany"
0a3863a0915256082aee613ba6dab6ede962cdcd,Early and Reliable Event Detection Using Proximity Space Representation,"Early and Reliable Event Detection Using Proximity Space Representation
Maxime Sangnier
LTCI, CNRS, T´el´ecom ParisTech, Universit´e Paris-Saclay, 75013, Paris, France
J´erˆome Gauthier
LADIS, CEA, LIST, 91191, Gif-sur-Yvette, France
Alain Rakotomamonjy
Normandie Universit´e, UR, LITIS EA 4108, Avenue de l’universit´e, 76801, Saint-Etienne-du-Rouvray, France"
0a81810af97e8ab5b8c483209b4d0ff7210436f9,Human Joint Angle Estimation and Gesture Recognition for Assistive Robotic Vision,"Human Joint Angle Estimation and Gesture Recognition
for Assistive Robotic Vision
Alp Guler1, Nikolaos Kardaris2, Siddhartha Chandra1, Vassilis Pitsikalis2, Christian
Werner3, Klaus Hauer3, Costas Tzafestas2, Petros Maragos2, Iasonas Kokkinos1
(1) INRIA GALEN & Centrale Sup´elec Paris,
(2) National Technical University of Athens, (3) University of Heidelberg"
0a9d38f74095c6840540dabb00c754f4b0c2d131,Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph,"Multimodal Language Analysis in the Wild:
CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph
Amir Zadeh1, Paul Pu Liang2, Jonathan Vanbriesen1, Soujanya Poria3,
Edmund Tong1, Erik Cambria4, Minghai Chen1, Louis-Philippe Morency1
{1- Language Technologies Institute, 2- Machine Learning Department}, CMU, USA
{3- A*STAR, 4- Nanyang Technological University}, Singapore"
0ac7488dfbab703fad126dbe8e5e1ed0e9f6629f,Pedestrian Detection via Classification on Riemannian Manifolds,"MITSUBISHI ELECTRIC RESEARCH LABORATORIES
http://www.merl.com
Pedestrian Detection Via Classification on
Riemannian Manifolds
Oncel Tuzel, Fatih Porikli, Peter Meer
TR2008-037 August 2008"
0af9141cdb644ea4eea659ec664d49cd083b9dc7,"Multibiometric Systems : Overview , Case Studies and Open Issues","Chapter 11
Multibiometric Systems: Overview, Case Studies
nd Open Issues
Arun Ross and Norman Poh"
26f5b8a79fac681ffb132c4863c51a55bc2b20e2,Visual speech synthesis from 3D mesh sequences driven by combined speech features,"VISUAL SPEECH SYNTHESIS FROM 3D MESH SEQUENCES DRIVEN BY COMBINED
SPEECH FEATURES
Felix Kuhnke and J¨orn Ostermann
Institut f¨ur Informationsverarbeitung, Leibniz Universit¨at Hannover, Germany"
2671d7085cb32fa3fe55672d9472ba22808e6fe3,An Integrative Approach to Face and Expression Recognition from 3D Scans,"An Integrative Approach to Face and Expression Recognition from 3D Scans
An Integrative Approach to Face and
Expression Recognition from 3D Scans
Chao Li
Florida A&M University
. Introduction
Face recognition, together with fingerprint recognition, speaker recognition, etc., is part of
the  research  area  known  as  ‘biometric  identification’  or  ‘biometrics’,  which  refers  to
identifying an individual based on his or her distinguishing characteristics. More precisely,
iometrics  is  the  science  of  identifying,  or  verifying  the  identity  of,  a  person  based  on
physiological  or  behavioral  characteristics  (Bolle  et  al.,  2003).  Biometric  characteristics
include something that a person is or produces. Examples of the former are fingerprints, the
iris,  the  face,  the  hand/finger  geometry  or  the  palm  print,  etc.  The  latter  include  voice,
handwriting, signature, etc. (Ortega-Garcia et al., 2004).
Face recognition is a particularly compelling biometric approach because it is the one used
every  day  by  nearly  everyone  as  the  primary  means  for  recognition  of  other  humans.
Because  of  its  natural  character,  face  recognition  is  more  acceptable  than  most  other
iometric methods. Face recognition also has the advantage of being noninvasive.
Face  recognition  has  a  wide  range  of  potential  applications  for  commercial,  security,  and
forensic purposes. These applications include automated crowd surveillance, access control,"
266b5b038750e1ab1311e38554e4c2c8ba6564fd,SLIC Superpixels Compared to State-of-the-Art Superpixel Methods,"JOURNAL OF LATEX CLASS FILES, VOL. 6, NO. 1, DECEMBER 2011
SLIC Superpixels Compared to State-of-the-art
Superpixel Methods
Radhakrishna Achanta, Appu Shaji, Kevin Smith,
Aurelien Lucchi, Pascal Fua, and Sabine S¨usstrunk"
267595dd40cd109c93e67874a1cf49ce79871f3a,A Compromise Principle in Deep Monocular Depth Estimation,"A Compromise Principle in Deep Monocular Depth
Estimation
Huan Fu, Mingming Gong, Chaohui Wang, and Dacheng Tao, Fellow, IEEE"
2677a79b6381f3e7787c5dca884fa53d0b28dfe2,Supplementary Document : Single-Shot Multi-Person 3 D Pose Estimation From Monocular RGB 1,"Supplementary Document:
Single-Shot Multi-Person 3D Pose
Estimation From Monocular RGB
. Read-out Process
An algorithmic description of the read-out process
is provided in Alg. 1.
Algorithm 1 3D Pose Inference
: Given: P 2D, C2D, M
: for all i ∈ (1..m) do
if C2D
[k] > thresh, k ∈ {pelvis, neck} then
Person i is detected
for all joints j ∈ (1..n) do
rloc = P2D
Pi[:, j] = ReadLocMap(j, rloc)
limbs
{arml, armr, legl, legr, head} do
{pelvis, neck}; j = parent(j) do
j = getExtremity(l); j
if isValidReadoutLoc(i, j) then"
2690264001ccd4b682b7b4c0334c80af6f5e9c9c,Sensor Transfer: Learning Optimal Sensor Effect Image Augmentation for Sim-to-Real Domain Adaptation,"Sensor Transfer: Learning Optimal Sensor Effect Image Augmentation
for Sim-to-Real Domain Adaptation
Alexandra Carlson1, Katherine A. Skinner1, Ram Vasudevan2 and Matthew Johnson-Roberson3"
26a32691321574ac1c90c58f47ec73fdfbc8507a,SATURN (Situational awareness tool for urban responder networks),"SATURN
(Situational Awareness Tool for Urban Responder Networks)
Heather Zwahlen
Aaron Yahr
Danielle Berven
Michael T. Chan
Maximilian Merfeld
Christine Russ
Jason Thornton
MIT Lincoln Laboratory
Lexington, MA
{heatherz | ayahr | danielle.berven | mchan | max.merfeld
| christine russ |"
266766818dbc5a4ca1161ae2bc14c9e269ddc490,Boosting a Low-Cost Smart Home Environment with Usage and Access Control Rules,"Article
Boosting a Low-Cost Smart Home Environment with
Usage and Access Control Rules
Paolo Barsocchi * ID , Antonello Calabrò, Erina Ferro, Claudio Gennaro ID and Eda Marchetti and
Claudio Vairo
Institute of Information Science and Technologies of CNR (CNR-ISTI)-Italy, 56124 Pisa, Italy;
(A.C.); (E.F.); (C.G.);
(E.M.); (C.V.)
* Correspondence: Tel.: +39-050-315-2965
Received: 27 April 2018; Accepted: 31 May 2018; Published: 8 June 2018"
26e425781e4090abfae65b5d68eac72282dd2e31,Image Captioning with Deep Bidirectional LSTMs,"Image Captioning with Deep Bidirectional LSTMs
Cheng Wang, Haojin Yang, Christian Bartz, Christoph Meinel
Hasso Plattner Institute, University of Potsdam
Prof.-Dr.-Helmert-Str. 2-3, 14482 Potsdam, Germany
{cheng.wang,"
2603efdc673e9c7cfa0c1e1dfda512b6ef54ea2c,On the Use of Simple Geometric Descriptors Provided by RGB-D Sensors for Re-Identification,"Sensors 2013, 13, 8222-8238; doi:10.3390/s130708222
OPEN ACCESS
sensors
ISSN 1424-8220
www.mdpi.com/journal/sensors
Article
On the Use of Simple Geometric Descriptors Provided by
RGB-D Sensors for Re-Identification
Javier Lorenzo-Navarro *, Modesto Castrill´on-Santana and Daniel Hern´andez-Sosa
SIANI, Universidad de Las Palmas de Gran Canaria, Campus de Tafira,
Las Palmas de Gran Canaria 35017, Spain; E-Mails: (M.C.-S.);
(D.H.-S.)
* Author to whom correspondence should be addressed; E-Mail:
Tel.: +34-928-458-747.
Received: 25 March 2013; in revised form: 7 June 2013 / Accepted: 20 June 2013 /
Published: 27 June 2013"
2608a2499819053468f4e6f77a715c2dbfefdfb0,Object Classification using Hybrid Holistic Descriptors: Application to Building Detection in Aerial Orthophotos,"Object Classification using Hybrid Holistic
Descriptors: Application to Building Detection
in Aerial Orthophotos
Fadi Dornaika, Abdelmalik Moujahid, Alireza Bosaghzadeh, Youssef El Merabet, and Yassine Ruichek"
26172460c2c47886f8b0e141c15de29c9766bfbe,An Iterative Co-Saliency Framework for RGBD Images,"An Iterative Co-Saliency Framework for RGBD
Images
Runmin Cong, Jianjun Lei, Senior Member, IEEE, Huazhu Fu, Weisi Lin, Fellow, IEEE,
Qingming Huang, Senior Member, IEEE, Xiaochun Cao, Senior Member, IEEE, and Chunping Hou"
2663fa2f1777dc779a73d678c7919cce37b5fb61,Relevance-Weighted ( 2 D ) 2 LDA Image Projection Technique for Face Recognition,"Relevance-Weighted (2D)2LDA
Image Projection Technique for Face Recognition
In  this  paper,  a  novel  image  projection  technique  for
face recognition application is proposed which is based on
linear  discriminant  analysis  (LDA)  combined  with  the
relevance-weighted  (RW)  method.  The  projection  is
performed through 2-directional and 2-dimensional LDA,
or  (2D)2LDA,  which  simultaneously  works  in  row  and
olumn directions to solve the small sample size problem.
Moreover, a weighted discriminant hyperplane is used in
the  between-class  scatter  matrix,  and  an  RW  method  is
used  in  the  within-class  scatter  matrix  to  weigh  the
information  to  resolve  confusable  data  in  these  classes.
This technique is called the relevance-weighted (2D)2LDA,
or  RW(2D)2LDA,  which  is  used  for  a  more  accurate
discriminant  decision  than  that  produced  by  the
onventional  LDA  or  2DLDA.  The  proposed  technique
has  been  successfully  tested  on  four  face  databases.
Experimental  results
the  proposed"
26c8cac8c6320bf49e2898e46bdf1504333fa257,Deep Predictive Models for Collision Risk Assessment in Autonomous Driving,"Deep Predictive Models for Collision Risk Assessment in Autonomous
Driving
Mark Strickland1, Georgios Fainekos1, Heni Ben Amor1"
26ad6ceb07a1dc265d405e47a36570cb69b2ace6,"Neural Correlates of Cross-Cultural Adaptation September , 2015 How to Improve the Training and Selection for Military Personnel Involved in Cross-Cultural Interactions","RESEARCH AND EXPLOR ATORY
DEVELOPMENT DEPARTMENT
REDD-2015-384
Neural Correlates of Cross-Cultural
How to Improve the Training and Selection for
Military Personnel Involved in Cross-Cultural
Operating Under Grant #N00014-12-1-0629/113056
Adaptation
September, 2015
Interactions
Jonathon Kopecky
Jason Spitaletta
Mike Wolmetz
Alice Jackson
Prepared for:
Office of Naval Research"
267e695d33c556164983014f1aec1552aa0388bc,Using Linear Kernel Entropy Component Analysis as a Feature Extraction Method in Face Recognition in video surveillance systems,"Using Linear Kernel Entropy Component Analysis as a
Feature Extraction Method in Face Recognition in video
surveillance systems
Sepehr Damavandinejadmonfared1, Sina Ashooritootkaboni2, and 3Taha Bahraminezhad Jooneghani
, 2 School of Electrical and Electronic Engineering, UniversitiSains Malaysia (USM), Penang, Malaysia
School of Software Engeenering, Jaber Ebn Hayan University, Rasht, Iran"
2603d8578a6c95a9b9d4cb8a73bc66f18d523f37,Deep Parts Similarity Learning for Person Re-Identification,
26679e1885b1ce186e80551befdf82e57b3f7455,TA RGETED BIOMETRIC IMPERSONATION,"TA RGETED BIOMETRIC IMPERSONATION
John D. Bustard, John N. Carter, Mark S. Nixon
School of Electronics
nd Computer Science, University
of Southampton"
26ad124271c118e207113ae42f0fd3d30f204ea1,State of the Art Report on Video-Based Graphics and Video Visualization,"General Copyright Notice
The documents distributed by this server have been provided by the contributing authors as a means to ensure timely
dissemination of scholarly and technical work on a noncommercial basis. Copyright and all rights therein are maintained by the
uthors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that
ll persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works
may not be reposted without the explicit permission of the copyright holder.
R. Borgo, M. Chen, B. Daubney, E. Grundy, G. Heidemann, B. Höferlin, M. Höferlin, H. Leitte, D.
Weiskopf, X. Xie:
State of the Art Report on Video-Based Graphics and Video Visualization,
Computer Graphics Forum, Vol. 31, No. 8, 2450-2477, 2012.
DOI: 10.1111/j.1467-8659.2012.03158.x
This is the author’s personal copy of the final, accepted version of the paper, which slightly differs from
the version published in Computer Graphics Form.
Copyright © 2012 The Eurographics Association and Blackwell Publishing Ltd.
Preprint"
26a72e9dd444d2861298d9df9df9f7d147186bcd,Collecting and annotating the large continuous action dataset,"DOI 10.1007/s00138-016-0768-4
ORIGINAL PAPER
Collecting and annotating the large continuous action dataset
Daniel Paul Barrett1 · Ran Xu2 · Haonan Yu1 · Jeffrey Mark Siskind1
Received: 18 June 2015 / Revised: 18 April 2016 / Accepted: 22 April 2016 / Published online: 21 May 2016
© The Author(s) 2016. This article is published with open access at Springerlink.com"
269c1f9df4a36b361d32bfdc81457b0a32b60966,Dimensionality reduction of visual features for efficient retrieval and classification,"SIP (2016), vol. 5, e14, page 1 of 14 © The Authors, 2016.
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unre-
stricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
doi:10.1017/ATSIP.2016.14
industrial technology advances
Dimensionality reduction of visual features
for efficient retrieval and classification
petros t. boufounos1, hassan mansour1, shantanu rane2 and anthony vetro1
Visual retrieval and classification are of growing importance for a number of applications, including surveillance, automotive,
s well as web and mobile search. To facilitate these processes, features are often computed from images to extract discriminative
spects of the scene, such as structure, texture or color information. Ideally, these features would be robust to changes in per-
spective, illumination, and other transformations. This paper examines two approaches that employ dimensionality reduction
for fast and accurate matching of visual features while also being bandwidth-efficient, scalable, and parallelizable. We focus on
two classes of techniques to illustrate the benefits of dimensionality reduction in the context of various industrial applications.
The first method is referred to as quantized embeddings, which generates a distance-preserving feature vector with low rate. The
second method is a low-rank matrix factorization applied to a sequence of visual features, which exploits the temporal redun-
dancy among feature vectors associated with each frame in a video. Both methods discussed in this paper are also universal in
that they do not require prior assumptions about the statistical properties of the signals in the database or the query. Further-
more, they enable the system designer to navigate a rate versus performance trade-off similar to the rate-distortion trade-off in
onventional compression."
263607a635d33d26612dce8af14682fb76d0550f,Improving Landmark Localization With Semi-Supervised Learning,"Improving Landmark Localization with Semi-Supervised Learning
Sina Honari1∗, Pavlo Molchanov2, Stephen Tyree2, Pascal Vincent1,4,5, Christopher Pal1,3, Jan Kautz2
MILA-University of Montreal, 2NVIDIA, 3Ecole Polytechnique of Montreal, 4CIFAR, 5Facebook AI Research.
{honaris,
{pmolchanov, styree,"
26288af02d522e50308ef8deb2def5cd3fe9878b,Learning to See by Moving,"Learning to See by Moving
Pulkit Agrawal
UC Berkeley
Jo˜ao Carreira
UC Berkeley
Jitendra Malik
UC Berkeley"
26861e41e5b44774a2801e1cd76fd56126bbe257,Personalized Tour Recommendation Based on User Interests and Points of Interest Visit Durations,"Personalized Tour Recommendation based on User Interests and Points of Interest
Visit Durations
Kwan Hui Lim*†, Jeffrey Chan*, Christopher Leckie*† and Shanika Karunasekera*
*Department of Computing and Information Systems, The University of Melbourne, Australia
Victoria Research Laboratory, National ICT Australia, Australia"
26d3887193808875115f68c7fd8ef9e86659fd3b,Don't Look Back: Robustifying Place Categorization for Viewpoint- and Condition-Invariant Place Recognition,"© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtainedfor all other uses, in any current or future media, including reprinting/republishing this materialfor advertising or promotional purposes, creating new collective works, for resale or redistributionto servers or lists, or reuse of any copyrighted component of this work in other works.Pre-print of article that will appear at the 2018 IEEE International Conference on Robotics and Automation.Please cite this paper as:S. Garg, N. Suenderhauf, and M. Milford, “Don't Look Back: Robustifying Place Categorization for Viewpoint- and Condition-Invariant Place Recognition,” in IEEE International Conference on Robotics and Automation (ICRA),   title={Don't Look Back: Robustifying Place Categorization for Viewpoint- and Condition-Invariant Place Recognition},   author={Garg, Sourav and Suenderhauf, Niko and Milford, Michael},   booktitle={IEEE International Conference on Robotics and Automation (ICRA)},   year={2018} }"
2661f38aaa0ceb424c70a6258f7695c28b97238a,Multilayer Architectures for Facial Action Unit Recognition,"IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 42, NO. 4, AUGUST 2012
Multilayer Architectures for Facial
Action Unit Recognition
Tingfan Wu, Nicholas J. Butko, Paul Ruvolo, Jacob Whitehill, Marian S. Bartlett, and Javier R. Movellan"
26cdb9b6d94c1d6c6a01792fee3c176585f594ac,Hybrid Person Detection and Tracking in H.264/AVC Video Streams,"Hybrid Person Detection and Tracking in H.264/AVC Video Streams
Philipp Wojaczek1, Marcus Laumer1,2, Peter Amon2, Andreas Hutter2 and André Kaup1
Multimedia Communications and Signal Processing,
Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
Imaging and Computer Vision, Siemens Corporate Technology, Munich, Germany
Keywords:
Object Detection, Person Detection, Tracking, Compressed Domain, Pixel Domain, H.264/AVC, Mac-
roblocks, Compression, Color Histogram, Hue, HSV, Segmentation."
26a07311397d47ccc438783eb256cb97309edbb2,Face Recognition using Feature of Integral Gabor-Haar Transformation,"-4244-1437-7/07/$20.00 ©2007 IEEE
IV - 505
ICIP 2007"
265af79627a3d7ccf64e9fe51c10e5268fee2aae,A Mixture of Transformed Hidden Markov Models for Elastic Motion Estimation,"A Mixture of Transformed Hidden Markov
Models for Elastic Motion Estimation
Huijun Di, Linmi Tao, and Guangyou Xu, Senior Member, IEEE"
26a6b2051fe7970f94584e9efbfcf7bdcfd1d6d6,Diffeomorphic image registration with applications to deformation modelling between multiple data sets,"Diffeomorphic image registration
with applications to deformation
modelling between multiple data sets
Bartłomiej Władysław Papież
A thesis submitted in partial fulfilment
for the requirements of the degree
of Doctor of Philosophy
The research presented in this thesis was carried out at the
Applied Digital Signal and Image Processing Research Centre,
School of Computing, Engineering and Physical Sciences,
University of Central Lancashire,
October 2012"
2606e6a5759c030e259ebf3f4261b9c04a36a609,Generating Semantically Precise Scene Graphs from Textual Descriptions for Improved Image Retrieval,"Proceedings of the 2015 Workshop on Vision and Language (VL’15), pages 70–80,
Lisbon, Portugal, 18 September 2015. c(cid:13)2015 Association for Computational Linguistics."
265644f1b6740ca34bfbe9762b90b33021adde62,Deep Learning in Medical Imaging: General Overview.,"Review Article | Experiment, Engineering, and Physics
https://doi.org/10.3348/kjr.2017.18.4.570
pISSN 1229-6929 · eISSN 2005-8330
Korean J Radiol 2017;18(4):570-584
Deep Learning in Medical Imaging: General Overview
June-Goo Lee, PhD1, Sanghoon Jun, PhD2, 3, Young-Won Cho, MS2, 3, Hyunna Lee, PhD2, 3,
Guk Bae Kim, PhD2, 3, Joon Beom Seo, MD, PhD2*, Namkug Kim, PhD2, 3*
Biomedical Engineering Research Center, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, Korea;  2Department of
Radiology, Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, Korea;  3Department of
Convergence Medicine, Biomedical Engineering Research Center, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, Korea
The artificial neural network (ANN)–a machine learning technique inspired by the human neuronal synapse system–was
introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing
gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of
sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data,
enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network.
Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition
tasks, which may portend its applications in medicine and healthcare, especially in medical imaging, in the foreseeable future.
This review article offers perspectives on the history, development, and applications of deep learning technology, particularly
regarding its applications in medical imaging.
Keywords: Artificial intelligence; Machine learning; Convolutional neural network; Recurrent Neural Network; Computer-aided;"
264a84f4d27cd4bca94270620907cffcb889075c,Deep motion features for visual tracking,"Deep Motion Features for Visual Tracking
Susanna Gladh, Martin Danelljan, Fahad Shahbaz Khan, Michael Felsberg
Computer Vision Laboratory, Department of Electrical Engineering, Link¨oping University, Sweden"
26d52680d610a2a19483e5fe9bb1421cc26207e6,An Asynchronous Hidden Markov Model for Audio-Visual Speech Recognition,"An Asynchronous  Hidden  Markov  Model
for  Audio-Visual  Speech Recognition
Samy Bengio
Dalle  Molle Institute for  Perceptual Artificial Intelligence  (IDIAP)
CP  592,  rue du Simplon 4,
920 Martigny, Switzerland"
2675a66b3f8743cf0551f284244af4f24537c19b,Learning Visually Grounded Sentence Representations,"Learning Visually Grounded Sentence Representations
Douwe Kiela
Facebook AI Research
Allan Jabri1
UC Berkeley"
26c7eda262dfda1c3a3597a3bf1f2f1cc4013425,Some Like It Hot — Visual Guidance for Preference Prediction,"Some like it hot - visual guidance for preference prediction
Rasmus Rothe
CVL, ETH Zurich
Radu Timofte
CVL, ETH Zurich
Luc Van Gool
KU Leuven, ETH Zurich"
26e570049aaedcfa420fc8c7b761bc70a195657c,Hybrid Facial Regions Extraction for Micro-expression Recognition System,"J Sign Process Syst
DOI 10.1007/s11265-017-1276-0
Hybrid Facial Regions Extraction for Micro-expression
Recognition System
Sze-Teng Liong1,2,3 · John See4 · Raphael C.-W. Phan2 · KokSheik Wong5 ·
Su-Wei Tan2
Received: 2 February 2016 / Revised: 20 October 2016 / Accepted: 10 August 2017
© Springer Science+Business Media, LLC 2017"
264dcfb5be3f89dc0950472a2a274ef7b641b1af,Dynamic Objects Segmentation for Visual Localization in Urban Environments,"Dynamic Objects Segmentation for Visual
Localization in Urban Environments
G. Zhou1, B. Bescos2, M. Dymczyk1, M. Pfeiffer1, J. Neira2, R. Siegwart1"
26437fb289cd7caeb3834361f0cc933a02267766,Innovative Assessment Technologies : Comparing ‘ Face-to-Face ’ and Game-Based Development of Thinking Skills in Classroom Settings,"012 International Conference on Management and Education Innovation
IPEDR vol.37 (2012) © (2012) IACSIT Press, Singapore
Innovative Assessment Technologies: Comparing ‘Face-to-Face’ and
Game-Based Development of Thinking Skills in Classroom Settings
Gyöngyvér Molnár 1 + and András Lőrincz 2
University of Szeged, 2 Eötvös Loránd University"
268afd5de8fa32cadd4a90bf0bb1c9938a245ab4,Image Compression Effects in Face Recognition Systems,"We are IntechOpen,
the world’s leading publisher of
Open Access books
Built by scientists, for scientists
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Open access books available
International  authors and editors
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in Web of Science™ Core Collection (BKCI)
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260081528f19f6f7e8e5ae16a776b62ad8c2ed0d,An Agent Based WCET Analysis for Top-View Person Re-Identification,"An agent-based WCET analysis for Top-View
Person Re-Identification
Marina Paolanti, Valerio Placidi,
Michele Bernardini, Andrea Felicetti, Rocco Pietrini, and
Emanuele Frontoni
Department of Information Engineering, Universit`a Politecnica delle Marche,
Via Brecce Bianche 12, 60131, Ancona, Italy"
151b6c519c77cda9ff5542fecee166a166e0928f,Mobile Applications Scene Text Recognition by Character Descriptor and Structure Configuration,"International Journal of Research
Available at https://edupediapublications.org/journals
p-ISSN: 2348-6848
e-ISSN: 2348-795X
Volume 03 Issue 04
February 2016
Mobile Applications Scene Text Recognition by Character
Descriptor and Structure Configuration
Ch.Lakshmi Prasad 1 & Koteswarao.M. 2
1.M.Tech student,Amara Institute of Engineering&Technology,JNTUK,NRT,AP.
. Assistant professor,Amara Institute of Engineering&Technology,JNTUK,NRT,AP."
153c8715f491272b06dc93add038fae62846f498,On Clustering Images of Objects,"(cid:13) Copyright by Jongwoo Lim, 2005"
15605634feb1a5770182a8f2c3515daf102ed463,Real-time human pose recognition in parts from single depth images,"Real-Time Human Pose Recognition in Parts from Single Depth Images
Mark Finocchio
Jamie Shotton
Andrew Fitzgibbon
Toby Sharp
Andrew Blake
Richard Moore
Mat Cook
Alex Kipman
Microsoft Research Cambridge & Xbox Incubation"
15f57134b42638cbd57d0d8c4437e8b6b6a8bac4,Learning Visual Reasoning Without Strong Priors,"Learning Visual Reasoning Without Strong Priors
Ethan Perez12, Harm de Vries1, Florian Strub3,
Vincent Dumoulin1, Aaron Courville14
MILA, Universit´e of Montr´eal, Canada; 2Rice University, U.S.A.
Univ. Lille, CNRS, Centrale Lille, Inria, UMR 9189 CRIStAL France
CIFAR Fellow, Canada"
15e6c983e74dcf70d8a557b75bdc172e36692191,VSO: Visual Semantic Odometry,"VSO: Visual Semantic Odometry
Konstantinos-Nektarios Lianos 1,⋆,
Johannes L. Sch¨onberger 2,
Marc Pollefeys 2,3, Torsten Sattler 2
Geomagical Labs, Inc., USA 3 Microsoft, Switzerland
Department of Computer Science, ETH Z¨urich, Switzerland"
152a7ca3a93d41c78ccb50687d8277e9a9247e26,Benchmarks in Robotics Research,"Lecture Notes for
IROS 2006 Workshop II (WS-2)
Tuesday, October 10, 2006
Benchmarks in Robotics Research
Organizer
Angel P. del Pobil
Universitat Jaume I"
153f5ad54dd101f7f9c2ae17e96c69fe84aa9de4,Overview of algorithms for face detection and tracking,"Overview of algorithms for face detection and
tracking
Nenad Markuˇs"
15f51d51c05c22e1dca3a40fb1af46941d91f598,Modeling Visual Compatibility through Hierarchical Mid-level Elements.,"Modeling Visual Compatibility through
Hierarchical Mid-level Elements
Jose Oramas M., Tinne Tuytelaars
KU Leuven, ESAT-PSI, iMinds"
15e6e1551ce9a4094c57db70985e420e57c6997a,Asymmetric cross-view dictionary learning for person re-identification,"978-1-5090-4117-6/17/$31.00 ©2017 IEEE
ICASSP 2017"
155033f2f096934042d659d10912ef29aa1cdbd1,Visual classification of coarse vehicle orientation using Histogram of Oriented Gradients features,"Visual Classification of Coarse Vehicle Orientation
using Histogram of Oriented Gradients Features
Paul E. Rybski and Daniel Huber and Daniel D. Morris and Regis Hoffman"
15f82c3a7f12b82281aca77d519403086611ae69,Comparative Study of Human Age Estimation Based on Hand-Crafted and Deep Face Features,"Máster Universitario en Ingeniería Computacional y Sistemas
Inteligentes
Master Thesis
Comparative Study of Human Age Estimation
Based on Hand-crafted and Deep Face Features
Carlos Belver
Director:
Fadi Dornaika
Co-director:
Ignacio Arganda Carreras"
15f3d47b48a7bcbe877f596cb2cfa76e798c6452,Automatic face analysis tools for interactive digital games,"Automatic face analysis tools for interactive digital games
Anonymised for blind review
Anonymous
Anonymous
Anonymous"
15623fe8875a36cac5283ff2f08cd50998599725,Semantic Instance Segmentation for Autonomous Driving Bert,"Semantic Instance Segmentation for Autonomous Driving
Bert De Brabandere
Davy Neven
ESAT-PSI, KU Leuven
Luc Van Gool"
15ebec3796a2e23d31c8c8ddf6d21555be6eadc6,Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks,"Recent Advances in Object Detection in the Age
of Deep Convolutional Neural Networks
Shivang Agarwal(∗
,1), Jean Ogier du Terrail(∗
,1,2), Fr´ed´eric Jurie(1)
(∗) equal contribution
(1)Normandie Univ, UNICAEN, ENSICAEN, CNRS
(2)Safran Electronics and Defense
September 11, 2018"
15c8443f8d9f1f6537fa8ff470ac407bf2185b0e,Learning Binary Code Representations for Effective and Efficient Image Retrieval,
15ec1faddbd61a9d50925c7b9b0c76642abe94e7,EFFICIENT TECHNIQUES FOR RECOVERING 2 D HUMAN BODY POSES FROM IMAGES,"EFFICIENT TECHNIQUES FOR RECOVERING 2D
HUMAN BODY POSES FROM IMAGES
TAI-PENG TIAN
Dissertation submitted in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
BOSTON
UNIVERSITY"
15292f380f5996f539f4d5e93dba3082d53338fb,Feature Space Optimization for Semantic Video Segmentation,"Feature Space Optimization for Semantic Video Segmentation
Abhijit Kundu∗
Georgia Tech
Vibhav Vineet∗
Vladlen Koltun
Intel Labs
Intel Labs
Figure 1. Semantic video segmentation on the Cityscapes dataset [6]. Input frame on the left, semantic segmentation computed by our
pproach on the right."
159b52158512481df7684c341401efbdbc5d8f02,Object Detection with Active Sample Harvesting,"Object Detection
with Active Sample Harvesting
Thèse no 7312
présentée le 5 Octobre 2016
à la Faculté des Sciences et Techniques de l'Ingénieur
Laboratoire LIDIAP (Idiap Research Institute)
École Polytechnique Fédérale de Lausanne
pour l'obtention du grade de Docteur ès Sciences
Olivier Canévet
devant le jury composé de :
Prof. Pascal Frossard, président du jury
Prof. Gilles Blanchard, rapporteur
Prof. Raphael Sznitman, rapporteur
Dr Mathieu Salzmann, rapporteur
Dr François Fleuret, directeur de thèse
Lausanne, EPFL, 2016"
155448563c354b01d12610b5864b511644cfeb27,Mapping Images to Sentiment Adjective Noun Pairs with Factorized Neural Nets,"Mapping Images to Sentiment Adjective Noun Pairs with Factorized Neural Nets
Takuya Narihira
Sony / ICSI
Damian Borth
DFKI / ICSI
Stella X. Yu
UC Berkeley / ICSI
Karl Ni
In-Q-Tel
Trevor Darrell
UC Berkeley / ICSI"
15aa6c457678e25f6bc0e818e5fc39e42dd8e533,Conditional Image Generation for Learning the Structure of Visual Objects,
15dea987f66386be14b7811f1f27784f3ed9e9c0,Face Detection with Mixtures of Boosted Discriminant Features,"Face Detection with Mixtures of Boosted Discriminant Features
Julien Meynet, Vlad popovici and Jean-Philippe Thiran
Ecole Polytechnique F´ed´erale de Lausanne (EPFL)
Signal Processing Institute
CH-1015 Lausanne, Switzerland.
Technical report TR-ITS-2005.35
November 23, 2005"
157ee7498320119f6f5da2d9c592448986edea7e,Learning Multiple Non-linear Sub-spaces Using K-RBMs,"Learning Multiple Non-Linear Sub-Spaces using K-RBMs
Siddhartha Chandra1, Shailesh Kumar2 & C. V. Jawahar3
CVIT, IIIT Hyderabad, 2Google, Hyderabad"
15c63e01ac051f01edcf76bf809ae41db0663d97,Wavelet Frame Accelerated Reduced Support Vector Machines,"IEEE TRANSACTION ON IMAGE PROCESSING, VOL. X, NO. XX, XXXXXX 2005
Wavelet Frame Accelerated
Reduced Support Vector Machines
Matthias R¨atsch, Gerd Teschke, Sami Romdhani, and Thomas Vetter Member, IEEE"
1565bf91f8fdfe5f5168a5050b1418debc662151,One-pass Person Re-identification by Sketch Online Discriminant Analysis,"One-pass Person Re-identification by
Sketch Online Discriminant Analysis
Wei-Hong Li, Zhuowei Zhong, and Wei-Shi Zheng∗"
15cd05baa849ab058b99a966c54d2f0bf82e7885,Structured Sparse Subspace Clustering: A unified optimization framework,"Structured Sparse Subspace Clustering: A Unified Optimization Framework
Chun-Guang Li1, René Vidal2
SICE, Beijing University of Posts and Telecommunications. 2Center for Imaging Science, Johns Hopkins University.
In many real-world applications, we need to deal with high-dimensional
datasets, such as images, videos, text, and more. In practice, such high-
dimensional datasets can be well approximated by multiple low-dimensional
subspaces corresponding to multiple classes or categories. For example, the
feature point trajectories associated with a rigidly moving object in a video
lie in an affine subspace (of dimension up to 4), and face images of a subject
under varying illumination lie in a linear subspace (of dimension up to 9).
Therefore, the task, known in the literature as subspace clustering [6], is
to segment the data into the corresponding subspaces and finds multiple
pplications in computer vision.
State of the art approaches [1, 2, 3, 4, 5, 7] for solving this problem fol-
low a two-stage approach: a) Construct an affinity matrix between points by
exploiting the ‘self-expressiveness’ property of the data, which allows any
data point to be represented as a linear (or affine) combination of the other
data points; b) Apply spectral clustering on the affinity matrix to recover
the data segmentation. Dividing the problem in two steps is, on the one
hand, appealing because the first step can be solved using convex optimiza-"
15728d6fd5c9fc20b40364b733228caf63558c31,EXPANDING THE BREADTH AND DETAIL OF OBJECT RECOGNITION,(cid:13) 2013 Ian N. Endres
15383ae2d86eb8c5e172168f94ef915a7a238b72,Learning Semantic Prediction using Pretrained Deep Feedforward Networks,"Learning Semantic Prediction using
Pretrained Deep Feedforward Networks
J¨org Wagner1,2, Volker Fischer1, Michael Herman1 and Sven Behnke2
- Robert Bosch GmbH - 70442 Stuttgart - Germany
- University of Bonn, Computer Science VI, Autonomous Intelligent Systems
Friedrich-Ebert-Allee 144, 53113 Bonn - Germany"
153e5cddb79ac31154737b3e025b4fb639b3c9e7,Active Dictionary Learning in Sparse Representation Based Classification,"PREPRINT SUBMITTED TO IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Active Dictionary Learning in Sparse
Representation Based Classification
Jin Xu, Haibo He, Senior Member, IEEE, and Hong Man, Senior Member, IEEE"
15d1326f054f4fadea463f217ce54bad6908705a,Sensor fusion in smart camera networks for Ambient Intelligence - Report on PhD Thesis and Defense,"Sensor fusion in smart camera networks for ambient
intelligence
Maatta, T.T.
0.6100/IR755363
Published: 01/01/2013
Document Version
Publisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)
Please check the document version of this publication:
• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences
etween the submitted version and the official published version of record. People interested in the research are advised to contact the
uthor for the final version of the publication, or visit the DOI to the publisher's website.
• The final author version and the galley proof are versions of the publication after peer review.
• The final published version features the final layout of the paper including the volume, issue and page numbers.
Link to publication
Citation for published version (APA):
Maatta, T. T. (2013). Sensor fusion in smart camera networks for ambient intelligence Eindhoven: Technische
Universiteit Eindhoven DOI: 10.6100/IR755363
General rights
Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners
nd it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights."
155ce5d596c7b525110ca24db11e47d521b487ce,STC: A Simple to Complex Framework for Weakly-Supervised Semantic Segmentation,"STC: A Simple to Complex Framework for
Weakly-supervised Semantic Segmentation
Yunchao Wei, Xiaodan Liang, Yunpeng Chen, Xiaohui Shen, Ming-Ming Cheng, Jiashi Feng, Yao Zhao,
Senior Member, IEEE and Shuicheng Yan Senior Member, IEEE"
158a8037ce1c577620550da385d2275a31b9ccaa,Combining motion detection and hierarchical particle filter tracking in a multi-player sports environment,"Combining motion detection and hierarchical particle filter tracking
in a multi-player sports environment
Robbie Vos, Willie Brink
Department of Mathematical Sciences
University of Stellenbosch, South Africa"
15e0b9ba3389a7394c6a1d267b6e06f8758ab82b,The OU-ISIR Gait Database comprising the Large Population Dataset with Age and performance evaluation of age estimation,"Xu et al. IPSJ Transactions on Computer Vision and
Applications  (2017) 9:24
DOI 10.1186/s41074-017-0035-2
IPSJ Transactions on Computer
Vision and Applications
TECHNICAL NOTE
Open Access
The OU-ISIR Gait Database comprising the
Large Population Dataset with Age and
performance evaluation of age estimation
Chi Xu1,2, Yasushi Makihara2*, Gakuto Ogi2, Xiang Li1,2, Yasushi Yagi2 and Jianfeng Lu1"
159b87e6e68b18f4daa3505bfc415be9b21a7db6,Tracking The Invisible Man - Hidden-object Detection for Complex Visual Scene Understanding,
150326137da214210b46e0b7f22e30f7e6529006,Pedestrian Detection at Warp Speed: Exceeding 500 Detections per Second,"Pedestrian Detection at Warp Speed: Exceeding 500 Detections per Second
Floris De Smedt∗, Kristof Van Beeck∗, Tinne Tuytelaars and Toon Goedem´e
EAVISE, ESAT-PSI-VISICS, KU Leuven, Belgium"
1550c3835822843a02b2144cef8abc534441f5d4,Human Pose Classification within the Context of Near-IR Imagery Tracking,"Human Pose Classification within the Context of Near-IR
Imagery Tracking
Jiwan Han, Anna Gaszczak, Ryszard Maciol, Stuart E. Barnes, Toby P. Breckon
School of Engineering, Cranfield University, Bedfordshire, UK"
15ff6356e3552b4dd7bd6bdd65090d988a7ce61f,PI-Edge: A Low-Power Edge Computing System for Real-Time Autonomous Driving Services,"π-Edge: A Low-Power Edge Computing System
for Real-Time Autonomous Driving Services
Jie Tang, Shaoshan Liu, Bo Yu, and Weisong Shi Fellow, IEEE
the DragonFly Pod (Figure 1), has been developed by us, for a
total  cost  under  $10,000  when  mass-produced  and  for  low-
speed scenarios, such as university campuses, industrial parks,
nd areas with limited traffic.
The DragonFly pod supports three basic services, real-time
localization through Simultaneous Localization And Mapping
(SLAM),  real-time  obstacle  detection
through  computer
vision, and speech recognition for user interaction [28]."
15eff32ccbf0f89e888df5a5128d89cea3e0060e,On preserving non-discrimination when combining expert advice,"On preserving non-discrimination when combining expert advice
Avrim Blum ∗
Suriya Gunasekar †
Thodoris Lykouris‡
Nathan Srebro §"
1546b65e5e95543cf2dc0ead92b758fb31a5f4d6,An inexpensive monocular vision system for tracking humans in industrial environments,"An Inexpensive Monocular Vision System for
Tracking Humans in Industrial Environments
Centre for Applied Autonomous Sensor Systems (AASS), ¨Orebro University, Sweden
Rafael Mosberger and Henrik Andreasson"
15136c2f94fd29fc1cb6bedc8c1831b7002930a6,Deep Learning Architectures for Face Recognition in Video Surveillance,"Deep Learning Architectures for Face
Recognition in Video Surveillance
Saman Bashbaghi, Eric Granger, Robert Sabourin and Mostafa Parchami"
15c7fe9c9154113f9824f68ca1870564600b66d6,Better Appearance Models for Pictorial Structures,"EICHNER, FERRARI: BETTER APPEARANCE MODELS FOR PICTORIAL STRUCTURES
Better appearance models
for pictorial structures
Marcin Eichner
Vittorio Ferrari
Computer Vision Laboratory
Zürich, Switzerland"
15cf11ddfc046b2ed2766c375e8ad067baaf8347,Active Pedestrian Safety by Automatic Braking and Evasive Steering,"Active Pedestrian Safety
y Automatic Braking and Evasive Steering
C. Keller, T. Dang, H. Fritz, A. Joos, C. Rabe and D. M. Gavrila"
15696370ff33b6e5a81bf5131d80065d6e59804f,Semantically guided location recognition for outdoors scenes,"Semantically Guided Location Recognition for Outdoors Scenes
Arsalan Mousavian and Jana Koˇseck´a and Jyh-Ming Lien"
157eb982da8fe1da4c9e07b4d89f2e806ae4ceb6,Connecting the dots in multi-class classification: From nearest subspace to collaborative representation,"MITSUBISHI ELECTRIC RESEARCH LABORATORIES
http://www.merl.com
Connecting the Dots in Multi-Class Classification: From
Nearest Subspace to Collaborative Representation
Chi, Y.; Porikli, F.
TR2012-043
June 2012"
15667845de2531b59736d866531728a771500d34,3-D Face Recognition Using Local Appearance-Based Models,"[4] L. Lee and W. E. L. Grimson, “Gait analysis for recognition and classi-
fication,” in Proc. IEEE Int. Conf. Automatic Face and Gesture Recog-
nition, Washington, DC, May 2002, pp. 734–742.
[5] L. Wang, H. Ning, W. Hu, and T. Tan, “Gait recognition based on pro-
rustes shape analysis,” in Proc. Int. Conf. Image Processing, 2002, pp.
33–436.
[6] L. Wang, H. Ning, T. Tan, and W. Hu, “Fusion of static and dynamic
ody biometrics for gait recognition,” IEEE Trans. Circuits Syst. Video
Technol., vol. 14, no. 2, pp. 149–158, Feb. 2004.
[7] D. Cunado, M. S. Nixon, and J. N. Carter, “Automatic extraction and
description of human gait models for recognition purposes,” in Comput.
Vis. Image Understand., Apr. 2003, vol. 90, pp. 1–41.
[8] P. J. Phillips, S. Sarkar, I. R. Vega, P. Grother, and K. W. Bowyer,
“The gait identification challenge problem: Data sets and baseline al-
gorithm,” in Proc. Int. Conf. Pattern Recognition, Quebec City, QC,
Canada, Aug. 2002, vol. 1, pp. 385–388.
[9] S. Sarkar, P. J. Phillips, Z. Liu, I. R. Vega, P. Grother, and K. W.
Bowyer, “The human ID gait challenge problem: Data sets, perfor-
mance, and analysis,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 27,
no. 2, pp. 162–177, Feb. 2005."
15e024d8f5625ec03c8ac592fbc093687cfb5f02,The Visual Object Tracking VOT2015 Challenge Results,"013 IEEE International Conference on Computer Vision Workshops
013 IEEE International Conference on Computer Vision Workshops
The Visual Object Tracking VOT2013 challenge results
Matej Kristan a
Luka ˇCehovin a
Roman Pflugfelder b
Georg Nebehay b
Aleˇs Leonardis c
Gustavo Fernandez b
Jiri Matas d
Tom´aˇs Voj´ıˇr d
Fatih Porikli e
Adam Gatt f
Ahmad Khajenezhad g
Alfredo Petrosino i
Chee Seng Chan m
Dorothy Monekosso n
Jin Gao q
Ahmed Salahledin h
Anthony Milton j"
bef2854893462ae28bdb2243bba4d010d3909289,TUBITAK UZAY at TRECVID 2010: Content-Based Copy Detection and Semantic Indexing,"TÜBİTAK UZAY at TRECVID 2010: Content-Based Copy Detection and
Semantic Indexing
Ahmet Saracoğlu1,2, Ersin Esen1,2, Medeni Soysal1,2, Tuğrul K. Ateş1,2, Berker Loğoğlu1, Mashar Tekin1,
Talha Karadeniz1, Müge Sevinç1, Hakan Sevimli1, Banu Oskay Acar1, Ezgi C. Ozan1,2,
Duygu Oskay Onur1, Sezin Selçuk1,
A. Aydın Alatan2, Tolga Çiloğlu2
TÜBİTAK Space Technologies Research Institute
Department of Electrical and Electronics Engineering, M.E.T.U.
{ahmet.saracoglu, ersin.esen, medeni.soysal, tugrul.ates, berker.logoglu, mashar.tekin,
talha.karadeniz, muge.sevinc, hakan.sevimli, banu.oskay, ezgican.ozan,
duygu.oskay, sezin.selcuk
Content-Based Copy Detection Task
.1  Visual Copy Detection
Mainline  approaches  for  content  description  for  copy  detection  utilize  global  or  local  descriptors  from
video and comparing these descriptors for similarity. In the literature [16], it has been shown that local
features  perform  better  in  terms  of  robustness  on  the  other  hand  global  features  are  computationally
simpler. Local features for content description can be extracted around pixels returned by interest point
detectors [17]. Thus, an interest point detector followed by a feature extractor is enough for describing
most local aspects of a video scene.
Our approach to CCD is based on the clustering of SIFT descriptors and comparing video scenes by their"
bea185a15d5df7bbfce83bc684c316412703efbb,PIXELNN: EXAMPLE-BASED IMAGE SYNTHESIS,"Under review as a conference paper at ICLR 2018
PIXELNN: EXAMPLE-BASED IMAGE SYNTHESIS
Anonymous authors
Paper under double-blind review"
be48780eb72d9624a16dd211d6309227c79efd43,Interactive Visual and Semantic Image Retrieval,"Interactive Visual and Semantic Image Retrieval
Joost van de Weijer, Fahad Khan and Marc Masana Castrillo
Introduction
One direct consequence of recent advances in digital visual data generation and
the direct availability of this information through the World-Wide Web, is a urgent
demand for efficient image retrieval systems. The disclosure of the content of these
millions of photos available on the internet is of great importance. The objective
of image retrieval is to allow users to efficiently browse through this abundance
of images. Due to the non-expert nature of the majority of the internet users, such
systems should be user friendly, and therefore avoid complex user interfaces.
Traditionally, two sources of information are exploited in the description of im-
ges on the web. The first approach, called text-based image retrieval, describes
images by a set of labels or keywords [1]. These labels can be automatically ex-
tracted from for example the image name (e.g. ’car.jpg’ would provide information
bout the presence of a car in the image), or alternatively from the webpage text
surrounding the image. Another, more expensive way would be to manually label
images with a set of keywords. Shortcomings of the text-based approach to image
retrieval are obvious: many objects in the scene will not be labeled, words suffer
from the confusions in case of synonyms or homonyms, and words often fall short
in describing the esthetics, composition and color scheme of a scene. However, un-"
be707bf7c7096df0fcf5bb07ef0fa53494d6a781,Effective classifiers for detecting objects,"Effective Classifiers for Detecting Objects
Michael Mayo
Dept. of Computer Science
University of Waikato
Private Bag 3105, Hamilton, New Zealand
in  the
literature:
Introduction
image. Many image databases such as Caltech-101 [1]
onsist  of  images  with  the  objects  of  interest  in  a
dominant foreground position, occupying most of the
image."
be5b455abd379240460d022a0e246615b0b86c14,"The MR2: A multi-racial, mega-resolution database of facial stimuli.","Behav Res
DOI 10.3758/s13428-015-0641-9
The MR2: A multi-racial, mega-resolution database of facial
stimuli
Nina Strohminger1,6 · Kurt Gray2 · Vladimir Chituc3 · Joseph Heffner4 ·
Chelsea Schein2 · Titus Brooks Heagins5
© Psychonomic Society, Inc. 2015"
be2d326fa588b4ffd1d8d3d4408ae680e1a26277,JOURNA A Survey on Modern Era ’ s Online Object Tracking Algorithms,"[Deshmukh, 2(3): March, 2013]
ISSN: 2277
ISSN: 2277-9655
IJESRT
INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH
INTERNATIONAL JOURNA
ENCES & RESEARCH
TECHNOLOGY
A Survey on Modern Era’s Online Object Tracking Algorithms
A Survey on Modern Era’s Online Object Tracking Algorithms
A Survey on Modern Era’s Online Object Tracking Algorithms
Khemraj Deshmukh*1, Vishal Moyal2
Khemraj Deshmukh"
bed7834ae7d371171977a590872f60d137c2f951,GuessWhat?! Visual Object Discovery through Multi-modal Dialogue,"GuessWhat?! Visual object discovery through multi-modal dialogue
Harm de Vries
University of Montreal
Florian Strub
Univ. Lille, CNRS, Centrale Lille,
Inria, UMR 9189 CRIStAL
Sarath Chandar
University of Montreal
Olivier Pietquin
DeepMind
Hugo Larochelle
Twitter
Aaron Courville
University of Montreal"
befa14324bb71e5d0f30808e54abc970d52f758c,A Convex Approach for Image Hallucination,"OAGM/AAPR Workshop 2013 (arXiv:1304.1876)
A Convex Approach for Image Hallucination
Institute for Computer Graphics and Vision, University of Technology Graz
Peter Innerhofer, Thomas Pock"
bef7d5d2c5951ae9ae85fcec4a7eaf5dfd8196c9,Self-learning Trajectory Prediction with Recurrent Neural Networks at Intelligent Intersections,
be13f17ca05bb0f80f1689254e516130874c6f6e,"Face Recognition using PCA , Deep Face Method","Gurpreet Kaur et al, International Journal of Computer Science and Mobile Computing, Vol.5 Issue.5, May- 2016, pg. 359-366
Available Online at www.ijcsmc.com
International Journal of Computer Science and Mobile Computing
A Monthly Journal of Computer Science and Information Technology
ISSN 2320–088X
IMPACT FACTOR: 5.258
IJCSMC, Vol. 5, Issue. 5, May 2016, pg.359 – 366
Face Recognition using PCA,
Deep Face Method
Gurpreet Kaur1, Sukhvir Kaur2, Amit Walia3
Department of CSE, I.K.G Punjab Technical University
2 3"
be25d7bff3b5928adf6c0a7f5495d47113f80997,LEARNING TO DRIVE: PERCEPTION FOR AUTONOMOUS CARS A DISSERTATION SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE AND THE COMMITTEE ON GRADUATE STUDIES OF STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY,"LEARNING TO DRIVE:
PERCEPTION FOR AUTONOMOUS CARS
A DISSERTATION
SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE
AND THE COMMITTEE ON GRADUATE STUDIES
OF STANFORD UNIVERSITY
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
David Michael Stavens
May 2011"
be24e5fd1ec27d444c66183e89b5033db9155de9,"A Continuous, Full-scope, Spatio-temporal Tracking Metric based on KL-divergence","A Continuous, Full-scope, Spatio-temporal Tracking
Metric based on KL-divergence
Terry Adams
U.S. Government
Suite 6587
Ft. Meade, MD 20755
Email:"
bee609ea6e71aba9b449731242efdb136d556222,Multi-Target Tracking in Multiple Non-Overlapping Cameras using Constrained Dominant Sets,"Multi-Target Tracking in Multiple
Non-Overlapping Cameras using Constrained
Dominant Sets
Yonatan Tariku Tesfaye*, Student Member, IEEE, Eyasu Zemene*, Student Member, IEEE,
Andrea Prati, Senior member, IEEE, Marcello Pelillo, Fellow, IEEE, and Mubarak Shah, Fellow, IEEE"
be6f29e129a99529f7ed854384d1f4da04c4ca1f,Spatially Consistent Nearest Neighbor Representations for Fine-Grained Classification. (Représentations d'images basées sur un principe de voisins partagés pour la classification fine),"Spatially Consistent Nearest Neighbor Representations
for Fine-Grained Classification
Valentin Leveau
To cite this version:
Valentin Leveau. Spatially Consistent Nearest Neighbor Representations for Fine-Grained Classifica-
tion. Computer Science [cs]. Université Montpellier, 2016. English. <tel-01410137>
HAL Id: tel-01410137
https://hal.archives-ouvertes.fr/tel-01410137
Submitted on 6 Dec 2016
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de
recherche français ou étrangers, des laboratoires"
be4c2b6fdde83179dd601541f57ee5d14fe1e98a,Graphical Generative Adversarial Networks,"Graphical Generative Adversarial Networks
Chongxuan Li 1 Max Welling 2 Jun Zhu 1 Bo Zhang 1"
be313072e9706df300d86bfac54079acfb9c1ef0,Descripteurs à divers niveaux de concepts pour la classification d ’ images multi-objets,"Descripteurs à divers niveaux de concepts pour la classification
d’images multi-objets
Y. Tamaazousti1 3
H. Le Borgne1
C. Hudelot2 3
CentraleSupélec, Laboratoire de Mathématiques et Informatique pour la Complexité et les Systèmes
CEA LIST, Laboratoire Vision et Ingénierie des Contenus
Université Paris-Saclay, Laboratoire MICS
{Youssef.tamaazousti,
Résumé
La classification d’images au moyen de descripteurs sé-
mantiques repose sur des caractéristiques formées par
les sorties de classifieurs binaires, chacun détectant un
oncept visuel dans l’image. Les approches existantes
onsidèrent souvent
les concepts visuels indépendam-
ment les uns des autres, alors qu’ils sont souvent liés.
Ces relations sont parfois prises en compte, au moyen
d’un schéma ascendant dépendant fortement de descrip-
teurs bas-niveaux, induisant des relations non-pertinentes"
bebea83479a8e1988a7da32584e37bfc463d32d4,Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning,"Discovery of Latent 3D Keypoints via
End-to-end Geometric Reasoning
Supasorn Suwajanakorn∗ Noah Snavely
Jonathan Tompson Mohammad Norouzi
{supasorn, snavely, tompson,
Google AI"
be62019734554152c4feef62ba3092894b402efb,ARISTA - image search to annotation on billions of web photos,"The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition
Poster Spotlights
Session: Thursday Poster Session, Thurs 17 June 2010, 10:30 - 12:10 am
ARISTA - Image Search to Annotation
on Billions of Web Photos
Xin-Jing Wang, Lei Zhang, Ming Liu, Yi Li,
Wei-Ying Ma"
be0bd420b78be8dfc0aad65dddae10ff1ec30a94,People Orientation Recognition by Mixtures of Wrapped Distributions on Random Trees,"People Orientation Recognition by Mixtures
of Wrapped Distributions on Random Trees
Davide Baltieri, Roberto Vezzani, and Rita Cucchiara
DIEF - University of Modena and Reggio Emilia
Via Vignolese 905, 41125 - Modena, Italy
http://imagelab.ing.unimore.it"
becb704450c6b2f7f57f03955036a5b66380b816,A Software Architecture for RGB-D People Tracking Based on ROS Framework for a Mobile Robot,"A software architecture for RGB-D
people tracking based on ROS
framework for a mobile robot
Matteo Munaro, Filippo Basso, Stefano Michieletto, Enrico Pagello, and
Emanuele Menegatti"
bec439c2a9a597c3aeb3f2932adc348d191ccba0,Zero-Shot Kernel Learning,"Zero-Shot Kernel Learning
Hongguang Zhang∗,2,1
Piotr Koniusz∗,1,2
Data61/CSIRO, 2Australian National University
nu.edu.au2}"
beab0d01cdbbbfdc52482b9ef65d6634e4f21b7e,Monocular Semantic Occupancy Grid Mapping With Convolutional Variational Encoder–Decoder Networks,"Monocular Semantic Occupancy Grid Mapping with
Convolutional Variational Encoder-Decoder
Networks
Chenyang Lu1, Marinus Jacobus Gerardus van de Molengraft2, and Gijs Dubbelman1"
beeeade98988e55afe81faaedf06dc00848ec751,ARBEE: Towards Automated Recognition of Bodily Expression of Emotion In the Wild,"Int J Comput Vis manuscript No.
(will be inserted by the editor)
ARBEE: Towards Automated Recognition of Bodily
Expression of Emotion In the Wild
Yu Luo · Jianbo Ye · Reginald B. Adams, Jr. · Jia Li ·
Michelle G. Newman · James Z. Wang
Received: date / Accepted: date"
beab10d1bdb0c95b2f880a81a747f6dd17caa9c2,DeepDeblur: Fast one-step blurry face images restoration.,"DeepDeblur: Fast one-step blurry face images restoration
Lingxiao Wang, Yali Li, Shengjin Wang
Tsinghua Unversity"
bed06e7ff0b510b4a1762283640b4233de4c18e0,Face Interpretation Problems on Low Quality Images,"Bachelor Project
Czech
Technical
University
in Prague
Faculty of Electrical Engineering
Department of Cybernetics
Face Interpretation Problems on Low
Quality Images
Adéla Šubrtová
Supervisor: Ing. Jan Čech, Ph.D
May 2018"
beb7a0329c3042c2ce63b5789e2581bb8e2dbbea,Generating Visual Representations for Zero-Shot Classification,"Generating Visual Representations for Zero-Shot Classification
Maxime Bucher, St´ephane Herbin
ONERA - The French Aerospace Lab
Palaiseau, France
Normandie Univ, UNICAEN, ENSICAEN, CNRS
Fr´ed´eric Jurie
Caen, France"
befd21f74248ca5f22f608043d64cdea67829737,Decoupled Access-Execute on ARM big.LITTLE,"Decoupled Access-Execute on ARM big.LITTLE
Anton Weber
Uppsala University
nton.weber.0295
Kim-Anh Tran
Uppsala University
kim-anh.tran
Stefanos Kaxiras
Uppsala University
stefanos.kaxiras
Alexandra Jimborean
lexandra.jimborean
Uppsala University"
be9dde86ebd10ecb05808e034e3cadd210fe0bfb,SLAMIT : A Sub-map based SLAM system On-line creation of multi-leveled map,"Master of Science Thesis in Electrical Engineering
Department of Electrical Engineering, Linköping University, 2016
SLAMIT: A Sub-map based
SLAM system
On-line creation of multi-leveled map
Karl Holmquist"
be75a0ff3999754f20e63fde90f4c68b4af22d60,R4-A.1: Dynamics-Based Video Analytics,"R4-A.1: Dynamics-Based Video Analytics
PARTICIPANTS
Octavia Camps
Mario Sznaier
Title
Co-PI
Co-PI
Faculty/Staff
Institution
Graduate, Undergraduate and REU Students
Oliver Lehmann
Mengran Gou
Yongfang Cheng
Yin Wang
Sadjad Asghari-Esfeden
Angels Rates
Degree Pursued
Institution
Email
Month/Year of Graduation"
be07f2950771d318a78d2b64de340394f7d6b717,3D HMM-based Facial Expression Recognition using Histogram of Oriented Optical Flow,"See	discussions,	stats,	and	author	profiles	for	this	publication	at:	https://www.researchgate.net/publication/290192867
D	HMM-based	Facial	Expression	Recognition
using	Histogram	of	Oriented	Optical	Flow
ARTICLE		in		SYNTHESIS	LECTURES	ON	ARTIFICIAL	INTELLIGENCE	AND	MACHINE	LEARNING	·	DECEMBER	2015
DOI:	10.14738/tmlai.36.1661
READS
AUTHORS,	INCLUDING:
Sheng	Kung
Oakland	University
Djamel	Bouchaffra
Institute	of	Electrical	and	Electronics	Engineers
PUBLICATION			0	CITATIONS
57	PUBLICATIONS			402	CITATIONS
SEE	PROFILE
SEE	PROFILE
All	in-text	references	underlined	in	blue	are	linked	to	publications	on	ResearchGate,
letting	you	access	and	read	them	immediately.
Available	from:	Djamel	Bouchaffra
Retrieved	on:	11	February	2016"
beec0138d21271379bdfa89317a0a1d648733bad,Model-Free Multiple Object Tracking with Shared Proposals,"Model-Free Multiple Object Tracking with
Shared Proposals
Gao Zhu1, Fatih Porikli1,2,3, Hongdong Li1,3
Australian National University1, Data61/CSIRO2,
ARC Centre of Excellence for Robotic Vision3"
be21529c47b79b688b420c5e296086698ba11350,CNN-Based Multimodal Human Recognition in Surveillance Environments,"Article
CNN-Based Multimodal Human Recognition in
Surveillance Environments
Ja Hyung Koo, Se Woon Cho, Na Rae Baek, Min Cheol Kim and Kang Ryoung Park *
Division of Electronics and Electrical Engineering, Dongguk University, 30 Pil-dong-ro, 1-gil, Jung-gu,
Seoul 100-715, Korea; (J.H.K.); (S.W.C.);
(N.R.B.); (M.C.K.)
* Correspondence: Tel.: +82-10-3111-7022; Fax: +82-2-2277-8735
Received: 7 August 2018; Accepted: 8 September 2018; Published: 11 September 2018"
beff22ec87148ce4eac32fde45ceb5368c735381,Boosted Projection: An Ensemble of Transformation Models,"Boosted Projection: An Ensemble of
Transformation Models
Ricardo Barbosa Kloss, Artur Jord˜ao, and William Robson Schwartz
Smart Surveillance Interest Group, Department of Computer Science
Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
{rbk,"
bea5780d621e669e8069f05d0f2fc0db9df4b50f,Convolutional Deep Belief Networks on CIFAR-10,"Convolutional Deep Belief Networks on CIFAR-10
Alex Krizhevsky
Introduction
We describe how to train a two-layer convolutional Deep Belief Network (DBN) on the 1.6 million tiny images
dataset.
When training a convolutional DBN, one must decide what to do with the edge pixels of teh images. As
the pixels near the edge of an image contribute to the fewest convolutional lter outputs, the model may
see it t to tailor its few convolutional lters to better model the edge pixels. This is undesirable becaue it
usually comes at the expense of a good model for the interior parts of the image. We investigate several ways
of dealing with the edge pixels when training a convolutional DBN. Using a combination of locally-connected
onvolutional units and globally-connected units, as well as a few tricks to reduce the eects of overtting,
we achieve state-of-the-art performance in the classication task of the CIFAR-10 subset of the tiny images
dataset.
The dataset
Throughout this paper we employ two subsets of the 80 million tiny images dataset [2]. The 80 million
tiny images dataset is a collection of 32 × 32 color images obtained by searching various online image search"
beb4546ae95f79235c5f3c0e9cc301b5d6fc9374,A Modular Approach to Facial Expression Recognition,"A Modular Approach to Facial Expression Recognition
Michal Sindlar
Cognitive Artificial Intelligence, Utrecht University, Heidelberglaan 6, 3584 CD, Utrecht
Marco Wiering
Intelligent Systems Group, Utrecht University, Padualaan 14, 3508 TB, Utrecht"
be437b53a376085b01ebd0f4c7c6c9e40a4b1a75,Face Recognition and Retrieval Using Cross Age Reference Coding,"ISSN (Online) 2321 – 2004
ISSN (Print) 2321 – 5526
INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN ELECTRICAL, ELECTRONICS, INSTRUMENTATION AND CONTROL ENGINEERING
Vol. 4, Issue 5, May 2016
IJIREEICE
Face Recognition and Retrieval Using Cross
Age Reference Coding
Sricharan H S1, Srinidhi K S1, Rajath D N1, Tejas J N1, Chandrakala B M2
BE, DSCE, Bangalore1
Assistant Professor, DSCE, Bangalore2"
be8c517406528edc47c4ec0222e2a603950c2762,CHAPTER 2 MEASURING FACIAL ACTION,"Harrigan / The new handbook of methods in nonverbal behaviour research 02-harrigan-chap02 Page Proof page 7
7.6.2005
5:45pm
B A S I C R E S E A RC H
M E T H O D S A N D
P RO C E D U R E S"
199aabb19ea78576a74d573739a7f35cf04fac6e,Fast globally optimal 2D human detection with loopy graph models,"Fast Globally Optimal 2D Human
Detection with Loopy Graph Models
Paper by
T.-P. Tian and S. Sclaroff
Slides by A. Vedaldi"
19359fb238888c0eb012a4ab5c6f0fa0e9be493b,Enhanced Facial Expression Recognition using 2DPCA Principal component Analysis and Gabor Wavelets,"Enhanced Facial Expression Recognition
using 2DPCA Principal component Analysis
nd Gabor Wavelets.
(1)Laboratory of Automatic and Signals Annaba (LASA) , Department of electronics, Faculty of Engineering,
Zermi.Narima(1), Saaidia.Mohammed(2),
Badji-Mokhtar University, P.O.Box 12, Annaba-23000, Algeria.
E-Mail :
(2) Département de Génie-électrique, Université M.C.M. Souk-Ahras, Algeria"
1962e4c9f60864b96c49d85eb897141486e9f6d1,Locality preserving embedding for face and handwriting digital recognition,"Neural Comput & Applic (2011) 20:565–573
DOI 10.1007/s00521-011-0577-7
O R I G I N A L A R T I C L E
Locality preserving embedding for face and handwriting digital
recognition
Zhihui Lai • MingHua Wan • Zhong Jin
Received: 3 December 2008 / Accepted: 11 March 2011 / Published online: 1 April 2011
Ó Springer-Verlag London Limited 2011
supervised manifold
the local sub-manifolds."
19aa506d04d3f7241fc71b595d28b5f1bb99edad,Compact Generalized Non-local Network,"Compact Generalized Non-local Network
Kaiyu Yue1,3 Ming Sun1 Yuchen Yuan1 Feng Zhou2 Errui Ding1 Fuxin Xu3
Baidu VIS 2Baidu Research
Central South University
{yuekaiyu, sunming05, yuanyuchen02, zhoufeng09,"
1936a73920c5a7eb97e8b73cb9a6096aa509e402,Robust Multi-Person Tracking from Moving Platforms,"Robust Multi-Person Tracking from Moving Platforms
Andreas Ess1, Konrad Schindler1, Bastian Leibe1,2 and Luc van Gool1,3
ETH Z¨urich
KU Leuven, IBBT
RWTH Aachen"
195d331c958f2da3431f37a344559f9bce09c0f7,Parsing occluded people by flexible compositions,"Parsing Occluded People by Flexible Compositions
Xianjie Chen, Alan Yuille
University of California, Los Angeles.
Figure 1: An illustration of the flexible compositions. Each connected sub-
tree of the full graph (include the full graph itself) is a flexible composition.
The flexible compositions that do not have certain parts are suitable for the
people with those parts occluded.
Figure 2: The absence of body parts evidence can help to predict occlusion.
However, absence of evidence is not evidence of absence.
It can fail in
some challenging scenes. The local image measurements near the occlusion
oundary (i.e., around the right elbow and left shoulder) can reliably provide
evidence of occlusion.
This paper presents an approach to parsing humans when there is signifi-
ant occlusion. We model humans using a graphical model which has a tree
structure building on recent work [1, 6] and exploit the connectivity prior
that, even in presence of occlusion, the visible nodes form a connected sub-
tree of the graphical model. We call each connected subtree a flexible com-
position of object parts. This involves a novel method for learning occlusion
ues. During inference we need to search over a mixture of different flexible"
194af94f1ea9357bebb0aab5ab98aa0daa21ddbd,Snapshot Distillation: Teacher-Student Optimization in One Generation,"Snapshot Distillation: Teacher-Student Optimization in One Generation
Chenglin Yang1 Lingxi Xie1 Chi Su2 Alan L. Yuille1
Johns Hopkins University 2Kingsoft"
19fcb95815e4c225b250f7deed9be3e90963933d,Evaluación de la calidad de las imágenes de rostros utilizadas para la identificación de las personas,"ISSN: 1405-5546
Instituto Politécnico Nacional
México
Méndez-Vázquez, Heydi; Chang, Leonardo; Rizo-Rodríguez, Dayron; Morales-González, Annette
Evaluación de la calidad de las imágenes de rostros utilizadas para la identificación de las personas
Instituto Politécnico Nacional
Distrito Federal, México
Disponible en: http://www.redalyc.org/articulo.oa?id=61523309003
Cómo citar el artículo
Número completo
Más información del artículo
Página de la revista en redalyc.org
Sistema de Información Científica
Red de Revistas Científicas de América Latina, el Caribe, España y Portugal
Proyecto académico sin fines de lucro, desarrollado bajo la iniciativa de acceso abierto"
191beb87f84326d2cc9c427efe2a5abee8f67574,Dual Local-Global Contextual Pathways for Recognition in Aerial Imagery,"Dual Local-Global Contextual Pathways for Recognition
in Aerial Imagery
Alina Marcu and Marius Leordeanu 1"
19cfe13e8196872b81d6f31d2849dc540d146f7c,A Bayesian Framework for Sparse Representation-Based 3-D Human Pose Estimation,"A Bayesian Framework for Sparse
Representation-Based 3D Human Pose Estimation
Behnam Babagholami-Mohamadabadi, Amin Jourabloo, Ali Zarghami, and Shohreh Kasaei Senior Member, IEEE"
193ec7bb21321fcf43bbe42233aed06dbdecbc5c,Automatic 3D Facial Expression Analysis in Videos,"UC Santa Barbara
UC Santa Barbara Previously Published Works
Title
Automatic 3D facial expression analysis in videos
Permalink
https://escholarship.org/uc/item/3g44f7k8
Authors
Chang, Y
Vieira, M
Turk, M
et al.
Publication Date
005-01-01
Peer reviewed
eScholarship.org
Powered by the California Digital Library
University of California"
19fd089807f8925b9384bae6e66cbfe7e6d318aa,Acume: A new visualization tool for understanding facial expression and gesture data,"Acume: A New Visualization Tool for
Understanding Facial Expression and Gesture
Daniel McDuff - MIT Media Lab
March 24, 2011"
193c9bd069e9457ac8650a8dfd4319bb3f4afd56,Improving Person Tracking Using an Inexpensive Thermal Infrared Sensor,"Improving Person Tracking Using an Inexpensive Thermal Infrared Sensor
Suren Kumar
Univ. of SUNY-Buffalo
Tim K. Marks
Mitsubishi Electric Research Labs
Michael Jones
Mitsubishi Electric Research Labs"
19296e129c70b332a8c0a67af8990f2f4d4f44d1,Is that you? Metric learning approaches for face identification,"Metric Learning Approaches for Face Identification
Is that you?
M. Guillaumin, J. Verbeek and C. Schmid
LEAR team, INRIA Rhˆone-Alpes, France
Supplementary Material"
19a3374ac2f917b408b4bcdca33fc9e9fd7ff260,Visual Fixation Patterns during Reciprocal Social Interaction Distinguish a Subgroup of 6-Month-Old Infants At-Risk for Autism from Comparison Infants.,"J Autism Dev Disord (2007) 37:108–121
DOI 10.1007/s10803-006-0342-4
O R I G I N A L P A P E R
Visual Fixation Patterns during Reciprocal Social Interaction
Distinguish a Subgroup of 6-Month-Old Infants At-Risk
for Autism from Comparison Infants
Noah Merin Æ Gregory S. Young Æ Sally Ozonoff Æ
Sally J. Rogers
Published online: 27 December 2006
Ó Springer Science+Business Media, LLC 2006"
19666b9eefcbf764df7c1f5b6938031bcf777191,Common and Individual Features Analysis: Beyond Canonical Correlation Analysis,"IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Group Component Analysis for Multi-block Data:
Common and Individual Feature Extraction
Guoxu Zhou, Andrzej Cichocki Fellow, IEEE, Yu Zhang, and Danilo Mandic Fellow, IEEE"
1927d86d26a1afdd4bb988b26ba89a8589675473,Traffic analysis using discrete wavelet transform and Bayesian regression,"IOSR Journal of Electronics & Communication Engineering  (IOSR-JECE)
ISSN(e) : 2278-1684  ISSN(p) : 2320-334X, PP 39-58
www.iosrjournals.org
TRAFFIC ANALYSIS USING DISCRETE WAVELET
TRANSFORM AND BAYESIAN REGRESSION
Nishidha.T, Dr. P.Janardhanan
(Electronics & Communication, KMCT College of Engineering / University of Calicut, India)
(Electronics & Communication, KMCT College of Engineering / University of Calicut, India)"
19b9583d0c1fa3ac86ac02fe5c10d8d4a59fc459,Dynamic Texture Feature Extraction Using Weber Local Descriptor,"D.G.Agrawal et al. Int. Journal of Engineering Research and Applications                    www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 3( Version 1), March 2014, pp.502-506
RESEARCH ARTICLE
OPEN ACCESS
Dynamic Texture Feature Extraction Using Weber Local
Descriptor
D.G.Agrawal*, Pranoti M. Jangale**
*(Department of Electronics and Communication Engineering, North Maharashtra University, Maharashtra-19)
**(Department of Electronics and Communication Engineering, North Maharashtra University, Maharashtra-19)"
1916a795d293aa3ddd9802ad5b5d50bb4a59b98f,Fast Multiple-Part Based Object Detection Using KD-Ferns,"Fast multiple-part based object detection using KD-Ferns
Dan Levi
Shai Silberstein
Aharon Bar-Hillel
General Motors R&D, Advanced Technical Center - Israel"
190d8bd39c50b37b27b17ac1213e6dde105b21b8,Mining Weakly Labeled Web Facial Images for Search-Based Face Annotation,"This document is downloaded from DR-NTU, Nanyang Technological
University Library, Singapore.
Title
Mining weakly labeled web facial images for search-
ased face annotation
Author(s) Wang, Dayong; Hoi, Steven C. H.; He, Ying; Zhu, Jianke
Citation
Wang, D., Hoi, S. C. H., He, Y., & Zhu, J. (2014). Mining
weakly labeled web facial images for search-based face
nnotation. IEEE Transactions on Knowledge and Data
Engineering, 26(1), 166-179.
http://hdl.handle.net/10220/18955
Rights
© 2014 IEEE. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other
uses, in any current or future media, including
reprinting/republishing this material for advertising or
promotional purposes, creating new collective works, for
resale or redistribution to servers or lists, or reuse of any
opyrighted component of this work in other works."
19766585a701749fc297a5ca6b8cdc0c62d4ba1b,A Bottom-Up Approach for Pancreas Segmentation Using Cascaded Superpixels and (Deep) Image Patch Labeling,"A Bottom-up Approach for Pancreas Segmentation using
Cascaded Superpixels and (Deep) Image Patch Labeling
Amal Faraga, Le Lua, Holger R. Rotha, Jiamin Liua, Evrim Turkbeya, Ronald M. Summersa,∗
Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes
of Health Clinical Center, Building 10 Room 1C224D, MSC 1182, Bethesda, MD 20892-1182, United States"
192e439b19824d06bb21ad6bd63cc7a55772549f,Face recognition using SURF features,"MIPPR 2009: Pattern Recognition and Computer Vision, edited by Mingyue Ding,
Bir Bhanu, Friedrich M. Wahl, Jonathan Roberts, Proc. of SPIE Vol. 7496, 749628
© 2009 SPIE · CCC code: 0277-786X/09/$18 · doi: 10.1117/12.832636
Proc. of SPIE Vol. 7496  749628-1
Downloaded from SPIE Digital Library on 22 Dec 2009 to 140.116.214.41. Terms of Use:  http://spiedl.org/terms"
197c64c36e8a9d624a05ee98b740d87f94b4040c,Regularized Greedy Column Subset Selection,"Regularized Greedy Column Subset Selection
Bruno Ordozgoiti*a, Alberto Mozoa, Jes´us Garc´ıa L´opez de Lacalleb
Department of Computer Systems, Universidad Polit´ecnica de Madrid
Department of Applied Mathematics, Universidad Polit´ecnica de Madrid"
19c53302bda8a82ec40d314a85b1713f43058a1a,Deep learning models of biological visual information processing,"Turcsány, Diána (2016) Deep learning models of
iological visual information processing. PhD thesis,
University of Nottingham.
Access from the University of Nottingham repository:
http://eprints.nottingham.ac.uk/35561/1/thesis_DianaTurcsany.pdf
Copyright and reuse:
The Nottingham ePrints service makes this work by researchers of the University of
Nottingham available open access under the following conditions.
This article is made available under the University of Nottingham End User licence and may
e reused according to the conditions of the licence.  For more details see:
http://eprints.nottingham.ac.uk/end_user_agreement.pdf
For more information, please contact"
19af008599fb17bbd9b12288c44f310881df951c,Discriminative Local Sparse Representations for Robust Face Recognition,"Discriminative Local Sparse Representations for
Robust Face Recognition
Yi Chen, Umamahesh Srinivas, Thong T. Do, Vishal Monga, and Trac D. Tran"
19841b721bfe31899e238982a22257287b9be66a,S KIP RNN : L EARNING TO S KIP S TATE U PDATES IN R ECURRENT N EURAL N ETWORKS,"Published as a conference paper at ICLR 2018
SKIP RNN: LEARNING TO SKIP STATE UPDATES IN
RECURRENT NEURAL NETWORKS
V´ıctor Campos∗†, Brendan Jou‡, Xavier Gir´o-i-Nieto§, Jordi Torres†, Shih-Fu ChangΓ
Barcelona Supercomputing Center, ‡Google Inc,
§Universitat Polit`ecnica de Catalunya, ΓColumbia University
{victor.campos,"
19158dfe2815e7f9eebc5822687e83d0a89ae147,Semantic Regularisation for Recurrent Image Annotation,[cs.CV]  16 Nov 2016
19d583bf8c5533d1261ccdc068fdc3ef53b9ffb9,FaceNet: A unified embedding for face recognition and clustering,"FaceNet: A Unified Embedding for Face Recognition and Clustering
Florian Schroff
Dmitry Kalenichenko
James Philbin
Google Inc.
Google Inc.
Google Inc."
191674c64f89c1b5cba19732869aa48c38698c84,FACE IMAGE RETRIEVAL USING ATTRIBUTE-ENHANCED SPARSE CODEWORDS,"International Journal of Advanced Technology in Engineering and Science                 www.ijates.com
Volume No.03, Issue No. 03, March 2015                                                   ISSN (online): 2348 – 7550
FACE IMAGE RETRIEVAL USING ATTRIBUTE -
ENHANCED SPARSE CODEWORDS
E.Sakthivel1 , M.Ashok kumar2
PG scholar, Communication Systems, Adhiyamaan College of Engineeing,Hosur,(India)
Asst. Prof., Electronics And Communication Engg., Adhiyamaan College of Engg.,Hosur,(India)"
19911c7e66b05d5aa28673608fdfc50ef00591dd,Recognizing Human Faces: Physical Modeling and Pattern Classification,
19a30ad283f2ab2d84f1c666d17492da14056d75,Visuomotor Coordination in Reach-To-Grasp Tasks: From Humans to Humanoids and Vice Versa,"Visuomotor Coordination in Reach-To-Grasp Tasks:
From Humans to Humanoids and Vice Versa
THÈSE NO 6695 (2015)
PRÉSENTÉE LE 4 JUIN 2015
À L’ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE
À LA FACULTÉ DES SCIENCES ET TECHNIQUES DE L'INGÉNIEUR
LABORATOIRE D'ALGORITHMES ET SYSTÈMES D'APPRENTISSAGE
À L’INSTITUTO SUPERIOR TÉCNICO (IST) DA UNIVERSIDADE DE LISBOA
INSTITUTO DE SISTEMA E ROBOTICA
PROGRAMME DOCTORAL EN GÉNIE ÉLECTRIQUE
DOUTORAMENTO EM ENGENHARIA ELECTROTÉCNICA E DE COMPUTADORES
POUR L’OBTENTION DU GRADE DE DOCTEUR ÈS SCIENCES (PhD)
Luka LUKIC
Prof. A. Billard, Prof. J. Santos-Victor, directeurs de thèse
cceptée sur proposition du jury:
Prof. J. Faria, président du jury
Prof. D. Vernon, rapporteur
Prof. E. Bicho, rapporteuse
Prof. A. Bernardino, rapporteur
Prof. G. Sandini, rapporteur"
19dc5a1156819230e6ae425e9c9d56e898d6bcb9,Comparing human and machine face recognition 1 Face Recognition Algorithms Surpass Humans,"Comparing human and machine face recognition1
Face Recognition Algorithms
Surpass Humans Matching Faces Over
Changes in Illumination
Alice J. O’TOOLE, P. Jonathon PHILLIPS, Fang JIANG, Janet AYYAD, Nils PENARD,
nd Hervé ABDI*"
198b6beb53e0e61357825d57938719f614685f75,Vaulted Verification : A Scheme for Revocable Face Recognition,"Vaulted Verification: A Scheme for Revocable Face
Recognition
Michael Wilber
University of Colorado, Colorado Springs"
19cfec264e863793dd96a5f308a3b603c6b9912e,Attention-Based Ensemble for Deep Metric Learning,"Attention-based Ensemble for
Deep Metric Learning
Wonsik Kim, Bhavya Goyal, Kunal Chawla, Jungmin Lee, Keunjoo Kwon
Samsung Research,
Samsung Electronics
{wonsik16.kim, bhavya.goyal, kunal.chawla, jm411.lee,"
197eafb6abb6b7d2813eec0891b143e27fc57386,Smile! Studying expressivity of happiness as a synergic factor in collaborative information seeking,"Smile! Studying expressivity of happiness as a synergic factor in collaborative
information seeking.
Rutgers University has made this article freely available.  Please share how this access benefits you.
Your story matters. [https://rucore.libraries.rutgers.edu/rutgers-lib/47408/story/]
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responsibility for the article. Content and layout is as set out by the author.
Citation to this Version: Shah, Chirag, González-Ibáñez, Roberto & Córdova-Rubio, Natalia. (2011). Smile! Studying
expressivity of happiness as a synergic factor in collaborative information seeking.. New Orleans
(La.). Retrieved from doi:10.7282/T3NK3GWF.
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197a3c1863c780507798c9550dd6faadeb65caaa,0 Processing and Recognising Faces in 3 D Images,",300+OPEN ACCESS BOOKS107,000+INTERNATIONALAUTHORS AND EDITORS113+ MILLIONDOWNLOADSBOOKSDELIVERED TO151 COUNTRIESAUTHORS AMONGTOP 1%MOST CITED SCIENTIST12.2%AUTHORS AND EDITORSFROM TOP 500 UNIVERSITIESSelection of our books indexed in theBook Citation Index in Web of Science™Core Collection (BKCI)Chapter from the book New Approaches to Characterization and Recognition of FacesDownloaded from: http://www.intechopen.com/books/new-approaches-to-characterization-and-recognition-of-facesPUBLISHED BYWorld's largest Science,Technology & Medicine Open Access book publisherInterested in publishing with InTechOpen?Contact us at"
197598af5be60fc75535c2f90849e60ac7122871,Fast multi-view face tracking with pose estimation,"Fast Multiview Face Tracking with Pose Estimation
Julien Meynet∗, Taner Arsan∗∗, Javier Cruz Mota∗ and Jean-Philippe Thiran∗
Ecole Polytechnique F´ed´erale de Lausanne (EPFL)
Signal Processing Institute
015 Lausanne, Switzerland
Kadir Has University
Computer Engineering Department
Istanbul 34230, Turkey
Technical report TR-ITS.2007.01
January 26, 2007"
19d4855f064f0d53cb851e9342025bd8503922e2,Learning SURF Cascade for Fast and Accurate Object Detection,"Learning SURF Cascade for Fast and Accurate Object Detection
Jianguo Li, Yimin Zhang
Intel Labs China"
19f7654f22416e6fdf430c1c873ad3e8c15e64f8,Zero-crossing based image projections encoding for eye localization,"0th European Signal Processing Conference (EUSIPCO 2012)
© EURASIP, 2012  -  ISSN 2076-1465
. INTRODUCTION"
19fb5e5207b4a964e5ab50d421e2549ce472baa8,Online emotional facial expression dictionary,"International Conference on Computer Systems and Technologies - CompSysTech’14
Online Emotional Facial Expression Dictionary
Léon Rothkrantz"
19b9e5127155730c618c0e1b41e1c723f143651d,Face Verification for Mobile Personal Devices,"Face Verification for Mobile Personal Devices
Qian Tao"
193a69489230de1013dff9af1232e5379cc5282f,Intelligent Multimodal Framework for Human Assistive Robotics Based on Computer Vision Algorithms,"Article
Intelligent Multimodal Framework for Human
Assistive Robotics Based on Computer
Vision Algorithms
Eugenio Ivorra 1,*
Luis Daniel Lledó 2, Nicolás Garcia-Aracil 2
, Mario Ortega 1, José M. Catalán 2,*
nd Mariano Alcañiz 1
, Santiago Ezquerro 2,
Institute for Research and Innovation in Bioengineering, Universitat Politècnica de València,
6022 Valencia, Spain; (M.O.); (M.A.)
Biomedical Neuroengineering Group, Universidad Miguel Hernández de Elche, 03202 Elche, Spain;
(S.E.); (L.D.L.); (N.G.-A.)
* Correspondence: or (E.I.); (J.M.C.)
Received: 4 July 2018; Accepted: 23 July 2018; Published: 24 July 2018"
194ea22b54f9aee6e0eb5d0dee100d46438b3cea,"Structured Tracking for Safety , Security , and Privacy : Algorithms for Fusing Noisy Estimates from Sensor , Robot , and Camera Networks","Structured Tracking for Safety, Security, and Privacy:
Algorithms for Fusing Noisy Estimates from Sensor,
Robot, and Camera Networks
Jeremy Ryan Schiff
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2009-104
http://www.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-104.html
July 23, 2009"
19f4020db2a37102ec3236ff72d8c7d3a0992ef9,Face Recognition by Kernel Independent Component Analysis,"Face Recognition by Kernel Independent Component
Analysis
T. Martiriggiano, M. Leo, T. D’Orazio, A. Distante,
CNR- ISSIA via Amendoda 122/D-I
70126 BARI ITALY
Abtract.  In  this  paper,  we  introduce  a  new  feature  representation  method  for
face recognition. The proposed method, referred as Kernel ICA, combines the
strengths of the Kernel and Independent Component Analysis approaches. For
performing Kernel ICA, we employ an algorithm developed by F. R. Bach and
M.  I.  Jordan.  This  algorithm  has  proven  successful  for  separating  randomly
mixed auditory signals, but it has never been applied on bidimensional signals
such as images. We compare the performance of Kernel ICA with classical al-
gorithms  such  as  PCA  and  ICA  within  the  context  of  appearance-based  face
recognition problem using the FERET database. Experimental results show that
oth  Kernel ICA and ICA representations are superior to representations based
on PCA for recognizing faces across days and changes in expressions.
Introduction
Face recognition has become one of most important biometrics technologies during
the past 20 years. It has a wide range of applications such as identity authentication,
ccess control, and surveillance."
e1966f234ad3f30302af3ddea70ed9eb5dcbe120,Entropy-Based Localization of Textured Regions,"Entropy-based Localization of Textured Regions
Liliana Lo Presti and Marco La Cascia
University of Palermo"
e17783170ecc48253fa16123a041ae298184f4ff,Graph Embedding Algorithms Based on Neighborhood Discriminant Embedding for Face Recognition,"International Journal of Computer Information Systems and Industrial Management Applications.
ISSN 2150-7988 Volume 4 (2012) pp. 374–382
(cid:13) MIR Labs, www.mirlabs.net/ijcisim/index.html
Graph Embedding Algorithms Based on
Neighborhood Discriminant Embedding for Face
Recognition
Dexing Zhong1,2, Jiuqiang Han1, Yongli Liu1 and Shengbin Li2
Ministry of Education Key Lab for Intelligent Networks and Network Security, Xi’an Jiaotong University,
8 Xianning West Road, Xian, 710049 P. R. China
State Key Laboratory of Ministry of Health for Forensic Sciences, Xian Jiaotong University,
76 Yanta West Road, Xian, 710061 P. R. China"
e1e5d64318ec0a493995fb83ef4f433ddde82e77,1 INTROVERSION / EXTRAVERSION AFFECTS THE GAZE-CUEING EFFECT,"(cid:5)(cid:36)(cid:57)(cid:50)(cid:44)(cid:39)(cid:44)(cid:49)(cid:42)(cid:3)(cid:50)(cid:53)(cid:3)(cid:36)(cid:51)(cid:51)(cid:53)(cid:50)(cid:36)(cid:38)(cid:43)(cid:44)(cid:49)(cid:42)(cid:3)(cid:40)(cid:60)(cid:40)(cid:54)(cid:5)(cid:34)(cid:3)(cid:44)(cid:49)(cid:55)(cid:53)(cid:50)(cid:57)(cid:40)(cid:53)(cid:54)(cid:44)(cid:50)(cid:49)(cid:18)(cid:40)(cid:59)(cid:55)(cid:53)(cid:36)(cid:57)(cid:40)(cid:53)(cid:54)(cid:44)(cid:50)(cid:49)
(cid:36)(cid:41)(cid:41)(cid:40)(cid:38)(cid:55)(cid:54)(cid:3)(cid:55)(cid:43)(cid:40)(cid:3)(cid:42)(cid:36)(cid:61)(cid:40)(cid:16)(cid:38)(cid:56)(cid:40)(cid:44)(cid:49)(cid:42)(cid:3)(cid:40)(cid:41)(cid:41)(cid:40)(cid:38)(cid:55)
(cid:16)(cid:16)(cid:48)(cid:68)(cid:81)(cid:88)(cid:86)(cid:70)(cid:85)(cid:76)(cid:83)(cid:87)(cid:3)(cid:39)(cid:85)(cid:68)(cid:73)(cid:87)(cid:16)(cid:16)
(cid:38)(cid:82)(cid:74)(cid:81)(cid:76)(cid:87)(cid:76)(cid:89)(cid:72)(cid:3)(cid:51)(cid:85)(cid:82)(cid:70)(cid:72)(cid:86)(cid:86)(cid:76)(cid:81)(cid:74)
(cid:3)
(cid:3)
(cid:48)(cid:68)(cid:81)(cid:88)(cid:86)(cid:70)(cid:85)(cid:76)(cid:83)(cid:87)(cid:3)(cid:49)(cid:88)(cid:80)(cid:69)(cid:72)(cid:85)(cid:29)
(cid:41)(cid:88)(cid:79)(cid:79)(cid:3)(cid:55)(cid:76)(cid:87)(cid:79)(cid:72)(cid:29)
(cid:36)(cid:85)(cid:87)(cid:76)(cid:70)(cid:79)(cid:72)(cid:3)(cid:55)(cid:92)(cid:83)(cid:72)(cid:29)
(cid:46)(cid:72)(cid:92)(cid:90)(cid:82)(cid:85)(cid:71)(cid:86)(cid:29)
(cid:38)(cid:82)(cid:85)(cid:85)(cid:72)(cid:86)(cid:83)(cid:82)(cid:81)(cid:71)(cid:76)(cid:81)(cid:74)(cid:3)(cid:36)(cid:88)(cid:87)(cid:75)(cid:82)(cid:85)(cid:29)
(cid:38)(cid:82)(cid:85)(cid:85)(cid:72)(cid:86)(cid:83)(cid:82)(cid:81)(cid:71)(cid:76)(cid:81)(cid:74)(cid:3)(cid:36)(cid:88)(cid:87)(cid:75)(cid:82)(cid:85)(cid:3)(cid:54)(cid:72)(cid:70)(cid:82)(cid:81)(cid:71)(cid:68)(cid:85)(cid:92)
(cid:44)(cid:81)(cid:73)(cid:82)(cid:85)(cid:80)(cid:68)(cid:87)(cid:76)(cid:82)(cid:81)(cid:29)
(cid:38)(cid:82)(cid:85)(cid:85)(cid:72)(cid:86)(cid:83)(cid:82)(cid:81)(cid:71)(cid:76)(cid:81)(cid:74)(cid:3)(cid:36)(cid:88)(cid:87)(cid:75)(cid:82)(cid:85)(cid:10)(cid:86)(cid:3)(cid:44)(cid:81)(cid:86)(cid:87)(cid:76)(cid:87)(cid:88)(cid:87)(cid:76)(cid:82)(cid:81)(cid:29)
(cid:38)(cid:82)(cid:85)(cid:85)(cid:72)(cid:86)(cid:83)(cid:82)(cid:81)(cid:71)(cid:76)(cid:81)(cid:74)(cid:3)(cid:36)(cid:88)(cid:87)(cid:75)(cid:82)(cid:85)(cid:10)(cid:86)(cid:3)(cid:54)(cid:72)(cid:70)(cid:82)(cid:81)(cid:71)(cid:68)(cid:85)(cid:92)
(cid:44)(cid:81)(cid:86)(cid:87)(cid:76)(cid:87)(cid:88)(cid:87)(cid:76)(cid:82)(cid:81)(cid:29)
(cid:41)(cid:76)(cid:85)(cid:86)(cid:87)(cid:3)(cid:36)(cid:88)(cid:87)(cid:75)(cid:82)(cid:85)(cid:29)
(cid:41)(cid:76)(cid:85)(cid:86)(cid:87)(cid:3)(cid:36)(cid:88)(cid:87)(cid:75)(cid:82)(cid:85)(cid:3)(cid:54)(cid:72)(cid:70)(cid:82)(cid:81)(cid:71)(cid:68)(cid:85)(cid:92)(cid:3)(cid:44)(cid:81)(cid:73)(cid:82)(cid:85)(cid:80)(cid:68)(cid:87)(cid:76)(cid:82)(cid:81)(cid:29)
(cid:50)(cid:85)(cid:71)(cid:72)(cid:85)(cid:3)(cid:82)(cid:73)(cid:3)(cid:36)(cid:88)(cid:87)(cid:75)(cid:82)(cid:85)(cid:86)(cid:29)
(cid:50)(cid:85)(cid:71)(cid:72)(cid:85)(cid:3)(cid:82)(cid:73)(cid:3)(cid:36)(cid:88)(cid:87)(cid:75)(cid:82)(cid:85)(cid:86)(cid:3)(cid:54)(cid:72)(cid:70)(cid:82)(cid:81)(cid:71)(cid:68)(cid:85)(cid:92)(cid:3)(cid:44)(cid:81)(cid:73)(cid:82)(cid:85)(cid:80)(cid:68)(cid:87)(cid:76)(cid:82)(cid:81)(cid:29)"
e151c99b5e55bfc03047a2c6c2118cd9e4ad829b,Perspectives on Deep Multimodel Robot Learning,"Perspectives on Deep Multimodel
Robot Learning
Wolfram Burgard, Abhinav Valada, Noha Radwan, Tayyab Naseer, Jingwei Zhang,
Johan Vertens, Oier Mees, Andreas Eitel and Gabriel Oliveira"
e19ebad4739d59f999d192bac7d596b20b887f78,Learning Gating ConvNet for Two-Stream based Methods in Action Recognition,"Learning Gating ConvNet for Two-Stream based Methods in Action
Recognition
Jiagang Zhu1,2, Wei Zou1, Zheng Zhu1,2"
e1cb5ff731dfb84ee46d8469c68964b7c4c0f3ea,Hierarchical Long Short-Term Concurrent Memory for Human Interaction Recognition.,"SUBMISSION FOR IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2018
Hierarchical Long Short-Term Concurrent Memory for Human
Interaction Recognition
Xiangbo Shu, Jinhui Tang, Senior Member, IEEE, Guo-Jun Qi, Wei Liu and Jian Yang"
e16311bf88192e1588826984d6761f7f26efe542,Image Segmentation Based Face Recognition Using Enhanced SPCA-KNN Method,"Image Segmentation Based Face Recognition
Using Enhanced SPCA-KNN Method
Mrs.J.Savitha M.Sc.,M.Phil.,
Ph.D Research Scholar,Karpagam University,
Coimbatore, Tamil Nadu, India.
Dr.A.V.Senthil Kumar.,
Director, Hindustan College of Arts and Science,
Coimbatore, Tamil Nadu, India."
e1cb110c45c4416f7aff490db2674abe1460259e,Hard-Aware Point-to-Set Deep Metric for Person Re-identification,"Hard-AwarePoint-to-SetDeepMetricforPersonRe-identificationRuiYu1,ZhiyongDou1,SongBai1,ZhaoxiangZhang2,YongchaoXu1(),andXiangBai1("
e10662a59b5f8e1f5684409023f11ca727647320,Performance Evaluation of Deep Learning Networks for Semantic Segmentation of Traffic Stereo-Pair Images,"Performance Evaluation of Deep Learning Networks for
Semantic Segmentation of Traffic Stereo-Pair Images
Vlad Taran, Nikita Gordienko, Yuriy Kochura, Yuri Gordienko, Alexandr Rokovyi, Oleg
Alienin, Sergii Stirenko
National Technical University of Ukraine ""Igor Sikorsky Kyiv Polytechnic Institute"",
Kyiv, Ukraine
Semantic  image  segmentation  is  one  the  most  demanding  task,  especially  for  analysis  of  traffic  conditions
for  self-driving  cars.  Here  the  results  of  application  of  several  deep  learning  architectures  (PSPNet  and
ICNet)  for  semantic  image  segmentation  of  traffic  stereo-pair  images  are  presented.  The  images  from
Cityscapes  dataset  and  custom  urban  images  were  analyzed  as  to  the  segmentation  accuracy  and  image
inference time. For the models pre-trained on Cityscapes dataset,  the inference time was equal in the limits
of  standard  deviation,  but  the  segmentation  accuracy  was  different  for  various  cities  and  stereo  channels
even. The distributions of accuracy (mean intersection over union — mIoU) values for each city and channel
re asymmetric, long-tailed, and have many extreme outliers, especially for PSPNet network in comparison
to  ICNet  network.  Some  statistical  properties  of  these  distributions  (skewness,  kurtosis)  allow  us  to
distinguish these two networks and open the question about relations between architecture of deep learning
networks and statistical distribution of the predicted results (mIoU here). The results obtained demonstrated
the  different  sensitivity  of  these  networks  to:  (1)  the  local  street  view  peculiarities  in  different  cities  that
should be  taken  into  account during  the  targeted  fine  tuning  the  models  before  their  practical  applications,
(2) the right and left data channels in stereo-pairs. For both networks, the difference in the predicted results"
e181aca6e4b7142d2254a93477170e75c335d616,A Combined SIFT / SURF Descriptor for Automatic Face Recognition,"A Combined SIFT/SURF Descriptor for Automatic Face Recognition
Ladislav Lenc, Pavel Král
Dept. of Computer Science & Engineering
Faculty of Applied Sciences
University of West Bohemia
Plzeň, Czech Republic
NTIS - New Technologies for the Information Society
Faculty of Applied Sciences
University of West Bohemia
Plzeň, Czech Republic"
e1cee76d2f9120e603b8c7fc586e6c346cf6476f,Automatic Detection and Recognition of Man-made Objects in High Resolution Remote Sensing Images Using Hierarchical Semantic Graph Model,"International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences,
Volume XL-1/W1, ISPRS Hannover Workshop 2013, 21 – 24 May 2013, Hannover, Germany"
e1d726d812554f2b2b92cac3a4d2bec678969368,Human Action Recognition Bases on Local Action Attributes,"J Electr Eng Technol.2015; 10(?): 30-40
http://dx.doi.org/10.5370/JEET.2015.10.2.030
ISSN(Print)
975-0102
ISSN(Online)  2093-7423
Human Action Recognition Bases on Local Action Attributes
Jing  Zhang*, Hong Liu*, Weizhi Nie† Lekha Chaisorn**, Yongkang Wong**
nd Mohan S Kankanhalli**"
e1fb8ab53996f06e9a35de6b553333bd6279bcbd,Learning Multilayer Channel Features for Pedestrian Detection,"Learning Multilayer Channel Features for
Pedestrian Detection
Jiale Cao, Yanwei Pang, and Xuelong Li"
e1e5d903887f8e1c412fab041726c4b34ffa820a,Failing to Learn: Autonomously Identifying Perception Failures for Self-Driving Cars,"Failing to Learn: Autonomously Identifying
Perception Failures for Self-driving Cars
Manikandasriram Srinivasan Ramanagopal1, Cyrus Anderson1, Ram Vasudevan2 and Matthew Johnson-Roberson3"
e1e6e6792e92f7110e26e27e80e0c30ec36ac9c2,Ranking with Adaptive Neighbors,"TSINGHUA SCIENCE AND TECHNOLOGY
ISSNll1007-0214
0?/?? pp???–???
DOI: 10.26599/TST.2018.9010000
Volume 1, Number 1, Septembelr 2018
Ranking with Adaptive Neighbors
Muge Li, Liangyue Li, and Feiping Nie∗"
e1c0beb01462d37a77c34909a02a29725c187f5e,GA-fisher: a new LDA-based face recognition algorithm with selection of principal components,"IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 35, NO. 5, OCTOBER 2005
GA-Fisher: A New LDA-Based Face Recognition
Algorithm With Selection of Principal Components
Wei-Shi Zheng, Jian-Huang Lai, and Pong C. Yuen"
e163118b4a5b8016754134215433eee1f2c0065a,3-D Shape Matching for Face Analysis and Recognition,"-D Shape Matching for Face Analysis and Recognition
Wei Quan, Bogdan J. Matuszewski and Lik-Kwan Shark
Robotics and Computer Vision Research Laboratory, Applied Digital Signal and Image Processing (ADSIP) Research
Centre, University of Central Lancashire, Preston PR1 2HE, U.K.
Keywords:
Face  Recognition,  Shape  Matching  and  Modelling,  Isometric  Embedding  Representation,  Non-Rigid
Deformation Registration."
e1e2b6a8944a4e6f195b6f7371ee9e6b0684ae6b,Generating Personalized Virtual Agent in Speech Dialogue System for People with Dementia,"Generating Personalized Virtual Agent
in Speech Dialogue System for People
with Dementia
Shota Nakatani1(B), Sachio Saiki1, Masahide Nakamura1, and Kiyoshi Yasuda2
Graduate School of System Informatics Kobe University,
-1 Rokkodai, Nada, Kobe, Japan
Chiba Rosai Hospital, 2-16 Tatsumidai-higashi, Ichihara, Japan"
e135f8118145b6a2e2a6a2088c04c26ca6d38642,Dynamic Biometrics Fusion at Feature Level for Video-Based Human Recognition,
e1f794bacd01eecb623bead652bdc9f86e17944e,Affective Environment for Java Programming Using Facial and EEG Recognition,"Affective Environment for Java Programming
Using Facial and EEG Recognition
María Lucía Barrón-Estrada, Ramón Zatarain-Cabada, Claudia Guadalupe
Aispuro-Gallegos, Catalina de la Luz Sosa-Ochoa, Mario Lindor-Valdez
Instituto Tecnológico de Culiacán, Culiacán, Sinaloa,
Mexico
{lbarron, rzatarain, m03171007, m07170739,"
e19b60e5b8083828285a2baa781ceaad27f6353c,The accuracy and value of machine-generated image tags: design and user evaluation of an end-to-end image tagging system,"The Accuracy and Value of Machine-Generated Image Tags
Design and User Evaluation of an End-to-End Image Tagging System
Lexing Xie, Apostol Natsev, Matthew Hill, John R. Smith
IBM Watson Research Center, Hawthorne, NY, USA
{xlx, natsev, mh,
Alex Phillips
IBM Global Business Services, United Kingdom"
e1e1b3683ac278386cf1569e97f9aced0923f4a0,Hyperdrive: A Systolically Scalable Binary-Weight CNN Inference Engine for mW IoT End-Nodes,"Hyperdrive: A Systolically Scalable Binary-Weight
CNN Inference Engine for mW IoT End-Nodes
Renzo Andri∗, Lukas Cavigelli∗, Davide Rossi†, Luca Benini∗†
Integrated Systems Laboratory, ETH Zurich, Zurich, Switzerland
DEI, University of Bologna, Bologna, Italy"
e18cc09c3d3d79df6cd40ea5cf13ad40eacb8a73,Visual Transfer Learning: Informal Introduction and Literature Overview,"Visual Transfer Learning: Informal Introduction
nd Literature Overview
Erik Rodner
University of Jena, Germany
August 2011"
e1371af87f6d5e22ef6d8c5f9977f5e924f176f6,Bidirectional Retrieval Made Simple Jônatas Wehrmann,"Bidirectional Retrieval Made Simple
Jˆonatas Wehrmann
School of Technology
Rodrigo C. Barros
School of Technology
Pontif´ıcia Universidade Cat´olica
Pontif´ıcia Universidade Cat´olica
do Rio Grande do Sul
do Rio Grande do Sul"
e1e1ae77cf37855ddc3493ac240551c28cfc5f7e,Face Detection with Skin Color Segmentation & Reorganization using Genetic Algorithm,"Advance in Electronic and Electric Engineering.
ISSN 2231-1297, Volume 3, Number 9 (2013), pp. 1197-1208
© Research India Publications
http://www.ripublication.com/aeee.htm
Face Detection with Skin Color Segmentation&
Reorganization using Genetic Algorithm
Mr.Tushar Gajame and 2Prof. C.L. Chandrakar
M.E. Scholar, Electronics Dept, S.S.G.I, BHILAI (C.G.), INDIA.
Associate, Prof. (E & I), S.S.G.I, BHILAI (C.G.), INDIA."
b9fb66f09b358a4ce167b54eed8c596772a392d9,Modal Regression based Atomic Representation for Robust Face Recognition,"Modal Regression based Atomic Representation for
Robust Face Recognition
Yulong Wang, Yuan Yan Tang, Life Fellow, IEEE, Luoqing Li, and Hong Chen"
b98aec5bbe7116fa3ae5f9b4d77cb1f1141eaabd,Appearance-Based 3D Upper-Body Pose Estimation and Person Re-identification on Mobile Robots,"Appearance-Based 3D Upper-Body Pose Estimation
nd Person Re-Identification on Mobile Robots
Christoph Weinrich, Michael Volkhardt, Horst-Michael Gross
Neuroinformatics and Cognitive Robotics Lab
Ilmenau University of Technology
Ilmenau, Germany"
b95acfe00686cc6f6526fcd1f30b6f38061d3a29,Revisiting Multiple-Instance Learning Via Embedded Instance Selection,"Revisiting Multiple-Instance Learning via
Embedded Instance Selection
James Foulds and Eibe Frank
Department of Computer Science, University of Waikato, New Zealand"
b97f694c2a111b5b1724eefd63c8d64c8e19f6c9,Group Affect Prediction Using Multimodal Distributions,"Group Affect Prediction Using Multimodal Distributions
Saqib Nizam Shamsi
Aspiring Minds
Bhanu Pratap Singh
Univeristy of Massachusetts, Amherst
Manya Wadhwa
Johns Hopkins University"
b9f2a755940353549e55690437eb7e13ea226bbf,Unsupervised Feature Learning from Videos for Discovering and Recognizing Actions,"Unsupervised Feature Learning from Videos for Discovering and Recognizing Actions
Carolina Redondo-Cabrera
Roberto J. López-Sastre"
b941d4a85be783a6883b7d41c1afa7a9db451831,Radiofrequency ablation planning for cardiac arrhythmia treatment using modeling and machine learning approaches,"Radiofrequency ablation planning for cardiac
rrhythmia treatment using modeling and machine
learning approaches
Roc´ıo Cabrera Lozoya
To cite this version:
Roc´ıo Cabrera Lozoya. Radiofrequency ablation planning for cardiac arrhythmia treatment
using modeling and machine learning approaches. Other. Universit´e Nice Sophia Antipolis,
015. English. <NNT : 2015NICE4059>. <tel-01206478v2>
HAL Id: tel-01206478
https://tel.archives-ouvertes.fr/tel-01206478v2
Submitted on 15 Dec 2015
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rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
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L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,"
b9bd9cab426f4d4a0b0d0077f6d9dca2ec01ce3c,Propositionalisation of Multi-instance Data Using Random Forests,"Propositionalisation of Multi-instance Data
using Random Forests
Eibe Frank and Bernhard Pfahringer
Department of Computer Science, University of Waikato"
b971266b29fcecf1d5efe1c4dcdc2355cb188ab0,On the Reconstruction of Face Images from Deep Face Templates.,"MAI et al.: ON THE RECONSTRUCTION OF FACE IMAGES FROM DEEP FACE TEMPLATES
On the Reconstruction of Face Images from
Deep Face Templates
Guangcan Mai, Kai Cao, Pong C. Yuen∗, Senior Member, IEEE, and Anil K. Jain, Life Fellow, IEEE"
b9a0aff228a697b87d89f5dfdfacc0dc9ac28fdb,A Deep Spatial Contextual Long-Term Recurrent Convolutional Network for Saliency Detection,"A Deep Spatial Contextual Long-term Recurrent
Convolutional Network for Saliency Detection
Nian Liu and Junwei Han, Senior Member, IEEE"
b92a057606a47eb7de6ecc180e4dbf53c4a8d4b7,Face Recognition Based on 2D and 3D Features,"Face Recognition Based on 2D and 3D Features
Stefano Arca, Ra(cid:11)aella Lanzarotti, and Giuseppe Lipori
Dipartimento di Scienze dell’Informazione
Universit(cid:18)a degli Studi di Milano
Via Comelico, 39/41 20135 Milano, Italy
farca, lanzarotti,"
b9128ff3b0b96815ff41a7d5fb2b4bef69f635ca,Deconvolutional Feature Stacking for Weakly-Supervised Semantic Segmentation,"Deconvolutional Feature Stacking for
Weakly-Supervised Semantic Segmentation
Hyo-Eun Kim and Sangheum Hwang
Lunit Inc., Seoul, South Korea
{hekim,"
b9e82ee9bb4cf016b5ed44b7acd2b42e1a5a6be2,Face recognition by applying wavelet subband representation and kernel associative memory,"Face Recognition by Applying Wavelet Subband
Representation and Kernel Associative Memory
Bai-Ling Zhang, Haihong Zhang, and Shuzhi Sam Ge, Senior Member, IEEE"
b9f8c8db31ab3882f513246b8d39386cea8cf764,Near Real-Time Object Recognition for Pepper based on Deep Neural Networks Running on a Backpack,"Near Real-Time Object Recognition for Pepper
ased on Deep Neural Networks Running on a
Backpack
Esteban Reyes1, Cristopher G´omez1, Esteban Norambuena1 and Javier
Ruiz-del-Solar1,2
Department of Electrical Engineering, Universidad de Chile
Advanced Mining Technology Center, Universidad de Chile"
b92175bf063bd73cabe8b222268c153e4466a82a,Background Subtraction with Dirichlet Process Mixture Models,"Background Subtraction with
Dirichlet Process Mixture Models
Tom S. F. Haines and Tao Xiang"
b955969e1077ca328018c9e4dcf27b87ed9f5076,Knowing When to Look: Adaptive Attention via a Visual Sentinel for Image Captioning,"Knowing When to Look: Adaptive Attention via
A Visual Sentinel for Image Captioning
Jiasen Lu2∗†, Caiming Xiong1†, Devi Parikh3, Richard Socher1
Salesforce Research, 2Virginia Tech, 3Georgia Institute of Technology
{cxiong,"
b9cad920a00fc0e997fc24396872e03f13c0bb9c,Face liveness detection under bad illumination conditions,"FACE LIVENESS DETECTION UNDER BAD ILLUMINATION CONDITIONS
Bruno Peixoto, Carolina Michelassi, and Anderson Rocha
University of Campinas (Unicamp)
Campinas, SP, Brazil"
b9953824b3d4cd2be77ecbc5db3f7dec3dfa031e,Guided Attention for Large Scale Scene Text Verification,"Large Scale Scene Text Verification with Guided
Attention
Dafang He1(cid:63), Yeqing Li2∗, Alexander Gorban2, Derrall Heath2, Julian Ibarz2,
Qian Yu2, Daniel Kifer1, C. Lee Giles1
The Pennsylvania State University1, Google Inc2."
b9696bdba6e16959258bad17ce26e6a643be5faf,Using Photometric Stereo for Face Recognition,"International Journal of Bio-Science and Bio-Technology
Vol. 3, No. 3, September, 2011
Using Photometric Stereo for Face Recognition
Gary A. Atkinson and Melvyn L. Smith
University of the West of England, Bristol, BS16 1QY, UK"
b9cedd1960d5c025be55ade0a0aa81b75a6efa61,Inexact Krylov Subspace Algorithms for Large Matrix Exponential Eigenproblem from Dimensionality Reduction,"INEXACT KRYLOV SUBSPACE ALGORITHMS FOR LARGE
MATRIX EXPONENTIAL EIGENPROBLEM FROM
DIMENSIONALITY REDUCTION
GANG WU∗, TING-TING FENG† , LI-JIA ZHANG‡ , AND MENG YANG§"
b94e57ee9278f06c65a96ce1b586cb7a5b2b7fbb,Group Re-identification via Unsupervised Transfer of Sparse Features Encoding,"Group Re-Identification via
Unsupervised Transfer of Sparse Features Encoding
Giuseppe Lisanti∗,1, Niki Martinel∗,2, Alberto Del Bimbo1 and Gian Luca Foresti2
MICC - University of Firenze, Italy
AViReS Lab - University of Udine, Italy"
b9d0774b0321a5cfc75471b62c8c5ef6c15527f5,Fishy Faces: Crafting Adversarial Images to Poison Face Authentication,"Fishy Faces: Crafting Adversarial Images to Poison Face Authentication
Giuseppe Garofalo
Vera Rimmer
Tim Van hamme
imec-DistriNet, KU Leuven
imec-DistriNet, KU Leuven
imec-DistriNet, KU Leuven
Davy Preuveneers
Wouter Joosen
imec-DistriNet, KU Leuven
imec-DistriNet, KU Leuven"
b908edadad58c604a1e4b431f69ac8ded350589a,Deep Face Feature for Face Alignment,"Deep Face Feature for Face Alignment
Boyi Jiang, Juyong Zhang, Bailin Deng, Yudong Guo and Ligang Liu"
40377a1bc15a9ec28ea54cc53d5cf0699365634f,Строительство автомобильных дорог на основе 3D-моделей,"НЕКООПЕРАТИВНАЯ БИОМЕТРИЧЕСКАЯ ИДЕНТИФИКАЦИЯ ПО 3D-
МОДЕЛЯМ ЛИЦА С ИСПОЛЬЗОВАНИЕМ ВИДЕОКАМЕР ВЫСОКОГО
РАЗРЕШЕНИЯ
А.И. Манолов,  А.Ю. Соколов,  О.В. Степаненко, А.C. Тумачек, А.В.Тяхт, А. К. Цискаридзе,
Д.Н. Заварикин, А.А. Кадейшвили,
Компания Vocord
Аннотация
Получены результаты по распознаванию лиц, основанные
на   3D  реконструкции   без   использования   какой-либо
структурированной подсветки. 3D реконструкция основана
на   использовании   камер   высокого   разрешения.
Вероятность распознавания составляет 92-98%.
Ключевые   слова:   3D  реконструкция,   3D  распознавание
. ВВЕДЕНИЕ
Системам распознавания лиц, основанным на двумерных
изображениях,  присущи  определенные  недостатки. Такие
системы   чувствительны   к   изменениям   яркости.   Свет,
собранный   с   лица,   является   функцией   геометрии   лица,
отражательной   способности   лица,   свойствами   источника
света и свойствами камеры. С учетом этого, сложно создать"
4071778aef122d2ba9f2525a56e375e072a4b186,Questioning the assumptions behind fairness solutions,"Questioning the assumptions behind fairness solutions∗
Rebekah Overdorf
Bogdan Kulynych
EPFL SPRING Lab
Ero Balsa
imec-COSIC KU Leuven
Carmela Troncoso
EPFL SPRING Lab
Seda G¨urses
imec-COSIC KU Leuven"
40d4fab85e2e1557e61d03b92429d64c6efba101,Detection-based multi-human tracking using a CRF model,"Detection-Based Multi-Human Tracking Using a CRF Model
Alexandre Heili1,2
Jean-Marc Odobez1,2
Idiap Research Institute – CH-1920 Martigny, Switzerland
Cheng Chen1
´Ecole Polytechnique F´ed´erale de Lausanne – CH-1015, Lausanne, Switzerland"
40b86ce698be51e36884edcc8937998979cd02ec,Finding Faces in News Photos Using Both Face and Name Information,"Yüz ve İsim İlişkisi kullanarak Haberlerdeki Kişilerin Bulunması
Finding Faces in News Photos Using Both Face and Name Information
Derya Ozkan, Pınar Duygulu
Bilgisayar Mühendisliği Bölümü, Bilkent Üniversitesi, 06800, Ankara
Özetçe
Bu  çalışmada,  haber  fotoğraflarından  oluşan  geniş  veri
kümelerinde  kişilerin  sorgulanmasını  sağlayan  bir  yöntem
sunulmuştur.  Yöntem  isim  ve  yüzlerin  ilişkilendirilmesine
dayanmaktadır.  Haber  başlığında  kişinin  ismi  geçiyor  ise
fotoğrafta da o kişinin yüzünün bulunacağı  varsayımıyla, ilk
olarak  sorgulanan  isim  ile  ilişkilendirilmiş,  fotoğraflardaki
tüm yüzler seçilir. Bu yüzler arasında sorgu kişisine ait farklı
koşul,  poz  ve  zamanlarda  çekilmiş  pek  çok  resmin  yanında,
haberde ismi geçen başka kişilere ait yüzler ya da kullanılan
yüz  bulma  yönteminin  hatasından  kaynaklanan  yüz  olmayan
resimler de bulunabilir. Yine de, çoğu  zaman, sorgu kişisine
it resimler daha çok olup, bu resimler birbirine diğerlerine
olduğundan  daha  çok  benzeyeceklerdir.  Bu  nedenle,  yüzler
rasındaki  benzerlikler  çizgesel  olarak  betimlendiğinde  ,
irbirine en çok benzeyen yüzler bu çizgede en yoğun bileşen"
4003b21fb29585ae55db154a5b3fa9945b1af88e,AN IMPROVED DOUBLE CODING LOCAL BINARY PATTERN ALGORITHM FOR FACE RECOGNITION,"IJRET: International Journal of Research in Engineering and Technology        eISSN: 2319-1163 | pISSN: 2321-7308
AN IMPROVED DOUBLE CODING LOCAL BINARY PATTERN
ALGORITHM FOR FACE RECOGNITION
Saurabh Asija1, Rakesh Singh2
Research Scholar (Computer Engineering Department), Punjabi University, Patiala.
Asst. Professor (Computer Engineering Department), Punjabi University, Patiala."
400aa5cb2fec558f7827c3638993bae34752ff31,Assessing post-detection filters for a generic pedestrian detector in a tracking-by-detection scheme,"(cid:13)2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including
reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,
or reuse of any copyrighted component of this work in other works.
Assessing Post-Detection Filters for a Generic Pedestrian Detector in a
Tracking-By-Detection Scheme
Volker Eiselein, Erik Bochinski and Thomas Sikora
Communication Systems Group, Technische Universit¨at Berlin"
40be718f23c163f12f88384a9ceb703578f89af4,Itinerary Recommendation for Cruises: User Study,"Itinerary Recommendation for Cruises: User Study
Diana Nurbakova, L´ea Laporte, Sylvie Calabretto, J´erˆome Gensel
To cite this version:
Diana Nurbakova, L´ea Laporte, Sylvie Calabretto, J´erˆome Gensel.
Itinerary Recommenda-
tion for Cruises: User Study. Julia Neidhardt; Daniel Fesenmaier; Tsvi Kuflik; Wolfgang
W¨orndl. RecTour 2017: 2nd Workshop on Recommenders in Tourism, Aug 2017, Como, Italy.
Proceedings of the 2nd Workshop on Recommenders in Tourism co-located with 11th ACM
Conference on Recommender Systems (RecSys 2017). Como, Italy, August 27, 2017. Vol-1906
(urn:nbn:de:0074-1906-6), pp.31-34, 2017, CEUR Workshop Proceedings. .
HAL Id: hal-01577228
https://hal.archives-ouvertes.fr/hal-01577228
Submitted on 25 Aug 2017
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
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broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est"
40a63746a710baf4a694fd5a4dd8b5a3d9fc2846,Invertible Conditional GANs for image editing,"Invertible Conditional GANs for image editing
Guim Perarnau, Joost van de Weijer, Bogdan Raducanu
Computer Vision Center
Barcelona, Spain
Jose M. Álvarez
Data61 CSIRO
Canberra, Australia"
40248cd4a742cb33c14e835fe6b847ad3f8d5b96,Learning View-Specific Deep Networks for Person Re-Identification,"Learning View-Specific Deep Networks for Person
Re-Identification
Zhanxiang Feng, Jianhuang Lai, and Xiaohua Xie"
4026dc62475d2ff2876557fc2b0445be898cd380,An affective user interface based on facial expression recognition and eye-gaze tracking,"An Affective User Interface Based on Facial Expression
Recognition and Eye-Gaze Tracking
Soo-Mi Choi and Yong-Guk Kim
School of Computer Engineering, Sejong University, Seoul, Korea"
40010e1918e1f342b14c8ec74e570101f07471b2,Flower Categorization using Deep Convolutional Neural Networks,"Flower Categorization using Deep Convolutional Neural Networks
Ayesha Gurnani
Viraj Mavani
Vandit Gajjar
Yash Khandhediya
L. D. College of Engineering
L. D. College of Engineering
L. D. College of Engineering
L. D. College of Engineering"
40fb4e8932fb6a8fef0dddfdda57a3e142c3e823,A mixed generative-discriminative framework for pedestrian classification,"A Mixed Generative-Discriminative Framework for Pedestrian Classification
Markus Enzweiler1
Dariu M. Gavrila2,3
Image & Pattern Analysis Group, Dept. of Math. and Comp. Sc., Univ. of Heidelberg, Germany
Environment Perception, Group Research, Daimler AG, Ulm, Germany
Intelligent Systems Lab, Faculty of Science, Univ. of Amsterdam, The Netherlands"
40389b941a6901c190fb74e95dc170166fd7639d,Automatic Facial Expression Recognition,"Automatic Facial Expression Recognition
Jacob Whitehill, Marian Stewart Bartlett, and Javier R. Movellan
Emotient
http://emotient.com
February 12, 2014
Imago animi vultus est, indices oculi. (Cicero)
Introduction
The face is innervated by two different brain systems that compete for control of its muscles:
cortical brain system related to voluntary and controllable behavior, and a sub-cortical
system responsible for involuntary expressions. The interplay between these two systems
generates a wealth of information that humans constantly use to read the emotions, inten-
tions, and interests [25] of others.
Given the critical role that facial expressions play in our daily life, technologies that can
interpret and respond to facial expressions automatically are likely to find a wide range of
pplications. For example, in pharmacology, the effect of new anti-depression drugs could
e assessed more accurately based on daily records of the patients’ facial expressions than
sking the patients to fill out a questionnaire, as it is currently done [7]. Facial expression
recognition may enable a new generation of teaching systems to adapt to the expression
of their students in the way good teachers do [61]. Expression recognition could be used
to assess the fatigue of drivers and air-pilots [58, 59]. Daily-life robots with automatic"
403e7fed4fa1785af8309b1c4c736d98fa75be5b,Supplemental Data : Social status gates social attention in monkeys,"Magazine
Social status
gates social
ttention in
monkeys
Stephen V. Shepherd1,
Robert O. Deaner1 and
Michael L. Platt1,2,3
Humans rapidly shift attention in
the direction other individuals are
looking, following gaze in a
manner suggestive of an
obligatory social reflex [1–4].
Monkeys’ attention also follows
gaze, and the similar magnitude
nd time-course of gaze-
following in rhesus macaques and
humans [5] is indicative of shared
neural mechanisms. Here we
show that low-status male rhesus"
40f6c9355dbf01a240b4c26b0fd00b5cfbd5f67d,An eye-tracking method to reveal the link between gazing patterns and pragmatic abilities in high functioning autism spectrum disorders,"ORIGINAL RESEARCH ARTICLE
published: 14 January 2015
doi: 10.3389/fnhum.2014.01067
An eye-tracking method to reveal the link between gazing
patterns and pragmatic abilities in high functioning autism
spectrum disorders
Ouriel Grynszpan 1* and Jacqueline Nadel 2
Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie, Centre National de la Recherche Scientifique, Paris, France
Centre Emotion, Hôpital de La Salpêtrière, Paris, France
Edited by:
John J. Foxe, Albert Einstein
College of Medicine, USA
Reviewed by:
Hans-Peter Frey, Albert Einstein
College of Medicine, USA
Julia Irwin, Haskins Laboratories,
Karri Gillespie-Smith, University of
West of Scotland, UK
*Correspondence:
Ouriel Grynszpan, Institut des"
404c7839afe2fec48a06f83d2a532c05ad8ba0d3,Vehicle Classification using Transferable Deep Neural Network Features,"Vehicle Classification using Transferable Deep
Neural Network Features
Yiren Zhou, Ngai-Man Cheung"
405b43f4a52f70336ac1db36d5fa654600e9e643,What can we learn about CNNs from a large scale controlled object dataset?,"What can we learn about CNNs from a large scale controlled object dataset?
Ali Borji
Saeed Izadi
Laurent Itti"
406caefc7f51e8a16833402e4757704d5d84a1f8,Dual-Tree Complex Wavelets Transform Based Facial Expression Recognition using Principal Component Analysis ( PCA ) and Local Binary Pattern ( LBP ),"ISSN XXXX XXXX © 2017 IJESC
Research Article                                                                                                                              Volume 7 Issue No.4
Dual-Tree Complex Wavelets Transform Based Facial Expression
Recognition using Principal Component Analysis (PCA) and Local
Binary Pattern(LBP)
Fahad Abdu Jibrin1, Abubakar Sadiq Muhammad2
Department of Electrical Engineering1, Department of Computer Engineering2
School of Technology, Kano State Polytechnic, Nigeria"
40b87d3b1e3dbbc82fb7d786004fe202e131c045,Multi-modal Egocentric Activity Recognition using Audio-Visual Features,"Submitted to IEEE Transactions on Human-Machine Systems
Multi-modal Egocentric Activity Recognition
using Audio-Visual Features
Mehmet Ali Arabacı, Fatih Özkan, Elif Surer, Peter Jančovič, Alptekin Temizel"
403b3d0594989629c95e5bc5230d4ccb1691f255,Automatic detection of pain from spontaneous facial expressions,"Meawad, F., Yang, S.-Y. and Loy, F. L. (2017) Automatic Detection of
Pain from Spontaneous Facial Expressions. In: 19th ACM International
Conference on Multimodal Interaction (ICMI 2017), Glasgow, Scotland,
3-17 Nov 2017, pp. 397-401. ISBN 9781450355438
(doi:10.1145/3136755.3136794)
This is the author’s final accepted version.
There may be differences between this version and the published version.
You are advised to consult the publisher’s version if you wish to cite from
http://eprints.gla.ac.uk/151491/
Deposited on: 22 December 2017
Enlighten – Research publications by members of the University of Glasgow
http://eprints.gla.ac.uk"
401f056e1017151018e83d2b13b5eaec573b4dbc,Rapid and accurate face depth estimation in passive stereo systems,"Noname manuscript No.
(will be inserted by the editor)
Rapid and accurate face depth estimation in passive
stereo systems
Amel AISSAOUI · Jean MARTINET ·
Chaabane DJERABA
Received: date / Accepted: date"
4053e3423fb70ad9140ca89351df49675197196a,Robust Face Detection Using the Hausdorff Distance,"(cid:13) In Proc. Third International Conference on Audio- and Video-based
Biometric Person Authentication, Springer, Lecture Notes in Computer
Science, LNCS-2091, pp. 90–95, Halmstad, Sweden, 6–8 June 2001.
Robust Face Detection
Using the Hausdorff Distance
Oliver Jesorsky, Klaus J. Kirchberg, and Robert W. Frischholz
BioID AG, Berlin, Germany
WWW home page: http://www.bioid.com"
40205181ed1406a6f101c5e38c5b4b9b583d06bc,Using Context to Recognize People in Consumer Images,"Using Context to Recognize People in Consumer Images
Andrew C. Gallagher and Tsuhan Chen"
40a34d4eea5e32dfbcef420ffe2ce7c1ee0f23cd,Bridging Heterogeneous Domains With Parallel Transport For Vision and Multimedia Applications,"Bridging Heterogeneous Domains With Parallel Transport For Vision and
Multimedia Applications
Raghuraman Gopalan
Dept. of Video and Multimedia Technologies Research
AT&T Labs-Research
San Francisco, CA 94108"
40f2b3af6b55efae7992996bd0c474a9c1574008,Oxytocin Increases Retention of Social Cognition in Autism,"ARTICLE  IN  PRESS
Oxytocin Increases Retention of Social Cognition
in Autism
Eric Hollander, Jennifer Bartz, William Chaplin, Ann Phillips, Jennifer Sumner, Latha Soorya,
Evdokia Anagnostou, and Stacey Wasserman
Background: Oxytocin dysfunction might contribute to the development of social deficits in autism, a core symptom domain and
potential target for intervention. This study explored the effect of intravenous oxytocin administration on the retention of social
information in autism.
Methods: Oxytocin and placebo challenges were administered to 15 adult subjects diagnosed with autism or Asperger’s disorder, and
omprehension of affective speech (happy, indifferent, angry, and sad) in neutral content sentences was tested.
Results: All subjects showed improvements in affective speech comprehension from pre- to post-infusion; however, whereas those who
received placebo first tended to revert to baseline after a delay, those who received oxytocin first retained the ability to accurately assign
emotional significance to speech intonation on the speech comprehension task.
Conclusions: These results are consistent with studies linking oxytocin to social recognition in rodents as well as studies linking
oxytocin to prosocial behavior in humans and suggest that oxytocin might facilitate social information processing in those with autism.
These findings also provide preliminary support for the use of oxytocin in the treatment of autism.
Key Words: Autism, oxytocin, neuropeptide, social cognition,
ffective speech
A utism is a developmental disorder characterized by ab-
normalities in speech and communication, impaired so-"
40b0fced8bc45f548ca7f79922e62478d2043220,Do Convnets Learn Correspondence?,"Do Convnets Learn Correspondence?
Trevor Darrell
Jonathan Long
{jonlong, nzhang,
University of California – Berkeley
Ning Zhang"
40f7ea135907d2f4abeae0475d9a88477239d504,Multimodal Explanations: Justifying Decisions and Pointing to the Evidence,"Multimodal Explanations: Justifying Decisions and Pointing to the Evidence
Dong Huk Park1, Lisa Anne Hendricks1, Zeynep Akata2,3, Anna Rohrbach1,3,
Bernt Schiele3, Trevor Darrell1, and Marcus Rohrbach4
EECS, UC Berkeley, 2University of Amsterdam, 3MPI for Informatics, 4Facebook AI Research"
401e6b9ada571603b67377b336786801f5b54eee,Active image clustering: Seeking constraints from humans to complement algorithms,"Active Image Clustering: Seeking Constraints from
Humans to Complement Algorithms
November 22, 2011"
40000b058cf80b7983a2c0f96562368a40a04580,Predicting human mobility through the assimilation of social media traces into mobility models,"Predicting human mobility through the assimilation of social media
traces into mobility models
Mariano G. Beir´o1
Andr´e Panisson1
Michele Tizzoni1
Ciro Cattuto1
ISI Foundation, Turin, Italy"
409220cf5137d6dc6c85f440d618e44d244f402e,Randomized Algorithms for Large-scale Strongly Over-determined Linear Regression Problems a Dissertation Submitted to the Institute for Computational and Mathematical Engineering and the Committee on Graduate Studies of Stanford University in Partial Fulfillment of the Requirements for the Degree Of,"RANDOMIZED ALGORITHMS FOR LARGE-SCALE STRONGLY
OVER-DETERMINED LINEAR REGRESSION PROBLEMS
A DISSERTATION
SUBMITTED TO THE INSTITUTE FOR
COMPUTATIONAL AND MATHEMATICAL ENGINEERING
AND THE COMMITTEE ON GRADUATE STUDIES
OF STANFORD UNIVERSITY
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
Xiangrui Meng
June 2014"
409ff083816d8357fe839e3ea0e62d648a5532aa,Proceedings of the 20 th Workshop on the Semantics and Pragmatics of Dialogue,"SEMDIAL 2016
JerSem
Proceedings of the 20th Workshop on
the Semantics and Pragmatics of Dialogue
Julie Hunter, Mandy Simons, and Matthew Stone (eds.)
New Brunswick, NJ, 16–18 July 2016"
40b10e330a5511a6a45f42c8b86da222504c717f,Implementing the Viola-Jones Face Detection Algorithm,"Implementing the Viola-Jones
Face Detection Algorithm
Ole Helvig Jensen
Kongens Lyngby 2008
IMM-M.Sc.-2008-93"
40041b80cef6dc23946ffa9628b6ac3b8dcc971a,Parallel Separable 3D Convolution for Video and Volumetric Data Understanding,"GONDA, WEI, PARAG, PFISTER: PARALLEL SEPARABLE 3D CONVOLUTION
Parallel Separable 3D Convolution for Video
nd Volumetric Data Understanding
Harvard John A. Paulson School of
Engineering and Applied Sciences
Camabridge MA, USA
Felix Gonda
Donglai Wei
Toufiq Parag
Hanspeter Pfister"
40932ccdd7cda22e90c1e16b4a4dc4930b122a9c,Learning to Look around Objects for Top-View Representations of Outdoor Scenes,"Learning to Look around Objects for Top-View
Representations of Outdoor Scenes
Samuel Schulter1,† Menghua Zhai2,†
Nathan Jacobs2
Manmohan Chandraker1,3
NEC-Labs1, Computer Science University of Kentucky2, UC San Diego3"
40c6a2b1cb312f11f8225a733545fdabd436e347,Deep Co-Training for Semi-Supervised Image Recognition,"Deep Co-Training for Semi-Supervised
Image Recognition
Siyuan Qiao1 Wei Shen1,2 Zhishuai Zhang1 Bo Wang3 Alan Yuille1
Johns Hopkins University 2Shanghai University 3Hikvision Research Institute"
40dd2b9aace337467c6e1e269d0cb813442313d7,Localizing spatially and temporally objects and actions in videos. (Localiser spatio-temporallement des objets et des actions dans des vidéos),"This thesis has been submitted in fulfilment of the requirements for a postgraduate degree
(e.g. PhD, MPhil, DClinPsychol) at the University of Edinburgh. Please note the following
terms and conditions of use:
This work is protected by copyright and other intellectual property rights, which are
retained by the thesis author, unless otherwise stated.
A copy can be downloaded for personal non-commercial research or study, without
prior permission or charge.
This thesis cannot be reproduced or quoted extensively from without first obtaining
permission in writing from the author.
The content must not be changed in any way or sold commercially in any format or
medium without the formal permission of the author.
When referring to this work, full bibliographic details including the author, title,
warding institution and date of the thesis must be given."
40dab43abef32deaf875c2652133ea1e2c089223,Facial Communicative Signals: valence recognition in task-oriented human-robot Interaction,"Noname manuscript No.
(will be inserted by the editor)
Facial Communicative Signals
Valence Recognition in Task-Oriented Human-Robot Interaction
Christian Lang · Sven Wachsmuth · Marc Hanheide · Heiko Wersing
Received: date / Accepted: date"
40c72f5699f87db0f4f5505e6fcf79254dfd13bd,Exploiting Multiple Cameras for Environmental Pathlets,"Exploiting Multiple Cameras
for Environmental Pathlets(cid:63)
Kevin Streib and James W. Davis
Dept. of Computer Science and Engineering
Ohio State University, Columbus, OH, 43210"
40229a034d2fcddc3df32f906ec4ef6a3b3e017e,A semi-automated system for accurate gaze coding in natural dyadic interactions,"A Semi-Automated System for Accurate Gaze Coding
in Natural Dyadic Interactions
Kenneth A. Funes-Mora, Laurent Nguyen, Daniel Gatica-Perez, Jean-Marc Odobez
Idiap Research Institute and École Polytechnique Fédérale de Lausanne (EPFL), Switzerland"
40ce2567ccc2552287f8a1c25e9f6086efa6bf8f,Identification and evaluation of children with autism spectrum disorders.,"CLINICAL REPORT
Identification and Evaluation of
Children With Autism Spectrum
Disorders
Chris Plauche´ Johnson, MD, MEd, Scott M. Myers, MD, and the Council on Children With Disabilities
Guidance for the Clinician in Rendering
Pediatric Care"
40012a8e480a1724cce1a71e2b8584332225492b,Greedy algorithm for subspace clustering from corrupted and incomplete data,"Fast Greedy Algorithm
for Subspace Clustering
from Corrupted and Incomplete Data
Alexander Petukhov, Inna Kozlov"
402f6db00251a15d1d92507887b17e1c50feebca,3D Facial Action Units Recognition for Emotional Expression,"D Facial Action Units Recognition for Emotional
Expression
Norhaida Hussain1, Hamimah Ujir, Irwandi Hipiny and Jacey-Lynn Minoi2
Department of Information Technology and Communication, Politeknik Kuching, Sarawak, Malaysia
Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Kota Samarahan, Sarawak, Malaysia
The  muscular  activities  caused  the  activation  of  certain  AUs  for  every  facial  expression  at  the  certain  duration  of  time
throughout the facial expression. This paper presents the methods to recognise facial Action Unit (AU) using facial distance
of the facial features which activates the muscles. The seven facial action units involved are AU1, AU4, AU6, AU12, AU15,
AU17 and AU25 that characterises happy and sad expression. The recognition is performed on each AU according to rules
defined based on the distance of each facial points. The facial distances chosen are extracted from twelve facial features.
Then the facial distances are trained using Support Vector Machine (SVM) and Neural Network (NN). Classification result
using SVM is presented with several different SVM kernels while result using NN is presented for each training, validation
nd testing phase.
Keywords: Facial action units recognition, 3D AU recognition, facial expression"
3cb8128b41b419a1fdc7a95bf8e65a37aff79676,Shifting the Baseline: Single Modality Performance on Visual Navigation&QA,"Single Modality Performance on Visual Navigation & QA
Shifting the Baseline:
Jesse Thomason
Yonatan Bisk
Paul G. Allen School of Computer Science and Engineering
Daniel Gordan"
3c0420a0dd90d0900613ac1f1a1174b626df26d9,Learning Discriminative Chamfer Regularization,"YARLAGADDA ∗, EIGENSTETTER ∗, OMMER: CHAMFER REGULARIZATION
Learning Discriminative Chamfer
Regularization
Pradeep Yarlagadda ∗
Angela Eigenstetter ∗
Björn Ommer
Interdisciplinary Center for Scientific
Computing (IWR)
University of Heidelberg
Germany"
3cfbe1f100619a932ba7e2f068cd4c41505c9f58,A Realistic Simulation Tool for Testing Face Recognition Systems under Real-World Conditions,"A Realistic Simulation Tool for Testing Face Recognition
Systems under Real-World Conditions∗
M. Correa, J. Ruiz-del-Solar, S. Parra-Tsunekawa, R. Verschae
Department of Electrical Engineering, Universidad de Chile
Advanced Mining Technology Center, Universidad de Chile"
3caebf3075e52483c7a7179b3491882af0aaaa37,Lateralization of Cognitive Functions : The Visual Half-Field Task Revisited,"Lateralization of Cognitive Functions: The Visual Half-Field
Task Revisited
Ark Verma
Promotor: Prof. Dr. Marc Brysbaert
Proefschrift ingediend tot het behalen van de academische graad
van Doctor in de Psychologie"
3c374cb8e730b64dacb9fbf6eb67f5987c7de3c8,Measuring Gaze Orientation for Human-Robot Interaction,"Measuring Gaze Orientation for Human-Robot
Interaction
R. Brochard∗, B. Burger∗, A. Herbulot∗†, F. Lerasle∗†
CNRS; LAAS; 7 avenue du Colonel Roche, 31077 Toulouse Cedex, France
Universit´e de Toulouse; UPS; LAAS-CNRS : F-31077 Toulouse, France
Introduction
In the context of Human-Robot interaction estimating gaze orientation brings
useful information about human focus of attention. This is a contextual infor-
mation : when you point something you usually look at it. Estimating gaze
orientation requires head pose estimation. There are several techniques to esti-
mate head pose from images, they are mainly based on training [3, 4] or on local
face features tracking [6]. The approach described here is based on local face
features tracking in image space using online learning, it is a mixed approach
since we track face features using some learning at feature level. It uses SURF
features [2] to guide detection and tracking. Such key features can be matched
etween images, used for object detection or object tracking [10]. Several ap-
proaches work on fixed size images like training techniques which mainly work
on low resolution images because of computation costs whereas approaches based
on local features tracking work on high resolution images. Tracking face features
such as eyes, nose and mouth is a common problem in many applications such as"
3c8e16de72af3f96af31b26aeeb01c8bf41148fd,Face Recognition: A Comparison of Appearance-Based Approaches,"Proc. VIIth Digital Image Computing: Techniques and Applications, Sun C., Talbot H., Ourselin S. and Adriaansen T. (Eds.), 10-12 Dec. 2003, Sydney
Face Recognition: A Comparison of Appearance-Based
Approaches
Thomas Heseltine1, Nick Pears, Jim Austin, Zezhi Chen
Advanced Computer Architectures Group, Department of Computer Science,
The University of York, York, England
http://www.cs.york.ac.uk/~tomh"
3c917f071bfc1244c75fca3ceed0a8c46bb975cc,Reduced acetylcholinesterase activity in the fusiform gyrus in adults with autism spectrum disorders.,"ORIGINAL ARTICLE
Reduced Acetylcholinesterase Activity
in the Fusiform Gyrus in Adults With Autism
Spectrum Disorders
Katsuaki Suzuki, MD, PhD; Genichi Sugihara, MD, PhD; Yasuomi Ouchi, MD, PhD; Kazuhiko Nakamura, MD, PhD;
Masatsugu Tsujii, MA; Masami Futatsubashi, BS; Yasuhide Iwata, MD, PhD; Kenji J. Tsuchiya, MD, PhD;
Kaori Matsumoto, MA; Kiyokazu Takebayashi, MD, PhD; Tomoyasu Wakuda, MD, PhD; Yujiro Yoshihara, MD, PhD;
Shiro Suda, MD, PhD; Mitsuru Kikuchi, MD, PhD; Nori Takei, MD, PhD, MSc; Toshirou Sugiyama, MD, PhD;
Toshiaki Irie, PhD; Norio Mori, MD, PhD
Context: Both neuropsychological and functional mag-
netic resonance imaging studies have shown deficien-
ies in face perception in subjects with autism spectrum
disorders (ASD). The fusiform gyrus has been regarded
s the key structure in face perception. The cholinergic
system is known to regulate the function of the visual
pathway, including the fusiform gyrus.
Objectives: To determine whether central acetylcho-
linesterase activity, a marker for the cholinergic system,
is altered in ASD and whether the alteration in acetyl-
holinesterase activity, if any, is correlated with their so-"
3cb2841302af1fb9656f144abc79d4f3d0b27380,When 3 D-Aided 2 D Face Recognition Meets Deep Learning : An extended UR 2 D for Pose-Invariant Face Recognition,"See	discussions,	stats,	and	author	profiles	for	this	publication	at:	https://www.researchgate.net/publication/319928941
When	3D-Aided	2D	Face	Recognition	Meets	Deep
Learning:	An	extended	UR2D	for	Pose-Invariant
Face	Recognition
Article	·	September	2017
CITATIONS
authors:
READS
Xiang	Xu
University	of	Houston
Pengfei	Dou
University	of	Houston
8	PUBLICATIONS			10	CITATIONS
9	PUBLICATIONS			29	CITATIONS
SEE	PROFILE
SEE	PROFILE
Ha	Le
University	of	Houston
7	PUBLICATIONS			2	CITATIONS
Ioannis	A	Kakadiaris"
3c9ad25e91cace6ac93069480745d4578b7f29f5,Automatic Article Commenting: the Task and Dataset,"Automatic Article Commenting: the Task and Dataset
Lianhui Qin1∗, Lemao Liu2, Victoria Bi2, Yan Wang2,
Xiaojiang Liu2, Zhiting Hu, Hai Zhao1, Shuming Shi2
Department of Computer Science and Engineering, Shanghai Jiao Tong University1, Tencent AI Lab2,"
3ca4ce8ab704b44701bf7ef8dda01c8dbb226fac,On-the-fly hand detection training with application in egocentric action recognition,"On-the-Fly Hand Detection Training with Application in Egocentric Action
Recognition
Jayant Kumar∗, Qun Li∗, Survi Kyal, Edgar A. Bernal, and Raja Bala
{Jayant.Kumar, Qun.Li, Survi.Kyal, Edgar.Bernal,
PARC, A Xerox Company
800 Phillips Road, Webster, NY 14580"
3c2f1d13b284823597c2c366312a6ae6ac6c7147,Diversity Regularized Spatiotemporal Attention for Video-Based Person Re-identification,"FeaturesSpatial  AttentionTemporal  Attention1                      2                      3                              N‘face’‘torso’‘bag’Figure1.SpatiotemporalAttention.Inchallengingvideore-identificationscenarios,apersonisrarelyfullyvisibleinallframes.However,framesinwhichonlypartofthepersonisvis-ibleoftencontainusefulinformation.Forexample,thefaceisclearlyvisibleintheframes1and2,thetorsoinframe2,andthehandbaginframes2,3andN.Insteadofaveragingfullframefeaturesacrosstime,weproposeanewspatiotemporalapproachwhichlearnstodetectasetofKdiversesalientimageregionswithineachframe(superimposedheatmaps).Anaggregaterep-resentationofeachbodypartisthenproducedbycombiningtheextractedper-frameregionsacrosstime(weightsshownaswhitetext).Ourspatiotemporalapproachcreatesacompactencodingofthevideothatexploitsusefulpartialinformationineachframebyleveragingmultiplespatialattentionmodels,andcombiningtheiroutputsusingmultipletemporalattentionmodels.personre-identification,whichisageneralizationofthestandardimage-basedre-identificationtask.InsteadofarXiv:1803.09882v1  [cs.CV]  27 Mar 2018"
3c793fa4d7f673f1e9f6799729ec266ce573ec60,Margin Sample Mining Loss: A Deep Learning Based Method for Person Re-identification,"Margin Sample Mining Loss: A Deep Learning Based Method for Person
Re-identification
Qiqi Xiao , Hao Luo , Chi Zhang"
3c4f6d24b55b1fd3c5b85c70308d544faef3f69a,A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics,"A Hybrid Deep Learning Architecture for
Privacy-Preserving Mobile Analytics
Seyed Ali Ossia(cid:63), Ali Shahin Shamsabadi(cid:63), Ali Taheri(cid:63), Hamid R. Rabiee(cid:63),
Nic Lane‡, Hamed Haddadi†
(cid:63)Sharif University of Technology, ‡University College London, †Queen Mary University of London"
3c8da376576938160cbed956ece838682fa50e9f,Aiding face recognition with social context association rule based re-ranking,"Chapter 4
Aiding Face Recognition with
Social Context Association Rule
ased Re-Ranking
Humans are very ef‌f‌icient at recognizing familiar face images even in challenging condi-
tions. One reason for such capabilities is the ability to understand social context between
individuals. Sometimes the identity of the person in a photo can be inferred based on the
identity of other persons in the same photo, when some social context between them is
known. This chapter presents an algorithm to utilize the co-occurrence of individuals as
the social context to improve face recognition. Association rule mining is utilized to infer
multi-level social context among subjects from a large repository of social transactions.
The results are demonstrated on the G-album and on the SN-collection pertaining to 4675
identities prepared by the authors from a social networking website. The results show that
ssociation rules extracted from social context can be used to augment face recognition and
improve the identification performance.
Introduction
Face recognition capabilities of humans have inspired several researchers to understand
the science behind it and use it in developing automated algorithms. Recently, it is also
rgued that encoding social context among individuals can be leveraged for improved
utomatic face recognition [175]. As shown in Figure 4.1, often times a person’s identity"
3c70360a4ba30b860d337308633842acbb908ee4,Because better detections are still possible: Multi-aspect Object Detection with Boosted Hough Forest,"REDONDO-CABRERA ET AL.: OBJECT DETECTION WITH BOOSTED HOUGH FOREST
Because better detections are still possible:
Multi-aspect Object Detection with Boosted
Hough Forest
Carolina Redondo-Cabrera
Roberto López-Sastre
University of Alcalá
Alcalá de Henares, ES"
3c5f390f99272c59fcf822ab78c90ee6bfa7926a,iCub : Learning Emotion Expressions using Human Reward,"iCub: Learning Emotion Expressions using Human Reward
Nikhil Churamani, Francisco Cruz, Sascha Griffiths and Pablo Barros"
3c1c8e171450a9b279df939d4c9209d8dbf6b2fe,Large scale mining and retrieval of visual data in a multimodal context,"Diss. ETH No. 18190
Large-Scale Mining and Retrieval of Visual Data in
Multimodal Context
A dissertation submitted to the
SWISS FEDERAL INSTITUTE OF TECHNOLOGY ZURICH
for the degree of
Doctor of Technical Sciences
presented by
Till Quack
MSc. ETH Zuerich
orn 15. September 1978
itizen of Germany
ccepted on the recommendation of
Prof. Dr. Luc Van Gool, examiner
Prof. Dr. Andrew Zisserman, co-examiner
September 2008"
3ca194773fe583661b988fbdf33f7680764438b3,Exploring Nearest Neighbor Approaches for Image Captioning,"Exploring Nearest Neighbor Approaches for
Image Captioning
Jacob Devlin, Saurabh Gupta, Ross Girshick, Margaret Mitchell, C. Lawrence Zitnick"
3cd10f6f24c49ce677a18f0984ff4466333d8d13,Correcting rolling-shutter distortion of CMOS sensors using facial feature detection,"© 2010 IEEE. Personal use of this material is permitted. Permission from IEEE
must  be  obtained  for  all  other  uses,  in  any  current  or  future  media,  including
reprinting/republishing  this  material  for  advertising  or  promotional  purposes,
reating  new  collective  works,  for  resale  or  redistribution  to  servers  or  lists,  or
reuse of any copyrighted component of this work in other works.
Pre-print of article that appeared at BTAS 2010.
The published article can be accessed from:
http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5634528"
3ca983d40b9de7dc12b989fce213b4abee652c9e,Will the Pedestrian Cross? A Study on Pedestrian Path Prediction,"Will the Pedestrian Cross?
A Study on Pedestrian Path Prediction
Christoph G. Keller and Dariu M. Gavrila"
3cd7b15f5647e650db66fbe2ce1852e00c05b2e4,"ACTIVE, an Extensible Cataloging Platform for Automatic Indexing of Audiovisual Content",
3c68763caa67dee55bca76f0f71dd4530f3fd57c,Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition Applications,"Ranking to Learn and Learning to Rank:
On the Role of Ranking in Pattern Recognition Applications
Giorgio Roffo
Submitted to the Department of Computer Science
in partial fulfillment of the requirements for the degree of
European Doctor of Philosophy
S.S.D. ING-INF05
Cycle XXIX/2014
t the
Universit`a degli Studi di Verona
May 2017
(cid:13) Universit`a degli Studi di Verona 2017. All rights reserved.
Author . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Department of Computer Science
May 25, 2017
Certified by . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Prof. Marco Cristani
Associate Professor
Thesis Tutor
Accepted by . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ."
3cb0ef5aabc7eb4dd8d32a129cb12b3081ef264f,Absolute Head Pose Estimation From Overhead Wide-Angle Cameras,"Absolute Head Pose Estimation From Overhead Wide-Angle Cameras
Ying-Li Tian, Lisa Brown, Jonathan Connell,
Sharat Pankanti, Arun Hampapur, Andrew Senior, Ruud Bolle
IBM T.J. Watson Research Center
9 Skyline Drive, Hawthorne, NY 10532 USA
{ yltian,lisabr,jconnell,sharat,arunh,aws,bolle"
3ceef6572b00bef961c0246a220edcc48553ed2d,Descriptor learning for omnidirectional image matching,"Descriptor learning for omnidirectional image matching
Jonathan Masci1,2,3
Davide Migliore1,4
Michael M. Bronstein2
J¨urgen Schmidhuber1,2,3
Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA), Manno, Switzerland
Faculty of Informatics, Universit`a della Svizzera Italiana (USI), Lugano, Switzerland
Scuola Universitaria Professionale della Svizzera Italiana (SUPSI), Lugano, Switzerland
Evidence Srl, Pisa, Italy"
3c2819dae899559f1c61b3b34aeb5d41a6398440,A Stable and Invariant Three-polar Surface Representation : Application to 3 D Face Description,"A Stable and Invariant Three-polar Surface Representation:
Application to 3D Face Description
Majdi Jribi
Faouzi Ghorbel
CRISTAL Laboratory,
GRIFT research group
ENSI,La Manouba
University
010, La manouba,
Tunisia
CRISTAL Laboratory,
GRIFT research group
ENSI,La Manouba
University
010, La manouba,
Tunisia"
3cbdb3c9eb3e97a9d12d84d2b62b76884cc0003d,State of the Art in Fair ML: From Moral Philosophy and Legislation to Fair Classifiers,"State of the Art in Fair ML: From Moral
Philosophy and Legislation to Fair Classifiers
Elias Baumann and Josef Lorenz Rumberger
Humboldt University Berlin"
3cba45bd70741f38ebf375fec33a9c077288d575,Efficient and Robust Pedestrian Detection using Deep Learning for Human-Aware Navigation,"Efficient and Robust Pedestrian Detection
using Deep Learning for Human-Aware Navigation
Andr´e Mateus, David Ribeiro, Pedro Miraldo, and Jacinto C. Nascimento
Instituto de Sistemas e Rob´otica (LARSyS),
Instituto Superior T´ecnico, Lisboa,
Torre Norte - 6 Piso Av.Rovisco Pais, 1 1049-001 Lisboa, Portugal.
Corresponding author:"
3ce8a74b47f81ec66046f2486afa1a89e3165dfd,LSH banding for large-scale retrieval with memory and recall constraints,"978-1-4244-2354-5/09/$25.00 ©2009 IEEE
ICASSP 2009"
3c3eb65a936296d6ae5058b564f6d0e0c07772cf,A metric for sets of trajectories that is practical and mathematically consistent,"A metric for sets of trajectories that is
practical and mathematically consistent
Jos´e Bento
Jia Jie Zhu"
3cea9573c592fc42c9f4c01535c1c3f26d42af8a,White and relaxed noises in optimal velocity models for pedestrian flow with stop-and-go waves,"White and relaxed noises in optimal velocity models
for pedestrian flow with stop-and-go waves
Antoine Tordeux1,2,∗ and Andreas Schadschneider3
J¨ulich Supercomputing Centre, Forschungszentrum J¨ulich GmbH, Germany
Computer Simulation for Fire Safety and Pedestrian Traffic, Bergische Universit¨at Wuppertal, Germany
Institut f¨ur Theoretische Physik, Universit¨at zu K¨oln, Germany"
3cec488a0910b69f50811cebe8c655dca22078d5,Evidence Extraction for Machine Reading Comprehension with Deep Probabilistic Logic,"Confidential TACL submission. DO NOT DISTRIBUTE.
Evidence Extraction for Machine Reading Comprehension
with Deep Probabilistic Logic
Anonymous TACL submission"
3c0bbfe664fb083644301c67c04a7f1331d9515f,The Role of Color and Contrast in Facial Age Estimation Paper,"The Role of Color and Contrast in Facial Age Estimation
Paper ID: 7
No Institute Given"
3c94f3380206bf4f53a6d971f9195d3811fab8f5,Exploiting Test Time Evidence to Improve Predictions of Deep Neural Networks,"Exploiting Test Time Evidence to Improve Predictions of Deep Neural Networks
Dinesh Khandelwal
Indian Institute of Technology Delhi
Suyash Agrawal
Parag Singla Chetan Arora"
3ca1e06dfbaeed0f8dc49bf345369fb8e43da53d,Cross-View Asymmetric Metric Learning for Unsupervised Person Re-Identification,"Cross-view Asymmetric Metric Learning for
Unsupervised Person Re-identification
Hong-Xing Yu, Ancong Wu, Wei-Shi Zheng
Code is available at the project page:
https://github.com/KovenYu/CAMEL
For reference of this work, please cite:
Hong-Xing Yu, Ancong Wu, Wei-Shi Zheng. “Cross-view Asymmetric
Metric Learning for Unsupervised Person Re-identification.” Proceedings
of the IEEE International Conference on Computer Vision. 2017.
title={Cross-view Asymmetric Metric Learning for Unsupervised Person
Re-identification},
uthor={Yu, Hong-Xing and Wu, Ancong and Zheng, Wei-Shi},
ooktitle={Proceedings of the IEEE International Conference on Computer
Vision},
year={2017}"
3c8aa33ccff8f959df28e4e883867af32e7b4b78,The impact of task relevance and degree of distraction on stimulus processing,"Biehl et al. BMC Neuroscience 2013, 14:107
http://www.biomedcentral.com/1471-2202/14/107
R ES EAR CH A R T I C LE
The impact of task relevance and degree of
distraction on stimulus processing
Stefanie C Biehl1,2*, Ann-Christine Ehlis3, Laura D Müller1, Andrea Niklaus1,5, Paul Pauli4 and Martin J Herrmann1
Open Access"
3c709c648f40158af31199aeb0733890ddf2bc58,A DATASET TO SUPPORT AND BENCHMARK COMPUTER VISION DEVELOPMENT FOR CLOSE RANGE ON-ORBIT SERVICING,"A DATASET TO SUPPORT AND BENCHMARK COMPUTER VISION DEVELOPMENT
FOR CLOSE RANGE ON-ORBIT SERVICING
Martin Lingenauber1, Simon Kriegel1, Michael Kaßecker1, and Giorgio Panin1
German Aerospace Center (DLR) - Institute of Robotics and Mechatronics - Departement of Perception and Cognition,
M¨unchener Str. 20, 82234 Wessling, Germany, Email:"
3c04bf7324eaf6a77822f0fb35f85dfa79eff781,EpicFlow: Edge-preserving interpolation of correspondences for optical flow,"EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow
Jerome Revauda
Philippe Weinzaepfela
Inria∗
Zaid Harchaouia,b
NYU
Cordelia Schmida"
3cc0d9c1f690addd2c82e60f2a460e3c557ff242,Sort Story: Sorting Jumbled Images and Captions into Stories,"Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 925–931,
Austin, Texas, November 1-5, 2016. c(cid:13)2016 Association for Computational Linguistics"
3c49dafc82ee24e70e338b896868cd9f82f0edd7,BIOLOGICALLY MOTIVATED 3 D FACE RECOGNITION by,"BIOLOGICALLY MOTIVATED 3D FACE RECOGNITION
Albert Ali Salah
B.S, in Computer Engineering, Bo˘gazi¸ci University, 1998
M.S, in Computer Engineering, Bo˘gazi¸ci University, 2000
Submitted to the Institute for Graduate Studies in
Science and Engineering in partial fulfillment of
the requirements for the degree of
Doctor of Philosophy
Graduate Program in
Bo˘gazi¸ci University"
e6e5a6090016810fb902b51d5baa2469ae28b8a1,Title Energy-Efficient Deep In-memory Architecture for NAND Flash Memories,"Title
Energy-Efficient Deep In-memory Architecture for NAND
Flash Memories
Archived version
Accepted manuscript: the content is same as the published
paper but without the final typesetting by the publisher
Published version
Published paper
Authors (contact)
0.1109/ISCAS.2018.8351458"
e66c1950de149e0ccf90d3796dacce8b4886544d,Thèse A contribution to mouth structure segmentation in images aimed towards automatic mouth gesture recognition,"Th`eseAcontributiontomouthstructuresegmentationinimagesaimedtowardsautomaticmouthgesturerecognitionPr´esent´eedevantL’institutnationaldessciencesappliqu´eesdeLyonPourobtenirLegradededocteur´Ecoledoctorale´Ecoledoctoraleelectronique,electrotechnique,automatique(EEA)ParJuanBernardoG´omez-Mendoza(Ing´enieur)Souten´uele15mai2012devantlaCommissiond’examenJuryMM.P.BOLONProfesseur(PolytechAnnecy-Chamb´ery)C-A.PARRA-RODR´IGUEZProfesseur(UniversidadJaveriana)M.ORKISZProfesseur(Universit´eLyonI)J-W.BRANCH-BEDOYAProfesseur(UniversidadNacionaldeColombia)H-T.REDARCEProfesseur(INSAdeLyon)F-A.PRIETO-ORTIZProfesseur(UniversidadNacionaldeColombia)Cette thèse est accessible à l'adresse : http://theses.insa-lyon.fr/publication/2012ISAL0074/these.pdf © [J. Gómez-Mendoza], [2012], INSA de Lyon, tous droits réservés"
e66304d30125a7adac3b1c9cce345c164c0317d7,Incremental Place Recognition in 3 D Point Clouds,"Research Collection
Master Thesis
Incremental Place Recognition in 3D Point Clouds
Author(s):
Gollub, Mattia
Publication Date:
Permanent Link:
https://doi.org/10.3929/ethz-b-000202826
Rights / License:
In Copyright - Non-Commercial Use Permitted
This page was generated automatically upon download from the ETH Zurich Research Collection. For more
information please consult the Terms of use.
ETH Library"
e6bbe7feb5633a361ffb6ed4c674d54574eb531e,Image quality and position variability assessment in minutiae-based fingerprint verification,"BIOMETRICS ON THE INTERNET
Image quality and position variability assessment in
minutiae-based fingerprint verification
D. Simon-Zorita, J. Ortega-Garcia, J. Fierrez-Aguilar and J. Gonzalez-Rodriguez"
e6fa9d4658610e699c71ac281762abf471983430,Simultaneous Perception and Path Generation Using Fully Convolutional Neural Networks,"Simultaneous Perception and Path Generation
Using Fully Convolutional Neural Networks
Luca Caltagirone∗, Mauro Bellone, Lennart Svensson, Mattias Wahde"
e6178de1ef15a6a973aad2791ce5fbabc2cb8ae5,Improving Facial Landmark Detection via a Super-Resolution Inception Network,"Improving Facial Landmark Detection via a
Super-Resolution Inception Network
Martin Knoche, Daniel Merget, Gerhard Rigoll
Institute for Human-Machine Communication
Technical University of Munich, Germany"
e6d50d65a87425e7f0b4ec08c53d200f12f75590,The Neural Dynamics of Facial Identity Processing: Insights from EEG-Based Pattern Analysis and Image Reconstruction,"New Research
Sensory and Motor Systems
The Neural Dynamics of Facial Identity
Processing: Insights from EEG-Based Pattern
Analysis and Image Reconstruction
Dan Nemrodov,1 Matthias Niemeier,1 Ashutosh Patel,1 and Adrian Nestor1
DOI:http://dx.doi.org/10.1523/ENEURO.0358-17.2018
Department of Psychology, University of Toronto Scarborough, 1265 Military Trail, Toronto, Ontario M1C1A4,
Canada"
e679d7d4a43b7549c0439bf00c05dc844e9ecfc6,Image Set Classification for Low Resolution Surveillance,"Image Set Classification
for Low Resolution Surveillance
Uzair Nadeem, Syed Afaq Ali Shah, Mohammed Bennamoun, Roberto Togneri
nd Ferdous Sohel"
e6e5949464c38ecea94c3c295ea65220bc19f338,BOP: Benchmark for 6D Object Pose Estimation,"BOP: Benchmark for 6D Object Pose Estimation
Tom´aˇs Hodaˇn1∗, Frank Michel2∗, Eric Brachmann3, Wadim Kehl4
Anders Glent Buch5, Dirk Kraft5, Bertram Drost6, Joel Vidal7, Stephan Ihrke2
Xenophon Zabulis8, Caner Sahin9, Fabian Manhardt10, Federico Tombari10
Tae-Kyun Kim9, Jiˇr´ı Matas1, Carsten Rother3
CTU in Prague, 2TU Dresden, 3Heidelberg University, 4Toyota Research Institute
5University of Southern Denmark, 6MVTec Software, 7Taiwan Tech
8FORTH Heraklion, 9Imperial College London, 10TU Munich"
e605242319ba495bc5f47abe9f1c08d508d83627,Importance-Aware Semantic Segmentation for Autonomous Driving System,Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
e63a0ea338dfc7293ddd68074baf250e99d0c6d5,Nonlinear Supervised Dimensionality Reduction via Smooth Regular Embeddings,"Nonlinear Supervised Dimensionality Reduction via
Smooth Regular Embeddings
Department of Electrical and Electronics Engineering, METU, Ankara
Cem ¨Ornek and Elif Vural"
e688a6535dbdd6ce6928bc4eb2978f39628e5302,Hand Drawn Sketch Classification Using Convolutional Neural Networks,"SUPPLEMENT ISSUE
ARTICLE
HAND DRAWN SKETCH CLASSIFICATION USING
CONVOLUTIONAL NEURAL NETWORKS
Habibollah Agh Atabay*
Department of Computer, Gonbad Kavous University, Gonbad Kavous, IRAN"
e6868f172df3736e052fec4c00b63780b3d739fe,Effects of a Common Variant in the CD38 Gene on Social Processing in an Oxytocin Challenge Study: Possible Links to Autism,"Effects of a Common Variant in the CD38 Gene on Social
Processing in an Oxytocin Challenge Study: Possible Links
to Autism
Carina Sauer*,1, Christian Montag2, Christiane Wo¨ rner1, Peter Kirsch1,3 and Martin Reuter2,3
Department of Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany;
Department of Differential and Biological Psychology, Rheinische Friedrich-Wilhelms-University, Bonn, Germany
The intranasal application of oxytocin (OT) has been shown to influence behavioral and neural correlates of social processing. These
effects are probably mediated by genetic variations within the OT system. One potential candidate could be the CD38 gene, which codes
for a transmembrane protein engaged in OT secretion processes. A common variation in this gene (rs3796863) was recently found to
e associated with autism spectrum disorders (ASD). Using an imaging genetics approach, we studied differential effects of an intranasal
OT application on neural processing of social stimuli in 55 healthy young men depending on their CD38 gene variant in a double-blind
placebo-controlled crossover design. Genotype had a significant influence on both behavioral and neuronal measures of social processing.
Homozygotic risk allele carriers showed slower reaction times (RT) and higher activation of left fusiform gyrus during visual processing of
social stimuli. Under OT activation differences between genotypes were more evident (though not statistically significantly increased) and
RT were accelerated in homozygotic risk allele carriers. According to our data, rs3796863 mainly influences fusiform gyrus activation, an
rea which has been widely discussed in ASD research. OT seems to modulate this effect by enhancing activation differences between
llele groups, which suggests an interaction between genetic makeup and OT availability on fusiform gyrus activation. These results
support recent approaches to apply OT as a pharmacological treatment of ASD symptoms.
Keywords: oxytocin; CD38; social processing; imaging genetics; autism
INTRODUCTION"
e6b45d5a86092bbfdcd6c3c54cda3d6c3ac6b227,Pairwise Relational Networks for Face Recognition,"Pairwise Relational Networks for Face
Recognition
Bong-Nam Kang1[0000−0002−6818−7532], Yonghyun Kim2[0000−0003−0038−7850],
nd Daijin Kim1,2[0000−0002−8046−8521]
Department of Creative IT Engineering, POSTECH, Korea
Department of Computer Science and Engineering, POSTECH, Korea"
e68b1fdc4e515f947c96f65ec7ac2521edbc06b2,ROS Wrapper for Real-Time Multi-Person Pose Estimation with a Single Camera,"Technical Report
IRI--TR-17-02
ROS Wrapper
for Real-Time Multi-Person
Pose Estimation
with a Single Camera
Autor
Miguel Arduengo
Sven Jens Jorgensen
Supervisors
Kimberly Hambuchen
Luis Sentis
Francesc Moreno
Guillem Alenyà
July 2017
Institut de Robòtica i Informàtica Industrial"
e6d8218e9859ecb4a0aab781493a2cac19632a63,Dynamic Models for Entity Trajectory Prediction Using Deep Learning,"Journal of Computers
Dynamic Models for Entity Trajectory Prediction Using
Deep Learning
Dhanya Raghu1*, Apoorva K H2, Anjana Anil Kumar3, S Natarajan4
PES Institute of Technology; Vijayanagar, Bangalore, Karnataka, India.
PES Institute of Technology; Rajarajeshwarinagar, Bangalore, Karnataka, India.
PES Institute of Technology; Rajajinagar, Bangalore, Karnataka, India.
PES University; Banashankari, Bangalore, Karnataka, India.
* Corresponding author. Tel.: +91-9731054855; email:
Manuscript submitted May 15, 2018; accepted July 8, 2018."
e624c73e3057a1de75e9d6d7e813771154ff1375,Incorporating Scalability in Unsupervised Spatio- Temporal Feature Learning,"INCORPORATING SCALABILITY IN UNSUPERVISED SPATIO-TEMPORAL FEATURE
LEARNING
Sujoy Paul, Sourya Roy and Amit K. Roy-Chowdhury
Dept. of Electrical and Computer Engineering, University of California, Riverside, CA 92521"
e654320739770029ec5cb22174772c935478b237,Paraphrase Thought: Sentence Embedding Module Imitating Human Language Recognition,"Paraphrase Thought: Sentence Embedding Module Imitating
Human Language Recognition
Myeongjun Jang 1 Pilsung Kang 1"
e6aadde93aedc06525523415e574507cf5c8cc44,End-to-end optimization of goal-driven and visually grounded dialogue systems,"End-to-end optimization of goal-driven and visually grounded dialogue systems
Florian Strub
Univ. Lille, CNRS, Centrale Lille, Inria,
UMR 9189 - CRIStAL, F-59000 Lille, France
Harm de Vries
University of Montreal
Jeremie Mary
Univ. Lille, CNRS, Centrale Lille, Inria,
UMR 9189 - CRIStAL, F-59000 Lille, France
Bilal Piot
DeepMind"
e63f4867c73eff9ff7cdf31246585a6915acef57,Digging Into Self-Supervised Monocular Depth Estimation,"Digging Into Self-Supervised
Monocular Depth Estimation
Cl´ement Godard
Oisin Mac Aodha
Gabriel J. Brostow"
e6af98d1567dad534262ec0863264bb26157533f,ON MULTI-SCALE DIFFERENTIAL FEATURES AND THEIR REPRESENTATIONS FOR IMAGE RETRIEVAL AND RECOGNITION,"ON MULTI-SCALE DIFFERENTIAL FEATURES AND THEIR
REPRESENTATIONS FOR IMAGE RETRIEVAL AND RECOGNITION
A Dissertation Presented
SRINIVAS S. RAVELA
Submitted to the Graduate School of the
University of Massachusetts Amherst in partial fulfillment
of the requirements for the degree of
DOCTOR OF PHILOSOPHY
February 2003
Department of Computer Science"
e6d8f332ae26e9983d5b42af4466ff95b55f2341,Pose-Normalized Image Generation for Person Re-identification,"Pose-Normalized Image Generation for Person Re-identification
Xuelin Qian1, Yanwei Fu1, Tao Xiang2, Wenxuan Wang1
Jie Qiu3, Yang Wu3, Yu-Gang Jiang1, Xiangyang Xue1
Fudan University; 2Queen Mary University of London;
Nara Institute of Science and Technology;"
e6d8ebfd88ee333deccce32b09ee41d271af6dc4,Grasp2Vec: Learning Object Representations from Self-Supervised Grasping,"Grasp2Vec: Learning Object Representations
from Self-Supervised Grasping
Eric Jang *,1, Coline Devin *,2,†, Vincent Vanhoucke1, and Sergey Levine1,2
*Both authors contributed equally
Google Brain
Department of Electrical Engineering and Computer Sciences, UC Berkeley
Work done while author was interning at Google Brain
{ejang, vanhoucke,"
e6ca412a05002b51d358c2e3061913c3dab6b810,MoFA: Model-Based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction,
e64b683e32525643a9ddb6b6af8b0472ef5b6a37,Face Recognition and Retrieval in Video,"Face Recognition and Retrieval in Video
Caifeng Shan"
e6865b000cf4d4e84c3fe895b7ddfc65a9c4aaec,The critical role of the cold-start problem and incentive systems in emotional Web 2 . 0 services,"Tobias Siebenlist, Kathrin Knautz
Chapter 15. The critical role of the
old-start problem and incentive systems
in emotional Web 2.0 services"
e6d689054e87ad3b8fbbb70714d48712ad84dc1c,Robust Facial Feature Tracking,"Robust Facial Feature Tracking
Fabrice Bourel, Claude C. Chibelushi, Adrian A. Low
School of Computing, Staffordshire University
Stafford ST18 0DG"
e6808679870e52a0945603a37d810146b1e2bada,Tı́tulo : Acquisition Scenario Analysis for Face Recognition at a Distance,"UNIVERSIDAD AUT ´ONOMA DE MADRID
ESCUELA POLIT´ECNICA SUPERIOR
DEPARTAMENTO DE TECNOLOG´IA Y DE LAS COMUNICACIONES
ACQUISITION SCENARIO ANALYSIS
FOR FACE RECOGNITION AT A
DISTANCE
–TRABAJO FIN DE M ´ASTER–
AN ´ALISIS DEL ESCENARIO DE ADQUISICI ´ON PARA RECONOCIMIENTO
BIOM ´ETRICO FACIAL A DISTANCIA
Author: Pedro Tom´e Gonz´alez
Ingeniero de Telecomunicaci´on,
Universidad Aut´onoma de Madrid
A Thesis submitted for the degree of:
M´aster Oficial en Ingenier´ıa Inform´atica y de Telecomunicaci´on
(Master of Science)
Madrid, October 2010"
e6beb5d95fa262b8717cc264d79a879285db15d4,Towards Transparent AI Systems: Interpreting Visual Question Answering Models,"Towards Transparent AI Systems:
Interpreting Visual Question Answering Models
Yash Goyal, Akrit Mohapatra, Devi Parikh, Dhruv Batra
{ygoyal, akrit, parikh,
Virginia Tech"
10ce3a4724557d47df8f768670bfdd5cd5738f95,Fisher Light-Fields for Face Recognition across Pose and Illumination,"Fihe igh	Fied f Face Recgii
Ac e ad 
	
iai
Rah G 
ai ahew ad Si
 Bake
The Rbic 
i	e Caegie e Uiveiy
5000 Fbe Ave	e ib	gh A 15213
Abac. 
 
ay face ecgii ak he e ad i	
iai
dii f he be ad gaey i
age ae di(cid:11)ee. 
 he cae

	ie gaey  be i
age 
ay be avaiabe each ca	ed f

di(cid:11)ee e ad 	de a di(cid:11)ee i	
iai. We e a face
ecgii agih
 which ca 	e ay 	
be f gaey i
age e
	bjec ca	ed a abiay e ad 	de abiay i	
iai
d ay 	
be f be i
age agai ca	ed a abiay e ad
de abiay i	
iai. The agih
 eae by ei
aig he
Fihe igh	(cid:12)ed f he 	bjec head f
 he i	 gaey  be
i
age. achig bewee he be ad gaey i he ef
ed 	ig
he Fihe igh	(cid:12)ed.

d	ci

 
ay face ecgii ceai he e f he be ad gaey i
age ae
di(cid:11)ee. The gaey cai he i
age 	ed d	ig aiig f he agih
.
The agih
 ae eed wih he i
age i he be e. F exa
e he"
10a285260e822b49023c4324d0fbbca7df8e128b,Objects2action: Classifying and Localizing Actions without Any Video Example,"Objects2action: Classifying and localizing actions without any video example
Mihir Jain(cid:63)
Jan C. van Gemert(cid:63)‡
Thomas Mensink(cid:63)
Cees G. M. Snoek(cid:63)†
(cid:63)University of Amsterdam ‡Delft University of Technology
Qualcomm Research Netherlands"
10773e5c1bc8a9a901a8baf4d0b891397975ea9d,Group encoding of local features in image classification,"1st International Conference on Pattern Recognition (ICPR 2012)
November 11-15, 2012. Tsukuba, Japan
978-4-9906441-1-6 ©2012 IAPR"
10554bc4fb045d303abee266bc2c548dae5e187d,Identifying Synapses Using Deep and Wide Multiscale Recursive Networks.,"Identifying Synapses using Deep and Wide Multiscale
Recursive Networks
Gary B. Huang and Stephen Plaza
Janelia Farm Research Campus
Howard Hughes Medical Institute
9700 Helix Drive, Ashburn, VA, USA
{huangg,"
10b3afc6a10149cd88bc6f4007b41895d661d5fe,SAN: Learning Relationship Between Convolutional Features for Multi-scale Object Detection,"SAN: Learning Relationship between
Convolutional Features
for Multi-Scale Object Detection
Yonghyun Kim1[0000−0003−0038−7850], Bong-Nam Kang2[0000−0002−6818−7532],
nd Daijin Kim1[0000−0002−8046−8521]
Department of Computer Science and Engineering, POSTECH, Korea
Department of Creative IT Engineering, POSTECH, Korea"
105fdf31d14ec55fda91c05059ec83162ba7ce3a,Automatic feature generation and selection in predictive analytics solutions,AutomaticfeaturegenerationandselectioninpredictiveanalyticssolutionsSuzannevandenBosch
10413ae7de5b234f5bdc5560a168fa2c2964a1c4,Public Document Title of Deliverable : Validation of a 'chimeric' Approach to Biometric Data Distribution Document Evolution Executive Summary,"Project N° IST-2002-507634 - BioSecure
D2.1.5– Revision: b3
9 July 2007
Contract Number :
Project Acronym :
Project Title :
Instrument :
Start Date of Project :
Duration :
Deliverable Number :
Title of Deliverable :
Contractual Due Date :
Actual Date of Completion :
IST-2002-507634
BioSecure
Biometrics for Secure Authentication
Network of Excellence
01 June, 2004
6 months
D2.1.5"
10f641aabdd8bc1eb87fae74c63b814d8ef274a5,Automatic Single-Image People Segmentation and Removal for Cultural Heritage Imaging,"Automatic Single-Image People Segmentation
nd Removal for Cultural Heritage Imaging
Marco Manfredi, Costantino Grana, and Rita Cucchiara
Universit`a degli Studi di Modena e Reggio Emilia, Modena MO 41125, Italy"
10554295addeae86571a26de6c2ad7e274963953,Re-ranking Object Proposals for Object Detection in Automatic Driving,"Re-ranking Object Proposals for Object Detection in Automatic
Driving
Zhun Zhong1, Mingyi Lei1, Shaozi Li1, Jianping Fan2"
104dd4963f7f0ef03fe09d505d31966666f9281d,Salient Object Subitizing,"Noname manuscript No.
(will be inserted by the editor)
Salient Object Subitizing
Jianming Zhang · Shugao Ma · Mehrnoosh Sameki · Stan Sclaroff ·
Margrit Betke · Zhe Lin · Xiaohui Shen · Brian Price · Radom´ır Mˇech
Received: date / Accepted: date"
10d8a48deae967b627839cc95c98b6c080ba9966,Overview of the ImageCLEF 2013 Scalable Concept Image Annotation Subtask,"Overview of the ImageCLEF 2013 Scalable
Concept Image Annotation Subtask
Mauricio Villegas,† Roberto Paredes† and Bart Thomee‡
ITI/DSIC, Universitat Polit`ecnica de Val`encia
Cam´ı de Vera s/n, 46022 Val`encia, Spain
Yahoo! Research
Avinguda Diagonal 177, 08018 Barcelona, Spain"
1068f6eca07c35426ca67961f00c3cac4866f155,Bilinear Models for 3-D Face and Facial Expression Recognition,"Bilinear Models for 3D Face and Facial
Expression Recognition
Iordanis Mpiperis, Sotiris Malassiotis and Michael G. Strintzis, Fellow,"
101c7bfc56091b627886636afcf1103c1cecccf6,Rapid Clothing Retrieval via Deep Learning of Binary Codes and Hierarchical Search,"Rapid Clothing Retrieval via Deep Learning of Binary
Codes and Hierarchical Search
Kevin Lin
Academia Sinica, Taiwan
Huei-Fang Yang
Academia Sinica, Taiwan
Kuan-Hsien Liu
Academia Sinica, Taiwan
Jen-Hao Hsiao
Yahoo! Taiwan
Chu-Song Chen
Academia Sinica, Taiwan"
1042683cf5733244238198ff486d3a65e70c9621,End-to-End Instance Segmentation with Recurrent Attention,"End-to-End Instance Segmentation with Recurrent Attention
Mengye Ren1, Richard S. Zemel1,2
University of Toronto1, Canadian Institute for Advanced Research2"
10ca3d8802ab0cc6ce000682a42fd9f6575a2006,Embedding Semantic Information into the Content of Natural Scenes Images,"http://dx.doi.org/10.5755/j01.eee.18.9.2808
ELEKTRONIKA IR ELEKTROTECHNIKA, ISSN 1392-1215, VOL. 18, NO. 9, 2012
Embedding Semantic Information into the
Content of Natural Scenes Images
G. Kazakeviciute-Januskeviciene1, E. Januskevicius2
Department of Graphical systems, Vilnius Gediminas Technical University,
Saulėtekio av.11, Vilnius, Lithuania, phone: +370 5 2744848
Department of Building Structures, Vilnius Gediminas Technical University,
Pylimo St. 26/1, Vilnius, Lithuania; phone: +370 5 2745205"
102e374347698fe5404e1d83f441630b1abf62d9,Facial Image Analysis for Fully Automatic Prediction of Difficult Endotracheal Intubation,"Facial Image Analysis for Fully-Automatic
Prediction of Difficult Endotracheal Intubation
Gabriel L. Cuendet, Student Member, IEEE, Patrick Schoettker, Anıl Y¨uce Student Member, IEEE, Matteo Sorci,
Hua Gao, Christophe Perruchoud, Jean-Philippe Thiran, Senior Member, IEEE"
102b27922e9bd56667303f986404f0e1243b68ab,Multiscale recurrent regression networks for face alignment,"Wang et al. Appl Inform  (2017) 4:13
DOI 10.1186/s40535-017-0042-5
RESEARCH
Multiscale recurrent regression networks
for face alignment
Open Access
Caixun Wang1,2,3, Haomiao Sun1,2,3, Jiwen Lu1,2,3*, Jianjiang Feng1,2,3 and Jie Zhou1,2,3
*Correspondence:
State Key Lab of Intelligent
Technologies and Systems,
Beijing 100084, People’s
Republic of China
Full list of author information
is available at the end of the
rticle"
10384cbe0ed2c44c4d0059745d8bf1509be75941,iQIYI-VID: A Large Dataset for Multi-modal Person Identification,"iQIYI-VID: A Large Dataset for Multi-modal Person Identification
Yuanliu Liu, Peipei Shi, Bo Peng, He Yan, Yong Zhou, Bing Han, Yi Zheng, Chao Lin,
Jianbin Jiang,Yin Fan, Tingwei Gao, Ganwen Wang, Jian Liu, Xiangju Lu, Danming Xie
iQIYI, Inc."
1099d475ee0807fc0e4aec55b636db4abc01dcb6,Perceptual Principles for Video Classification With Slow Feature Analysis,"Perceptual principles for video classification with
Slow Feature Analysis
Christian Th´eriault(1), Nicolas Thome(1), Matthieu Cord(1), Patrick P´erez(2)
(1)UPMC-Sorbonne Universities, Paris, France (2)Technicolor, France"
10be82098017fc2d60b0572cea8032afabad5d1a,A Dataset for Multimodal Question Answering in the Cultural Heritage Domain,"Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities (LT4DH),
pages 10–17, Osaka, Japan, December 11-17 2016."
10d710c01acb10c4aea702926d21697935656c3d,Infrared Colorization Using Deep Convolutional Neural Networks,"Infrared Colorization Using Deep Convolutional
Neural Networks
Matthias Limmer∗, Hendrik P.A. Lensch†
Daimler AG, Ulm, Germany
Department of Computer Graphics, Eberhard Karls Universität, Tübingen, Germany"
100641ed8a5472536dde53c1f50fa2dd2d4e9be9,Visual attributes for enhanced human-machine communication,"Visual Attributes for Enhanced Human-Machine Communication*
Devi Parikh1"
10114df7ddbb221337cc1e99e1de0eab8e47c95d,Evaluating Feature Importance for Re-identification,"Chapter 9
Evaluating Feature Importance for
Re-Identification
Chunxiao Liu, Shaogang Gong, Chen Change Loy, and Xinggang Lin"
109df0e8e5969ddf01e073143e83599228a1163f,Scheduling heterogeneous multi-cores through performance impact estimation (PIE),"Scheduling Heterogeneous Multi-Cores through
Performance Impact Estimation (PIE)
Kenzo Van Craeynest•∗ Aamer Jaleel†
Lieven Eeckhout•
Paolo Narvaez†
Joel Emer†‡
Ghent University•
Ghent, Belgium
{kenzo.vancraeynest,
Intel Corporation, VSSAD†
{aamer.jaleel,paolo.narvaez,
Hudson, MA
Cambridge, MA"
1060e223710a9472ffa5bb68bbb4d629014f7dbf,Title of thesis: REAL-TIME POSE BASED HUMAN DETECTION AND RE-IDENTIFICATION WITH A SINGLE CAMERA FOR ROBOT PERSON FOLLOWING,
10678172baa93d8318dd1945d09f38721a0c1ffa,A Comparison of Adaptive Appearance Methods for Tracking Faces in Video Surveillance,"A Comparison of Adaptive Appearance Methods for Tracking
Faces in Video Surveillance
M. Ali Akber Dewan*, E. Granger*, F. Roli†, R. Sabourin*, and G. L. Marcialis†
*Laboratoire d’imagerie, de vision et d’intelligence artificielle, École de technologie supérieure,
Université du Québec, Montréal, Canada
Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d'Armi, Cagliari, Italy
Keywords:  Biometrics,  Face  Tracking,  Spatiotemporal  Face
Recognition,  Video  Surveillance,  On-Line  and  Incremental
Learning, Adaptive Appearance Methods."
103590b36d026928a90eae7ade9d7da318202168,Indoor Scene Recognition Using Local Semantic Concepts,"Indoor Scene Recognition Using Local Semantic
Concepts
Elham Seifossadat1, Niloofar Gheissari2 and Ali Fanian3
Electrical and Computer Department,Isfahan University of Technology
Isfahan, Iran
Electrical and Computer Department,Isfahan University of Technology
Isfahan, Iran
3 Electrical and Computer Department,Isfahan University of Technology
Isfahan, Iran"
1048c753e9488daa2441c50577fe5fdba5aa5d7c,Recognising faces in unseen modes: A tensor based approach,"Recognising faces in unseen modes: a tensor based approach
Santu Rana, Wanquan Liu, Mihai Lazarescu and Svetha Venkatesh
{santu.rana, wanquan, m.lazarescu,
Dept. of Computing, Curtin University of Technology
GPO Box U1987, Perth, WA 6845, Australia."
10177800e4d1dfc0c88460b52e746b336bd393db,Discovering objects and their location in images with Latent Dirichlet Allocation,"Discovering objects and their location in images with Latent Dirichlet Allocation
Bryan C. Russell∗
Research Supervisor: William T. Freeman"
1059729bcca57731c81d8a9c866ceb8ed3547d8d,Coupled Object Detection and Tracking from Static Cameras and Moving Vehicles,"Coupled Object Detection and Tracking from
Static Cameras and Moving Vehicles
Bastian Leibe, Konrad Schindler, Nico Cornelis, and Luc Van Gool"
106150b707a31f0825bdae44eca4139b715547d6,Robust Semantic Segmentation with Ladder-DenseNet Models,"Robust Semantic Segmentation with Ladder-DenseNet Models
Ivan Kreˇso
Marin Orˇsi´c
Petra Bevandi´c
Siniˇsa ˇSegvi´c
Faculty of Electrical Engineering and Computing
University of Zagreb, Croatia"
10fb32ef34f815e9056ba71bc4b67a9951b4475b,End-to-End Audio Visual Scene-Aware Dialog using Multimodal Attention-Based Video Features,"End-to-End Audio Visual Scene-Aware Dialog using
Multimodal Attention-Based Video Features
Chiori Hori†, Huda Alamri∗†, Jue Wang†, Gordon Wichern†,
Vincent Cartillier∗, Raphael Gontijo Lopes∗, Abhishek Das∗,
Takaaki Hori†, Anoop Cherian†, Tim K. Marks†,
Irfan Essa∗, Dhruv Batra∗ Devi Parikh∗,
Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA, USA
School of Interactive Computing, Georgia Tech"
10c4b2489d7e1ee43a1d19724d3c1e9c33ca3f29,A Question-Answering framework for plots using Deep learning,"A Question-Answering framework for plots using Deep learning
Revanth Reddy1, Rahul Ramesh1, Ameet Deshpande1 and Mitesh M. Khapra1
Indian Institute of Technology Madras"
10916d4eeacbf63a178c229868160189c6ce8850,Extraction of Illumination Invariant Features using Fuzzy Threshold based Approach,"International Conference on Intelligent Systems and Data Processing (ICISD) 2011
Special Issue published by International Journal of Computer Applications® (IJCA)
Extraction of Illumination Invariant Features using
Fuzzy Threshold based Approach
R. M. Makwana
V. K. Thakar
N.C. Chauhan
Dept. of Computer Engineering
A. D. Patel Inst. of Technology,
S.P. University, New V.V. Nagar
Dept. of Electronics and Commu.
A. D. Patel Inst. of Technology
S.P. University, New V.V. Nagar
Dept. of Information Technology
A. D. Patel Inst. of Technology
S.P. University, New V.V. Nagar
in  unconstrained  environment"
10261848b16292a5c8c700de6c6c9f692867c9c8,Cleaning Training-Datasets with Noise-Aware Algorithms,"Cleaning Training-Datasets with Noise-Aware Algorithms
Instituto Nacional de Astrof´ısica ´Optica y Electr´onica,
H. Jair Escalante
Computer Science Department
Tonantzintla, Puebla, 72840, M´exico"
10d3f77225eca1d576268ba84ed83f230a5e47c4,Crafting a multi-task CNN for viewpoint estimation,"F. MASSA ET AL.: CRAFTING A MULTI-TASK CNN FOR VIEWPOINT ESTIMATION
Crafting a multi-task CNN
for viewpoint estimation
Francisco Massa
http://imagine.enpc.fr/~suzano-f/
Renaud Marlet
http://imagine.enpc.fr/~marletr/
Mathieu Aubry
http://imagine.enpc.fr/~aubrym/
LIGM, UMR 814, Imagine,
Ecole des Ponts ParisTech,
UPEM,ESIEE Paris, CNRS, UPE
Champs-sur-Marne, France"
100f57d2eb737d6cb467bfac6e4bbfa9b39e774f,Mixing Body-Part Sequences for Human Pose Estimation,"Mixing Body-Part Sequences for Human Pose Estimation
Anoop Cherian∗
Julien Mairal∗ Karteek Alahari∗ Cordelia Schmid∗
Inria"
1040a32d5bd5e6f4c8bc1932345ef93671e2c019,Real-time RGB-D based template matching pedestrian detection,"Real-Time RGB-D based Template Matching Pedestrian Detection
Omid Hosseini jafari and Michael Ying Yang"
101569eeef2cecc576578bd6500f1c2dcc0274e2,Multiaccuracy: Black-Box Post-Processing for Fairness in Classification,"Multiaccuracy: Black-Box Post-Processing for Fairness in
Michael P. Kim∗†
Classification
Amirata Ghorbani∗
James Zou"
10c7575e7db69a208bfb21e3fc0cbc3f7698e99d,New sparse representation methods; application to image compression and indexing. (Nouvelles méthodes de représentations parcimonieuses ; application à la compression et l'indexation d'images),"New sparse representation methods; application to
image compression and indexing
Joaquin Zepeda Salvatierra
To cite this version:
Joaquin Zepeda Salvatierra. New sparse representation methods; application to image compression
nd indexing. Human-Computer Interaction [cs.HC]. Université Rennes 1, 2010. English. <tel-
00567851>
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https://tel.archives-ouvertes.fr/tel-00567851
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scientifiques de niveau recherche, publiés ou non,
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10bb4ef7a6719ea132e00f0ab5680919a4131d99,BAM: Bottleneck Attention Module,"PARK, WOO, LEE, KWEON: BOTTLENECK ATTENTION MODULE
BAM: Bottleneck Attention Module
Jongchan Park*†1
Sanghyun Woo*2
Joon-Young Lee3
In So Kweon2
Lunit Inc.
Seoul, Korea
Korea Advanced Institute of Science
nd Technology (KAIST)
Daejeon, Korea
Adobe Research
San Jose, CA, USA"
10c077bf2dd1bed928926feb37837862ab786808,"Multiple Target Tracking and Identity Linking under Split, Merge and Occlusion of Targets and Observations","Multiple target tracking and identity linking under split, merge and
occlusion of targets and observations
nonymous submission
Keywords:
Tracking, graphical models, MAP inference, particle tracking, live cell tracking, intelligent headlights."
10e3b9fe646c6e81ec824cdc2391cc412a1b2730,Solving Three Czech NLP Tasks End-to-End with Neural Models,"S. Krajˇci (ed.): ITAT 2018 Proceedings, pp. 138–143
CEUR Workshop Proceedings Vol. 2203, ISSN 1613-0073, c(cid:13) 2018 Jindˇrich Libovický, Rudolf Rosa, Jindˇrich Helcl, and Martin Popel"
1038aa6c1f63c1de9045f10e47ed573810cb4a52,A Video-Based Method for Objectively Rating Ataxia,"A Video-Based Method for Objectively Rating Ataxia
Ronnachai Jaroensri∗1, Amy Zhao∗1, Guha Balakrishnan1, Derek Lo2, Jeremy Schmahmann3,
John Guttag1, and Fr´edo Durand1
MIT CSAIL 2Yale University 3Massachusetts General Hospital"
10cdb31a23c3233527ad2f8beebe7803b7a51a8c,Altered Neocortical Microcircuitry in the Valproic Acid Rat Model of Autism,"Altered Neocortical Microcircuitry in the
Valproic Acid Rat Model of Autism
THÈSE N° 3701 (2006)
PRÉSENTÉE LE 20 NOVEMBRE
À LA FACULTÉ DES SCIENCES DE LA VIE
LABORATOIRE DE NEUROSCIENCE DES MICROCIRCUITS
PROGRAMME DOCTORAL EN NEUROSCIENCES
ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE
POUR L’OBTENTION DU GRADE DE DOCTEUR ÈS SCIENCES
Tania Rinaldi
ingénieur chimiste diplômée EPF
de nationalité suisse et originaire de Vouvry (VS)
cceptée sur proposition du jury:
Prof. R. Schneggenburger, président du jury
Prof. H. Markram, directeur de thèse
Prof. B. Gähwiler, rapporteur
Prof. A. Lüthi, rapporteur
Prof. C. Petersen, rapporteur
Suisse
(2006) année d’impression"
10d39dedfaf34d862e3ca7216521c6290044ff87,Synthesized Classifiers for Zero-Shot Learning,"Synthesized Classifiers for Zero-Shot Learning
Soravit Changpinyo∗, Wei-Lun Chao∗
U. of Southern California
Los Angeles, CA
Boqing Gong
U. of Central Florida
Orlando, FL
schangpi,
Fei Sha
U. of California
Los Angeles, CA"
4c1ef2a628627798939dccc072d33f9e12b48640,Advanced Hybrid Color Space Normalization for Human Face Extraction and Detection,"IJSRD - International Journal for Scientific Research & Development| Vol. 1, Issue 4, 2013 | ISSN (online): 2321-0613
Advanced Hybrid Color Space Normalization for Human Face
Extraction and Detection
Jayakrishna.V1  Akhila G.P.2 Shafeena Basheer3
, 2Faculty  3PG Student
, 3Amal Jyothi College of Engineering, Kanjirappally
UKF College of Engineering &Technology,Parippally
S.P.B.Patel Engineering College, Mehsana, Gujarat
(CSN)
technique
enhancing
is  contained
in  Y  component,  and"
4cdfef0fec0918dcf5c40b9b53c9e3f48be0462b,Unsupervised robotic sorting: Towards autonomous decision making robots,"Unsupervised robotic sorting:
Towards autonomous decision making
robots
Joris Gu´erin, St´ephane Thiery, Eric Nyiri and Olivier Gibaru
Arts et M´etiers ParisTech, Lille, FRANCE"
4c500c84e16e5ebb50b33f9bcff36854e5131c16,All-Transfer Learning for Deep Neural Networks and its Application to Sepsis Classification,"All-Transfer Learning for Deep Neural Networks and
its Application to Sepsis Classification
Yoshihide Sawada1 and Yoshikuni Sato2 and Toru Nakada2 and Kei Ujimoto2 and Nobuhiro Hayashi3"
4c39000bbd6761dd9e5609fe310af51facb835a9,Kinects and human kinetics : A new approach for studying pedestrian behavior,"This paper might be a pre-copy-editing or a post-print author-produced .pdf of an article accepted for publication. For the
definitive publisher-authenticated version, please refer directly to publishing house’s archive system."
4c81789b13b016afc5ef47591268533674c2a3f6,Mapless Online Detection of Dynamic Objects in 3D Lidar,"Mapless Online Detection of Dynamic Objects in 3D Lidar
David J. Yoon, Tim Y. Tang, and Timothy D. Barfoot
model-free,"
4ca8ff09f24f0838022f1d0b94af4331f6e538cd,Semantic Parsing to Probabilistic Programs for Situated Question Answering,"Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 160–170,
Austin, Texas, November 1-5, 2016. c(cid:13)2016 Association for Computational Linguistics"
4cfa2fe87c250534fd2f285c2300e7ca2cd9e325,"Visual, Auditory, and Cross Modal Sensory Processing in Adults with Autism: An EEG Power and BOLD fMRI Investigation","ORIGINAL RESEARCH
published: 19 April 2016
doi: 10.3389/fnhum.2016.00167
Visual, Auditory, and Cross Modal
Sensory Processing in Adults with
Autism: An EEG Power and BOLD
fMRI Investigation
Elizabeth’ C. Hames1, Brandi Murphy2, Ravi Rajmohan3, Ronald C. Anderson1,
Mary Baker1*, Stephen Zupancic2, Michael O’Boyle4 and David Richman5
Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA, 2 Department of Audiology,
Texas Tech University Health Sciences Center, Lubbock, TX, USA, 3 Department of Pharmacology and Neuroscience, Texas
Tech University Health Sciences Center, Lubbock, TX, USA, 4 College of Human Sciences, Texas Tech University, Lubbock,
TX, USA, 5 Burkhart Center for Autism Education and Research, Texas Tech University, Lubbock, TX, USA
Electroencephalography (EEG) and blood oxygen level dependent functional magnetic
resonance imagining (BOLD fMRI) assessed the neurocorrelates of sensory processing
of visual and auditory stimuli
in 11 adults with autism (ASD) and 10 neurotypical (NT)
ontrols between the ages of 20–28. We hypothesized that ASD performance on
ombined audiovisual trials would be less accurate with observable decreased EEG
power across frontal, temporal, and occipital channels and decreased BOLD fMRI"
4c4e49033737467e28aa2bb32f6c21000deda2ef,Improving Landmark Localization with Semi-Supervised Learning,"Improving Landmark Localization with Semi-Supervised Learning
Sina Honari1∗, Pavlo Molchanov2, Stephen Tyree2, Pascal Vincent1,4,5, Christopher Pal1,3, Jan Kautz2
MILA-University of Montreal, 2NVIDIA, 3Ecole Polytechnique of Montreal, 4CIFAR, 5Facebook AI Research.
{honaris,
{pmolchanov, styree,"
4cfd15e9d3c01028bcda22e68791a95aa54c2a7c,"DeepLesion: Automated Deep Mining, Categorization and Detection of Significant Radiology Image Findings using Large-Scale Clinical Lesion Annotations","DeepLesion: Automated Deep Mining, Categorization
nd Detection of Significant Radiology Image Findings
using Large-Scale Clinical Lesion Annotations
Ke Yan(cid:63), Xiaosong Wang(cid:63), Le Lu, and Ronald M. Summers
Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, MD 20892
{ke.yan, xiaosong.wang, le.lu,"
4c05dc45b82b79e87f7b337ccf9f48d537c0e6e2,Exploring Heterogeneity within a Core for Improved Power Efficiency,"Exploring Heterogeneity within a Core for
Improved Power Efficiency
Sudarshan Srinivasan, Nithesh Kurella, Israel Koren, Fellow, IEEE, and Sandip Kundu, Fellow, IEEE"
4c822705edd305d04f2c02ac9b1b73421e857961,Towards fully automated person re-identification,"Towards Fully Automated Person Re-Identification
Matteo Taiana, Dario Figueira, Athira Nambiar, Jacinto Nascimento and Alexandre Bernardino
Institute for Systems and Robotics, IST, Lisboa, Portugal
Re-Identification, Pedestrian Detection, Camera Networks, Video Surveillance
Keywords:"
4c5a07ab1700a67afaf16fc9a7a2647f51358255,DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection,"DeepSaliency: Multi-Task Deep Neural Network
Model for Salient Object Detection
Xi Li, Liming Zhao, Lina Wei, Ming-Hsuan Yang, Fei Wu, Yueting Zhuang, Haibin Ling, and Jingdong Wang"
4c477ba5513ec9c629ca3442c1fee15612259905,Complex Relations in a Deep Structured Prediction Model for Fine Image Segmentation,"Complex Relations in a Deep Structured Prediction
Model for Fine Image Segmentation
Cristina Mata, Guy Ben-Yosef, Boris Katz
Computer Science and Artificial Intelligence Laboratory
{cfmata, gby,
Center for Brains, Minds and Machines"
4c6d6bb5bafba9e04d8f2ce128be71fba1d1e0e8,Human parsing with a cascade of hierarchical poselet based pruners,"HUMAN PARSING WITH A CASCADE OF HIERARCHICAL POSELET BASED PRUNERS
Duan Tran†
Yang Wang‡
University of Illinois at Urbana Champaign†
David Forsyth†
University of Manitoba‡"
4c4454aa7a2a244c678f507a982fe8827ba419bb,Adversarial Examples for Semantic Image Segmentation,"Workshop track - ICLR 2017
ADVERSARIAL EXAMPLES FOR
SEMANTIC IMAGE SEGMENTATION
Volker Fischer1, Mummadi Chaithanya Kumar2, Jan Hendrik Metzen1 & Thomas Brox2
Bosch Center for Artificial Intelligence, Robert Bosch GmbH
University of Freiburg
{volker.fischer,"
4c797506d610525591288f813621b271ce879452,The automaticity of face perception is influenced by familiarity,"Atten Percept Psychophys (2017) 79:2202–2211
DOI 10.3758/s13414-017-1362-1
The automaticity of face perception is influenced by familiarity
Xiaoqian Yan 1 & Andrew W. Young 1 & Timothy J. Andrews 1
Published online: 5 July 2017
# The Author(s) 2017. This article is an open access publication"
4cf5437ef31e587435ba1531da9109d714c0ee3e,Vehicle Re-identification Using Quadruple Directional Deep Learning Features,"Vehicle Re-identification Using Quadruple
Directional Deep Learning Features
Jianqing Zhu, Huanqiang Zeng, Jingchang Huang, Shengcai Liao, Zhen Lei, Canhui Cai and LiXin Zheng"
4cbb370f0bb6af0052bad6855cf1d9776376ceb3,Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection,"Towards Safe Autonomous Driving: Capture Uncertainty in the Deep
Neural Network For Lidar 3D Vehicle Detection
Di Feng1, Lars Rosenbaum1, Klaus Dietmayer2"
4cfdd0c8313ac4f92845dcd658115beb115b97ce,Multi-Task Learning as Multi-Objective Optimization,"Multi-Task Learning as Multi-Objective Optimization
Ozan Sener
Intel Labs
Vladlen Koltun
Intel Labs"
4c0d7c0c4b0a1dd2fd11853ab98ea1fa79e0716b,Feature Extraction for Human Detection using HOG and CS-LBP methods,"International Journal of Computer Applications (0975 – 8887)
National Conference “Electronics, Signals, Communication and Optimization"" (NCESCO 2015)
Feature Extraction for Human Detection using HOG and
CS-LBP methods
Srujana B.J.
PG Student
Dept. of Computer Science and Engineering
PES College of Engineering, Mandya
The Histogram of Oriented Gradient (HOG) [2] [5] is a good
descriptor for human detection. HOG features are now widely
used  in  object  recognition  and  detection  [6].  They  describe
ody  shape  through  the  extraction  of  edge  directions  or
gradient directions in the window. Each region of the window
is  divided  into  64  blocks  with  each  block  having  32*32  in
dimensions.  Each  block  is  composed  of  2*2  cells.  A
histogram of oriented gradients is computed for each cell. The
final  descriptor  is  obtained  by  combining  all  the  block
features  in  a  window.  The  main  drawback  of  HOG  is  that,  it
produces too many feature patterns and is time consuming.
The  drawback  of  HOG  is  overcome  with  the  use  of  CS-LBP"
4c710141c3800b91d099893fc2ef5d902a5a3f9b,An Eye Detection System Based on Neural Autoassociators,"An eye detection system
ased on neural autoassociators
Monica Bianchini and Lorenzo Sarti
Dipartimento di Ingegneria dell’Informazione
Universit`a degli Studi di Siena
Via Roma 56 — 53100 Siena, ITALY"
4c60815ea05ff4b9f7301b28b8dd6017d64877bd,LIDAR-camera fusion for road detection using fully convolutional neural networks,"LIDAR-Camera Fusion for Road Detection
Using Fully Convolutional Neural Networks
Luca Caltagirone∗, Mauro Bellone, Lennart Svensson, Mattias Wahde
{luca.caltagirone, mauro.bellone, lennart.svensson,"
4c7659079b3df5bc746f76b2b1685b0b539832d6,Domain Adaptive Faster R-CNN for Object Detection in the Wild,"Domain Adaptive Faster R-CNN for Object Detection in the Wild
Yuhua Chen1 Wen Li1 Christos Sakaridis1 Dengxin Dai1
Luc Van Gool1,2
Computer Vision Lab, ETH Zurich
VISICS, ESAT/PSI, KU Leuven"
4cd30e654deedb5cab98ad73096ac73c9645860d,Data Imputation Through the Identification of Local Anomalies,"Data Imputation through the Identification of Local
Anomalies
Huseyin Ozkan, Ozgun S. Pelvan and Suleyman S. Kozat, Senior Member, IEEE"
4c73baaf624280b49eb73e5d72406fab5ae05011,On the impact of outliers on high-dimensional data analysis methods for face recognition,"On the Impact of Outliers on High-Dimensional Data
Analysis Methods for Face Recognition
Sid-Ahmed Berrani
France Telecom R&D – TECH/IRIS
, rue du Clos Courtel – BP 91226
5512 Cesson Sévigné Cedex, France
Christophe Garcia
France Telecom R&D – TECH/IRIS
, rue du Clos Courtel – BP 91226
5512 Cesson Sévigné Cedex, France"
4c0ce0ed9cc92115874be4397f6240769d3ed84f,The effect of familiarity on face adaptation.,"doi:10.1068/p6774
The effect of familiarity on face adaptation
Sarah Laurence, Graham Hole
School of Psychology, University of Sussex, Falmer, Brighton BN1 9QH, Sussex, UK;
e-mail:
Received 14 July 2010, in revised form 30 March 2011"
4cc5fb6cf48b2c58b283460b19f3beeb7e5b6a22,Clickage: towards bridging semantic and intent gaps via mining click logs of search engines,"Clickage: Towards Bridging Semantic and Intent Gaps
via Mining Click Logs of Search Engines
Xian-Sheng Hua, Linjun Yang, Jingdong Wang, Jing Wang
Ming Ye, Kuansan Wang, Yong Rui, Jin Li
Microsoft Corporation, One Microsoft Way, Redmond WA 98052, USA
{xshua; linjuny; jingdw; v-wangji; mingye; kuansanw; yongrui;"
4c1e47ba68b81d210718f837b197253164decaf0,Evaluation of Quality Factors for the Captured Facial Image,"International Journal of Computer Applications (0975 – 8887)
Volume 142 – No.10, May 2016
Evaluation of Quality Factors for the Captured Facial
Image
Abhay Goyal
M.Tech. Student
Department of ECE
SBSSTC, Ferozepur, Pujnab"
4ce68170f85560942ee51465e593b16560f9c580,Practical Matrix Completion and Corruption Recovery Using Proximal Alternating Robust Subspace Minimization,"(will be inserted by the editor)
Practical Matrix Completion and Corruption Recovery using
Proximal Alternating Robust Subspace Minimization
Yu-Xiang Wang · Choon Meng Lee · Loong-Fah Cheong · Kim-Chuan Toh
Introduction
Completing a low-rank matrix from partially observed
entries, also known as matrix completion, is a central
task in many real-life applications. The same abstrac-
tion of this problem has appeared in diverse fields such
s signal processing, communications, information re-
trieval, machine learning and computer vision. For in-
stance, the missing data to be filled in may correspond
to plausible movie recommendations (Koren et al 2009;
Funk 2006), occluded feature trajectories for rigid or
non-rigid structure from motion, namely SfM (Hart-
ley and Schaffalitzky 2003; Buchanan and Fitzgibbon
005) and NRSfM (Paladini et al 2009), relative dis-
tances of wireless sensors (Oh et al 2010), pieces of un-
ollected measurements in DNA micro-array (Friedland
et al 2006), just to name a few."
4c60a78722404bcbcd9afab4636993e79cf96c72,Learning Invariant Representations Of Planar Curves,"Published as a conference paper at ICLR 2017
LEARNING INVARIANT REPRESENTATIONS OF
PLANAR CURVES
Gautam Pai, Aaron Wetzler & Ron Kimmel
Department of Computer Science
Technion-Israel Institute of Technology"
4c863a15c4da0d0ccd20c5897a4e33fb771fe3eb,The effect of forced choice on facial emotion recognition: a comparison to open verbal classification of emotion labels,"OPEN ACCESS
Research Article
The effect of forced choice on facial emotion recognition:
comparison to open verbal classification of emotion
labels
Der Effekt eines geschlossenen Antwortformats auf die mimische
Emotionserkennung: ein Vergleich mit der freien verbale Zuordnung von
Emotionswörtern
Kerstin
Limbrecht-Ecklundt1
Andreas Scheck1
Lucia Jerg-Bretzke1
Steffen Walter1
Holger Hoffmann1
Harald C. Traue1
University of Ulm, University
Clinic of Psychosomatic
Medicine and Psychotherapy,
Medical Psychology, Ulm,
Germany"
4c815f367213cc0fb8c61773cd04a5ca8be2c959,Facial expression recognition using curvelet based local binary patterns,"978-1-4244-4296-6/10/$25.00 ©2010 IEEE
ICASSP 2010"
4c5041f8b93fd71a851445e84bfca0d7d0c3bb9b,Enhancing Memory-Based Particle Filter with Detection-Based Memory Acquisition for Robustness under Severe Occlusion,"ENHANCING MEMORY-BASED PARTICLE FILTER WITH
DETECTION-BASED MEMORY ACQUISITION FOR ROBUSTNESS
UNDER SEVERE OCCLUSION
Dan Mikami, Kazuhiro Otsuka, Shiro Kumano and Junji Yamato
NTT Communication Science Laboratories, NTT, 3-1 Morinosato-Wakamiya, Atsugi, Kanagawa, 243-0198, Japan
Keywords:
Pose Tracking, Face Pose, Memory-based Prediction, Memory Acquisition."
4c88e41424022c7c5f111d34d931fae15f52a551,"CUR Decompositions, Similarity Matrices, and Subspace Clustering","CUR Decompositions, Similarity Matrices, and
Subspace Clustering
Akram Aldroubi, Keaton Hamm, Ahmet Bugra Koku, and Ali Sekmen"
4cfe0a11f11a2b8f9d16c4226280774de9a43f07,Can Object Detectors Aid Internet Video Event Retrieval ?,"Can Object Detectors Aid Internet Video Event Retrieval?
, b University of Amsterdam, Science Park 904, 1098 XG, Amsterdam, The Netherlands;
Davide Modolo a and Cees G.M. Snoekb"
4c6e1840451e1f86af3ef1cb551259cb259493ba,HAND POSTURE DATASET CREATION FOR GESTURE RECOGNITION,"HAND POSTURE DATASET CREATION FOR GESTURE
RECOGNITION
Instituto de Sistemas Inteligentes y Aplicaciones Numericas en Ingenieria
Luis Anton-Canalis
Campus Universitario de Tafira, 35017 Gran Canaria, Spain
Elena Sanchez-Nielsen
Departamento de E.I.O. y Computacion
8271 Universidad de La Laguna, Spain
Keywords:
Image understanding, Gesture recognition, Hand dataset."
4c987baaf0587798a40b56d4fdc9e2518bdc139b,TuringBox: An Experimental Platform for the Evaluation of AI Systems,"TuringBox: An Experimental Platform for the Evaluation of AI Systems
Ziv Epstein * 1 Blakeley H. Payne * 1 Judy Hanwen Shen 1 Casey Jisoo Hong 1 Bjarke Felbo 1
Abhimanyu Dubey 1 Matthew Groh 1 Nick Obradovich 1 Manuel Cebrian 1 Iyad Rahwan 1"
4cf74211e635c73ca5816199ef33d10c3462beae,OF FACIAL EXPRESSION RECOGNITION SYSTEM AND USED DATASETS,"IJRET: International Journal of Research in Engineering and Technology       eISSN: 2319-1163 | pISSN: 2321-7308
REVIEW OF FACIAL EXPRESSION RECOGNITION SYSTEM AND
USED DATASETS
Shyna Dutta1, V.B. Baru2,
ME Student, Department of Electronics and Telecommunication, Sinhgad College of Engineering Vadgaon, Pune,
Associate Professor, Department of Electronics and Telecommunication, Sinhgad College of Engineering Vadgaon,"
4ce18536eec7917da848be6b5f783d3ee3d49677,Fast Face Detection in One Line of Code,"Fast Face Detection in One Line of Code
Michael Zucchi, B.E. (Comp. Sys. Eng.)
Unaliated, unfunded, personal research."
4c69da79843016d5d934464d3777030741978180,Neuromorphic Atomic Switch Networks,"Neuromorphic Atomic Switch Networks
Audrius V. Avizienis1.
Adam Z. Stieg2,3*, James K. Gimzewski1,2,3
, Henry O. Sillin1.
, Cristina Martin-Olmos1, Hsien Hang Shieh2, Masakazu Aono3,
Department of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, California, United States of America, 2 California NanoSystems Institute,
University of California Los Angeles, Los Angeles, California, United States of America, 3 World Premier International Center for Materials Nanoarchitectonics, National
Institute for Materials Science, Tsukuba, Ibaraki, Japan"
4c55ea9c04d46d60ec5789f4e4c3224c41360768,Dimensionality Reduction Using Similarity-Induced Embeddings,"IEEE Copyright Notice
Copyright c(cid:13)2017 IEEE
Personal use of this material is permitted. Permission from
IEEE must be obtained for all other uses, in any current or fu-
ture media, including reprinting/republishing this material for
dvertising or promotional purposes, creating new collective
works, for resale or redistribution to servers or lists, or reuse
of any copyrighted component of this work in other works.
Published in: IEEE Transactions on Neural Networks and
Learning Systems
URL: http://ieeexplore.ieee.org/document/8004500
DOI: 10.1109/TNNLS.2017.2728818
DOI 10.1109/TNNLS.2017.2728818 c(cid:13)2017 IEEE"
4c81c76f799c48c33bb63b9369d013f51eaf5ada,Multi-modal Score Fusion and Decision Trees for Explainable Automatic Job Candidate Screening from Video CVs,"Multi-modal Score Fusion and Decision Trees for Explainable Automatic Job
Candidate Screening from Video CVs
Heysem Kaya1, Furkan G¨urpınar2, and Albert Ali Salah2
Department of Computer Engineering, Namık Kemal University, Tekirda˘g, Turkey
Department of Computer Engineering, Bo˘gazic¸i University, Istanbul, Turkey"
86b105c3619a433b6f9632adcf9b253ff98aee87,A Mutual Information based Face Clustering Algorithm for Movies,"­4244­0367­7/06/$20.00 ©2006 IEEE
ICME 2006"
869a2fbe42d3fdf40ed8b768edbf54137be7ac71,Relative Attributes for Enhanced Human-Machine Communication,"Relative Attributes for Enhanced Human-Machine Communication
Devi Parikh1, Adriana Kovashka3, Amar Parkash2, and Kristen Grauman3
Toyota Technological Institute, Chicago
Indraprastha Institute of Information Technology, Delhi
University of Texas, Austin"
8645fe95f3f503f854b08096c2874a3f7ea6b79b,BoxCars: 3D Boxes as CNN Input for Improved Fine-Grained Vehicle Recognition,"BoxCars: 3D Boxes as CNN Input
for Improved Fine-Grained Vehicle Recognition
Jakub Sochor∗, Adam Herout, Jiˇr´ı Havel
Brno University of Technology
Brno, Czech Republic"
86dd8db0587b570ea2237c03cb0126ab3a53317c,A Novel Face Detection and Facial Feature Detection Algorithm using Skin Colour and Back Propagation Neural Network,"A Novel Face Detection and Facial Feature Detection
Algorithm using Skin Colour and Back Propagation
International Journal of Computer Applications (0975 – 8887)
Volume 90 – No 2, March 2014
Neural Network
Pijush Chakraborty
Student, Computer Science and Engineering,
Calcutta Institute of Engineering and Management,
Kolkata
Akashdeep Ghosh
Student, Information Technology,
RCC Institute of Information Technology,
Kolkata"
86904aee566716d9bef508aa9f0255dc18be3960,Learning Anonymized Representations with Adversarial Neural Networks,"Learning Anonymized Representations with
Adversarial Neural Networks
Cl´ement Feutry, Pablo Piantanida, Yoshua Bengio, and Pierre Duhamel"
869df5e8221129850e81e77d4dc36e6c0f854fe6,A metric for sets of trajectories that is practical and mathematically consistent,"A metric for sets of trajectories that is
practical and mathematically consistent
Jos´e Bento
Jia Jie Zhu"
8646f22a46b65c2018bc39ad3cbdb939e788a1fc,Learning a Confidence Measure for Optical Flow,"Learning a Confidence Measure
for Optical Flow
Oisin Mac Aodha, Ahmad Humayun, Marc Pollefeys and Gabriel J. Brostow"
8629c779581a0f46452bc4ca45b571bfbd3cd063,Defoiling Foiled Image Captions,"Defoiling Foiled Image Captions
Pranava Madhyastha, Josiah Wang and Lucia Specia
Department of Computer Science
University of Sheffield, UK
{p.madhyastha, j.k.wang,"
863b96896ffa920bf5ae6f41c4997741d47c3e17,Multithreading cascade of SURF for facial expression recognition,"Chen et al. EURASIP Journal on Image and Video
Processing  (2016) 2016:37
DOI 10.1186/s13640-016-0140-7
EURASIP Journal on Image
nd Video Processing
RESEARCH
Open Access
Multithreading cascade of SURF for facial
expression recognition
Jinhui Chen1*
, Zhaojie Luo2, Tetsuya Takiguchi3 and Yasuo Ariki3"
8623945e67548becb658ac2866c2fd28ad0aebac,Studying Human Face Recognition with the Gaze-Contingent Window Technique,"UC Merced
Proceedings of the Annual Meeting of the Cognitive Science
Society
Title
Studying Human Face Recognition with the Gaze-Contingent Window Technique
Permalink
https://escholarship.org/uc/item/5rt9n3c7
Journal
Proceedings of the Annual Meeting of the Cognitive Science Society, 26(26)
Authors
Maw, Naing Naing
Pomplun, Marc
Publication Date
004-01-01
Peer reviewed
eScholarship.org
Powered by the California Digital Library
University of California"
867fd4914a265b5dd4494f14273b8d28257c7b5b,A Comparison of FPGA and GPGPU Designs for Bayesian Occupancy Filters,"Article
A Comparison of FPGA and GPGPU Designs for
Bayesian Occupancy Filters
Luis Medina 1, Miguel Diez-Ochoa 2, Raul Correal 2, Sergio Cuenca-Asensi 1,
Alejandro Serrano 1 ID , Jorge Godoy 3, Antonio Martínez-Álvarez 1 and Jorge Villagra 3,* ID
University Institute for Computing Research, University of Alicante, 03690 San Vicente del Raspeig, Spain;
(L.M.); (S.C.-A.); (A.S.);
(A.M.-Á.)
Ixion Industry & Aerospace SL, Julian Camarilo 21B, 28037 Madrid, Spain; (M.D.-O.);
(R.C.)
Centre for Automation and Robotics (UPM-CSIC), 28500 Arganda del Rey, Spain;
* Correspondence: Tel.: +34-918-711-900
Received: 15 September 2017 ; Accepted: 6 November 2017 ; Published: 11 November 2017"
86e1bdbfd13b9ed137e4c4b8b459a3980eb257f6,The Kinetics Human Action Video Dataset,"The Kinetics Human Action Video Dataset
Will Kay
Jo˜ao Carreira
Karen Simonyan
Brian Zhang
Chloe Hillier
Sudheendra Vijayanarasimhan
Fabio Viola
Tim Green
Trevor Back
Paul Natsev
Mustafa Suleyman
Andrew Zisserman"
862f19f8317971fabc46cf0f994f4a8616f17b78,Human Re-identification through Distance Metric Learning based on Jensen-Shannon Kernel,"HUMAN RE-IDENTIFICATION THROUGH DISTANCE METRIC
LEARNING BASED ON JENSEN-SHANNON KERNEL
Yoshihisa Ijiri1, Shihong Lao2, Tony X. Han3 and Hiroshi Murase4
Corporate R&D, OMRON Corp., Kizugawa, Kyoto, Japan
OMRON Social Solutions Co. Ltd., Kizugawa, Kyoto, Japan
Electrical & Computer Engineering Dept., Univ. of Missouri, Columbia, MO, U.S.A.
Graduate School of Information Science, Nagoya Univ., Chigusaku, Nagoya, Japan
Keywords:
Human Re-identification, Distance Metric Learning, Jensen-Shannon Kernel."
8641593c67d87d81e528448a527e45fc9a5aa145,Complex Urban LiDAR Data Set,"Complex Urban LiDAR Data Set
Jinyong Jeong1, Younggun Cho1, Young-Sik Shin1, Hyunchul Roh1 and Ayoung Kim1
Fig. 1: This paper provides the complex urban data set including metropolitan area, apartment building complex and
underground parking lot. Sample scenes from the data set can be found in https://youtu.be/IguZjmLf5V0."
864f0e5e317a7d304dcc1dfca176b7afd230f4c2,Focal loss dense detector for vehicle surveillance,"Focal Loss Dense Detector for Vehicle Surveillance
Xiaoliang Wang, Peng Cheng, Xinchuan Liu, Benedict Uzochukwu"
86e19f1899e67f4bdab015ad46cb72d0fb9b01a1,A Computer-Aided System for Indexing People in Historical Images,"A Computer-Aided System for Indexing People in Historical Images
David Lunardi Flam, Camillo Jorge Santos Oliveira, Arnaldo de Albuquerque Araújo
Núcleo de Processamento Digital de Imagens – NPDI
Departamento de Ciência da Computação – DCC/ICEX
Universidade Federal de Minas Gerais - UFMG
{david, arnaldo,"
86519cfb71135dd15eb6be3769052ef11e5ab257,DPC-Net: Deep Pose Correction for Visual Localization,"PERETROUKHIN et al.: DPC-NET: DEEP POSE CORRECTION FOR VISUAL LOCALIZATION
DPC-Net: Deep Pose Correction for Visual
Localization
Valentin Peretroukhin1, and Jonathan Kelly1"
8627248c6e3c3e316e3964d12e0a44e23aa969f3,Automated Annotations,"Automated Annotations
Richard Brath and Martin Matusiak*
Uncharted Software Inc."
86ae83ce79f05952bd4a5448e901e626b8ba1af4,Structure-aware classification using supervised dictionary learning,"STRUCTURE-AWARE CLASSIFICATION USING SUPERVISED DICTIONARY LEARNING
Yael Yankelevsky and Michael Elad
Computer Science Department
Technion - Israel Institute of Technology
Haifa 32000, Israel"
869abfc258f5512fd95da179f7d92b624900eadd,Autonomous face recognition,"Autonomous Face Recognition
Dissertation
zur Erlangung des akademischen Grades eines
Doktor-Ingenieurs (Dr.-Ing.)
der Fakultät für Ingenieurwissenschaften
der Universität Ulm
Mou, Dengpan
us China
. Gutachter:
Prof. Dr.-Ing. Albrecht Rothermel
. Gutachter:
Prof. Dr. Heiko Neumann
Amtierender Dekan:
Prof. Dr.-Ing. Hans-Jörg Pfleiderer
Datum der Promotion:
2. August, 2005"
86e71e8cb5bd15a6530cbe684be0775249665f3c,Extended Patch Prioritization for Depth Filling Within Constrained Exemplar-Based RGB-D Image Completion,"Extended Patch Prioritization for Depth Filling within
Constrained Exemplar-based RGB-D Image Completion
Amir Atapour-Abarghouei and Toby P. Breckon
Computer Science and Engineering, Durham University, UK"
86a417327acd84980a72cc6205174b4d58e5287b,Web Enabled Based Face Recognition Using Partitioned Iterated Function System,"Web Enabled Based Face Recognition using Partitioned Iterated Function System
{tag}                                  {/tag}
International Journal of Computer Applications
© 2010 by IJCA Journal
Number 2 - Article 6
Year of Publication: 2010
Authors:
Amol D.Potgantwar
Dr.S.G.Bhirud
10.5120/57-159"
86c1bf121851aa901e3e7eb11a3b8cc5a08a921b,"Motion , Blur , Illumination based Face Recognition","ISSN: 2455-5797                International Journal of Innovative Works in Engineering and Technology (IJIWET)
Motion, Blur, Illumination based Face Recognition
Anand M.S
PG Student
Department of ECE
Satyam College of Engineering
E-mail :"
860196a306c9303ddaf323d702dacba68db658d2,Open-Ended Content-Style Recombination Via Leakage Filtering,"OPEN-ENDED CONTENT-STYLE RECOMBINATION
VIA LEAKAGE FILTERING
Karl Ridgeway+∗ & Michael C. Mozer+†
+ Department of Computer Science, University of Colorado, Boulder
Sensory, Inc.
presently at Google Brain, Mountain View"
86e87d276b5b01a6b4b09b5487781fab740aca2e,Deep Ranking Model by Large Adaptive Margin Learning for Person Re-identification,"Deep Ranking Model by Large Adaptive Margin Learning for
Person Re-identification
Jiayun Wanga, Sanping Zhoua, Jinjun Wanga,∗, Qiqi Houa
The institute of artificial intelligence and robotic, Xi’an Jiaotong University, Xianning West Road
No.28, Shaanxi, 710049, P.R. China"
86614c2d2f6ebcb9c600d4aef85fd6bf6eab6663,Benchmarks for Cloud Robotics,"Benchmarks for Cloud Robotics
Arjun Singh
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2016-142
http://www.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-142.html
August 12, 2016"
861802ac19653a7831b314cd751fd8e89494ab12,"Time-of-Flight and Depth Imaging. Sensors, Algorithms, and Applications","Marcin Grzegorzek, Christian Theobalt, Reinhard Koch,
Andreas Kolb
Time-of-Flight and Depth Imaging. Sensors, Algorithms
nd Applications: Dagstuhl Seminar 2012 and GCPR
Workshop on Imaging New Modalities (Lecture ... Vision,
Pattern Recognition, and Graphics)
Publisher: Springer; 2013 edition
(November 8, 2013)
Language: English
Pages: 320
ISBN: 978-3642449635
Size: 20.46 MB
Format: PDF / ePub / Kindle
Cameras for 3D depth imaging, using
either time-of-flight (ToF) or
structured light sensors, have received
lot of attention recently and have
een improved considerably over the
last few years. The present
techniques..."
86c053c162c08bc3fe093cc10398b9e64367a100,Cascade of forests for face alignment,"Cascade of Forests for Face Alignment
Heng Yang, Changqing Zou, Ioannis Patras"
86b1751b265b289b09de79956e77a01d82e12086,Face recognition in multi-camera surveillance videos,"1st International Conference on Pattern Recognition (ICPR 2012)
November 11-15, 2012. Tsukuba, Japan
978-4-9906441-1-6 ©2012 IAPR"
8602b2ef26a0f851f1f6f2f2ae0ce142eb64300a,Is it a face ? How to find and validate a face on 3 D scans,"Is it a face ? How to find and validate a face on 3D scans
Przemyslaw Szeptycki,
Mohsen Ardabilian,
Liming Chen
Ecole Centrale de Lyon, 36 av. Guy de Collongue, 69134 Lyon, France
{przemyslaw.szeptycki, mohsen.ardabilian,
Introduction"
86585bd7288f41a28eeda883a35be6442224110a,A Variational Observation Model of 3 D Object for Probabilistic Semantic SLAM,"A Variational Observation Model of 3D Object for Probabilistic
Semantic SLAM
H. W. Yu and B. H. Lee"
8609035f1b9fa5bddfbbffd287a98ba47a1ecba0,Making Bertha See,"Making Bertha See
Uwe Franke, David Pfeiffer, Clemens Rabe, Carsten Knoeppel,
Markus Enzweiler, Fridtjof Stein, and Ralf G. Herrtwich
Daimler AG - Research & Development, 71059 Sindelfingen, Germany"
867d3aa95bb6a764ce3a03cfb5e99a81aea4a980,Computer-based recognition of dysmorphic faces,"& 2003 Nature Publishing Group All rights reserved 1018-4813/03 $25.00
www.nature.com/ejhg
ARTICLE
Computer-based recognition of dysmorphic faces
Hartmut S Loos1, Dagmar Wieczorek*,2, Rolf P Wu¨rtz1, Christoph von der Malsburg1 and
Bernhard Horsthemke2
Institut fu¨r Neuroinformatik, Ruhr-Universita¨t Bochum, Germany; 2Institut fu¨r Humangenetik, Universita¨tsklinikum
Essen, Germany
Genetic syndromes often involve craniofacial malformations. We have investigated whether a computer
an recognize disease-specific facial patterns in unrelated individuals. For this, 55 photographs (256 256
pixel) of patients with mucopolysaccharidosis type III (n¼ 6), Cornelia de Lange (n¼ 12), fragile X (n¼ 12),
Prader –Willi (n¼ 12), and Williams–Beuren (n¼ 13) syndromes were preprocessed by a Gabor wavelet
transformation. By comparing the feature vectors at 32 facial nodes, 42/55 (76%) of the patients were
orrectly classified. In another four patients (7%), the correct and an incorrect diagnosis scored equally
well. Clinical geneticists who were shown the same photographs achieved a recognition rate of 62%. Our
results prove that certain syndromes are associated with a specific facial pattern and that this pattern can
e described in mathematical terms.
Keywords: face recognition; facial pattern
Introduction
Humans have a remarkable ability to recognize and"
8616ff1d0fd7bcfc5fd81d1e8a9b189c21f3b93d,Visual Reference Resolution using Attention Memory for Visual Dialog,"Visual Reference Resolution using Attention Memory
for Visual Dialog
Paul Hongsuck Seo†
POSTECH
Andreas Lehrmann§
{hsseo, {andreas.lehrmann,
Bohyung Han†
§Disney Research
Leonid Sigal§"
da995212c9c8a933307cd893d862f5bf7d99f3ec,Elephant Panda Lion Dolphin Dog Monkey ... ... Classes Elephant Dolphin Lion Classifier Training Data Synthesizing Prediction,"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
EmbeddingSample EmbeddingElephantLionPandaMonkeyDolphinDog0.140.490.660.721.060.59Figure1:FrameworkofembeddingbasedZSLapproaches.occurfrequentlyenough,andthenewconceptsemergeev-erydayespeciallyintheWeb,whichmakesitdifficultandex-pensivetocollectandlabelasufficientlylargetrainingsetformodellearning[Changpinyoetal.,2016].Howtotraineffec-tiveclassificationmodelsfortheuncommonclasseswithoutusingthelabeledsamplesbecomesanimportantandpracti-calproblemandhasgatheredconsiderableresearchinterestsfromthemachinelearningandcomputervisioncommunities.Itisestimatedthathumanscanrecognizeapproximate30;000basicobjectcategoriesandmanymoresubordinateonesandtheyareabletoidentifynewclassesgivenanat-tributedescription[Lampertetal.,2014].Basedonthisob-servation,manyzero-shotlearning(ZSL)approacheshavebeenproposed[Akataetal.,2015;Romera-ParedesandTorr,2015;ZhangandSaligrama,2016a;Guoetal.,2017a].ThegoalofZSListobuildclassifiersfortargetunseenclassesgivennolabeledsamples,withclassattributesassidein-formationandfullylabeledsourceseenclassesasknowl-edgesource.Differentfrommanysupervisedlearningap-proacheswhichtreateachclassindependently,ZSLasso-ciatesclasseswithanintermediaryattributeorsemantics-paceandthentransfersknowledgefromthesourceseenclassestothetargetunseenclassesbasedontheassocia-tion.Inthisway,onlytheattributevectorofatarget(un-seen)classisrequiredandtheclassificationmodelcanbebuiltevenwithoutanylabeledsamplesforthisclass.Inparticular,anembeddingfunctionislearnedusingthela-beledsamplesofsourceseenclassesthatmapstheimagesandclassesintoacommonembeddingspacewherethedis-tanceorsimilaritybetweenthemcanbemeasured.Becausetheattributesaresharedbybothsourceandtargetclass-es,theembeddingfunctionlearnedbysourceclassescanbedirectlyappliedtotargetclasses[Farhadietal.,2009;Socheretal.,2013].Finally,givenatestimage,wemapit"
da61e3f62eda5e1cea027f73a156da36262722b0,Un nouvel ensemble de descripteurs de Fourier Clifford pour les images couleur. Les GCFD3,"Un nouvel ensemble de descripteurs de Fourier Clifford
pour les images couleur : les GCFD3
José Mennesson, Christophe Saint-Jean, Laurent Mascarilla
To cite this version:
José Mennesson, Christophe Saint-Jean, Laurent Mascarilla. Un nouvel ensemble de descripteurs
(3-4-5), pp.359-382. <10.3166/TS.29.359-382>. <hal-00808069>
HAL Id: hal-00808069
https://hal.archives-ouvertes.fr/hal-00808069
Submitted on 4 Apr 2013
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de
recherche français ou étrangers, des laboratoires"
daef6fa60c7d79930ad0a341aab69f1f4fa80442,Supplement for BIER,"Supplement for BIER
. Introduction
In this document we provide further insights into Boost-
ing Independent Embeddings Robustly (BIER). First, in
Section 2 we describe our method for loss functions op-
erating on triplets. Next, in Section 3 we show how our
method behaves when we vary the embedding size and the
number of groups. In Section 4 we summarize the effect of
our boosting based training approach and our initialization
pproach. We provide an experiment evaluating the impact
of end-to-end training in Section 5. Further, in Section 6 we
demonstrate that our method is applicable to generic im-
ge classification problems. Finally, we show a qualitative
omparison of the different embeddings in our ensemble in
Section 7 and some qualitative results in Section 8.
. BIER for Triplets
For loss functions operating on triplets of samples, we
illustrate our training method in Algorithm 1. In contrast
to our tuple based algorithm, we sample triplets x(1), x(2)
nd x(3) which satisfy the constraint that the first pair (x(1),"
da523ee3b7e8077713ebb7d903c3dc3bcb78921a,Multi-person tracking-by-detection based on calibrated multi-camera systems,"Multi-Person Tracking-by-Detection based on
Calibrated Multi-Camera Systems
Xiaoyan Jiang, Erik Rodner, and Joachim Denzler
Computer Vision Group Jena
Friedrich Schiller University of Jena
http://www.inf-cv.uni-jena.de"
da9080d5b433f73444078ac79c3a8a4515ad958e,IIS at ImageCLEF 2015: Multi-label Classification Task,"IIS at ImageCLEF 2015:
Multi-label classification task
Antonio J Rodr´ıguez-S´anchez1, Sabrina Fontanella1,2,
Justus Piater1, and Sandor Szedmak1
Intelligent and Interactive Systems, Department of Computer Science,
University of Innsbruck, Austria
Department of Computer Science, University of Salerno, Italy
https://iis.uibk.ac.at/"
dabd3f276bc815865ab7c9f375368a1e31903860,Deformable face mapping for person identification,"DEFORMABLE FACE MAPPING FOR PERSON IDENTIFICATION
Florent Perronnin , Jean-Luc Dugelay
Institut Eurecom
Multimedia Communications Department
BP 193, F-06904 Sophia Antipolis Cedex

 perronni, dugelay"
da1e0b9e445493d3e6dc0e3c23be194228c5d796,Video Segmentation using Teacher-Student Adaptation in a Human Robot Interaction (HRI) Setting,"Video Segmentation using Teacher-Student Adaptation
in a Human Robot Interaction (HRI) Setting
Mennatullah Siam1, Chen Jiang1, Steven Lu1, Laura Petrich1,
Mahmoud Gamal2, Mohamed Elhoseiny3, Martin Jagersand1"
da013b84a93cc89d78f2d9a346fc275e3c159565,Affordable Self Driving Cars and Robots with Semantic Segmentation,"Affordable Self Driving Cars and Robots with Semantic Segmentation
Gaurav Bansal
Jeff Chen
Evan Darke"
da288fca6b3bcaee87a034529da5621bb90123d1,Aesthetics and Emotions in Images,"[ Dhiraj Joshi,
Ritendra Datta,
Elena Fedorovskaya,
Quang-Tuan Luong,
James Z. Wang,
Jia Li, and Jiebo Luo]
PUBLICDOMAINPICTURES.NET &
© BRAND X PICTURES
[ A computational perspective]
In this tutorial, we define and discuss key aspects of the problem of computational inference of aesthetics
nd emotion from images. We begin with a background discussion on philosophy, photography, paintings,
visual arts, and psychology. This is followed by introduction of a set of key computational problems that the
research  community  has  been  striving  to  solve  and  the  computational  framework  required  for  solving
them. We also describe data sets available for performing assessment and outline several real-world applica-
tions where research in this domain can be employed. A significant number of papers that have attempted to
solve problems in aesthetics and emotion inference are surveyed in this tutorial. We also discuss future direc-
tions  that  researchers  can  pursue  and  make  a  strong  case  for  seriously  attempting  to  solve  problems  in  this
research domain.
Digital Object Identifier 10.1109/MSP.2011.941851
Date of publication: 22 August 2011"
da55917aa3a8a95179bae92c5b01e4c8f2f61b75,What makes a place? Building bespoke place dependent object detectors for robotics,"What Makes a Place? Building Bespoke Place Dependent Object Detectors
for Robotics
Jeffrey Hawke, Alex Bewley, Ingmar Posner"
dae420b776957e6b8cf5fbbacd7bc0ec226b3e2e,RECOGNIZING EMOTIONS IN SPONTANEOUS FACIAL EXPRESSIONS,"RECOGNIZING EMOTIONS IN SPONTANEOUS FACIAL EXPRESSIONS
Michael Grimm, Dhrubabrata Ghosh Dastidar, and Kristian Kroschel
Institut f¨ur Nachrichtentechnik
Universit¨at Karlsruhe (TH), Germany"
da1ba46027b7236c937d276fb54e99906036c4ef,Using 3 D Representations of the Nasal Region for Improved Landmarking and Expression Robust Recognition,"Using 3D Representations of the Nasal
Region for Improved Landmarking and
Expression Robust Recognition
Jiangning Gao1
Adrian N Evans1
Department of Electronic and
Electrical Engineering, University
of Bath, Bath, UK, BA2 7AY."
dac07680925b6c56b7ddf184dbdaf143a5d4816d,Object Ordering with Bidirectional Matchings for Visual Reasoning,"Object Ordering with Bidirectional Matchings for Visual Reasoning
Hao Tan and Mohit Bansal
UNC Chapel Hill
{haotan,"
dae35f1f2c581d9e632cccb8d279b56a4f1deb79,Contribution to the Fusion of Biometric Modalities by the Choquet Integral,"I.J. Image, Graphics and Signal Processing, 2012, 10, 1-7
Published Online September 2012 in MECS (http://www.mecs-press.org/)
DOI: 10.5815/ijigsp.2012.10.01
Contribution to the Fusion of Biometric
Modalities by the Choquet Integral
Research Unit of Advanced Systems in Electrical Engineering, National Engineering School of Sousse, Tunisia.
Anouar Ben Khalifa1 ,
Research Unit of Advanced Systems in Electrical Engineering, National Engineering School of Sousse, Tunisia.
e-mail:
Najoua Essoukri BenAmara2
e-mail:
to  a  digital  fingerprint  [8].  It  is  presented  as  a  reliable
means  of  authentication.  Convenient,  even  more
difficult to borrow, steal, forget or falsify [7]. However,
the  performance  of  authentication  systems  associated
with  these  biometrics  are  still  weak  to  even  consider
their large scale use [18].  In this context, multimodality
ppears a promising way to improve the performance of
biometric  system.  However,  multimodality  poses
problems at the level of information fusion."
dabd413deabad57bef8d426f7016db0b25ccbeb7,Face Recognition System Using Doubly Truncated Multivariate Gaussian Mixture Model and DCT Coefficients Under Logarithm Domain,"I.J. Image, Graphics and Signal Processing, 2012, 10, 8-17
Published Online September 2012 in MECS (http://www.mecs-press.org/)
DOI: 10.5815/ijigsp.2012.10.02
Face Recognition System Using Doubly
Truncated Multivariate Gaussian Mixture Model
nd DCT Coefficients Under Logarithm Domain
University college of Engineering, Jawaharlal Nehru Technological University Kakinada, Kakinada
D. Haritha
E-mail:
K.Srinivasa Rao
Department of Statistics, Andhra University, Visakhapatnam.
E-mail:
Ch. Satyanarayana
University college of Engineering, Jawaharlal Nehru Technological University Kakinada, Kakinada
E-mail:"
dac3fcb9fd51a22ea27cb911b95051387c5885ba,Extraction of object from the video,"(cid:1)(cid:2)(cid:3)(cid:4)(cid:5)(cid:6)(cid:7)(cid:8)(cid:9)(cid:7)(cid:10)(cid:2)(cid:11)(cid:7)(cid:12)(cid:9)(cid:7)(cid:13)(cid:6)(cid:14)(cid:15)(cid:16)(cid:17)(cid:18)(cid:15)(cid:7)(cid:19)(cid:20)(cid:8)(cid:20)(cid:7)
ISSN No. 0976-5697
(cid:21)(cid:22)(cid:15)(cid:6)(cid:23)(cid:22)(cid:24)(cid:15)(cid:25)(cid:2)(cid:22)(cid:24)(cid:3)(cid:7)(cid:26)(cid:2)(cid:4)(cid:23)(cid:22)(cid:24)(cid:3)(cid:7)(cid:2)(cid:27)(cid:7)(cid:28)(cid:29)(cid:30)(cid:24)(cid:22)(cid:18)(cid:6)(cid:29)(cid:7)(cid:31)(cid:6) (cid:6)(cid:24)(cid:23)(cid:18)!(cid:7)(cid:25)(cid:22)(cid:7)""(cid:2)(cid:5)(cid:14)(cid:4)(cid:15)(cid:6)(cid:23)(cid:7)(cid:13)(cid:18)(cid:25)(cid:6)(cid:22)(cid:18)(cid:6)(cid:7)
(cid:31)#(cid:13)#(cid:28)(cid:31)""$(cid:7)%(cid:28)%#(cid:31)(cid:7)
(cid:28)(cid:30)(cid:24)(cid:25)(cid:3)(cid:24)&(cid:3)(cid:6)(cid:7)(cid:17)(cid:22)(cid:3)(cid:25)(cid:22)(cid:6)(cid:7)(cid:24)(cid:15)(cid:7)’’’(cid:11)(cid:25)((cid:24)(cid:23)(cid:18) (cid:11)(cid:25)(cid:22)(cid:27)(cid:2)(cid:7)
Extraction of object from the video
Dr. M. Mohamed Sathik*
Associate Professor in Computer Science,
Sadakathullah Appa College,
Thirunelveli, India-627 011.
Ms. M. Parveen
Research Scholar in Computer Science,
Research and Development Centre,
Bharathiyar University,
Coimbatore, India.
Ms. P. Peer Fatima
Research Scholar in Computer Science,
M.S.University,
Thirunelveli, India."
dadb7ddfde3478238d23a8bacf5eddecc59e84c9,Vocabulary Image Captioning with Constrained Beam Search,"Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 947–956
Copenhagen, Denmark, September 7–11, 2017. c(cid:13)2017 Association for Computational Linguistics
image containing previously unseen object (‘suitcase’)CNN-RNNCaptioning ModelA catsitting insideofa suitcase.cat, suitcase, insideConstrainedBeamSearchBeamSearchA cat sitting on top ofa refrigerator.Image TagsFigure1:Wesuccessfullycaptionimagescontain-ingpreviouslyunseenobjectsbyincorporatingse-manticattributes(i.e.,imagetags)duringRNNde-coding.ActualexamplefromSection4.2.prisingly,modelstrainedonthesedatasetsdonotgeneralizewelltoout-of-domainimagescontain-ingnovelscenesorobjects(Tranetal.,2016).Thislimitationseverelyhinderstheuseofthesemodelsinrealworldapplicationsdealingwithim-agesinthewild.Althoughavailableimage-captiontrainingdataislimited,manyimagecollectionsareaugmentedwithground-truthtextfragmentssuchassemanticattributes(i.e.,imagetags)orobjectannotations.Eveniftheseannotationsdonotexist,theycanbegeneratedusing(potentiallytaskspecific)imagetaggers(Chenetal.,2013;Zhangetal.,2016)orobjectdetectors(Renetal.,2015;Krauseetal.,2016),whichareeasiertoscaletonewconcepts.Inthispaperourgoalistoincorporatetextfrag-mentssuchastheseduringcaptiongeneration,toimprovethequalityofresultingcaptions.Thisgoalposestwokeychallenges.First,RNNsaregenerallyopaque,anddifficulttoinfluenceattesttime.Second,textfragmentsmayincludewords"
da833d8ec9c91d55256effccd370b2e62a896ccb,Front-view Gait Recognition,"Front-view Gait Recognition
Michela Goffredo, John N. Carter and Mark S. Nixon"
dab6921a578c9ded6904a5a18bdd054aee62d2ad,Learning to Recognize Faces by Successive Meetings,"Learning to recognize faces
y successive meetings
M. Castrill´on-Santana, O. D´eniz-Su´arez,
J. Lorenzo-Navarro and M. Hern´andez-Tejera
IUSIANI
Edif. Ctral. del Parque Cient´ıfico Tecnol´ogico
Universidad de Las Palmas de Gran Canaria
Las Palmas de Gran Canaria, 35017
Spain"
daefac0610fdeff415c2a3f49b47968d84692e87,Multimodal Frame Identification with Multilingual Evaluation,"New Orleans, Louisiana, June 1 - 6, 2018. c(cid:13)2018 Association for Computational Linguistics
Proceedings of NAACL-HLT 2018, pages 1481–1491"
dad7b8be074d7ea6c3f970bd18884d496cbb0f91,Super-Sparse Regression for Fast Age Estimation from Faces at Test Time,"Super-Sparse Regression for Fast Age
Estimation From Faces at Test Time
Ambra Demontis, Battista Biggio, Giorgio Fumera, and Fabio Roli
Dept. of Electrical and Electronic Engineering, University of Cagliari
Piazza d’Armi, 09123 Cagliari, Italy
WWW home page: http://prag.diee.unica.it"
da8d0855e7760e86fbec47a3cfcf5acd8c700ca8,F 2 ConText : How to Extract Holistic Contexts of Persons of Interest for Enhancing Exploratory Analysis,"Accepted on 15 Sep 2018. To appear in Knowledge and Information Systems.
Under consideration for publication in Knowledge and Information Sys-
F2ConText: How to Extract Holistic
Contexts of Persons of Interest for
Enhancing Exploratory Analysis
Md Abdul Kader1, Arnold P. Boedihardjo2 and M. Shahriar Hossain3
IBM Innovation Center, Austin, TX 78758
Radiant Solutions, Herndon, VA 20171
The University of Texas at El Paso, El Paso, TX 79968"
da7ffe21508ad8d6dd9de7da378e184cb43a56c8,D Landmark Localisation,"D Landmark Localisation
Luke Gahan, Supervised by Prof. Paul F. Whelan"
dabf269f516adc6bf87a7ceb455cceda4466917a,Investigation of Facial Artifacts on Face Biometrics using Eigenface based Single and Multiple Neural Networks,"Investigation of Facial Artifacts on Face Biometrics
using Eigenface based Single and Multiple Neural Networks
K. Sundaraj
University Malaysia Perlis (UniMAP)
School of Mechatronics Engineering
02600 Jejawi - Perlis
MALAYSIA"
bff354d05823c83215183c8824faefbc093de011,A new efficient SVM and its application to real-time accurate eye localization,"Proceedings of International Joint Conference on Neural Networks, San Jose, California, USA, July 31 – August 5, 2011
A New Efficient SVM and Its Application to
Real-time Accurate Eye Localization
Shuo Chen and Chengjun Liu"
bf4825474673246ae855979034c8ffdb12c80a98,"UNIVERSITY OF CALIFORNIA RIVERSIDE Active Learning in Multi-Camera Networks, With Applications in Person Re-Identification A Dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Electrical Engineering by","UNIVERSITY OF CALIFORNIA
RIVERSIDE
Active Learning in Multi-Camera Networks, With Applications in Person
Re-Identification
A Dissertation submitted in partial satisfaction
of the requirements for the degree of
Doctor of Philosophy
Electrical Engineering
Abir Das
December 2015
Dissertation Committee:
Professor Amit K. Roy-Chowdhury, Chairperson
Professor Anastasios Mourikis
Professor Walid Najjar"
bf1e0279a13903e1d43f8562aaf41444afca4fdc,Different Viewpoints of Recognizing Fleeting Facial Expressions with DWT,"International Research Journal of Engineering and Technology (IRJET)       e-ISSN: 2395-0056
Volume: 04 Issue: 10 | Oct -2017                     www.irjet.net                                                                 p-ISSN: 2395-0072
Different Viewpoints of Recognizing Fleeting Facial Expressions with
VAIBHAV SHUBHAM1, MR. SANJEEV SHRIVASTAVA2, DR. MOHIT GANGWAR3
information
to  get  desired
information
Introduction
---------------------------------------------------------------------***---------------------------------------------------------------------"
bf514f38c6549700fd51355bdc6b86ff4707d4dd,Performance Analysis of Joint EM/SAGE Estimation and Multistage Detection in UTRA/WCDMA Uplink,"Publications
L. Vandendorpe
Laboratoire de T´el´ecommunications et T´el´ed´etection
Universit´e catholique de Louvain
December 2006"
bf9d47987943e8c763ea42fbfd4b71c08ffda266,LECTURE ATTENDANCE SYSTEM WITH FACE RECOGNITION AND IMAGE PROCESSING,"International Journal Of Advance Research In Science And Engineering            http://www.ijarse.com
IJARSE, Vol. No.2, Issue No.3, March, 2013                                                       ISSN-2319-8354(E)
LECTURE ATTENDANCE SYSTEM WITH FACE RECOGNITION
AND IMAGE PROCESSING"
bf4f76c3da8a46783dfd2b72651e2300901ced25,Robust aggregation of GWAP tracks for local image annotation,"Robust aggregation of GWAP tracks
for local image annotation
C. Bernaschina, P. Fraternali, L. Galli, D. Martinenghi, M. Tagliasacchi
Dipartimento di Elettronica, Informazione e Bioingegneria
Politecnico di Milano, Italy"
bfbee49e2c193fd0aa0f119bb2603450895dbf14,Rethinking Monocular Depth Estimation with Adversarial Training,"Rethinking Monocular Depth Estimation with Adversarial Training
Richard Chen1, Faisal Mahmood2, Alan Yuille1 and Nicholas J. Durr2
Department of Computer Science 2Department of Biomedical Engineering
Johns Hopkins University, Baltimore, MD
{rchen40, faisalm, ayuille,"
bfa763e7cec812f855c712895fa48eae89a34a00,Face Retrieval using Frequency Decoded Local Descriptor,"PREPRINT: ACCEPTED IN MULTIMEDIA TOOLS AND APPLICATIONS, SPRINGER
Face Retrieval using Frequency Decoded Local
Descriptor
Shiv Ram Dubey"
bf86c65a4a3d81ca422600fdbc5d31eb56e098b9,Fusion Algorithms for Face Localization,"Algorithms Fusion for Face
Localization
R. BELAROUSSI
L. PREVOST
M. MILGRAM
Institute of Intelligent Systems and Robotics–PRC
University Pierre and Marie Curie
Face localization is a face detection problem where the number
of people is known. We present a comparison between different
lgorithms fusion methods dedicated to the localization of faces in
olor images. Data to combine result from an appearance model
supported by an auto-associative network, an ellipse model based
on Generalized Hough Transform, and a skin color model. We intro-
duce and compare several fusion methods like the Bayesian classi-
fier with parametric or non-parametric technique, a fuzzy inference
system, and a weighted average. Given an input image, we compute
kind of probability map on it using a sliding window. The face
position is then determined as the location of the absolute maximum
over this map. Improvement of basic detectors localization rates is
learly shown and prevalence of the weighted average is reported."
bf4fcd80083f3145176b64d15bab78456a7e5e43,Title Fast Randomized Algorithms for Convex Optimization and Statistical Estimation Permalink,"Fast Randomized Algorithms for Convex Optimization and
Statistical Estimation
Mert Pilanci
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2016-147
http://www.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-147.html
August 14, 2016"
bf3aae7293f664d512c0904916d804327af22f52,STDnet: A ConvNet for Small Target Detection,"BOSQUET, MUCIENTES, BREA: STDNET FOR SMALL TARGET DETECTION
STDnet: A ConvNet for Small Target
Detection
Brais Bosquet
Manuel Mucientes
Víctor M. Brea
Centro Singular de Investigación en
Tecnoloxías da Información (CiTIUS)
University of Santiago de Compostela
Santiago de Compostela, Spain"
bfa71537839a81a03569a702a7cdc07647f7de4d,Target re-identification in low-quality camera networks,"Target Re-Identi(cid:12)cation in Low Quality Camera Networks
Federica Battisti, Marco Carli, Giovanna Farinella, Alessandro Neri
(cid:3)
Applied Electronics Department
Universit(cid:19)a degli Studi Roma TRE
Rome, Italy"
bf8bcda2e4d04b6bd6f5e70622e972baf525a1c7,Three decades of Cognition & Emotion: A brief review of past highlights and future prospects.,"COGNITION AND EMOTION, 2018
VOL. 32, NO. 1, 1–12
https://doi.org/10.1080/02699931.2018.1418197
nd future prospects
Klaus Rothermunda and Sander L. Kooleb
Institute of Psychology, Friedrich-Schiller-Universität Jena, Jena, Germany; bDepartment of Psychology, VU Amsterdam,
Amsterdam, the Netherlands"
bfa6ad4d71008505729274d008a9b4a7d92b2985,Semantic Understanding of Scenes Through the ADE20K Dataset,"Semantic Understanding of Scenes through the ADE20K Dataset
Bolei Zhou · Hang Zhao · Xavier Puig · Tete Xiao · Sanja Fidler · Adela
Barriuso · Antonio Torralba"
bf39babab5648ff64cc4b79bfec96e8c6c93b812,The Impact of Disappointment in Decision Making: Inter-Individual Differences and Electrical Neuroimaging,"(cid:65)(cid:114)(cid:116)(cid:105)(cid:99)(cid:108)(cid:101)
(cid:84)(cid:104)(cid:101)(cid:32)(cid:73)(cid:109)(cid:112)(cid:97)(cid:99)(cid:116)(cid:32)(cid:111)(cid:102)(cid:32)(cid:68)(cid:105)(cid:115)(cid:97)(cid:112)(cid:112)(cid:111)(cid:105)(cid:110)(cid:116)(cid:109)(cid:101)(cid:110)(cid:116)(cid:32)(cid:105)(cid:110)(cid:32)(cid:68)(cid:101)(cid:99)(cid:105)(cid:115)(cid:105)(cid:111)(cid:110)(cid:32)(cid:77)(cid:97)(cid:107)(cid:105)(cid:110)(cid:103)(cid:58)(cid:32)(cid:73)(cid:110)(cid:116)(cid:101)(cid:114)(cid:45)(cid:73)(cid:110)(cid:100)(cid:105)(cid:118)(cid:105)(cid:100)(cid:117)(cid:97)(cid:108)
(cid:68)(cid:105)(cid:102)(cid:102)(cid:101)(cid:114)(cid:101)(cid:110)(cid:99)(cid:101)(cid:115)(cid:32)(cid:97)(cid:110)(cid:100)(cid:32)(cid:69)(cid:108)(cid:101)(cid:99)(cid:116)(cid:114)(cid:105)(cid:99)(cid:97)(cid:108)(cid:32)(cid:78)(cid:101)(cid:117)(cid:114)(cid:111)(cid:105)(cid:109)(cid:97)(cid:103)(cid:105)(cid:110)(cid:103)
(cid:84)(cid:90)(cid:73)(cid:69)(cid:82)(cid:79)(cid:80)(cid:79)(cid:85)(cid:76)(cid:79)(cid:83)(cid:44)(cid:32)(cid:72)(cid:233)(cid:108)(cid:232)(cid:110)(cid:101)(cid:44)(cid:32)(cid:101)(cid:116)(cid:32)(cid:97)(cid:108)(cid:46)"
bf05e710dae791f82cc639a09dbe5ec66fed2008,Generating Video Description using Sequence-to-sequence Model with Temporal Attention,"Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers,
pages 44–52, Osaka, Japan, December 11-17 2016."
bf4ec5068e6ff0b008a09f0c94bfaac290ae7d3b,Co-attention CNNs for Unsupervised Object Co-segmentation,Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18)
bf96a0f037e7472e4b6cb1dae192a5fedbbbd88a,Visual Listening In: Extracting Brand Image Portrayed on Social Media,"Visual Listening In: Extracting Brand Image
Portrayed on Social Media
Liu Liu
NYU Stern School of Business,
Daria Dzyabura
NYU Stern School of Business,
University of Washington - Foster School of Business,
Natalie Mizik
Marketing academics and practitioners recognize the importance of monitoring consumer online conversations
bout brands. The focus so far has been on user generated content in the form of text. However, images are
on their way to surpassing text as the medium of choice for social conversations. In these images, consumers
often tag brands. We propose a “visual listening in” approach to measuring how brands are portrayed on
social media (Instagram), by mining visual content posted by users. Our approach consists of two stages. We
first use two supervised machine learning methods, traditional support vector machine classifiers and deep
onvolutional neural networks, to measure brand attributes (glamorous, rugged, healthy, fun) from images.
We then apply the classifiers to brand-related images posted on social media to measure what consumers
re visually communicating about brands. We study 56 brands in the apparel and beverages categories, and
ompare their portrayal in consumer-created images with images on the firm’s of‌f‌icial Instagram account, as
well as with consumer brand perceptions measured in a national brand survey. Although the three measures
exhibit convergent validity, we find key differences between how consumers and firms portray the brands on"
bfb98423941e51e3cd067cb085ebfa3087f3bfbe,Sparseness helps: Sparsity Augmented Collaborative Representation for Classification,"Sparseness helps: Sparsity Augmented
Collaborative Representation for Classification
Naveed Akhtar, Faisal Shafait, and Ajmal Mian"
bfef76d0e287fc6401d69a9f65ff174e4fbf0970,Nonnegative Matrix Factorization with Outliers,"978-1-4799-9988-0/16/$31.00 ©2016 IEEE
ICASSP 2016"
bfcba38d563a4a75f69f892a9638f464049723b9,Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos,"Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised
Learning from Monocular Videos
Vincent Casser∗1
Soeren Pirk
Reza Mahjourian2
Anelia Angelova
Institute for Applied Computational Science, Harvard University; Google Brain
Google Brain
University of Texas at Austin; Google Brain
{pirk, rezama,"
bf15ba4db09fd805763738ec2cb48c09481785dd,Training Deep Neural Network in Limited Precision,"Training Deep Neural Network in Limited Precision
Hyunsun Park∗, Jun Haeng Lee∗, Youngmin Oh, Sangwon Ha, Seungwon Lee
Samsung Advanced Institute of Technology
Samsung-ro 130, Suwon-si, Republic of Korea
{h-s.park,"
bf4e6ec60e5603324f6a40d2a060420322dbdd62,Kinects and Human Kinetics: A New Approach for Studying Crowd Behavior,"Kinects and Human Kinetics: A New Approach for
Studying Crowd Behavior
Stefan Seera,b,∗, Norbert Br¨andlea, Carlo Rattib
Austrian Institute of Technology (AIT), Giefinggasse 2, 1210 Vienna, Austria
MIT Senseable City Lab, Massachusetts Institute of Technology (MIT), 77
Massachusetts Avenue, 02139 Cambridge, MA, USA"
448efcae3b97aa7c01b15c6bc913d4fbb275f644,Style Finder : Fine-Grained Clothing Style Recognition and Retrieval,"Style Finder: Fine-Grained Clothing Style Recognition and Retrieval
Wei Di2, Catherine Wah1, Anurag Bhardwaj2, Robinson Piramuthu2, and Neel Sundaresan2
Department of Computer Science and Engineering, University of California, San Diego
eBay Research Labs, 2145 Hamilton Ave. San Jose, CA"
446a99fdedd5bb32d4970842b3ce0fc4f5e5fa03,A Pose-Adaptive Constrained Local Model for Accurate Head Pose Tracking,"A Pose-Adaptive Constrained Local Model For
Accurate Head Pose Tracking
Lucas Zamuner
Eikeo
1 rue Leon Jouhaux,
F-75010, Paris, France
Kevin Bailly
Sorbonne Universit´es
UPMC Univ Paris 06
CNRS UMR 7222, ISIR
F-75005, Paris, France
Erwan Bigorgne
Eikeo
1 rue Leon Jouhaux,
F-75010, Paris, France"
4419215162eb6ec20206fb70f6890c5286ced188,Audiovisual Speech SynchronyMeasure : Application to Biometrics,"Hindawi Publishing Corporation
EURASIP Journal on Advances in Signal Processing
Volume 2007, Article ID 70186, 11 pages
doi:10.1155/2007/70186
Research Article
Audiovisual Speech Synchrony Measure:
Application to Biometrics
Herv ´e Bredin and G ´erard Chollet
D´epartement Traitement du Signal et de l’Image, ´Ecole Nationale Sup´erieure des T´el´ecommunications,
CNRS/LTCI, 46 rue Barrault, 75013 Paris Cedex 13, France
Received 18 August 2006; Accepted 18 March 2007
Recommended by Ebroul Izquierdo
Speech is a means of communication which is intrinsically bimodal: the audio signal originates from the dynamics of the articu-
lators. This paper reviews recent works in the field of audiovisual speech, and more specifically techniques developed to measure
the level of correspondence between audio and visual speech. It overviews the most common audio and visual speech front-end
processing, transformations performed on audio, visual, or joint audiovisual feature spaces, and the actual measure of correspon-
dence between audio and visual speech. Finally, the use of synchrony measure for biometric identity verification based on talking
faces is experimented on the BANCA database.
Copyright © 2007 H. Bredin and G. Chollet. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly"
4452c36dc4c5e9f11d041489c8ff2e7006d33c80,"A Computational Analysis of Recent Multi-Object Tracking Methods Based on Particle Filter , HMM and Appearance Information of Objects","International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 02, February 2013)
A Computational Analysis of Recent Multi-Object Tracking
Methods Based on Particle Filter, HMM and Appearance
Information of Objects
Raksha Shrivastava1, Professor Rajesh Nema 2
,2Department of Electronics and Communication, NRI Institute of Information Science and Technology, Bhopal (M.P)"
4430d41c36c731c16020037d25df3dcd237fd863,IJATRD 2012019 ) THERMAL RECOGNITION IN BIOMETRICS AUTHENTICATION Mr,"International Journal for Advancements in Technical Research & Development
(IJATRD2012019) THERMAL RECOGNITION IN
BIOMETRICS
AUTHENTICATION
Mr. Gopal Sakarkar
Asst. Prof.  MCA Dept.,
haracteristic  the  biometric  system  is  based  on,  can
e  either  central  or  distributed.  In  the  case  of  a
distributed  database,  each  individual  has  a  magnetic
or smart card in which his biometric characteristic is
recorded.
The  techniques  of  user  authentication  are  linked  to
passwords,  user  IDs,  identification  cards  and  PINs
(personal  identification  numbers).  These  techniques
suffer  from  several  limitations:  Passwords  and  PINs
an  be  guessed,  stolen  or illicitly  acquired  by  covert
observation[2].
The biometrics systems provide a more accurate and
reliable user authentication method.
Existing user authentication techniques include:"
44e3d382ce8d765f705706d40716cb81575281e8,Automatic Parameter Adaptation for Multi-object Tracking,"Automatic Parameter Adaptation for
Multi-Object Tracking
Duc Phu CHAU, Monique THONNAT, and Fran¸cois BREMOND
{Duc-Phu.Chau, Monique.Thonnat,
STARS team, INRIA Sophia Antipolis, France
http://team.inria.fr/stars"
44736c0c7cfced2c0f06c5ae8dd0111d9ea0dc20,On the Robustness of Speech Emotion Recognition for Human-Robot Interaction with Deep Neural Networks,"On the Robustness of Speech Emotion Recognition for Human-Robot
Interaction with Deep Neural Networks
Egor Lakomkin1, Mohammad Ali Zamani1, Cornelius Weber1, Sven Magg1 and Stefan Wermter1"
449f93b4be37087236c6a13e9db4c1c323683a58,Abnormalities in early visual processes are linked to hypersociability and atypical evaluation of facial trustworthiness: An ERP study with Williams syndrome,"Cogn Affect Behav Neurosci (2017) 17:1002–1017
DOI 10.3758/s13415-017-0528-6
Abnormalities in early visual processes are linked
to hypersociability and atypical evaluation of facial
trustworthiness: An ERP study with Williams syndrome
Danielle M. Shore 1 & Rowena Ng 2 & Ursula Bellugi 3 & Debra L. Mills 4
Published online: 6 July 2017
# The Author(s) 2017. This article is an open access publication"
44682707bf06626bb1f0d9b181fb5c45cb446d30,Stable Affine Frames on Isophotes,"Stable Af‌f‌ine Frames on Isophotes
Michal Perd’och
Jiˇr´ı Matas
ˇStˇep´an Obdrˇz´alek
Center for Machine Perception, CTU in Prague, Czech Republic"
44880df54e6caa3e7263db7a4d5cb77838f4698f,Learning Optimal Parameters for Multi-target Tracking with Contextual Interactions,"Learning Optimal Parameters for Multi-target Tracking with Contextual
Interactions
Shaofei Wang · Charless C. Fowlkes"
44bb6ccb3526bb38364550263bc608116910da32,Model-Driven Simulations for Computer Vision,"017 IEEE Winter Conference on Applications of Computer Vision
Model-driven Simulations for Computer Vision
VSR Veeravasarapu1, Constantin Rothkopf2, Ramesh Visvanathan1
Center for Cognition and Computation, Dept. of Computer Science, Goethe University, Frankfurt
Center for Cognitive Science & Dept. of Psychology, Technical University Darmstadt.
(a) Lambertian
(Direct-lighting based rendering)
(b) Ray tracing
(appearance-driven rendering)
(c) Monte-Carlo rendering
(physics-driven rendering)
(d) Semantic labels
(e) Day light
(f) Night
Figure 1: Rendering fidelity and Virtual scene diversity. This work aims to quantify the impact of photorealism and physics
fidelity on transfer learning from virtual reality. (a)-(c): Images of same scene state rendered with different rendering engines.
(e)-(g): Same scene under different lighting. (d) and (h) semantic labels. Color coding scheme for labels is same as [5].
(g) Rain
(h) Semantic labels"
44993de87bbbce71f14d7917944d055700217696,A late fusion approach to combine multiple pedestrian detectors,"A Late Fusion Approach to Combine Multiple
Pedestrian Detectors
Artur Jord˜ao, Jessica Sena de Souza, William Robson Schwartz
Smart Surveillance Interest Group, Computer Science Department
Universidade Federal de Minas Gerais, Minas Gerais, Brazil"
44703dea094eb9558965db9439a07b9a74fd36b5,"Multiculturalism, Colorblindness, and Prejudice: Examining How Diversity Ideologies Impact Intergroup Attitudes","University of Arkansas, Fayetteville
Theses and Dissertations
8-2018
Multiculturalism, Colorblindness, and Prejudice:
Examining How Diversity Ideologies Impact
Intergroup Attitudes
David Sparkman
University of Arkansas, Fayetteville
Follow this and additional works at: https://scholarworks.uark.edu/etd
Part of the Social Psychology Commons
Recommended Citation
Sparkman, David, ""Multiculturalism, Colorblindness, and Prejudice: Examining How Diversity Ideologies Impact Intergroup
Attitudes"" (2018). Theses and Dissertations. 2923.
https://scholarworks.uark.edu/etd/2923
This Dissertation is brought to you for free and open access by It has been accepted for inclusion in Theses and Dissertations by
n authorized administrator of For more information, please contact"
44984f97c8c5ff0a734dc4496116df195789beba,Random Forest with Adaptive Local Template for Pedestrian Detection,"Publishing CorporationMathematical Problems in EngineeringVolume 2015, Article ID 767423, 11 pageshttp://dx.doi.org/10.1155/2015/767423"
44f4b1b90f8d5515f2486e07e4cb4b9589c27518,Deep Learning and Its Applications to Machine Health Monitoring: A Survey,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
Deep Learning and Its Applications to Machine
Health Monitoring: A Survey
Rui Zhao, Ruqiang Yan, Zhenghua Chen, Kezhi Mao, Peng Wang, and Robert X. Gao"
4439746eeb7c7328beba3f3ef47dc67fbb52bcb3,An Efficient Face Detection Method Using Adaboost and Facial Parts,"YASAMAN HEYDARZADEH at al: AN EFFICIENT FACE DETECTION METHOD USING ADABOOST . . .
An Efficient Face Detection Method Using Adaboost and Facial Parts
Yasaman Heydarzadeh, Abolfazl Toroghi Haghighat
Computer, IT and Electronic department
Azad University of Qazvin
Tehran, Iran
qiau.ac.ir ,"
44241248f16c172a1c2fb90e48fd728ba26220fc,Expression-invariant Non-rigid 3 D Face Recognition : A Robust Approach to Expression-aware Morphing,"Expression-invariant Non-rigid 3D Face Recognition: A Robust Approach to
Expression-aware Morphing
F. R. Al-Osaimi
M. Bennamoun
A. Mian"
44a3ec27f92c344a15deb8e5dc3a5b3797505c06,A Taxonomy of Part and Attribute Discovery Techniques,"A Taxonomy of Part and Attribute Discovery
Techniques
Subhransu Maji"
44b30a1048465cd56904cdcbec8e79dffab693bd,Semantic based Query Approach For Web Image Search Through reranking algorithm,"Scientific Journal of Impact Factor (SJIF): 3.134
E-ISSN (O): 2348-4470
P-ISSN (P): 2348-6406
International Journal of Advance Engineering and Research
Development
Volume 2,Issue 12,December -2015
Semantic based Query Approach For Web Image Search
Through reranking algorithm
Pushpak Waghmare1, Shubham Katkamwan2, Abhijeet Markand3,  Abuj Pratiksha4, Prof. Navale Girish Jaysingh5
-5Department Of Computer,All India shri Shivaji Memorial Society’s"
4414a328466db1e8ab9651bf4e0f9f1fe1a163e4,Weighted voting of sparse representation classifiers for facial expression recognition,"© EURASIP, 2010   ISSN 2076-1465
8th European Signal Processing Conference (EUSIPCO-2010)
INTRODUCTION"
44f65e3304bdde4be04823fd7ca770c1c05c2cef,On the use of phase of the Fourier transform for face recognition under variations in illumination,"SIViP
DOI 10.1007/s11760-009-0125-4
ORIGINAL PAPER
On the use of phase of the Fourier transform for face recognition
under variations in illumination
Anil Kumar Sao · B. Yegnanarayana
Received: 17 November 2008 / Revised: 20 February 2009 / Accepted: 7 July 2009
© Springer-Verlag London Limited 2009"
446dc1413e1cfaee0030dc74a3cee49a47386355,Recent Advances in Zero-shot Recognition,"Recent Advances in Zero-shot Recognition
Yanwei Fu, Tao Xiang, Yu-Gang Jiang, Xiangyang Xue, Leonid Sigal, and Shaogang Gong"
4461a1b70e461ec298d7066ba103deda48d4ba22,Classification via Minimum Incremental Coding Length,"Vol. 2, No. 2, pp. 367–395
(cid:2) 2009 Society for Industrial and Applied Mathematics
Classification via Minimum Incremental Coding Length
John Wright
, Yi Ma
, Yangyu Tao
, Zhouchen Lin
, and Heung-Yeung Shum"
442cc39db208a66acf3acc22589b13981bb303fd,Design of Non-Linear Discriminative Dictionaries for Image Classification,"CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE.
Design of Non-Linear Discriminative
Dictionaries for Image Classi(cid:12)cation
Anonymous ACCV 2012 submission
Paper ID 662"
4443ee5eaa56e41acddb62cacbc2f6d8c84ccd59,Multiple Objects Fusion Tracker Using a Matching Network for Adaptively Represented Instance Pairs,"Article
Multiple Objects Fusion Tracker Using a Matching
Network for Adaptively Represented Instance Pairs
Sang-Il Oh and Hang-Bong Kang *
Department of Media Engineering, Catholic University of Korea, 43-1, Yeoggok 2-dong, Wonmmi-gu,
Bucheon-si, Gyeonggi-do 14662, Korea;
* Correspondence: Tel.: +82-2-2164-4598
Academic Editor: Simon X. Yang
Received: 27 February 2017; Accepted: 14 April 2017; Published: 18 April 2017"
4425df6cc10917644c44a7f4177a5d7cc1c8b7bc,Object Localization based on Structural SVM using Privileged Information,"Object Localization based on Structural SVM
using Privileged Information
Jan Feyereisl, Suha Kwak∗, Jeany Son, Bohyung Han
Dept. of Computer Science and Engineering, POSTECH, Pohang, Korea"
447a5e1caf847952d2bb526ab2fb75898466d1bc,LEARNING NON-LINEAR TRANSFORM WITH DISCRIM- INATIVE AND MINIMUM INFORMATION LOSS PRIORS,"Under review as a conference paper at ICLR 2018
LEARNING NON-LINEAR TRANSFORM WITH DISCRIM-
INATIVE AND MINIMUM INFORMATION LOSS PRIORS
Anonymous authors
Paper under double-blind review"
383f874ba7975c83b55c694ec0a70f51dc3a0ee5,Towards Automatic Image Understanding and Mining via Social Curation,"Towards Automatic Image Understanding and Mining via Social Curation
Katsuhiko Ishiguro, Akisato Kimura, and Koh Takeuchi
NTT Communication Science Laboratories
NTT Corporation, Kyoto, Japan"
38215c283ce4bf2c8edd597ab21410f99dc9b094,The SEMAINE Database: Annotated Multimodal Records of Emotionally Colored Conversations between a Person and a Limited Agent,"The SEMAINE Database: Annotated Multimodal Records of
Emotionally Colored Conversations between a Person and a Limited
Agent
McKeown, G., Valstar, M., Cowie, R., Pantic, M., & Schröder, M. (2012). The SEMAINE Database: Annotated
Multimodal Records of Emotionally Colored Conversations between a Person and a Limited Agent. IEEE
Transactions on Affective Computing, 3(1), 5-17. DOI: 10.1109/T-AFFC.2011.20
Published in:
Document Version:
Peer reviewed version
Queen's University Belfast - Research Portal:
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Download date:05. Nov. 2018"
38b0a67727dea3fe563e8662517bd0fda2fd5e06,Perceiving and expressing feelings through actions in relation to individual differences in empathic traits: the Action and Feelings Questionnaire (AFQ),"Cogn Affect Behav Neurosci (2016) 16:248–260
DOI 10.3758/s13415-015-0386-z
Perceiving and expressing feelings through actions in relation
to individual differences in empathic traits: the Action
nd Feelings Questionnaire (AFQ)
Justin H. G. Williams 1,4 & Isobel M. Cameron 1 & Emma Ross 2 & Lieke Braadbaart 3 &
Gordon D Waiter 3
Published online: 20 October 2015
# The Author(s) 2015. This article is published with open access at Springerlink.com"
389b2390fd310c9070e72563181547cf23dceea3,β-VAE : L EARNING B ASIC,"Published as a conference paper at ICLR 2017
β-VAE: LEARNING BASIC VISUAL CONCEPTS WITH A
CONSTRAINED VARIATIONAL FRAMEWORK
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot,
Matthew Botvinick, Shakir Mohamed, Alexander Lerchner
Google DeepMind
{irinah,lmatthey,arkap,cpburgess,glorotx,"
3837f81524286ed5f9142d245743733766aa4017,Houdini: Fooling Deep Structured Visual and Speech Recognition Models with Adversarial Examples,"Houdini: Fooling Deep Structured Visual and Speech
Recognition Models with Adversarial Examples
Moustapha Cisse
Facebook AI Research
Natalia Neverova*
Facebook AI Research"
3898a9dcb22f87413f08bb44c656f4129e1c42df,On binary representations for biometric template protection,"ON BINARY REPRESENTATIONS FOR
BIOMETRIC TEMPLATE PROTECTION
Chun Chen"
38cc2896058131e4656443aedfb1b9dae61b99cd,Functional Connectivity Imaging Analysis : Interhemispheric Integration in Autism,"Functional Connectivity Imaging Analysis:
Interhemispheric Integration in Autism
Daniel J. Kelley"
38eea307445a39ee7902c1ecf8cea7e3dcb7c0e7,Multi-distance Support Matrix Machines,"Noname manuscript No.
(will be inserted by the editor)
Multi-distance Support Matrix Machine
Yunfei Ye1
· Dong Han1
Received: date / Accepted: date"
3824a648507000b7f319b9bf2ec0b7d07bcdfee4,A performance evaluation of local descriptors,"A performance evaluation of local descriptors
K. Mikolajczyk
C. Schmid
INRIA Rhône-Alpes, GRAVIR-CNRS
655, av. de l’Europe, 38330 Montbonnot, France"
38679355d4cfea3a791005f211aa16e76b2eaa8d,Title Evolutionary cross-domain discriminative Hessian Eigenmaps,"Title
Evolutionary cross-domain discriminative Hessian Eigenmaps
Author(s)
Si, S; Tao, D; Chan, KP
Citation
Ieee Transactions On Image Processing, 2010, v. 19 n. 4, p. 1075-
Issued Date
http://hdl.handle.net/10722/127357
Rights
IEEE Transactions on Image Processing. Copyright © IEEE.;
This work is licensed under a Creative Commons Attribution-
NonCommercial-NoDerivatives 4.0 International License.; ©2010
IEEE. Personal use of this material is permitted. However,
permission to reprint/republish this material for advertising or
promotional purposes or for creating new collective works for
resale or redistribution to servers or lists, or to reuse any
opyrighted component of this work in other works must be
obtained from the IEEE."
38d56ddcea01ce99902dd75ad162213cbe4eaab7,Sense Beauty by Label Distribution Learning,Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
38a3611138388490c2cd60dfbf795932d5e55a79,2 D pose estimation in the Restaurant of the Future,"D pose estimation in the Restaurant
of the Future
Frederik (Frank) Evers
supervision by
dr. ir. Nico P. van der Aa
Noldus IT B.V.
Wageningen, NL
dr. Robby T. Tan
University of Utrecht
Utrecht, NL
March 29, 2012"
3807f0c1b7360f99fe2d30d2fd1722fbddd276b0,Czech Technical University in Prague F 3 Faculty of Electrical EngineeringDepartment of Cybernetics Object Scene Flow in Video Sequences,"Master Thesis
Czech
Technical
University
in Prague
Faculty of Electrical Engineering
Department of Cybernetics
Object Scene Flow in Video Sequences
Bc. Michal Neoral
Supervisor: Mgr. Jan Šochman, Ph.D.
Field of study: Open Informatics
Subfield: Computer Vision and Image Processing
May 2017"
382f1ebe6009e580949d5513bc298cb253a1eeda,Interpreting Complex Regression Models,"Interpreting Complex Regression Models
Noa Avigdor-Elgrabli∗, Alex Libov†, Michael Viderman∗, Ran Wolff∗
Yahoo Research, Haifa, Israel,
Amazon Research, Haifa, Israel,"
389363432ee9fcf0e0cfe67b7b4f62618e1f4b59,Performing content-based retrieval of humans using gait biometrics,"Performing Content-Based Retrieval of Humans
Using Gait Biometrics
Sina Samangooei and Mark S. Nixon
School of Electronics and Computer Science, Southampton University, Southampton,
SO17 1BJ, United Kingdom"
384f972c81c52fe36849600728865ea50a0c4670,"Multi-Fold Gabor, PCA, and ICA Filter Convolution Descriptor for Face Recognition","Multi-Fold Gabor, PCA and ICA Filter
Convolution Descriptor for Face Recognition
Cheng Yaw Low, Andrew Beng Jin Teoh, Senior Member, IEEE, Cong Jie Ng"
38998d58a0c1048ad4c08d0022066e22ba6d1201,RE-IDENTIFICATION THROUGH A VIDEO,"UNIVERSIT´EDENICE-SOPHIAANTIPOLIS´ECOLEDOCTORALESTICSCIENCESETTECHNOLOGIESDEL’INFORMATIONETDELACOMMUNICATIONTH`ESEpourl’obtentiondugradedeDocteurenSciencesdel’Universit´edeNice-SophiaAntipolisMention:AUTOMATIQUETRAITEMENTDUSIGNALETDESIMAGESpr´esent´eeetsoutenueparMalikSOUDEDPEOPLEDETECTION,TRACKINGANDRE-IDENTIFICATIONTHROUGHAVIDEOCAMERANETWORKTh`esedirig´eeparFranc¸oisBR´EMONDSoutenancepr´evuele20/12/2013Jury:MoniqueTHONNATDirectrice,INRIASophia-Antipolis,FrancePr´esidenteJamesFERRYMANProfesseur,UniversityofReading,UKRapporteurCarloREGAZZONIProfesseur,UniversityofGenova,ItalyRapporteurPatrickBOUTHEMYDirecteur,INRIARennes,FranceExaminateurFranc¸oisBREMONDDirecteur,INRIASophia-Antipolis,FranceDirecteurdeth`eseMarie-ClaudeFRASSONDirectrice,DigitalBarriers,Sophia-Antipolis,FranceInvit´ee"
380d5138cadccc9b5b91c707ba0a9220b0f39271,Deep Imbalanced Learning for Face Recognition and Attribute Prediction,"Deep Imbalanced Learning for Face Recognition
nd Attribute Prediction
Chen Huang, Yining Li, Chen Change Loy, Senior Member, IEEE and Xiaoou Tang, Fellow, IEEE"
386a5c06d334d20227e8b2daf5433a2bef385648,Cross and Learn: Cross-Modal Self-Supervision,"Cross and Learn: Cross-Modal Self-Supervision
Nawid Sayed1, Biagio Brattoli2, and Bj¨orn Ommer2
Heidelberg University, HCI / IWR, Germany"
3805d47da61527137b6f44b92af3017a2dfe7bd5,Greedy column subset selection for large-scale data sets,"(will be inserted by the editor)
Greedy Column Subset Selection for Large-scale
Data Sets
Ahmed K. Farahat · Ahmed Elgohary ·
Ali Ghodsi · Mohamed S. Kamel
Received: date / Accepted: date"
380b8df0f340e5bbc3a953c62f9bc573ce073b92,Joint Image-Text News Topic Detection and Tracking by Multimodal Topic And-Or Graph,"Joint Image-Text News Topic Detection and
Tracking by Multimodal Topic And-Or Graph
Weixin Li, Jungseock Joo, Hang Qi, and Song-Chun Zhu"
38a169b6e67ef7768f91fa208c9b5544f6f57f16,Object Bank: An Object-Level Image Representation for High-Level Visual Recognition,"Int J Comput Vis
DOI 10.1007/s11263-013-0660-x
Object Bank: An Object-Level Image Representation
for High-Level Visual Recognition
Li-Jia Li · Hao Su · Yongwhan Lim · Li Fei-Fei
Received: 2 January 2012 / Accepted: 11 September 2013
© Springer Science+Business Media New York 2013"
38b18585e4bdb78347d44caa561e69a0045ade8d,Differential Attention for Visual Question Answering,"Differential Attention for Visual Question Answering
Badri Patro, Vinay P. Namboodiri
IIT Kanpur
{ badri,vinaypn"
3858e5175799b97805b2b70ff54e8a7e0718870f,Deep Learning For Smile Recognition,"July 26, 2017
WSPC - Proceedings Trim Size: 9in x 6in
paper
Deep Learning For Smile Recognition
Patrick O. Glauner
Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg
721 Luxembourg, Luxembourg
Email:
snt.uni.lu
Inspired by recent successes of deep learning in computer vision, we propose
novel application of deep convolutional neural networks to facial expression
recognition, in particular smile recognition. A smile recognition test accuracy
of 99.45% is achieved for the Denver Intensity of Spontaneous Facial Action
(DISFA) database, significantly outperforming existing approaches based on
hand-crafted features with accuracies ranging from 65.55% to 79.67%. The
novelty of this approach includes a comprehensive model selection of the ar-
hitecture parameters, allowing to find an appropriate architecture for each
expression such as smile. This is feasible because all experiments were run on a
Tesla K40c GPU, allowing a speedup of factor 10 over traditional computations
on a CPU."
3802da31c6d33d71b839e260f4022ec4fbd88e2d,Deep Attributes for One-Shot Face Recognition,"Deep Attributes for One-Shot Face Recognition
Aishwarya Jadhav1,3, Vinay P. Namboodiri2, and K. S. Venkatesh 3
Xerox Research Center India, 2Department of Computer Science,
Department of Electrical Engineering, IIT Kanpur"
38e3c26829e38c6b56f7c541e0c4445820fab0fe,BOLD5000: A public fMRI dataset of 5000 images,"BOLD5000
A public fMRI dataset of 5000 images
Nadine Chang1, John A. Pyles1, Abhinav Gupta1,
Michael J. Tarr1, Elissa M. Aminoff2*
September 6, 2018
thor: Elissa Aminoff"
38e509fc0d94e954a512128760f7a1f0d6fbc384,A Framework for Application-Guided Task Management on Heterogeneous Embedded Systems,"A Framework for Application Guided Task Management on
Heterogeneous Embedded Systems
FRANCISCO GASPAR, INESC-ID, Instituto Superior T´ecnico, Universidade de Lisboa
LUIS TANIC¸ A, INESC-ID, Instituto Superior T´ecnico, Universidade de Lisboa
PEDRO TOM ´AS, INESC-ID, Instituto Superior T´ecnico, Universidade de Lisboa
ALEKSANDAR ILIC, INESC-ID, Instituto Superior T´ecnico, Universidade de Lisboa
LEONEL SOUSA, INESC-ID, Instituto Superior T´ecnico, Universidade de Lisboa
In this paper, we propose a general framework for fine-grain application-aware task management in hetero-
geneous embedded platforms, which allows integration of different mechanisms for an efficient resource uti-
lization, frequency scaling and task migration. The proposed framework incorporates several components for
ccurate run-time monitoring by relying on the OS facilities and performance self-reporting for parallel and
iterative applications. The framework efficiency is experimentally evaluated on a real hardware platform,
where significant power and energy savings are attained for SPEC CPU2006 and PARSEC benchmarks, by
guiding frequency scaling and inter-cluster migrations according to the run-time application behavior and
predefined performance targets.
CCS Concepts:rComputer systems organization → Multicore architectures; Heterogeneous (hybrid)
systems;rSoftware and its engineering → Process management;
Additional Key Words and Phrases: Heterogeneous multi processor; scheduling; embedded systems; quality
of service; big.LITTLE; task migration; dynamic voltage and frequency control
ACM Reference Format:"
3837f3faa722c91aa21d6f17ea1ac1cb5187bda1,Human Action Attribute Learning From Video Data Using Low-Rank Representations,"Human Action Attribute Learning From Video
Data Using Low-Rank Representations
Tong Wu, Student Member, IEEE, Prudhvi Gurram, Senior Member, IEEE,
Raghuveer M. Rao, Fellow, IEEE, and Waheed U. Bajwa, Senior Member, IEEE"
383d64b27fb3cdf2beff43f3beb8caac8c21a886,Detecting activities of daily living in first-person camera views,"Detecting Activities of Daily Living in First-person Camera Views
Hamed Pirsiavash Deva Ramanan
Department of Computer Science, University of California, Irvine"
38f56240c642677f2245aebe94fb846988487570,Mining patterns of orientations and magnitudes for face recognition,"Mining patterns of orientations and magnitudes for face recognition
Ngoc-Son Vu, Alice Caplier
Gipsa-lab, Grenoble Institute of Technology"
3810b6299140bf2c7d6d0cced765c0777d603923,Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?,"Do Deep Features Generalize from Everyday Objects
to Remote Sensing and Aerial Scenes Domains?
Ot´avio A. B. Penatti
Advanced Technologies Group
SAMSUNG Research Institute
Campinas, SP, 13097-160, Brazil
Keiller Nogueira, Jefersson A. dos Santos
Department of Computer Science
Universidade Federal de Minas Gerais
Belo Horizonte, MG, 31270-010, Brazil"
38f1fac3ed0fd054e009515e7bbc72cdd4cf801a,Finding Person Relations in Image Data of the Internet Archive,"Finding Person Relations in Image Data of the
Internet Archive
Eric M¨uller-Budack1,2[0000−0002−6802−1241],
Kader Pustu-Iren1[0000−0003−2891−9783], Sebastian Diering1, and
Ralph Ewerth1,2[0000−0003−0918−6297]
Leibniz Information Centre for Science and Technology (TIB), Hannover, Germany
L3S Research Center, Leibniz Universit¨at Hannover, Germany"
38d26057acdae8d66378db4b1a2fbebed0a14f27,Similarity Join and Similarity Self-Join Size Estimation in a Streaming Environment,"Similarity Join and Similarity Self-Join Size
Estimation in a Streaming Environment
Davood Rafiei and Fan Deng"
389334e9a0d84bc54bcd5b94b4ce4c5d9d6a2f26,Facial parameter extraction system based on active contours,"FACIAL PARAMETER EXTRACTION SYSTEM BASED ON ACTIVE CONTOURS
Montse Pardàs, Marcos Losada
Universitat Politècnica de Catalunya, Barcelona, Spain"
38192f06ac19172299ab543483d2e0eca2f889c0,Mining Mid-level Features for Image Classification,"(will be inserted by the editor)
Mining Mid-level Features for Image Classification
Basura Fernando · Elisa Fromont · Tinne Tuytelaars
Received: date / Accepted: date"
3851ed2e3c00083f68c2811694736ebdaa9ed8b5,DeepStory: Video Story QA by Deep Embedded Memory Networks,Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
d008e8662b7e482b88e6dee7c50bd939d28e7628,"People detection, tracking and re-identification through a video camera network. (Détection, suivi et ré-identification de personnes à travers un réseau de caméra vidéo)","People detection, tracking and re-identification through
video camera network
Malik Souded
To cite this version:
Malik Souded. People detection, tracking and re-identification through a video camera network.
Other [cs.OH]. Université Nice Sophia Antipolis, 2013. English. <NNT : 2013NICE4152>. <tel-
00913072v2>
HAL Id: tel-00913072
https://tel.archives-ouvertes.fr/tel-00913072v2
Submitted on 29 Jan 2014
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
d03e4e938bcbc25aa0feb83d8a0830f9cd3eb3ea,Face Recognition with Patterns of Oriented Edge Magnitudes,"Face Recognition with Patterns of Oriented
Edge Magnitudes
Ngoc-Son Vu1,2 and Alice Caplier2
Vesalis Sarl, Clermont Ferrand, France
Gipsa-lab, Grenoble INP, France"
d03f1257066ce5dd843c6977858a1daef0671f3d,Stories for Images-in-Sequence by using Visual and Narrative Components,"Stories for Images-in-Sequence by using Visual
nd Narrative Components (cid:63)
Marko Smilevski1,2, Ilija Lalkovski2, and Gjorgji Madjarov1,3
Ss. Cyril and Methodius University, Skopje, Macedonia
Pendulibrium, Skopje, Macedonia
Elevate Global, Skopje, Macedonia"
d03265ea9200a993af857b473c6bf12a095ca178,Multiple deep convolutional neural networks averaging for face alignment,"Multiple deep convolutional neural
networks averaging for face
lignment
Shaohua Zhang
Hua Yang
Zhouping Yin
Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 05/28/2015 Terms of Use: http://spiedl.org/terms"
d0b936f643f7462068517e0a840e775d6bd4abfb,Improving Video Generation for Multi-functional Applications,"Improving Video Generation for Multi-functional
Applications
Bernhard Kratzwald, Zhiwu Huang, Danda Pani Paudel, Acharya Dinesh,
Luc Van Gool
ETH Zurich"
d0e20aa3d61b77d17f005a1d24d7cf47600836ef,Rethinking Atrous Convolution for Semantic Image Segmentation,"Rethinking Atrous Convolution for Semantic Image Segmentation
Liang-Chieh Chen George Papandreou Florian Schroff Hartwig Adam
{lcchen, gpapan, fschroff,
Google Inc."
d0a9bbd3bd9dcb62f9874fc1378a7f1a17f44563,Prototype Generation Using Self-Organizing Maps for Informativeness-Based Classifier,"Hindawi
Computational Intelligence and Neuroscience
Volume 2017, Article ID 4263064, 15 pages
https://doi.org/10.1155/2017/4263064
Research Article
Prototype Generation Using Self-Organizing Maps for
Informativeness-Based Classifier
Leandro Juvêncio Moreira1 and Leandro A. Silva2
Graduate Program in Electrical Engineering and Computing, Mackenzie Presbyterian University, Sao Paulo, SP, Brazil
Computing and Informatics Faculty & Graduate Program in Electrical Engineering and Computing,
Mackenzie Presbyterian University, Sao Paulo, SP, Brazil
Correspondence should be addressed to Leandro A. Silva;
Received 31 January 2017; Revised 13 June 2017; Accepted 15 June 2017; Published 25 July 2017
Academic Editor: Toshihisa Tanaka
Copyright © 2017 Leandro Juvˆencio Moreira and Leandro A. Silva. This is an open access article distributed under the Creative
Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the
original work is properly cited.
The 𝑘 nearest neighbor is one of the most important and simple procedures for data classification task. The 𝑘NN, as it is called,
requires only two parameters: the number of𝑘 and a similarity measure. However, the algorithm has some weaknesses that make it
nalysis and all training dataset is necessary. Another weakness is the optimal choice of 𝑘 parameter when the object analyzed"
d01e65591745fc46a3f69a6c9387be17caf55c16,State-Driven Particle Filter for Multi-person Tracking,"State-Driven Particle Filter
for Multi-Person Tracking
David Gerónimo1, Frédéric Lerasle2,3, and Antonio M. López1
Computer Vision Center and Department of Computer Science
Edifici O, 08193 Campus Universitat Autònoma de Barcelona, Bellaterra, Spain.
CNRS-LAAS, 7 avenue du Colonel Roche, F-31077 Toulouse, France
Université de Toulouse (UPS), F-31077 Toulouse, France"
d0a6a700779ac8cb70d7bb95f9a5afdda60152d9,Pyramid Mean Representation of Image Sequences for Fast Face Retrieval in Unconstrained Video Data,"Pyramid Mean Representation of Image Sequences for
Fast Face Retrieval in Unconstrained Video Data
Christian Herrmann1,2 and J¨urgen Beyerer1,2
Vision and Fusion Lab, Karlsruhe Institute of Technology KIT, Karlsruhe, Germany
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB,
Karlsruhe, Germany"
d041c8cb05a5555046f6e62a4efbb964fb560c31,Generating faces for affect analysis,"Noname manuscript No.
(will be inserted by the editor)
Generating faces for affect analysis
Dimitrios Kollias (cid:63) · Shiyang Cheng † · Evangelos Ververas ∗ · Irene
Kotsia1 · Stefanos Zafeiriou2
Received: Sept 30th 2018 / Accepted: date"
d0d186779ae4a4e53101a26dc741254e822e07ab,Multi Camera for Surveillance System Ground Detection and 3 D Reconstruction,"Multi Camera for Surveillance System Ground Detection and
International Journal of Smart Home
Vol. 9, No. 1 (2015), pp. 103-110
http://dx.doi.org/10.14257/ijsh.2015.9.1.11
D Reconstruction
Xu Yongzhe1 and Byungsoo Lee1
Department of Computer Engineering, University of Incheon, Korea"
d00787e215bd74d32d80a6c115c4789214da5edb,Faster and Lighter Online Sparse Dictionary Learning Project report,"Faster and Lighter Online
Sparse Dictionary Learning
Project report
By: Shay Ben-Assayag, Omer Dahary
Supervisor: Jeremias Sulam"
d00c335fbb542bc628642c1db36791eae24e02b7,Deep Learning-Based Gaze Detection System for Automobile Drivers Using a NIR Camera Sensor,"Article
Deep Learning-Based Gaze Detection System for
Automobile Drivers Using a NIR Camera Sensor
Rizwan Ali Naqvi, Muhammad Arsalan, Ganbayar Batchuluun, Hyo Sik Yoon and
Kang Ryoung Park *
Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu,
Seoul 100-715, Korea; (R.A.N.); (M.A.);
(G.B.); (H.S.Y.)
* Correspondence: Tel.: +82-10-3111-7022; Fax: +82-2-2277-8735
Received: 5 January 2018; Accepted: 1 February 2018; Published: 3 February 2018"
d0a21f94de312a0ff31657fd103d6b29db823caa,Facial Expression Analysis,"Facial Expression Analysis
Fernando De la Torre and Jeffrey F. Cohn"
d0de92865a53576af3dd118f4d1fa73be12aee9b,PCANet-II: When PCANet Meets the Second Order Pooling,"PCANet-II:WhenPCANetMeetstheSecondOrderPoolingLeiTian,XiaopengHong"
d07e9b04c1480d65e37e44bec3be95fc3206c17b,Combining classifiers for face recognition,- 130-7803-7965-9/03/$17.00 ©2003 IEEEICME 2003(cid:224)
d02f45670fa6eb1fbac7ed7ed3eaa442579c73b2,Covariance Pooling for Facial Expression Recognition,"Covariance Pooling for Facial Expression Recognition
Computer Vision Lab, ETH Zurich, Switzerland
VISICS, KU Leuven, Belgium
Dinesh Acharya†, Zhiwu Huang†, Danda Pani Paudel†, Luc Van Gool†‡
{acharyad, zhiwu.huang, paudel,"
d00f6ec074bbe777ba2e419b39729283a28101c5,Hashtag Recommendation for Multimodal Microblog Using Co-Attention Network,Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
d0462aa7754ffdf39962e2003344937258a0e42e,You Can ’ t Gamble on Others : Dissociable Systems for Strategic Uncertainty and Risk in the Brain,"You Can’t Gamble on Others: Dissociable Systems for
Strategic Uncertainty and Risk in the Brain
W. Gavin Ekins1, Ricardo Caceda, C. Monica Capra1, and Gregory S. Berns1*
1Center for Neuropolicy and Economics Department, Emory University, Atlanta, GA 30322 USA
*Correspondance:"
d0eab36a4c76da09d2393ff549c2d6de2106a4cb,"Semantic Segmentation via Multi-task, Multi-domain Learning","Semantic Segmentation via Multi-task,
Multi-domain Learning
Damien Fourure1 & R´emi Emonet1 & Elisa Fromont 1
Damien Muselet1 & Alain Tr´emeau 1 & Christian Wolf2
Univ Lyon, UJM, CNRS, Lab Hubert Curien UMR5516, F-42000
Universite de Lyon, CNRS, France, INSA-Lyon, LIRIS, UMR5205, F-69621"
d05825a394f11a391c8815f6b0d394cdb4cfaa95,I2T2I: Learning text to image synthesis with textual data augmentation,
d02c54192dbd0798b43231efe1159d6b4375ad36,3 D Reconstruction and Face Recognition Using Kernel-Based ICA and Neural Networks,"D Reconstruction and Face Recognition Using Kernel-Based
ICA and Neural Networks
Cheng-Jian Lin                  Ya-Tzu Huang
Chi-Yung Lee
Dept. of Electrical                Dept. of CSIE                    Dept. of CSIE
Engineering              Chaoyang University              Nankai Institute of
National University            of Technology                        Technology
of Kaohsiung"
d0e1ad4f3f608124cd3efc2d5bd01b421ffc3274,Suppressing behaviour related to discomfort induced with a cold pressure task does not influence working memory capacity in a 2-back task,"Running
head:
SUPPRESSING
BEHAVIOUR
INFLUENCE
WORKING
MEMORY
CAPACITY
DEPARTMENT OF PSYCHOLOGY
Suppressing behaviour related to discomfort
induced with a cold pressure task does not
influence working memory capacity in a 2-back
task.
Erik Danielski
Master thesis spring 2013
Supervisors: Martin Wolgast & Emelie Stiernströmer"
d0ad7324fab174609f26c617869fa328960617e2,Person Identification From Text Independent Lip Movement Using the Longest Matching Segment Method,"Person Identification From Text Independent Lip Movement
Using the Longest Matching Segment Method
Paul C. Brown, Ji Ming, Daryl Stewart
Institute of ECIT, Electronics and Computer Engineering Cluster, Queen(cid:48)s University Belfast,
Belfast BT7 1NN, UK"
d096bdd5743cbb33f0cd0ae984d188b2c302f054,EXTRACTIVE AND ABSTRACTIVE CAPTION GENERATION MODEL FOR NEWS IMAGES,"ISSN:2321-1156
International Journal of Innovative Research in Technology & Science(IJIRTS)"
d0f709ab39e280467d854064132570c1d5316de5,Multi-Object Tracking and Identification over Sets,"Multi-Object Tracking and Identification over Sets
Aijun Bai
UC Berkeley"
d0144d76b8b926d22411d388e7a26506519372eb,Improving Regression Performance with Distributional Losses,"Improving Regression Performance with Distributional Losses
Ehsan Imani 1 Martha White 1"
d0631ba22add59684fff926d80d2e6948dfb7d7e,MUTT: Metric Unit TesTing for Language Generation Tasks,"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pages 1935–1943,
Berlin, Germany, August 7-12, 2016. c(cid:13)2016 Association for Computational Linguistics"
d0765e76ea93e70ad6873560541531b5f572fb4f,ANALÝZA POHYBU OSOB STACIONÁRNÍ KAMEROU ANALYSIS OF MOTION OF PEOPLE BY A STATIONARY CAMERA,"VYSOKE´ UCˇ ENI´ TECHNICKE´ V BRNEˇ
BRNO UNIVERSITY OF TECHNOLOGY
FAKULTA INFORMACˇ NI´CH TECHNOLOGII´
U´ STAV POCˇ I´TACˇ OVE´ GRAFIKY A MULTIME´ DII´
FACULTY OF INFORMATION TECHNOLOGY
DEPARTMENT OF COMPUTER GRAPHICS AND MULTIMEDIA
ANALY´ ZA POHYBU OSOB STACIONA´ RNI´ KAMEROU
ANALYSIS OF MOTION OF PEOPLE BY A STATIONARY CAMERA
BAKALA´ Rˇ SKA´ PRA´ CE
BACHELOR’S THESIS
AUTOR PRA´ CE
AUTHOR
VEDOUCI´ PRA´ CE
SUPERVISOR
BRNO 2014
STANISLAV SMATANA
Ing. ADAM HEROUT, Ph.D."
5f02e49aa0fe467bbeb9de950e4abb6c99133feb,"Enhancing person re-identification by late fusion of low-, mid- and high-level features","Aalborg Universitet
Enhancing Person Re-identification by Late Fusion of Low-, Mid-, and High-Level
Features
Lejbølle, Aske Rasch; Nasrollahi, Kamal; Moeslund, Thomas B.
Published in:
DOI (link to publication from Publisher):
0.1049/iet-bmt.2016.0200
Publication date:
Document Version
Accepted author manuscript, peer reviewed version
Link to publication from Aalborg University
Citation for published version (APA):
Lejbølle, A. R., Nasrollahi, K., & Moeslund, T. B. (2018). Enhancing Person Re-identification by Late Fusion of
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5f344a4ef7edfd87c5c4bc531833774c3ed23542,Semisupervised Learning of Classifiers with Application to Human-computer Interaction," Copyright by Ira Cohen, 2003"
5f4a873118e033e5e168ee99d64474b4cc4d94a3,Lessons Learned from Crime Caught on Camera,"Article
Lessons Learned
from Crime Caught
on Camera
Marie Rosenkrantz Lindegaard1,2
nd Wim Bernasco1,3
Journal of Research in Crime and
Delinquency
018, Vol. 55(1) 155-186
ª The Author(s) 2018
Reprints and permission:
sagepub.com/journalsPermissions.nav
DOI: 10.1177/0022427817727830
journals.sagepub.com/home/jrc"
5f34c96ddcf992e1b8660b5cb01e3c311b05023c,Towards Online Iris and Periocular Recognition Under Relaxed Imaging Constraints,"IEEE Trans. Image Processing, 2013
Towards Online Iris and Periocular Recognition under
Relaxed Imaging Constraints
Chun-Wei Tan, Ajay Kumar"
5f71adb3df33e1dac408e1c211043aa95e75988c,A Novel Characterization of the Alternative Hypothesis Using Kernel Discriminant Analysis for LLR-Based Speaker Verification,"Computational Linguistics and Chinese Language Processing
Vol. 12, No. 3, September 2007, pp. 255-272                                                                255
© The Association for Computational Linguistics and Chinese Language Processing
A Novel Characterization of the Alternative Hypothesis
Using Kernel Discriminant Analysis for
LLR-Based Speaker Verification
Yi-Hsiang Chao∗+ , Hsin-Min Wang∗ and Ruei-Chuan Chang∗+"
5fac23560b5ba4d45045f1edd837ed4bb6c1f5ca,Optimization of a Face Verification System using Bayesian Screening Techniques.,"OPTIMIZATION OF A FACE VERIFICATION SYSTEM
USING BAYESIAN SCREENING TECHNIQUES
Raquel Montes Diez, Cristina Conde, Ángel Serrano, Licesio J. Rodríguez-Aragón, Enrique Cabello
Universidad Rey Juan Carlos (URJC)
Escuela Superior de Ciencias Experimentales y Tecnología (ESCET)
Face Recognition & Artificial Vision Group (FRAV)
C/ Tulipán, s/n. Móstoles E-29833, Madrid, Spain
{r.montes, cristina.conde, aserranos, ljrodriguez,
http://frav.escet.urjc.es/
the  150  most"
5f3a513351a75f4eb86dd57e1651e33691c62417,Gender Recognition via Face Area Analysis,
5f0f8c9acc3e8eb50ca6e7d9c33cf3d9a8a54985,Structured Inhomogeneous Density Map Learning for Crowd Counting,"Structured Inhomogeneous Density Map Learning
for Crowd Counting
Hanhui Li, Xiangjian He, Hefeng Wu, Saeed Amirgholipour Kasmani, Ruomei Wang, Xiaonan Luo, Liang Lin"
5fa6e4a23da0b39e4b35ac73a15d55cee8608736,RED-Net: A Recurrent Encoder–Decoder Network for Video-Based Face Alignment,"IJCV special issue (Best papers of ECCV 2016) manuscript No.
(will be inserted by the editor)
RED-Net:
A Recurrent Encoder-Decoder Network for Video-based Face Alignment
Xi Peng · Rogerio S. Feris · Xiaoyu Wang · Dimitris N. Metaxas
Submitted: April 19 2017 / Revised: December 12 2017"
5feb32a73dd1bd9e13f84a7b3344497a5545106b,FastText.zip: Compressing text classification models,"Under review as a conference paper at ICLR 2017
FASTTEXT.ZIP:
COMPRESSING TEXT CLASSIFICATION MODELS
Armand Joulin, Edouard Grave, Piotr Bojanowski, Matthijs Douze, Herv´e J´egou & Tomas Mikolov
Facebook AI Research"
5f5906168235613c81ad2129e2431a0e5ef2b6e4,A Unified Framework for Compositional Fitting of Active Appearance Models,"Noname manuscript No.
(will be inserted by the editor)
A Unified Framework for Compositional Fitting of
Active Appearance Models
Joan Alabort-i-Medina · Stefanos Zafeiriou
Received: date / Accepted: date"
5f769ba95ffea0ce76ac9d8e7cd47e2d1c91e1bf,Localizing antipodal grasps in point clouds,"Localizing antipodal grasps in point clouds
Andreas ten Pas and Robert Platt"
5f0e9cc18374a670dfea4698424c9d48494f3093,Online Domain Adaptation for Multi-Object Tracking,"GAIDON & VIG: ONLINE DOMAIN ADAPTATION FOR MULTI-OBJECT TRACKING
Online Domain Adaptation for Multi-Object Tracking
Computer Vision Group
Xerox Research Centre Europe
Meylan, France
Adrien Gaidon
Eleonora Vig"
5fdb3533152f9862e3e4c2282cd5f1400af18956,A Variational U-Net for Conditional Appearance and Shape Generation,"A Variational U-Net for Conditional Appearance and Shape Generation
Patrick Esser∗, Ekaterina Sutter∗, Bj¨orn Ommer
Heidelberg Collaboratory for Image Processing
IWR, Heidelberg University, Germany"
5ffd74d2873b7cba2cbc5fd295cc7fbdedca22a2,The Cityscapes Dataset,"The Cityscapes Dataset
Marius Cordts1,2
Mohamed Omran3
Rodrigo Benenson3
Sebastian Ramos1,4
Uwe Franke1
Timo Scharw¨achter1,2
Markus Enzweiler1
Stefan Roth2
Bernt Schiele3
Daimler AG R&D, 2TU Darmstadt, 3MPI Informatics, 4TU Dresden
www.cityscapes-dataset.net"
5f6116b6e5f21da66a304e9f59f3e224e188caef,Behavior Is Everything: Towards Representing Concepts with Sensorimotor Contingencies,"Behavior is Everything – Towards Representing Concepts
with Sensorimotor Contingencies
Nicholas Hay, Michael Stark, Alexander Schlegel, Carter Wendelken,
Dennis Park, Eric Purdy, Tom Silver, D. Scott Phoenix, and Dileep George
Vicarious AI, San Francisco, CA, USA"
5f7354634e13c9fad64163d53beb0a8eb5df30e1,Sketch-Based Image Retrieval: Benchmark and Bag-of-Features Descriptors,"Sketch-Based Image Retrieval: Benchmark
nd Bag-of-Features Descriptors
Mathias Eitz, Kristian Hildebrand, Tamy Boubekeur and Marc Alexa"
5f943f9bfe3154fbd368034903ea11620d2946eb,Cascade Category-Aware Visual Search,"MiniManuscript.com
The one stop shop for academic literature.
07:00am  8 Dec, 2018
Cascade Category-Aware Visual Search.
Authors Zhang S, Tian Q, Huang Q, Gao W, Rui Y
Volume
Issue
Pages"
5fc621cdef59c38ef898a2adc2b4472a8396119a,Elephant Panda Lion Dolphin Dog Monkey ... ... Classes Elephant Dolphin Lion Classifier Training Data Synthesizing,"Synthesizing Samples for Zero-shot Learning
IJCAI Anonymous Submission 2625"
5f96af88dfef2bff4ed8a49ceca909efb701d1d5,Addressing the Dark Side of Vision Research: Storage,"Addressing the Dark Side of Vision Research: Storage
Vishakha Gupta-Cledat
Intel Labs
Luis Remis
Intel Labs
Christina R. Strong
Intel Labs"
5fc664202208aaf01c9b62da5dfdcd71fdadab29,Automatic Face Recognition from Video,rXiv:1504.05308v1  [cs.CV]  21 Apr 2015
5f58bf2c25826cb6ee927a1461aa72bd623157ff,Tasting Families of Features for Image Classification,"ICCV 2011 Submission #549. CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE.
Tasting Families of Features for Image Classification
Anonymous ICCV submission
Paper ID 549"
5fd147f57fc087b35650f7f3891d457e4c745d48,Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields,"Published as a conference paper at ICLR 2018
COULOMB GANS: PROVABLY OPTIMAL NASH EQUI-
LIBRIA VIA POTENTIAL FIELDS
Thomas Unterthiner1
Bernhard Nessler1
Calvin Seward1,2
Günter Klambauer1
Martin Heusel1
Hubert Ramsauer1
Sepp Hochreiter1
LIT AI Lab & Institute of Bioinformatics, Johannes Kepler University Linz, Austria
Zalando Research, Mühlenstraße 25, 10243 Berlin, Germany"
5fff61302adc65d554d5db3722b8a604e62a8377,Additive Margin Softmax for Face Verification,"Additive Margin Softmax for Face Verification
Feng Wang
UESTC
Weiyang Liu
Georgia Tech
Haijun Liu
UESTC
Jian Cheng
UESTC
haijun"
5fa1724a79a9f7090c54925f6ac52f1697d6b570,The Development of Multimodal Lexical Resources,"Proceedings of the Workshop on Grammar and Lexicon: Interactions and Interfaces,
pages 41–47, Osaka, Japan, December 11 2016."
5fa587381a9e4308163b3a5395985f0375c3cf7d,Deep Extreme Cut: From Extreme Points to Object Segmentation,"Deep Extreme Cut: From Extreme Points to Object Segmentation
K.-K. Maninis* S. Caelles∗
Computer Vision Lab, ETH Z¨urich, Switzerland
J. Pont-Tuset
L. Van Gool
Figure 1. Example results of DEXTR: The user provides the extreme clicks for an object, and the CNN produces the segmented masks."
5f534bacc658f620a15b5647adecb0ea813286c8,Reliable object detection and segmentation using inpainting,"Reliable Object Detection and Segmentation using Inpainting
Ji Hoon Joung, M. S. Ryoo, Sunglok Choi, and Sung-Rak Kim"
5fcde9236d654a0f92a76c1a3f07c0cad954985c,Personality-Dependent Referring Expression Generation,"Personality-dependent Referring Expression Generation
Ivandr´e Paraboni, Danielle Sampaio Monteiro, and Alex Gwo Jen Lan
University of S˜ao Paulo, School of Arts, Sciences and Humanities, S˜ao Paulo, Brazil"
5f5164cf998a10d2bef37741adb562ab07fac413,A Comprehensive Study on Cross-View Gait Based Human Identification with Deep CNNs,"This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TPAMI.2016.2545669, IEEE
Transactions on Pattern Analysis and Machine Intelligence
A Comprehensive Study on Cross-View Gait Based
Human Identification with Deep CNNs
Zifeng Wu, Yongzhen Huang, Liang Wang, Xiaogang Wang, and Tieniu Tan"
d904f945c1506e7b51b19c99c632ef13f340ef4c,0 ° 15 ° 30 ° 45 ° 60 ° 75 ° 90 °,"A scalable 3D HOG model for fast object detection and viewpoint estimation
Marco Pedersoli
Tinne Tuytelaars
KU Leuven, ESAT/PSI - iMinds
Kasteelpark Arenberg 10 B-3001 Leuven, Belgium"
d9f9b35800da98e7bde1966225b8734d2ca5e94f,ASSESSMENT OF THE EFFICIENCY OF CORRELATION COEFFICIENT FOR FACE AUTHENTICATION,"International Journal of Advance Research In Science And Engineering           http://www.ijarse.com
IJARSE, Vol. No.3, Issue No.8, August 2014                                                        ISSN-2319-8354(E)
ASSESSMENT OF THE EFFICIENCY OF
CORRELATION COEFFICIENT FOR FACE
AUTHENTICATION
Sheela Shankar, 2V.R Udupi
Department of Electronics & Communication Engg,
KLE Dr. M. S. Sheshgiri CET, Udyambag, Belgaum, (India)
Department of Electronics and Communication Engg,
Gogte Institute of Technology, Belgaum, (India)"
d9984fac91cb4f469cf36f140b6c8c07c45afe6f,Face Recognition Using LAPP Algorithm,"International Journal of Engineering Trends and Applications (IJETA) – Volume 2 Issue 5, Sep-Oct 2015
RESEARCH  ARTICLE
OPEN  ACCESS
Face Recognition Using LAPP Algorithm
Priyanka  Kumbham  [1], Dr. G. R. Sakthidharan[2]
M.Tech Student [1], Associate Professor [2]
Department of Computer Science and Engineering
Gokaraju Rangaraju Institute of Engineering  and Technology
Telangana – India"
d950c5b7be9e46cba46f6811845c9f45189e900c,Correlation Method Based PCA Subspace using Accelerated Binary Particle Swarm Optimization for Enhanced Face Recognition,"ISSN: 2321-8169
International Journal on Recent and Innovation Trends in Computing and Communication
Volume: 4 Issue: 9
69- 72
_______________________________________________________________________________________________
Correlation Method Based PCA Subspace using Accelerated Binary Particle
Swarm Optimization for Enhanced Face Recognition
Ms. Navpreet Kaur
M.Tech Scholar
Ms. Rasleen Kaur
Assistant professor
Depar tmen t of Computer  Scien ce An d  Engin eer ing,
Depar tmen t of Computer Scien ce An d  Engin eer ing
Global Institute of Management And Emerging
Technologies,Amritsar,punjab,India.
Global Institute of Management And Emerging
Technologies,Amritsar,punjab,India."
d98c7dd22a7927b377e523a3a36408729a523f42,Extended Abstract Object Recognition via Robust Learning,
d9433ba8bc58a94dffbfebe85e1aca1b6229d6c4,Audio‐Visual Speaker Tracking,"We are IntechOpen,
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d930d20ba42a5d868dd78dd73bac0f72110e0bc5,Multivariate Shape Modeling and Its Application to Characterizing Abnormal Amygdala Shape in Autism,"Multivariate Shape Modeling and Its Application to
Characterizing Abnormal Amygdala Shape in Autism
Moo K. Chunga,b∗,Keith J. Worsleyd, Brendon, M. Nacewiczb,
Kim M. Daltonb, Richard J. Davidsonb,c
Department of Biostatistics and Medical Informatics
Waisman Laboratory for Brain Imaging and Behavior
Department of Psychology and Psychiatry
University of Wisconsin, Madison, WI 53706, USA
dDepartment of Statistics
University of Chicago, Chicago, IL 60637, USA
September 22, 2009"
d944ff789af84cecc0a913da964e017408687d62,"Image Parsing: Segmentation, Detection, and Recognition","Image Parsing: Segmentation, Detection, and
Recognition.
Zhuowen Tu
Department of Statistics
Alex Chen
Department of Statistics
University of California at Los Angeles
University of California at Los Angeles
Los Angeles, CA 90095
Los Angeles, CA 90095
Alan Yuille
Department of Statistics
Song-Chun Zhu
Department of Statistics
University of California at Los Angeles
University of California at Los Angeles
Los Angeles, CA 90095
Los Angeles, CA 90095
In International Conference of Computer Vision. Nice, France. pp 18-25. October. 2003."
d9ee64038aea3a60120e9f7de16eb4130940a103,Message Passing Multi-Agent GANs,"Message Passing Multi-Agent GANs
Arnab Ghosh∗, Viveka Kulharia∗, Vinay Namboodiri
IIT Kanpur"
d9f0640716ec25278e6f1a4fdda5596660504c54,A Correlated Parts Model for Object Detection in Large 3D Scans,"EUROGRAPHICS 2013 / I. Navazo, P. Poulin
(Guest Editors)
Volume 32 (2013), Number 2
A Correlated Parts Model for Object Detection in Large 3D
Scans
M. Sunkel1, S. Jansen1, M. Wand1,2, H.-P. Seidel1
MPI Informatik
Saarland University
Figure 1: Based on sparse user annotations a shape model is learned. The detected instances are transformed into descriptors
for the second hierarchy level. Hierarchical detections shown on the right are obtained using only the example marked red."
d9cc8bc5c4a4b29ab40f75b721bd9e5140d2baf6,Object Detection for Crime Scene Evidence Analysis Using Deep Learning,"Object Detection for Crime Scene Evidence
Analysis Using Deep Learning
Surajit Saikia1,2(B), E. Fidalgo1,2, Enrique Alegre1,2,
nd Laura Fern´andez-Robles2,3
Department of Electrical, Systems and Automation, University of Le´on,
Le´on, Spain
INCIBE (Spanish National Cybersecurity Institute), Le´on, Spain
Department of Mechanical, Informatics and Aerospace Engineering,
University of Le´on, Le´on, Spain"
d9fe0b257ec50a12ba1af749fad56a6f705d16a4,HIGH FREQUENCY REGIONS FOR FACE RECOGNITION,"The International Journal of Multimedia & Its Applications (IJMA) Vol.4, No.1, February 2012
FEATURE IMAGE GENERATION USING LOW, MID
AND HIGH FREQUENCY REGIONS FOR FACE
RECOGNITION
Vikas Maheshkar1, Sushila Kamble2, Suneeta Agarwal3 and Vinay Kumar
Srivastava4
-3Department of Computer Science and Engineering, MNNIT, Allahabad
Department of Electronics & Communication Engineering, MNNIT, Allahabad"
d929534024614e3153c986e55d758ea7471d3fff,How Not to Evaluate a Developmental System,"How Not to Evaluate a Developmental System
Frederick Shic and Brian Scassellati"
d9bc16dcbc13502389704e4a0bdd8ee7af618069,Learning pullback HMM distances for action recognition,Learning pullback HMM distances for action recognition
d94b37958657aa703d8a3d02a66ee251b4c3f597,Learning deep features from body and parts for person re-identification in camera networks,"Zhang and Si EURASIP Journal on Wireless Communications and
Networking  (2018) 2018:52
https://doi.org/10.1186/s13638-018-1060-2
RESEARCH
Open Access
Learning deep features from body and
parts for person re-identification in camera
networks
Zhong Zhang1,2* and Tongzhen Si1,2"
d94d7ff6f46ad5cab5c20e6ac14c1de333711a0c,Face Album: Towards automatic photo management based on person identity on mobile phones,"978-1-5090-4117-6/17/$31.00 ©2017 IEEE
ICASSP 2017"
d94c7a89adf6f568bbe1510910850d5083a58b4f,Deep Cross Modal Learning for Caricature Verification and Identification (CaVINet),"Deep Cross Modal Learning for Caricature Verification and
Identification(CaVINet)
https://lsaiml.github.io/CaVINet/
Jatin Garg∗
Indian Institute of Technology Ropar
Himanshu Tolani∗
Indian Institute of Technology Ropar
Skand Vishwanath Peri∗
Indian Institute of Technology Ropar
Narayanan C Krishnan
Indian Institute of Technology Ropar"
d9810786fccee5f5affaef59bc58d2282718af9b,Adaptive Frame Selection for Enhanced Face Recognition in Low-Resolution Videos,"Adaptive Frame Selection for
Enhanced Face Recognition in
Low-Resolution Videos
Raghavender Reddy Jillela
Thesis submitted to the
College of Engineering and Mineral Resources
t West Virginia University
in partial fulfillment of the requirements
for the degree of
Master of Science
Electrical Engineering
Arun Ross, PhD., Chair
Xin Li, PhD.
Donald Adjeroh, PhD.
Lane Department of Computer Science and Electrical Engineering
Morgantown, West Virginia
Keywords: Face Biometrics, Super-Resolution, Optical Flow, Super-Resolution using
Optical Flow, Adaptive Frame Selection, Inter-Frame Motion Parameter, Image Quality,
Image-Level Fusion, Score-Level Fusion
Copyright 2008 Raghavender Reddy Jillela"
d9c4b1ca997583047a8721b7dfd9f0ea2efdc42c,Learning Inference Models for Computer Vision,Learning Inference Models for Computer Vision
d97c4da8cbc6b3c5de06eb457acfdf5c7241a522,Monocular Depth Estimation by Learning from Heterogeneous Datasets,"Monocular Depth Estimation by Learning from Heterogeneous
Datasets
Akhil Gurram1,2, Onay Urfalioglu2, Ibrahim Halfaoui2, Fahd Bouzaraa2 and Antonio M. L´opez1"
d9fda0030ca349da7b1dafca015bea95a6aabea0,ISA2: Intelligent Speed Adaptation from Appearance,"ISA2: Intelligent Speed Adaptation from Appearance
Carlos Herranz-Perdiguero1 and Roberto J. L´opez-Sastre1"
d97e7799142e2c66b63fe63bc52632fdf305f313,Lanczos Vectors versus Singular Vectors for Effective Dimension Reduction,"Lanczos Vectors versus Singular Vectors for
Effective Dimension Reduction
Jie Chen and Yousef Saad"
d914c53cdf26acc64259d381fbd45c4e150633ee,Pedestrian Tracking in the Compressed Domain Using Thermal Images,"Pedestrian Tracking in the Compressed Domain
Using Thermal Images
Ichraf Lahouli1,2,3, Robby Haelterman1, Zied Chtourou2, Geert De Cubber1,
nd Rabah Attia3
Royal Military Academy,
Brussels, Belgium
VRIT Lab, Military Academy of Tunisia,
Nabeul, Tunisia
SERCOM Lab, Tunisia Polytechnic School,
La Marsa, Tunisia"
d9bad7c3c874169e3e0b66a031c8199ec0bc2c1f,"It All Matters: Reporting Accuracy, Inference Time and Power Consumption for Face Emotion Recognition on Embedded Systems","It All Matters:
Reporting Accuracy, Inference Time and Power Consumption
for Face Emotion Recognition on Embedded Systems
Jelena Milosevic
Institute of Telecommunications, TU Wien
Andrew Forembsky
Movidius an Intel Company
Dexmont Pe˜na
Movidius an Intel Company
David Moloney
Movidius an Intel Company
Miroslaw Malek
ALaRI, Faculty of Informatics, USI"
d92581c452e780710938cfbfa0f1ca2ffccc5d5e,Facial Feature Extraction Based on Local Color and Texture for Face Recognition using Neural Network,"International Journal of Science and Engineering Applications
Volume 2 Issue 4, 2013, ISSN-2319-7560 (Online)
Facial Feature Extraction Based on Local Color and Texture
for Face Recognition using Neural Network
S.Cynthia Christabel
M.Annalakshmi
Sethu Institute of Technology.
Sethu Institute of Technology.
Kariapatti.
Kariapatti.
Mr.D.Prince Winston
Aruppukottai."
d930ec59b87004fd172721f6684963e00137745f,Face Pose Estimation using a Tree of Boosted Classifiers,"Face Pose Estimation using a
Tree of Boosted Classifiers
Javier Cruz Mota
Project Assistant: Julien Meynet
Professor: Jean-Philippe Thiran
Signal Processing Institute,
´Ecole Polytechnique F´ed´erale de Lausanne (EPFL)
September 11, 2006"
d951ff5f378b2a5f878423029123ad6b3491b444,Foveal Vision for Instance Segmentation of Road Images,"Foveal Vision for Instance Segmentation of Road Images
Benedikt Ortelt1, Christian Herrmann2,3, Dieter Willersinn2, J¨urgen Beyerer2,3
Robert Bosch GmbH, Leonberg, Germany
Fraunhofer IOSB, Karlsruhe, Germany
Karlsruhe Institute of Technology KIT, Vision and Fusion Lab, Karlsruhe, Germany
Keywords:
Instance Segmentation, Multi-Scale Analysis, Foveated Imaging, Cityscapes."
9679d15c6699b521740408b2e899c03af89390ac,ARTICULATED BODY TRACKING AND HUMAN ACTION ANALYSIS,"DIMENSIONALITY REDUCTION FOR 3D
ARTICULATED BODY TRACKING AND HUMAN
ACTION ANALYSIS
Leonid Raskin
Research Supervisors:
Prof. Ehud Rivlin, Dr. Michael Rudzsky
Prof. Michael Lindenbaum
Submitted in Partial Fulfillment of the Requirements for the
Degree of Doctor of Philosophy
Technion IIT - Israel Institute of Technology
Haifa, Israel
March 2010
(cid:176) Copyright by Leonid Raskin, 2010
Technion - Computer Science Department - Ph.D. Thesis  PHD-2010-11 - 2010"
9696b172d66e402a2e9d0a8d2b3f204ad8b98cc4,Region-Based Facial Expression Recognition in Still Images,"J Inf Process Syst, Vol.9, No.1, March 2013
pISSN 1976-913X
eISSN 2092-805X
Region-Based Facial Expression Recognition in
Still Images
Gawed M. Nagi*, Rahmita Rahmat*, Fatimah Khalid* and Muhamad Taufik*"
96dfa2a7630fc6800ba8a546df526b8174f54b5c,Appearance-Based Facial Recognition Using Visible and Thermal Imagery : A Comparative Study ∗,"Appearance-Based Facial Recognition Using Visible and Thermal
Imagery: A Comparative Study ∗
Andrea Selinger†
Diego A. Socolinsky‡
Equinox Corporation
07 East Redwood Street
Equinox Corporation
9 West 57th Street
New York, NY 10019
Baltimore, MD 21202"
96e9bc6b54d1c79406cf37ae45fd35ef04d647c6,A Fully Automated System for Sizing Nasal PAP Masks Using Facial Photographs,"A Fully Automated System for Sizing Nasal PAP Masks Using Facial
Photographs
Benjamin Johnston Student Member, IEEE and Philip de Chazal Senior Member, IEEE"
96a7f2faf4baa09184deb458a03146805d62beed,Passive Three Dimensional Face Recognition Using Iso-Geodesic Contours and Procrustes Analysis,"Int J Comput Vis (2013) 105:87–108
DOI 10.1007/s11263-013-0631-2
Passive Three Dimensional Face Recognition Using Iso-Geodesic
Contours and Procrustes Analysis
Sina Jahanbin · Rana Jahanbin · Alan C. Bovik
Received: 11 November 2011 / Accepted: 11 May 2013 / Published online: 19 June 2013
© Springer Science+Business Media New York 2013"
96390f95a73a6bd495728b6cd2a97554ef187f76,Pan Olympus : Sensor Privacy through Utility Aware,"Proceedings on Privacy Enhancing Technologies ..; .. (..):1–21
Nisarg Raval, Ashwin Machanavajjhala, and Jerry Pan
Olympus: Sensor Privacy through Utility Aware
Obfuscation"
964e43f4983a42ef3790c265bdce42c1fce56d79,A virtual environment tool for benchmarking face analysis systems,"A Virtual Environment Tool for Benchmarking Face
Analysis Systems
Mauricio Correa+,*, Javier Ruiz-del-Solar+,*, Rodrigo Verschae*
+Department of Electrical Engineering, Universidad de Chile
*Advanced Mining Technology Center, Universidad de Chile
{macorrea,"
966541dca2aa4fe5ddd1a371a1c999073fa0d737,Recognition of Damaged Arrow-Road Markings by Visible Light Camera Sensor Based on Convolutional Neural Network,"Article
Recognition of Damaged Arrow-Road Markings by
Visible Light Camera Sensor Based on Convolutional
Neural Network
Husan Vokhidov, Hyung Gil Hong, Jin Kyu Kang, Toan Minh Hoang and Kang Ryoung Park *
Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu,
Seoul 100-715, Korea; (H.V.); (H.G.H.);
(J.K.K.); (T.M.H.)
* Correspondence: Tel.: +82-10-3111-7022; Fax: +82-2-2277-8735
Academic Editor: Felipe Jimenez
Received: 31 October 2016; Accepted: 14 December 2016; Published: 16 December 2016"
9691055b1fcbe626b5bce9d8d43903094a5c0339,Generating an item pool for translational social cognition research: methodology and initial validation.,"Behav Res (2015) 47:228–234
DOI 10.3758/s13428-014-0464-0
Generating an item pool for translational social cognition
research: Methodology and initial validation
Michael K. Keutmann & Samantha L. Moore &
Adam Savitt & Ruben C. Gur
Published online: 10 April 2014
# Psychonomic Society, Inc. 2014"
96f4a1dd1146064d1586ebe86293d02e8480d181,COMPARATIVE ANALYSIS OF RERANKING TECHNIQUES FOR WEB IMAGE SEARCH,"COMPARATIVE ANALYSIS OF RERANKING
TECHNIQUES FOR WEB IMAGE SEARCH
Suvarna V. Jadhav1, A.M.Bagade2
,2Department of Information Technology, Pune Institute of Computer Technology, Pune,( India)"
9645e8b4829c04879a642d8dd6b3cdf5cf264afb,Finding Beans in Burgers: Deep Semantic-Visual Embedding with Localization,"Finding beans in burgers:
Deep semantic-visual embedding with localization
Martin Engilberge1,2, Louis Chevallier2, Patrick P´erez2, Matthieu Cord1
Sorbonne universit´e, Paris, France 2Technicolor, Cesson S´evign´e, France
{martin.engilberge,"
9626bcb3fc7c7df2c5a423ae8d0a046b2f69180c,A deep learning approach for action classification in American football video sequences,"UPTEC STS 17033
Examensarbete 30 hp
November 2017
A deep learning approach for
ction classification in American
football video sequences
Jacob Westerberg"
96fdc0131dc80ffa6d7b9c526e07f080414c54ec,1 Paying More A ention to Saliency : Image Captioning with Saliency and Context A ention,"Paying More A(cid:130)ention to Saliency: Image Captioning with
Saliency and Context A(cid:130)ention
MARCELLA CORNIA, University of Modena and Reggio Emilia
LORENZO BARALDI, University of Modena and Reggio Emilia
GIUSEPPE SERRA, University of Udine
RITA CUCCHIARA, University of Modena and Reggio Emilia
Image captioning has been recently gaining a lot of a(cid:138)ention thanks to the impressive achievements
shown by deep captioning architectures, which combine Convolutional Neural Networks to extract image
representations, and Recurrent Neural Networks to generate the corresponding captions. At the same time,
signi(cid:128)cant research e(cid:130)ort has been dedicated to the development of saliency prediction models, which
an predict human eye (cid:128)xations. Even though saliency information could be useful to condition an image
aptioning architecture, by providing an indication of what is salient and what is not, research is still struggling
to incorporate these two techniques. In this work, we propose an image captioning approach in which a
generative recurrent neural network can focus on di(cid:130)erent parts of the input image during the generation of
the caption, by exploiting the conditioning given by a saliency prediction model on which parts of the image
re salient and which are contextual. We show, through extensive quantitative and qualitative experiments on
large scale datasets, that our model achieves superior performances with respect to captioning baselines with
nd without saliency, and to di(cid:130)erent state of the art approaches combining saliency and captioning.
CCS Concepts: •Computing methodologies →Scene understanding; Natural language generation;
Additional Key Words and Phrases: saliency, visual saliency prediction, image captioning, deep learning."
96f0da034d090a3ecadd0fb92333bb681f23ab14,Temporal-Spatial Mapping for Action Recognition,"Temporal-Spatial Mapping for Action Recognition
Xiaolin Song, Cuiling Lan, Wenjun Zeng, Junliang Xing, Jingyu Yang, and Xiaoyan Sun"
96788880589a514c3ae9de29695c0127d6e76b8f,Attention-Based Multimodal Fusion for Video Description,"Attention-Based Multimodal Fusion for Video Description
Chiori Hori
Takaaki Hori
Teng-Yok Lee
Kazuhiro Sumi∗
John R. Hershey
Tim K. Marks
Mitsubishi Electric Research Laboratories (MERL)
{chori, thori, tlee, sumi, hershey,"
96813c14f3de1b8f7472caff7a99b4145267f25c,Argus: the digital doorman,"A p p l i c a t i o n s :   C o m p u t e r   V i s i o n
Argus: The Digital Doorman
Rahul Sukthankar, Just Research and Carnegie Mellon University
Robert Stockton, Just Research
W hen you’ve visited someone in a large apartment
or office complex, chances are that a security
guard in the lobby granted you access. Perhaps over time,
the guard has learned to associate you with the person you
plan to visit and immediately notifies that person over the
uilding intercom when you arrive. Argus, named after the
vigilant watchman from Greek mythology, is an auto-
mated version of such a security guard: a system for auto-
matic visitor identification. We successfully implemented
nd tested Argus at Just Research.
To detect visitors, Argus’s digital camera photographs
the building entrance at regular intervals, and a motion
detection algorithm identifies potential scenes containing
visitors. Using a neural-network-based face detector,1
Argus extracts faces from these images. A memory-based
face recognition system2 examines these faces and at-"
9606b1c88b891d433927b1f841dce44b8d3af066,Principal Component Analysis with Tensor Train Subspace,"Principal Component Analysis with Tensor Train
Subspace
Wenqi Wang, Vaneet Aggarwal, and Shuchin Aeron"
96881897605eb3d9575c5379b29083b793a24bd2,Application of bidirectional two-dimensional principal component analysis to curvelet feature based face recognition,"Application of Bidirectional Two-dimensional
Principal Component Analysis to Curvelet Feature
Based Face Recognition
Abdul A. Mohammed, Q. M. Jonathan Wu, Maher A. Sid-Ahmed
Department of Electrical and Computer Engineering
Windsor, Ontario, Canada
{mohammea, jwu,"
9634348d3bc7b86d0b644f6c14ab0c4294341905,Investigating Redundancy in Emoji Use: Study on a Twitter Based Corpus,"Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 118–126
Copenhagen, Denmark, September 7–11, 2017. c(cid:13)2017 Association for Computational Linguistics"
968f472477a8afbadb5d92ff1b9c7fdc89f0c009,Firefly-based Facial Expression Recognition: Extended Abstract,Firefly-based Facial Expression Recognition
968c62bb2927ca300ef953644e652ba7d2c2e5e6,Learning person-object interactions for action recognition in still images,"Learning person-object interactions for
ction recognition in still images
Vincent Delaitre∗
´Ecole Normale Sup´erieure
Josef Sivic*
INRIA Paris - Rocquencourt
Ivan Laptev*
INRIA Paris - Rocquencourt"
96e7142ab905c54c033696ac3692e85692c43bf3,Sparse Illumination Learning and Transfer for Single-Sample Face Recognition with Image Corruption and Misalignment,"Noname manuscript No.
(will be inserted by the editor)
Sparse Illumination Learning and Transfer for
Single-Sample Face Recognition with Image Corruption and
Misalignment
Liansheng Zhuang · Tsung-Han Chan ·
Allen Y. Yang · S. Shankar Sastry · Yi Ma
Received: date / Accepted: date"
a726858df7c9503116504206577a938df1a67815,Unsupervised Vehicle Re-Identification using Triplet Networks,"Unsupervised Vehicle Re-Identification using Triplet Networks
Pedro Antonio Mar´ın-Reyes
Andrea Palazzi
University of Las Palmas de Gran Canaria
University of Modena and Reggio Emilia
Luca Bergamini
Simone Calderara
University of Modena and Reggio Emilia
University of Modena and Reggio Emilia
Javier Lorenzo-Navarro
Rita Cucchiara
University of Las Palmas de Gran Canaria
University of Modena and Reggio Emilia"
a7eee3222623778294461102d0dc770d4e09a7c5,A novel fusion-based method for expression-invariant gender classification,"978-1-4244-2354-5/09/$25.00 ©2009 IEEE
ICASSP 2009"
a71106ef95103276fac010c10291f6dd6fd9d9f5,Social status level and dimension interactively influence person evaluations indexed by P300s.,"ISSN: 1747-0919 (Print) 1747-0927 (Online) Journal homepage: http://www.tandfonline.com/loi/psns20
Social status level and dimension interactively
influence person evaluations indexed by P300s
Ivo Gyurovski, Jennifer Kubota, Carlos Cardenas-Iniguez & Jasmin Cloutier
To cite this article: Ivo Gyurovski, Jennifer Kubota, Carlos Cardenas-Iniguez & Jasmin Cloutier
(2017): Social status level and dimension interactively influence person evaluations indexed by
To link to this article:  http://dx.doi.org/10.1080/17470919.2017.1326400
Accepted author version posted online: 02
May 2017.
Published online: 15 May 2017.
Submit your article to this journal
Article views: 11
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Full Terms & Conditions of access and use can be found at
http://www.tandfonline.com/action/journalInformation?journalCode=psns20
Download by: [University of Chicago Library]
Date: 22 May 2017, At: 09:19"
a75ee7f4c4130ef36d21582d5758f953dba03a01,Human face attributes prediction with Deep Learning,"DD2427 Final Project Report
Mohamed Abdulaziz Ali Haseeb
DD2427 Final Project Report
Human face attributes prediction with Deep
Learning
Mohamed Abdulaziz Ali Haseeb"
a77d9e54bf6917e56054c2b70836b535dcfbcbac,Semi-supervised learning for image classification,"Semi-Supervised Learning
for Image Classification
A dissertation for the degree of
Doktor-Ingenieur (Dr.-Ing.)
pproved by
Saarland University
Computer Science
presented by
Sandra Ebert
Dipl.-Inform.
orn in Leipzig, Germany
Saarbrücken, 2012"
a7e8ce268c16ea8c10e4c5ccd8d6e53702423faa,The Ciona17 Dataset for Semantic Segmentation of Invasive Species in a Marine Aquaculture Environment,"The Ciona17 Dataset for Semantic Segmentation
of Invasive Species in a Marine Aquaculture Environment
Angus Galloway∗, Graham W. Taylor∗, Aaron Ramsay†, Medhat Moussa∗
School of Engineering
University of Guelph
Guelph, ON, Canada
{gallowaa, gwtaylor,
Department of Agriculture and Fisheries
Government of PEI
Montague, PEI, Canada"
a7066c13ba21817abcf8ff955740493adf95b02c,Points2Pix: 3D Point-Cloud to Image Translation using conditional Generative Adversarial Networks,"Points2Pix: 3D Point-Cloud to Image Translation
using conditional Generative Adversarial Networks
Stefan Milz 1 * Martin Simon 1 * Kai Fischer 1 * Maximillian Poepperl 1"
a7e78f80e0e37d0c17bc09058c27996e32e4454e,UNAM at SemEval-2018 Task 10: Unsupervised Semantic Discriminative Attribute Identification in Neural Word Embedding Cones,"Proceedings of the 12th International Workshop on Semantic Evaluation (SemEval-2018), pages 977–984
New Orleans, Louisiana, June 5–6, 2018. ©2018 Association for Computational Linguistics"
a76d471a409904c084746a56334453450f1c6c1a,Study of the Changing Trends in Facial Expression Recognition,"Study of the Changing Trends in Facial Expression Recognition
{tag}                                                                   {/tag}
International Journal of Computer Applications
© 2011 by IJCA Journal
Number 5 - Article 3
Year of Publication: 2011
Authors:
Dr. S. Ravi
Mahima S
10.5120/2509-3397"
a73a16203b644353a287a4759bc951450e67d700,BodyNet: Volumetric Inference of 3D Human Body Shapes,"BodyNet: Volumetric Inference of
D Human Body Shapes
G¨ul Varol1,*
Ersin Yumer2,‡
Duygu Ceylan2
Bryan Russell2
Jimei Yang2
Ivan Laptev1,*
Cordelia Schmid1,†
Inria, France
Adobe Research, USA"
a7e274db8f1389b95469588995f18c1c42b62534,VideoStory Embeddings Recognize Events when Examples are Scarce,
a7a1d3036c542824f2c681c3bf08f5b85f05d9e9,A Fast and Precise HOG-Adaboost Based Visual Support System Capable to Recognize Pedestrian and Estimate Their Distance,"A fast and precise HOG-Adaboost based based visual support
system capable to recognize Pedestrian and estimate their distance.
Yokohama City University, Graduate School of Nanobioscience, 22-2 Seto Kanazawa-ku, 236-0027 Yokohama, Japan
Takahisa Kishino1, Sun Zhe1,Ruggero Micheletto1"
a7663528eb6c9b79a68b94800e30da952c0b6bb2,IFQ-Net : Integrated Fixed-point Quantization Networks for Embedded Vision,"IFQ-Net: Integrated Fixed-point Quantization Networks for Embedded Vision
Hongxing Gao, Wei Tao, Dongchao Wen
Canon Information Technology (Beijing) Co., LTD
Tse-Wei Chen, Kinya Osa, Masami Kato
Device Technology Development Headquarters, Canon Inc."
a7d23c699a5ae4ad9b8a5cbb8c38e5c3b5f5fb51,A Summary of literature review : Face Recognition,"Postgraduate Annual Research Seminar 2007 (3-4 July 2007)
A Summary of literature review : Face Recognition
Kittikhun Meethongjan & Dzulkifli Mohamad
Faculty of Computer Science & Information System,
University Technology of Malaysia, 81310 Skudai, Johor, Malaysia."
a759570e6ef674cd93068020c2e6bd036961f7c6,SPEECH-COCO: 600k Visually Grounded Spoken Captions Aligned to MSCOCO Data Set,"SPEECH-COCO: 600k Visually Grounded Spoken Captions Aligned to
MSCOCO Data Set
William N. Havard1, Laurent Besacier1, Olivier Rosec2
Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG, F-38000 Grenoble, France
Voxygen, France"
a77091cee259e37536196238096bc1753b4c0027,FIELD ESTIMATION USING BRIGHTNESS CONSTANCY ASSUMPTION AND EPIPOLAR GEOMETRY CONSTRAINT,"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-1, 2014
ISPRS Technical Commission I Symposium, 17 – 20 November 2014, Denver, Colorado, USA
ontribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.doi:10.5194/isprsannals-II-1-9-20149"
a7bfb6426359140a0bc0c84741ad9a3ac83eff04,Object-Level Context Modeling For Scene Classification with Context-CNN,"Object-Level Context Modeling For Scene Classification with Context-CNN
Syed Ashar Javed1 and Anil Kumar Nelakanti2
IIIT Hyderabad, 2Amazon"
a75db530b5a905c87aba6b65676809b35f9614f5,Dynamic Eye Gaze Tracking for Foveated Rendering and Retinal Blur,"Dynamic Eye Gaze Tracking for Foveated Rendering and Retinal Blur
Kushagr Gupta, Suleman Kazi, Terry Kong"
a73bc57fb0aa429ba5f7f12b6d02e2c6274cabdd,A Superior Tracking Approach: Building a Strong Tracker through Fusion,"A Superior Tracking Approach:
Building a Strong Tracker through Fusion
Christian Bailer1, Alain Pagani1, and Didier Stricker1,2
German Research Center for Artificial Intelligence, Kaiserslautern, Germany
University of Kaiserslautern, Germany"
a764cba765648c6e36782b02393ea2eed5cd69c7,Contributions to large-scale learning for image classification. (Contributions à l'apprentissage grande échelle pour la classification d'images),"CONTRIBUTIONSTOLARGE-SCALELEARNINGFORIMAGECLASSIFICATIONZeynepAkataPhDThesisl’´EcoleDoctoraleMath´ematiques,SciencesetTechnologiesdel’Information,InformatiquedeGrenoble"
a71e3cf566de457336aab9dd6a5f5d6282b4a6af,Visual Abstraction for Zero-Shot Learning,
a705b6b6de4ed62891099cbe5734fb7d3d3044cc,Enhancing the retrieval performance by combing the texture and edge features,"Enhancing the retrieval performance by combing the
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. XXX, No. XXX, 2010
texture and edge features
Mohamed Eisa
Amira Eletrebi
Ebrahim Elhenawy
Computer Science Department
Computer Science Department
Computer Science Department
PortSaid University
Mansoura University
Zagazig University"
a7664247a37a89c74d0e1a1606a99119cffc41d4,Modal Consistency based Pre-Trained Multi-Model Reuse,Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
a70fa8af52e4cc32dae09e6e753f1dd3ec198327,Neural Task Representations as Weak Supervision for Model Agnostic Cross-Lingual Transfer,"Neural Task Representations as Weak Supervision for Model Agnostic
Cross-Lingual Transfer
Sujay Kumar Jauhar
Microsoft Research AI
Redmond, WA, USA
Michael Gamon
Microsoft Research AI
Redmond, WA, USA
Patrick Pantel∗
Facebook Inc.
Seattle, WA, USA"
a7152589980ec27375023d719eec6acc04b7d4fd,Generating Facial Expressions,"Generating Facial Expressions
Jonathan Suit
Georgia Tech"
a7887d32c86cda13872c5408713b21a27d0fc17a,Detecting Drivable Area for Self-driving Cars: An Unsupervised Approach,"Detecting Drivable Area for Self-driving Cars:
An Unsupervised Approach
Ziyi Liu, Siyu Yu, Xiao Wang and Nanning Zheng∗
Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, Shannxi, P.R.China
National Engineering Laboratory for Visual Information Processing and Applications,
Xi’an Jiaotong University, Xi’an, Shannxi, P.R.China
to locate road surface by using lane line,"
a7267bc781a4e3e79213bb9c4925dd551ea1f5c4,Proceedings of eNTERFACE 2015 Workshop on Intelligent Interfaces,"Proceedings of eNTERFACE’15
The 11th Summer Workshop
on Multimodal Interfaces
August 10th - September 4th, 2015
Numediart Institute, University of Mons
Mons, Belgium"
a702fc36f0644a958c08de169b763b9927c175eb,Facial expression recognition using Hough forest,"FACIAL EXPRESSION RECOGNITION USING HOUGH FOREST
Chi-Ting Hsu1, Shih-Chung Hsu1, and Chung-Lin Huang1,2
.  Department of Electrical Engineering, National Tsing-Hua University, Hsin-Chu, Taiwan
Email:
.  Department of Applied Informatics and Multimedia, Asia University, Taichung, Taiwan"
a760ce8baddf2da7946d2ed6f02ac3927f39a9da,Face Recognition Using a Unified 3D Morphable Model,"Face Recognition Using a Unified 3D Morphable Model
Hu, G., Yan, F., Chan, C-H., Deng, W., Christmas, W., Kittler, J., & Robertson, N. M. (2016). Face Recognition
Using a Unified 3D Morphable Model. In Computer Vision – ECCV 2016: 14th European Conference,
Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part VIII (pp. 73-89). (Lecture Notes in
Computer Science; Vol. 9912). Springer Verlag. DOI: 10.1007/978-3-319-46484-8_5
Published in:
Computer Vision – ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14,
016, Proceedings, Part VIII
Document Version:
Peer reviewed version
Queen's University Belfast - Research Portal:
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opyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated
with these rights.
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The Research Portal is Queen's institutional repository that provides access to Queen's research output. Every effort has been made to"
5ad88a16e2efe9bb67c20cdbd9b003ffb79c12ef,Real-Time Video Event Detection in Crowded Scenes using MPEG Derived Features : a Multiple,"Elsevier Editorial System(tm) for Pattern Recognition Letters
Manuscript Draft
Manuscript Number: PRLETTERS-D-13-00222R2
Title: Real-Time Video Event Detection in Crowded Scenes using MPEG Derived Features: a Multiple
Instance Learning Approach
Article Type: Special Issue: SIPRCA
Keywords: Event Detection; Crowded Scene; Multiple Instance Learning;
MPEG domain; Sparse Approximation; Random Matrix; Traffic
Surveillance; Naive Bayes Model
Corresponding Author: Mr. Jingxin Xu, M.D
Corresponding Author's Institution: Queensland University of Technology
First Author: Jingxin Xu, M.D
Order of Authors: Jingxin Xu, M.D; Simon  Denman, PhD; Vikas  Reddy, PhD; Clinton Fookes, PhD;
Sridha Sridhran, PhD"
5a14209a5241877f92743d04282598f41fd3e50f,From BoW to CNN: Two Decades of Texture Representation for Texture Classification,"From BoW to CNN: Two Decades of Texture Representation for Texture
Classification
Li Liu 1,2 · Jie Chen 2 · Paul Fieguth 3 ·
Guoying Zhao 2 · Rama Chellappa 4 · Matti Pietik¨ainen 2
Received: date / Accepted: date"
5a3da29970d0c3c75ef4cb372b336fc8b10381d7,CNN-based Real-time Dense Face Reconstruction with Inverse-rendered Photo-realistic Face Images.,"CNN-based Real-time Dense Face Reconstruction
with Inverse-rendered Photo-realistic Face Images
Yudong Guo, Juyong Zhang†, Jianfei Cai, Boyi Jiang and Jianmin Zheng"
5abaaf9c222398caea40d62e45b81a847436b784,An adaptive gamma correction for image enhancement,"Rahman et al. EURASIP Journal on Image and Video
Processing  (2016) 2016:35
DOI 10.1186/s13640-016-0138-1
EURASIP Journal on Image
nd Video Processing
RESEARCH
Open Access
An adaptive gamma correction for image
enhancement
Shanto Rahman1*, Md Mostafijur Rahman1, M. Abdullah-Al-Wadud2, Golam Dastegir Al-Quaderi3
nd Mohammad Shoyaib1"
5af1e8a38b64c6694b9a34cd0b1596f2c905d3ff,Context-based trajectory descriptor for human activity profiling,"Context-based Trajectory Descriptor for Human
Activity Profiling
Eduardo M. Pereira
INESC TEC and
Faculty of Engineering
of the University of Porto
Rua Dr. Roberto Frias, 378
Porto, Portugal 4200 - 465
Email:
Lucian Ciobanu
INESC TEC
Rua Dr. Roberto Frias, 378
Porto, Portugal 4200 - 465
Email:
Jaime S. Cardoso
INESC TEC and
Faculty of Engineering
of the University of Porto
Rua Dr. Roberto Frias, 378
Porto, Portugal 4200 - 465"
5aeaee0e3a324970c02ae8463e1b358597457d03,Towards a Types-As-Classifiers Approach to Dialogue Processing in Human-Robot Interaction,"Towards a Types-As-Classifiers Approach to Dialogue Processing in
Human-Robot Interaction
HOUGH, J; JAMONE, L; Schlangen, D; Walck, G; Haschke, R; Workshop on Dialogue and
Perception (DaP 2018)
© The Author(s) 2018
For additional information about this publication click this link.
http://qmro.qmul.ac.uk/xmlui/handle/123456789/45947
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make corrections to records, please therefore check the published record when citing. For
more information contact"
5a1669abdc4f958c589843cff2f4d83a11fe8007,Robust Recognition via ` 1-Minimization April,"Robust Recognition via ‘1-Minimization
April 13, 2007"
5a6e624b35175fa031dce373c906a340f668407e,Holistic Processing in the Composite Task Depends on Face Size.,"This article was downloaded by: [David Ross]
On: 15 July 2015, At: 09:13
Publisher: Routledge
Informa Ltd Registered in England and Wales Registered Number: 1072954
Registered office: 5 Howick Place, London, SW1P 1WG
Publication details, including instructions for authors
nd subscription information:
http://www.tandfonline.com/loi/pvis20
Holistic processing in the
omposite task depends on face
David A. Rossab & Isabel Gauthiera
Department of Psychology, University of
Massachusetts Amherst, Amherst, MA, USA
Department of Psychological and Brain Sciences,
Vanderbilt University, Nashville, TN, USA
Published online: 19 Jun 2015.
Click for updates
To cite this article: David A. Ross & Isabel Gauthier (2015): Holistic
0.1080/13506285.2015.1049678
To link to this article:  http://dx.doi.org/10.1080/13506285.2015.1049678"
5ad4e9f947c1653c247d418f05dad758a3f9277b,WLFDB: Weakly Labeled Face Databases,"IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (TPAMI)
WLFDB: Weakly Labeled Face Databases
Dayong Wang†, Steven C.H. Hoi∗, and Jianke Zhu‡"
5ab2791fbd8d39d02eb8ee76b0f03f5d64371309,A Methodology for Empirical Performance Evaluation of Page Segmentation Algorithms Song Mao and Tapas Kanungo a Methodology for Empirical Performance Evaluation of Page Segmentation Algorithms,"LAMP-TR-
December

CAR-TR-	
CS-TR-	
AMethodologyforEmpirical
PerformanceEvaluation
ofPageSegmentationAlgorithms
SongMaoandTapasKanungo"
5a4c6246758c522f68e75491eb65eafda375b701,Contourlet structural similarity for facial expression recognition,"978-1-4244-4296-6/10/$25.00 ©2010 IEEE
ICASSP 2010"
5aad5e7390211267f3511ffa75c69febe3b84cc7,Driver Gaze Estimation Without Using Eye Movement,"Driver Gaze Estimation
Without Using Eye Movement
Lex Fridman, Philipp Langhans, Joonbum Lee, Bryan Reimer
MIT AgeLab"
5a6b2f3a542322be153fc9104f3064f2a1bc76eb,"A French-Spanish Multimodal Speech Communication Corpus Incorporating Acoustic Data, Facial, Hands and Arms Gestures Information","Interspeech 2018
-6 September 2018, Hyderabad
0.21437/Interspeech.2018-2212"
5a86842ab586de9d62d5badb2ad8f4f01eada885,Facial Emotion Recognition and Classification Using Hybridization Method,"International Journal of Engineering Research and General Science Volume 3, Issue 3, May-June, 2015
ISSN 2091-2730
Facial Emotion Recognition and Classification Using Hybridization
Method
Anchal Garg , Dr. Rohit Bajaj
Deptt. of CSE, Chandigarh Engg. College,  Mohali, Punjab, India.
07696449500"
5a3092b266ddf97ac9dc9a07a0ae7c40b54470cd,HSM: A New Color Space used in the Processing of Color Images,"HSM: A New Color Space used in the Processing of Color Images
Severino Jr, Osvaldo1 and Gonzaga, Adilson2
Department of Electrical Engineering
School of Engineering - USP
Av. Trabalhador S˜ao-carlense, 400
ZIP Code 13566-590, S˜ao Carlos, Brazil
. 2."
5a1bf442073b95a3392e673dd14bf73fc1e99f6b,Internal Report 96{08 Face Recognition by Elastic Bunch Graph Matching Face Recognition by Elastic Bunch Graph Matching,"InternalReport	{
FaceRecognitionbyElasticBunchGraphMatching
LaurenzWiskott,Jean-MarcFellous,NorbertKr(cid:127)uger,
ndChristophvonderMalsburg
IR-INI	{
Ruhr-Universit(cid:127)atBochum
April
		
Institutf(cid:127)urNeuroinformatik
ISSN	-
Bochum
(cid:13)
		Institutf(cid:127)urNeuroinformatik,Ruhr-Universit(cid:127)atBochum,FRG"
5ac707ab88c565b1ed34fac89939f0cd2451eb22,Automated Object Recognition in Baggage Screening using Multiple X-ray Views,"Automated Object Recognition in Baggage Screening
using Multiple X-ray Views
Domingo Mery and Vladimir Riffo
Department of Computer Science – Pontificia Universidad Cat´olica de Chile
Av. Vicu˜na Mackenna 4860(143) – Santiago de Chile
http://dmery.ing.puc.cl"
5ac18d505ed6d10e8692cbb7d33f6852e6782692,"The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale","IJCV submission in review
The Open Images Dataset V4
Unified image classification, object detection, and visual relationship detection at scale
Alina Kuznetsova Hassan Rom Neil Alldrin
Shahab Kamali
Stefan Popov Matteo Malloci Tom Duerig Vittorio Ferrari
Jasper Uijlings
Ivan Krasin
Jordi Pont-Tuset"
5a8d20ecd92d22bf077208a5e7b1bb008a9b7dbc,A new manifold distance measure for visual object categorization,"A New Manifold Distance Measure for Visual Object
Categorization
Fengfu Li, Xiayuan Huang, Hong Qiao and Bo Zhang
index. The proposed distance is more robust"
5aaa84090c50da903ea1d61495c0fe96a5470909,Image-embodied Knowledge Representation Learning,"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
of armourhas partFigure1:Examplesofentityimages.Fig.1demonstratessomeexamplesofentityimages.Eachentityhasmultipleimageswhichcanprovidesignificantvisu-alinformationthatintuitivelydescribestheappearancesandbehavioursofthisentity.Toutilizetherichinformationinimages,weproposetheImage-embodiedKnowledgeRepre-sentationLearningmodel(IKRL).Morespecifically,wefirstproposeanimageencoderwhichconsistsofaneuralrep-resentationmoduleandaprojectionmoduletogeneratetheimage-basedrepresentationforeachimageinstance.Second,weconstructtheaggregatedimage-basedrepresentationforeachentityjointlyconsideringallitsimageinstanceswithanattention-basedmethod.Finally,wejointlylearntheknowl-edgerepresentationswithtranslation-basedmethods.WeevaluatetheIKRLmodelonknowledgegraphcom-pletionandtripleclassification.Experimentalresultsdemon-stratethatourmodelachievesthestate-of-the-artperfor-mancesonbothtasks,whichconfirmsthesignificanceofvi-sualinformationinknowledgerepresentationlearning.ItalsoindicatesthatourIKRLmodeliscapableofencodingimageinformationwellintoknowledgerepresentations.Wedemon-stratethemaincontributionsofthisworkasfollows:(cid:15)WeproposeanovelIKRLmodelconsideringvisualin-formationinentityimagesforknowledgerepresentationlearning.Tothebestofourknowledge,thisisthefirstattempttocombineimageswithknowledgegraphsforknowledgerepresentationlearning.(cid:15)Weevaluateourmodelsonareal-worlddatasetandre-ceivepromisingperformancesonbothknowledgegraph"
5a4ec5c79f3699ba037a5f06d8ad309fb4ee682c,Automatic age and gender classification using supervised appearance model,"Downloaded From: https://www.spiedigitallibrary.org/journals/Journal-of-Electronic-Imaging on 12/17/2017 Terms of Use: https://www.spiedigitallibrary.org/terms-of-use
AutomaticageandgenderclassificationusingsupervisedappearancemodelAliMainaBukarHassanUgailDavidConnahAliMainaBukar,HassanUgail,DavidConnah,“Automaticageandgenderclassificationusingsupervisedappearancemodel,”J.Electron.Imaging25(6),061605(2016),doi:10.1117/1.JEI.25.6.061605."
5ae9e0cd2debd9e1b114bd44d9311c07a08e693a,JÚNIOR A SCALABLE AND VERSATILE FRAMEWORK FOR SMART VIDEO SURVEILLANCE,"A SCALABLE AND VERSATILE FRAMEWORK FOR
SMART VIDEO SURVEILLANCE"
5a9126f4478384f6615bf57b6da7299dc17b9a6b,3-D Facial Landmark Localization With Asymmetry Patterns and Shape Regression from Incomplete Local Features,"JOURNAL OF LATEX CLASS FILES, VOL. 11, NO. 4, DECEMBER 2012
D Facial Landmark Localization with Asymmetry
Patterns and Shape Regression from Incomplete
Local Features
Federico M. Sukno, John L. Waddington, and Paul F. Whelan"
5acd67f9102db0bed8c8465a0ea0e85efe89bcec,CalibNet: Self-Supervised Extrinsic Calibration using 3D Spatial Transformer Networks,"CalibNet: Self-Supervised Extrinsic Calibration using 3D Spatial
Transformer Networks
Ganesh Iyer, Karnik Ram R., J. Krishna Murthy, and K. Madhava Krishna
Fig. 1: CalibNet estimates the extrinsic calibration parameters between a 3D LiDAR and a 2D camera. It takes as input an
RGB image (a) from a calibrated camera, a raw LiDAR point cloud (b), and outputs a 6-DoF rigid-body transformation
T that best aligns the two inputs. (c) shows the colorized point cloud output for a mis-calibrated setup, and (d) shows the
output after calibration using our network. As shown, using the mis-calibrated point cloud to recover a colorized 3D map
of the world results in an incoherent reconstruction. Notice how the 3D structures highlighted in (c) using red rectangles
fail to project to their 2D counterparts. However, using the extrinsic calibration parameters predicted by CalibNet produces
more consistent and accurate reconstructions (d), even for large initial mis-calibrations."
5a93f9084e59cb9730a498ff602a8c8703e5d8a5,Face Recognition using Local Quantized Patterns,"HUSSAIN ET. AL: FACE RECOGNITION USING LOCAL QUANTIZED PATTERNS
Face Recognition using Local Quantized
Patterns
Sibt ul Hussain
Thibault Napoléon
Fréderic Jurie
GREYC — CNRS UMR 6072,
University of Caen Basse-Normandie,
Caen, France"
5a363ac89f93377bb72af4346957ce36a01090c8,Face Recognition of Database of Compressed Images using Local Binary Patterns,"International Journal of Computer Applications (0975 – 8887)
Volume 74– No.16, July 2013
Face Recognition of Database of Compressed Images
using Local Binary Patterns
Padmaja Vijay Kumar
M.N.Giri Prasad,Ph.D        Padmaja.K.V,Ph.D
Jawaharlal Nehru Technological          HOD and Professor-EC             Professor, IT
University, JNTU-AP
JNTU-Anantapur-A.P
R.V.C.E-Bangalore"
5a0209515ab62e008efeca31f80fa0a97031cd9d,Dataset fingerprints: Exploring image collections through data mining,"Dataset Fingerprints: Exploring Image Collections Through Data Mining
Konstantinos Rematas1, Basura Fernando1, Frank Dellaert2, and Tinne Tuytelaars1
KU Leuven, ESAT-PSI, iMinds
Georgia Tech
Figure 1: Given an image collection, our system extracts patterns of discriminative mid level features and uses the connection
etween them to enable structure specific browsing."
5ad65c6474c135a6c15e7127d8bb91de8c8a55a1,Designing Empathetic Animated Agents for a B-Learning Training Environment within the Electrical Domain,"Hernández,  Y.,  Pérez-Ramírez,  M.,  Zatarain-Cabada,  R.,  Barrón-Estrada,  L.,  &  Alor-Hernández,  G.  (2016).  Designing
Empathetic  Animated Agents for a B-Learning Training Environment  within the Electrical Domain. Educational Technology  &
Society, 19 (2), 116–131.
Designing Empathetic Animated Agents for a B-Learning Training
Environment within the Electrical Domain
Yasmín Hernández1*, Miguel Pérez-Ramírez1, Ramón Zatarain-Cabada2, Lucía Barrón-
Estrada2 and Giner Alor-Hernández3
Instituto de Investigaciones Eléctricas, Gerencia de Tecnologías de la Información, Cuernavaca, México // 2Instituto
Tecnológico de Culiacán, Departamento de Posgrado, Culiacán, México // 3Instituto Tecnológico de Orizaba,
División de Estudios de Posgrado e Investigación, Orizaba, México // // //
// //
*Corresponding author"
5afd6c5eb5cc1e8496bb78b8f7b3a00b2900deb3,Self-Supervised Learning of Pose Embeddings from Spatiotemporal Relations in Videos,"Self-supervised Learning of Pose Embeddings
from Spatiotemporal Relations in Videos
¨Omer S¨umer∗
Tobias Dencker∗
Bj¨orn Ommer
Heidelberg Collaboratory for Image Processing
IWR, Heidelberg University, Germany"
5aa29373a59c60b01df4f8986ed4902e6dd60b96,GENDER CLASSIFICATION USING SUPPORT VECTOR MACHINES,"GENDER CLASSIFICATION USING SUPPORT VECTOR MACHINES
Ashwin Swaminathan
ENEE633: Statistical and Neural Pattern Recognition
Instructor : Prof. Rama Chellappa
Project 2, Part (a)
. INTRODUCTION
Gender classification has been known as one of the most important tasks for humans [13]. Recently, the
field of gender classification has caught considerable attention due to its emerging applications in the
rea of human computer interactions systems [14] and other applications.
The problem of gender classification has been widely studied in psychology where they try to model
the human recognition system to classify gender. Over the past few years, there have been a few learning
ased algorithms proposed for gender classification. Most of these methods use neural networks for
gender classification [16, 17]. In these works, the authors perform tests over 30 images and show that
the neural network can help determine gender from 8x8 images with an average accuracy of 92%. RBF
type networks have been widely used for this purpose.
The use of Support vector machines for gender classification was proposed by Moghaddam et al. in
[14]. It is well known that SVM can provide superior performace and hence it has been widely used
in many classification problems. Specifically, it has been very helpful in cases where it is difficult to
estimate the density model such as in classification of images e.g. face recognition, etc. The support
vector machines employing kernel functions have been useful in cases where the data are not linearly"
5ade87a54c8baec555c37d59071c6fb4a9a55cf7,Deep Learning For Video Saliency Detection,"Deep Learning For Video Saliency Detection
Wenguan Wang, and Jianbing Shen, Senior Member, IEEE, and Ling Shao, Senior Member,"
5acfacbe8bf93ab1ee17bd6f65667abe0d6e1b5e,Where Should We Place LiDARs on the Autonomous Vehicle? - An Optimal Design Approach,"Where Should We Place LiDARs on the Autonomous Vehicle?
- An Optimal Design Approach
Zuxin Liu1 , Mansur Arief2 and Ding Zhao2,∗"
5ab9f00a707a55f4955b378981ad425aa1cb8ea3,Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets,"IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. XX, NO. YY, NOVEMBER 2018
Forecasting People Trajectories and Head Poses
y Jointly Reasoning on Tracklets and Vislets
Irtiza Hasan, Francesco Setti, Theodore Tsesmelis, Vasileios Belagiannis,
Sikandar Amin, Alessio Del Bue, Marco Cristani, and Fabio Galasso"
5ac8edd62fe23911e19d639287135f91e22421cc,Gender and 3D facial symmetry: What's the relationship?,"Gender and 3D Facial Symmetry: What’s the
Relationship?
Baiqiang Xia, Boulbaba Ben Amor, Hassen Drira, Mohamed Daoudi,
Lahoucine Ballihi
To cite this version:
Baiqiang Xia, Boulbaba Ben Amor, Hassen Drira, Mohamed Daoudi, Lahoucine Ballihi. Gender
nd 3D Facial Symmetry: What’s the Relationship?. 10th IEEE Conference on Automatic Face and
Gesture Recognition (FG 2013), Apr 2013, shanghai, China. 2013. <hal-00771988>
HAL Id: hal-00771988
https://hal.archives-ouvertes.fr/hal-00771988
Submitted on 9 Jan 2013
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,"
5aed0f26549c6e64c5199048c4fd5fdb3c5e69d6,Human Expression Recognition using Facial Features,"International Journal of Computer Applications® (IJCA) (0975 – 8887)
International Conference on Knowledge Collaboration in Engineering, ICKCE-2014
Human Expression Recognition using Facial Features
G.Saranya
Post graduate student, Dept. of ECE
Parisutham Institute of Technology & Science
Thanjavur.
Affiliated to Anna university, Chennai
recognition  can  be  used"
185360fe1d024a3313042805ee201a75eac50131,Person De-Identification in Videos,"Person De-Identification in Videos
Prachi Agrawal and P. J. Narayanan"
182df40034d937c74df0edb0acb295088e63f1ed,University of Michigan North Campus long-term vision and lidar dataset,"University of Michigan North Campus
Long-Term Vision and Lidar Dataset
The International Journal of
Robotics Research
000(00):1–16
©The Author(s) 2010
Reprints and permission:
sagepub.co.uk/journalsPermissions.nav
DOI:doi number
http://mms.sagepub.com
Nicholas Carlevaris-Bianco∗, Arash K. Ushani†, and Ryan M. Eustice‡"
18dd7bf12de78f8d9afa4d107ddbf814d5a8ad49,PROBE-GK: Predictive robust estimation using generalized kernels,"PROBE-GK: Predictive Robust Estimation using Generalized Kernels
Valentin Peretroukhin1, William Vega-Brown2, Nicholas Roy2 and Jonathan Kelly1"
1888bf50fd140767352158c0ad5748b501563833,A GUIDED TOUR OF FACE PROCESSING,"PA R T 1
THE BASICS"
1883387726897d94b663cc4de4df88e5c31df285,Measures of Effective Video Tracking,"Measures of effective video tracking
Tahir Nawaz, Fabio Poiesi, Andrea Cavallaro"
18fcbf5c7f83aff072feb116b8a561d40afa11e9,COMS 4701 Artificial Intelligence Final Project Report Applications of Machine Learning to Facial Recognition in Video,"in Video
David Lariviere
John Petrella
Jian Pan
COMS 4701 Artificial Intelligence
Final Project Report
Applications of Machine Learning to Facial Recognition"
181045164df86c72923906aed93d7f2f987bce6c,T R ] 2 8 Se p 20 15 Correctness of Backtest Engines,"RHEINISCH-WESTFÄLISCHE TECHNISCHE
HOCHSCHULE AACHEN
KNOWLEDGE-BASED SYSTEMS GROUP
PROF. GERHARD LAKEMEYER, PH. D.
Detection and Recognition of Human
Faces using Random Forests for a
Mobile Robot
MASTER OF SCIENCE THESIS
VAISHAK BELLE
MATRICULATION NUMBER: 26 86 51
SUPERVISOR:
SECOND SUPERVISOR:
PROF. GERHARD LAKEMEYER, PH. D.
PROF. ENRICO BLANZIERI, PH. D.
ADVISERS:
STEFAN SCHIFFER, THOMAS DESELAERS"
18d51a366ce2b2068e061721f43cb798177b4bb7,Looking into your eyes: observed pupil size influences approach-avoidance responses.,"Cognition and Emotion
ISSN: 0269-9931 (Print) 1464-0600 (Online) Journal homepage: http://www.tandfonline.com/loi/pcem20
Looking into your eyes: observed pupil size
influences approach-avoidance responses
Marco Brambilla, Marco Biella & Mariska E. Kret
To cite this article: Marco Brambilla, Marco Biella & Mariska E. Kret (2018): Looking into your
eyes: observed pupil size influences approach-avoidance responses, Cognition and Emotion, DOI:
0.1080/02699931.2018.1472554
To link to this article:  https://doi.org/10.1080/02699931.2018.1472554
View supplementary material
Published online: 11 May 2018.
Submit your article to this journal
View related articles
View Crossmark data
Full Terms & Conditions of access and use can be found at
http://www.tandfonline.com/action/journalInformation?journalCode=pcem20"
1856e71437886af2366b620bcfe4caf891425f7b,Analyzing the Distribution of a Large-Scale Character Pattern Set Using Relative Neighborhood Graph,"Analyzing the Distribution of
Large-scale Character Pattern Set
Using Relative Neighborhood Graph
Masanori Goto(cid:3), Ryosuke Ishiday, Yaokai Fengy and Seiichi Uchiday
(cid:3)GLORY LTD., Hyogo, Japan
Email:
yKyushu University, Fukuoka, Japan
Email:"
18de899c853120a1a2cd502ebc3e970b92e1882f,Age Regression from Soft Aligned Face Images Using Low Computational Resources,"Age regression from soft aligned face images
using low computational resources
Juan Bekios-Calfa1, Jos´e M. Buenaposada2, and Luis Baumela3
Dept. de Ingenier´ıa de Sistemas y Computaci´on, Universidad Cat´olica del Norte
Av. Angamos 0610, Antofagasta, Chile
Dept. de Ciencias de la Computaci´on, Universidad Rey Juan Carlos
Calle Tulip´an s/n, 28933, M´ostoles, Spain
Dept. de Inteligencia Artificial, Universidad Polit´ecnica de Madrid
Campus Montegancedo s/n, 28660 Boadilla del Monte, Spain"
180cf5ab4e021e64b9bf08f2ffc4a4712acd9a30,Multi-view anchor graph hashing,"MULTI-VIEW ANCHOR GRAPH HASHING
Saehoon Kim1 and Seungjin Choi1,2
Department of Computer Science and Engineering, POSTECH, Korea
Division of IT Convergence Engineering, POSTECH, Korea
{kshkawa,"
18f348d56a2ff1c0904685ce8b6818b84867b7a4,ML-o-scope : a diagnostic visualization system for deep machine learning pipelines,"ML-o-scope: a diagnostic visualization system for
deep machine learning pipelines
Daniel Bruckner
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2014-99
http://www.eecs.berkeley.edu/Pubs/TechRpts/2014/EECS-2014-99.html
May 16, 2014"
18ab703c9959fbea7ad253a4062eb705b245552c,Efficient trajectory extraction and parameter learning for data-driven crowd simulation,"Efficient Trajectory Extraction and Parameter Learning for Data-Driven
Crowd Simulation
Aniket Bera∗
Sujeong Kim†
Dinesh Manocha‡
The University of North Carolina at Chapel Hill"
1832204e7a0a17390e1335cba7be9b922ee4fa57,Bilateral random projections,"Bilateral Random Projections
December 23, 2011"
188d43b5be038c705c0e552d5cc3e59980899a4d,Zero-Shot Recognition with Unreliable Attributes ( Supplementary material ),"Zero-Shot Recognition with Unreliable Attributes
(Supplementary material)
Dinesh Jayaraman
Kristen Grauman
In this document, we provide supplementary material for our NIPS 2014 paper “Zero-Shot Recognition with
Unreliable Attributes”.
Sec 1 shows how unlearnable attributes are avoided by our method. Sec 2 discusses the details of the signature
uncertainty model introduced in Sec 3.2.3 of the paper. Sec 3 gives additional details for our controlled noise
experiments (Sec 4.1 of the paper). Sec 4 lists the 10 SUN database test classes chosen at random. Sec 5 shows
more few-shot results, as a continuation of Sec 4.2 in the paper. Sec 6 contains pseudocode for our proposed
method, and Sec 7 contains schematics illustrating our method and its ablated variants.
Unlearnable attributes
As a sanity check, we show how accounting for classifier unreliability as detailed in Sec 3.2.2 of the paper also
inherently avoids unlearnable attributes. For the extreme case of completely unlearnable attributes, the classifier
annot tell between positives and negatives, so that TPR=FPR (regardless of threshold). If a candidate split (m, t)
tested at any node involves such an attribute m, then signatures of all classes are equally likely to propagate to the
left or right, i.e., I(cid:48)
l and
r are multiples of I(cid:48)
I(cid:48)"
1819d9a9099dafc987dd236c2174945e7922be13,Eigenfeature Regularization and Extraction in Face Recognition,"Eigenfeature Regularization and Extraction
in Face Recognition
Xudong Jiang, Senior Member, IEEE, Bappaditya Mandal, and Alex Kot, Fellow, IEEE"
18cc17c06e34baaa3e196db07e20facdbb17026d,Describing Videos by Exploiting Temporal Structure,"Describing Videos by Exploiting Temporal Structure
Li Yao
Universit´e de Montr´eal
Atousa Torabi
Universit´e de Montr´eal
Kyunghyun Cho
Universit´e de Montr´eal
Nicolas Ballas
Universit´e de Montr´eal
Christopher Pal
´Ecole Polytechnique de Montr´eal
Hugo Larochelle
Universit´e de Sherbrooke
Aaron Courville
Universit´e de Montr´eal"
1824b1ccace464ba275ccc86619feaa89018c0ad,One millisecond face alignment with an ensemble of regression trees,"One Millisecond Face Alignment with an Ensemble of Regression Trees
Vahid Kazemi and Josephine Sullivan
KTH, Royal Institute of Technology
Computer Vision and Active Perception Lab
Teknikringen 14, Stockholm, Sweden"
18d0f6445abeecf1fd2ce2b79924916d5f635af2,� BY Matthew Turk,"There are still obstacles to achieving general, robust,
high-performance computer vision systems. The last
decade, however, has seen significant progress in vision
technologies for human-computer interaction.
Computer Vision
IN THE INTERFACE
V isual information is clearly important as people
onverse and interact with one another. Through
the modality of vision, we can instantly determine
number of salient facts and features about oth-
ers, including their location, identity, approximate
ge, focus of attention, facial expression, posture,
gestures, and general activity. These visual cues affect the content
nd flow of conversation, and they impart contextual informa-
tion different from, but related to, speech—for example, a ges-
ture or facial expression may be a key signal, or the direction of
gaze may disambiguate the object referred to in speech as “this”
or the direction “over there.” In other words, vision and speech
re co-expressive and complementary channels in human-human
interaction [6]. Just as automatic speech recognition seeks to"
18858cc936947fc96b5c06bbe3c6c2faa5614540,Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification,"Proceedings of Machine Learning Research 81:1–15, 2018
Conference on Fairness, Accountability, and Transparency
Gender Shades: Intersectional Accuracy Disparities in
Commercial Gender Classification∗
Joy Buolamwini
MIT Media Lab 75 Amherst St. Cambridge, MA 02139
Timnit Gebru
Microsoft Research 641 Avenue of the Americas, New York, NY 10011
Editors: Sorelle A. Friedler and Christo Wilson"
18d7684c6b96caf51adb519738720eceb1b13050,Hidden Relationships: Bayesian Estimation With Partial Knowledge,"Hidden Relationships:
Bayesian Estimation with Partial Knowledge
Tomer Michaeli and Yonina C. Eldar, Senior Member, IEEE
the joint probability function of"
18f9a6045ba01cb079c4fa49a630d71bbd27cd92,A dataset of clinically generated visual questions and answers about radiology images,"www.nature.com/scientificdata
Received: 18 June 2018
Accepted: 16 September 2018
Published: 20 November 2018
Data Descriptor: A dataset of
linically generated visual
questions and answers about
radiology images
Jason J. Lau, Soumya Gayen, Asma Ben Abacha & Dina Demner-Fushman
Radiology images are an essential part of clinical decision making and population screening, e.g., for cancer.
Automated systems could help clinicians cope with large amounts of images by answering questions about
the image contents. An emerging area of artificial intelligence, Visual Question Answering (VQA) in the
medical domain explores approaches to this form of clinical decision support. Success of such machine
learning tools hinges on availability and design of collections composed of medical images augmented with
question-answer pairs directed at the content of the image. We introduce VQA-RAD, the first manually
onstructed dataset where clinicians asked naturally occurring questions about radiology images and
provided reference answers. Manual categorization of images and questions provides insight into clinically
relevant tasks and the natural language to phrase them. Evaluating with well-known algorithms, we
demonstrate the rich quality of this dataset over other automatically constructed ones. We propose VQA-
RAD to encourage the community to design VQA tools with the goals of improving patient care."
1868aeb7f13e64ebc78869b371ef321572d6167f,Weakly Supervised Automatic Annotation of Pedestrian Bounding Boxes,"Weakly Supervised Automatic Annotation of Pedestrian Bounding Boxes
David V´azquez1, Jiaolong Xu1, Sebastian Ramos1, Antonio M. L´opez1,2 and Daniel Ponsa1,2
Computer Vision Center
Dept. of Computer Science
Autonomous University of Barcelona
08193 Bellaterra, Barcelona, Spain
{dvazquez, jiaolong, sramosp, antonio,"
1806665f9571bbdcf654f3bdf5e009bcb8eac799,Markov random field terrain classification for autonomous robots in unstructured terrain,"Fachbereich 4: Informatik
Arbeitsgruppe
Aktives Sehen
MARKOV RANDOM FIELD
TERRAIN CLASSIFICATION
FOR AUTONOMOUS ROBOTS
IN UNSTRUCTURED TERRAIN
Vom Promotionsausschuss des Fachbereichs 4: Informatik
n der Universität Koblenz-Landau
zur Verleihung des akademischen Grades
Doktor der Naturwissenschaften (Dr. rer. nat.)
genehmigte
DISSERTATION
Dipl.-Inform. Marcel Häselich
Koblenz - 2014
Datum der Einreichung:
Datum der wissenschaftlichen Aussprache:
7. Juni 2014
7. Dezember 2014
Vorsitzender des Promotionsausschusses:"
18d5b0d421332c9321920b07e0e8ac4a240e5f1f,Collaborative Representation Classification Ensemble for Face Recognition.,"Collaborative Representation Classification
Ensemble for Face Recognition
Xiao Chao Qu, Suah Kim, Run Cui and Hyoung Joong Kim"
18bca470bf51f5cc42148cd7e34fa58280be8eb2,Face Expressional Recognition using Geometry and Behavioral Traits,"IJCSNS International Journal of Computer Science and Network Security, VOL.9 No.8, August 2009
Face Expressional Recognition using Geometry and Behavioral
Traits
J. K. Kani Mozhi, Sr. Lect / Dept. of MCA, K. S. Rangasamy College of Technology, Tiruchengode. India.
J. K. Kani Mozhi 1 and Dr. R. S. D. Wahida Banu 2
Dr. R. S. D. Wahida Banu, Prof. & Head / Dept. of ECE, Govt. College of Engg., Salem, India.
recognition"
18fe745e0840b7b086fb7d14850a95ebbd5ae57b,Evaluation and Acceleration of High-Throughput Fixed-Point Object Detection on FPGAs,"This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
Evaluation and Acceleration of High-Throughput
Fixed-Point Object Detection on FPGAs
Xiaoyin Ma, Student Member, IEEE, Walid A. Najjar, Fellow, IEEE, Amit K. Roy-Chowdhury, Sr. Member, IEEE"
18bf90d6f77bb3731cdae14315c0cf4724f0e6c1,When VLAD Met Hilbert,"When VLAD met Hilbert
Mehrtash Harandi
NICTA and Australian National University
Canberra, Australia
Mathieu Salzmann
NICTA and Australian National University
Canberra, Australia
Fatih Porikli
NICTA and Australian National University
Canberra, Australia"
18727adf3e63de90674fcafd8b1f5e0059669e84,"A COMPARATIVE STUDY OF PCA , ICA AND LDA","A COMPARATIVE STUDY OF PCA, ICA AND LDA
Kresimir Delac 1, Mislav Grgic 2 and Sonja Grgic 2
Croatian Telecom, Savska 32, Zagreb, Croatia, e-mail:
University of Zagreb, FER, Unska 3/XII, Zagreb, Croatia
implementations
in  all  possible  algorithm"
1885acea0d24e7b953485f78ec57b2f04e946eaf,Combining Local and Global Features for 3D Face Tracking,"Combining Local and Global Features for 3D Face Tracking
Pengfei Xiong, Guoqing Li, Yuhang Sun
Megvii (face++) Research
{xiongpengfei, liguoqing,"
18ccd8bd64b50c1b6a83a71792fd808da7076bc9,Object detection and segmentation from joint embedding of parts and pixels,"Object Detection and Segmentation
from Joint Embedding of Parts and Pixels
Michael Maire1, Stella X. Yu2, Pietro Perona1
California Institute of Technology - Pasadena, CA 91125
Boston College - Chestnut Hill, MA 02467"
18babfe4c7230522527a068654eeea10b1a827fd,Discriminative Label Propagation for Multi-object Tracking with Sporadic Appearance Features,"Discriminative Label Propagation for Multi-Object Tracking with Sporadic
Appearance Features
Amit Kumar K.C. and Christophe De Vleeschouwer
ISPGroup, ELEN Department, ICTEAM Institute
Universit´e catholique de Louvain
Louvain-la-Neuve, B-1348, Belgium
{amit.kc,"
189fa973bc09cf3f3416e875fc853c3c489edeb0,Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks,"Multimodal Trajectory Predictions for Autonomous
Driving using Deep Convolutional Networks
Henggang Cui, Vladan Radosavljevic, Fang-Chieh Chou, Tsung-Han Lin,
Thi Nguyen, Tzu-Kuo Huang, Jeff Schneider and Nemanja Djuric1"
18a7edd0bfe5a3d6ceb4d2053081e479cfa1e920,Transductive Kernel Map Learning and Its Application Image Annotation,"TRANSDUCTIVE LEARNING, KERNEL MAP, IMAGE ANNOTATION: BMVC SUBMISSION 1
Transductive Kernel Map Learning
nd its Application to Image Annotation
Dinh-Phong Vo
Hichem Sahbi
LTCI CNRS Telecom ParisTech
6 rue Barrault, 75013, Paris, France"
18269fcaba9feba85552b039a9052cd67e6d9c8b,Emotional facial sensing and multimodal fusion in a continuous 2D affective space,"J Ambient Intell Human Comput (2012) 3:31–46
DOI 10.1007/s12652-011-0087-6
O R I G I N A L R E S E A R C H
Emotional facial sensing and multimodal fusion in a continuous
D affective space
Eva Cerezo • Isabelle Hupont • Sandra Baldassarri •
Sergio Ballano
Received: 3 February 2011 / Accepted: 24 September 2011 / Published online: 30 October 2011
Ó Springer-Verlag 2011"
18a849b1f336e3c3b7c0ee311c9ccde582d7214f,"Efficiently Scaling Up Crowdsourced Video Annotation A Set of Best Practices for High Quality , Economical Video Labeling","Int J Comput Vis
DOI 10.1007/s11263-012-0564-1
Efficiently Scaling up Crowdsourced Video Annotation
A Set of Best Practices for High Quality, Economical Video Labeling
Carl Vondrick · Donald Patterson · Deva Ramanan
Received: 31 October 2011 / Accepted: 20 August 2012
© Springer Science+Business Media, LLC 2012"
182c91f619e0b7a8cd2120139d530750aa0b85a7,Compressing the Input for CNNs with the First-Order Scattering Transform,"Compressing the Input for CNNs with the
First-Order Scattering Transform
Edouard Oyallon,1,4,5 Eugene Belilovsky,2 Sergey Zagoruyko,3 Michal Valko4
CentraleSupelec, Universit´e Paris-Saclay
MILA, University of Montreal
WILLOW – Inria Paris, 4SequeL – Inria Lille, 5GALEN – Inria Saclay"
85188c77f3b2de3a45f7d4f709b6ea79e36bd0d9,"Combined model for detecting , localizing , interpreting and recognizing faces","Author manuscript, published in ""Workshop on Faces in 'Real-Life' Images: Detection, Alignment, and Recognition, Marseille :
France (2008)"""
85387549277d6131dc8596ffacc7a21aeee0c6d1,Attribute Enhanced Face Aging with Wavelet-based Generative Adversarial Networks,"Attribute Enhanced Face Aging with Wavelet-based Generative Adversarial
Networks
Yunfan Liu, Qi Li, and Zhenan Sun∗
Center for Research on Intelligent Perception and Computing, CASIA
National Laboratory of Pattern Recognition, CASIA
{qli,"
851e78906e1307773b664953bf2830f32b28511f,Lie Algebra-Based Kinematic Prior for 3D Human Pose Tracking,"Lie Algebra-Based Kinematic Prior for 3D Human Pose Tracking
Edgar Simo-Serra, Carme Torras, and Francesc Moreno-Noguer
Institut de Rob`otica i Inform`atica Industrial (CSIC-UPC). Barcelona, Spain"
8518b501425f2975ea6dcbf1e693d41e73d0b0af,Relative Hidden Markov Models for Evaluating Motion Skill,"Relative Hidden Markov Models for Evaluating Motion Skills
Qiang Zhang and Baoxin Li
Computer Science and Engineering
Arizona State Univerisity, Tempe, AZ 85281"
8502c089c9affe5955810073b4c814457790065c,Learning Single-view 3D Reconstruction of Objects and Scenes,"UC Berkeley
UC Berkeley Electronic Theses and Dissertations
Title
Learning Single-view 3D Reconstruction of Objects and Scenes
Permalink
https://escholarship.org/uc/item/3dc5m39p
Author
Tulsiani, Shubham
Publication Date
Peer reviewed|Thesis/dissertation
eScholarship.org
Powered by the California Digital Library
University of California"
853bd61bc48a431b9b1c7cab10c603830c488e39,Learning Face Representation from Scratch,"Learning Face Representation from Scratch
Dong Yi, Zhen Lei, Shengcai Liao and Stan Z. Li
Center for Biometrics and Security Research & National Laboratory of Pattern Recognition
Institute of Automation, Chinese Academy of Sciences (CASIA)
dong.yi, zlei, scliao,"
85956f6431543d1bf62bb5d5143de4348f14a95c,Detecting Adversarial Perturbations Through Spatial Behavior in Activation Spaces,"Detecting Adversarial Perturbations Through Spatial
Behavior in Activation Spaces
Ziv Katzir
Yuval Elovici
Department of Software and Information
Department of Software and Information
Systems Engineering, Ben-Gurion University
Systems Engineering, Ben-Gurion University
of the Negev
of the Negev"
8562ca7f86e7cc144aa2d34a9cce41431b9e13e9,BioMechanical Enginering Face Recognition for Cognitive Robots,"Face Recognition for Cognitive
Robots
F. Gaisser
BioMechanical Enginering"
856b8576999517c0cb7d95aef0159432604a8447,The 19 th Meeting on Image Recognition and Understanding Weighted Heterogeneous Learning for Deep Convolutional Neural Network Based Facial Image Analysis,The 19th Meeting on Image Recognition and Understanding
8558ea46c8f7e56c57073b27408c6638e81293f0,Morphable crowds,
85955fe6cdf4f9f35fc9eab6cc4fccbb819e68a1,3D Face Reconstruction by Learning from Synthetic Data,"D Face Reconstruction by Learning from Synthetic Data
Elad Richardson*
Matan Sela*
Ron Kimmel
Department of Computer Science, Technion - Israel Institute of Technology"
852998c60e4a62d0508de11b6297e6e97a0d3fac,Autonomous Vehicles that Interact with Pedestrians: A Survey of Theory and Practice,"THIS WORK HAS BEEN SUBMITTED TO THE IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS.
Autonomous Vehicles that Interact with Pedestrians:
A Survey of Theory and Practice
Amir Rasouli and John K. Tsotsos"
85fd2bda5eb3afe68a5a78c30297064aec1361f6,"Are You Smiling, or Have I Seen You Before? Familiarity Makes Faces Look Happier.","702003 PSSXXX10.1177/0956797617702003Carr et al.Are You Smiling, or Have I Seen You Before?
research-article2017
Research Article
Are You Smiling, or Have I Seen You
Before? Familiarity Makes Faces Look
Happier
017, Vol. 28(8) 1087 –1102
© The Author(s) 2017
Reprints and permissions:
sagepub.com/journalsPermissions.nav
DOI: 10.1177/0956797617702003
https://doi.org/10.1177/0956797617702003
www.psychologicalscience.org/PS
Evan W. Carr1, Timothy F. Brady2, and Piotr Winkielman2,3,4
Columbia Business School, Columbia University; 2Psychology Department, University of California, San Diego;
Behavioural Science Group, Warwick Business School, University of Warwick; and 4Faculty of Psychology,
SWPS University of Social Sciences and Humanities"
8558fc2016fc9b3db3248b2e75b81d9aa9cfc390,Spatial orientations of visual word pairs to improve Bag-of-Visual-Words model,"KHAN et al.: SP. ORIENTATIONS OF VIS. WORD PAIRS TO IMPROVE BOVW MOD.
Spatial orientations of visual word pairs to
improve Bag-of-Visual-Words model
Rahat Khan
Cecile Barat
Damien Muselet
Christophe Ducottet
Université de Lyon, F-42023,
Saint-Etienne, France,
CNRS, UMR5516, Laboratoire Hubert
Curien, F-42000, Saint-Etienne, France,
Université de Saint-Etienne, Jean Mon-
net, F-42000, Saint-Etienne, France."
858901405086056361f8f1839c2f3d65fc86a748,ON TENSOR TUCKER DECOMPOSITION : THE CASE FOR AN ADJUSTABLE CORE SIZE,"ON TENSOR TUCKER DECOMPOSITION: THE CASE FOR AN
ADJUSTABLE CORE SIZE
BILIAN CHEN ∗, ZHENING LI † , AND SHUZHONG ZHANG ‡"
858ddff549ae0a3094c747fb1f26aa72821374ec,"Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications","Survey on RGB, 3D, Thermal, and Multimodal
Approaches for Facial Expression Recognition:
History, Trends, and Affect-related Applications
Ciprian A. Corneanu, Marc Oliu, Jeffrey F. Cohn, and Sergio Escalera"
857fb344977e5181bf5a99593ceba09a158d412c,VCI 2 R at the NTCIR-13 Lifelog-2 Lifelog Semantic Access Task,"VCI2R at the NTCIR-13 Lifelog-2 Lifelog Semantic Access
Jie Lin
Institute for Infocomm
Research, A*STAR, Singapore
Ana Garcia del Molino
Institute for Infocomm
Research, A*STAR, Singapore
Nanyang Technological
University, Singapore
star.edu.sg
Qianli Xu
Institute for Infocomm
Research, A*STAR, Singapore
Fen Fang
Vigneshwaran Subbaraju
Joo Hwee Lim
Institute for Infocomm
Research, A*STAR, Singapore
star.edu.sg
Institute for Infocomm"
853feff8674f4a856e6568c9ddce5eace014de8c,NISTIR 8045 Performance Evaluation Methods for Human Detection and Tracking Systems for Robotic Applications,"NISTIR 8045
Performance Evaluation Methods for
Human Detection and Tracking
Systems for Robotic Applications
Michael Shneier
Tsai Hong
Geraldine Cheok
Kamel Saidi
Will Shackleford
This publication is available free of charge from:
http://dx.doi.org/10.6028/NIST.IR.8045"
8566231abd7e5bc71ee0bc0da84b8d76ce07a501,On The Stability of Video Detection and Tracking,"On The Stability of Video Detection and Tracking
Hong Zhang
Chinese University of Hong Kong
Naiyan Wang
TuSimple LLC"
85c1926ea23ff4f472774fec8c6a993bb499e4f4,Eigenbands fusion for frontal face recognition,"EIGENBANDS FUSION FOR FRONTAL FACE RECOGNITION
George D. C. Cavalcanti1s2 and Edson C. B. Cawalho Filho’
’ UFPE-Universidade  Federal de Pemambuco, 50732-970, Recife, PE, Brad
FIR-Faculdade  Integrada do Recife 50720-635 Recife, PE, Brad"
8575adafc04a7915bd71c3733e379577da0c4406,Sistema tutor afectivo para la enseñanza de lógica algorítmica y programación,"Sistema tutor afectivo para la enseñanza de lógica
lgorítmica y programación
Ramón Zatarain-Cabada1, María Lucia Barrón-Estrada1,
José Mario Ríos-Félix1, Giner Alor-Hernandez2
Instituto Tecnológico de Culiacán, Culiacán Sinaloa,
México
Instituto Tecnológico de Orizaba,
División de Estudios de Posgrado e Investigación, Orizaba, Veracruz,
México
{rzatarain, lbarron,
Resumen.  La  creciente  demanda  de    herramientas  de  software  que  motiven  y
poyen  a  los  estudiantes  en  el  aprendizaje  de  diseño  e  implementación  de
lgoritmos  y  programas,  ha  motivado  la  creación  de  este  tipo  de  sistemas  de
software.    En  este  artículo  presentamos  un  nuevo  e  innovador  sistema  tutor
fectivo de lógica algorítmica y programación, basado en la técnica de bloques.
Nuestro  enfoque  combina  la  interfaz  de  Google  Blockly  con  técnicas  de
gamificación y ejercicios que son monitoreados para obtener el estado afectivo
del estudiante. Dependiendo de la emoción manifestada (aburrido, enganchado,
frustrado y neutral), el sistema evalúa una serie de variables, para determinar si
el estudiante requiere asistencia. En base a las pruebas preliminares con varios"
85cad2b23e2ed7098841285bae74aafbff921659,Pa-gan: Improving Gan Training by Progressive Augmentation,"Under review as a conference paper at ICLR 2019
PA-GAN: IMPROVING GAN TRAINING BY
PROGRESSIVE AUGMENTATION
Anonymous authors
Paper under double-blind review"
85401b669a989da15bb3d2b37d4598c21d9d061b,"The effect of intranasal oxytocin versus placebo treatment on the autonomic responses to human sounds in autism: a single-blind, randomized, placebo-controlled, crossover design study","Lin et al. Molecular Autism 2014, 5:20
http://www.molecularautism.com/content/5/1/20
Open Access
R ES EAR CH
The effect of intranasal oxytocin versus placebo
treatment on the autonomic responses to human
sounds in autism: a single-blind, randomized,
placebo-controlled, crossover design study
I-Fan Lin1*, Makio Kashino1,2, Haruhisa Ohta3, Takashi Yamada3, Masayuki Tani3, Hiromi Watanabe3, Chieko Kanai3,
Taisei Ohno3, Yuko Takayama3, Akira Iwanami3 and Nobumasa Kato3,4"
8529c0b98ab4f6eb21715a54395420988dd69633,Adapting Semantic Segmentation Models for Changes in Illumination and Camera Perspective,"Adapting Semantic Segmentation Models for Changes
in Illumination and Camera Perspective
Wei Zhou, Alex Zyner, Stewart Worrall, and Eduardo Nebot"
857fface5ccd0fd4f30d6b1b3d2cd25a2b471501,Head pose estimation via probabilistic high-dimensional regression,"Head Pose Estimation Via Probabilistic
High-Dimensional Regression
Vincent Drouard 1 Sil`eye Ba 1 Georgios Evangelidis 1
Antoine Deleforge 2 Radu Horaud 1
Team Perception - Inria Grenoble Rhˆone-Alpes, France
Friedrich-Alexander-Universit¨at, Erlangen, Germany
September 28, 2015, Qu´ebec, Canada
Work supported by EU-FP7 ERC Advanced Grant VHIA (#340113) and STREP project EARS (#609645)"
858b51a8a8aa082732e9c7fbbd1ea9df9c76b013,Can Computer Vision Problems Benefit from Structured Hierarchical Classification?,"Can Computer Vision Problems Benefit from
Structured Hierarchical Classification?
Thomas Hoyoux1, Antonio J. Rodr´ıguez-S´anchez2, Justus H. Piater2, and
Sandor Szedmak2
INTELSIG, Montefiore Institute, University of Li`ege, Belgium
Intelligent and Interactive Systems, Institute of Computer Science, University of
Innsbruck, Austria"
85b1ca3089d503781af9de23d24567461cbb65c6,A Mobile Real Time Interactive Communication Assistant for Cerebral Palsy,"A Mobile Real Time Interactive Communication Assistant for Cerebral
Palsy
LAU BEE THENG & HII KIING SHI*
Swinburne University of Technology Sarawak Campus, Malaysia
LOW TIONG KIE
University Malaysia Sarawak, Malaysia"
8509abbde2f4b42dc26a45cafddcccb2d370712f,A way to improve precision of face recognition in SIPP without retrain of the deep neural network model,"Improving precision and recall of face recognition in SIPP with combination of
modified mean search and LSH
Xihua.Li"
850c5d1f97eee47a1fdaefc0894b52e51a3145fc,Improved Semantic Stixels via Multimodal Sensor Fusion,"Improved Semantic Stixels via
Multimodal Sensor Fusion
Florian Piewak(cid:63)1,2, Peter Pinggera1, Markus Enzweiler1,
David Pfeiffer(cid:63)(cid:63)1, and Marius Z¨ollner2,3
Daimler AG, R&D, Stuttgart, Germany
Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Forschungszentrum Informatik (FZI), Karlsruhe, Germany"
85489639f395608174f686d634d6e27ef44c9d77,Social ‘wanting’ dysfunction in autism: neurobiological underpinnings and treatment implications,"Kohls et al. Journal of Neurodevelopmental Disorders 2012, 4:10
http://www.jneurodevdisorders.com/content/4/1/10
RE VI E W
Open Access
Social ‘wanting’ dysfunction in autism:
neurobiological underpinnings and
treatment implications
Gregor Kohls*, Coralie Chevallier, Vanessa Troiani and Robert T Schultz"
854f9fb21853d1e50302dddcc1fd5c2e933ed8f4,Information Constraints on Auto-Encoding Variational Bayes,"Information Constraints on Auto-Encoding Variational Bayes
Romain Lopez1, Jeffrey Regier1, Michael I. Jordan1,2, and Nir Yosef1,3,4
{romain_lopez, regier,
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley
Department of Statistics, University of California, Berkeley
Ragon Institute of MGH, MIT and Harvard
Chan-Zuckerberg Biohub"
854cfc218b506b54edeee15d32b0869e77755006,Preserving privacy in structural neuroimages,"Preserving Privacy in Structural Neuroimages
Nakeisha Schimke, Mary Kuehler, and John Hale
Institute of Bioinformatics and Computational Biology
The University of Tulsa
800 South Tucker Drive
Tulsa, Oklahoma 74104"
854890f35fc7955d94777395f6a66da433426d98,Human Gaze Following for Human-Robot Interaction,"Human Gaze Following for Human-Robot Interaction
Akanksha Saran1, Srinjoy Majumdar2, Elaine Schaertl Short2, Andrea Thomaz2 and Scott Niekum1"
35692e80fa2fc17a1d37a40b3d4ffca28a1bcc7b,Appearance-based people recognition by local dissimilarity representations,"Appearance-based People Recognition by Local
Dissimilarity Representations
Riccardo Satta, Giorgio Fumera, Fabio Roli
Dept. of Electrical and Electronic Engineering, University of Cagliari
Piazza d’Armi, 09123 Cagliari, Italy
riccardo.satta, fumera,"
35c0954acde9c86df8bbcb6edccbcd702796f5eb,"Multimodal Database of Emotional Speech , Video and Gestures","World Academy of Science, Engineering and Technology
International Journal of Computer and Information Engineering
Vol:12, No:10, 2018
Multimodal Database of Emotional Speech, Video
nd Gestures
Tomasz Sapi´nski, Dorota Kami´nska, Adam Pelikant, Egils Avots, Cagri Ozcinar, Gholamreza Anbarjafari"
3516d0c6918bcf977ad004b337fb066a9d5a8e17,A Novel Face Hallucination with an Error Regression Model and MPCA in RGB Color Space,"International Journal of Future Computer and Communication, Vol. 2, No. 4, August 2013
A Novel Face Hallucination with an Error Regression
Model and MPCA in RGB Color Space
Krissada Asavaskulkiet and Phumin Kirawanich
framework,  many  color"
35e4b6c20756cd6388a3c0012b58acee14ffa604,Gender Classification in Large Databases,"Gender Classification in Large Databases
E. Ram´on-Balmaseda, J. Lorenzo-Navarro, and M. Castrill´on-Santana (cid:63)
Universidad de Las Palmas de Gran Canaria
SIANI
Spain"
353a89c277cca3e3e4e8c6a199ae3442cdad59b5,Title of dissertation : LEARNING FROM MULTIPLE VIEWS OF DATA,
35d94887e4eb075f2603b2c69b19d31471351ff7,People detection and tracking from aerial thermal views,
3555d849b85e9416e9496c9976084b0e692b63cd,TOWARDS EFFECTIVE GANS,"Under review as a conference paper at ICLR 2018
TOWARDS EFFECTIVE GANS
FOR DATA DISTRIBUTIONS WITH DIVERSE MODES
Anonymous authors
Paper under double-blind review"
35047c9667a4c5160f9f40f16b455839e63662ff,Color Object Recognition Based on a Clifford Fourier Transform,"Author manuscript, published in ""Guide to Geometric Algebra in Practice, Leo Dorst and Joan Lasenby (Ed.) (2011) 175-191""
DOI : 10.1007/978-0-85729-811-9_9"
35e730f7967155b9394f9e5d3cadf2b955ce9a7b,Deep Affinity Network for Multiple Object Tracking,"JOURNAL OF LATEX CLASS FILES, VOL. 13, NO. 9, SEPTEMBER 2017
Deep Affinity Network
for Multiple Object Tracking
ShiJie Sun, Naveed Akhtar, HuanSheng Song, Ajmal Mian, Mubarak Shah"
352d61eb66b053ae5689bd194840fd5d33f0e9c0,Analysis Dictionary Learning based Classification: Structure for Robustness,"Analysis Dictionary Learning based
Classification: Structure for Robustness
Wen Tang, Ashkan Panahi, Hamid Krim, and Liyi Dai"
35f345ebe3831e4741dcdc1931da59043acf4b83,Towards High Performance Video Object Detection for Mobiles 3 2 Revisiting Video Object Detection Baseline,"Towards High Performance
Video Object Detection for Mobiles
Xizhou Zhu(cid:63)
Jifeng Dai Xingchi Zhu(cid:63) Yichen Wei
Lu Yuan
Microsoft Research Asia"
35e6f6e5f4f780508e5f58e87f9efe2b07d8a864,Summarization of User-Generated Sports Video by Using Deep Action Recognition Features,"This paper is a preprint (IEEE accepted status). IEEE copyright notice. 2018 IEEE.
Personal use of this material is permitted. Permission from IEEE must be obtained for all
other uses, in any current or future media, including reprinting/republishing this material for
dvertising or promotional purposes, creating new collective works, for resale or redistribu-
tion to servers or lists, or reuse of any copyrighted.
A. Tejero-de-Pablos, Y. Nakashima, T. Sato, N. Yokoya, M. Linna and E. Rahtu, ”Sum-
marization of User-Generated Sports Video by Using Deep Action Recognition Features,” in
doi: 10.1109/TMM.2018.2794265
keywords: Cameras; Feature extraction; Games; Hidden Markov models; Semantics;
Three-dimensional displays; 3D convolutional neural networks; Sports video summarization;
ction recognition; deep learning; long short-term memory; user-generated video,
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8259321&isnumber=4456689"
35ebe95db7ab148e25904604d3b06a9412f6b4a4,Illustrative Language Understanding: Large-Scale Visual Grounding with Image Search,"Illustrative Language Understanding:
Large-Scale Visual Grounding with Image Search
Jamie Ryan Kiros*
William Chan*
Geoffrey E. Hinton
{kiros, williamchan,
Google Brain Toronto"
35308a3fd49d4f33bdbd35fefee39e39fe6b30b7,Efficient and effective human action recognition in video through motion boundary description with a compact set of trajectories,"biblio.ugent.be   The UGent Institutional Repository is the electronic archiving and dissemination platform for allUGent research publications. Ghent University has implemented a mandate stipulating that allacademic publications of UGent researchers should be deposited and archived in this repository.Except for items where current copyright restrictions apply, these papers are available in OpenAccess.   This item is the archived peer-reviewed author-version of:   Efficient and effective human action recognition in video through motion boundary description witha compact set of trajectories   Jeong-Jik Seo, Jisoo Son, Hyung-Il Kim, Wesley De Neve, and Yong Man Ro   In: 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition,1, 1-6, 2015.     To refer to or to cite this work, please use the citation to the published version:   Seo, J., Son, J., Kim, H., De Neve, W., and Ro, Y. M. (2015). Efficient and effective human actionrecognition in video through motion boundary description with a compact set of trajectories. 11thIEEE International Conference and Workshops on Automatic Face and Gesture Recognition 1 1-6.http://dx.doi.org/10.1109/FG.2015.7163123"
35800a537017803dd08274710388734db66b54f0,Sliced Wasserstein Generative Models,"Sliced Wasserstein Generative Models
Jiqing Wu * 1 Zhiwu Huang * 1 Wen Li 1 Janine Thoma 1 Luc Van Gool 1 2"
3596c23a0f13c36d2c71c4cba4351363954dd02a,PathFinder : An autonomous mobile robot guided by Computer Vision,"PathFinder: An autonomous mobile robot guided by Computer
Vision
Andre R. de Geus1,2, Marcelo H. Stoppa1, Sergio F. da Silva1,2
Modeling and Optimization Program, Federal University of Goias, Catalao, Goias, Brazil
Biotechnology Institute, Federal University of Goias, Catalao, Goias, Brazil
Email:"
357df3ee0f0c30d5c8abc5a1bdf70122322d6fbd,O BJECT DETECTORS EMERGE IN D EEP S CENE CNN S,"Under review as a conference paper at ICLR 2015
OBJECT DETECTORS EMERGE IN DEEP SCENE CNNS
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, Antonio Torralba
Department of Computer Science and Artificial Intelligence, MIT"
35410a58514cd5fd66d9c43d42e8222526170c1b,Shared mechanism for emotion processing in adolescents with and without autism,"Received: 04 August 2016
Accepted: 05 January 2017
Published: 20 February 2017
Shared mechanism for emotion
processing in adolescents with and
without autism
Christina Ioannou1, Marwa El Zein1, Valentin Wyart1, Isabelle Scheid2,3,
Frédérique Amsellem3,4, Richard Delorme3,4, Coralie Chevallier1,* & Julie Grèzes1,*
Although, the quest to understand emotional processing in individuals with Autism Spectrum Disorders
(ASD) has led to an impressive number of studies, the picture that emerges from this research remains
inconsistent. Some studies find that Typically Developing (TD) individuals outperform those with
ASD in emotion recognition tasks, others find no such difference. In this paper, we move beyond
focusing on potential group differences in behaviour to answer what we believe is a more pressing
question: do individuals with ASD use the same mechanisms to process emotional cues? To this end,
we rely on model-based analyses of participants’ accuracy during an emotion categorisation task in
which displays of anger and fear are paired with direct vs. averted gaze. Behavioural data of 20 ASD
nd 20 TD adolescents revealed that the ASD group displayed lower overall performance. Yet, gaze
direction had a similar impact on emotion categorisation in both groups, i.e. improved accuracy for
salient combinations (anger-direct, fear-averted). Critically, computational modelling of participants’
ehaviour reveals that the same mechanism, i.e. increased perceptual sensitivity, underlies the"
35b1c1f2851e9ac4381ef41b4d980f398f1aad68,Geometry Guided Convolutional Neural Networks for Self-Supervised Video Representation Learning,"Geometry Guided Convolutional Neural Networks for
Self-Supervised Video Representation Learning
Chuang Gan1, Boqing Gong2, Kun Liu3, Hao Su 4, Leonidas J. Guibas 5
MIT-IBM Watson AI Lab , 2 Tencent AI Lab, 3 BUPT, 4 UCSD, 5 Stanford University"
3597ca03bded3717f5c88273e4b7dbf24545ff83,Mouse Pose Estimation From Depth Images,"Mouse Pose Estimation From Depth Images
Ashwin Nanjappa1, Li Cheng∗1, Wei Gao1, Chi Xu1, Adam Claridge-Chang2, and
Zoe Bichler3
Bioinformatics Institute, A*STAR, Singapore
Institute of Molecular and Cell Biology, A*STAR, Singapore
National Neuroscience Institute, Singapore"
35035f79256a3f19a111fff34df6d14876d83fab,Satyam: Democratizing Groundtruth for Machine Vision,"SATYAM: DEMOCRATIZING GROUNDTRUTH FOR MACHINE VISION
Hang Qiu?, Krishna Chintalapudi†, Ramesh Govindan?"
35794480e556b946c82d498ce868fa62235befa7,Real-Time Detection and Tracking of Multiple Humans from High Bird's-Eye Views in the Visual and Infrared Spectrum,"Real-Time Detection and Tracking of Multiple
Humans from High Bird’s-Eye Views in the
Visual and Infrared Spectrum
Julius K¨ummerle, Timo Hinzmann,
Anurag Sai Vempati and Roland Siegwart
Autonomous Systems Lab, ETH Zurich"
35fd6c9f2e651259ee565475f88faeb523511d4e,On the Performance of Kernel Methods for Skin Color Segmentation,"Hindawi Publishing Corporation
EURASIP Journal on Advances in Signal Processing
Volume 2009, Article ID 856039, 13 pages
doi:10.1155/2009/856039
Research Article
On the Performance of Kernel Methods for
Skin Color Segmentation
A. Guerrero-Curieses,1 J. L. Rojo- ´Alvarez,1 P. Conde-Pardo,2 I. Landesa-V´azquez,2
J. Ramos-L ´opez,1 and J. L. Alba-Castro2
Departamento de Teor´ıa de la Se˜nal y Comunicaciones, Universidad Rey Juan Carlos, 28943 Fuenlabrada, Spain
Departamento de Teor´ıa de la Se˜nal y Comunicaciones, Universidad de Vigo, 36200 Vigo, Spain
Correspondence should be addressed to A. Guerrero-Curieses,
Received 26 September 2008; Revised 23 March 2009; Accepted 7 May 2009
Recommended by C.-C. Kuo
Human skin detection in color images is a key preprocessing stage in many image processing applications. Though kernel-based
methods have been recently pointed out as advantageous for this setting, there is still few evidence on their actual superiority.
Specifically, binary Support Vector Classifier (two-class SVM) and one-class Novelty Detection (SVND) have been only tested
in some example images or in limited databases. We hypothesize that comparative performance evaluation on a representative
pplication-oriented database will allow us to determine whether proposed kernel methods exhibit significant better performance
than conventional skin segmentation methods. Two image databases were acquired for a webcam-based face recognition"
35e855b0c1af4cc7bf62a8eb459c949776fbe7ee,Local Features and Kernels for Classification of Texture and Object Categories : An In-Depth Study,"INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET EN AUTOMATIQUE
Local Features and Kernels for Classi(cid:2)cation of
Texture and Object Categories: An In-Depth Study
Jianguo Zhang (cid:151) Marcin Marsza(cid:7)ek (cid:151) Svetlana Lazebnik (cid:151) Cordelia Schmid
N(cid:176) 5737
Novembre 2005
Th(cid:30)eme COG
p p o r t  (cid:13)
(cid:13)d e  r e c h e r c h e (cid:13)"
35be5bea87c465c97127c64919d115e235d62e82,"The automatic detection of chronic pain-related expression : requirements , challenges and a multimodal dataset","IEEE TRANSACTIONS ON JOURNAL NAME,  MANUSCRIPT ID
The automatic detection of chronic pain-
related expression: requirements, challenges
nd a multimodal dataset
Min S. H. Aung, Sebastian Kaltwang, Bernardino Romera-Paredes, Brais Martinez, Aneesha
Singh, Matteo Cella, Michel Valstar, Hongying Meng, Andrew Kemp, Moshen Shafizadeh, Aaron
C. Elkins, Natalie Kanakam, Amschel de Rothschild, Nick Tyler, Paul J. Watson, Amanda C. de C.
Williams, Maja Pantic, and Nadia Bianchi-Berthouze*
face  videos,  head  mounted  and  room  audio  signals,"
35197862aed9a73415bdaea2fb8ad29d66675604,Mixture of SVMs for Face Class Modeling,"Mixture of SVMs for Face Class Modeling
Julien Meynet1, Vlad Popovici, and Jean-Philippe Thiran
Signal Processing Institute, Swiss Federal Institute of Technology Lausanne,
CH-1015 Lausanne, Switzerland
http://itswww.epfl.ch"
35570297681daa3973498eabead361d0be961672,Configuration Estimates Improve Pedestrian Finding,"Configuration Estimates Improve Pedestrian Finding
Duan Tran∗
U.Illinois at Urbana-Champaign
Urbana, IL 61801 USA
D.A. Forsyth
U.Illinois at Urbana-Champaign
Urbana, IL 61801 USA"
355c8c0dbd80de9d23affb37ac102179b6b2a908,“A Distorted Skull Lies in the Bottom Center...” Identifying Paintings from Text Descriptions,"Anupam Guha, Mohit Iyyer, and Jordan Boyd-Graber. A Distorted Skull Lies in the Bottom Center:
Identifying Paintings from Text Descriptions. NAACL Human-Computer Question Answering Workshop, 2016.
Title = {A Distorted Skull Lies in the Bottom Center: Identifying Paintings from Text Descriptions},
Author = {Anupam Guha and Mohit Iyyer and Jordan Boyd-Graber},
Booktitle = {NAACL Human-Computer Question Answering Workshop},
Year = {2016},
Location = {San Diego, CA},
Url = {docs/2016_naacl_paintings.pdf},
Links:
• Data [http://www.cs.umd.edu/~aguha/data/paintdata.rar]
Downloaded from http://cs.colorado.edu/~jbg/docs/2016_naacl_paintings.pdf"
355de7460120ddc1150d9ce3756f9848983f7ff4,Midge: Generating Image Descriptions From Computer Vision Detections,"Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pages 747–756,
Avignon, France, April 23 - 27 2012. c(cid:13)2012 Association for Computational Linguistics"
35f3c4012e802332faf0a1426e9acf8365601551,Bidirectional Conditional Generative Adversarial Networks,"Bidirectional Conditional
Generative Adversarial Networks
Ayush Jaiswal, Wael AbdAlmageed, Yue Wu, and Premkumar Natarajan
USC Information Sciences Institute, Marina del Rey, CA, USA
{ajaiswal, wamageed, yue wu,"
35b9ded80ce2b30ee115b8198d146890b9028d51,Regularizing max-margin exemplars by reconstruction and generative models,"Regularizing Max-Margin Exemplars by Reconstruction and Generative Models
Jose C. Rubio and Bj¨orn Ommer
Heidelberg Collaboratory for Image Processing
IWR, Heidelberg University, Germany"
3538d2b5f7ab393387ce138611ffa325b6400774,A DSP-BASED APPROACH FOR THE IMPLEMENTATION OF FACE RECOGNITION ALGORITHMS,"A DSP-BASED APPROACH FOR THE IMPLEMENTATION OF FACE RECOGNITION
ALGORITHMS
A. U. Batur
B. E. Flinchbaugh
M. H. Hayes IIl
Center for Signal and Image Proc.
Georgia Inst. Of Technology
Atlanta, GA
Imaging and Audio Lab.
Texas Instruments
Dallas, TX
Center for Signal and Image Proc.
Georgia Inst. Of Technology
Atlanta, CA"
351de1f7862bd13a82fcfcaa698b4efd53bc2c35,Automatic 3D face verification from range data,- 1930-7803-7663-3/03/$17.00 ©2003 IEEEICASSP 2003(cid:224)
35fe83665c61adb513781c7208b92706ae2a1578,Answering Visual What-If Questions: From Actions to Predicted Scene Descriptions,
3506ef7168e07840187ec978b47f3a05a753101d,Robust 3D Face Landmark Localization Based on Local Coordinate Coding,"Robust 3D Face Landmark Localization based on
Local Coordinate Coding
Mingli Song, Senior Member, IEEE, Dacheng Tao, Senior Member, IEEE, Shengpeng Sun, Chun Chen, and
Stephen J. Maybank Fellow, IEEE,"
35e87e06cf19908855a16ede8c79a0d3d7687b5c,Strategies for MultiView Face Recognition for Identification of Human Faces : A Review,"Strategies for Multi-View Face Recognition for
Identification of Human Faces: A Review
Pritesh G. Shah
Department of Computer Science
Mahatma Gandhi Shikshan Mandal’s,
Arts, Science and Commerce College, Chopda
Dist: Jalgaon (M.S)
Dr. R.R.Manza
Department of Computer Science and IT
Dr. Babasaheb Ambedkar Marathwada University
Aurangabad."
3533a7714b19396bba8297e0ca22f85ac68ca18a,Dense Captioning with Joint Inference and Visual Context,"Dense Captioning with Joint Inference and Visual Context
Linjie Yang
Kevin Tang
Jianchao Yang
Li-Jia Li
{linjie.yang, kevin.tang,
Snap Inc."
3574bd487622fc1d6d88cda4ba31d87f3d1a5bd6,Automatic Pose Correction for Local Feature-Based Face Authentication,"Automatic Pose Correction for Local
Feature-Based Face Authentication
Daniel Gonz´alez-Jim´enez1, Federico Sukno2,
Jos´e Luis Alba-Castro1, and Alejandro Frangi2
Departamento de Teor´ıa de la Se˜nal y Comunicaciones, Universidad de Vigo, Spain
{danisub,
Departamento de Tecnolog´ıa, Universidad Pompeu Fabra, Barcelona, Spain
{federico.sukno,"
35f084ddee49072fdb6e0e2e6344ce50c02457ef,A bilinear illumination model for robust face recognition,"A Bilinear Illumination Model
for Robust Face Recognition
The Harvard community has made this
rticle openly available.  Please share  how
this access benefits you. Your story matters
Citation
Lee, Jinho, Baback Moghaddam, Hanspeter Pfister, and Raghu
Machiraju. 2005. A bilinear illumination model for robust face
recognition. Proceedings of the Tenth IEEE International Conference
on Computer Vision: October 17-21, 2005, Beijing, China. 1177-1184.
Los Almamitos, C.A.: IEEE Computer Society.
Published Version
doi:10.1109/ICCV.2005.5
Citable link
http://nrs.harvard.edu/urn-3:HUL.InstRepos:4238979
Terms of Use
This article was downloaded from Harvard University’s DASH
repository, and is made available under the terms and conditions
pplicable to Other Posted Material, as set forth at http://
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35e808424317cf03b51516df7d083f45791311ae,A Survey for Action Recognition Research,"A Survey for Action Recognition Research
Yuancheng Ye"
359a4142f6a55a58a3e18628e3ee52c76744fcb0,Prevalence of face recognition deficits in middle childhood.,"ISSN: 1747-0218 (Print) 1747-0226 (Online) Journal homepage: http://www.tandfonline.com/loi/pqje20
Prevalence of face recognition deficits in middle
hildhood
Rachel J Bennetts, Ebony Murray, Tian Boyce & Sarah Bate
To cite this article: Rachel J Bennetts, Ebony Murray, Tian Boyce & Sarah Bate (2016):
Prevalence of face recognition deficits in middle childhood, The Quarterly Journal of
Experimental Psychology, DOI: 10.1080/17470218.2016.1167924
To link to this article:  http://dx.doi.org/10.1080/17470218.2016.1167924
View supplementary material
Accepted author version posted online: 21
Mar 2016.
Submit your article to this journal
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Full Terms & Conditions of access and use can be found at
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Download by: [Rachel Bennetts]
Date: 22 March 2016, At: 07:06"
353480b21d5745590db5f70b016a27e25f5b9aec,Cross-Modal and Hierarchical Modeling of Video and Text,"Cross-Modal and Hierarchical Modeling of Video
nd Text
Bowen Zhang(cid:63)1, Hexiang Hu(cid:63)1, and Fei Sha2
Dept. of Computer Science, U. of Southern California, Los Angeles, CA 90089
Netflix, 5808 Sunset Blvd, Los Angeles, CA 90028"
3521904cced380b849325d6fda2a4d855edbe405,Finding Images of Rare and Ambiguous Entities,"Finding Images of Rare and
Ambiguous Entities
Bilyana Taneva
Mouna Kacimi
Gerhard Weikum
MPI–I–2011–5–002
May 2011"
3504907a2e3c81d78e9dfe71c93ac145b1318f9c,An End-to-End System for Unconstrained Face Verification with Deep Convolutional Neural Networks,"Noname manuscript No.
(will be inserted by the editor)
Unconstrained Still/Video-Based Face Verification with Deep
Convolutional Neural Networks
Jun-Cheng Chen∗
Kumar∗ · Ching-Hui Chen∗ · Vishal M. Patel · Carlos D. Castillo ·
Rama Chellappa
· Rajeev Ranjan∗ · Swami Sankaranarayanan∗ · Amit
Received: date / Accepted: date"
359c9b9282424f198ef7b348a2fbab38c0c8f4fc,Supervised learning and evaluation of KITTI's cars detector with DPM,"Supervised learning and evaluation of KITTI’s cars detector with DPM
J. Javier Yebes, Luis M. Bergasa, Roberto Arroyo and Alberto Lázaro"
35457de70ea13415b8abd3898a4a83021946501f,Learning Robust and Discriminative Subspace With Low-Rank Constraints,"Calhoun: The NPS Institutional Archive
Faculty and Researcher Publications
Funded by Naval Postgraduate School
Learning Robust and Discriminative Subspace
With Low-Rank Constraints
Sheng Li
http://hdl.handle.net/10945/52406"
35ccf703df2dd37fc857ab9b438e18c9a65ba5b9,Two-step supervised confidence measure for automatic face recognition,"TWO-STEP SUPERVISED CONFIDENCE MEASURE FOR AUTOMATIC FACE
RECOGNITION
Ladislav Lenc1
, Pavel Kr´al1
Dept. of Computer Science & Engineering 2New Technologies for the Information Society
Faculty of Applied Sciences
University of West Bohemia
Plzeˇn, Czech Republic
Faculty of Applied Sciences
University of West Bohemia
Plzeˇn, Czech Republic"
53078a5692d493685fd2d63abf297a39b2edb36d,Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking,"Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
Filip Radenovi´c1 Ahmet Iscen1 Giorgos Tolias1 Yannis Avrithis2 Ondˇrej Chum1
VRG, FEE, CTU in Prague
Inria Rennes"
53079196041fedeb5f1e236b1c76c7108fd8346e,"Multiple Object Detection, Tracking and Long-Term Dynamics Learning in Large 3D Maps","Multiple Object Detection, Tracking
nd Long-Term Dynamics Learning
in Large 3D Maps
Local
D Maps
Object
Posteriors
Learn
Dynamics
Location l1
Location l3
Object jump
probability:
pjump = 0.036
Object spatial
process variance:
q = 0.137
Measurement
ovariance:
 0.14 −0.03 0.02"
53c5f995e76ead002f1b0a78bfd50de3b1faf593,Enhancing the Symmetry and Proportion of 3D Face Geometry,"Enhancing the symmetry and proportion of 3D
face geometry
Qiqi Liao, Xiaogang Jin, Wenting Zeng"
53a41c711b40e7fe3dc2b12e0790933d9c99a6e0,Recurrent Memory Addressing for Describing Videos,"Recurrent Memory Addressing for describing videos
Arnav Kumar Jain∗ Abhinav Agarwalla∗
Kumar Krishna Agrawal∗
Pabitra Mitra
{arnavkj95, abhinavagarawalla, kumarkrishna,
Indian Institute of Technology Kharagpur"
53bed2d3d75c4320ad5af4a85e31bf92e3c704ef,Reinforced Video Captioning with Entailment Rewards,"Reinforced Video Captioning with Entailment Rewards
Ramakanth Pasunuru and Mohit Bansal
UNC Chapel Hill
{ram,"
5357e6e5d5fe06934bfe693d18b9f44bbd98f73b,Landmark Detection for Unconstrained Face Recognition,"Landmark Detection for
Unconstrained Face Recognition
Panagiotis B. Perakis (cid:63)
National and Kapodistrian University of Athens
Department of Informatics and Telecommunications"
53ac22fff7ae3ed08565439ac30656846cac2465,Learning 3D Human Pose from Structure and Motion,"Learning 3D Human Pose from Structure and Motion
Rishabh Dabral1, Anurag Mundhada1, Uday Kusupati1, Safeer Afaque1, Abhishek
Sharma2, Arjun Jain1
{rdabral, safeer, {anuragmundhada,
Indian Institute of Technology Bombay, 2Gobasco AI Labs
kusupatiuday,"
5365892b23db7e8bd526441d339c9165657f7efb,MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation,"MPIIGaze: Real-World Dataset and Deep
Appearance-Based Gaze Estimation
Xucong Zhang, Yusuke Sugano∗, Mario Fritz, Andreas Bulling"
53f0b39c88f3973f74f65455d0d77dfe6feede84,Fine-grained sketch-based image retrieval by matching deformable part models,"Fine-grained sketch-based image retrieval by matching deformable part
models
Li, Y; Hospedales, TM; Song, YZ; Gong, S
For additional information about this publication click this link.
http://qmro.qmul.ac.uk/jspui/handle/123456789/6440
Information about this research object was correct at the time of download; we occasionally
make corrections to records, please therefore check the published record when citing. For
more information contact"
53baabad70915a852477c2191b8bdb005b8cc4a5,Temporal-Enhanced Convolutional Network for Person Re-Identification,"The Thirty-Second AAAI Conference
on Artificial Intelligence (AAAI-18)
Temporal-Enhanced Convolutional
Network for Person Re-identification
Yang Wu, Jie Qiu, Jun Takamatsu, Tsukasa Ogasawara
Nara Institute of Science and Technology
8916-5 Takayama-cho, Ikoma, Nara, 630-0192, Japan
{qiu.jie.qf3, j-taka,"
53bb52eb910c3a0ac5dc7f379b1f3f7c29af529d,Pain recognition using spatiotemporal oriented energy of facial muscles,"Pain Recognition using Spatiotemporal Oriented Energy of Facial Muscles
Ramin Irani, Kamal Nasrollahi, and Thomas B. Moeslund
Visual Analysis of People (VAP) Laboratory
Rendsburggade 14, 9000 Aalborg, Denmark
{ri, kn,"
5345822a30293c9989fd1ec19b5a2da96956347b,Acoustic-labial Speaker Verication 1,"Acoustic(cid:0)Labial Speaker Veri(cid:1)cation 

P(cid:0) Jourlin a
(cid:0)
(cid:1) J(cid:0) Luettin a(cid:1) D(cid:0) Genoud a(cid:1) H(cid:0) Wassner a
IDIAP(cid:0) rue du Simplon (cid:0) CP 	(cid:0) CH(cid:5)
	 Martigny(cid:0) Switzerland
LIA(cid:0) 	 chemin des Meinajari(cid:9)es(cid:0) BP 
(cid:0) 	

 Avignon Cedex 	(cid:0) France"
5322f56b8768f8053e8b0c4944d81377d1d60411,MEE 09 : 79 FACE ALIGNMENT USING BOOSTED APPEARANCE MODEL ( Discriminative Appearance Model ),"MEE09:79
FACE ALIGNMENT USING
BOOSTED APPEARANCE MODEL
(Discriminative Appearance Model)
Satya Mahesh Muddamsetty
This thesis is presented as part of Degree of
Master of Science in Electrical Engineering
Blekinge Institute of Technology
November 2009
Blekinge Institute of Technology
School of Engineering
Department of Applied Signal Processing
Supervisor:  Mr. Tommaso Gritti, PHILIPS RESEARCH, Eindhoven, Netherlands
Examiner:    Mr. Mikael Nilsson"
5367610430dc0380dfbe8344e08537267875968c,Tracking 3 D Surfaces Using Multiple Cameras : A Probabilistic Approach,"Tracking 3D Surfaces Using
Multiple Cameras: A
Probabilistic Approach
Thomas Popham
Thesis
Submitted to the University of Warwick
for the degree of
Doctor of Philosophy
Department of Computer Science
August 2010"
537061f3601965b5aab9f402763d9dcf451e1cef,A Deep Neural Model Of Emotion Appraisal,"Noname manuscript No.
(will be inserted by the editor)
A Deep Neural Model Of Emotion Appraisal
Pablo Barros · Emilia Barakova · Stefan Wermter
Received: date / Accepted: date"
53f8f1ddd83a9e0e0821aaa883fbf7c1f7f5426e,Face Recognition using Principal Component Analysis and Log-Gabor Filters,"Face Recognition using Principal Component
Analysis and Log-Gabor Filters
Vytautas Perlibakas
Image Processing and Analysis Laboratory, Computational Technologies Centre,
Kaunas University of Technology, Studentu st. 56-305, LT-51424 Kaunas,
Lithuania"
53d0cef0b415f0cab8b82e1f62a66f9511ea6eab,Similarity Function Learning with Data Uncertainty,
531b211d4cbe766e0b86c4bb6f24e924494360c5,"SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation","SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation
Sudeep Pillai, Rares, Ambrus,, Adrien Gaidon
Toyota Research Institute (TRI)"
53492cb14b33a26b10c91102daa2d5a2a3ed069d,Improving Online Multiple Object tracking with Deep Metric Learning,"Improving Online Multiple Object tracking with Deep Metric Learning
Michael Thoreau, Navinda Kottege"
534551fe2c4ce76c9a9a752364cd9c3af0dc9093,Gait Dynamics for Recognition and Classification,"m a s s a c h u s e t t s   i n s t i t u t e   o f
t e c h n o l o g y   — a r t i f i c i a l   i n t e l l i g e n c e   l a b o r a t o r y
Gait Dynamics for Recognition
nd Classification
Lily Lee
AI Memo 2001-019
September 2001
© 2 0 0 1   m a s s a c h u s e t t s   i n s t i t u t e   o f
t e c h n o l o g y, c a m b r i d g e , m a   0 2 1 3 9   u s a   —   w w w. a i . m i t . e d u"
538f735450463f40c78f60797899fcee47df72bc,Discriminative Dictionary Learning With Motion Weber Local Descriptor for Violence Detection,"© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for
ll other uses, in any current or future media, including reprinting/republishing this material for
dvertising or promotional purposes, creating new collective works, for resale or redistribution to
servers or lists, or reuse of any copyrighted component of this work in other works."
53881bb35cb98c788f75fbc8c76198ccbc50edbf,Selective experience replay in reinforcement learning for reidentification,"SELECTIVE EXPERIENCE REPLAY IN REINFORCEMENT LEARNING FOR
REIDENTIFICATION
Ninad Thakoor , Bir Bhanu
Center for Research in Intelligent Systems,
University of California, Riverside, Riverside, CA 92521, USA"
537a00082b413b40fbdd02b5584791614f5071d2,Face Recognition Using Principal Component Analysis for Security Based System,"International Journal of Science and Research (IJSR)
ISSN (Online): 2319-7064
Index Copernicus Value (2013): 6.14 | Impact Factor (2013): 4.438
Face Recognition Using Principal Component
Analysis for Security Based System
Madhuri M. Ghodake1, Parul S. Arora2
Savitribai Phule Pune University, G.H.Raisoni College of Engg & Management, Domkhel Road, Wagholi, Pune
Assistant Professor, G.H.Raisoni College of Engg & Management, Domkhel Road, Wagholi, Pune, Savitribai Phule University, Pune"
53993c7fabf631cbd8a44ab3e42c6bdf784db456,Understanding and Predicting Image Memorability at a Large Scale,"Understanding and Predicting Image Memorability at a Large Scale
Aditya Khosla
Akhil S. Raju
Antonio Torralba
Aude Oliva"
535919a5ffa0f214df2ad39e80015ecba174a10d,k-fold Subsampling based Sequential Backward Feature Elimination,
5366573e96a1dadfcd4fd592f83017e378a0e185,"Server, server in the cloud. Who is the fairest in the crowd?","Böhlen, Chandola and Salunkhe
Server, server in the cloud.
Who is the fairest in the crowd?"
5383473d1a669beb0089f72a9a5075e943f0270f,Higher-order Occurrence Pooling on Mid-and Low-level Features : Visual Concept Detection,
533d5e62a983325cdbd44010018696ece433ea67,A Novel Georeferenced Dataset for Stereo Visual Odometry,"Proceedings of the Croatian Computer Vision Workshop, Year 1
September 19, 2013, Zagreb, Croatia
A novel georeferenced dataset
for stereo visual odometry
Ivan Kreˇso
Marko ˇSevrovi´c
Siniˇsa ˇSegvi´c
University of Zagreb Faculty of
Electrical Engineering and Computing
University of Zagreb Faculty of
Transport and Traffic Engineering
University of Zagreb Faculty of
Electrical Engineering and Computing
Zagreb, HR-10000, Croatia
Zagreb, HR-10000, Croatia
Zagreb, HR-10000, Croatia
Email:
Email:
Email:"
539b66c91f41719ef94f50e6d42ab470f2a2702c,Agreeing to cross: How drivers and pedestrians communicate,"Agreeing To Cross: How Drivers and Pedestrians Communicate*
Amir Rasouli, Iuliia Kotseruba and John K. Tsotsos1"
53822d61e829ef02a95a6c89fea082114fd3e16b,A General Framework for Tracking Multiple People from a Moving Camera,"This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.
IEEE TRANSACTION ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
A General Framework for Tracking Multiple
People from a Moving Camera
Wongun Choi, Caroline Pantofaru, Silvio Savarese"
53819049f41998a5a1587dfccccc2db8612b45af,Deep Semantic Lane Segmentation for Mapless Driving,"018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Madrid, Spain, October 1-5, 2018
978-1-5386-8093-3/18/$31.00 ©2018 IEEE"
53c8f841cbf2c8f09c6ece9d7f164504fe39409b,Deep Clustering for Unsupervised Learning of Visual Features,"Deep Clustering for Unsupervised Learning
of Visual Features
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze
Facebook AI Research"
53e081f5af505374c3b8491e9c4470fe77fe7934,Unconstrained realtime facial performance capture,"Unconstrained Realtime Facial Performance Capture
Pei-Lun Hsieh⇤
⇤ University of Southern California
Chongyang Ma⇤
Jihun Yu†
Hao Li⇤
Industrial Light & Magic
Figure 1: Calibration-free realtime facial performance capture on highly occluded subjects using an RGB-D sensor."
5357bdaf7c54619016bdb7ebfa991a65a6cc8353,"Infants’ Temperament and Mothers’, and Fathers’ Depression Predict Infants’ Attention to Objects Paired with Emotional Faces","J Abnorm Child Psychol (2016) 44:975–990
DOI 10.1007/s10802-015-0085-9
Infants’ Temperament and Mothers’, and Fathers’ Depression
Predict Infants’ Attention to Objects Paired with Emotional Faces
Evin Aktar 1,2 & Dorothy J. Mandell 1 & Wieke de Vente 2 & Mirjana Majdandžić 2 &
Maartje E. J. Raijmakers 1,3 & Susan M. Bögels 2
Published online: 8 October 2015
# The Author(s) 2015. This article is published with open access at Springerlink.com"
5305a899c3f45e312f2ae583fd3abb2d1fede458,Absolute Localization using Visual Data for Autonomous Vehicles,
53f981cb6f1cf19b08255c571d62cc1073fd792b,Deconvolutional networks for point-cloud vehicle detection and tracking in driving scenarios,"Deconvolutional Networks for Point-Cloud Vehicle Detection
nd Tracking in Driving Scenarios
V´ıctor Vaquero∗, Ivan del Pino∗, Francesc Moreno-Noguer, Joan Sol`a, Alberto Sanfeliu and Juan Andrade-Cetto"
89742f28108330f97df94df98f73b459b02ca33d,Query Specific Semantic Signature for Improved Web Image Re-Ranking Joshith,"International Journal of Engineering and Technical Research (IJETR)
ISSN: 2321-0869, Volume-3, Issue-3, March 2015
Query Specific Semantic Signature for Improved
Web Image Re-Ranking
Joshith.K, S.Krishnamoorthi"
8949563597276246f9f480d4b38b3b7851fd5495,Toward Efficient and Robust Large-scale Structure-from-motion Systems,"TOWARD EFFICIENT AND ROBUST LARGE-SCALE
STRUCTURE-FROM-MOTION SYSTEMS
Jared S. Heinly
A dissertation submitted to the faculty of the University of North Carolina at Chapel Hill in partial
fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of
Computer Science.
Chapel Hill
Approved by:
Jan-Michael Frahm
Enrique Dunn
Alexander C. Berg
Marc Niethammer
Sameer Agarwal"
89588a4055b69abf99de321ce36b03ad5e8f8f05,Huffman Code Function and Mahalanobis Distance-base Face Recognition,"Huffman Code Function and Mahalanobis Distance-base Face Recognition
{tag}                                                 {/tag}
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 153
Number 5
Year of Publication: 2016
Authors:
Babatunde R. S., Ajao. J. F., Balogun B. F.
10.5120/ijca2016911784
{bibtex}2016911784.bib{/bibtex}"
8948e9dce2dfaeb1d93ce146fab5364b6cd342c9,Dual Attention Network for Scene Segmentation,"Dual Attention Network for Scene Segmentation
Jun Fu, Jing Liu, Haijie Tian, Zhiwei Fang, Hanqing Lu
{jun.fu, jliu, zhiwei.fang,
CASIA IVA"
89d3a57f663976a9ac5e9cdad01267c1fc1a7e06,Neural Class-Specific Regression for face verification,"Neural Class-Specific Regression for face
verification
Guanqun Cao, Alexandros Iosifidis, Moncef Gabbouj"
89c51f73ec5ebd1c2a9000123deaf628acf3cdd8,Face Recognition Based on Nonlinear Feature Approach,"American Journal of Applied Sciences 5 (5): 574-580, 2008
ISSN 1546-9239
© 2008 Science Publications
Face Recognition Based on Nonlinear Feature Approach
Eimad E.A. Abusham, 1Andrew T.B. Jin, 1Wong E. Kiong and 2G. Debashis
Faculty of Information Science and Technology,
Faculty of Engineering and Technology, Multimedia University (Melaka Campus),
Jalan Ayer Keroh Lama, 75450 Bukit Beruang, Melaka, Malaysia"
89d7cc9bbcd2fdc4f4434d153ecb83764242227b,Face-Name Graph Matching For The Personalities In Movie Screen Einstein,"Einstein.J, DivyaBaskaran / International Journal of Engineering Research and Applications
(IJERA)             ISSN: 2248-9622           www.ijera.com
Vol. 3, Issue 2, March -April 2013, pp.351-355
Face-Name Graph Matching For The Personalities In Movie
Screen
*(Asst. Professor, Dept. of IT, VelTech HighTech Dr. Rangarajan Dr.Sakunthala Engineering College,
Einstein.J*, DivyaBaskaran**
** (Final Year Student, M.Tech IT, Vel Tech Dr. RR &Dr. SR Technical University, Chennai.)
Chennai.)"
89967f456c7680c193d7c2d18d2ec267ec59dbf6,Biometrics of Asymmetrical Face,"Biometrics of Asymmetrical Face
Leonid Kompanets
Czestochowa University of Technology
Institute of Computer and Information Sciences
Center for Intelligent Multimedia Techniques
Dabrowski str., 69, 42-200 Czestochowa, Poland"
89d02ceae9e972eca633ae6ff9da9ee8a85fb171,Using Explanations to Improve Ensembling of Visual Question Answering Systems,"In Proceedings of the IJCAI 2017 Workshop on Explainable Artificial
Intelligence (XAI), pp. 43-47, Melbourne, Australia, August 2017."
89670862270d31fe7a02d863be51fbe83324ece2,Ear Recognition Based on Statistical Shape Model,"Ear Recognition Based on Statistical Shape Model
LU Lu, ZHANG Xiaoxun, ZHAO Youdong and JIA Yunde
Department of computer science and engineering,
Beijing Institute of Technology, Beijing 100081, PR China"
89f9225a7223133fa687e1c44bb758c3567f4f26,F 3F : A System Theoretic Approach to Robust Detection Of Potential Threats From Video,"F3-F: A System Theoretic Approach to Robust
Detection Of Potential Threats from Video"
891b10c4b3b92ca30c9b93170ec9abd71f6099c4,2 New Statement for Structured Output Regression Problems,"Facial landmark detection using structured output deep
neural networks
Soufiane Belharbi ∗1, Cl´ement Chatelain∗1, Romain H´erault∗1, and S´ebastien
Adam∗2
LITIS EA 4108, INSA de Rouen, Saint ´Etienne du Rouvray 76800, France
LITIS EA 4108, UFR des Sciences, Universit´e de Rouen, France.
September 24, 2015"
8966af6a8049192556e9c9356886a135595c19b8,Temporally Coherent CRP: A Bayesian Non-Parametric Approach for Clustering Tracklets with applications to Person Discovery in Videos,"Temporally Coherent CRP: A Bayesian Non-Parametric Approach for
Clustering Tracklets with applications to Person Discovery in Videos
Adway Mitra∗
Soma Biswas†
Chiranjib Bhattacharyya‡"
8935ffe454758e2e5def0b5190de6e28c350b3b8,Learning to Reconstruct Face Geometries Research,"Learning to Reconstruct Face
Geometries
Elad Richardson
Technion - Computer Science Department - M.Sc. Thesis  MSC-2017-11 - 2017"
89945b7cd614310ebae05b8deed0533a9998d212,Divide-and-Conquer Method for L1 Norm Matrix Factorization in the Presence of Outliers and Missing Data,"Divide-and-Conquer Method for L1 Norm Matrix
Factorization in the Presence of Outliers and
Missing Data
Deyu Meng and Zongben Xu"
898a66979c7e8b53a10fd58ac51fbfdb6e6e6e7c,Dynamic vs. Static Recognition of Facial Expressions,"Dynamic vs. Static Recognition of Facial
Expressions
No Author Given
No Institute Given"
8961677300a9ee30ca51e1a3cf9815b4a162265b,Deep Representation Learning with Part Loss for Person Re-Identification,"Deep Representation Learning with Part Loss for Person Re-Identification
Hantao Yao, Shiliang Zhang, Yongdong Zhang, Jintao Li, Qi Tian"
890103cb8d3d869298421da817d0a181487ec79a,Learning the Hierarchical Parts of Objects by Deep Non-Smooth Nonnegative Matrix Factorization,"Learning the Hierarchical Parts of Objects by Deep
Non-Smooth Nonnegative Matrix Factorization
Jinshi Yu, Guoxu Zhou, Andrzej Cichocki
IEEE Fellow, and Shengli Xie IEEE Senior Member"
89c73b1e7c9b5e126a26ed5b7caccd7cd30ab199,Application of an Improved Mean Shift Algorithm in Real-time Facial Expression Recognition,"Application of an Improved Mean Shift Algorithm
in Real-time Facial Expression Recognition
School of Computer and Communication, Hunan University of Technology, Hunan, Zhuzhou, 412008，china
School of Electrical and Information Engineering, Hunan University of Technology, Hunan, Zhuzhou, 412008，china
School of Computer and Communication, Hunan University of Technology, Hunan, Zhuzhou, 412008，china
Zhao-yi PENG
Yu ZHOU
Yan-hui  ZHU
Email:
Zhi-qiang WEN
Email:
School of Computer and Communication, Hunan University of Technology, Hunan, Zhuzhou, 412008，china
facial
real-time
expression"
895c5a6f2915d95d518e78d6a0224dad7399492b,Beyond Bounding Boxes: Precise Localization of Objects in Images,"UC Berkeley
UC Berkeley Electronic Theses and Dissertations
Title
Beyond Bounding Boxes: Precise Localization of Objects in Images
Permalink
https://escholarship.org/uc/item/54v2z91n
Author
Hariharan, Bharath
Publication Date
Peer reviewed|Thesis/dissertation
eScholarship.org
Powered by the California Digital Library
University of California"
894f1e924dfb8dfb843c42835fa79e386ac07383,Dimensional emotion recognition using visual and textual cues,"Dimensional emotion recognition using visual and textual cues
Pedro M. Ferreira1, Diogo Pernes2, Kelwin Fernandes1, Ana Rebelo3 and Jaime S. Cardoso1"
89ef75b43bc329f21fd3e81d36235d4824478831,A real-time Deep Learning pedestrian detector for robot navigation,"A Real-Time Deep Learning Pedestrian Detector for Robot Navigation
David Ribeiro, Andr´e Mateus, Pedro Miraldo, and Jacinto C. Nascimento
Instituto de Sistemas e Rob´otica, Instituto Superior T´ecnico, Universidade de Lisboa, Lisboa, Portugal"
8913a5b7ed91c5f6dec95349fbc6919deee4fc75,BigBIRD: A large-scale 3D database of object instances,"BigBIRD: A Large-Scale 3D Database of Object Instances
Arjun Singh, James Sha, Karthik S. Narayan, Tudor Achim, Pieter Abbeel"
89bc311df99ad0127383a9149d1684dfd8a5aa34,Towards ontology driven learning of visual concept detectors.,"Towards ontology driven learning of
visual concept detectors
Sanchit ARORA, Chuck CHO, Paul FITZPATRICK, Franc¸ois SCHARFFE 1
Dextro Robotics, Inc. 101 Avenue of the Americas, New York, USA"
89d590d7013433304aae1c97debd257b8dd801fa,Outdoor Human Motion Capture by Simultaneous Optimization of Pose and Camera Parameters,"Volume xx (200y), Number z, pp. 1–13
Outdoor Human Motion Capture by Simultaneous
Optimization of Pose and Camera Parameters
A. Elhayek C. Stoll K. I. Kim and C. Theobalt
Max-Planck-Institute for Informatics, Saarbrücken, Germany
Figure 1: Examples of multi-person tracking with moving cameras. (Left two images) two actors, and two moving and 3 static
ameras (Soccer1). (Right two images) One actor, and three moving and two static cameras (Walk2)."
89ed007d1dc8cd26033e71dfad7698c835b7336f,"Statistical modeling, level-set and ensemble learning for automatic segmentation of 3D high-frequency ultrasound data : towards expedited quantitative ultrasound in lymph nodes from cancer patients. (Modélisation statistique, méthodes d'ensemble de niveaux et apprentissage automatique pour la segmen","Statistical modeling, level-set and ensemble learning for
utomatic segmentation of 3D high-frequency
ultrasound data : towards expedited quantitative
ultrasound in lymph nodes from cancer patients
Thanh Bui Minh
To cite this version:
Thanh Bui Minh. Statistical modeling, level-set and ensemble learning for automatic segmen-
tation of 3D high-frequency ultrasound data : towards expedited quantitative ultrasound in
lymph nodes from cancer patients. Medical Imaging. Universit´e Pierre et Marie Curie - Paris
VI, 2016. English. ¡ NNT : 2016PA066146 ¿.
HAL Id: tel-01408283
https://tel.archives-ouvertes.fr/tel-01408283
Submitted on 4 Dec 2016
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
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broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est"
8954d46e1d7a11b20b2c688e5fb8bce4901650d6,Looking at movies and cartoons: eye-tracking evidence from Williams syndrome and autism.,"Looking at Movies and Cartoons: Eye-tracking evidence from Williams syndrome
nd Autism
Deborah M Riby and Peter J B Hancock
Journal of Intellectual Disability Research
http://dx.doi.org/10.1111/j.1365-2788.2008.01142.x"
89d967cb778cc99ff9b3f3e26257b0636f522af1,Decision tree simplification for classifier ensembles,"7305629
UNIVERSITY  OF  SURREY  LIBRARY"
8929e704b6af7f09ad027714b75972cb9df57483,Image Inpainting for Irregular Holes Using Partial Convolutions,
893239f17dc2d17183410d8a98b0440d98fa2679,UvA-DARE ( Digital Academic Repository ) Expression-Invariant Age Estimation,"UvA-DARE (Digital Academic Repository)
Expression-Invariant Age Estimation
Alnajar, F.; Lou, Z.; Alvarez Lopez, J.M.; Gevers, T.
Published in:
Proceedings of the British Machine Vision Conference 2014
0.5244/C.28.14
Link to publication
Citation for published version (APA):
Alnajar, F., Lou, Z., Alvarez, J., & Gevers, T. (2014). Expression-Invariant Age Estimation. In M. Valstar, A.
French, & T. Pridmore (Eds.), Proceedings of the British Machine Vision Conference 2014 (pp. 14.1-14.11).
BMVA Press. DOI: 10.5244/C.28.14
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Download date: 04 Aug 2017"
8988384f492b1b277be41a894a2c911ed884e095,Reliability evaluation and error mitigation in pedestrian detection algorithms for embedded GPUs,"UNIVERSIDADE FEDERAL DO RIO GRANDE DO SUL
INSTITUTO DE INFORMÁTICA
PROGRAMA DE PÓS-GRADUAÇÃO EM COMPUTAÇÃO
FERNANDO FERNANDES DOS SANTOS
Reliability evaluation and error mitigation
in pedestrian detection algorithms for
embedded GPUs
Thesis presented in partial fulfillment
of the requirements for the degree of
Master of Computer Science
Advisor: Prof. Dr. Paolo Rech
Porto Alegre
May 2017"
89f44f756c230e104cdf2ec0152d5f015586399c,WIDE-AREA BASED TRAFFIC SITUATION DETECTION AT AN UNGATED LEVEL CROSSING,"M. Junghans, et al., Int. J. of Safety and Security Eng., Vol. 6, No. 2 (2016) 383–393
WIDE-AREA BASED TRAFFIC SITUATION DETECTION
AT AN UNGATED LEVEL CROSSING
M. JUNGHANS, A. LEICH, K. KOZEMPEL, H. SAUL & S. KNAKE-LANGHORST
Institute of Transportation Systems, German Aerospace Center (DLR), Berlin, Germany."
89358e65aec4d6665098c7dbbe3975296cc7a2fc,Discriminative Feature Based Algorithm for Detecting And Classifying Frames In Image Sequences,"M. A. A Victoria et al. Int. Journal of Engineering Research and Applications               www.ijera.com
Vol. 3, Issue 5, Sep-Oct 2013, pp.446-450
RESEARCH ARTICLE                                                                               OPEN ACCESS
Discriminative Feature Based Algorithm for Detecting And
Classifying Frames In Image Sequences
M. Antony Arockia Victoria, R. Sahaya Jeya Sutha
B.E,M.E. Assistant Professor, Department of MCA, Dr.Sivanthi Aditanar College of Engineering,
MCA,M.Phil. Assistant Professor, Department of MCA, Dr. Sivanthi Aditanar College of Engineering"
89a245eae1e7eda7aa8e360c0cdb4bf6a72da225,A Survey of Pedestrian Detection in Video,"(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 5, No. 10, 2014
A Survey of Pedestrian Detection in Video
Achmad Solichin
Department of Informatics
Budi Luhur University
Jakarta, Indonesia
Agus Harjoko
Agfianto Eko Putra
Dept. of Computer Science and
Dept. of Computer Science and
Electronics Gadjah Mada University
Electronics Gadjah Mada University
Yogyakarta, Indonesia
Yogyakarta, Indonesia"
89174737423d87258d3b9d5a660236a0bb66a470,On the usage of Sensor Pattern Noise for Picture-to-Identity linking through social network accounts,"On the usage of Sensor Pattern Noise for Picture-to-Identity linking
through social network accounts
Riccardo Satta1 and Pasquale Stirparo1,2
Institute for the Protection and Security of the Citizen
Joint Research Centre (JRC), European Commission, Ispra (VA), Italy
Royal Institute of Technology (KTH), Stockholm, Sweden
{riccardo.satta,
Keywords:
social network, account, Sensor Pattern Noise, identity, linking, digital image forensics, multimedia forensics"
89e324b9c64a800e57ad82eddecc03f2cc0b7cc5,Long-Term Identity-Aware Multi-Person Tracking for Surveillance Video Summarization.,"Long-Term Identity-Aware Multi-Person Tracking
for Surveillance Video Summarization
Shoou-I Yu, Yi Yang, Xuanchong Li, and Alexander G. Hauptmann"
d2b86b6dc93631990e21a12278e77f002fb4b116,Aalborg Universitet Attention in Multimodal Neural Networks for Person Re-identification,"Aalborg Universitet
Attention in Multimodal Neural Networks for Person Re-identification
Lejbølle, Aske Rasch; Krogh, Benjamin; Nasrollahi, Kamal; Moeslund, Thomas B.
Published in:
018 IEEE Computer Vision and Pattern Recognition Workshops: Visual Understanding of Humans in Crowd
Scene
Publication date:
Document Version
Accepted author manuscript, peer reviewed version
Link to publication from Aalborg University
Citation for published version (APA):
Lejbølle, A. R., Krogh, B., Nasrollahi, K., & Moeslund, T. B. (2018). Attention in Multimodal Neural Networks for
Person Re-identification. In 2018 IEEE Computer Vision and Pattern Recognition Workshops: Visual
Understanding of Humans in Crowd Scene (pp. 179-187). IEEE.
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? Users may download and print one copy of any publication from the public portal for the purpose of private study or research.
? You may not further distribute the material or use it for any profit-making activity or commercial gain
? You may freely distribute the URL identifying the publication in the public portal ?"
d2f3ba37ef34d5d39f799f8dd3557f1eb795aedd,Learning Unified Embedding for Apparel Recognition,"Learning Unified Embedding for Apparel Recognition
Yang Song
Google
Yuan Li
Google
Xiao Zhang
Google
Bo Wu
Google
Chao-Yeh Chen
Google
Hartwig Adam
Google"
d29cc6d2007920beaa47b24d222981a1e6802ff9,Modeling Affection Mechanisms using Deep and Self-Organizing Neural Networks,"Modeling Affection Mechanisms using
Deep and Self-Organizing Neural
Networks
Dissertation
zur Erlangung des Doktorgrades
n der Fakult¨at f¨ur Mathematik, Informatik und Naturwissenschaften
Fachbereich Informatik
der Universit¨at Hamburg
eingereicht beim Fach-Promotionsausschuss Informatik von
Pablo Vinicius Alves de Barros
Hamburg, August 2016"
d2bc2c3c933131c1e4b4fedd5e3db1734244b220,City-Scale Road Audit System using Deep Learning,"City-Scale Road Audit System using Deep Learning
Sudhir Yarram
Girish Varma
C.V. Jawahar"
d2384ce437e4e6ec82aaade981cda79db13b4dc3,Removing Algorithmic Discrimination (With Minimal Individual Error),"Removing Algorithmic Discrimination
(With Minimal Individual Error)
El Mahdi El Mhamdi, Rachid Guerraoui, Lˆe Nguyˆen Hoang and Alexandre Maurer"
d2df37ecfbf914d5b81e2e5e342e3907c6f55a14,Can Convolution Neural Network ( CNN ) Triumph in Ear Recognition of Uniform Illumination Invariant ?,"Indonesian Journal of Electrical Engineering and Computer Science
Vol. 11, No. 2, August 2018, pp. 558~566
ISSN: 2502-4752, DOI: 10.11591/ijeecs.v11.i2.pp558-566
      558
Can Convolution Neural Network (CNN) Triumph in Ear
Recognition of Uniform Illumination Invariant?
Nursuriati Jamil1, Ali Abd Almisreb2, Syed Mohd Zahid Syed Zainal Ariffin3, N. Md Din4,
Raseeda Hamzah5
,3,5Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA,
40450 Shah Alam, Selangor, Malaysia
,4College of Graduate Studies, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, 43000 Kajang, Malaysia
Article Info
Article history:
Received Mar 1, 2018
Revised Apr 21, 2018
Accepted May 1, 2018
Keywords:
Convolution Neural Network
Ear Recognition
Uniform Illumination Invariant"
d2518b01092160cecec2e986935b0129b0bbff45,Looking around the Backyard Helps to Recognize Handwritten Digits Anonymous CVPR submission Paper ID 2611,"#2611
CVPR 2008 Submission #2611. CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE.
#2611
Looking around the Backyard Helps to Recognize Handwritten Digits
Anonymous CVPR submission
Paper ID 2611"
d20e7d7ab8e767dc1c170ca2141d8ba64a4d092b,“ Please Draw Me a Face ... ” Atypical Face Mental Concept in Autism,"Psychology, 2014, 5, 1392-1403
Published Online August 2014 in SciRes. http://www.scirp.org/journal/psych
http://dx.doi.org/10.4236/psych.2014.511150
“Please Draw Me a Face…” Atypical Face
Mental Concept in Autism
Emilie Meaux1*, David Bakhos2, Frédérique Bonnet-Brilhault1, Patrice Gillet3,
Emmanuel Lescanne4, Catherine Barthélémy1, Magali Batty1
UMRS Imagerie et Cerveau, Inserm U930 Equipe 1, CNRS ERL 3106, Université François Rabelais de Tours,
CHRU de Tours, Tours, France
Unité Pédiatrique d’ORL et CCF, Centre Hospitalier Régional Universitaire de Tours, Université François
Rabelais de Tours, CHRU de Tours, Tours, France
Université François Rabelais de Tours, CHRU de Tours, Tours, France
Service d’ORL et CCF Pédiatrique, CHU de Tours Gatien-de-Clocheville, Université François Rabelais de Tours,
Tours, France
Email:
Received 16 May 2014; revised 12 June 2014; accepted 5 July 2014
Copyright © 2014 by authors and Scientific Research Publishing Inc.
This work is licensed under the Creative Commons Attribution International License (CC BY).
http://creativecommons.org/licenses/by/4.0/"
d231a81b38fde73bdbf13cfec57d6652f8546c3c,SUPERRESOLUTION TECHNIQUES FOR FACE RECOGNITION FROM VIDEO by,"SUPERRESOLUTION TECHNIQUES
FOR FACE RECOGNITION FROM VIDEO
Osman Gökhan Sezer
B.S., E.E., Boğaziçi University, 2003
Submitted to the Graduate School of Engineering
and Natural Sciences in partially fulfillment of
the requirement for the degree of
Master of Science
Graduate Program in Electronics Engineering and Computer Science
Sabancı University
Spring 2005"
d2eb1079552fb736e3ba5e494543e67620832c52,DeSTNet: Densely Fused Spatial Transformer Networks,"ANNUNZIATA, SAGONAS, CALÌ: DENSELY FUSED SPATIAL TRANSFORMER NETWORKS1
DeSTNet: Densely Fused Spatial
Transformer Networks1
Roberto Annunziata
Christos Sagonas
Jacques Calì
Onfido Research
Finsbury Avenue
London, UK"
d24a7a7ceb2ddce42ac64d1d07ccebc2a55ed053,A Bayesian Architecture for Combining Saliency Detectors,"A Bayesian Architecture for Combining Saliency Detectors
Dashan Gao and Nuno Vasconcelos
SVCL-TR 2005/01
June 2005"
d24a30ed78b749f3730e25dcef89472dd5fb439c,Improving Face Recognition Performance Using a Hierarchical Bayesian Model,"Improving Face Recognition
Performance Using a Hierarchical
Bayesian Model
Ashwini Shikaripur Nadig
Submitted to the graduate degree program in
Electrical Engineering & Computer Science and the
Graduate Faculty of the University of Kansas
School of Engineering in partial fulfillment of the
requirements for the degree of Master of Science
Thesis Committee:
Dr. Brian Potetz: Chairperson
Dr. Prasad Kulkarni
Dr. Luke Huan
Date Defended"
d250e57f6b7e06bb1dac41c8b89700086a85999e,Self-Supervised Generalisation with Meta Auxiliary Learning,"Self-Supervised Generalisation with Meta Auxiliary Learning
Shikun Liu 1 Andrew J. Davison 1 Edward Johns 1"
d2b2b56dd8c1daa61152595caf759a62596a85c9,Revocable and Non-Invertible Multibiometric Template Protection based on Matrix Transformation,"Pertanika J. Sci. & Technol. 26 (1): 133 - 160 (2018)
Revocable and Non-Invertible Multibiometric Template
Protection based on Matrix Transformation
Jegede, A.1,2*, Udzir, N. I.1, Abdullah, A.1 and Mahmod, R.1
Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 UPM,
Serdang, Selangor, Malaysia
Department of Computer Science, University of Jos, 930001 Nigeria"
d2f254780699bafff02eafc92e00822ee597c864,A comparative evaluation of global representation-based schemes for face verification,"A Comparative Evaluation of Global Representation-Based Schemes
for Face Verification
S. Cruz-Llanas, J. Fierrez-Aguilar, J. Ortega-Garcia, J. Gonzalez-Rodriguez
Biometrics Research Lab. – Universidad Politecnica de Madrid"
d28c12e270a06e977b59194cc6564787c87caa7e,HUMAN ACTION POSELETS ESTIMATION VIA COLOR G-SURF IN STILL IMAGES,"HUMAN ACTION POSELETS ESTIMATION VIA COLOR G-SURF IN STILL IMAGES
M. Favorskaya *, D. Novikov, Y. Savitskaya
Institute of Informatics and Telecommunications, Siberian State Aerospace University, 31 Krasnoyarsky Rabochy av., Krasnoyarsk,
660014 Russian Federation - (favorskaya,
Commission WG V/5, WG III/3
KEY WORDS: Human Action, Poselets, Gauge-SURF, Random Forest, Still Image"
d2f717d1799b5cec5f1f426511527bd7e6e05d9d,Image-Based Synthesis for Deep 3D Human Pose Estimation,"Noname manuscript No.
(will be inserted by the editor)
Image-based Synthesis for Deep 3D Human Pose Estimation
Grégory Rogez · Cordelia Schmid
Received: date / Accepted: date"
d27ea28543cc55d84b0700a74743fb1ccd205135,Lernen mit wenigen Beispielen für die visuelle Objekterkennung,"Lernen mit wenigen Beispielen f¨ur die visuelle
Objekterkennung
Erik Rodner
Lehrstuhl Digitale Bildverarbeitung
Friedrich-Schiller Universit¨at Jena"
d2ada7d9424c056cc555f331dbb23ddab84eeee7,Background Subtraction with Dirichlet Processes,"Background Subtraction with Dirichlet
Processes
Tom S. F. Haines and Tao Xiang
Electronic Engineering and Computer Science, Queen Mary, Uni. of London"
d2860bb05f747e4628e95e4d84018263831bab0d,Learning to Generate Samples from Noise through Infusion Training,"Published as a conference paper at ICLR 2017
LEARNING TO GENERATE SAMPLES FROM NOISE
THROUGH INFUSION TRAINING
Florian Bordes, Sina Honari, Pascal Vincent∗
Montreal Institute for Learning Algorithms (MILA)
D´epartement d’Informatique et de Recherche Op´erationnelle
Universit´e de Montr´eal
Montr´eal, Qu´ebec, Canada"
d259d3652f03c7b80e29c986e9540ab00b1f1133,3 D Face Detection and Recognition under Occlusion,"Dr.V.Ramaswamy1, Parashuram Baraki2
Research Guide, Jain University, Bangalore,
Doctoral Student, Jain University, Bangalore
& Asst.Professor , CS&E, Dept,
GM Institute of Technology, Davanagere
D Face Detection and Recognition under Occlusion
is  very  vital.  Three-dimensional"
d2cda0dbb8b2e83ce3e70d818f78d2add803c661,Automatic Video Captioning via Multi-channel Sequential Encoding,"Automatic Video Captioning via Multi-channel
Sequential Encoding
Chenyang Zhang and Yingli Tian
Department of Electrical Engineering
The City College of New York
New York, NY 10031"
d2b8459b41172dc332cf00dc18a309c442347a7d,Deep Spatial Feature Reconstruction for Partial Person Re-identification: Alignment-Free Approach,"Deep Spatial Feature Reconstruction for Partial Person Re-identification:
Alignment-free Approach
Lingxiao He∗1,2, Jian Liang∗1,2, Haiqing Li1,2, and Zhenan Sun1,2,3
CRIPAC & NLPR, CASIA 2 University of Chinese Academy of Sciences, Beijing, P.R. China
Center for Excellence in Brain Science and Intelligence Technology, CAS
{lingxiao.he, jian.liang, hqli,"
d23ac99cdab20a9a3eca2784a5b262649c717988,Rotation Invariant Angular Descriptor Via A Bandlimited Gaussian-like Kernel,"Rotation Invariant Angular Descriptor Via A
Bandlimited Gaussian-like Kernel
Michael T. McCann, Matthew Fickus, Jelena Kovaˇcevi´c"
cda08b49c91c805c4820238f30a5118f30e55bfe,SFS Based View Synthesis for Robust Face Recognition,"SFSBasedViewSynthesisforRobustFaceRecognition
WenYiZhao
RamaChellappa
CenterforAutomationResearch
UniversityofMaryland
CollegePark,MD-
seemtohavereceivedmuchattentioninthecontext"
cd0f7b3f545cc4bfa5e2d7185789e8ead7e3cee2,"Children ’ s and Adults ’ Predictions of Black , White , and Multiracial Friendship Patterns","Journal of Cognition and Development
ISSN: 1524-8372 (Print) 1532-7647 (Online) Journal homepage: http://www.tandfonline.com/loi/hjcd20
Children’s and Adults’ Predictions of Black, White,
nd Multiracial Friendship Patterns
Steven O. Roberts, Amber D. Williams & Susan A. Gelman
To cite this article: Steven O. Roberts, Amber D. Williams & Susan A. Gelman (2017) Children’s
nd Adults’ Predictions of Black, White, and Multiracial Friendship Patterns, Journal of Cognition
nd Development, 18:2, 189-208, DOI: 10.1080/15248372.2016.1262374
To link to this article:  http://dx.doi.org/10.1080/15248372.2016.1262374
Accepted author version posted online: 22
Nov 2016.
Published online: 22 Nov 2016.
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cd9666858f6c211e13aa80589d75373fd06f6246,A Novel Time Series Kernel for Sequences Generated by LTI Systems,"A Novel Time Series Kernel for
Sequences Generated by LTI Systems
Liliana Lo Presti, Marco La Cascia
V.le delle Scienze Ed.6, DIID, Universit´a degli studi di Palermo, Italy"
cdd3b727a8c3129d265ddccd01112655e45c0f4c,Bio-Inspired Pedestrian Detection and Tracking,"Proc. of the Third Intl. Conf. Advances in Bio-Informatics, Bio-Technology and Environmental Engineering- ABBE 2015
Copyright © Institute of Research Engineers and Doctors, USA .All rights reserved.
ISBN: 978-1-63248-060-6 doi: 10.15224/ 978-1-63248-060-6-07
Bio-Inspired Pedestrian Detection and Tracking
Miguel Farrajota, João M.F. Rodrigues and J.M.H. du Buf"
cd827a9dbf82240cb711f049989decada01c8ac7,"Object Localization , Segmentation , and Classification in 3 D Images by","City University of New York (CUNY)
CUNY Academic Works
Dissertations, Theses, and Capstone Projects
Graduate Center
-2018
Object Localization, Segmentation, and
Classification in 3D Images
Allan Zelener
The Graduate Center, City University of New York
How does access to this work benefit you? Let us know!
Follow this and additional works at: https://academicworks.cuny.edu/gc_etds
Part of the Artificial Intelligence and Robotics Commons
Recommended Citation
Zelener, Allan, ""Object Localization, Segmentation, and Classification in 3D Images"" (2018). CUNY Academic Works.
https://academicworks.cuny.edu/gc_etds/2531
This Dissertation is brought to you by CUNY Academic Works. It has been accepted for inclusion in All Dissertations, Theses, and Capstone Projects
y an authorized administrator of CUNY Academic Works. For more information, please contact"
cd855c776240150f4dba7a5975c7011a9c6737ac,On Accurate and Reliable Anomaly Detection for Gas Turbine Combustors : A Deep Learning Approach,"On Accurate and Reliable Anomaly Detection for Gas Turbine
Combustors: A Deep Learning Approach
Weizhong Yan1 and Lijie Yu2
General Electric Global Research Center, Niskayuna, New York 12309, USA
General Electric Power & Water Engineering, Atlanta, Georgia 30339, USA"
cd516ae100bcb698ce90b7d588ee06a27284c1a8,FaceID-Grid : A Grid Platform for Face Detection and Identification in Video Storage,"FaceID-Grid: A Grid Platform for Face Detection and
Identification in Video Storage
Filipe Rodrigues
Instituto Superior T´ecnico"
cd2c1e542ae8c08cfb8baea3dff788d143232de8,Multiview Human Synthesis From a Single View,"Multiview Human Synthesis From a Singleview
Si Wen (06246679), Tiancong Zhou (06247022), Honghao Qiu (06246258)
{wensi, longztc,"
cd4252d1f0a124dcc91af28f527ad1fa7be3a195,Development of an Efficient Face Recognition System based on Linear and Nonlinear Algorithms,"Development of an Efficient Face Recognition System based on Linear and Nonlinear Algorithms
{tag}                                                 {/tag}
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 134
Number 7
Year of Publication: 2016
Authors:
Filani Araoluwa S., Adetunmbi Adebayo O.
10.5120/ijca2016907932
{bibtex}2016907932.bib{/bibtex}"
cd32ea4eae50f60584fac6001d2af22a8c1d8648,HEVC Inter Coding Using Deep Recurrent Neural Networks and Artificial Reference Pictures,"HEVC INTER CODING USING DEEP RECURRENT NEURAL NETWORKS AND
ARTIFICIAL REFERENCE PICTURES
Felix Haub, Thorsten Laude and J¨orn Ostermann
Leibniz University Hannover, Institut f¨ur Informationsverarbeitung,
Appelstr. 9a, 30167 Hannover, Germany"
cd0a04c0af9b6c523884415ba54bff370fd02fab,Generalized Sparselet Models for Real-Time Multiclass Object Recognition,"Generalized Sparselet Models for Real-Time
Multiclass Object Recognition
Hyun Oh Song, Ross Girshick, Stefan Zickler, Christopher Geyer, Pedro Felzenszwalb, and Trevor Darrell"
cd36768795c696c990ff5c89be8d8b3b205858bd,CliCR: A Dataset of Clinical Case Reports for Machine Reading Comprehension,"CliCR: A Dataset of Clinical Case Reports for Machine
Reading Comprehension∗
Simon ˇSuster and Walter Daelemans
Computational Linguistics & Psycholinguistics Research Center,
University of Antwerp, Belgium"
cdba015be9db1e047a51b7e06403528b3551587e,SHOG - Spherical HOG Descriptors for Rotation Invariant 3D Object Detection,"SHOG - Spherical HOG Descriptors for
Rotation Invariant 3D Object Detection
Henrik Skibbe1,3, Marco Reisert2 and Hans Burkhardt1,3
Department of Computer Science, University of Freiburg, Germany
Dept. of Diagnostic Radiology, Medical Physics, University Medical Center, Freiburg
Center for Biological Signalling Studies (BIOSS), University of Freiburg"
cd01a0018f2b8f1211e8dfe311c28e32773c58dc,Globally-Optimal Inlier Set Maximisation for Simultaneous Camera Pose and Feature Correspondence,"Globally-Optimal Inlier Set Maximisation for
Simultaneous Camera Pose and Feature Correspondence
Dylan Campbell1,2, Lars Petersson1,2, Laurent Kneip1 and Hongdong Li1
Australian National University*
Data61 – CSIRO"
cda4fb9df653b5721ad4fe8b4a88468a410e55ec,Gabor wavelet transform and its application,"Gabor wavelet transform and its application
Wei-lun Chao R98942073"
cd6978bf6b98794552bd52d166b5e04626fb6d6d,A Review on Face Recognition in various Illuminations,"A Review on Face Recognition in various
Illuminations
Saurabh D. Parmar , Vaishali j. kalariya
CE/IT Department-School of Engineering,R.K. University,Rajkot"
cd444ee7f165032b97ee76b21b9ff58c10750570,Table of Contents.,"UNIVERSITY OF CALIFORNIA,
IRVINE
Relational Models for Human-Object Interactions and Object Affordances
DISSERTATION
submitted in partial satisfaction of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
in Computer Science
Chaitanya Desai
Dissertation Committee:
Professor Deva Ramanan, Chair
Professor Charless Fowlkes
Professor Padhraic Smyth
Professor Serge Belongie"
cd5ef3aeebc231e2c833ef55cf0571aa990c5ff8,IMAGE QUALITY ASSESSMENT TECHNIQUES IMPROVE TRAINING,"Under review as a conference paper at ICLR 2018
IMAGE QUALITY ASSESSMENT TECHNIQUES IMPROVE
TRAINING AND EVALUATION OF ENERGY-BASED
GENERATIVE ADVERSARIAL NETWORKS
Anonymous authors
Paper under double-blind review"
cdf817a64c389615c2d3098ea1c4f48d10ca24cd,Recognizing faces with single sample per subject using fusion of transforms,"Recognizing faces with single sample per
subject using fusion of transforms
Sujata G. Bhele
Priyadarshini College of Engineering, CRPF gate, Nagpur
Vijay H. Mankar
Government Polytechnic, Bramhapuri"
cd3005753012409361aba17f3f766e33e3a7320d,Multilinear Biased Discriminant Analysis: A Novel Method for Facial Action Unit Representation,"Multilinear Biased Discriminant Analysis: A Novel Method for Facial
Action Unit Representation
Mahmoud Khademi†, Mehran Safayani†and Mohammad T. Manzuri-Shalmani†
: Sharif University of Tech., DSP Lab,"
cd8398e82e0c0cc4276a1694fd333214ede337ea,Decoupled Spatial Neural Attention for Weakly Supervised Semantic Segmentation,"Decoupled Spatial Neural Attention for Weakly
Supervised Semantic Segmentation
Tianyi Zhang, Guosheng Lin, Jianfei Cai, Tong Shen, Chunhua Shen, Alex C. Kot,"
cd687ddbd89a832f51d5510c478942800a3e6854,A game to crowdsource data for affective computing,"A Game to Crowdsource Data for Affective Computing
Chek Tien Tan
Hemanta Sapkota
Daniel Rosser
Yusuf Pisan
Games Studio, Faculty of Engineering and IT, University of Technology, Sydney"
cd596a2682d74bdfa7b7160dd070b598975e89d9,Mood Detection : Implementing a facial expression recognition system,"Mood Detection: Implementing a facial
expression recognition system
Neeraj Agrawal, Rob Cosgriff and Ritvik Mudur
. Introduction
Facial  expressions  play  a  significant  role  in  human  dialogue.  As  a  result,  there  has  been
onsiderable work done on the recognition of emotional expressions and the  application of this
research  will  be  beneficial  in  improving  human-machine  dialogue.  One  can  imagine  the
improvements  to  computer  interfaces,  automated  clinical  (psychological)  research  or  even
interactions between humans and autonomous robots.
Unfortunately,  a  lot  of  the  literature  does  not  focus  on  trying  to  achieve  high  recognition  rates
cross  multiple  databases.  In  this  project  we  develop  our  own  mood  detection  system  that
ddresses  this  challenge.  The  system  involves  pre-processing  image  data  by  normalizing  and
pplying a simple mask, extracting certain (facial) features using PCA and Gabor filters and then
using SVMs for classification and recognition of expressions. Eigenfaces for each class are used
to  determine  class-specific  masks  which  are  then  applied  to  the  image  data  and  used  to  train
multiple,  one  against  the  rest,  SVMs.  We  find  that  simply  using  normalized  pixel  intensities
works well with such an approach.
Figure 1 – Overview of our system design
. Image pre-processing
We performed pre-processing on the images used to train and test our algorithms as follows:"
b20ccf72adae61b3c0831eae137a45c55d7f7a32,AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation,"DomainAdaDepthPredicted DepthSynthetic DomainResnet-50EncoderDecoder CNNFigure1.Illustrationoftheproposeddomainadaptationmethodwithinputimagedomaindiscrepancy(redandbluebackground)followedbydepth-mapprediction.ColorcodedarrowsrepresentcorrespondingRGBimageanddepthpredictionsforthesynthetic-trainedencoder(redandpinkbordered)andfortheadapteden-coder(bluebordered);indicatingthatsynthetic-trainedmodelshowssub-optimalperformanceonnaturalimages.oftenperformsub-optimallywhentestedonrealscenes,showinglackofgeneralization[19,35].Fromaprobabilis-ticperspective,consideringinputsamplesforanetworkbe-ingdrawnfromacertainsourcedistribution,thenetworkcanperformsufficientlywellontestsetonlyifthetestdataisalsosampledfromthesamedistribution.Hence,thegen-eralapproachhasbeentotransferlearnedrepresentationsfromsynthetictorealdatasetsbyfine-tuningthemodelonamixedsetofsamples[42].Fordepthestimationtasks,theground-truthacquiredus-ingdeviceslikeKinectorotherdepthsensorsexhibitsnoisyartifacts[40]andhenceseverelylimitstheperformanceofasuperviseddepthpredictionnetwork.InthewidelyusedNYUDepthDataset[34],suchcasesareaddressedbyman-uallyinpaintingthedepthvaluesinthedistortedregions.Butthedatasethasonlyahandfulofsuchcraftedsamples,mainlybecausetheprocessislaboriousandpronetopixel-levelannotationerrors.Theseshortcomingsshowtheneedforaframeworkthatisminimallydependentonscarceclean"
b2026f7ba664e976afe8d83560d7538e47e9af5e,Generalizing multistain immunohistochemistry tissue segmentation using one-shot color deconvolution deep neural networks,"Generalizing multistain immunohistochemistry tissue
segmentation using one-shot color deconvolution deep
neural networks
Amal Lahiani1,3, Jacob Gildenblat2, Irina Klaman1, Nassir Navab3, and Eldad Klaiman1
Pathology and Tissue Analytics, Pharma Research and Early Development, Roche Innovation
Computer Aided Medical Procedures, Technische Universität München, Germany
{amal.lahiani, irina.klaman,
Center Munich
SagivTech LTD., Israel"
b2e1b3b2342d29b1abbe92caffca703622ee4274,Automating the Processes Involved in Facial Composite Production and Identification,"Automating the Processes Involved in Facial Composite Production and
Identification
Charlie Frowd*, Anna Petkovic, Kamran Nawaz and Yasmeen Bashir
School of Psychology, University of Central Lancashire, Preston, UK
*Corresponding author: Dr Charlie Frowd, School of Psychology, University of Central Lancashire,
Preston PR1 2HE. Email: Phone: (01772) 893439."
b2624c3cb508bf053e620a090332abce904099a1,Dynamic Memory Networks for Visual and Textual Question Answering,"Dynamic Memory Networks for Visual and Textual Question Answering
Caiming Xiong*, Stephen Merity*, Richard Socher
MetaMind, Palo Alto, CA USA
{CMXIONG,SMERITY,RICHARD}METAMIND.IO
*indicates equal contribution."
b261439b5cde39ec52d932a222450df085eb5a91,Facial Expression Recognition using Analytical Hierarchy Process,"International Journal of Computer Trends and Technology (IJCTT) – volume 24 Number 2 – June 2015
Facial Expression Recognition using Analytical Hierarchy
Process
MTech Student 1 , Assistant Professor 2  , Department of Computer Science and Engineeringt1, 2, Disha Institute of
Management and Technology, Raipur Chhattisgarh, India1, 2
Vinita Phatnani1, Akash Wanjari2,
its  significant  contribution"
b277bde51641d6b08693c171aea761beb14af800,FACE KERNEL EXTRACTION FROM LOCAL FEATURES,"FACE KERNEL EXTRACTION FROM
LOCAL FEATURES
A thesis submitted to the University of Manchester
for the degree of Doctor of Philosophy
in the Faculty of Engineering and Physical Sciences
Maria Pavlou
School of Electrical Engineering and Electronics"
b2abaffc4d68ebf910dd85c0f7a367895ab90e2a,Iris recognition using scattering transform and textural features,"IRIS RECOGNITION USING SCATTERING TRANSFORM AND TEXTURAL FEATURES
Shervin Minaee, AmirAli Abdolrashidi and Yao Wang
ECE Department, NYU Polytechnic School of Engineering, USA
{shervin.minaee, abdolrashidi,"
b2504b0b2a7e06eab02a3584dd46d94a3f05ffdf,Conditional Neural Processes,"Conditional Neural Processes
Marta Garnelo 1 Dan Rosenbaum 1 Chris J. Maddison 1 Tiago Ramalho 1 David Saxton 1 Murray Shanahan 1 2
Yee Whye Teh 1 Danilo J. Rezende 1 S. M. Ali Eslami 1"
b28e142376a2dd639f58935f2f63a9dc7651131e,Investigation of Gait Representations in Lower Knee Gait Recognition,
b235b4ccd01a204b95f7408bed7a10e080623d2e,Regularizing Flat Latent Variables with Hierarchical Structures,"Regularizing Flat Latent Variables with Hierarchical Structures
Rongcheng Lin(cid:117) , Huayu Li(cid:117) , Xiaojun Quan† , Richang Hong(cid:63) , Zhiang Wu∓ , Yong Ge(cid:117)
(cid:117)UNC Charlotte. Email: {rlin4, hli38,
(cid:63) Hefei University of Technology. Email:
Institute for Infocomm Research. Email:
∓ Nanjing University of Finance and Economics. Email:"
b285e50220fb6c09cf3c724c7e48093373df3c58,Semisupervised Classifier Evaluation and Recalibration,"Semisupervised Classifier Evaluation
nd Recalibration
Peter Welinder∗, Max Welling†, and Pietro Perona‡
October 7, 2012"
b29d70f38bd4759cd9d8c2fdc9312f7f807f4fa9,Face Recognition in Subspaces,"Chapter 2
Face Recognition in Subspaces
Gregory Shakhnarovich and Baback Moghaddam
.1 Introduction
Images of faces, represented as high-dimensional pixel arrays, often belong to a
manifold of intrinsically low dimension. Face recognition, and computer vision re-
search in general, has witnessed a growing interest in techniques that capitalize on
this observation and apply algebraic and statistical tools for extraction and analy-
sis of the underlying manifold. In this chapter, we describe in roughly chronologic
order techniques that identify, parameterize, and analyze linear and nonlinear sub-
spaces, from the original Eigenfaces technique to the recently introduced Bayesian
method for probabilistic similarity analysis. We also discuss comparative experi-
mental evaluation of some of these techniques as well as practical issues related to
the application of subspace methods for varying pose, illumination, and expression.
.2 Face Space and Its Dimensionality
Computer analysis of face images deals with a visual signal (light reflected off the
surface of a face) that is registered by a digital sensor as an array of pixel values.
The pixels may encode color or only intensity. In this chapter, we assume the latter
ase (i.e., gray-level imagery). After proper normalization and resizing to a fixed
m-by-n size, the pixel array can be represented as a point (i.e., vector) in an mn-"
b2c25af8a8e191c000f6a55d5f85cf60794c2709,A novel dimensionality reduction technique based on kernel optimization through graph embedding,"Noname manuscript No.
(will be inserted by the editor)
A Novel Dimensionality Reduction Technique based on
Kernel Optimization Through Graph Embedding
N. Vretos, A. Tefas and I. Pitas
the date of receipt and acceptance should be inserted later"
b2444e837095706998b03fa5fed223411b9d4d55,Color Based Tracing in Real-Life Surveillance Data,"Color Based Tracing in Real-life Surveillance
Michael J. Metternich, Marcel Worring, and Arnold W.M. Smeulders
ISLA-University of Amsterdam,
Science Park 107, 1098 XG Amsterdam, The Netherlands
http://www.science.uva.nl/research/isla/"
b2e67e67e5bbb19a02524afcc217929b0a76a9a7,Chapter 12 Using Ocular Data for Unconstrained Biometric Recognition,"Face Recognition in Adverse ConditionsMaria De MarsicoSapienza University of Rome, ItalyMichele NappiUniversity of Salerno, ItalyMassimo TistarelliUniversity of Sassari, ItalyA volume in the Advances in Computational Intelligence and Robotics (ACIR) Book Series"
b2f4871cf9f61c44b16c733369d8730e90d9cc0d,The role of emotion in problem solving: first results from observing chess,"The Role of Emotion in Problem Solving: First Results
from Observing Chess
Thomas Guntz1, James L. Crowley1, Dominique Vaufreydaz1, Raffaella Balzarini1,
Philippe Dessus1,2
Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LIG, 38000 Grenoble, France
Univ. Grenoble Alpes, LaRAC, 38000 Grenoble, France
Author version"
b2e2260b8d811948e71898d3adfa8aa6b64fe125,Learning Arbitrary Potentials in CRFs with Gradient Descent,"Learning Arbitrary Potentials in CRFs with Gradient Descent
M˚ans Larsson1
Fredrik Kahl1,2
Chalmers Univ. of Technology 2Lund Univ.
Shuai Zheng3 Anurag Arnab3
Oxford Univ.
Philip Torr3 Richard Hartley4
Australian National Univ."
b26f6e3cad2b3d129c0e70e9307ce9197cad2123,Robust Wearable Camera Localization as a Target Tracking Problem on SE(3),"G.BOURMAUD ET AL.: ROBUST WEARABLE CAMERA LOCALIZATION
Robust Wearable Camera Localization as a
Target Tracking Problem on SE(3)
Guillaume Bourmaud
Audrey Giremus
IMS Laboratory CNRS UMR 5218
University of Bordeaux
France"
b266be4d9fab8bf307ee2e6fdd6180ac7f6ef893,Look into Person: Joint Body Parsing & Pose Estimation Network and A New Benchmark,"Look into Person: Joint Body Parsing & Pose
Estimation Network and A New Benchmark
Xiaodan Liang, Ke Gong, Xiaohui Shen, and Liang Lin"
b2891e43c7b5dcd889f1a270721209a9cb9cfb49,Human Emotion Detection and Recognition from Still Images,"Human Emotion Detection and Recognition from Still Images
Zareena
Student, VTU RC, Mysore
E-mail ID-"
b22b4817757778bdca5b792277128a7db8206d08,SCAN: Learning Hierarchical Compositional Visual Concepts,"Published as a conference paper at ICLR 2018
SCAN: LEARNING HIERARCHICAL
COMPOSITIONAL VISUAL CONCEPTS
Irina Higgins, Nicolas Sonnerat, Loic Matthey, Arka Pal,
Christopher P Burgess, Matko Bošnjak, Murray Shanahan,
Matthew Botvinick, Demis Hassabis, Alexander Lerchner
DeepMind, London, UK
{irinah,sonnerat,lmatthey,arkap,cpburgess,"
b288353fcd8da86e9c55f9f82768492b2de25596,DATA PROCESSING AND RECORDING USING A VERSATILE MULTI-SENSOR VEHICLE,"DATA PROCESSING AND RECORDING USING A VERSATILE MULTI-SENSOR
VEHICLE
Bj¨orn Borgmann∗, Volker Schatz, Hilke Kieritz, Clemens Scherer-Kl¨ockling, Marcus Hebel, Michael Arens
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, Gutleuthausstr. 1, 76275 Ettlingen, Germany
(bjoern.borgmann, volker.schatz, clemens.scherer-kloeckling, marcus.hebel,
Commission I, WG 6
KEY WORDS: Mobile, Multi-sensor, System, LiDAR, Camera"
b20a5427d79c660fe55282da2533071629bfc533,Deep Learning Advances on Different 3D Data Representations: A Survey,"Deep Learning Advances on Different 3D Data
Representations: A Survey
Eman Ahmed, Alexandre Saint, Abd El Rahman Shabayek, Kseniya Cherenkova, Rig Das, Gleb Gusev,
Djamila Aouada and Bj¨orn Ottersten"
b2f63863e73a8565895ca3d9d7d6a1e10a7695b1,Efficient Neural Network Compression via Transfer Learning for Industrial Optical Inspection,"Efficient Neural Network Compression via Transfer
Learning for Industrial Optical Inspection
Anonymous Author(s)
Affiliation
Address
email"
0ec17d929f62660fb3d1bcdd791f9639034f5344,How Do We Evaluate Facial Emotion Recognition ?,"Psychology & Neuroscience
016, Vol. 9, No. 2, 153–175
983-3288/16/$12.00
© 2016 American Psychological Association
http://dx.doi.org/10.1037/pne0000047
How Do We Evaluate Facial Emotion Recognition?
Ana Idalina de Paiva-Silva
Universidade de Brasília and Universidade Federal
de Goiás
Marta Kerr Pontes,
Juliana Silva Rocha Aguiar, and
Wânia Cristina de Souza
Universidade de Brasília
The adequate interpretation of facial expressions of emotion is crucial for social
functioning and human interaction. New methods are being applied, and a review of the
methods that are used to evaluate facial emotion recognition is timely for the field. An
extensive review was conducted using the Web of Science, PsycINFO, and PubMed
databases. The following keywords were used to identify articles that were published
within the past 20 years: emotion recognition, face, expression, and assessment. The
initial search yielded 291 articles. After applying the exclusion criteria, 115 articles"
0ef40a21edf2b48c73fd51c21d213ee69ca30a4b,Hidden Markov model as a framework for situational awareness,
0e5640677feb2e1d78639b516f7977e80d9d394f,Volume-based Human Re-identification with RGB-D Cameras,"Cosar, S., Coppola, C. and Bellotto, N.
Volume-based Human Re-identification with RGB-D Cameras.
In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - Volume 4: VISAPP, pages
89-397
ISBN: 978-989-758-225-7
Copyright c(cid:13) 2017 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved"
0e78af9bd0f9a0ce4ceb5f09f24bc4e4823bd698,Spontaneous Subtle Expression Recognition: Imbalanced Databases and Solutions,"Spontaneous Subtle Expression Recognition:
Imbalanced Databases & Solutions (cid:63)
Anh Cat Le Ngo1, Raphael Chung-Wei Phan1, John See2
Faculty of Engineering,
Multimedia University (MMU), Cyberjaya, Malaysia
Faculty of Computing & Informatics,
Multimedia University (MMU), Cyberjaya, Malaysia"
0e986f51fe45b00633de9fd0c94d082d2be51406,"Face detection, pose estimation, and landmark localization in the wild","Face Detection, Pose Estimation, and Landmark Localization in the Wild
Xiangxin Zhu Deva Ramanan
Dept. of Computer Science, University of California, Irvine"
0e2c88bc1c7e79d6759b3ed26d78746d0c848373,HUMAN FACIAL ILLUSTRATIONS : CREATION AND EVALUATION USING BEHAVIORAL STUDIES AND FUNCTIONAL MAGNETIC RESONANCE IMAGING,"HUMAN FACIAL ILLUSTRATIONS: CREATION
AND EVALUATION USING BEHAVIORAL STUDIES
AND FUNCTIONAL MAGNETIC RESONANCE
IMAGING
Bruce Gooch
A dissertation submitted to the faculty of
The University of Utah
in partial ful(cid:12)llment of the requirements for the degree of
Doctor of Philosophy
Computer Science
School of Computing
The University of Utah
July 2003"
0efdd82a4753a8309ff0a3c22106c570d8a84c20,LDA WITH SUBGROUP PCA METHOD FOR FACIAL IMAGE RETRIEVAL,"LDA WITH SUBGROUP PCA METHOD FOR FACIAL IMAGE RETRIEVAL
Wonjun Hwang, Tae-Kyun Kim, Seokcheol Kee
Human Computer Interaction Lab., Samsung Advanced Institute of Technology, Korea."
0ef99317c2ab67923c1657c05f2eba5a280a54eb,Incremental adaptation of fuzzy ARTMAP neural networks for video-based face classification,"Proceedings of the 2009 IEEE Symposium on Computational Intelligence
in Security and Defense Applications (CISDA 2009)
Incremental Adaptation of Fuzzy ARTMAP Neural Networks for
Video-Based Face Classification
Jean-Franc¸ois Connolly, Eric Granger, and Robert Sabourin"
0edd3517579a110da989405309e4235e47dd8937,Performance and security analysis of Gait-based user authentication,"Performance and Security Analysis
of Gait-based User Authentication
Doctoral Dissertation by
Davrondzhon Gafurov
Submitted to the Faculty of Mathematics and Natural Sciences at the
University of Oslo in partial fulfillment of the requirements for the degree
Philosophiae Doctor (PhD) in Computer Science"
0eac652139f7ab44ff1051584b59f2dc1757f53b,Efficient Branching Cascaded Regression for Face Alignment under Significant Head Rotation,"Efficient Branching Cascaded Regression
for Face Alignment under Significant Head Rotation
Brandon M. Smith
Charles R. Dyer
University of Wisconsin–Madison"
0e13f7fc698cbe78ddbf3412b13ca27a4d878fa8,Greater need to belong predicts a stronger preference for extraverted faces ☆,"See	discussions,	stats,	and	author	profiles	for	this	publication	at:	https://www.researchgate.net/publication/306357929
Greater	need	to	belong	predicts	a	stronger
preference	for	extraverted	faces	☆
Article		in		Personality	and	Individual	Differences	·	January	2017
DOI:	10.1016/j.paid.2016.08.012
CITATION
authors,	including:
READS
Mitch	Brown
University	of	Southern	Mississippi
6	PUBLICATIONS			5	CITATIONS
SEE	PROFILE
Some	of	the	authors	of	this	publication	are	also	working	on	these	related	projects:
Metaphor	and	Disease	View	project
Limbal	Rings	View	project
All	content	following	this	page	was	uploaded	by	Mitch	Brown	on	10	November	2016.
The	user	has	requested	enhancement	of	the	downloaded	file."
0e8f529458db2fbede1274758f4651580ef5fda4,Chapter 1 Talking-face authentication,"Chapter 1
Talking-face authentication
Herv´e Bredin, Aur´elien Mayoue and G´erard Chollet
.1 Introduction
Numerous studies have exposed the limits of biometric identity verification based on
single modality (such as fingerprint, iris, handwritten signature, voice, face). The
talking face modality, that includes both face recognition and speaker verification,
is a natural choice for multimodal biometrics. Talking faces provide richer opportu-
nities for verification than does any ordinary multimodal fusion. The signal contains
not only voice and image but also a third source of information: the simultaneous
dynamics of these features. Natural lip motion and the corresponding speech signal
re synchronized.
However, this specificity is often forgotten and most of the existing talking-face au-
thentication systems are based on the fusion of the scores of two separate modules of
face verification and speaker verification. Even though this prevalent paradigm may
lead to the best performance on widespread evaluation frameworks based on ran-
dom impostor scenarios, the question of its performance against real life impostor
ttacks has to be studied.
.2 State of the art
In the existing literature, talking-face identity verification is often refered to as"
0ee1916a0cb2dc7d3add086b5f1092c3d4beb38a,The Pascal Visual Object Classes (VOC) Challenge,"Int J Comput Vis (2010) 88: 303–338
DOI 10.1007/s11263-009-0275-4
The PASCAL Visual Object Classes (VOC) Challenge
Mark Everingham · Luc Van Gool ·
Christopher K. I. Williams · John Winn ·
Andrew Zisserman
Received: 30 July 2008 / Accepted: 16 July 2009 / Published online: 9 September 2009
© Springer Science+Business Media, LLC 2009"
0efa6a6f385ce1d9eff6410ffc43e1f5a3c24f21,A minimax approach to Bayesian estimation with partial knowledge of the observation model,"A MINIMAX APPROACH TO BAYESIAN ESTIMATION WITH PARTIAL KNOWLEDGE OF
THE OBSERVATION MODEL
Tomer Michaeli and Yonina C. Eldar
Department of Electrical Engineering
Technion–Israel Institute of Technology, Haifa, Israel"
0eb45876359473156c0d4309f548da63470d30ee,A Deeply-Initialized Coarse-to-fine Ensemble of Regression Trees for Face Alignment,"A Deeply-initialized Coarse-to-fine Ensemble of
Regression Trees for Face Alignment
Roberto Valle1[0000−0003−1423−1478], Jos´e M.
Buenaposada2[0000−0002−4308−9653], Antonio Vald´es3, and Luis Baumela1
Univ. Polit´ecnica de Madrid, Spain.
Univ. Rey Juan Carlos, Spain.
Univ. Complutense de Madrid, Spain."
0e2ea7af369dbcaeb5e334b02dd9ba5271b10265,Multi-Level Feature Abstraction from Convolutional Neural Networks for Multimodal Biometric Identification,
0ed91520390ebdee13a0ac13d028f65d959bdc10,Hard Example Mining with Auxiliary Embeddings,"Hard Example Mining with Auxiliary Embeddings
Evgeny Smirnov
Speech Technology Center
Aleksandr Melnikov
ITMO University
Andrei Oleinik
ITMO University
melnikov
Elizaveta Ivanova
Ilya Kalinovskiy
Speech Technology Center
Speech Technology Center
Eugene Luckyanets
ITMO University"
0ef93418f420f1f0f71d6a66d54d13b143179cc4,Intrinsic image decomposition from multiple photographs,"UNIVERSITYOFNICE-SOPHIAANTIPOLISDOCTORALSCHOOLSTICSCIENCESETTECHNOLOGIESDEL’INFORMATIONETDELACOMMUNICATIONPHDTHESIStoobtainthetitleofPhDofScienceoftheUniversityofNice-SophiaAntipolisSpecialty:COMPUTERSCIENCEDefendedbyPierre-YvesLAFFONTIntrinsicimagedecompositionfrommultiplephotographsThesisAdvisor:GeorgeDRETTAKISCo-Advisor:AdrienBOUSSEAUpreparedatINRIASophiaAntipolis,REVESTeamdefendedonOctober12,2012Reviewers:BrianCURLESS-UniversityofWashingtonHendrikP.A.LENSCH-UniversitätTübingenAdvisor:GeorgeDRETTAKIS-REVES/INRIASophiaAntipolisCo-advisor:AdrienBOUSSEAU-REVES/INRIASophiaAntipolisPresident:FrédéricPRECIOSO-Polytech’Nice-SophiaExaminer:DiegoGUTIERREZ-UniversidaddeZaragoza"
0e49a23fafa4b2e2ac097292acf00298458932b4,Unsupervised Detection of Outlier Images Using Multi-Order Image Transforms,"Theory and Applications of Mathematics & Computer Science 3 (1) (2013) 13–31
Unsupervised Detection of Outlier Images Using Multi-Order
Image Transforms
Lior Shamira,∗
Lawrence Technological University, 21000 W Ten Mile Rd., Southfield, MI 48075, United States."
0e767d8a88cdeea1a4338817c0e6ea3640ba4232,Deep Learning Architectures for Face Recognition in Video Surveillance,"Deep Learning Architectures for Face
Recognition in Video Surveillance
Saman Bashbaghi, Eric Granger, Robert Sabourin and Mostafa Parchami"
0ecaabbf846bbc78c91bf7ff71b998b61c0082d8,Automated Visual Fin Identification of Individual Great White Sharks,"Noname manuscript No.
(will be inserted by the editor)
Automated Visual Fin Identification
of Individual Great White Sharks
Benjamin Hughes and Tilo Burghardt
Received: date / Accepted: date"
0eff410cd6a93d0e37048e236f62e209bc4383d1,Learning discriminative MspLBP features based on Ada-LDA for multi-class pattern classification,"Anchorage Convention District
May 3-8, 2010, Anchorage, Alaska, USA
978-1-4244-5040-4/10/$26.00 ©2010 IEEE"
0e7862580028aa80c409b52ef1fa683444afcbcd,Reducing the Dimensionality of Face Space in a Sparse Distributed Local-Features Representation,"(cid:3)
Reducing the Dimensionality of Face Space in a Sparse Distributed
Local-Features Representation
Laboratory of Computational Neuroscience
230 York Avenue, New York, NY 10021
The Rockefeller University
Penio S. Penev"
0e9f7d8554e065a586163845dd2bfba26e55cefb,Registration of 3 D Face Scans with Average Face Models ∗,"Registration of 3D Face Scans with Average Face Models
Albert Ali Salah1,2, Ne¸se Aly¨uz1, Lale Akarun1
{salah, nese.alyuz,
Bo˘gazi¸ci University,
4342 Bebek, ˙Istanbul, Turkey
Phone: +90 212 359 4523-24
Fax: +90 212 287 2461
Centrum voor Wiskunde en Informatica,
Kruislaan 413, 1098 SJ, 94079, The Netherlands
Phone: +31 020 592 4214
Fax: +31 020 592 4199"
0e50fe28229fea45527000b876eb4068abd6ed8c,Angle Principal Component Analysis,Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
0efb7d1413ada560ab1aee1ea4cc94d80737e662,Performance Analysis of Eye localization Methods for Real Time Vision Interface using Low Grade Video Camera,"International Journal of Computer Applications (0975 – 8887)
Volume 114 – No. 2, March 2015
Performance Analysis of Eye localization Methods for
Real Time Vision Interface using Low Grade Video
Krupa Jariwala
Assistant Professor
Computer Engineering Department
SVNIT, Surat
Camera
Upena Dalal, Ph.D.
Associate Professor
Electronics Engineering Department
SVNIT, Surat"
0e031312cb6e1634e3115e428505e2be9ef46b75,Explicit Knowledge-based Reasoning for Visual Question Answering,"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
giraffe people people Attributes: glass house room standing walking wall zoo  Scenes: museum indoor  Visual Question: How many giraffes are there in the image? Answer: Two.  Common-Sense Question: Is this image related to zoology? Answer: Yes. Reason: Object/Giraffe  -->  Herbivorous animals    --> Animal --> Zoology; Attribute/Zoo --> Zoology. KB-Knowledge Question: What are the common properties between the animal in this image and zebra? Answer: Herbivorous animals; Animals; Megafauna of Africa. Figure1:ArealexampleoftheproposedKB-VQAdatasetandtheresultsgivenbyAhab,theproposedVQAapproach.Ourapproachanswersquestionsbyextractingseveraltypesofvisualconceptsfromanimageandaligningthemtolarge-scalestructuredknowl-edgebases.Apartfromanswers,ourapproachcanalsoproviderea-sonsandexplanationsforcertaintypesofquestions.itisansweringthequestionbasedonimageinformation,orjusttheprevalenceofaparticularanswerinthetrainingset.Thesecondproblemisthatbecausethemodelistrainedonindividualquestion/answerpairs,therangeofquestionsthatcanbeaccuratelyansweredislimited.Answeringgeneralquestionsposedbyhumansaboutimagesinevitablyrequiresreferencetoadiversevarietyofinformationnotcontainedintheimageitself.CapturingsuchlargeamountofinformationwouldrequireanimplausiblylargeLSTM,andacompletelyimpracticalamountoftrainingdata.Thethird,andmajor,problemwiththeLSTMapproachisthatitisincapableofex-plicitreasoningexceptinverylimitedsituations[Rockt¨ascheletal.,2016].OurmaincontributionisamethodwecallAhab1foran-sweringawidevarietyofquestionsaboutimagesthatrequire1Ahab,thecaptaininthenovelMobyDick,iseitherabrilliantvisionary,oradeludedfanatic,dependingonyourperspective."
e9c8594ce3b8c555df59ff32b1a71eb62722b38c,On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach,"On the Importance of Stereo for Accurate Depth Estimation:
An Efficient Semi-Supervised Deep Neural Network Approach
Nikolai Smolyanskiy
{nsmolyanskiy, akamenev,
Alexey Kamenev
NVIDIA
Stan Birchfield"
e9af96d478b487fec9a06dde9e43b2ed3355ea7b,Automatic thresholding of SIFT descriptors,"AUTOMATIC THRESHOLDING OF SIFT DESCRIPTORS
Matthew R. Kirchner
Image and Signal Processing Branch, Research Office, Code 4F0000D
Naval Air Warfare Center Weapons Division, China Lake, CA 93555 USA"
e941ee2d584938e6509c0676466023f8b43b9486,Appearance based tracking with background subtraction,"The 8th International
Computer Science
April 26-28,  2013. Colombo,
Sri Lanka
& Education (ICCSE 2013)
Conference on
SuD1.4
Appearance Based Tracking with Background
Dileepa Joseph Jayamanne
Subtraction
Jayathu Samarawickrama
Ranga Rodrigo
Electronic
Engineering
Telecommunication
Electronic
Engineering
Telecommunication
Telecommunication
Electronic"
e917bb1f7efdfc448b8b63c52e8f643e68630a11,3 D information is valuable for the detection of humans in video streams,"D information is valuable for the detection of humans
in video streams
Sébastien Piérard
Antoine Lejeune
Marc Van Droogenbroeck
INTELSIG Laboratory
Montefiore Institute
University of Liège, Belgium
INTELSIG Laboratory
Montefiore Institute
University of Liège, Belgium
INTELSIG Laboratory
Montefiore Institute
University of Liège, Belgium
Email :
Email :
Email :"
e9fcd15bcb0f65565138dda292e0c71ef25ea8bb,Analysing Facial Regions for Face Recognition Using Forensic Protocols,"Repositorio Institucional de la Universidad Autónoma de Madrid
https://repositorio.uam.es
Esta es la versión de autor de la comunicación de congreso publicada en:
This is an author produced version of a paper published in:
Highlights on Practical Applications of Agents and Multi-Agent Systems:
International Workshops of PAAMS. Communications in Computer and
Information Science, Volumen 365. Springer, 2013. 223-230
DOI:    http://dx.doi.org/10.1007/978-3-642-38061-7_22
Copyright:  © 2013 Springer-Verlag
El acceso a la versión del editor puede requerir la suscripción del recurso
Access to the published version may require subscription"
e909b9e0bbfc37d0b99acad5014e977daac7e2bd,Adversarial Training of Variational Auto-Encoders for High Fidelity Image Generation,"Adversarial Training of Variational Auto-encoders for
High Fidelity Image Generation
Salman H. Khan†, Munawar Hayat ‡, Nick Barnes †
Data61 - CSIRO and ANU, Australia, ‡University of Canberra, Australia,"
e91c7dbd33a3047c70d550e201ebdf4353cbe929,Re-identification for Online Person Tracking by Modeling Space-Time Continuum,"Re-identification for Online Person Tracking by Modeling Space-Time
Continuum
Neeti Narayan, Nishant Sankaran, Srirangaraj Setlur and Venu Govindaraju
University at Buffalo, SUNY
{neetinar, n6, setlur,"
e952994fbcd3b6ad9bc52101ee0240e9de6095c8,"Comparative study of speaker verification techniques based on vector quantization , sphericity models and dynamic time warping ∗","Comparative study of speaker verification techniques
ased on vector quantization, sphericity models and
dynamic time warping
Sofia Tsekeridou†
C. Kotropoulos†
A. Xafopoulos†
I. Pitas†"
e9c9da57bbf9a968489cb90ec7252319bcab42fb,Hard Mixtures of Experts for Large Scale Weakly Supervised Vision,"Hard Mixtures of Experts for Large Scale Weakly Supervised Vision
Sam Gross, Marc’Aurelio Ranzato, and Arthur Szlam
Facebook AI Research (FAIR)"
e939fb6b762de242b22e295940e0d9d7d259e442,Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos,"Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised
Learning from Monocular Videos
Vincent Casser∗1
Soeren Pirk
Reza Mahjourian2
Anelia Angelova
Institute for Applied Computational Science, Harvard University; Google Brain
Google Brain
University of Texas at Austin; Google Brain
{pirk, rezama,"
e988be047b28ba3b2f1e4cdba3e8c94026139fcf,Multi-Task Convolutional Neural Network for Pose-Invariant Face Recognition,"Multi-Task Convolutional Neural Network for
Pose-Invariant Face Recognition
Xi Yin and Xiaoming Liu Member, IEEE,"
e9ac109c395ededb23dfc78fe85d76eeb772ee7e,A Multilevel Mixture-of-Experts Framework for Pedestrian Classification,"A Multilevel Mixture-of-Experts Framework for
Pedestrian Classification
Markus Enzweiler and Dariu M. Gavrila"
e991ff3b28c46279a3bee2446d989326a682bd33,Robust non-negative matrix factorization,"Front. Electr. Electron. Eng. China 2011, 6(2): 192–200
DOI 10.1007/s11460-011-0128-0
Lijun ZHANG, Zhengguang CHEN, Miao ZHENG, Xiaofei HE
Robust non-negative matrix factorization
(cid:2) Higher Education Press and Springer-Verlag Berlin Heidelberg 2011"
e9d43231a403b4409633594fa6ccc518f035a135,Deformable Part Models with CNN Features,"Deformable Part Models with CNN Features
Pierre-Andr´e Savalle1, Stavros Tsogkas1,2, George Papandreou3, Iasonas
Kokkinos1,2
Ecole Centrale Paris,2 INRIA, 3TTI-Chicago (cid:63)"
e9891ca60aa39df5819e5c417a6f00d56335ffb9,A dataset of stereoscopic images and ground-truth disparity mimicking human fixations in peripersonal space,"www.nature.com/scientificdata
Received: 05 September 2016
Accepted: 13 January 2017
Published: 28 March 2017
Data Descriptor: A dataset of
stereoscopic images and ground-
truth disparity mimicking human
fixations in peripersonal space
Andrea Canessa1,*, Agostino Gibaldi1,*, Manuela Chessa1, Marco Fato1, Fabio Solari1
& Silvio P. Sabatini1
Binocular stereopsis is the ability of a visual system, belonging to a live being or a machine, to interpret the
different visual information deriving from two eyes/cameras for depth perception. From this perspective,
the ground-truth information about three-dimensional visual space, which is hardly available, is an ideal
tool both for evaluating human performance and for benchmarking machine vision algorithms. In the
present work, we implemented a rendering methodology in which the camera pose mimics realistic eye
pose for a fixating observer, thus including convergent eye geometry and cyclotorsion. The virtual
environment we developed relies on highly accurate 3D virtual models, and its full controllability allows us
to obtain the stereoscopic pairs together with the ground-truth depth and camera pose information. We
thus created a stereoscopic dataset: GENUA PESTO—GENoa hUman Active fixation database: PEripersonal
space STereoscopic images and grOund truth disparity. The dataset aims to provide a unified framework"
54c5e9cded7da1f9dc695f5397d9d1a5ac5350af,Person Re-identification Based on Color Histogram and Spatial Configuration of Dominant Color Regions,"Person Re-identification Based on Color Histogram and Spatial
Configuration of Dominant Color Regions
Kwangchol Jang, Sokmin Han, Insong Kim
College of Computer Science, KIM IL SUNG University, Pyongyang, D.P.R of Korea
illumination,  pose  and  viewpoint,  camera  parameters.  Being  related"
54f0fa07dee7bd270d3bd8da9011ca90df78af59,Comparison of Laser-Based Person Tracking at Feet and Upper-Body Height,"Comparison of Laser-based Person Tracking at
Feet and Upper-Body Height
Konrad Schenk, Markus Eisenbach,
Alexander Kolarow, and Horst-Michael Gross (cid:63)
Neuroinformatics and Cognitive Robotics
Ilmenau University of Technologies"
54b309443f53ed960f588f64d6aefe53f87504b6,TVD: A Reproducible and Multiply Aligned TV Series Dataset,"TVD: a reproducible and multiply aligned TV series dataset
Anindya Roy1, Camille Guinaudeau1,2, Herv´e Bredin1, Claude Barras1,2
Spoken Language Processing Group, CNRS-LIMSI, B.P. 133, Orsay, France.
Universit´e Paris Sud, Orsay, France.
{roy, guinaudeau, bredin,"
54983972aafc8e149259d913524581357b0f91c3,ReSEED: social event dEtection dataset,"ReSEED: Social Event dEtection Dataset
Timo Reuter
Universität Bielefeld, CITEC
Bielefeld, Germany
ielefeld.de
Symeon Papadopoulos
CERTH-ITI
Thermi, Greece
Vasilios Mezaris
CERTH-ITI
Thermi, Greece
Philipp Cimiano
Universität Bielefeld, CITEC
Bielefeld, Germany
ielefeld.de"
549c719c4429812dff4d02753d2db11dd490b2ae,YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video,"YouTube-BoundingBoxes: A Large High-Precision
Human-Annotated Data Set for Object Detection in Video
Esteban Real
Google Brain
Jonathon Shlens
Google Brain
Stefano Mazzocchi
Google Research
Xin Pan
Google Brain
Vincent Vanhoucke
Google Brain"
54d78ad2ed30557474fabd1d3a9e5db1c76fbeaa,Deep Person Re-identification for Probabilistic Data Association in Multiple Pedestrian Tracking,"Deep Person Re-identification for Probabilistic Data Association in
Multiple Pedestrian Tracking
Brian H. Wang1, Yan Wang2, Kilian Q. Weinberger2, and Mark Campbell1"
541b13515480c0371bb8bb79cf17120645edccc7,Development of an Efficient Face Recognition System based on Linear and Nonlinear Algorithms,"Development of an Efficient Face Recognition System based on Linear and Nonlinear Algorithms
{tag}                                                 {/tag}
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 134
Number 7
Year of Publication: 2016
Authors:
Filani Araoluwa S., Adetunmbi Adebayo O.
10.5120/ijca2016907932
{bibtex}2016907932.bib{/bibtex}"
5456166e3bfe78a353df988897ec0bd66cee937f,Improved boosting performance by exclusion of ambiguous positive examples,"Improved Boosting Performance by Exclusion
of Ambiguous Positive Examples
Miroslav Kobetski, Josephine Sullivan
Computer Vision and Active Perception, KTH, Stockholm 10800, Sweden
{kobetski,
Keywords:
Boosting, Image Classification, Algorithm Evaluation, Dataset Pruning, VOC2007."
546cef6f86fb5a9fd59d40d9df63301c8a9d7d15,PathTrack: Fast Trajectory Annotation with Path Supervision,"PathTrack: Fast Trajectory Annotation with Path Supervision
Santiago Manen1
Michael Gygli1
Dengxin Dai1
Luc Van Gool1,2
Computer Vision Laboratory
ESAT - PSI / IBBT
{smanenfr, gygli, daid,
ETH Zurich
K.U. Leuven"
54969bcd728b0f2d3285866c86ef0b4797c2a74d,Learning for Video Compression,"IEEE TRANSACTION SUBMISSION
Learning for Video Compression
Zhibo Chen, Senior Member, IEEE, Tianyu He, Xin Jin, Feng Wu, Fellow, IEEE"
54ed052738ca0f4570c74931857b3275fca9993b,Knowledge-Guided Deep Fractal Neural Networks for Human Pose Estimation,"Knowledge-Guided Deep Fractal Neural Networks
for Human Pose Estimation
Guanghan Ning, Student Member, IEEE, Zhi Zhang, Student Member, IEEE, and Zhihai He, Fellow, IEEE"
54830a1cf8606a5183561357b4004088718e4141,Deep Watershed Detector for Music Object Recognition,"DEEP WATERSHED DETECTOR FOR MUSIC OBJECT RECOGNITION
Lukas Tuggener1,3
J¨urgen Schmidhuber3
Ismail Elezi1,2
Thilo Stadelmann1
ZHAW Datalab, Zurich University of Applied Sciences, Winterthur, Switzerland
Dept. of Environmental Sciences, Informatics and Statistics, Ca’Foscari University of Venice, Italy
Faculty of Informatics, Universit`a della Svizzera Italiana, Lugano, Switzerland"
540b39ba1b8ef06293ed793f130e0483e777e278,Biologically Inspired Emotional Expressions for Artificial Agents,"ORIGINAL RESEARCH
published: 13 July 2018
doi: 10.3389/fpsyg.2018.01191
Biologically Inspired Emotional
Expressions for Artificial Agents
Beáta Korcsok 1*, Veronika Konok 2, György Persa 3, Tamás Faragó 2, Mihoko Niitsuma 4,
Ádám Miklósi 2,5, Péter Korondi 1, Péter Baranyi 6 and Márta Gácsi 2,5
Department of Mechatronics, Optics and Engineering Informatics, Budapest University of Technology and Economics,
Budapest, Hungary, 2 Department of Ethology, Eötvös Loránd University, Budapest, Hungary, 3 Institute for Computer Science
nd Control, Hungarian Academy of Sciences, Budapest, Hungary, 4 Department of Precision Mechanics, Chuo University,
Tokyo, Japan, 5 MTA-ELTE Comparative Ethology Research Group, Budapest, Hungary, 6 Department of Telecommunications
nd Media Informatics, Budapest University of Technology and Economics, Budapest, Hungary
A special area of human-machine interaction,
the expression of emotions gains
importance with the continuous development of artificial agents such as social robots or"
5458ccf22bdea7197e28b433ef06d5225fb030a7,Video Description using Bidirectional Recurrent Neural Networks,"Video Description using Bidirectional Recurrent
Neural Networks
´Alvaro Peris1, Marc Bola˜nos2,3, Petia Radeva2,3, and Francisco Casacuberta1
PRHLT Research Center, Universitat Polit`ecnica de Val`encia, Valencia (Spain)
Universitat de Barcelona, Barcelona (Spain)
Computer Vision Center, Bellaterra (Spain)"
542289d1acfebb9d79ea7a10c8e1516924e09973,Video Highlight Prediction Using Audience Chat Reactions,"Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 972–978
Copenhagen, Denmark, September 7–11, 2017. c(cid:13)2017 Association for Computational Linguistics"
5454c5900b6b6a0cf36df65d667129fcbd5262dc,Benchmarking asymmetric 3D-2D face recognition systems,"Benchmarking Asymmetric 3D-2D Face Recognition Systems
Xi Zhao, Wuming Zhang, Georgios Evangelopoulos, Di Huang, Shishir K. Shah, Yunhong Wang,
Ioannis A. Kakadiaris and Liming Chen"
5478a70badcf4d6da383d86163f0acc2c28b6bd3,Enhancing pedestrian detection using optical flow for surveillance,"Int. J. Computational Vision and Robotics, Vol. 7, Nos. 1/2, 2017
Enhancing pedestrian detection using optical flow for
surveillance
Redwan A.K. Noaman*,
Mohd Alauddin Mohd Ali and
Nasharuddin Zainal
Department of Electrical, Electronic and Systems Engineering,
Faculty of Engineering and Built Environment,
Universiti Kebangsaan Malaysia,
3600 Bandar Baru Bangi, Selangor, Malaysia
Email:
Email:
Email:
*Corresponding author"
548bc4203770450c21133bfb72c58f5fae0fbdf2,Visual-Inertial-Semantic Scene Representation for 3D Object Detection,"Visual-Inertial-Semantic Scene Representation for 3D Object Detection
Jingming Dong∗
Xiaohan Fei∗
Stefano Soatto
UCLA Vision Lab, University of California, Los Angeles, CA 90095
{dong, feixh,"
54756f824befa3f0c2af404db0122f5b5bbf16e0,Statement Computer Vision — Visual Recognition,"Research Statement
Computer Vision — Visual Recognition
Alexander C. Berg
Computational visual recognition concerns identifying what is in an image, video, or other visual data, enabling
pplications such as measuring location, pose, size, activity, and identity as well as indexing for search by content.
Recent progress in making economical sensors and improvements in network, storage, and computational power
make visual recognition practical and relevant in almost all experimental sciences and commercial applications
such as image search. My work in visual recognition brings together machine learning, insights from psychology
nd physiology, computer graphics, algorithms, and a great deal of computation.
While I am best known for my work on general object category detection – creating techniques and building
systems for some of the best performing approaches to categorizing and localizing objects in images, recognizing
ction in video, and searching large collections of video and images – my research extends widely across visual
recognition including:
• Creating low-level image descriptors – procedures for converting pixel values to features that can be used
to model appearance for recognition. These include widely used descriptors for category recognition in
images [4, 2], object detection in images and video [11, 10, 2], and optical flow based descriptors for action
recognition in video [8].
• Developing models for recognition – ranging from what is becoming seminal work in recognizing human
ctions in video [8], to formulating object localization as approximate subgraph isomorphism [2], to models
for parsing architectural images [3], to a novel approach for face recognition based on high level describable"
543c601f8ebc0995040f4b8de4a339fd4c860cbb,Eye localization : a survey,"Eye localization: a survey
Paola CAMPADELLI, Raffaella LANZAROTTI, Giuseppe LIPORI 1
Dipartimento di Scienze dell’Informazione.
Università degli Studi di Milano.
Via Comelico, 39/41 - 20135 Milano (Italy)"
5479da1038a530beb760a38dbb5b08947dfaefbd,Fusing continuous spectral images for face recognition under indoor and outdoor illuminants,"DOI 10.1007/s00138-008-0151-1
ORIGINAL PAPER
Fusing continuous spectral images for face recognition
under indoor and outdoor illuminants
H. Chang · A. Koschan · B. Abidi · M. Abidi
Received: 4 December 2007 / Accepted: 14 May 2008 / Published online: 17 June 2008
© Springer-Verlag 2008
image fusion approaches,"
545b2ce4bc5ed7b1c1089020b3e53c1d67186370,CONVOLUTIONAL NEURAL NETWORKS IN AUTONOMOUS VEHICLE CONTROL SYSTEMS,"Session C11
Paper #77
Disclaimer—This  paper  partially  fulfills  a  writing  requirement  for  first  year  (freshman)  engineering  students  at  the
University  of  Pittsburgh  Swanson  School  of  Engineering.  This  paper  is  a  student,  not  a  professional,  paper.  This  paper  is
ased on publicly available information and may not provide complete analyses of all relevant data.  If this paper is used for
ny  purpose  other  than  these  authors’  partial  fulfillment  of  a  writing  requirement  for  first  year  (freshman)  engineering
students at the University of Pittsburgh Swanson School of Engineering, the user does so at his or her own risk.
CONVOLUTIONAL NEURAL NETWORKS IN AUTONOMOUS VEHICLE
CONTROL SYSTEMS
Erin Welling, Mahboobin 4:00, Maddie Oppelt, Vidic 2:00"
541c68e2c65f6dce6179801c9f92dc7803dc71b5,Unsupervised and Transfer Learning under Uncertainty - From Object Detections to Scene Categorization,"Unsupervised and Transfer Learning under Uncertainty:
from Object Detections to Scene Categorization
Gr´egoire Mesnil1,2, Salah Rifai1, Antoine Bordes3,
Xavier Glorot1, Yoshua Bengio1 and Pascal Vincent1
LISA, Universit´e de Montr´eal, Qu´ebec, Canada
LITIS, Universit´e de Rouen, France
CNRS - Heudiasyc UMR 7253, Universit´e de Technologie de Compi`egne, France
Keywords:
Unsupervised Learning, Transfer Learning, Deep Learning, Scene Categorization, Object Detection"
548f94f82bf28efa299a64c2527aad36d76b81af,Adaptive Kernels for Texture Based Analysis of Object Based Classification of Forest Stands by Ziab Khan,"Adaptive Kernels for Texture Based
Analysis of Object Based Classification
of Forest Stands
Ziab Khan
A thesis submitted in partial fulfilment for the
degree of Master of Philosophy
in the
Department of Geography
University of Leicester
August 26, 2014"
54bb3a17d536c7b88e56d294464f3d54de2ea9b3,Video surveillance online repository (ViSOR): www.openvisor.org,"Video Surveillance Online Repository (ViSOR)
www.openvisor.org
Roberto Vezzani, Rita Cucchiara
Dipartimento di Ingegneria “Enzo Ferrari”
University of Modena and Reggio Emilia, Italy"
54f0588b379b25ee6c22952e486d7da45bad7bab,Optimal feature selection for support vector machines,Author's personal copy
54f8a2cc202a65ede4da8593c9dd218eed0b0109,R 4-A . 3 : Human Detection and Re-Identifi cation for Mass Transit Environments,"R4-A.3: Human Detection and Re-Identifi cation
for Mass Transit Environments
PARTICIPANTS
Rich Radke
Title
Faculty/Staff
Institution
Email
Graduate, Undergraduate and REU Students
Degree Pursued
Srikrishna Karanam M.S. & PhD
Austin Li
Gyanendra Sharma
Institution
Month/Year of Graduation
2/2017
5/2015
5/2019
PROJECT DESCRIPTION
Overview and Signi(cid:980)icance"
54509dbe70cd3015007bbd5fa1fd8793b388319e,Fast Pedestrian Detection by Cascaded Random Forest with Dominant Orientation Templates.,"TANG ET AL.: FAST PEDESTRIAN DETECTION BY RANDOM FORESTS WITH DOT
Fast Pedestrian Detection by Cascaded
Random Forest with Dominant Orientation
Templates
Danhang Tang
http://www.iis.ee.ic.ac.uk/~dtang
Yang Liu
http://www.iis.ee.ic.ac.uk/~yliu
Tae-Kyun Kim
http://www.iis.ee.ic.ac.uk/~tkkim
Department of Electrical Engineering,
Imperial College,
London, UK"
544829d3b2e878c8f28fae5aa0c226e65ba6242a,Human Body Segmentation with Multi-limb Error-Correcting Output Codes Detection and Graph Cuts Optimization,"Human Pose Recovery and Behavior Analysis Group
Human Body Segmentation with
Multi-limb Error-Correcting Output
Codes Detection and Graph Cuts
Optimization
Daniel Sánchez, Juan Carlos Ortega,
Miguel Ángel Bautista & Sergio Escalera
All rights reserved HuBPA©"
5432392d916e730c53962be202c115133e6d7777,Face processing in a case of high functioning autism with developmental prosopagnosia.,"RESEARCH PAPER
Acta Neurobiol Exp 2018, 78: 114–131
DOI: 10.21307/ane‑2018‑011
Face processing in a case of high functioning autism
with developmental prosopagnosia
Hanna B. Cygan1,3*, Hanna Okuniewska2, Katarzyna Jednoróg3, Artur Marchewka4,
Marek Wypych4 and Anna Nowicka3
Laboratory of Social Psychology, Department of Ergonomics, Central Institute for Labour Protection, National Research Institute,
Warsaw, Poland, 2 Faculty of Psychology, University of Warsaw, Warsaw, Poland, 3 Laboratory of Psychophysiology, Department of
Neurophysiology, Nencki Institute of Experimental Biology, Polish Academy of Science, Warsaw, Poland, 4 Laboratory of Brain Imaging,
Neurobiology Center, Nencki Institute of Experimental Biology, Polish Academy of Science, Warsaw, Poland,
* Email:
The ability to “read” the information about facial identity, expressed emotions, and intentions is crucial for non‑verbal social interaction.
Neuroimaging and clinical studies consequently link face perception with fusiform gyrus (FG) and occipital face area (OFA) activity. Here
we investigated face processing in an adult, patient PK, diagnosed with both high functioning autism spectrum disorder (ASD) and
developmental prosopagnosia (DP). Both disorders have a significant impact on face perception and recognition, thus creating a unique
neurodevelopmental condition. We used eye‑tracking and functional magnetic resonance imaging (fMRI) method. Eye‑tracking and fMRI
results of PK were compared to results of control subjects. Patient PK showed atypical gaze‑fixation strategy during face perception and
typical patterns of brain activations in the FG and OFA. However, a significant difference between PK and control subjects was found in
the left anterior superior temporal sulcus/middle temporal gyrus (aSTS/MTG). In PK the left aSTS/MTG was hypo‑activated in comparison"
541bccf19086755f8b5f57fd15177dc49e77d675,A few days of a robot's life in the human's world: toward incremental individual recognition,"Computer Science and ArtificialIntelligence LaboratoryTechnical Reportmassachusetts institute of technology, cambridge, ma 02139 usa — www.csail.mit.eduMIT-CSAIL-TR-2007-022April 3, 2007A Few Days of A Robot’s Life in the Human’s World: Toward Incremental Individual RecognitionLijin Aryananda"
8b21c89b436e0aa540f7b9648e4344eb30a6e372,Robust coding schemes for indexing and retrieval from large face databases,"IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 9, NO. 1, JANUARY 2000
Correspondence________________________________________________________________________
Robust Coding Schemes for Indexing and Retrieval from
Large Face Databases
Chengjun Liu and Harry Wechsler"
8bb4d90d5b97e8d08d2aaa99e9c075a506b3108a,Generating Diverse Clusterings,"Generating Diverse Clusterings
Anonymous Author(s)"
8b36c4675732249963b3e9294a0f52f26c6dd931,A Survey on Local Invariant Features,"FnT Computer Graphics and Vision 2:4
FnTCGV
Foundations and Trends in Computer Graph-
ics and Vision
Vol. 1, No 1 (1) 1–94
(cid:13) 1 now Publishers Inc.
A Survey on Local Invariant Features
Tinne Tuytelaars1 and Krystian Mikolajczyk2
Kasteelpark
Arenberg
0Leuven
B-3001,
Belgium,
Surrey GU27XH, United Kingdom,"
8b20737b454fa8c2848979b5c76be9915a65a75f,Automated Object Recognition Using Multiple X-ray Views,"Automated Object Recognition
Using Multiple X-ray Views
Domingo Mery1 – Vladimir Riffo1, 2
Department of Computer Science, Pontificia
Universidad Católica de Chile.
Department of Computer Engineering and
Computer Science, Universidad de Atacama.
Av. Vicuña Mackenna 4860(143) – Santiago de
Chile.
Av. Copayapu 485 – Copiapó, Chile.
http://dmery.ing.puc.cl
http://www.ing.puc.cl/~vriffo1"
8bff7353fa4f75629ea418ca8db60477a751db93,Invariance of Weight Distributions in Rectified MLPs,"Invariance of Weight Distributions in Rectified MLPs
Russell Tsuchida 1 Farbod Roosta-Khorasani 2 3 Marcus Gallagher 1"
8b7724e2e5dec9a0a51e84a270c9b9db84b205a6,Analysis Dictionary Learning based Classification: Structure for Robustness,"Analysis Dictionary Learning based
Classification: Structure for Robustness
Wen Tang, Ashkan Panahi, Hamid Krim, and Liyi Dai"
8b5b8db6a2a2880c14894140ea70ceb5f96c3b9b,Learning a Text-Video Embedding from Incomplete and Heterogeneous Data,"Learning a Text-Video Embedding from
Incomplete and Heterogeneous Data
Antoine Miech1,2
Ivan Laptev1,2
Josef Sivic1,2,3
´Ecole Normale Sup´erieure(cid:63)
Inria
CIIRC(cid:63)(cid:63)
http://www.di.ens.fr/willow/research/mee/"
8ba474aa327cd078c2b85f0c1ab752043253016a,Face Detection Based on Skin Color Segmentation Using Fuzzy Entropy,"Article
Face Detection Based on Skin Color Segmentation
Francisco A. Pujol 1,*, Mar Pujol 2, Antonio Jimeno-Morenilla 1 and María José Pujol 3
Department of Computer Technology, University of Alicante, Alicante 03690, Spain;
Department of Computer Science and Artificial Intelligence, University of Alicante, Alicante 03690, Spain;
Department of Applied Mathematics, University of Alicante, Alicante 03690, Spain;
* Correspondence: Tel.: +34-96-590-3681
Academic Editor: Raúl Alcaraz Martínez
Received: 14 November 2016; Accepted: 22 December 2016; Published: 11 January 2017"
8bbafa3efb7b96adb95128ea2a30a363bfe06812,Towards usable authentication on mobile phones : An evaluation of speaker and face recognition on off-the-shelf handsets,"Towards usable authentication on mobile phones: An
evaluation of speaker and face recognition on off-the-shelf
handsets
Rene Mayrhofer
University of Applied Sciences Upper Austria
Softwarepark 11, A-4232 Hagenberg, Austria
University of Applied Sciences Upper Austria
Softwarepark 11, A-4232 Hagenberg, Austria
Thomas Kaiser
hagenberg.at"
8bba26895022749e2273729f96051571eabc7b99,Natural language acquisition in recurrent neural architectures,"Natural Language Acquisition in
Recurrent Neural Architectures
Dissertation
submitted to the Universität Hamburg,
Faculty of Mathematics,
Informatics
nd Natural Sciences, Department
fulfilment
of the requirements for the degree of
Doctor rerum naturalium (Dr. rer. nat.)
Informatics,
in partial
Dipl.-Inform. Stefan Heinrich
Hamburg, 2016"
8b632db02220806cd62e35fdebb3ede58243dee0,Recognizing Partially Occluded Faces from a Single Sample Per Class Using String-Based Matching,"Recognizing Partially Occluded Faces from a
Single Sample Per Class Using String-Based
Matching
Weiping Chen1 and Yongsheng Gao1,2
School of Engineering, Grif‌f‌ith University, Australia
National ICT Australia, Queensland Research Lab"
8b2f99b0106143fd0193fcbf2b07eba80dc7f8dd,Enhancing Recommender Systems for TV by Face Recognition,
8b2c090d9007e147b8c660f9282f357336358061,Emotion Classification based on Expressions and Body Language using Convolutional Neural Networks,"Lake Forest College
Lake Forest College Publications
Senior Theses
-23-2018
Student Publications
Emotion Classification based on Expressions and
Body Language using Convolutional Neural
Networks
Aasimah S. Tanveer
Lake Forest College,
Follow this and additional works at: https://publications.lakeforest.edu/seniortheses
Part of the Neuroscience and Neurobiology Commons
Recommended Citation
Tanveer, Aasimah S., ""Emotion Classification based on Expressions and Body Language using Convolutional Neural Networks""
(2018). Senior Theses.
This Thesis is brought to you for free and open access by the Student Publications at Lake Forest College Publications. It has been accepted for
inclusion in Senior Theses by an authorized administrator of Lake Forest College Publications. For more information, please contact"
8b7e5be7faa1c78065baa21179c258697fcaa730,Detection of Complex Salient Regions,"Hindawi Publishing Corporation
EURASIP Journal on Advances in Signal Processing
Volume 2008, Article ID 451389, 11 pages
doi:10.1155/2008/451389
Research Article
Detection of Complex Salient Regions
Sergio Escalera,1, 2 Oriol Pujol,1, 2 and Petia Radeva1, 2
Computer Vision Center, Campus UAB, Edifici O, 08193 Bellaterra, Barcelona, Spain
Departamento de Matem`atica Aplicada i An`alisi, Universitat de Barcelona (UB), 08007 Barcelona, Spain
Correspondence should be addressed to Sergio Escalera,
Received 16 October 2007; Revised 8 February 2008; Accepted 12 March 2008
Recommended by Irene Gu
The goal of interest point detectors is to find, in an unsupervised way, keypoints easy to extract and at the same time robust to
image transformations. We present a novel set of saliency features based on image singularities that takes into account the region
ontent in terms of intensity and local structure. The region complexity is estimated by means of the entropy of the gray-level
information; shape information is obtained by measuring the entropy of significant orientations. The regions are located in their
representative scale and categorized by their complexity level. Thus, the regions are highly discriminable and less sensitive to
onfusion and false alarm than the traditional approaches. We compare the novel complex salient regions with the state-of-the-art
keypoint detectors. The presented interest points show robustness to a wide set of image transformations and high repeatability as
well as allow matching from different camera points of view. Besides, we show the temporal robustness of the novel salient regions"
8b1db0894a23c4d6535b5adf28692f795559be90,How Reliable are Your Visual Attributes ?,"Biometric and Surveillance Technology for Human and Activity Identification X, edited by Ioannis Kakadiaris,
Walter J. Scheirer, Laurence G. Hassebrook, Proc. of SPIE Vol. 8712, 87120Q · © 2013 SPIE
CCC code: 0277-786X/13/$18 · doi: 10.1117/12.2018974
Proc. of SPIE Vol. 8712  87120Q-1
From: http://proceedings.spiedigitallibrary.org/ on 06/07/2013 Terms of Use: http://spiedl.org/terms"
8bcee0c84759a2bf17d8f8e77b8a393c6f823ded,Optimized Performance of 2 DPCA Approach in Face Recognition System,"Internati onal Journal of Innovations & Advance me nt in Computer  Science
IJ IACS
ISSN 2347 – 8616
Volume  3, Issue 5
July 2014
Optimized Performance of 2DPCA Approach in Face
Recognition System
in  the  face database. Today, several approaches
like PCA, KPCA, MPCA and 2DPCA   provide
y  researches  but  till  date  it  is  not  completely
solve  issues.  Therefore,  it  required  to  review
these approaches one again.
. LITERATURE SURVEY
Abhishek Sharma
M.Tech Research scholar
Department of CSE, SET
IFTMU Moradabad.
provide
redit/debit
identification,"
8b744786137cf6be766778344d9f13abf4ec0683,And Summarization by Sub-modular Inference,"978-1-4799-9988-0/16/$31.00 ©2016 IEEE
ICASSP 2016"
8b9db19d0d3e2a7d740be811810a043a04d6226a,An Attention-based Regression Model for Grounding Textual Phrases in Images,Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
8b879863237d315997857a5585afb2bbbf78c622,Social network analysis as a tool for improving enterprise architecture,"Proceedings of the 5th International KES Symposium on Agents and Multi-agent
Systems, KES-AMSTA 2011. Manchester, UK, June 29 - July 1, 2011
Lecture Notes in Artificial Intelligence LNAI, Volume 6682, 2011,  pp. 651-660
DOI: 10.1007/978-3-642-22000-5_67
Social Network Analysis as a Tool
for Improving Enterprise Architecture
Przemysław Kazienko, Radosław Michalski, Sebastian Palus
Institute of Informatics, Wrocław University of Technology
Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland
{kazienko, radoslaw.michalski,"
8b9e94fb3bb64389e9765ffde365862231b5972c,Fast Eye Tracking and Feature Measurement using a Multi-stage Particle Filter,
8b3288421ee4fa7f9ff45ddc6adbf04698c4b8ba,On the Iterative Refinement of Densely Connected Representation Levels for Semantic Segmentation,"On the iterative refinement of densely connected representation levels for
semantic segmentation
Arantxa Casanova1,2 Guillem Cucurull1,2 Michal Drozdzal1,3 Adriana Romero1,3 Yoshua Bengio1
Montreal Institute for Learning Algorithms
Computer Vision Center, Barcelona
Facebook AI Research"
8bddd0afd064e2d45ab6cf9510f2631f7438c17b,Outlier Detection using Generative Models with Theoretical Performance Guarantees,"Outlier Detection using Generative Models with
Theoretical Performance Guarantees∗
Jirong Yi†
Anh Duc Le‡
Tianming Wang§
Xiaodong Wu¶
Weiyu Xu(cid:107)
October 29, 2018"
8b29ee0a47efc11071ab8baec8369fd54970bfbb,Thèse présentée à la faculté des sciences pour l ’ obtention du grade de docteur ès sciences Features Extraction for Low-Power Face Verification,"Thèse présentée à la faculté des sciences pour
l’obtention du grade de docteur ès sciences
Features Extraction for
Low-Power Face Verification
Patrick Stadelmann
Acceptée sur proposition du jury :
Prof. Fausto Pellandini, directeur de thèse
PD Dr. Michael Ansorge, co-directeur de thèse
Prof. Pierre-André Farine, rapporteur
Dr. Nicolas Blanc, rapporteur
Soutenue le 23 mai 2008
Institut de Microtechnique
Université de Neuchâtel"
8b64dbeac77fe8d6bf440311337451f9f61b9ea0,Image-based approaches to hair modeling,"Image-Based Approaches to Hair Modeling
Dissertation
Erlangung des Doktorgrades (Dr. rer. nat)
Mathematisch-Naturwissenschaftlichen Fakult¨at
Rheinischen Friedrich-Wilhelms-Universit¨at Bonn
vorgelegt von
Tom´as Lay Herrera
Havanna
Bonn, November 2012"
8b607928c7af70259a9f8af9e08e28e6037411c8,Bayesian teaching of image categories,"Bayesian teaching of image categories
Wai Keen Vong∗
Ravi B. Sojitra*
Newark, NJ, 07102
Anderson Reyes
Scott Cheng-Hsin Yang
Patrick Shafto
Department of Mathematics and Computer Science, 110 Warren Street,"
8bb21b1f8d6952d77cae95b4e0b8964c9e0201b0,Multimodal Interaction on a Social Robotic Platform,"Methoden
t 11/2013
(cid:2)(cid:2)(cid:2)
Multimodale Interaktion
uf einer sozialen Roboterplattform
Multimodal Interaction on a Social Robotic Platform
Jürgen Blume
Korrespondenzautor:
, Tobias Rehrl, Gerhard Rigoll, Technische Universität München
Zusammenfassung Dieser Beitrag beschreibt die multimo-
dalen Interaktionsmöglichkeiten mit der Forschungsroboter-
plattform ELIAS. Zunächst wird ein Überblick über die Ro-
oterplattform sowie die entwickelten Verarbeitungskompo-
nenten gegeben, die Einteilung dieser Komponenten erfolgt
nach dem Konzept von wahrnehmenden und agierenden Mo-
dalitäten. Anschließend wird das Zusammenspiel der Kom-
ponenten in einem multimodalen Spieleszenario näher be-
trachtet. (cid:2)(cid:2)(cid:2) Summary
This paper presents the mul-
timodal"
8b9f529700a93a2ff6e227c76a1333883a1f6213,PREMOC: Plataforma de reconocimiento multimodal de emociones,"PREMOC: Plataforma de reconocimiento multimodal
de emociones
Ramón Zatarain-Cabada, María Lucia Barrón-Estrada, Gilberto Muñoz-Sandoval
Instituto Tecnológico de Culiacán, Culiacán, Sinaloa,
México
{rzaratain, lbarron,
Resumen.  En  años  recientes  la  computación  afectiva  ha  venido  a  mejorar  la
interacción  humano-computadora,  pues  ayuda  a  la  computadora  a  conocer  el
estado  afectivo  del  usuario  para  mejorar  la  toma  de  decisiones.  Este  artículo
presenta  los  avances  en  el  proyecto  PREMOC,  una  plataforma  que  brinda  un
servicio web para el reconocimiento de emociones en texto, imágenes de rostros,
sonidos de voz y señales EEG de manera mono-modal y multimodal. PREMOC
yuda  a  los  desarrolladores  a  integrar  el  reconocimiento  de  afecto  a  sus
plicaciones  o  sistemas  de  software.  Cada  uno  de  los  reconocedores  se
implementó aplicando diferentes técnicas tanto para extraer características como
para  clasificar  emociones;  además  para  el  reconocimiento  multimodal  se
integraron  las  emociones  mediante  un  sistema  difuso.  Esta  plataforma  ya  está
siendo  utilizada  por  diferentes  proyectos  en  el  laboratorio  de  la  Maestría  en
Ciencias de la Computación del Instituto Tecnológico de Culiacán.
Palabras  claves:  Computación  afectiva,  inteligencia  artificial,  reconocimiento"
8b547b87fd95c8ff6a74f89a2b072b60ec0a3351,Initial perceptions of a casual game to crowdsource facial expressions in the wild,"Initial Perceptions of a Casual Game to Crowdsource
Facial Expressions in the Wild
Chek Tien Tan
Hemanta Sapkota
Daniel Rosser
Yusuf Pisan
Games Studio, Faculty of Engineering and IT, University of Technology, Sydney"
8b9c53e7d65ba7a7be3d588d00481f2ff49b5ef4,Orienting in response to gaze and the social use of gaze among children with autism spectrum disorder.,23Journal of Autism andDevelopmental Disorders ISSN 0162-3257Volume 43Number 7 J Autism Dev Disord (2013)43:1584-1596DOI 10.1007/s10803-012-1704-8Orienting in Response to Gaze and theSocial Use of Gaze among Children withAutism Spectrum DisorderAdrienne Rombough & Grace Iarocci
b774d7c951b9c444572085e15f6a81a063abf123,Diversity Regularized Spatiotemporal Attention for Video-based Person Re-identification,"FeaturesSpatial  AttentionTemporal  Attention1                      2                      3                              N‘face’‘torso’‘bag’Figure1.SpatiotemporalAttention.Inchallengingvideore-identificationscenarios,apersonisrarelyfullyvisibleinallframes.However,framesinwhichonlypartofthepersonisvis-ibleoftencontainusefulinformation.Forexample,thefaceisclearlyvisibleintheframes1and2,thetorsoinframe2,andthehandbaginframes2,3andN.Insteadofaveragingfullframefeaturesacrosstime,weproposeanewspatiotemporalapproachwhichlearnstodetectasetofKdiversesalientimageregionswithineachframe(superimposedheatmaps).Anaggregaterep-resentationofeachbodypartisthenproducedbycombiningtheextractedper-frameregionsacrosstime(weightsshownaswhitetext).Ourspatiotemporalapproachcreatesacompactencodingofthevideothatexploitsusefulpartialinformationineachframebyleveragingmultiplespatialattentionmodels,andcombiningtheiroutputsusingmultipletemporalattentionmodels.personre-identification,whichisageneralizationofthestandardimage-basedre-identificationtask.InsteadofarXiv:1803.09882v1  [cs.CV]  27 Mar 2018"
b704f8360c369e65f0826ca23dac2d4e221d8997,A Knowledge Base for Automatic Feature Recognition from Point Clouds in an Urban Scene,"Article
A Knowledge Base for Automatic Feature Recognition
from Point Clouds in an Urban Scene
Xu-Feng Xing 1,2,* ID , Mir-Abolfazl Mostafavi 1,2 ID and Seyed Hossein Chavoshi 1,2
Department of Geomatics Sciences, Université Laval, Québec, QC G1V 0A6, Canada;
(M.-A.M.); (S.H.C.)
Center for Research in Geomatics, Université Laval, Québec, QC G1V 0A6, Canada
* Correspondence: Tel.: +1-581-888-9786
Received: 4 October 2017; Accepted: 11 January 2018; Published: 16 January 2018"
b7f05d0771da64192f73bdb2535925b0e238d233,Robust Active Shape Model using AdaBoosted Histogram Classifiers,"MVA2005  IAPR  Conference on Machine VIsion Applications, May 16-18, 2005 Tsukuba Science City, Japan
Robust Active Shape Model using AdaBoosted Histogram Classifiers
Yuanzhong Li
W ataru Ito
Imaging Software Technology Center
Imaging Software Technology Center
FUJI PHOTO FILM  CO., LTD.
fujifilm.co.jp
FUJI PHOTO FILM  CO., LTD.
fujifilm.co.jp"
b7b23814948afc5525975ed44f3dd247100e6722,Relevant Feature Selection for Human Pose Estimation and Localization in Cluttered Images,"Relevant Feature Selection for Human Pose Estimation
nd Localization in Cluttered Images
Ryuzo Okada(cid:2) and Stefano Soatto
Computer Science Department, University of California, Los Angeles"
b7f7a4df251ff26aca83d66d6b479f1dc6cd1085,Handling missing weak classifiers in boosted cascade: application to multiview and occluded face detection,"Bouges et al. EURASIP Journal on Image and Video Processing 2013, 2013:55
http://jivp.eurasipjournals.com/content/2013/1/55
RESEARCH
Open Access
Handling missing weak classifiers in boosted
ascade: application to multiview and
occluded face detection
Pierre Bouges1*, Thierry Chateau1*, Christophe Blanc1 and Gaëlle Loosli2"
b7f0d1d65763fb57ee9a3624116a42a2fe763707,Predicting psychological attributions from face photographs with a deep neural network,"Predicting psychological attributions from face
photographs with a deep neural network
Edward Grant1∗, Stephan Sahm1∗, Mariam Zabihi1∗, Marcel van Gerven1
Radboud University, Nijmegen, the Netherlands
Denotes equal contribution"
b755505bdd5af078e06427d34b6ac2530ba69b12,NFRAD: Near-Infrared Face Recognition at a Distance,"To appear in the International Joint Conf. Biometrics, Washington D.C., October, 2011
NFRAD: Near-Infrared Face Recognition at a Distance
Hyunju Maenga, Hyun-Cheol Choia, Unsang Parkb, Seong-Whan Leea and Anil K. Jaina,b
Dept. of Brain and Cognitive Eng. Korea Univ., Seoul, Korea
Dept. of Comp. Sci. & Eng. Michigan State Univ., E. Lansing, MI, USA 48824
{hjmaeng, ,"
b7cf7bb574b2369f4d7ebc3866b461634147041a,From NLDA to LDA/GSVD: a modified NLDA algorithm,"Neural Comput & Applic (2012) 21:1575–1583
DOI 10.1007/s00521-011-0728-x
O R I G I N A L A R T I C L E
From NLDA to LDA/GSVD: a modified NLDA algorithm
Jun Yin • Zhong Jin
Received: 2 August 2010 / Accepted: 3 August 2011 / Published online: 19 August 2011
Ó Springer-Verlag London Limited 2011"
b7c2798e136feb85847c8a9aa693d75bc3f9b08c,Classifying a specific image region using convolutional nets with an ROI mask as input,"Classifying  a specific image  region using
onvolutional nets with an ROI mask as input
Sagi Eppel  1"
b76af8fcf9a3ebc421b075b689defb6dc4282670,Face Mask Extraction in Video Sequence,"Face Mask Extraction in Video Sequence
Yujiang Wang 1 · Bingnan Luo 1 · Jie Shen 1 · Maja Pantic 1"
b73a6c7083f3dbc8b355f934aaf84438c10a7963,The 54th Annual Meeting of the Association for Computational Linguistics,"The54thAnnualMeetingoftheAssociationforComputationalLinguisticsProceedingsoftheConference,Vol.2(ShortPapers)August7-12,2016Berlin,Germany"
b7c7721e5f19e02ef56c17cd74d24476a53856dd,Bi-Real Net: Binarizing Deep Network Towards Real-Network Performance,"Noname manuscript No.
(will be inserted by the editor)
Bi-Real Net: Binarizing Deep Network Towards
Real-Network Performance
Zechun Liu · Wenhan Luo · Baoyuan
Wu · Xin Yang · Wei Liu · Kwang-Ting
Cheng
Received: date / Accepted: date"
b7c4b22d44be82b2e1074c5c40b76461db4b0292,Generating Multiple Diverse Hypotheses for Human 3D Pose Consistent with 2D Joint Detections,"Generating Multiple Diverse Hypotheses for Human 3D Pose Consistent with 2D
Joint Detections
Ehsan Jahangiri, Alan L. Yuille
Johns Hopkins University, Baltimore, USA"
b701f11ecf5d465c7d5c427914db2ad8c97bb8a9,JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets,"JointGAN: Multi-Domain Joint Distribution Learning with
Generative Adversarial Nets
Yunchen Pu 1 Shuyang Dai 2 Zhe Gan 3 Weiyao Wang 2 Guoyin Wang 2 Yizhe Zhang 3 Ricardo Henao 2
Lawrence Carin 2"
b797f3fa4e732d52092f9eb863350440d5de8bb1,Unsupervised Category Discovery via Looped Deep Pseudo-Task Optimization Using a Large Scale Radiology Image Database.,"Unsupervised Category Discovery via Looped Deep Pseudo-Task Optimization
Using a Large Scale Radiology Image Database
Xiaosong Wang
Le Lu
Hoo-chang Shin
Lauren Kim Isabella Nogues
Jianhua Yao
Ronald Summers
Imaging Biomarkers and Computer-aided Detection Laboratory
Department of Radiology and Imaging Sciences
National Institutes of Health Clinical Center
0 Center Drive, Bethesda, MD 20892"
b705ca751a947e3b761e2305b41891051525d9df,Exploring Context with Deep Structured Models for Semantic Segmentation,"Exploring Context with Deep Structured models
for Semantic Segmentation
Guosheng Lin, Chunhua Shen, Anton van den Hengel, Ian Reid"
b7d425ea6b476c4af208a6b6a9e84ab17921dab4,Heuristic-based automatic face detection,"HEURISTIC-BASED AUTOMATIC FACE DETECTION
Geovany Ramírez1, Vittorio Zanella1,2, Olac Fuentes2
Universidad Popular Autónoma del Estado de Puebla
1 sur #1103 Col. Santiago Puebla 72160, México
Instituto Nacional de Astrofísica Optica y Electrónica
Luis Enrique Erro #1 Sta. María  Tonantzintla Puebla 72840, México
E-mail:"
b7c4fe5c89df51ebd1f89a34c66b94cc6019d8e6,Model Cards for Model Reporting,"Model Cards for Model Reporting
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben
Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, Timnit Gebru"
b7c7411eed2f3b6211abfe0aeea7c18adb028d11,Robust De-noising by Kernel PCA,"Robust De-noising by Kernel PCA
Takashi Takahashi1 and Takio Kurita2
Department of Applied Mathematics and Informatics,
Ryukoku University, Ootsu, Shiga 520–2194, JAPAN
National Institute of Advanced Industrial Science and Technology(AIST),
Tsukuba, Ibaraki 305–8568, JAPAN"
b7a0e7dab11781c252e1145f3526aee388b4136d,Facing humanness: Facial width-to-height ratio predicts ascriptions of humanity.,"Journal of Personality and Social
Psychology
Facing Humanness: Facial Width-to-Height Ratio
Predicts Ascriptions of Humanity
Jason C. Deska, E. Paige Lloyd, and Kurt Hugenberg
Online First Publication, August 28, 2017. http://dx.doi.org/10.1037/pspi0000110
CITATION
Deska, J. C., Lloyd, E. P., & Hugenberg, K. (2017, August 28). Facing Humanness: Facial Width-to-
Advance online publication. http://dx.doi.org/10.1037/pspi0000110"
b7eead8586ffe069edd190956bd338d82c69f880,A VIDEO DATABASE FOR FACIAL BEHAVIOR UNDERSTANDING,"A VIDEO DATABASE FOR FACIAL
BEHAVIOR UNDERSTANDING
D. Freire-Obreg´on and M. Castrill´on-Santana.
SIANI, Universidad de Las Palmas de Gran Canaria, Spain"
b778c0e5ec6cebbabc77fc56f9b7438f2974a4ea,Altered activity of the primary visual area during gaze processing in individuals with high-functioning autistic spectrum disorder: a magnetoencephalography study.,"Altered Activity of the Primary Visual Area during Gaze Processing in
Individuals with High-Functioning Autistic Spectrum Disorder: A
Magnetoencephalography Study
Naoya Hasegawaa, Hideaki Kitamuraa, Hiroatsu Murakamib, Shigeki Kameyamab, Mutsuo
Sasagawac, Jun Egawaa, Ryu Tamuraa, Tarou Endoa, Toshiyuki Someyaa
Department of Psychiatry, Niigata University Graduate School of Medical and Dental Sciences,
-757 Asahimachi-dori, Chuo-ku, Niigata 951-8510, Japan
Department of Neurosurgery, Epilepsy Center, Nishi-Niigata Chuo National Hospital, 1-14-1
Masago, Nishi-ku, Niigata 950-2085, Japan
Department of Psychiatry, Epilepsy Center, Nishi-Niigata Chuo National Hospital, 1-14-1
Masago, Nishi-ku, Niigata 950-2085, Japan
Short title:
Altered activity of the primary visual area of autistic spectrum disorder during gaze processing
Correspondence: Hideaki Kitamura
Department of Psychiatry, Niigata University Graduate School of Medical and Dental Sciences,
-757 Asahimachi-dori, Chuo-ku, Niigata 951-8510, Japan
Tel: +81-25-227-2213; Fax: +81-25-227-0777;
E-mail:"
b7a09eaadcb21bf9ab234d87c954e329518580c5,Learning to Fuse 2D and 3D Image Cues for Monocular Body Pose Estimation,"Learning to Fuse 2D and 3D Image Cues for Monocular Body Pose Estimation
Bugra Tekin
Pablo M´arquez-Neila
Mathieu Salzmann
Pascal Fua
EPFL, Switzerland"
b7a827bb393361c309fbba652967dee11d16857c,Comparative Analysis of various Illumination Normalization Techniques for Face Recognition,"International Journal of Computer Applications (0975 – 8887)
Volume 28– No.9, August 2011
Comparative Analysis of various Illumination
Normalization Techniques for Face Recognition
Tripti Goel
GPMCE, Delhi
Vijay Nehra
BPSMV, Khanpur
Virendra P.Vishwakarma
JIIT, Noida
explained"
b788f1c013c6786cd44d6f35682a388975058488,Towards Collaborative Group Activity Recognition Using Mobile Devices,"Mobile Netw Appl
DOI 10.1007/s11036-012-0415-x
Towards Collaborative Group Activity Recognition
Using Mobile Devices
Dawud Gordon · Jan-Hendrik Hanne ·
Martin Berchtold · Ali Asghar Nazari Shirehjini ·
Michael Beigl
© Springer Science+Business Media New York 2012"
b7b461f82c911f2596b310e2b18dd0da1d5d4491,K-mappings and Regression trees,"014 IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP)
978-1-4799-2893-4/14/$31.00 ©2014 IEEE
K-MAPPINGS AND REGRESSION TREES
SAMSI and Duke University
. INTRODUCTION
rgminM1,...,MK
P1,...PK
Arthur Szlam†
.1. Partitioning Y
K(cid:2)
(cid:2)
(cid:3)
(cid:4)"
b7ac537d97efcb968ca8e353ff5b0563e26b9dbe,Object-Aware Dense Semantic Correspondence,"Object-aware Dense Semantic Correspondence
Fan Yang1, Xin Li1 ∗, Hong Cheng2, Jianping Li1, Leiting Chen1
School of Computer Science & Engineering, UESTC
Center for Robotics, School of Automation Engineering, UESTC
fanyang xinli"
b7216846c743d94fcd43e1b543c9d16ae11d3c48,Engaging Image Chat: Modeling Personality in Grounded Dialogue,"Engaging Image Chat: Modeling Personality in Grounded Dialogue
Kurt Shuster Samuel Humeau Antoine Bordes Jason Weston
{kshuster, samuelhumeau, abordes, jase}
Facebook AI Research"
b7c2173668a4c23b79450111887d8b1e4199f89c,Complex event recognition by latent temporal models of concepts,"COMPLEX EVENT RECOGNITION BY LATENT TEMPORAL MODELS OF CONCEPTS
Ehsan Zare Borzeshi1, Afshin Dehghan2, Massimo Piccardi1, and Mubarak Shah2
School of Computing and Communications, University of Technology, Sydney (UTS)1,
Centre for Research in Computer Vision, University of Central Florida (UCF)2"
b79f3d9f8de4d1cc6679676146a40d2a8596f32d,Composing Simple Image Descriptions using Web-scale N-grams,"Proceedings of the Fifteenth Conference on Computational Natural Language Learning, pages 220–228,
Portland, Oregon, USA, 23–24 June 2011. c(cid:13)2011 Association for Computational Linguistics"
b73fdae232270404f96754329a1a18768974d3f6,International Journal of Advanced Robotic Systems Local Relation Map : A Novel Illumination Invariant Face Recognition Approach Regular Paper,
b74c410ea8db3babb9a307d1fb8f08b90ce8b490,Automatic Visual Speech Recognition,"6 Automatic Visual Speech Recognition Alin Chiţu¹ and Léon J.M. Rothkrantz¹,² ¹Delft University of Technology ²Netherlands Defence Academy The Netherlands 1. Introduction Lip reading was thought for many years to be specific to hearing impaired persons. Therefore, it was considered that lip reading is one possible solution to an abnormal situation. Even the name of the domain suggests that lip reading was considered to be a rather artificial way of communication because it associates lip reading with the written language which is a relatively new cultural phenomenon and is not an evolutionary inherent ability. Extensive lip reading research was primarily done in order to improve the teaching methodology for hearing impaired persons to increase their chances for integration in the society. Later on, the research done in human perception and more exactly in speech perception proved that lip reading is actively employed in different degrees by all humans irrespective to their hearing capacity. The most well know study in this respect was performed by Harry McGurk and John MacDonald in 1976. In their experiment the two researchers were trying to understand the perception of speech by children. Their finding, now called the McGurk effect, published in Nature (Mcgurk & Macdonald, 1976), was that if a person is presented a video sequence with a certain utterance (i.e. in their experiments utterance 'ga'), but in the same time the acoustics present a different utterance (i.e. in their experiments the sound 'ba'), in a large majority of cases the person will perceive a third utterance (i.e. in this case 'da'). Subsequent experiments showed that this is true as well for longer utterances and that is not a particularity of the visual and aural senses but also true for other perception functions. Therefore, lip reading is part of our multi-sensory speech perception process and could be better named visual speech recognition. Being an evolutionary acquired capacity, same as speech perception, some scientists consider the lip reading's neural mechanism the one that enables humans to achieve high literacy skills with relative easiness (van Atteveldt, 2006). Another source of confusion is the “lip” word, because it implies that the lips are the only part of the speaker face that transmit information about what is being said. The teeth, the tongue and the cavity were shown to be of great importance for lip reading by humans (Williams et al., 1998). Also other face elements were shown to be important during face to face communication; however, their exact influence is not completely elucidated. During experiments in which a gaze tracker was used to track the speaker's areas of attention during communication it was found that the human lip readers focus on four major areas: the mouth, the eyes and the centre of the face depending on the task and the noise level (Buchan et al., 2007). In normal situations the listener scans the mouth and the other areas www.intechopen.com"
b768cb6fc2616f3dbe9ef4e25dedd7d95781ba66,Distribution Matching in Variational Inference,"Distribution Matching in Variational Inference
Mihaela Rosca Balaji Lakshminarayanan
Shakir Mohamed
DeepMind"
b7aee9dfb027d6061c6a653684c0fa9a9bba750d,RECURRENT NEURAL NETWORKS,"Accepted as a workshop contribution at ICLR 2015
LEARNING LONGER MEMORY IN
RECURRENT NEURAL NETWORKS
Tomas Mikolov, Armand Joulin, Sumit Chopra, Michael Mathieu & Marc’Aurelio Ranzato
Facebook Artificial Intelligence Research
770 Broadway
New York City, NY, 10003, USA"
b75df22c7c52b8d85dd7f155f7b495907ff3561f,Benchmark data and method for real-time people counting in cluttered scenes using depth sensors,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, APRIL 2018
Benchmark data and method for real-time people
ounting in cluttered scenes using depth sensors
ShiJie Sun, Naveed Akhtar, HuanSheng Song, ChaoYang Zhang, JianXin Li, Ajmal Mian
Computer Vision techniques are well-suited to the problem
of automatic people counting for public transportations. How-
ever, using conventional RGB videos for this purpose is chal-
lenged by multiple issues resulting from real-world conditions
such as clutter, occlusions, illumination variations, handling
shadows etc. In comparison to the conventional video systems,
RGB-D cameras (e.g. Kinect V1 [4], Prime Sense Camera [5])
an mitigate these issues by providing ‘depth’ information
of the scene in addition to its color video. Nevertheless,
effective people counting in real-world conditions using depth
information still remains a largely unsolved problem due to
noise and occlusion [6]."
b732393cd3877f7e6d3cf3ca033a42415bd6db56,Statistical and Geometric Modeling of Spatio-Temporal Patterns for Video Understanding,
2f23f7d08c7b8670289cfedd1e571f44a3bace8b,Contextual Information and Covariance Descriptors for People Surveillance: An Application for Safety of Construction Workers,"Hindawi Publishing Corporation
EURASIP Journal on Image and Video Processing
Volume 2011, Article ID 684819, 16 pages
doi:10.1155/2011/684819
Research Article
Contextual Information and Covariance Descriptors for People
Surveillance: An Application for Safety of Construction Workers
Giovanni Gualdi,1 Andrea Prati,2 and Rita Cucchiara1
DII, University of Modena and Reggio Emilia, 41122 Modena, Italy
DISMI, University of Modena and Reggio Emilia, 42122 Reggio Emilia, Italy
Correspondence should be addressed to Andrea Prati,
Received 30 April 2010; Revised 7 October 2010; Accepted 10 December 2010
Academic Editor: Luigi Di Stefano
Copyright © 2011 Giovanni Gualdi et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
ited.
In computer science, contextual information can be used both to reduce computations and to increase accuracy. This paper
discusses how it can be exploited for people surveillance in very cluttered environments in terms of perspective (i.e., weak scene
alibration) and appearance of the objects of interest (i.e., relevance feedback on the training of a classifier). These techniques are
pplied to a pedestrian detector that uses a LogitBoost classifier, appropriately modified to work with covariance descriptors which"
2fda164863a06a92d3a910b96eef927269aeb730,Names and faces in the news,"Names and Faces in the News
Tamara L. Berg, Alexander C. Berg, Jaety Edwards, Michael Maire,
Ryan White, Yee-Whye Teh, Erik Learned-Miller and D.A. Forsyth
Computer Science Division
U.C. Berkeley
Berkeley, CA 94720"
2f8cf3747f3c7d8e230ad9ab3dbc5ea4e6b9cdf1,NIMBLE: a kernel density model of saccade-based visual memory.,"http://journalofvision.org/8/14/17/
NIMBLE: A kernel density model of saccade-based
visual memory
Luke Barrington
Tim K. Marks
Janet Hui-wen Hsiao
Garrison W. Cottrell
Department of Electrical and Computer Engineering,
University of California, San Diego, La Jolla, CA, USA
Department of Computer Science and Engineering,
University of California, San Diego, La Jolla, CA, USA
Department of Computer Science and Engineering,
University of California, San Diego, La Jolla, CA, USA
Department of Computer Science and Engineering,
University of California, San Diego, La Jolla, CA, USA
We present a Bayesian version of J. Lacroix, J. Murre, and E. Postma’s (2006) Natural Input Memory (NIM) model of
saccadic visual memory. Our model, which we call NIMBLE (NIM with Bayesian Likelihood Estimation), uses a cognitively
plausible image sampling technique that provides a foveated representation of image patches. We conceive of these
memorized image fragments as samples from image class distributions and model the memory of these fragments using
kernel density estimation. Using these models, we derive class-conditional probabilities of new image fragments and"
2fa4f66a7c3846a189ea1f962592d7c20d9683b1,Object Detection with YOLO on Artwork Dataset,"Object Detection with YOLO on Artwork Dataset
Yihui He∗
Computer Science Department, Xi’an Jiaotong University"
2fa1fc116731b2b5bb97f06d2ac494cb2b2fe475,A novel approach to personal photo album representation and management,"A novel approach to personal photo album representation
nd management
Edoardo Ardizzone, Marco La Cascia, and Filippo Vella
Universit`a di Palermo - Dipartimento di Ingegneria Informatica
Viale delle Scienze, 90128, Palermo, Italy"
2fa3ad0329386bf9f55eb2c011e031ca71a11299,Weakly-supervised Semantic Parsing with Abstract Examples,
2f0d5cd2d25ea2f3add0139cf4b61f358435bab8,A New Effective System for Filtering Pornography Videos,"Tarek Abd El Hafeez / (IJCSE) International Journal on Computer Science and Engineering
Vol. 02, No. 09, 2010, 2847-2852
A New Effective System for Filtering
Pornography Videos
Tarek Abd El-Hafeez
Department of Computer Science,
Faculty of Science, Minia University
El-Minia, Egypt"
2f000034f040f6a23c756671477f5f573514af8a,Learning transferable distance functions for human action recognition and detection,"-)41/ 64)5.-4)*- ,156)+- .7+615
.4 07) )+61 4-+/161 ),
,-6-+61
9AEC ;=C
*-C 5KJDA=IJ 7ELAHIEJO +DE=  %
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>A E MEJD JDA =M F=HJE?K=HO EB =FFHFHE=JAO"
2f1a5e5b2cc9d2b31913f1b873e2a617b6aaca05,A Particle Filter based Multi-person Tracking with Occlusion Handling,
2f0c30d6970da9ee9cf957350d9fa1025a1becb4,Deformable Convolutional Networks,"Deformable Convolutional Networks
Jifeng Dai∗ Haozhi Qi∗,† Yuwen Xiong∗,† Yi Li∗,† Guodong Zhang∗,† Han Hu Yichen Wei
Microsoft Research Asia"
2ffc045f48f25bfcf9fe6dbbc86d3f60ff7b112d,Selection of a Visible-Light vs . Thermal Infrared Sensor in Dynamic Environments Based on Confidence Measures,"Appl. Sci. 2014, 4, 331-350; doi:10.3390/app4030331
pplied sciences
OPEN ACCESS
ISSN 2076-3417
www.mdpi.com/journal/applsci
Article
Selection of a Visible-Light vs. Thermal Infrared Sensor in
Dynamic Environments Based on Confidence Measures
Juan Serrano-Cuerda 1, Antonio Fernández-Caballero 1;2;* and María T. López 1;2
Instituto de Investigación en Informática de Albacete, Albacete 02071, Spain;
E-Mails: (J.S.-C.); (M.T.L.)
Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete 02071, Spain
* Author to whom correspondence should be addressed; E-Mail:
Tel.: +34-967-599-200; Fax: +34-967-599-224.
Received: 16 June 2014; in revised form: 26 July 2014 / Accepted: 4 August 2014 /
Published: 8 August 2014"
2fd9ecb40df6c7cd4f27c047223a1e45aae1bb95,Feature-based affine-invariant localization of faces,"Feature-based affine-invariant localization of
faces
M. Hamouz, J. Kittler, J.-K. Kamarainen, P. Paalanen, H. K¨alvi¨ainen, J. Matas"
2f1972f1fa665f3306a487de9d004dfece719c0b,A Deep Learning Perspective on the Origin of Facial Expressions,"BREUER, KIMMEL: A DEEP LEARNING PERSPECTIVE ON FACIAL EXPRESSIONS
A Deep Learning Perspective on the Origin
of Facial Expressions
Ran Breuer
Ron Kimmel
Department of Computer Science
Technion - Israel Institute of Technology
Technion City, Haifa, Israel
Figure 1: Demonstration of the filter visualization process."
2f005b31b41face8a8b157e2ce7f97ece5b61391,L 1 Graph Based Sparse Model for Label Denoising,"Pages 74.1-74.12
DOI: https://dx.doi.org/10.5244/C.30.74"
2f33884d0612fcc3f7eed66e1a4acc229860d6b5,Survey on Spatio-Temporal View Invariant Human Pose Recovery,"Survey on Spatio-Temporal View
Invariant Human Pose Recovery
Xavier Perez-Sala, Email: a;c,
Sergio Escalera, Email: b;c and
Cecilio Angulo, Email: a
CETpD-UPC Technical Research Center for Dependency Care and Autonomous
Living, Universitat Polit`ecnica de Catalunya, Ne`apolis, Rambla de l’Exposici´o, 59-69,
Dept. Mathematics, Universitat de Barcelona, Gran Via de les Corts Catalanes 585,
08800 Vilanova i la Geltru, Spain
Computer Vision Center, Campus UAB, Edifici 0, 08193, Bellaterra, Spain
08007, Barcelona, Spain"
2fa241edb56734539c3b3487eda159e0b3e0f31c,Kinematic Pose Rectification for Performance Analysis and Retrieval in Sports,"Kinematic Pose Rectification for Performance Analysis and Retrieval in Sports
Dan Zecha, Moritz Einfalt, Christian Eggert and Rainer Lienhart
Multimedia Computing and Computer Vision Lab
University of Augsburg"
2f88d3189723669f957d83ad542ac5c2341c37a5,Attribute-correlated local regions for deep relative attributes learning,"Downloaded From: https://www.spiedigitallibrary.org/journals/Journal-of-Electronic-Imaging on 9/13/2018
Terms of Use: https://www.spiedigitallibrary.org/terms-of-use
Attribute-correlatedlocalregionsfordeeprelativeattributeslearningFenZhangXiangweiKongZeJiaFenZhang,XiangweiKong,ZeJia,“Attribute-correlatedlocalregionsfordeeprelativeattributeslearning,”J.Electron.Imaging27(4),043021(2018),doi:10.1117/1.JEI.27.4.043021."
2f02328dc09396e37e159141c5e21bef3e6ff06e,Combining face detection and people tracking in video sequences,"Author manuscript, published in ""The 3rd International Conference on Imaging for Crime Detection and Prevention - ICDP09,
Kingston Upon Thames (London) : Royaume-Uni (2009)"""
2ff9618ea521df3c916abc88e7c85220d9f0ff06,Facial Tic Detection Using Computer Vision,"Facial Tic Detection Using Computer Vision
Christopher D. Leveille
Advisor: Prof. Aaron Cass
March 20, 2014"
2fc15f80080b4317cad60ad645300b49afddb19e,Low cognitive load strengthens distractor interference while high load attenuates when cognitive load and distractor possess similar visual characteristics.,"Atten Percept Psychophys
DOI 10.3758/s13414-015-0866-9
Low cognitive load strengthens distractor interference while high
load attenuates when cognitive load and distractor possess similar
visual characteristics
Takehiro Minamoto & Zach Shipstead & Naoyuki Osaka &
Randall W. Engle
# The Psychonomic Society, Inc. 2015"
2f29b13fcf7a92a3cc438014068f11f9e45d62be,"AMIGOS: A Dataset for Affect, Personality and Mood Research on Individuals and Groups","AMIGOS: A dataset for Mood, personality and
ffect research on Individuals and GrOupS
Juan Abdon Miranda-Correa, Student Member, IEEE, Mojtaba Khomami Abadi, Student Member, IEEE,
Nicu Sebe, Senior Member, IEEE, and Ioannis Patras, Senior Member, IEEE"
2f529605ed776d4fbeac2d73054247b495504ac7,Person Re-identification for Real-world Surveillance Systems,"Person Re-identification for Real-world
Surveillance Systems
Furqan M. Khan and Fran¸cois Br´emond
INRIA Sophia Antipolis - M´editerran´ee
004 Route des Lucioles, Sophia Antipolis
{furqan.khan |"
2fc8f46ed3e679fa50ecddd7e394235d6b983b4e,AudioPairBank : towards a large-scale tag-pair-based audio content analysis,"IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, JULY 2017
AudioPairBank: Towards A Large-Scale
Tag-Pair-Based Audio Content Analysis
Sebastian S¨ager, and Benjamin Elizalde, and Damian Borth IEEE Member, and Christian Schulze,
nd Bhiksha Raj IEEE Fellow, and Ian Lane IEEE Member"
2f7e9b45255c9029d2ae97bbb004d6072e70fa79,cvpaper.challenge in 2015 - A review of CVPR2015 and DeepSurvey,"Noname manuscript No.
(will be inserted by the editor)
vpaper.challenge in 2015
A review of CVPR2015 and DeepSurvey
Hirokatsu Kataoka · Yudai Miyashita · Tomoaki Yamabe · Soma
Shirakabe · Shin’ichi Sato · Hironori Hoshino · Ryo Kato · Kaori Abe ·
Takaaki Imanari · Naomichi Kobayashi · Shinichiro Morita · Akio
Nakamura
Received: date / Accepted: date"
2fda461869f84a9298a0e93ef280f79b9fb76f94,OpenFace: An open source facial behavior analysis toolkit,"OpenFace: an open source facial behavior analysis toolkit
Tadas Baltruˇsaitis
Peter Robinson
Louis-Philippe Morency"
2faa09413162b0a7629db93fbb27eda5aeac54ca,Quantifying how lighting and focus affect face recognition performance,"NISTIR 7674
Quantifying How Lighting and Focus
Affect Face Recognition Performance
Phillips, P. J.
Beveridge, J. R.
Draper, B.
Bolme, D.
Givens, G. H.
Lui, Y. M."
2f7452476910a7dbf6231b6b27aed67d9ed455d3,Seam carving for content-aware image resizing,"Seam Carving for Content-Aware Image Resizing
Shai Avidan
Mitsubishi Electric Research Labs
Ariel Shamir
The Interdisciplinary Center & MERL
Figure 1: A seam is a connected path of low energy pixels in an image. On the left is the original image with one horizontal and one vertical
seam. In the middle the energy function used in this example is shown (the magnitude of the gradient), along with the vertical and horizontal
path maps used to calculate the seams. By automatically carving out seams to reduce image size, and inserting seams to extend it, we achieve
ontent-aware resizing. The example on the top right shows our result of extending in one dimension and reducing in the other, compared to
standard scaling on the bottom right."
2f988201fb8316772bca1b44cc0e42f774edd714,Conditional Random Field (CRF)-Boosting: Constructing a Robust Online Hybrid Boosting Multiple Object Tracker Facilitated by CRF Learning,"Article
Conditional Random Field (CRF)-Boosting:
Constructing a Robust Online Hybrid Boosting
Multiple Object Tracker Facilitated by CRF Learning
Ehwa Yang, Jeonghwan Gwak and Moongu Jeon *
School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology,
Gwangju 61005, Korea; (E.Y.); (J.G.)
* Correspondence: Tel.: +82-62-715-2406
Academic Editor: Joonki Paik
Received: 6 December 2016; Accepted: 14 March 2017; Published: 17 March 2017"
2f8ef26bfecaaa102a55b752860dbb92f1a11dc6,A Graph Based Approach to Speaker Retrieval in Talk Show Videos with Transcript-Based Supervision,"A Graph Based Approach to Speaker Retrieval in Talk
Show Videos with Transcript-Based Supervision
Yina Han  1, Guizhong Liu, Hichem Sahbi, Gérard Chollet"
2fdb3576715829aa9bbaf74825236bbb71d06f1a,Where-and-When to Look: Deep Siamese Attention Networks for Video-based Person Re-identification,"Where-and-When to Look: Deep Siamese Attention
Networks for Video-based Person Re-identification
Lin Wu, Yang Wang, Junbin Gao, Xue Li"
2f78e471d2ec66057b7b718fab8bfd8e5183d8f4,An Investigation of a New Social Networks Contact Suggestion Based on Face Recognition Algorithm,"SOFTWARE ENGINEERING
VOLUME: 14 | NUMBER: 5 | 2016 | DECEMBER
An Investigation of a New Social Networks
Contact Suggestion Based on Face Recognition
Algorithm
Ivan ZELINKA1,2, Petr SALOUN 2, Jakub STONAWSKI 2, Adam ONDREJKA2
Modeling Evolutionary Algorithms Simulation and Artificial Intelligence, Faculty of Electrical & Electronics
Engineering, Ton Duc Thang University, 19 Nguyen Huu Tho Street, Ho Chi Minh City, Vietman
Department of Computer Science, Faculty of Electrical Engineering and Computer Science,
VSB–Technical University of Ostrava, 17. listopadu 15, 708 33 Ostrava, Czech Republic
DOI: 10.15598/aeee.v14i5.1116"
2f184c6e2c31d23ef083c881de36b9b9b6997ce9,Polichotomies on imbalanced domains by one-per-class compensated reconstruction rule,"Polichotomies on Imbalanced Domains
y One-per-Class Compensated Reconstruction Rule
Roberto D’Ambrosio and Paolo Soda
Integrated Research Centre, Universit´a Campus Bio-Medico of Rome, Rome, Italy"
2f3125bf303bca19d9cdc9ffe1de2aacf7a23023,In-Bed Pose Estimation: Deep Learning with Shallow Dataset,"JOURNAL OF , VOL. , NO. , MONTH YEAR
In-Bed Pose Estimation:
Deep Learning with Shallow Dataset
Shuangjun Liu, Yu Yin, and Sarah Ostadabbas"
2fdc469096f72533726964260c80b4c14ae62fab,A KERNEL MAXIMUM UNCERTAINTY DISCRIMINANT ANALYSIS AND ITS APPLICATION TO FACE RECOGNITION,"A KERNEL MAXIMUM UNCERTAINTY DISCRIMINANT
ANALYSIS AND ITS APPLICATION TO FACE RECOGNITION
Department of Electrical Engineering, Centro Universitario da FEI, FEI, Sao Paulo, Brazil
Carlos Eduardo Thomaz
Gilson Antonio Giraldi
Department of Computer Science, National Laboratory for Scientific Computing, LNCC, Rio de Janeiro, Brazil
Keywords:"
2f43bfedb8cffc9e44de9f95db80b26395a29cc8,Generalized Hadamard-Product Fusion Operators for Visual Question Answering,"Generalized Hadamard-Product Fusion Operators
for Visual Question Answering
Brendan Duke∗†, Graham W. Taylor∗†‡
School of Engineering, University of Guelph
Vector Institute for Artificial Intelligence
Canadian Institute for Advanced Research"
2f04c7aaac3a884088be550d1be51b4a0b585a2e,"Robust, Real-Time 3D Tracking of Multiple Objects with Similar Appearances","Robust, Real-Time 3D Tracking of Multiple Objects with Similar Appearances
Taiki Sekii
Panasonic System Networks R&D Lab. Co., Ltd."
2f48f1cb1cfef964fa70d7868b87d81455e7be2e,A new image centrality descriptor for wrinkle frame detection in WCE videos,"MVA2013 IAPR International Conference on Machine Vision Applications, May 20-23, 2013, Kyoto, JAPAN
A new image centrality descriptor for wrinkle frame detection in
WCE videos.
Santi Segu´ı1,2, Ekaterina Zaytseva1,2, Michal Drozdzal1,2, Carolina Malagelada3,
Fernando Azpiroz3, Petia Radeva1,2 and Jordi Vitri`a1,2
Computer Vision Center (CVC), Universitat Aut`onoma de Barcelona, Barcelona, Spain
Dept. Matem`atica Aplicada i An`alisis, Universitat de Barcelona, Barcelona, Spain
Digestive System Research Unit, Hospital Vall dHebron, Barcelona, Spain"
2f59f28a1ca3130d413e8e8b59fb30d50ac020e2,Children Gender Recognition Under Unconstrained Conditions Based on Contextual Information,"Children Gender Recognition Under Unconstrained
Conditions Based on Contextual Information
Riccardo Satta, Javier Galbally and Laurent Beslay
Joint Research Centre, European Commission, Ispra, Italy
Email:"
2f3c60ebd92240099e52c1cb6d33832b0b8979d1,Spatial pyramid co-occurrence for image classification,"011 IEEE International Conference on Computer Vision
978-1-4577-1102-2/11/$26.00 c(cid:13)2011 IEEE"
2f3a67394deb32f265bcff9daf2c829d4be36336,Improving Visual Relationship Detection Using Semantic Modeling of Scene Descriptions,"Improving Visual Relationship Detection using
Semantic Modeling of Scene Descriptions
Stephan Baier1, Yunpu Ma1,2, and Volker Tresp1,2
Ludwig Maximilian University, 80538 Munich, Germany
Siemens AG, Corporate Technology, Munich, Germany"
2feea7d50b312a6c07673fda325838139b2e7820,Analysis of technologies and resources for multimodal information kiosk for deaf users,"Analysis of Technologies and Resources
for Multimodal Information Kiosk for Deaf Users
Miloˇs ˇZelezn´y
Department of Cybernetics, Faculty of Applied Sciences,
University of West Bohemia, Pilsen, Czech Republic"
2f587ab6694fdcfe6bd2977120ebeb758e28d77f,Coupled Generative Adversarial Nets,"MITSUBISHI ELECTRIC RESEARCH LABORATORIES
http://www.merl.com
Coupled Generative Adversarial Nets
Liu, M.-Y.; Tuzel, O.
TR2016-070
June 2016"
2fa1629d75a03b950c56bf9b3430b2983abd7881,Learning geometrical transforms between multi camera views using Canonical Correlation Analysis,"CONRAD, MESTER: LEARNING GEOMETRICAL TRANSFORMS USING CCA
Learning geometrical transforms between
multi camera views using Canonical
Correlation Analysis
Christian Conrad
Rudolf Mester
Visual Sensorics and Information
Processing Lab, Goethe University
Frankfurt am Main, Germany
Computer Vision Laboratory
Electr. Eng. Dept. (ISY)
Linköping University, Sweden"
2f95340b01cfa48b867f336185e89acfedfa4d92,Face expression recognition with a 2-channel Convolutional Neural Network,"Face Expression Recognition with a 2-Channel
Convolutional Neural Network
Dennis Hamester, Pablo Barros, Stefan Wermter
University of Hamburg — Department of Informatics
Vogt-K¨olln-Straße 30, 22527 Hamburg, Germany
http://www.informatik.uni-hamburg.de/WTM/"
2f489bd9bfb61a7d7165a2f05c03377a00072477,Structured Semi-supervised Forest for Facial Landmarks Localization with Face Mask Reasoning,"JIA, YANG: STRUCTURED SEMI-SUPERVISED FOREST
Structured Semi-supervised Forest for
Facial Landmarks Localization with Face
Mask Reasoning
Department of Computer Science
The Univ. of Hong Kong, HK
School of EECS
Queen Mary Univ. of London, UK
Xuhui Jia1
Heng Yang2
Angran Lin1
Kwok-Ping Chan1
Ioannis Patras2"
2fce767ad830e0203d62ce30bbe75213b959d19c,Histogram of Log-Gabor Magnitude Patterns for face recognition,"014 IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP)
978-1-4799-2893-4/14/$31.00 ©2014 IEEE
School of Information and Communication Engineering,
{yijun,
Jun Yi†, Fei Su†‡
. INTRODUCTION"
2f77c0908716b0febfda19ff6a0e2970c23af440,A face recognition system dealing with expression variant faces,"A face recognition system dealing with expression variant faces
Stefano Arca∗, Paola Campadelli, Raffaella Lanzarotti, Giuseppe Lipori
Dipartimento di Scienze dell’Informazione
Universit`a degli Studi di Milano
Via Comelico, 39/41 20135 Milano, Italy"
2f13dd8c82f8efb25057de1517746373e05b04c4,Evaluation of state-of-the-art algorithms for remote face recognition,"EVALUATION OF STATE-OF-THE-ART ALGORITHMS FOR REMOTE FACE
RECOGNITION
Jie Ni and Rama Chellappa
Department of Electrical and Computer Engineering and Center for Automation Research, University
of Maryland, College Park, MD 20742, USA"
2fe5bea76853da079512595fb8bdd74ef4ccf12f,Face recognition using two-dimensional nonnegative principal component analysis,"Face recognition using two-dimensional
nonnegative principal component
nalysis
Peng Ma
Dan Yang
Yongxin Ge
Xiaohong Zhang
Ying Qu
Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 08/24/2012 Terms of Use: http://spiedl.org/terms"
2ff37f7e00c624066f596b71ca3331171d0f66d1,‘ vVISWa ’ – A Multilingual Multi-Pose Audio Visual Database for Robust Human Computer Interaction,"International Journal of Computer Applications (0975 – 8887)
Volume 137 – No.4, March 2016
‘vVISWa’ – A Multilingual Multi-Pose Audio Visual
Database for Robust Human Computer Interaction
Ramesh Manza
Biomedical Image
Processing Lab
Dr. Babasaheb
Ambedkar
Bharti Gawali
SpeechCommunication
nd Machine Research
Lab Dr. Babasaheb
Ambedkar
Pravin Yannawar
Vision and Intelligent
System Lab
Dr. Babasaheb
Ambedkar
Prashant Borde"
2fc2250d843326f3eefab1941e5a6e54eef239b3,Appearance Based Facial Recognition System Using Dhmm with Linear Discriminant Analysis,"Daffodil International University
Institutional Repository
DIU Journal of Science and Technology
Volume 10, Issue 1-2, July 2015
016-06-18
Appearance Based Facial Recognition
System Using Dhmm with Linear
Discriminant Analysis
Islam, Md. Rabiul
http://hdl.handle.net/20.500.11948/1487
Downloaded from  http://dspace.library.daffodilvarsity.edu.bd, Copyright Daffodil International University Library"
aa09ade36424fd83f067f234baffde294800e705,Is a Picture Worth Ten Thousand Words in a Review Dataset?,"Is a Picture Worth Ten Thousand Words
in a Review Dataset?
Roberto Camacho, Laura M. Rodriguez, Rebecca Urbina, and M. Shahriar Hossain
Dept. of Computer Science, University of Texas at El Paso, El Paso, TX 79968
{lmrodriguez3,"
aabcbe3ee81ff4f696c7cefc49d28a41fb760987,Automated annotation of coral reef survey images,"Automated Annotation of Coral Reef Survey Images
Oscar Beijbom†
Peter J. Edmunds∗
David I. Kline‡
B. Greg Mitchell‡
David Kriegman†
{obeijbom, dkline, gmitchell,
{†Department of Computer Science and Engineering, ‡Scripps Institution of Oceanography},
University of California, San Diego. ∗Department of Biology, California State University Northridge."
aad8d2e32f1cc21eedbdd5e8ebff9f367daa6d92,Online multi-target tracking by large margin structured learning,"Online Multi-Target Tracking
y Large Margin Structured Learning
Suna Kim, Suha Kwak, Jan Feyereisl, and Bohyung Han
Department of Computer Science and Engineering
POSTECH, Korea"
aaa021feeec2f84c4a5f3c56b4c0fecb5a85a352,A Riemannian Network for SPD Matrix Learning,"A Riemannian Network for SPD Matrix Learning
Zhiwu Huang and Luc Van Gool
Computer Vision Lab, ETH Zurich, Switzerland
{zhiwu.huang,"
aa261599d70a9e649501cae5cf46fbc56229fad8,The effect of the Distance in Pedestrian Detection,"Master in Computer Vision and Artificial Intelligence - Universitat Aut`onoma de Barcelona
September 2009
The effect of the Distance in Pedestrian Detection
David V´azquez Berm´udez
Computer Vision Center
Edifici O, Universitat Aut`onoma de Barcelona
08193, Bellaterra (Spain)
Advisors: Dr. Antonio M. L´opez and David Ger´onimo"
aaa82dfc7942ae16c1d7155a109582505ccee4ec,Properties of Datasets Predict the Performance of Classifiers,"AGHAZADEH, CARLSSON: PROPERTIES OF DATASETS PREDICT THE PERFORMANCE ... 1
Properties of Datasets Predict the
Performance of Classifiers
Omid Aghazadeh
http://www.csc.kth.se/~omida
Stefan Carlsson
http://www.csc.kth.se/~stefanc
Computer Vision Group
Computer Vision and Active Perception
Laboratory
KTH, Sweden"
aa17a5e76ae21d0d068c7f10a4bb24c19888b685,SPARSE BAYESIAN LEARNING IN CLASSIFYING FACE FEATURE VECTORS,"JOURNAL OF MEDICAL INFORMATICS & TECHNOLOGIES Vol. 9/2005, ISSN 1642-6037
Alina MOMOT, Michał KAWULOK *
supervised learning, Bayesian inference,
face recognition
SPARSE BAYESIAN LEARNING IN CLASSIFYING FACE FEATURE VECTORS
The  Relevance  Vector  Machine  (RVM),  a Bayesian  treatment  of  generalized  linear  model  of  identical
functional form to the Support Vector Machine (SVM), is the recently developed machine learning framework
apable of building simple models from large sets of candidate features. The paper describes the application of
the RVM to a classification algorithm of face feature vectors, obtained by Eigenfaces method. Moreover, the
results of the RVM classification are compared with those obtained by using both the Support Vector Machine
method and the method based on the Euclidean distance.
.  INTRODUCTION
The aim of the machine learning is extracting the structure from the data. Anyhow it is often
difficult  to  solve  problems  like  classification  in  the  space,  in  which  the  underlying  observations
have been made. Kernel-based learning methods like the Support Vector Machine (SVM) [7] or the
Relevance  Vector  Machine  (RVM)  [4]  use  implicit  mapping  of  the  input  data  into  a  high
dimensional feature space defined by a kernel function and the learning takes place in the feature
space. An interesting property of kernel-based systems is that, once a valid kernel function has been
selected, one can practically work in spaces of any dimension without paying any computational
ost, since feature mapping is never effectively performed."
aac101dd321e6d2199d8c0b48c543b541c181b66,USING CONTEXT TO ENHANCE THE UNDERSTANDING OF FACE IMAGES,"USING CONTEXT TO ENHANCE THE
UNDERSTANDING OF FACE IMAGES
A Dissertation Presented
VIDIT JAIN
Submitted to the Graduate School of the
University of Massachusetts Amherst in partial fulfillment
of the requirements for the degree of
DOCTOR OF PHILOSOPHY
September 2010
Department of Computer Science"
aa23d33983b1abd2d8a677040eb875e93c478a7f,Measuring the Objectness of Image Windows,"Measuring the objectness of image windows
Bogdan Alexe, Thomas Deselaers, and Vittorio Ferrari"
aad7f9eeb10d4f655c3e3d18d3542603ad3071b4,Deep Unsupervised Learning of Visual Similarities,"Deep Unsupervised Learning of Visual Similarities
Artsiom Sanakoyeu∗, Miguel A. Bautista, Björn Ommer
Heidelberg Collaboratory for Image Processing and Interdisciplinary Center for Scientific Computing, Heidelberg University, Germany"
aad03480c30c0a3d917d171d8d6b914026fe5105,Affordances Provide a Fundamental Categorization Principle for Visual Scenes,"Affordances
Provide
Fundamental
Categorization
Principle
Visual
Scenes
Michelle
Greene
Christopher
Baldassano
Andre
Esteva
Diane
(1) Stanford
University,
Department
Computer
Science
(2) Stanford"
aa8cec9cec1f15f95bbe0ef4d7809e199de0f30b,Vitamin D hormone regulates serotonin synthesis. Part 1: relevance for autism.,"The FASEB Journal (cid:129) Review
Vitamin D hormone regulates serotonin synthesis.
Part 1: relevance for autism
Rhonda P. Patrick1 and Bruce N. Ames1
Nutrition and Metabolism Center, Children’s Hospital Oakland Research Institute, Oakland,
California, USA
Serotonin and vitamin D have been pro-"
aa9b924c88b75909871585cbadbed2d76df7dbdf,The effects of item familiarity on the neural correlates of successful associative memory encoding.,"Cogn Affect Behav Neurosci
DOI 10.3758/s13415-015-0359-2
The effects of item familiarity on the neural correlates
of successful associative memory encoding
Nancy A. Dennis 1 & Indira C. Turney 1 & Christina E. Webb 1 & Amy A. Overman 2
# Psychonomic Society, Inc. 2015"
aa5efcc4331da6b1902f2c900b79120226fdcf20,A ROBUST CLASS-BASED REFLECTANCE RENDERING FOR FACE IMAGES,A ROBUST CLASS-BASED REFLECTANCE RENDERING FOR FACE IMAGES
aa5ed6ee0b2fd53df5cab952aa368f8c4908ffeb,REACH - Realtime crowd tracking using a hybrid motion model,"REACH - Realtime Crowd tracking using a Hybrid motion model
Aniket Bera1 and Dinesh Manocha1
http://gamma.cs.unc.edu/REACH"
aa2ad3df24d8d8c4a4d2fe85f0d4e635d595f0a2,PedCut: an iterative framework for pedestrian segmentation combining shape models and multiple data cues,"F. FLOHR, D. M. GAVRILA: PEDCUT
PedCut: an iterative framework for
pedestrian segmentation combining
shape models and multiple data cues
Fabian Flohr1,2
Dariu M. Gavrila1,2
www.gavrila.net
Environment Perception Department,
Daimler R&D, Ulm, Germany
Intelligent Systems Laboratory,
Univ. of Amsterdam, The Netherlands"
aa36d50e7cb584d68a3eef0d3345954aa58f63df,Sub-Image Homomorphic Filtering Technique for Improving Facial Identification under Difficult Illumination Conditions,"International Conference on Systems, Signals and Image Processing (IWSSIP’06)
September 21-23, 2006. Budapest, Hungary"
aadfcaf601630bdc2af11c00eb34220da59b7559,Multi-view Hybrid Embedding: A Divide-and-Conquer Approach,"Multi-view Hybrid Embedding:
A Divide-and-Conquer Approach
Jiamiao Xu∗, Shujian Yu∗, Xinge You†, Senior Member, IEEE, Mengjun Leng,
Xiao-Yuan Jing, and C. L. Philip Chen, Fellow, IEEE"
aa782f4af587ee68936f0f5361fc1448ef61bdd9,Human Tracking using Wearable Sensors in the Pocket Double blind submission,"Human Tracking using Wearable Sensors in the Pocket
Double blind submission
Address
e-mail address"
aaa4c625f5f9b65c7f3df5c7bfe8a6595d0195a5,Biometrics in ambient intelligence,"Biometrics in Ambient Intelligence
Massimo Tistarelli§ and Ben Schouten§§"
aaaeca92457a72ec4e7e538cf6393c4c1dc8e670,Life-long Learning Perception using Cloud Database Technology,"Life-long Learning Perception using Cloud Database Technology
Tim Niemueller
Stefan Schiffer
Gerhard Lakemeyer
Knowledge-based Systems Group
Safoura Rezapour Lakani
Intelligent and Interactive Systems
RWTH Aachen University (Aachen, Germany)
University of Innsbruck (Innsbruck, Austria)"
aa5c2ac60a288132efeeb85c5af1fd0b39294eed,Directed Markov Stationary Features for visual classification,"978-1-4244-2354-5/09/$25.00 ©2009 IEEE
ICASSP 2009"
aa49556ee4f1ee3fcc9f0f713c755da30b0f505c,Exactly Robust Kernel Principal Component Analysis,"Exactly Robust Kernel Principal Component
Analysis
Jicong Fan, Tommy W.S. Chow"
aaf1d8237524ebe671b3e56f803747b7092dd1f8,Robust visual SLAM with point and line features,"Robust Visual SLAM with Point and Line Features
Xingxing Zuo1, Xiaojia Xie1, Yong Liu1,2 and Guoquan Huang3"
aa5fbe092f8a4dcb43c31ab93af0290900b4f0e2,Visual Question Answering using Natural Language Object Retrieval and Saliency Cues,"Visual Question Answering using Natural Language Object Retrieval and
CS381V Final Project Report
Saliency Cues
Aishwarya Padmakumar
Akanksha Saran"
aa8cc8a80fe591676b09ef50b9cc695c28460c73,Sensing requirements for an automated vehicle for highway and rural environments,Sensingrequirementsforanautomatedvehicleforhigh-wayandruralenvironmentsMScthesisK.J.BussemakerTechnischeUniversiteitDelft
aae1bf434983545c8a99a5dbfc2ce37435c76e03,SampleAhead: Online Classifier-Sampler Communication for Learning from Synthesized Data,"CHEN ET.AL: SAMPLEAHEAD – ONLINE CLASSIFIER-SAMPLER COMMUNICATION
SampleAhead: Online Classifier-Sampler
Communication for Learning from
Synthesized Data
Department of Computer Science,
The Johns Hopkins Univerisity
* This work is supported by IARPA via DOI/IBC contract
#D17PC00345 and ONR grant N00014-15-1-2356.
Qi Chen, Weichao Qiu, Yi Zhang
Lingxi Xie(), Alan L. Yuille"
aafb8dc8fda3b13a64ec3f1ca7911df01707c453,Excitation Backprop for RNNs,"Excitation Backprop for RNNs
Sarah Adel Bargal∗1, Andrea Zunino∗ 2, Donghyun Kim1, Jianming Zhang3,
Vittorio Murino2,4, Stan Sclaroff1
Department of Computer Science, Boston University 2Pattern Analysis & Computer Vision (PAVIS),
Istituto Italiano di Tecnologia 3Adobe Research 4Computer Science Department, Universit`a di Verona
Figure 1: Our proposed framework spatiotemporally highlights/grounds the evidence that an RNN model used in producing a class label
or caption for a given input video. In this example, by using our proposed back-propagation method, the evidence for the activity class
CliffDiving is highlighted in a video that contains CliffDiving and HorseRiding. Our model employs a single backward pass to produce
saliency maps that highlight the evidence that a given RNN used in generating its outputs."
aa2a4f7cf8866d513053873a410879ab5b34b53a,Improving robot manipulation with data-driven object-centric models of everyday forces,"Noname manuscript No.
(will be inserted by the editor)
Improving Robot Manipulation with Data-Driven
Object-Centric Models of Everyday Forces
Advait Jain · Charles C. Kemp
Received: date / Accepted: date"
aa420d32c48a3fd526a91285673cd55ca9fe2447,R 4-A . 1 : Dynamics-Based Video Analytics,"R4-A.1: Dynamics-Based Video Analytics
PARTICIPANTS
Octavia Camps
Mario Sznaier
Title
Co-PI
Co-PI
Faculty/Staff
Institution
Graduate, Undergraduate and REU Students
Oliver Lehmann
Mengran Gou
Yongfang Cheng
Yin Wang
Sadjad Ashari-Esfeden
Tom Hebble
Rachel Shaff er
Burak Yilmaz
Degree Pursued
MSEE/ PhD"
aa3e1824af497dc16ae27e6818a0e89c78a18371,Local Gray Code Pattern ( LGCP ) : A Robust Feature Descriptor for Facial Expression Recognition,"International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064
Local Gray Code Pattern (LGCP): A Robust
Feature Descriptor for Facial Expression
Recognition
Mohammad Shahidul Islam
Atish Dipankar University of Science & Technology, School, Department of Computer Science and Engineering, Dhaka, Bangladesh."
aa6854612062edff9978b33e0a410f2717bc3027,LPT: Eye Features Localizer in an N-Dimensional Image Space.,"LPT: Eye Features Localizer in an N-Dimensional Image
Space
Mohammad Mahdi Dehshibi1, Azam Bastanfard2, and Alireza Abdi3
Young Researchers Club, Islamic Azad University South Tehran Branch, Tehran, Iran
IT Research Laboratory, Faculty of Engineering, Islamic Azad University Karaj Branch, Karaj, Iran
Faculty of Electrical, Computer and IT, Islamic Azad University Qazvin Branch, Qazvin, Iran"
aa94f214bb3e14842e4056fdef834a51aecef39c,Reconhecimento de padrões faciais : Um estudo,"Reconhecimento de padrões faciais: Um estudo
Alex Lima Silva, Marcos Evandro Cintra
Universidade Federal
Rural do Semi-Árido
Departamento de Ciências Naturais
Mossoró, RN - 59625-900
Email:
Resumo—O reconhecimento facial tem sido utilizado em di-
versas áreas para identificação e autenticação de usuários. Um
dos principais mercados está relacionado a segurança, porém há
uma grande variedade de aplicações relacionadas ao uso pessoal,
onveniência, aumento de produtividade, etc. O rosto humano
possui um conjunto de padrões complexos e mutáveis. Para
reconhecer esses padrões, são necessárias técnicas avançadas de
reconhecimento de padrões capazes, não apenas de reconhecer,
mas de se adaptar às mudanças constantes das faces das pessoas.
Este documento apresenta um método de reconhecimento facial
proposto a partir da análise comparativa de trabalhos encontra-
dos na literatura.
iométrica é o uso da biometria para reconhecimento, identi-"
aa6f094f17d78380f927555a348ad514a505cc3b,SlowFast Networks for Video Recognition,"SlowFast Networks for Video Recognition
Christoph Feichtenhofer
Haoqi Fan
Jitendra Malik
Kaiming He
Facebook AI Research (FAIR)"
aa74e63360c341f47a921e0043c5d58d55807fe4,Multi-Residual Networks: Improving the Speed and Accuracy of Residual Networks,"SUBMITTED FOR PUBLICATION, 2016
Multi-Residual Networks: Improving the Speed and
Accuracy of Residual Networks
Masoud Abdi, and Saeid Nahavandi, Senior Member, IEEE"
74d4224989b5937ee6c97eec1955e64ab0699f57,Facial Emotional Classifier For Natural Interaction,"Electronic Letters on Computer Vision and Image Analysis 7(4):1-12, 2008
Facial Emotional Classifier For Natural Interaction
Isabelle Hupont, Eva Cerezo, Sandra Baldassarri
Departamento de Informática e Ingeniería de Sistemas,
Instituto de Investigación en Ingeniería de Aragón, Universidad de Zaragoza (Spain)
Received 29th  November 2007,  Revised 26th February 2008,  Accepted 3rd June 2008
{478953, ecerezo,"
74408cfd748ad5553cba8ab64e5f83da14875ae8,Facial Expressions Tracking and Recognition: Database Protocols for Systems Validation and Evaluation,"Facial Expressions Tracking and Recognition: Database Protocols for Systems Validation
nd Evaluation"
74f21f2edfa985280be63f8a01aa00541f3a5625,People Groping by Spatio-Temporal Features of Trajectories,"4-13
MVA2013 IAPR International Conference on Machine Vision Applications, May 20-23, 2013, Kyoto, JAPAN
People Groping by Spatio-Temporal Features of Trajectories
Asami Okada†, Yusuke Moriguchi†, Norimichi Ukita†,
nd Norihiro Hagita†‡
Nara Institute od Science and Technology
Advanced Telecommunications Research Institute International
e-mail"
74e6110466306f41f703d84bb3d136ba414b1998,Face Recognition System under Varying Lighting Conditions,"IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 14, Issue 3 (Sep. - Oct. 2013), PP 79-88
www.iosrjournals.org
Face Recognition System under Varying Lighting Conditions
P.Kalaiselvi1, S.Nithya2
(Asst. Professor, Department of ECE, NSN College of Engineering and Technology, Karur, Tamilnadu, India)
(Asst. Professor, Department of ECE, NSN College of Engineering and Technology, Karur, Tamilnadu, India)"
7444adac0f4c0004fe557e7fd68fdc89c99ca10b,CUNI System for the WMT18 Multimodal Translation Task,"CUNI System for the WMT18 Multimodal Translation Task
Jindˇrich Helcl and Jindˇrich Libovick´y and Duˇsan Variˇs
Charles University, Faculty of Mathematics and Physics
Institute of Formal and Applied Linguistics
Malostransk´e n´amˇest´ı 25, 118 00 Prague, Czech Republic
{helcl, libovicky,"
74dbe6e0486e417a108923295c80551b6d759dbe,An HMM based Model for Prediction of Emotional Composition of a Facial Expression using both Significant and Insignificant Action Units and Associated Gender Differences,"International Journal of Computer Applications (0975 – 8887)
Volume 45– No.11, May 2012
An HMM based Model for Prediction of Emotional
Composition of a Facial Expression using both
Significant and Insignificant Action Units and
Associated Gender Differences
Suvashis Das
Koichi Yamada
Department of Management and Information
Department of Management and Information
Systems Science
603-1 Kamitomioka, Nagaoka
Niigata, Japan
Systems Science
603-1 Kamitomioka, Nagaoka
Niigata, Japan"
74b0095944c6e29837c208307a67116ebe1231c8,Manifold learning using Euclidean k-nearest neighbor graphs [image processing examples],"be
independentandidenticallydis-tributed(i.i.d.)randomvectorswithvaluesinacompactsubsetof.The(-)nearestneighborofinisgivenby!""$%&(*,.%13575where575istheusualEuclidean(<=)distanceinbe-tweenvectorand.Forgeneralinteger?,the-nearestneighborofapointisdefinedinasimilarway.The-NNgraphputsanedgebetweeneachpointinandits-nearestneighbors.LetBCDBCDFHbethesetof-nearestneighborsofin.Thetotaledgelengthofthe-NNgraphisdefinedas:<JDCFHMN
M%&QRS1575J(1)whereVWXisapowerweightingconstant.2.1.ConvergencetoExtrinsicZ-EntropyThe-NNedgelengthliesinthelargeclassoffunctionalscalledcontinuousquasi-additiveEuclideanfunctionals[7].Othergraphsinthisclassincludetheminimalspanningtree,theminimalmatch-inggraphorthetravelingsalesmantouramongothers.Thesefunc-tionalshaveremarkableasymptoticbehavioras
increases:Theorem1([7,Theorem8.3])Let
bei.i.d.randomvectorswithvaluesinacompactsubsetofandLebesgueden-sity\.Let]?_,aVb]anddefineZF]7VHf].Then,withprobability(w.p.)gh""jk<JDCFH
moDJDCp\mFrHtr(2)whereoDJDCisaconstantindependentof\.Furthermore,themeanlengthuv<JDCFHwf
mconvergestothesamelimit.Thequantitythatdeterminesthelimit(2)inTheorem1istheex-trinsicR´enyiZ-entropyofthemultivariateLebesguedensity\:yz{mF\H7Zg!pz{\mFrHtr(3)III - 9880-7803-8484-9/04/$20.00 ©2004 IEEEICASSP 2004(cid:224)"
74839baf9ca8199b850c9cec0abc7c064112a48f,Advances in Radio Science Improving pedestrian detection using MPEG-7 descriptors,"Adv. Radio Sci., 11, 101–105, 2013
www.adv-radio-sci.net/11/101/2013/
doi:10.5194/ars-11-101-2013
© Author(s) 2013. CC Attribution 3.0 License.
Advances in
Radio Science
Improving pedestrian detection using MPEG-7 descriptors
H. Lietz1, M. Ritter2, R. Manthey2, and G. Wanielik1
Chemnitz University of Technology, Professorship on Communications Engineering, Reichenhainer Straße 70,
09126 Chemnitz, Germany
Chemnitz University of Technology, Chair Media Informatics, Straße der Nationen 62, 09111 Chemnitz, Germany
Correspondence to: H. Lietz"
74a1336f1fbc8b7bb3b6e159711af1a91336ce22,An overview of traffic sign detection methods,"An overview of traffic sign detection methods
Department of Electronics, Microelectronics, Computer and Intelligent Systems
Faculty of Electrical Engineering and Computing
Karla Brki´c
Unska 3, 10000 Zagreb, Croatia
Email:"
74760efe2a2bbc4b44375a0175b07b84ca24374b,A Novel Similarity Metric for Retinal Images based Authentication,"A NOVEL SIMILARITY METRIC FOR RETINAL IMAGES BASED
AUTHENTICATION
M. Ortega, M. G. Penedo, C. Mari˜no
Department of Computer Science, University of A Coru˜na, A Coru˜na, Spain
Department of Computer Science and Electronics, University of Santiago de Compostela, Santiago de Compostela, Spain
M. J. Carreira
Keywords:"
74618fb4ce8ce0209db85cc6069fe64b1f268ff4,Rendering and animating expressive caricatures,"Rendering and Animating Expressive
Caricatures
Mohammad Obaid* t, Ramakrishnan
Mukundan
*HITLab New Zealand,
University
of Canterbury,
t, and Mark Billinghurst*
Christchurch,
New Zealand
tComputer
Science
nd Software Engineering
Email: {mohammad.obaid,
Dept., University
of Canterbury,
New Zealand
stylized
nd control
on the generated caricature."
74156a11c2997517061df5629be78428e1f09cbd,"Preparatory coordination of head, eyes and hands: Experimental study at intersections","Cancún Center, Cancún, México, December 4-8, 2016
978-1-5090-4846-5/16/$31.00 ©2016 IEEE"
74c19438c78a136677a7cb9004c53684a4ae56ff,RESOUND: Towards Action Recognition Without Representation Bias,"RESOUND: Towards Action Recognition
without Representation Bias
Yingwei Li, Yi Li, and Nuno Vasconcelos
UC San Diego"
748260579dc2fb789335a88ae3f63c114795d047,Action and Interaction Recognition in First-Person Videos,"Action and Interaction Recognition in First-person videos
Sanath Narayan
Dept. of Electrical Engg.,
IISc, Bangalore
Mohan S. Kankanhalli
School of Computing,
NUS, Singapore
Kalpathi R. Ramakrishnan
Dept. of Electrical Engg.,
IISc, Bangalore"
7484911e00afec5c08e7b83f3a1259d60035d77f,In Your Face: Startle to Emotional Facial Expressions Depends on Face Direction,"Article
In Your Face: Startle to
Emotional Facial Expressions
Depends on Face Direction
i-Perception
January-February 2017, 1–13
! The Author(s) 2017
DOI: 10.1177/2041669517694396
journals.sagepub.com/home/ipe
Ole A˚ sli, Henriette Michalsen and Morten Øvervoll
Department of Psychology, University of Tromsø—The Arctic University
of Norway, Tromsø, Norway"
744fe47157477235032f7bb3777800f9f2f45e52,"Progressive Growing of GANs for Improved Quality, Stability, and Variation","Published as a conference paper at ICLR 2018
PROGRESSIVE GROWING OF GANS FOR IMPROVED
QUALITY, STABILITY, AND VARIATION
Tero Karras
NVIDIA
Samuli Laine
NVIDIA
Timo Aila
NVIDIA
Jaakko Lehtinen
NVIDIA and Aalto University"
743bbf46557ec767f389c1ec5ac901ef64ffab37,DeepV2D: Video to Depth with Differentiable Structure from Motion,"DeepV2D: Video to Depth with Differentiable Structure from Motion
Zachary Teed1
Jia Deng1,2
Princeton University 2University of Michigan"
747b15ecd9a9e28bbd733527c59e5dd0aa5de7a1,Learning Visual Features from Large Weakly Supervised Data,"Learning Visual Features from Large Weakly Supervised Data
Armand Joulin∗
Laurens van der Maaten∗
Allan Jabri
Nicolas Vasilache
Facebook AI Research
770 Broadway, New York NY 10003"
747e9b36c5a1b0b8a9572da0ab416ddd1e1d2d33,Augmentation for Visual Question Answering,"Proceedings of The 10th International Natural Language Generation conference, pages 198–202,
Santiago de Compostela, Spain, September 4-7 2017. c(cid:13)2017 Association for Computational Linguistics"
74ba4ab407b90592ffdf884a20e10006d2223015,Partial Face Detection in the Mobile Domain,"Partial Face Detection in the Mobile Domain
Upal Mahbub, Student Member, IEEE, Sayantan Sarkar, Student Member, IEEE,
nd Rama Chellappa, Fellow, IEEE"
7478c2351c75183527f258aecce6931be9c9d624,Periodic Variance Maximization using Generalized Eigenvalue Decomposition applied to Remote Photoplethysmography estimation,"Periodic Variance Maximization using Generalized Eigenvalue
Decomposition applied to Remote Photoplethysmography estimation
Richard Macwan, Serge Bobbia, Yannick Benezeth, Julien Dubois, Alamin Mansouri
LE2I EA7508, Arts et M´etiers
Univ. Bourgogne Franche-Comt´e
{richard.macwan, serge.bobbia, yannick.benezeth, julien.dubois,"
743e582c3e70c6ec07094887ce8dae7248b970ad,Face Recognition based on Deep Neural Network,"International Journal of Signal Processing, Image Processing and Pattern Recognition
Vol.8, No.10 (2015), pp.29-38
http://dx.doi.org/10.14257/ijsip.2015.8.10.04
Face Recognition based on Deep Neural Network
Li Xinhua,Yu Qian
Shandong Women’s University"
7411761e789ccb1da80984472f5df5cb084e8ba3,Towards Scene Understanding with Detailed 3D Object Representations,"Towards Scene Understanding with Detailed 3D Object Representations
M. Zeeshan Zia1, Michael Stark2, and Konrad Schindler1
Photogrammetry and Remote Sensing, ETH Z¨urich, Switzerland
Stanford University and Max Planck Institute for Informatics"
749d605dd12a4af58de1fae6f5ef5e65eb06540e,Multi-Task Video Captioning with Video and Entailment Generation,"Multi-Task Video Captioning with Video and Entailment Generation
Ramakanth Pasunuru and Mohit Bansal
UNC Chapel Hill
{ram,"
7430d964331327475f62d8b1051b044c453ed660,Talk Resource-Efficiently to Me: Optimal Communication Planning for Distributed Loop Closure Detection,"Optimal Communication Planning for Distributed Loop Closure Detection
Talk Resource-Efficiently to Me:
Matthew Giamou† Kasra Khosoussi†
Jonathan P. How"
74a3618f3268d8d55a27e53dba2f76034dddaacf,Multilayer Neural Networks and Nearest Neighbor Classifier Performances for Image Annotation,"(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 3, No. 11, 2012
Multilayer Neural Networks and Nearest Neighbor
Classifier Performances for Image Annotation
Mustapha OUJAOURA
Laboratory of Information
Processing and Telecommunications,
Computer Science Department,
Faculty of Science and Technology,
Sultan Moulay Slimane University,
PO Box. 523, Béni Mellal, Morocco.
Brahim MINAOUI
Laboratory of Information
Mohammed FAKIR
Laboratory of Information
Processing and Telecommunications,
Processing and Telecommunications,
Computer Science Department,
Computer Science Department,
Faculty of Science and Technology,"
74032e526edb45bc6c79cb5576e69486e72a316d,Animated 3D Human Models for Use in Person Recognition Experiments,"Animated 3D Human Models for Use in Person Recognition Experiments
Jean M. Vettel1,2,3, Justin Kantner1,2, Matthew Jaswa4, Michael Miller2
U.S. Army Research Laboratory, 2University of California, Santa Barbara, 3University of
Pennsylvania, 4DCS Corporation
Jean M Vettel
U.S. Army Research Laboratory
59 Mulberry Point Road
Aberdeen Proving Ground, MD 21005
10.278.7431"
745ec003b7fbeb52aecd00c41ac889fcd4d88bcd,Guiding Intelligent Surveillance System by learning-by-synthesis gaze estimation,"Pattern Recognition Letters
journal homepage: www.elsevier.com
Guiding Intelligent Surveillance System by learning-by-synthesis gaze estimation
Tongtong Zhaoa, Yuxiao Yana, Jinjia Penga, Zetian Mia, Xianping Fua,∗∗
Information Science and Technology College, Dalian Maritime University, Dalian, China."
74410df341f44f5c915d97725ce396a862d44a7b,Shadow extraction and application in pedestrian detection,"Wang and Yagi EURASIP Journal on Image and Video Processing 2014, 2014:12
http://jivp.eurasipjournals.com/content/2014/1/12
RESEARCH
Open Access
Shadow extraction and application in
pedestrian detection
Junqiu Wang1* and Yasushi Yagi2"
7480d8739eb7ab97c12c14e75658e5444b852e9f,MLBoost Revisited: A Faster Metric Learning Algorithm for Identity-Based Face Retrieval,"NEGREL ET AL.: REVISITED MLBOOST FOR FACE RETRIEVAL
MLBoost Revisited: A Faster Metric
Learning Algorithm for Identity-Based Face
Retrieval
Romain Negrel
Alexis Lechervy
Frederic Jurie
Normandie Univ, UNICAEN,
ENSICAEN, CNRS
France"
747ca08cbf258da8d2b89ba31f24bdb17d7132bb,Tall and skinny QR factorizations in MapReduce architectures,"Tall and Skinny QR factorizations
in MapReduce architectures
Paul G. Constantine
Sandia National Laboratories∗
Albuquerque, NM
David F. Gleich
Sandia National Laboratories∗
Livermore, CA"
740b0f180c64c71bc218da7fd275ce8dff640079,"Capture, Annotate, Browse, Find, Share: Novel Interfaces for Personal Photo Management","Capture, Annotate, Browse, Find, Share:
Novel Interfaces for Personal Photo Management
Hyunmo Kang1, Benjamin B. Bederson1,2
Bongwon Suh
Human-Computer Interaction Laboratory
Palo Alto Research Center
University of Maryland Institute
for Advanced Computer Studies1
Computer Science Department2
College Park, MD 20742
{kang,
333 Coyote Hill Road
Palo Alto, CA 94304"
74a1e28dd2c03076124282482074e10bb02bc643,Coulomb Gans: Provably Optimal Nash Equi-,"Under review as a conference paper at ICLR 2018
COULOMB GANS: PROVABLY OPTIMAL NASH EQUI-
LIBRIA VIA POTENTIAL FIELDS
Anonymous authors
Paper under double-blind review"
744d23991a2c48d146781405e299e9b3cc14b731,Aging Face Recognition: A Hierarchical Learning Model Based on Local Patterns Selection,"This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TIP.2016.2535284, IEEE
Transactions on Image Processing
Aging Face Recognition: A Hierarchical Learning
Model Based on Local Patterns Selection
Zhifeng Li, Senior Member, IEEE, Dihong Gong, Xuelong Li, Fellow, IEEE, and Dacheng Tao, Fellow, IEEE"
74368040bdd21380365963baab6a83da6c5cff81,"Talking-Face Identity Verification, Audiovisual Forgery, and Robustness Issues","Hindawi Publishing Corporation
EURASIP Journal on Advances in Signal Processing
Volume 2009, Article ID 746481, 15 pages
doi:10.1155/2009/746481
Research Article
Talking-Face Identity Verification, Audiovisual
Forgery, and Robustness Issues
Walid Karam,1 Herv´e Bredin,2 Hanna Greige,3 G´erard Chollet,4 and Chafic Mokbel1
Computer Science Department, University of Balamand, 100 El-Koura, Lebanon
SAMoVA Team, IRIT-UMR 5505, CNRS, 5505 Toulouse, France
Mathematics Department, University of Balamand, 100 El-Koura, Lebanon
TSI, Ecole Nationale Sup´erieure des T´el´ecommunications, 46 rue Barrault, 75634 Paris, France
Correspondence should be addressed to Walid Karam,
Received 1 October 2008; Accepted 3 April 2009
Recommended by Kevin Bowyer
The robustness of a biometric identity verification (IV) system is best evaluated by monitoring its behavior under impostor
ttacks. Such attacks may include the transformation of one, many, or all of the biometric modalities. In this paper, we present the
transformation of both speech and visual appearance of a speaker and evaluate its effects on the IV system. We propose MixTrans,
novel method for voice transformation. MixTrans is a mixture-structured bias voice transformation technique in the cepstral
domain, which allows a transformed audio signal to be estimated and reconstructed in the temporal domain. We also propose a"
744db9bd550bf5e109d44c2edabffec28c867b91,FX e-Makeup for Muscle Based Interaction,"FX e-Makeup for Muscle Based Interaction
Katia Canepa Vega1, Abel Arrieta2, Felipe Esteves3, and Hugo Fuks1
Department of Informatics, PUC-Rio, Rio de Janeiro, Brazil
Department of Mechanical Engineering, PUC-Rio, Rio de Janeiro, Brazil
Department of Administration, PUC-Rio, Rio de Janeiro, Brazil"
748305f0b2d686d51ccf893207697d6cfb39e890,COVER LOCALIZATION IN AERIAL IMAGES WITH A DEEP LEARNING APPROACH,"MANHOLE COVER LOCALIZATION IN AERIAL IMAGES WITH A DEEP LEARNING
APPROACH
B. Commandrea,e, D. En-Nejjarya,b, L. Pibreb,c, M. Chaumontd, C. Delennea,e∗, N. Chahinianf
HSM, Univ. Montpellier, IRD, CNRS, Montpellier, France
LIRMM, Univ. Montpellier, CNRS, Montpellier, France
Berger-Levrault, Montpellier, France
d LIRMM, Univ. Nˆımes, Univ. Montpellier, CNRS, Montpellier, France
e Lemon, Inria, Montpellier, France
f HSM, IRD, Univ. Montpellier, CNRS, Montpellier, France
KEY WORDS: Deep learning, high resolution imagery, urban object detection, Convolutional Neural Network"
74671fd8dd510db4abdcb93864fb5d5f77c878a0,Real-Time Viola-Jones Face Detection in a Web Browser,"Real-Time Viola-Jones
Face Detection in a
Web Browser
Theo Ephraim - Tristan Himmelman - Kaleem Siddiqi
McGill University - School of Computer Science
Centre For Intelligent Machines (CIM)
http://flashfacedetection.com"
747c25bff37b96def96dc039cc13f8a7f42dbbc7,EmoNets: Multimodal deep learning approaches for emotion recognition in video,"EmoNets: Multimodal deep learning approaches for emotion
recognition in video
Samira Ebrahimi Kahou · Xavier Bouthillier · Pascal Lamblin · Caglar Gulcehre ·
Vincent Michalski · Kishore Konda · S´ebastien Jean · Pierre Froumenty · Yann
Dauphin · Nicolas Boulanger-Lewandowski · Raul Chandias Ferrari · Mehdi Mirza ·
David Warde-Farley · Aaron Courville · Pascal Vincent · Roland Memisevic ·
Christopher Pal · Yoshua Bengio"
749382d19bfe9fb8d0c5e94d0c9b0a63ab531cb7,A Modular Framework to Detect and Analyze Faces for Audience Measurement Systems,"A Modular Framework to Detect and Analyze Faces for
Audience Measurement Systems
Andreas Ernst, Tobias Ruf, Christian Kueblbeck
Fraunhofer Institute for Integrated Circuits IIS
Department Electronic Imaging
Am Wolfsmantel 33, 91058 Erlangen, Germany
{andreas.ernst, tobias.ruf,"
020d97ca2bf617b7ffed5a31aa8a27ffa5efadbb,An Efficient and Flexible FPGA Implementation of a Face Detection System,"Fekih, H. B., Elhossini, A., & Juurlink, B.
An Efficient and Flexible FPGA
Implementation of a Face Detection
System.
Chapter in book   |
This version is available at https://doi.org/10.14279/depositonce-6778
Accepted manuscript (Postprint)
This is a post-peer-review, pre-copyedit version of an article published in Lecture Notes in Computer
Science. The final authenticated version is available online at:
http://dx.doi.org/10.1007/978-3-319-16214-0_20.
Fekih, H. B., Elhossini, A., & Juurlink, B. (2015). An Efficient and Flexible FPGA Implementation of a Face
Detection System. In Lecture Notes in Computer Science (pp. 243–254). Springer International
Publishing. https://doi.org/10.1007/978-3-319-16214-0_20
Terms of Use
Copyright applies. A non-exclusive, non-transferable and
limited right to use is granted. This document is intended
solely for personal, non-commercial use."
02b8e7e8724359e4a830b001914a8ba54046fbd2,Efficient 2D and 3D Facade Segmentation Using Auto-Context,"Efficient 2D and 3D Facade Segmentation using
Auto-Context
Raghudeep Gadde*, Varun Jampani*, Renaud Marlet, and Peter V. Gehler"
02e5372e439c09f8a8ce8c4784b044ce116ca11c,DeepCorrect: Correcting DNN models against Image Distortions,"Image Distortions
Tejas Borkar, Student Member, IEEE, and Lina Karam, Fellow, IEEE"
02d6df5060281cf13fbef68a8f1ddc29983fe8b3,An Enhanced Default Approach Bias Following Amygdala Lesions in Humans.,"583804 PSSXXX10.1177/0956797615583804Harrison et al.Default Approach Bias Following Amygdala Lesions
research-article2015
Research Article
An Enhanced Default Approach Bias
Following Amygdala Lesions in Humans
1 –13
© The Author(s) 2015
Reprints and permissions:
sagepub.com/journalsPermissions.nav
DOI: 10.1177/0956797615583804
pss.sagepub.com
Laura A. Harrison1, Rene Hurlemann2, and Ralph Adolphs1
California Institute of Technology and 2University of Bonn"
02c50ddcb8ed626090b967a7015d1ddaf55aa149,A Human Body Analysis System,"A Human Body Analysis System
Vincent Girondel, Laurent Bonnaud, Alice Caplier
To cite this version:
Vincent Girondel, Laurent Bonnaud, Alice Caplier. A Human Body Analysis System. Eurasip Journal
on Applied Signal Processing, Hindawi Publishing Corporation, 2006, Volume 2006, 18 p. <hal-
00121790>
HAL Id: hal-00121790
https://hal.archives-ouvertes.fr/hal-00121790
Submitted on 22 Dec 2006
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02ccd5f0eb9a48a6af088197b950fb30a8e3abcc,Scaling for Multimodal 3D Object Detection,"Scaling for Multimodal 3D Object Detection
Andrej Karpathy
Stanford"
02f038ed453de0551813159284746126168f5e15,Multi Channel-Kernel Canonical Correlation Analysis for Cross-View Person Re-Identification,"This is a pre-print version, the final version of the manuscript with more experiments can be found at:
https://doi.org/10.1145/3038916
Analysis for Cross-View Person Re-Identification
Giuseppe Lisanti, Svebor Karaman, Iacopo Masi"
022edc074693c52d4e689947bd2def8b2117fa8b,A super-resolution method for low-quality face image through RBF-PLS regression and neighbor embedding,"978-1-4673-0046-9/12/$26.00 ©2012 IEEE
ICASSP 2012"
02b72a5a4389cb32a7dd784b1c9084e8412e2e78,Hierarchical Bayesian Image Models,"We are IntechOpen,
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0273414ba7d56ab9ff894959b9d46e4b2fef7fd0,Photographic home styles in Congress: a computer vision approach,"Photographic home styles in Congress: a
omputer vision approach∗
L. Jason Anastasopoulos†.
Dhruvil Badani‡
Crystal Lee§
Shiry Ginosar¶
Jake Williams(cid:107)
December 1, 2016"
02fbf86b975c3f45b04de8288d1565cce8b53f62,A real-time pedestrian detection system based on structure and appearance classification,"Anchorage Convention District
May 3-8, 2010, Anchorage, Alaska, USA
978-1-4244-5040-4/10/$26.00 ©2010 IEEE"
02cce8b08e4839d16f2142c5723fc009ccb4e3e1,Improving spatial codification in semantic segmentation,"IMPROVING SPATIAL CODIFICATION IN SEMANTIC SEGMENTATION
Carles Ventura(cid:63)
Kevin McGuinness†
Xavier Gir´o-i-Nieto(cid:63)
Ferran Marqu´es(cid:63)
Ver´onica Vilaplana(cid:63)
Noel E. O’Connor†
(cid:63) Universitat Polit`ecnica de Catalunya (UPC), Barcelona, Spain
Insight Centre for Data Analytics, Dublin City University (DCU), Ireland"
02aff7faf2f6b775844809805424417eed30f440,"A Tale of Three Probabilistic Families: Discriminative, Descriptive and Generative Models","QUARTERLY OF APPLIED MATHEMATICS
VOLUME , NUMBER 0
XXXX XXXX, PAGES 000–000
A TALE OF THREE PROBABILISTIC FAMILIES: DISCRIMINATIVE,
DESCRIPTIVE AND GENERATIVE MODELS
YING NIAN WU (Department of Statistics, University of California, Los Angeles),
RUIQI GAO (Department of Statistics, University of California, Los Angeles),
TIAN HAN (Department of Statistics, University of California, Los Angeles),
SONG-CHUN ZHU (Department of Statistics, University of California, Los Angeles)"
02b0bf28f34c3c403abecd2fb4fb7d4969c0e0db,Learning Disentangled Joint Continuous and Discrete Representations,"Learning Disentangled Joint Continuous and Discrete
Representations
Schlumberger Software Technology Innovation Center
Emilien Dupont
Menlo Park, CA, USA"
026ca771bd3995748b477e100ed4283a9bf8215a,Predicting Performance of a Face Recognition System Based on Image Quality,"Predicting Performance of a Face
Recognition System Based on
Image Quality
Abhishek Dutta"
0278acdc8632f463232e961563e177aa8c6d6833,Selective Transfer Machine for Personalized Facial Expression Analysis,"Selective Transfer Machine for Personalized
Facial Expression Analysis
Wen-Sheng Chu, Fernando De la Torre, and Jeffrey F. Cohn
INTRODUCTION
Index Terms—Facial expression analysis, personalization, domain adaptation, transfer learning, support vector machine (SVM)
A UTOMATIC facial AU detection confronts a number of"
028dc6a134f1204bd9ae28213e2e6665e82ddcb0,Integral Normalized Gradient Image A Novel Illumination Insensitive Representation,"Integral Normalized Gradient Image
A Novel Illumination Insensitive
Representation
Samsung Advanced Institute of Technology
E-mail:"
0297fbba5d7eacc48b2e1682d64d33a7dc6be88c,Face anti-spoofing methods,"REVIEW ARTICLES
Face anti-spoofing methods
Sajida Parveen1,3,*, Sharifah Mumtazah Syed Ahmad1,2, Marsyita Hanafi1 and
Wan Azizun Wan Adnan1
Department of Computer and Communication Systems Engineering, and
Wireless and Photonic Networks Research Centre of Excellence, Universiti Putra, Malaysia
Department of Computer Systems Engineering, Quaid-e-Awam University of Engineering, Science and Technology, Nawabshah, Pakistan
In recent years, facial biometric systems have received
increased deployment in various applications such as
surveillance,  access  control  and  forensic  investiga-
tions. However, one of the limitations of face recogni-
tion system is the high possibility of the system being
deceived  or  spoofed  by  non-real faces such  as  photo-
graph, video clips or dummy faces. In order to identify
the  spoofing  attacks  on  such  biometric  systems,  face
liveness  detection  approaches  have  been  developed.
Thus, the current approach is to integrate liveness de-
tection within facial biometrics by using life sign indi-
ators  of  individual  features.  This  article  presents  a
review  of  state-of-the-art  techniques  in  face  liveness"
02bee2cef6b04e6b57cfa3fd54cabc756f0c2e8d,Data-driven Methods for Interactive Visual Content Creation and Manipulation,"Data-driven Methods for
Interactive Visual Content Creation
nd Manipulation
Dissertation zur Erlangung des Grades des
Doktors der Ingenieurwissenschaften der
Naturwissenschaftlich-Technischen Fakultäten der
Universität des Saarlandes
Vorgelegt durch
Arjun Jain
Max-Planck-Institut Informatik
Campus E1 4
66123 Saarbrücken
Germany
m 4. February 2013 in Saarbrücken"
02a2c5b332d883d726929474060a7e62411c010a,Totally Corrective Multiclass Boosting with Binary Weak Learners,"SEPTEMBER 2010
with Binary Weak Learners
Zhihui Hao, Chunhua Shen, Nick Barnes, and Bo Wang"
0252256fa23eceb54d9eea50c9fb5c775338d9ea,Application-driven Advances in Multi-biometric Fusion,"Application-driven Advances
in Multi-biometric Fusion
dem Fachbereich Informatik
der Technischen Universität Darmstadt
vorzulegende
DISSERTATION
zur Erlangung des akademischen Grades eines
Doktor-Ingenieurs (Dr.-Ing.)
M.Sc. Naser Damer
geboren in Amman, Jordanien
Referenten der Arbeit:
Prof. Dr. Arjan Kuijper
Technische Universität Darmstadt
Prof. Dr. Dieter W. Fellner
Technische Universität Darmstadt
Prof. Dr. Raghavendra Ramachandra
Norwegian University of Science and Technology
Tag der Einreichung:
Tag der mündlichen Prüfung:
2/01/2018"
02f1d5c896ced7f6f002eb7514ba49eca940b75c,A Comparison of Efficient Global Image Features for Localizing Small Mobile Robots,"A Comparison of Efficient Global Image Features
for Localizing Small Mobile Robots
Marius Hofmeister, Philipp Vorst and Andreas Zell
Computer Science Department, University of Tübingen, Tübingen, Germany"
0241513eeb4320d7848364e9a7ef134a69cbfd55,Supervised translation-invariant sparse coding,"Supervised Translation-Invariant Sparse
Coding
¹Jianchao Yang, ²Kai Yu, and ¹Thomas Huang
¹University of Illinois at Urbana Champaign
²NEC Laboratories America at Cupertino"
02a99a43670ab83e77de9d935eb8d3d164e1972c,Joint Segmentation and Pose Tracking of Human in Natural Videos,"Joint Segmentation and Pose Tracking of Human in Natural Videos∗
Taegyu Lim1,2
Seunghoon Hong2
Bohyung Han2
Joon Hee Han2
DMC R&D Center, Samsung Electronics, Korea
Department of Computer Science and Engineering, POSTECH, Korea"
02e43d9ca736802d72824892c864e8cfde13718e,Transferring a semantic representation for person re-identification and search,"Transferring a Semantic Representation for Person Re-Identification and
Search
Shi, Z; Yang, Y; Hospedales, T; XIANG, T; IEEE Conference on Computer Vision and
Pattern Recognition
© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be
obtained for all other uses, in any current or future media, including reprinting/republishing
this material for advertising or promotional purposes, creating new collective works, for resale
or redistribution to servers or lists, or reuse of any copyrighted component of this work in
other works.
For additional information about this publication click this link.
http://qmro.qmul.ac.uk/xmlui/handle/123456789/10075
Information about this research object was correct at the time of download; we occasionally
make corrections to records, please therefore check the published record when citing. For
more information contact"
02dd0af998c3473d85bdd1f77254ebd71e6158c6,PPP: Joint Pointwise and Pairwise Image Label Prediction,"PPP: Joint Pointwise and Pairwise Image Label Prediction
Yilin Wang1 Suhang Wang1
Jiliang Tang2 Huan Liu1 Baoxin Li1
Department of Computer Science, Arizona State Univerity
Yahoo Research"
026509ad687f9cdaba8f2dac0fe5720e0553a8bd,Integrated pedestrian classification and orientation estimation,"Integrated Pedestrian Classification
nd Orientation Estimation
Markus Enzweiler1
Dariu M. Gavrila2,3
Image & Pattern Analysis Group, Univ. of Heidelberg, Germany
Environment Perception, Group Research, Daimler AG, Ulm, Germany
Intelligent Autonomous Systems Group, Univ. of Amsterdam, The Netherlands"
02e9f1bb203a5ade98308eaff4f6a5c96a2c11e0,Self-Supervised Relative Depth Learning for Urban Scene Understanding,"Self-Supervised Relative Depth Learning for
Urban Scene Understanding
Huaizu Jiang1,
Erik Learned-Miller1
Gustav Larsson2, Michael Maire3, Greg Shakhnarovich3
UMass Amherst
University of Chicago
TTI-Chicago"
026050f71175d235f3f91ca0e99e994c00f9b5a6,Supervised Discrete Hashing,"Supervised Discrete Hashing
Fumin Shen1, Chunhua Shen2, Wei Liu3, Heng Tao Shen4
University of Electronic Science and Technology of China. 2 University of Adelaide; and Australian Centre for Robotic Vision. 3IBM Research.
The University of Queensland.
Recently, learning based hashing techniques have attracted broad research
interests due to the resulting efficient storage and retrieval of images, videos,
documents, etc. However, a major difficulty of learning to hash lies in han-
dling the discrete constraints imposed on the needed hash codes. In general,
the discrete constraints imposed on the binary codes that the target hash
functions generate lead to mixed-integer optimization problems—which is
generally NP hard. To simplify the optimization involved in a binary code
learning procedure, most of the aforementioned methods choose to first
solve a relaxed problem through directly discarding the discrete constraints,
nd then threshold the continuous outputs to be binary. This greatly simpli-
fies the optimization but, unfortunately, the approximated solution is typi-
ally of low quality and often makes the final hash functions less effective,
possibly due to the accumulated quantization errors. This is especially the
ase when long-length codes are needed.
Directly learning the binary codes without relaxations would be pre-
ferred if (and only if) a tractable and scalable solver is available. The impor-"
023cc7f9f3544436553df9548a7d0575bb309c2e,Bag of Tricks for Efficient Text Classification,"Bag of Tricks for Efficient Text Classification
Armand Joulin
Edouard Grave
Piotr Bojanowski
Tomas Mikolov
Facebook AI Research"
02f86370fd467f0d03948a94a346034d8a111ffd,Semantic Video Retrieval Using High Level Context,"SEMANTIC VIDEO RETRIEVAL USING HIGH LEVEL CONTEXT
YUSUF AYTAR
B.S. Ege University
A thesis submitted in partial fulfillment of the requirements
for the degree of Master of Science
in the School of Electrical Engineering and Computer Science
in the College of Engineering and Computer Science
t the University of Central Florida
Orlando, Florida
Spring Term
Major Professor: Mubarak Shah"
02da0cab1152eb1c43266687ebff8389c2d6b27b,Infrastructure Development for Integration of Lip Reading into the SUMMIT Speech,"
fa	c	e Deve
e f 
egai f i
Readig i he SU
T Seech Recgize
Chia	a a
S.B. aach	e 
i	e f Techgy 2002
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Eecica Egieeig ad C
	e Sciece
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ay 2003
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. A igh eeved
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Cei(cid:12)ed by . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ti
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022d74ae2f8680e780b18e0cbb041d5c5a57c7a5,Video Salient Object Detection via Fully Convolutional Networks,"Video Salient Object Detection via
Fully Convolutional Networks
Wenguan Wang, Jianbing Shen, Senior Member, IEEE, and Ling Shao, Senior Member, IEEE"
027beed800f7d5e20194caf6d689345045e8d0d4,Smoothed Dilated Convolutions for Improved Dense Prediction,"Smoothed Dilated Convolutions for Improved Dense Prediction
Zhengyang Wang
Washington State University
Pullman, Washington, USA
Shuiwang Ji
Washington State University
Pullman, Washington, USA"
02a88a2f2765b17c9ea76fe13148b4b8a9050b95,DeepPose: Human Pose Estimation via Deep Neural Networks,"DeepPose: Human Pose Estimation via Deep Neural Networks
Alexander Toshev
Christian Szegedy
Google
600 Amphitheatre Pkwy
Mountain View, CA 94043
mainly by the first challenge, the need to search in the large
space of all possible articulated poses. Part-based models
lend themselves naturally to model articulations ([16, 8])
nd in the recent years a variety of models with efficient
inference have been proposed ([6, 19]).
The above efficiency, however, is achieved at the cost of
limited expressiveness – the use of local detectors, which
reason in many cases about a single part, and most impor-
tantly by modeling only a small subset of all interactions
etween body parts. These limitations, as exemplified in
Fig. 1, have been recognized and methods reasoning about
pose in a holistic manner have been proposed [15, 21] but
with limited success in real-world problems.
In this work we ascribe to this holistic view of human"
026e3363b7f76b51cc711886597a44d5f1fd1de2,Vision meets robotics: The KITTI dataset,"Vision meets Robotics: The KITTI Dataset
Andreas Geiger, Philip Lenz, Christoph Stiller and Raquel Urtasun"
02086be014c4a276663e66ffde4d14f9c4cebe7e,BiggerPicture: data-driven image extrapolation using graph matching,"This is an Open Access document downloaded from ORCA, Cardiff University's institutional
repository: http://orca.cf.ac.uk/67868/
This is the author’s version of a work that was submitted to / accepted for publication.
Citation for final published version:
Wang, Miao, Lai, Yukun, Liang, Yuan, Martin, Ralph Robert and Hu, Shi-Min 2014. Biggerpicture:
data-driven image extrapolation using graph matching. ACM Transactions on Graphics 33 (6) , 173.
0.1145/2661229.2661278 file
Publishers page: http://dx.doi.org/10.1145/2661229.2661278
<http://dx.doi.org/10.1145/2661229.2661278>
Changes made as a result of publishing processes such as copy-editing, formatting and page
numbers may not be reflected in this version. For the definitive version of this publication, please
refer to the published source. You are advised to consult the publisher’s version if you wish to cite
Please note:
this paper.
This version is being made available in accordance with publisher policies. See
http://orca.cf.ac.uk/policies.html for usage policies. Copyright and moral rights for publications
made available in ORCA are retained by the copyright holders."
02e97e65fd0ec9a6d98a255d0396eb796a5e444a,Online Multiple View Tracking: Targets Association Across Cameras,"Q.LE, D.CONTE, M.HIDANE: COLLABORATIVE TRACKING
Online Multiple View Tracking:
Targets Association Across Cameras
Quoc Cuong LE1
Donatello CONTE1
Moncef HIDANE2
LIFAT
University of Tours,
Tours, France
Computer Science Department
INSA Centre Val de Loire,
Blois, France"
02af5e40653b5a545b62aa6aebfaca6557f4173d,Sensor fusion for human safety in industrial workcells,"Sensor Fusion for Human Safety in Industrial Workcells*
Paul Rybski1, Peter Anderson-Sprecher1, Daniel Huber1, Chris Niessl1, Reid Simmons1
Figure 1: An example of our approach. (a) The workcell as seen
y one of the 3D sensors. The red region indicates the adaptive
danger zone surrounding the moving robot arm. (b) As the person
enters the workcell, the green region indicates the adaptive safety
zone surrounding the person. (c) When the person gets too close
to the robot, the safety zone and danger zones intersect (shown
with a red circle), and the robot automatically halts. LIGHTEN THE
CONTRAST ON THESE FIGURES TO MAKE THEM EASIER TO SEE"
02534fabd5ffbb98d1c09581eb410e29bec9da01,Fast Vehicle Detection in Aerial Imagery.,"Jennifer Carlet
KeyW Corp.
Beavercreek, OH"
027b940ef4de5aef8b2fd0bebc739445147baf1d,Secure remote matching with privacy: Scrambled support vector vaulted verification (S2V3),"Secure remote matching with privacy:
Scrambled Support Vector Vaulted Verification (S2V 3)
Michael J. Wilber and Terrance E. Boult
Vision and Security Technology Lab, UCCS
Colorado Springs, CO, 80918, USA"
02e133aacde6d0977bca01ffe971c79097097b7f,Convolutional Neural Fabrics,
023da8828f9c039c20ac9267a6b37813b74d4824,Free supervision from video games,"Free supervision from video games
Philipp Kr¨ahenb¨uhl
UT Austin"
029fa43a49a2f5df4bee8aa6a9574f8da5098f98,"Learning event representation: As sparse as possible, but not sparser","Learning event representation: As sparse as possible, but not sparser
Tuan Do and James Pustejovsky
Department of Computer Science
Brandeis University
Waltham, MA 02453 USA"
02e4025fd63f168810724156fb6b20b0b14dccdc,Local inter-session variability modelling for object classification,"This is the author’s version of a work that was submitted/accepted for pub-
lication in the following source:
Anantharajah, Kaneswaran, Ge, ZongYuan, McCool, Christopher, Den-
man, Simon, Fookes, Clinton B., Corke, Peter, Tjondronegoro, Dian W., &
Sridharan, Sridha
(2014)
Local inter-session variability modelling for object classification. In
014), 24-26 March 2014, Steamboat Springs, CO.
This file was downloaded from: https://eprints.qut.edu.au/67786/
(cid:13) Copyright 2014 [please consult the author]
Notice: Changes introduced as a result of publishing processes such as
opy-editing and formatting may not be reflected in this document. For a
definitive version of this work, please refer to the published source:"
ba20e58fcca7537fe3cb46a7dea03b5915373ba7,Face Recognition by Extending Elastic Bunch Graph Matching with Particle Swarm Optimization,"Face Recognition by Extending
Elastic Bunch Graph Matching with
Particle Swarm Optimization
Department of Mechanical Engineering, Melbourne School of Engineering, The University of Melbourne, Australia;
Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Australia
Rajinda Senaratne1, Saman Halgamuge1, Arthur Hsu2
Email: rajinda"
ba051292ca6e8c689542831479e436be7035c147,Superpixel Sampling Networks,"Superpixel Sampling Networks
Varun Jampani1, Deqing Sun1, Ming-Yu Liu1,
Ming-Hsuan Yang1,2, Jan Kautz1
NVIDIA
UC Merced"
bafb8812817db7445fe0e1362410a372578ec1fc,Image-Quality-Based Adaptive Face Recognition,"Image-Quality-Based Adaptive Face Recognition
Harin Sellahewa and Sabah A. Jassim"
babc76d0550a647dc605dca9a69bda1af8e0c872,AN EFFECTIVE COLOR FACE RECOGNITION BASED ON BEST COLOR FEATURE SELECTION ALGORITHM USING WEIGHTED FEATURES FUSION SYSTEM,"ISSN 22773061
AN EFFECTIVE COLOR FACE RECOGNITION BASED ON BEST
COLOR FEATURE SELECTION ALGORITHM USING WEIGHTED
FEATURES FUSION SYSTEM
J.Umadevi, B. Sankara gomathi, B. Sasi Kumar,
Department of Computer  Science, The Rajaas Engineering  College
Dean (Academic), National Engineering College
Professor, Department of Computer  Science, The  Rajaas Engineering  College"
baeb207ea6f4b52eea129b9d8597d4b7a0891ad6,"Sparse , Smart Contours to Represent and Edit Images","Sparse, Smart Contours to Represent and Edit Images
Tali Dekel 1
Chuang Gan 2
Dilip Krishnan 1
Ce Liu 1 William T. Freeman 1,3
Google Research 2 MIT-Watson AI Lab 3 MIT-CSAIL
Reconstruction from Sparse Contour Represenation
Editing in the Contour Domain
.4% px
.5% px
(a) Source
(b) Contours
(c) Source Reconstuction
(d) Edited/blended Contours
(e) Recon. from Edit
Reference
Figure 1. Our method produces high quality reconstructions of images from information along a small number of contours: a source
(512×512) image in (a) is reconstructed in (c) from gradient information stored at the set of colored contours in (b)2, which are less than
5% of the pixels. The model synthesizes hair texture, facial lines and shading even in regions where no input information is provided.
Our model allows for semantically intuitive editing in the contour domain. Top-right: a caricature-like result (e) is created by moving and"
ba1cf2d0493f25da61bd816f92712291999c0ef6,Simple online and realtime tracking with a deep association metric,"SIMPLE ONLINE AND REALTIME TRACKING WITH A DEEP ASSOCIATION METRIC
Nicolai Wojke†, Alex Bewley(cid:5), Dietrich Paulus†
University of Koblenz-Landau†, Queensland University of Technology(cid:5)"
ba8a99d35aee2c4e5e8a40abfdd37813bfdd0906,Uporaba emotivno pogojenega računalništva v priporočilnih sistemih,"ELEKTROTEHNI ˇSKI VESTNIK 78(1-2): 12–17, 2011
EXISTING SEPARATE ENGLISH EDITION
Uporaba emotivno pogojenega raˇcunalniˇstva v
priporoˇcilnih sistemih
Marko Tkalˇciˇc, Andrej Koˇsir, Jurij Tasiˇc
Univerza v Ljubljani, Fakulteta za elektrotehniko, Trˇzaˇska 25, 1000 Ljubljana, Slovenija
Univerza v Ljubljani, Fakulteta za raˇcunalniˇstvo in informatiko, Trˇzaˇska 25, 1000 Ljubljana, Slovenija
E-poˇsta:
Povzetek. V ˇclanku predstavljamo rezultate treh raziskav, vezanih na izboljˇsanje delovanja multimedijskih
priporoˇcilnih sistemov s pomoˇcjo metod emotivno pogojenega raˇcunalniˇstva (ang. affective computing).
Vsebinski priporoˇcilni sistem smo izboljˇsali s pomoˇcjo metapodatkov, ki opisujejo emotivne odzive uporabnikov.
Pri skupinskem priporoˇcilnem sistemu smo dosegli znaˇcilno izboljˇsanje v obmoˇcju hladnega zagona z uvedbo
nove mere podobnosti, ki temelji na osebnostnem modelu velikih pet (ang. five factor model). Razvili smo tudi
sistem za neinvazivno oznaˇcevanje vsebin z emotivnimi parametri, ki pa ˇse ni zrel za uporabo v priporoˇcilnih
sistemih.
Kljuˇcne besede: priporoˇcilni sistemi, emotivno pogojeno raˇcunalniˇstvo, strojno uˇcenje, uporabniˇski profil,
emocije
Uporaba emotivnega raˇcunalniˇstva v priporoˇcilnih
sistemih
In this paper we present the results of three investigations of"
ba87bcf4bf799001641b7afd7d1025600f57c4a1,A HYBRID ARCHITECTURE FOR TRACKING PEOPLE IN REAL-TIME USING A VIDEO SURVEILLANCE CAMERA: APPLICATION FOR BEHAVIOURAL MARKETING,"Signal & Image Processing : An International Journal (SIPIJ) Vol.6, No.6, December 2015
A HYBRID ARCHITECTURE FOR TRACKING
PEOPLE IN REAL-TIME USING A VIDEO
SURVEILLANCE CAMERA: APPLICATION FOR
BEHAVIOURAL MARKETING
Kheireddine AZIZ1, Djamal MERAD2, Jean-Luc DAMOISEAUX3 and
Pierre DRAP2
SeaTech Toulon, Toulon University, La Gardes, France
LSIS Lab, Aix-Marseille University, Marseille, France
IUT R&T, Aix-Marseille University, Marseille, France"
badcd992266c6813063c153c41b87babc0ba36a3,Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks,"Recent Advances in Object Detection in the Age
of Deep Convolutional Neural Networks
Shivang Agarwal(∗
,1), Jean Ogier du Terrail(∗
,1,2), Fr´ed´eric Jurie(1)
(∗) equal contribution
(1)Normandie Univ, UNICAEN, ENSICAEN, CNRS
(2)Safran Electronics and Defense
September 11, 2018"
ba25c0c330da1460cc817a98e7c55a100c255039,A Two Level Face Recognition Algorithm For Efficient Attendance Marking,"IOSR Journal of Electronics and Communication Engineering (IOSR-JECE)
e-ISSN: 2278-2834,p- ISSN: 2278-8735.Volume 12, Issue 3, Ver. II (May - June 2017), PP 80-85
www.iosrjournals.org
A Two Level  Face Recognition Algorithm For Efficient
Attendance Marking
Bhaskar B1,Shwetha B2, Rashmi K3, Dr.K.V.Mahendra Prashanth4
Associate Professor,
Postgraduate Student,
Postgraduate Student,
Professor
Department of Electronics and communication S.J.B Institute of Technology, Bengaluru-60"
ba51ce1ec7b18fa808985b919f4a201fe5e4bafb,Semantic parsing for priming object detection in indoors RGB-D scenes,"Semantic Parsing for Priming Object Detection in
Indoors RGB-D Scenes∗
C´esar Cadena †and Jana Koˇseck´a ‡"
ba0d84d97eeec7774534b91da78b10c5d924fdc8,Classification with Repulsion Tensors: A Case Study on Face Recognition,"Classification with Repulsion Tensors: A Case Study on Face
Recognition
Hawren Fang∗
March 16, 2016"
bac5906adc227e390f2f70705e990a3e1ec369df,Active Control of Camera Parameters for Object Detection Algorithms,"Active Control of Camera Parameters for Object
Detection Algorithms
Yulong Wu, John Tsotsos
Department of Electrical Engineering and Computer Science
York Univeristy
Toronto, ON M3J 1P3
Email: {yulong,"
bab69d0954213851bc4aae50ece0ce8ac52bdedf,' s personal copy Optimal feature selection for support vector machines,Author's personal copy
bab47c7bf80c9310f947cbdaf71b3c983c497b68,Systematic Parameter Optimization and Application of Automated Tracking in Pedestrian Dominant Situations Date of submission : 2014-0801,"Systematic Parameter Optimization and Application of Automated
Tracking in Pedestrian Dominant Situations
Date of submission: 2014-08-01
Dariush Ettehadieh*
M.Sc. Student,
Polytechnique Montréal,
500, Chemin de Polytechnique, Montreal
phone : 1-514-266-5544
Bilal Farooq
Assistant Professor,
Polytechnique Montréal
500, Chemin de Polytechnique, Montreal
phone : 1-514-340-4711 ext. 4802
Nicolas Saunier
Associate Professor,
Polytechnique Montréal
500, Chemin de Polytechnique, Montreal
phone : 1-514-340-4711 ext. 4962
5029 Words + 4 Figures + 3 Tables = 6779
Submitted for presentation to the 94th Annual Meeting of the Transportation Research Board and publication in"
ba180547d0b1cf7de24248051ff87bf34f75783d,Video Surveillance and Sensor Networks,"Multimedia Surveillance Systems
Dipartimento di Ingegneria dell’ Informazione
University of Modena and Reggio Emilia
Rita Cucchiara
Italy"
bade9b38c45afd4f988e246974427685f3ff599f,Pairwise Rotation Hashing for High-dimensional Features,"Pairwise Rotation Hashing for High-dimensional
Features
Kohta Ishikawa, Ikuro Sato, and Mitsuru Ambai
Denso IT Laboratory, Inc."
bad7254ae08f8bf1305e70c7de28374f67f151fd,Ré-identification de personnes à partir des séquences vidéo. (Person re-identification from video sequence),"Ré-identification de personnes à partir des séquences
vidéo
Mohamed Ibn Khedher
To cite this version:
Mohamed Ibn Khedher. Ré-identification de personnes à partir des séquences vidéo. Réseaux et
télécommunications [cs.NI]. Institut National des Télécommunications, 2014. Français. <NNT :
014TELE0018>. <tel-01149691>
HAL Id: tel-01149691
https://tel.archives-ouvertes.fr/tel-01149691
Submitted on 7 May 2015
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
bac748bf117cb5b06037413de92378b76ff358d0,Entropy-based sim(3) calibration of 2D lidars to egomotion sensors,"Entropy-Based Sim(3) Calibration of 2D Lidars to Egomotion Sensors
Jacob Lambert1, Lee Clement1, Matthew Giamou2, and Jonathan Kelly1"
ba816806adad2030e1939450226c8647105e101c,MindLAB at the THUMOS Challenge,"MindLAB at the THUMOS Challenge
Fabi´an P´aez
Jorge A. Vanegas
Fabio A. Gonz´alez
MindLAB Research Group
MindLAB Research Group
MindLAB Research Group
Bogot´a, Colombia
Bogot´a, Colombia
Bogot´a, Colombia"
ba09dd1844427cff803082dda7366bd966456932,Automated analysis of nonverbal behavior in depression View project Automated analysis of facial expressions View project,"See	discussions,	stats,	and	author	profiles	for	this	publication	at:	https://www.researchgate.net/publication/227031714
Facial	Expression	Recognition
Chapter	·	January	2011
DOI:	10.1007/978-0-85729-932-1_19
CITATIONS
authors:
READS
,261
Yingli	Tian
City	College	of	New	York
Takeo	Kanade
Carnegie	Mellon	University
89	PUBLICATIONS			4,076	CITATIONS
511	PUBLICATIONS			51,663	CITATIONS
SEE	PROFILE
SEE	PROFILE
Jeffrey	F	Cohn
University	of	Pittsburgh
30	PUBLICATIONS			18,287	CITATIONS
SEE	PROFILE"
ba8e0bda11af08b6037666b67cf54ae1f780822d,1 Spatial Pyramid Matching,"Author manuscript, published in ""Object Categorization: Computer and Human Vision Perspectives Cambridge University Press (Ed.)
(2009) 401--415"""
baf0af0ac2f2fbbf0c04141e12886ff850d77413,Feature-based 3d Slam,"KERNEL{BASED CLASSIFIERS WITH
APPLICATIONS TO FACE DETECTION
TH(cid:18)ESE No 3141 (2004)
PR(cid:19)ESENT(cid:19)EE (cid:18)A LA FACULT(cid:19)E SCIENCES ET TECHNIQUES DE L’ING(cid:19)ENIEUR
INSTITUT DE TRAITEMENT DES SIGNAUX
SECTION DE G(cid:19)ENIE (cid:19)ELECTRIQUE ET (cid:19)ELECTRONIQUE
(cid:19)ECOLE POLYTECHNIQUE F(cid:19)ED(cid:19)ERALE DE LAUSANNE
POUR L’OBTENTION DU GRADE DE DOCTEUR (cid:18)ES SCIENCES
Vlad POPOVICI
DEA de sciences des syst(cid:18)emes et des calculateurs, Universit(cid:19)e Technique de Cluj-Napoca, Roumanie
et de nationalit(cid:19)e roumaine
ccept(cid:19)ee sur proposition du jury:
Prof. J.-P. Thiran, directeur de th(cid:18)ese
Dr. S. Bengio, rapporteur
Prof. J. Kittler, rapporteur
Prof. M. Kunt, rapporteur
Lausanne, EPFL
D(cid:19)ecembre 2004"
ba99c37a9220e08e1186f21cab11956d3f4fccc2,A Fast Factorization-Based Approach to Robust PCA,"A Fast Factorization-based Approach to Robust PCA
Department of Computer Science, Southern Illinois University,Carbondale, IL 62901 USA
Chong Peng, Zhao Kang, and Qiang Cheng
Email:"
ba85ccde9dd53a9c78a9dbb1fe1e34d7800ad1c1,ENHANCEMENT OF FAST PEDESTRIAN DETECTION USING HOG FEATURES,"International Journal of Industrial Electronics and Electrical Engineering, ISSN: 2347-6982
Volume-3, Issue-4, April-2015
ENHANCEMENT OF FAST PEDESTRIAN DETECTION USING HOG
AHMAD BAGHDADI, 2SUHAIMI ABD LATIF
,2Electrical and Computer Engineering Department, IIUM
E-mail:
FEATURES
reasons:
first,  HOG"
656f05741c402ba43bb1b9a58bcc5f7ce2403d9a,Supervised Learning Approaches for Automatic Structuring of Videos. (Méthodes d'apprentissage supervisé pour la structuration automatique de vidéos),"THÈSEPour obtenir le grade deDOCTEUR DE L’UNIVERSITÉ GRENOBLE ALPESSpécialité : Mathématiques et InformatiqueArrêté ministériel : 7 août 2006Présentée parDanila POTAPOVThèse dirigée par Cordelia SCHMID et codirigée par Zaid HARCHAOUIpréparée au sein de Inria Grenoble Rhône-Alpesdans l'École Doctorale Mathématiques, Sciences et technologies de l'information, InformatiqueSupervised Learning Approaches for Automatic Structuring of VideosThèse soutenue publiquement le « 22 Juillet 2015 »,devant le jury composé de : Prof. Cordelia SCHMID Inria Grenoble Rhône-Alpes, France, Directeur de thèseDr. Zaid HARCHAOUIInria Grenoble Rhône-Alpes, France, Co-encadrant de thèse Prof. Patrick PEREZTechnicolor Rennes, France, RapporteurProf. Ivan LAPTEVInria Paris Rocquencourt, France, Rapporteur, PrésidentDr. Florent PERRONNINFacebook AI Research, Paris, France, ExaminateurDr. Matthijs DOUZEInria Grenoble Rhône-Alpes, France, Examinateur"
65ec52a3e0a0f6a46fd140ff83bb82d7d02a2d45,Learning Hierarchical Features from Generative Models,"Learning Hierarchical Features from Generative Models
Shengjia Zhao 1 Jiaming Song 1 Stefano Ermon 1"
65639b79576f22b705a601f062bb6905f0a396af,A Preliminary Investigation into the Impact of Training for Example-Based Facial Blendshape Creation,"EUROGRAPHICS 2018/ O. Diamanti and A. Vaxman
Short Paper
A Preliminary Investigation into the Impact of Training for
Example-Based Facial Blendshape Creation
Emma Carrigan1, Ludovic Hoyet2, Rachel McDonnell1 and Quentin Avril3
Graphics Vision and Visualisation Group, Trinity College Dublin, Ireland
Inria Rennes, France 3 Technicolor"
65a858ca95dcfa032e812a7f1fc7ee5bdac88f5b,Using Pre-Trained Models for Fine-Grained Image Classification in Fashion Field,"Using Pre-Trained Models for Fine-Grained Image
Classification in Fashion Field
Anna Iliukovich-Strakovskaia
Moscow Institute of Physics and
Technology
Moscow Institute of Physics and
Alexey Dral
Technology
“А” Kerchenskaya st., Moscow,
17303, Russian Federation
“А” Kerchenskaya st., Moscow,
17303, Russian Federation
+7 495 408 45 54
+7 495 408 45 54
Emeli Dral
Moscow Institute of Physics and
Technology & Yandex Data Factory
“А” Kerchenskaya st., Moscow,
17303, Russian Federation
+7 495 408 45 54"
656e7c7739e3f334d4f275c71499485501aabc44,A Two-Step Methodology for Human Pose Estimation Increasing the Accuracy and Reducing the Amount of Learning Samples Dramatically,"A two-step methodology for human pose
estimation increasing the accuracy and reducing
the amount of learning samples dramatically
Samir Azrour, Sébastien Piérard, Pierre Geurts, and Marc Van Droogenbroeck
INTELSIG Laboratory, Department of Electrical Engineering and Computer Science,
University of Liège, Belgium"
65a8c76a25c1eb95436bab22e99f78f6b390c87a,Local Multi-Grouped Binary Descriptor With Ring-Based Pooling Configuration and Optimization,"Local Multi-Grouped Binary Descriptor with
Ring-based Pooling Configuration and Optimization
Yongqiang Gao, Weilin Huang, and Yu Qiao
them are hand-crafted descriptors, which significantly limit
their generality to various tasks or different databases by
using pre-defined filters and unfeasible pooling configurations.
Recently, learning-based descriptors have been proposed by
optimising both local filters and pooling regions using training
data, with promising improvements achieved [16]–[18]. The
discriminative capability and computational complexity are
two crucial but conflicted issues, which need to be balanced
arefully in the learning processing."
6581c5b17db7006f4cc3575d04bfc6546854a785,Contextual Person Identification in Multimedia Data,"Contextual Person Identification
in Multimedia Data
zur Erlangung des akademischen Grades eines
Doktors der Ingenieurwissenschaften
der Fakultät für Informatik
des Karlsruher Instituts für Technologie (KIT)
genehmigte
Dissertation
Dipl.-Inform. Martin Bäuml
us Erlangen
Tag der mündlichen Prüfung:
8. November 2014
Hauptreferent:
Korreferent:
Prof. Dr. Rainer Stiefelhagen
Karlsruher Institut für Technologie
Prof. Dr. Gerhard Rigoll
Technische Universität München
KIT – Universität des Landes Baden-Württemberg und nationales Forschungszentrum in der Helmholtz-Gemeinschaft
www.kit.edu"
651125ca22947e95e5be6206c3056988b850266a,Swifter: improved online video scrubbing,"Swifter: Improved Online Video Scrubbing
Justin Matejka, Tovi Grossman, and George Fitzmaurice
Autodesk Research, Toronto, Ontario, Canada
Figure  1.  Scrubbing  behavior  of  a  traditional  streaming  video  player,  the  Swift  interface  [16],  and  our  new  Swifter
interface, which shows multiple frames around the active timeline location and allows for direct selection of each frame.
Traditional
Swift
Swifter"
656aeb92e4f0e280576cbac57d4abbfe6f9439ea,USE OF IMAGE ENHANCEMENT TECHNIQUES FOR IMPROVING REAL TIME FACE RECOGNITION EFFICIENCY ON WEARABLE GADGETS,"Journal of Engineering Science and Technology
Vol. 12, No. 1 (2017) 155 - 167
© School of Engineering, Taylor’s University
USE OF IMAGE ENHANCEMENT TECHNIQUES
FOR IMPROVING REAL TIME FACE RECOGNITION EFFICIENCY
ON WEARABLE GADGETS
MUHAMMAD EHSAN RANA1,*, AHMAD AFZAL ZADEH2,
AHMAD MOHAMMAD MAHMOOD ALQURNEH3
, 3Asia Pacific University of Technology & Innovation, Kuala Lumpur 57000, Malaysia
Staffordshire University, Beaconside Stafford ST18 0AB, United Kingdom
*Corresponding Author:"
65eff143b099e53dcf39692c2fb542b0ee1fdfb6,Real-time Scale-invariant Object Recognition from Light Field Imaging,
650f4ccbe7d4aa49ae80e246df394ca6c60894ec,Pedestrian detection in urban environments based on vision and depth data Personenerkennung in städtischen,"DEPARTMENT OF INFORMATICS
TECHNISCHE UNIVERSITÄT MÜNCHEN
Bachelor’s Thesis in Informatics
Pedestrian detection in urban environments
ased on vision and depth data
Andreas Kreutz"
6527cf0b9dbddbd0c6429a35a3cbded3ca336583,MCMC Supervision for People Re-identification in Nonoverlapping Cameras,"MEDEN, LERASLE, SAYD: MCMC TRACKING-BY-REIDENTICATION
MCMC Supervision for People
Reidentification in Nonoverlapping Cameras
Boris Meden1
Frédéric Lerasle2
lerasle.laas.fr
Patrick Sayd1
CEA, LIST,
Laboratoire Vision et Ingénierie des
Contenus,
BP 94, F-91191 Gif-sur-Yvette, France
CNRS ; LAAS ;
Université de Toulouse ; UPS, LAAS ;
F-31077 Toulouse Cedex 4, France"
656a59954de3c9fcf82ffcef926af6ade2f3fdb5,Convolutional Network Representation for Visual Recognition,"Convolutional Network Representation
for Visual Recognition
ALI SHARIF RAZAVIAN
Doctoral Thesis
Stockholm, Sweden, 2017"
65817963194702f059bae07eadbf6486f18f4a0a,WhittleSearch: Interactive Image Search with Relative Attribute Feedback,"http://dx.doi.org/10.1007/s11263-015-0814-0
WhittleSearch: Interactive Image Search with Relative Attribute
Feedback
Adriana Kovashka · Devi Parikh · Kristen Grauman
Received: date / Accepted: date"
659fc2a483a97dafb8fb110d08369652bbb759f9,Improving the Fisher Kernel for Large-Scale Image Classification,"Improving the Fisher Kernel
for Large-Scale Image Classification
Florent Perronnin, Jorge S´anchez, and Thomas Mensink
Xerox Research Centre Europe (XRCE)"
65d588e2ff7b4f2903efbeded978885f7da5d0e0,UMPM benchmark: A multi-person dataset with synchronized video and motion capture data for evaluation of articulated human motion and interaction,"UMPM benchmark: a multi-person dataset with synchronized video and motion
apture data for evaluation of articulated human motion and interaction
N.P. van der Aa1,2, X. Luo1, G.J. Giezeman1, R.T. Tan1, R.C. Veltkamp1
{x.luo, g.j.giezeman, r.t.tan,
Utrecht University
Noldus Information Technology"
653942873bc7ea6f1056739dc5015ec3631d9bbe,Face Detection Techniques-A Review,"International Journal of Current Engineering and Technology
ISSN 2277 - 4106
© 2013 INPRESSCO. All Rights Reserved.
Available at http://inpressco.com/category/ijcet
Research Article
Face Detection Techniques- A Review
Chandrashekhar S.PatilȦ and  Gopal N.DhootȦ*
ȦDepartment of E&TC, University of North Maharashtra,  State Maharashtra, Country India
Accepted 20 November 2013, Available online 01 December 2013, Vol.3, No.5 (December 2013)"
65237b5e96c7492a0e5d01ddea5b1d381da408cd,A human-machine collaborative approach to tracking human movement in multi-camera video,"A Human-Machine Collaborative Approach to Tracking
Human Movement in Multi-Camera Video
Philip DeCamp
MIT Media Lab
0 Ames Street, E15-441
Cambridge, Massachusetts 02139
Deb Roy
MIT Media Lab
0 Ames Street, E15-488
Cambridge, Massachusetts 02139"
65edab091e437d3b9d093dcb8be7c5dc4ce0fe0f,DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation,"DeepOrgan: Multi-level Deep Convolutional
Networks for Automated Pancreas Segmentation
Holger R. Roth, Le Lu, Amal Farag, Hoo-Chang Shin, Jiamin Liu,
Evrim B. Turkbey, and Ronald M. Summers
Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and
Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD
0892-1182, USA"
659fc18b1ec79a7437e6e7b1dce145d423e82199,Real time person detection and tracking by mobile robots using RGB-D images,"Real Time Person Detection and Tracking by Mobile Robots using
RGB-D Images
Duc My Vo, Lixing Jiang and Andreas Zell"
657ae9ecb59cb2a27e57784577a9efb60de81126,The Task Matters: Comparing Image Captioning and Task-Based Dialogical Image Description,"The Task Matters: Comparing Image
Captioning and Task-Based Dialogical Image Description
Nikolai Ilinykh, Sina Zarrieß, David Schlangen
Dialogue Systems Group
University of Bielefeld
Germany"
658c802890c7133e2ade778b5d88b68bcd0dca9c,Learning to Segment via Cut-and-Paste,"Learning to Segment via Cut-and-Paste
Tal Remez, Jonathan Huang, Matthew Brown
Google"
65126e0b1161fc8212643b8ff39c1d71d262fbc1,Occlusion Coherence: Localizing Occluded Faces with a Hierarchical Deformable Part Model,"Occlusion Coherence: Localizing Occluded Faces with a
Hierarchical Deformable Part Model
Golnaz Ghiasi Charless C. Fowlkes
Dept. of Computer Science, University of California, Irvine"
6506e45c15cf24d9cbf3d694994195ef56063a80,Dynamic and Robust Object Tracking for Activity Recognition. (Suivi dynamique et robuste d'objets pour la reconnaissance d'activités),"Dynamic and Robust Object Tracking for Activity
Recognition
Duc Phu Chau
To cite this version:
Duc Phu Chau. Dynamic and Robust Object Tracking for Activity Recognition. Computer Vision
nd Pattern Recognition [cs.CV]. Institut National de Recherche en Informatique et en Automatique
(INRIA), 2012. English. <tel-00695567>
HAL Id: tel-00695567
https://tel.archives-ouvertes.fr/tel-00695567
Submitted on 22 Nov 2012
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
6574eaab393aa8d674cd785fab16cae06a53151a,A study on polymorphing superscalar processor dynamically to improve power efficiency,"A Study on Polymorphing Superscalar Processor Dynamically
to Improve Power Efficiency
Sudarshan Srinivasan, Rance Rodrigues, Arunachalam Annamalai, Israel Koren and Sandip Kundu
Department of Electrical and Computer Engineering
University of Massachusetts at Amherst, MA, USA
Email: {ssrinivasan, rodrigues, annamalai, koren,"
6520367a3ff4f863a95e0d34eda54cd4903f557c,European Robotics Network Network of Excellence Information Society Technologies Priority DR 2 . 4 IROS ’ 06 Workshop on Benchmarks,"FP6-507728
EURON
European Robotics Network
Network of Excellence
Information Society Technologies Priority
DR 2.4
IROS’06 Workshop on Benchmarks
Due date of deliverable: 31 December 2006
Actual submission date: 4 June 2007
Start date of the project: 1 May 2004
Duration: 48 months
Organisation name of lead contractor for this deliverable: UJI
Revision: Revised version, June 2007
Dissemination level: PU"
652d3f33fd0a99808dd646aed228b45eacdaf34f,A Framework for Binding and Retrieving Class-Specific Information to and from Image Patterns Using Correlation Filters,"A Framework for Binding and Retrieving
Class-Specific Information to and from Image
Patterns using Correlation Filters
Vishnu Naresh Boddeti, Student Member, IEEE, and B.V.K Vijaya Kumar, Fellow, IEEE"
65341edca2f74ffad7d0aa56f20558f5305b307e,Co-design of architectures and algorithms for mobile robot localization and model-based detection of obstacles. (Co-conception d'architectures et d'algorithmes pour la localisation de robots mobiles et la détection d'obstacles basée sur des modèles),"Co-design of architectures and algorithms for mobile
robot localization and model-based detection of obstacles
Daniel Törtei
To cite this version:
Daniel Törtei. Co-design of architectures and algorithms for mobile robot localization and model-based
detection of obstacles. Embedded Systems. Université Paul Sabatier - Toulouse III, 2016. English.
<NNT : 2016TOU30294>. <tel-01477662v2>
HAL Id: tel-01477662
https://tel.archives-ouvertes.fr/tel-01477662v2
Submitted on 16 Feb 2018
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
659fe7f32475bec73ed763f50cea929352b1309a,Facial image prediction using exemplar-based algorithm and non-negative matrix factorization,"Facial Image Prediction Using Exemplar-based
Algorithm and Non-negative Matrix Factorization
Department of Electrical Engineering, National Yunlin University of Science and Technology, Douliou, Taiwan
Hsuan T. Chang* and Hsiao W. Peng†
* E-mail:    Tel: +886-5-5342601#4263
E-mail:    Tel: +886-5-5342601#4299
Therefore, we used the non-family people images sometime.
Currently, many human face databases in the internet are open
for  public.  For  example,  FG-NET  aging  database  [11].
Although, GF-NET database provides numerous face images,
it does not offer the facial image sequence of different ages of
the persons in the same family
THE PROPOSED METHOD"
656a5d4d84c450792402b3c69eecbdbca4cad4cb,2.1. Imagenet and Related Datasets,"Figure 4: Percent of clean images at different tree depth levels in
ImageNet. A total of 80 synsets are randomly sampled at every
tree depth of the mammal and vehicle subtrees. An independent
group of subjects verified the correctness of each of the images.
An average of 99.7% precision is achieved for each synset.
ImageNet
TinyImage
LabelMe
LHill
LabelDisam
Clean
DenseHie
FullRes
PublicAvail
Segmented
Table 1: Comparison of some of the properties of ImageNet ver-
sus other existing datasets.
ImageNet offers disambiguated la-
els (LabelDisam), clean annotations (Clean), a dense hierarchy
(DenseHie), full resolution images (FullRes) and is publicly avail-"
a67e7ca0c7e1e3020169b5c59dc492e9f62f0022,FACE RECOGNITION PERFORMANCE UNDER ADVERSARIAL CONDITIONS,
a6ebe013b639f0f79def4c219f585b8a012be04f,Facial Expression Recognition Based on Hybrid Approach,"Facial Expression Recognition Based on Hybrid
Approach
Md. Abdul Mannan, Antony Lam, Yoshinori Kobayashi, and Yoshinori Kuno
Graduate School of Science and Engineering, Saitama University,
55 Shimo-Okubo, Sakura-ku, Saitama-shi, Saitama 338-8570, Japan
E-mail"
a6eb6ad9142130406fb4ffd4d60e8348c2442c29,"Video Description: A Survey of Methods, Datasets and Evaluation Metrics","Video Description: A Survey of Methods,
Datasets and Evaluation Metrics
Nayyer Aafaq, Syed Zulqarnain Gilani, Wei Liu, and Ajmal Mian"
a6404e91af8d1644aa7eea307ffceefa715dd7ea,Human Motion Capture Using a Drone,"Human Motion Capture Using a Drone
Xiaowei Zhou, Sikang Liu, Georgios Pavlakos, Vijay Kumar, Kostas Daniilidis"
a6eb8cb1c35d0f53f8d2c9a404e374c01275544b,NovaSearch on Medical ImageCLEF 2013,"NovaSearch on medical ImageCLEF 2013
Andr´e Mour˜ao, Fl´avio Martins and Jo˜ao Magalh˜aes
Universidade Nova de Lisboa, Faculdade de Ciˆencias e Tecnologia,
Caparica, Portugal,"
a6f477f3c1cb2ab230fe8d89c31ae6af0b9c2346,Relevance Subject Machine: A Novel Person Re-identification Framework,"Relevance Subject Machine: A Novel Person
Re-identification Framework
Igor Fedorov, Student Member, IEEE, Ritwik Giri, Student Member, IEEE, Bhaskar D. Rao, Fellow, IEEE,
Truong Q. Nguyen, Fellow, IEEE"
a62ca056821a3179b116662b28338433ba5b5e7d,How far can we go without convolution: Improving fully-connected networks,"Under review as a conference paper at ICLR 2016
HOW FAR CAN WE GO WITHOUT CONVOLUTION: IM-
PROVING FULLY-CONNECTED NETWORKS
Zhouhan Lin & Roland Memisevic
Universit´e de Montr´eal
Canada
{zhouhan.lin,
Kishore Konda
Goethe University Frankfurt
Germany"
a694180a683f7f4361042c61648aa97d222602db,Face recognition using scattering wavelet under Illicit Drug Abuse variations,"Face Recognition using Scattering Wavelet under Illicit Drug Abuse Variations
Prateekshit Pandey, Richa Singh, Mayank Vatsa
fprateekshit12078, rsingh,
IIIT-Delhi India"
a65e953df1dbc007862f8eaa8c12ceb225d15837,Robust Head-shoulder Detection using Deformable Part-based Models,"Robust Head-shoulder Detection using Deformable Part-based Models
Enes Dayangac, Christian Wiede, Julia Richter and Gangolf Hirtz
Faculty of Electrical Engineering and Information Technology, Technische Universit¨at Chemnitz,
Chemnitz, Germany
Keywords:
Person Detection, Head-shoulder Detection, Ambient Assisted Living, Latent SVM, DPM, ACF-Detector."
a618cc9c513762d4eb5db2f7f7b686e7e2b758ca,Learning Semi-Riemannian Metrics for Semisupervised Feature Extraction,"Learning Semi-Riemannian Metrics
for Semisupervised Feature Extraction
Wei Zhang, Zhouchen Lin, Senior Member, IEEE, and Xiaoou Tang, Fellow, IEEE"
a6e7513371a49cd7b8b30bb444e8fc448c5326cb,Simple online and realtime tracking,"SIMPLE ONLINE AND REALTIME TRACKING
Alex Bewley†, Zongyuan Ge†, Lionel Ott(cid:5), Fabio Ramos(cid:5), Ben Upcroft†
Queensland University of Technology†, University of Sydney(cid:5)"
a6161e53d77d7cbd6e69d1b84e6d03d7041cb93e,Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime,"Dark Model Adaptation: Semantic Image Segmentation from Daytime
to Nighttime
Dengxin Dai1 and Luc Van Gool1,2"
a63638b26d36bab8db10bd95fb287c727bab33ec,Joint Sparse and Low-Rank Representation for Emotion Recognition,"MAY 2014
Joint Sparse and Low-Rank Representation for
Emotion Recognition
Xiang Xiang, Fabian Prada, Hao Jiang"
a60146c458adfe9207f015d7a77cb7dfb54f744f,Understanding Dynamic Social Grouping Behaviors of Pedestrians,"Understanding Dynamic Social Grouping
Behaviors of Pedestrians
Linan Feng, Student Member, IEEE, and Bir Bhanu, Fellow, IEEE"
a649bc66524e5e61e4d34cc00159099b6b58db2f,Chapter 3 Large-Scale Image Geolocalization,"Chapter 3
Large-Scale Image Geolocalization
James Hays and Alexei A. Efros"
a67d54cf585c9491ab8a3e2d58d9c4b223359602,Spatial information and end-to-end learning for visual recognition. (Informations spatiales et apprentissage bout-en-bout pour la reconnaissance visuelle),"Spatial information and end-to-end learning for visual
recognition
Mingyuan Jiu
To cite this version:
Mingyuan Jiu. Spatial information and end-to-end learning for visual recognition. Computer Science
[cs]. INSA de Lyon, 2014. English. <NNT : 2014ISAL0038>. <tel-01127462>
HAL Id: tel-01127462
https://tel.archives-ouvertes.fr/tel-01127462
Submitted on 7 Mar 2015
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de
recherche français ou étrangers, des laboratoires"
a6ce2f0795839d9c2543d64a08e043695887e0eb,Driver Gaze Region Estimation Without Using Eye Movement,"Driver Gaze Region Estimation
Without Using Eye Movement
Lex Fridman, Philipp Langhans, Joonbum Lee, and Bryan Reimer
Massachusetts Institute of Technology (MIT)"
a6a7fa90d44a1afc4217e627cde83704668ec53f,Automatic Face Recognition using Radial Basis Function Networks,"Automatic Face Recognition using Radial
Basis Function Networks
Andrew Jonathan Howell
CSRP 488
September, 1997
ISSN 1350–3162
Cognitive Science
Research Papers"
a6990136d9db12d50245f574941aa992f01116c4,Moving visual focus in salient object segmentation,"Moving Visual Focus in Salient Object Segmentation
Manuscript ID:  IPR-2014-0987
Manuscript Type:  Research Paper
Date Submitted by the Author:  13-Dec-2014
Complete List of Authors:  Chen, Zhihua; East China University of Science and Technology,
Department of Computer Science and Engineering
Xiao, Xiaolong; East China University of Science and Technology,
Department of Computer Science and Engineering
Liu, Yi; East China University of Science and Technology, Department of
Computer Science and Engineering
Ying, Fangli; East China University of Science and Technology, Department
of Computer Science and Engineering
Zhang, Jing; East China University of Science and Technology, Department
of Computer Science and Engineering
Yuan, Yu-Bo; East China University of Science and Technology, Department
of Computer Science and Engineering
Keyword:  IMAGE PROCESSING, IMAGE RECOGNITION, IMAGE RETRIEVAL
IET Review Copy Only"
a66373beaad40fb5a8e2e1b42c5a2213b166a55c,Childhood abuse is related to working memory impairment for positive emotion in female university students.,"Childhood abuse is related to working memory impairment for positive
emotion in female university students
Cromheeke S, Herpoel LA, Mueller SC.
014; 19(1):38-48
ARTICLE IDENTIFIERS
DOI: 10.1177/1077559513511522
PMID: 24271026
PMCID: not available
JOURNAL IDENTIFIERS
LCCN: not available
pISSN: 1077-5595
eISSN: 1552-6119
OCLC ID: 30832620
CONS ID: sn 94001296
US National Library of Medicine ID: 9602869
This article was identified from a query of the SafetyLit database.
Powered by TCPDF (www.tcpdf.org)"
a66d89357ada66d98d242c124e1e8d96ac9b37a0,Failure Detection for Facial Landmark Detectors,"Failure Detection for Facial Landmark Detectors
Andreas Steger, Radu Timofte, and Luc Van Gool
Computer Vision Lab, D-ITET, ETH Zurich, Switzerland
{radu.timofte,"
a6a6cfae45e8633c01793debf43592b7d515f65d,From ImageNet to Mining: Adapting Visual Object Detection with Minimal Supervision,"From ImageNet to Mining: Adapting Visual
Object Detection with Minimal Supervision
Alex Bewley and Ben Upcroft"
a6590c49e44aa4975b2b0152ee21ac8af3097d80,3D Interpreter Networks for Viewer-Centered Wireframe Modeling,"https://doi.org/10.1007/s11263-018-1074-6
D Interpreter Networks for Viewer-Centered Wireframe Modeling
Jiajun Wu1 · Tianfan Xue2 · Joseph J. Lim3 · Yuandong Tian4 ·
Joshua B. Tenenbaum1 · Antonio Torralba1 · William T. Freeman1,5
Received: date / Accepted: date"
dbf360fa9997b2918a8076f1d0df9a1fffa94ed7,Recommending and Planning Trip Itineraries for Individual Travellers and Groups of Tourists,"Recommending and Planning Trip Itineraries for Individual Travellers and
Groups of Tourists
*Department of Computing and Information Systems, The University of Melbourne, Australia
Victoria Research Laboratory, National ICT Australia, Australia
Kwan Hui Lim*†"
dbb7f37fb9b41d1aa862aaf2d2e721a470fd2c57,Face image analysis with convolutional neural networks,"Face Image Analysis With
Convolutional Neural Networks
Dissertation
Zur Erlangung des Doktorgrades
der Fakult¨at f¨ur Angewandte Wissenschaften
n der Albert-Ludwigs-Universit¨at Freiburg im Breisgau
Stefan Duffner"
db85195e171f7b75e4e6f99ed3029d31ee557e13,the influence of a verticality metaphor in the processing of happy and sad faces,"RIPS / IRSP, 27 (2), 51-77 © 2014, Presses universitaires de Grenoble
the influence of a verticality metaphor
in the processing of happy and sad faces
L’influence de la métaphore de verticalité sur le traitement
des émotions faciales de gaieté et de tristesse
Timothée Mahieu*,**
Olivier Corneille**
Vincent Y. Yzerbyt**
Key-words
Metaphorical thinking,
grounded cognition,
facial emotions, gender
Mots-clés
Pensée métaphorique,
ognition incarnée,
émotions faciales,
genre"
db24a2c27656db88486479b26f99d8754a44f4b8,Age estimation via face images : a survey,"Angulu et al. EURASIP Journal on Image and Video
Processing  (2018) 2018:42
https://doi.org/10.1186/s13640-018-0278-6
EURASIP Journal on Image
nd Video Processing
REVIEW
Open Access
Age estimation via face images: a survey
Raphael Angulu1*†
, Jules R. Tapamo2 and Aderemi O. Adewumi1"
db545026d0b42c2dcf935a1505a68edb36381194,Development of an Encrypting System for an Image Viewer based on Hill Cipher Algorithm,"Covenant Journal of Engineering Technology (CJET) Vol.1 No. 2, Dec. 2017
An Open Access Journal Available Online
Development of an Encrypting System for an Image
Viewer based on Hill Cipher Algorithm
Okereke Chinonso, Osemwegie Omoruyi,
Kennedy Okokpujie & Samuel  John
Department of Electrical and Information Engineering,
Covenant University,
Ota, Ogun state, Nigeria
Corresponding Author:"
db480f100004e3ef075f9404041fe4f89fcf4e0c,Human Pose Estimation for RGBD Imagery with Multi-Channel Mixture of Parts and Kinematic Constraints,"Human Pose Estimation for RGBD Imagery with Multi-Channel
Mixture of Parts and Kinematic Constraints
ENRIQUE MARTINEZ-BERTI
Universitat Politecnica de Valencia
Instituto AI2
Camino de Vera s/n, Valencia
SPAIN
ANTONIO J. SNCHEZ-SALMERN
Universitat Politecnica de Valencia
CARLOS RICOLFE-VIALA
Universitat Politecnica de Valencia
Instituto AI2
Camino de Vera s/n, Valencia
SPAIN
Instituto AI2
Camino de Vera s/n, Valencia
SPAIN
Center for Research in Computer Vision
Center for Research in Computer Vision
OLIVER NINA"
db458242dd526d84579aeee563355ca1a7dea5ea,Face Detection in Nighttime Images Using Visible-Light Camera Sensors with Two-Step Faster Region-Based Convolutional Neural Network,"Article
Face Detection in Nighttime Images Using
Visible-Light Camera Sensors with Two-Step Faster
Region-Based Convolutional Neural Network
Se Woon Cho, Na Rae Baek, Min Cheol Kim, Ja Hyung Koo, Jong Hyun Kim and
Kang Ryoung Park *
Division of Electronics and Electrical Engineering, Dongguk University, 30 Pil-dong-ro 1-gil, Jung-gu,
Seoul 04620, Korea; (S.W.C.); (N.R.B.);
(M.C.K.); (J.H.K.); (J.H.K.)
* Correspondence: Tel.: +82-10-3111-7022; Fax: +82-2-2277-8735
Received: 31 July 2018; Accepted: 4 September 2018; Published: 7 September 2018"
dbd5e9691cab2c515b50dda3d0832bea6eef79f2,Image-based Face Recognition: Issues and Methods 1,"Image-basedFaceRecognition:IssuesandMethods

WenYiZhao
RamaChellappa
Sarno(cid:11)Corporation
CenterforAutomationResearch

WashingtonRoad
UniversityofMaryland
Princeton,NJ
CollegePark,MD-"
db68105a787d7ef60ebb5bb4cfce151d47748123,Improved RGB-D-T based face recognition,"Aalborg Universitet
Improved RGB-D-T based Face Recognition
Oliu Simon, Marc;   Corneanu, Ciprian; Nasrollahi, Kamal; Nikisins, Olegs; Guerrero, Sergio
Escalera; Sun, Yunlian; Li, Haiqing; Sun, Zhenan; Moeslund, Thomas B.; Greitans, Modris
Published in:
DOI (link to publication from Publisher):
0.1049/iet-bmt.2015.0057
Publication date:
Document Version
Accepted author manuscript, peer reviewed version
Link to publication from Aalborg University
Citation for published version (APA):
Oliu Simon, M.,   Corneanu, C., Nasrollahi, K., Nikisins, O., Guerrero, S. E., Sun, Y., ... Greitans, M. (2016).
mt.2015.0057
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? You may not further distribute the material or use it for any profit-making activity or commercial gain
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dbaf89ca98dda2c99157c46abd136ace5bdc33b3,Nonlinear Cross-View Sample Enrichment for Action Recognition,"Nonlinear Cross-View Sample Enrichment for
Action Recognition
Ling Wang, Hichem Sahbi
Institut Mines-T´el´ecom; T´el´ecom ParisTech; CNRS LTCI"
db0d33590dc15de2d30cf0407b7a26ae79cd51b5,Deep Probabilistic Modeling of Natural Images using a Pyramid Decomposition,"Deep Probabilistic Modeling of Natural Images using a Pyramid Decomposition
Alexander Kolesnikov
IST Austria, Am Campus 1, Klosterneuburg, 3400 Austria
Christoph H. Lampert
IST Austria, Am Campus 1, Klosterneuburg, 3400 Austria"
db9ddb2c730d75ab741544654c7c227831ed1243,Recent Advances in Zero-shot Recognition,"Recent Advances in Zero-shot Recognition
Yanwei Fu, Tao Xiang, Yu-Gang Jiang, Xiangyang Xue, Leonid Sigal, and Shaogang Gong"
dbb0a527612c828d43bcb9a9c41f1bf7110b1dc8,Machine Learning Techniques for Face Analysis,"Chapter 7
Machine Learning Techniques
for Face Analysis
Roberto Valenti, Nicu Sebe, Theo Gevers, and Ira Cohen"
db227f72bb13a5acca549fab0dc76bce1fb3b948,Characteristic Based Image Search Using Re-Ranking Method,"International Refereed Journal of Engineering and Science (IRJES)
ISSN (Online) 2319-183X, (Print) 2319-1821
Volume 4, Issue 6 (June 2015), PP.169-169-174
Characteristic Based Image Search using Re-Ranking method
Chitti Babu, 2Yasmeen Jaweed, 3G.Vijay Kumar
,2,3Computer Science Engineering Dept, Sree Dattha Institute of Engineering & Science"
dba3ec4420a0bcca3264f75f4c975cabdbb1af74,"""Edutainment 2017"" a visual and semantic representation of 3D face model for reshaping face in images","J Vis (2018) 21:649–660
https://doi.org/10.1007/s12650-018-0476-4
R E G UL A R P A P E R
Jiang Du • Dan Song • Yanlong Tang • Ruofeng Tong • Min Tang
‘‘Edutainment 2017’’ a visual and semantic
representation of 3D face model for reshaping face
in images
Received: 15 September 2017 / Revised: 20 December 2017 / Accepted: 22 January 2018 / Published online: 16 February 2018
Ó The Visualization Society of Japan 2018"
db186bd2a276a574b2246e3e4d136f8a07c53ff2,Verisimilar Percept Sequences Tests for Autonomous Driving Intelligent Agent Assessment,"Verisimilar Percept Sequences Tests for
Autonomous Driving Intelligent Agent Assessment
Thomio Watanabe
University of Sao Paulo
Denis Wolf
University of Sao Paulo"
db6d00f9237cce392c08b422662b48baa2ed1b80,A New Framework for Biometric Face Recognition Using Visual,"Annals of DAAAM for 2012 & Proceedings of the 23rd International DAAAM Symposium, Volume 23, No.1, ISSN 2304-1382
ISBN 978-3-901509-91-9, CDROM version, Ed. B. Katalinic, Published by DAAAM International, Vienna, Austria, EU, 2012
Make Harmony between Technology and Nature, and Your Mind will Fly Free as a Bird
Annals & Proceedings of DAAAM International 2012
A NEW FRAMEWORK FOR BIOMETRIC FACE RECOGNITION USING VISUAL
CRYPTOGRAPY
MIHAILESCU, M[arius] I[ulian] & PIRLOAGA, M[arian] D[orin]"
db3814e155e77cefaa71016b0e5ebdc6be34a925,Enhanced Clustering Method using 3 D Laser Range Data for an Autonomous Vehicle,"Enhanced Clustering Method using 3D Laser Range Data
for an Autonomous Vehicle
KUK CHO, SEUNGHO BAEG, and SANGDEOK PARK
Korea Institute of Industrial Technology
Department of Robot Research & Business Development
271-18, Sa-1dong, Sangruk-gu, Ansan
Republic of Korea
googi33, shbaeg,"
dbb065aa2a6e6804e0ab8aee27314a6f68c4cde1,Advanced Hypothesis Testing Techniques and Their Application to Image Classification Advanced Hypothesis Testing Techniques and Their Application to Image Classification Title: Advanced Hypothesis Testing Techniques and Their Application to Image Classification Acknowledgements,"Dipartimento di Informatica e
Scienze dell’Informazione
•• ••
Advanced Hypothesis testing techniques and their
pplication to image classification
Emanuele Franceschi
Theses Series
DISI-TH-2005-XX
DISI, Universit`a di Genova
v. Dodecaneso 35, 16146 Genova, Italy
http://www.disi.unige.it/"
dbe2f62c086b2e6617c7744f8c242de401f17cc0,Particle-based Pedestrian Path Prediction using LSTM-MDL Models,"Particle-based Pedestrian Path Prediction using LSTM-MDL Models
Ronny Hug∗, Stefan Becker∗, Wolfgang H¨ubner∗ and Michael Arens∗"
318d7a4bc9c7b1e3a01056815479564ed8ad78a4,University of Oklahoma Graduate College Reinforcement Learning Scheduler for Heterogeneous Multi-core Processors Reinforcement Learning Scheduler for Heterogeneous Multi-core Processors a Thesis Approved for the School of Computer Science,"UNIVERSITY OF OKLAHOMA
GRADUATE COLLEGE
REINFORCEMENT LEARNING SCHEDULER FOR HETEROGENEOUS
MULTI-CORE PROCESSORS
A THESIS
SUBMITTED TO THE GRADUATE FACULTY
in partial fulfillment of the requirements for the
Degree of
MASTER OF SCIENCE
XIAOLEI YAN
Norman, Oklahoma"
3137870bf1314e25c2246d4a9d77d941aadd5398,Influence of Positive Instances on Multiple Instance Support Vector Machines,"Influence of Positive Instances on
Multiple Instance Support Vector Machines
Nuno Barroso Monteiro1,2, Jo˜ao Pedro Barreto2, and Jos´e Gaspar1
Institute for Systems and Robotics (ISR/IST), LARSyS, Univ. of Lisbon, Portugal
Institute for Systems and Robotics, Univ. of Coimbra, Portugal"
318ee553c61888f2418280cb1d342c698d3444c9,Towards face unlock: on the difficulty of reliably detecting faces on mobile phones,"Towards Face Unlock: On the Difficulty of Reliably
Detecting Faces on Mobile Phones
Rainhard D. Findling
Softwarepark 11
Hagenberg, Austria
Rene Mayrhofer
Softwarepark 11
Hagenberg, Austria
Department for Mobile Computing
Upper Austria University of Applied Sciences
Department for Mobile Computing
Upper Austria University of Applied Sciences"
3130eb9bfab5e5a095ab989ba3cc6a2ec62c156d,Generating Facial Ground Truth with Synthetic Faces,"Generating Facial Ground Truth with Synthetic Faces
Rossana Queiroz, Marcelo Cohen, Juliano L. Moreira, Adriana Braun, J´ulio C. Jacques J´unior, Soraia Raupp Musse
Pontif´ıcia Universidade Cat´olica do Rio Grande do Sul - PUCRS
Graduate Programme in Computer Science
Virtual Human Laboratory -www.inf.pucrs.br/∼vhlab
Porto Alegre, Brazil
Figure 1. A sample of 3D faces generated by our prototype."
31786e6d5187d7bc41678cbd2d1bf8edf1ddfed9,Capture de mouvements humains par capteurs RGB-D. (Capture human motions by RGB-D sensor ),"Capture de mouvements humains par capteurs RGB-D
Jean-Thomas Masse
To cite this version:
Jean-Thomas Masse. Capture de mouvements humains par capteurs RGB-D. Robotique
[cs.RO]. Universit´e Paul Sabatier - Toulouse III, 2015. Fran¸cais.
¡ NNT : 2015TOU30361
HAL Id: tel-01280163
https://tel.archives-ouvertes.fr/tel-01280163v2
Submitted on 26 Apr 2017
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,
´emanant des ´etablissements d’enseignement et de
recherche fran¸cais ou ´etrangers, des laboratoires"
318c4c25d86511690cc5df7b041a6392e8cc4ea8,Fashion-Gen: The Generative Fashion Dataset and Challenge,"Fashion-Gen: The Generative Fashion Dataset and Challenge
Negar Rostamzadeh 1 Seyedarian Hosseini 1 2 Thomas Boquet 1 Wojciech Stokowiec 1 Ying Zhang 1
Christian Jauvin 1 Chris Pal 3 1"
31c174f2190889d5792358713e078336926d7ee4,Image Categorization using Codebooks Built from Scored and Selected Local Features,"Image Categorization using Codebooks Built from
Scored and Selected Local Features
Department of Computer Science, Northern Illinois University DeKalb IL USA 60115
Bala S. Divakaruni and Jie Zhou
follows
(M&C)  process"
31625522950e82ad4dffef7ed0df00fdd2401436,Motion Representation with Acceleration Images,"Motion Representation with Acceleration Images
Hirokatsu Kataoka, Yun He, Soma Shirakabe, Yutaka Satoh
National Institute of Advanced Industrial Science and Technology (AIST)
Tsukuba, Ibaraki, Japan
{hirokatsu.kataoka, yun.he, shirakabe-s,"
31da1da2d4e7254dd8f2a4578d887c57e0678438,Unsupervised Person Re-identification: Clustering and Fine-tuning,"Unsupervised Person Re-identification:
Clustering and Fine-tuning
Hehe Fan, Liang Zheng and Yi Yang"
317f5a56519df95884cce81cfba180ee3adaf5a5,Operator-In-The-Loop Deep Sequential Multi-camera Feature Fusion for Person Re-identification,"FusionCam C1Cam C2Classical re-id schemeProposed re-idschemeQueryQueryRanked List: Cam 𝐶1Ranked List: Cam 𝐶2Ranked List: Cam 𝐶1OperatorFeedbackRanked List: Cam 𝐶2Fig.1:(Top)Classicalre-idschemewherequeryimage’sfeaturerepresentationisusedtosearcheachcamerainthenetworkinde-pendently.Theretrievedlistsarereturnedtothehumanoperator.(Bottom)Ourproposedsequentialre-idschemewhereoperatorfeedbackregardingtargetsightingisutilizedtowardsbetterre-idperformanceinanonlinefashion.Inthefigure,cameraC1isqueriedfirstandrankedlistofmatchesisobtained.Thecorrectmatch(pinkbox)inretrievedrankedlistisidentifiedbyoperator.Thecorrectmatchisfusedwithqueryimageatfeaturelevel(orangeblock).ThisfusedrepresentationisusedtoquerycameraC2.NoticethatrankingofquerytargetinC2’slistimprovesinourapproachunliketheclassicalversionwhichcannotexploitoperatorinputstoimprovesubsequentqueries.arXiv:1807.07295v3  [cs.CV]  6 Nov 2018"
318eb316c0117059dd47978854cfa92baeaac1d2,Deterministic CUR for Improved Large-Scale Data Analysis: An Empirical Study,"Deterministic CUR for Improved Large-Scale Data Analysis:
An Empirical Study
Christian Thurau, Kristian Kersting, and Christian Bauckhage
Fraunhofer IAIS, Germany"
31fc3b044ec908f7f61386422727ef23784178c0,Enhancing Face Recognition using Average per Region,"International Journal of Computer Applications (0975 – 8887)
Volume 65– No.3, March 2013
Enhancing Face Recognition using Average per Region
Basheer M. Nasef
Teaching Assistant
Dept of Computer and Systems Engineering,
Zagazig University, Sharkia, Egypt
Ibrahim E. Ziedan
Dept of Computer and Systems Engineering,
Professor
Zagazig University, Sharkia, Egypt"
316e67550fbf0ba54f103b5924e6537712f06bee,Multimodal semi-supervised learning for image classification,"Multimodal semi-supervised learning
for image classification
Matthieu Guillaumin, Jakob Verbeek, Cordelia Schmid
LEAR team, INRIA Grenoble, France"
315ac7b1c160eb593b6ce80d46b4ae2bcab49e12,SLAM++ -A highly efficient and temporally scalable incremental SLAM framework,"SLAM++1. A Highly Efficient and Temporally Scalable
Incremental SLAM Framework
Viorela Ila, Lukas Polok, Marek Solony and Pavel Svoboda"
3199a528e5e68f48827544abee7fd6b47678dba5,Human Face Recognition Using Weighted Vote of Gabor Magnitude Filters,"Information Technology and Applications Journal
Human Face Recognition Using Weighted Vote of
Gabor Magnitude Filters
Iqbal Nouyed, Graduate Student member IEEE, M. Ashraful Amin, Member IEEE, Bruce
Poon, Senior Member IEEE, Hong Yan, Fellow IEEE"
312b807a24b8c30876c1750530b08e4d9627e231,Increasing Trustworthiness of Face Authentication in Mobile Devices by Modeling Gesture Behavior and Location Using Neural Networks,"Article
Increasing Trustworthiness of Face Authentication in
Mobile Devices by Modeling Gesture Behavior and
Location Using Neural Networks
Blerim Rexha 1 ID , Gresa Shala 2,* and Valon Xhafa 3
Faculty of Electrical and Computer Engineering, University of Prishtina, Kodra e Diellit p.n.,
0000 Prishtina, Kosovo;
Department of Computer Science, Freiburg University, Georges-Köhler Alley 101,
79110 Freiburg im Breisgau, Germany
Department of Informatics, Technical University of Munich, Boltzmannstraße 3,
85748 Garching bei München, Germany;
* Correspondence:
Received: 18 January 2018; Accepted: 2 February 2018; Published: 5 February 2018"
31de9b3dd6106ce6eec9a35991b2b9083395fd0b,FERET ( Face Recognition Technology ) Recognition Algorithm Development and Test Results,"FERET (Face Recognition Technology)
Recognition Algorithm Development and
Test Results
y P. Jonathon Phillips, Patrick J. Rauss, and Sandor Z. Der
ARL-TR-995
October 1996
Approved for public release; distribution unlimited."
318df1761d01ebafc955eca683392dc98a5056d0,On the Use of SIFT Features for Face Authentication,"On the use of SIFT features for face authentication
Manuele Bicego, Andrea Lagorio, Enrico Grosso, Massimo Tistarelli
University of Sassari"
3176ee88d1bb137d0b561ee63edf10876f805cf0,Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation,"Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation
Sina Honari1, Jason Yosinski2, Pascal Vincent1,4, Christopher Pal3
University of Montreal, 2Cornell University, 3Ecole Polytechnique of Montreal, 4CIFAR
{honaris,"
31ea3186aa7072a9e25218efe229f5ee3cca3316,A ug 2 01 7 Reinforced Video Captioning with Entailment Rewards,"Reinforced Video Captioning with Entailment Rewards
Ramakanth Pasunuru and Mohit Bansal
UNC Chapel Hill
{ram,"
31ca0d6488a27a140263291c51ec924b8a49967b,"Show, Ask, Attend, and Answer: A Strong Baseline For Visual Question Answering","Show, Ask, Attend, and Answer:
A Strong Baseline For Visual Question Answering
Vahid Kazemi
Ali Elqursh
Google Research
600 Amphitheater Parkway
{vahid,"
3107486fe666a3004b720125bd2b05ff9382fdb8,Generalized two-dimensional linear discriminant analysis with regularization,"JOURNAL OF LATEX CLASS FILES, VOL.
, NO.
Generalized two-dimensional linear discriminant
nalysis with regularization
Chun-Na Li, Yuan-Hai Shao,Wei-Jie Chen, Zhen Wang and Nai-Yang Deng"
31c0968fb5f587918f1c49bf7fa51453b3e89cf7,Deep Transfer Learning for Person Re-Identification,"Deep Transfer Learning for Person Re-identification
Mengyue Geng
Yaowei Wang
Tao Xiang
Yonghong Tian"
3174fceef3cf09ac35e8d1eb4e1b8b73a3b2c713,Unsupervised learning from videos using temporal coherency deep networks,"Computer Vision and Image Understanding
journal homepage: www.elsevier.com
Unsupervised learning from videos using temporal coherency deep networks
Carolina Redondo-Cabreraa,∗∗, Roberto Lopez-Sastrea
GRAM, University of Alcal´a, Alcal´a de Henares, 28805, Spain"
31d30089d00d89715167ca4a130a5d262e1d79d3,Measuring the effect of nuisance variables on classifiers,"FAWZI, FROSSARD: MEASURING THE EFFECT OF NUISANCE VARIABLES
Measuring the effect of nuisance variables
on classifiers
Alhussein Fawzi
Pascal Frossard
Signal Processing Laboratory (LTS4)
Ecole Polytechnique Fédérale de
Lausanne (EPFL)
Lausanne, Switzerland"
318f7b59fc22d6326f77b24939860b0137bf8e77,Multiple Classifier Boosting and Tree-Structured Classifiers,"Multiple Classifier Boosting and
Tree-Structured Classifiers
Tae-Kyun Kim and Roberto Cipolla"
3137eede6bbada4442e0193dc5918788b7e88aa1,Hyper-class augmented and regularized deep learning for fine-grained image classification,"Hyper-class Augmented and Regularized Deep Learning for Fine-grained Image Classification
Saining Xie1, Tianbao Yang2 Xiaoyu Wang3, Yuanqing Lin4
University of California, San Diego. 2University of Iowa. 3Snapchat Research. 4NEC Labs America, Inc.
Fine-grained image classification (FGIC) is challenging because (i) fine-
grained labeled data is much more expensive to acquire (usually requir-
ing domain expertise); (ii) there exists large intra-class and small inter-
lass variance. In this paper, we propose a systematic framework of learn-
ing a deep CNN that addresses the challenges from two new perspectives:
(i) identifying easily annotated hyper-classes inherent in the fine-grained
data and acquiring a large number of hyper-class-labeled images from read-
ily available external sources, and formulating the problem into multi-task
learning, to address the data scarcity issue. We use two common types of
hyper-classes to augment our data, with one being the super-type hyper-
lasses that subsume a set of fine-grained classes, and another being named
factor-type hyper-classes (e.g., different view-points of a car) that explain
the large intra-class variance. (ii) a novel learning model by exploiting a reg-
ularization between the fine-grained recognition model and the hyper-class
recognition model to mitigate the issue of large intra-class variance and im-
prove the generalization performance. The proposed approach also closely
relates to attribute-based learning, since one can consider that factor-type"
3123e97a6b86913d994e44f8d9d5c639e0e2dc96,A Method of Initialization for Nonnegative Matrix Factorization,"A METHOD OF INITIALIZATION FOR NONNEGATIVE MATRIX FACTORIZATION
Yong-Deok Kim and Seungjin Choi
Department of Computer Science, POSTECH, Korea
{karma13,"
31afdb6fa95ded37e5871587df38976fdb8c0d67,Quantized fuzzy LBP for face recognition,"QUANTIZED FUZZY LBP FOR FACE RECOGNITION
Jianfeng
Xudong Jiang,
Junsong
BeingThere
Centre
Institute
of Media Innovation
Nanyang
50 Nanyang
Technological
Singapore
Drive,
637553.
University
School of Electrical
& Electronics
Engineering
Nanyang
50 Nanyang"
31ef5419e026ef57ff20de537d82fe3cfa9ee741,Facial Expression Analysis Based on High Dimensional Binary Features,"Facial Expression Analysis Based on
High Dimensional Binary Features
Samira Ebrahimi Kahou, Pierre Froumenty, and Christopher Pal
´Ecole Polytechique de Montr´eal, Universit´e de Montr´eal, Montr´eal, Canada
{samira.ebrahimi-kahou, pierre.froumenty,"
316bed02e22aa6742dffcd50c29a7365c5a5a437,Representation Learning for Visual-Relational Knowledge Graphs,"Representation Learning for Visual-Relational
Knowledge Graphs
Daniel Oñoro-Rubio, Mathias Niepert, Alberto García-Durán, Roberto
González-Sánchez and Roberto J. López-Sastre*
NEC Labs Europe, Alcalá de Henares*
{daniel.onoro, mathias.niepert, alberto.duran,
https://github.com/nle-ml/mmkb.git"
310a88a60ffa2d8a0fa7ef9fc77fa842d16eed57,View Invariant Gait Recognition,"View Invariant Gait Recognition
Richard D. Seely, Michela Goffredo, John N. Carter and Mark S. Nixon"
3151b110ecdcf2105def494bfb0775f21259d7e8,Asymmetric Cuts: Joint Image Labeling and Partitioning,"Asymmetric Cuts : Joint Image Labeling and
Partitioning
Thorben Kroeger1, J¨org H. Kappes2, Thorsten Beier1, Ullrich Koethe1 and
Fred A. Hamprecht1,2
Multidimensional Image Processing Group, Heidelberg University
Heidelberg Collaboratory for Image Processing, Heidelberg University"
31ace8c9d0e4550a233b904a0e2aabefcc90b0e3,Learning Deep Face Representation,"Learning Deep Face Representation
Haoqiang Fan
Megvii Inc.
Zhimin Cao
Megvii Inc.
Yuning Jiang
Megvii Inc.
Qi Yin
Megvii Inc.
Chinchilla Doudou
Megvii Inc."
31470cf8fda53c4460de4373e5ac4544236c44af,Biased information processing as an endophenotype for depression,"PDF hosted at the Radboud Repository of the Radboud University
Nijmegen
The following full text is a publisher's version.
For additional information about this publication click this link.
http://repository.ubn.ru.nl/handle/2066/127113
Please be advised that this information was generated on 2017-04-19 and may be subject to
hange."
31b9251dedce1e10467a0a33f56ac4eb05ed0451,Viewpoint-dependent 3D human body posing for sports legacy recovery from images and video,"VIEWPOINT-DEPENDENT 3D HUMAN BODY POSING FOR SPORTS LEGACY
RECOVERY FROM IMAGES AND VIDEO
Luis Unzueta, Jon Goenetxea, Mikel Rodriguez and Maria Teresa Linaza
Vicomtech-IK4, Paseo Mikeletegi, 57, Parque Tecnológico, 20009, Donostia, Spain"
318985dc2b8d5a1882b709eedeaac4a2e7de1d81,Accelerating Message Passing for MAP with Benders Decomposition,"Accelerating Message Passing for MAP with
Benders Decomposition
Julian Yarkony
Experian Data Lab.
Shaofei Wang
Baidu Inc.
May 15, 2018"
31bec2ba11ccf217461a491e810dc1d8ef33f9a9,Multimodal Probabilistic Model-Based Planning for Human-Robot Interaction,"Multimodal Probabilistic Model-Based Planning
for Human-Robot Interaction
Edward Schmerling1 Karen Leung2 Wolf Vollprecht3 Marco Pavone2"
c791682fa3f716401bca46c6e9a2af495b0d9d51,Interested in publishing with us ? Contact book,"We are IntechOpen,
the world’s leading publisher of
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Built by scientists, for scientists
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Downloads
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Numbers displayed above are based on latest data collected."
c72914e2e999c99753d1d0058c459af69af6662a,CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation,"MACKOWIAK ET AL.: CEREALS
CEREALS – Cost-Effective REgion-based
Active Learning for Semantic Segmentation
Robert Bosch GmbH
Corporate Research - Computer Vision
Robert-Bosch-Straße 200
1139 Hildesheim, DE
Heidelberg Collaboratory for Image
Processing (HCI)
Berliner Straße 43,
69120 Heidelberg, DE
Radek Mackowiak1
Philip Lenz1
Omair Ghori1
Ferran Diego1
Oliver Lange1
Carsten Rother2"
c7391b43bd0216daf697fb77906b76c71f5c50e2,Learning where to attend like a human driver,"Where Should You Attend While Driving?
Simone Calderara
Stefano Alletto
Andrea Palazzi∗
Francesco Solera∗
Rita Cucchiara
University of Modena and Reggio Emilia"
c758b9c82b603904ba8806e6193c5fefa57e9613,Heterogeneous Face Recognition with CNNs,"Heterogeneous Face Recognition with CNNs
Shreyas Saxena
Jakob Verbeek
INRIA Grenoble, Laboratoire Jean Kuntzmann"
c71f71200fc85d9fdbd5b3d8b4b6a9daf5a2b424,Machine Learning for Image Based Motion Capture,"INSTITUTNATIONALPOLYTECHNIQUEDEGRENOBLENum´eroattribu´eparlabiblioth`equeTH`ESEpourobtenirlegradedeDOCTEURDEL’INSTITUTNATIONALPOLYTECHNIQUEDEGRENOBLESp´ecialit´e:Imagerie,VisionetRobotiqueEcoleDoctorale:Math´ematiques,SciencesetTechnologiedel’Informationpr´esent´eeetsoutenuepubliquementparAnkurAgarwalle26Avril2006MachineLearningforImageBasedMotionCaptureDirecteurdeth`ese:M.WilliamTriggsJURYM.RogerMohrPr´esidentM.AndrewZissermanRapporteurM.PascalFuaRapporteurM.WilliamTriggsDirecteurdeth`eseM.PhilipTorrExaminateurTh`esepr´epar´eedanslelaboratoireGRAVIR–IMAGauseinduprojetLEARINRIARhˆone-Alpes,655avenuedel’Europe,38334SaintIsmier,France."
c7f80cd585117ae7028148c38ddce8e63b311ba5,Real-Time Face Detection and Recognition via Local Binary Pattern Plus Sample Selective Biomimetic Pattern Recognition,"IEICE TRANS. INF. & SYST., VOL.E96–D, NO.3 MARCH 2013
PAPER SpecialSectiononFacePerceptionandRecognition
Real-Time Face Detection and Recognition via Local Binary
Pattern Plus Sample Selective Biomimetic Pattern Recognition
Yikui ZHAI†,††a), Student Member, Junying GAN†, Jinwen LI††, Junying ZENG†,
nd Ying XU†,†††, Nonmembers
SUMMARY
Due to security demand of society development, real-time
face recognition has been receiving more and more attention nowadays.
In this paper, a real-time face recognition system via Local Binary Pat-
tern (LBP) plus Improved Biomimetic Pattern Recognition (BPR) has been
proposed. This system comprises three main steps: real-time color face de-
tection process, feature extraction process and recognition process. Firstly,
color face detector is proposed to detect face with eye alignment and si-
multaneous performance; while in feature extraction step, LBP method is
dopted to eliminate the negative effect of the light heterogeneity. Finally,
n improved BPR method with Selective Sampling construction is applied
to the recognition system. Experiments on our established database named
WYU Database, PUT Database and AR Database show that this real-time
face recognition system can work with high ef‌f‌iciency and has achieved"
c76d143b3fa0d25e21580c583d39ab07fc937e71,3D Position Estimation of a Person of Interest in Multiple Video Sequences: People Detection,"Institutionen för systemteknik
Department of Electrical Engineering
Examensarbete
D Position Estimation of a Person of Interest in
Multiple Video Sequences: People Detection
Examensarbete utfört i Datorseende
vid Tekniska högskolan vid Linköpings universitet
Johannes Markström
LiTH-ISY-EX--13/4721--SE
Linköping 2013
Department of Electrical Engineering
Linköpings universitet
SE-581 83 Linköping, Sweden
Linköpings tekniska högskola
Linköpings universitet
581 83 Linköping"
c7ea9611446817f7b668882061ab11c7e998296c,Towards a Crowd Analytic Framework For Crowd Management in Majid-al-Haram,"Towards a Crowd Analytic Framework For Crowd
Management in Majid-al-Haram
Sultan Daud Khan1,*, Muhammad Tayyab1, Muhammad Khurram Amin1, Akram Nour1,
Anas Basalamah1, Saleh Basalamah1, and Sohaib Ahmad Khan1,2,*
Technology Innovation Center, Wadi Makkah, Makkah Al Mukarramah, Saudi Arabia
Science and Technology Unit, Umm Al Qura University, Makkah Al Mukarramah, Saudi Arabia"
c7e4c7be0d37013de07b6d829a3bf73e1b95ad4e,MO : A VIDEO DATABASE OF NATURAL FACIAL EXPRESSIONS OF EMOTIONS,"The International Journal of Multimedia & Its Applications (IJMA) Vol.5, No.5, October 2013
DYNEMO: A VIDEO DATABASE OF NATURAL FACIAL
EXPRESSIONS OF EMOTIONS
Anna Tcherkassof1, Damien Dupré1, Brigitte Meillon2, Nadine Mandran2,
Michel Dubois1 and Jean-Michel Adam2
LIP, Univ. Grenoble Alpes, BP 47 - 38040 Grenoble Cedex 9, France
LIG, Univ. Grenoble Alpes, BP 53 - 38041 Grenoble Cedex 9, France"
c7fff0d0a6312965b269c6180b2112babd40564c,Unsupervised Person Re-identification: Clustering and Fine-tuning,"Unsupervised Person Re-identification:
Clustering and Fine-tuning
Hehe Fan, Liang Zheng and Yi Yang"
c7eb127e9cd67d645b9a7f59c03bc73183faefeb,Human Detection in Indoor Environments Using Multiple Visual Cues and a Mobile Robot,"Human Detection in Indoor Environments Using
Multiple Visual Cues and a Mobile Robot
Stefan Pszcz´o(cid:2)lkowski and Alvaro Soto
Pontificia Universidad Catolica de Chile
Santiago 22, Chile"
c7c03324833ba262eeaada0349afa1b5990c1ea7,A Wearable Face Recognition System on Google Glass for Assisting Social Interactions,"A Wearable Face Recognition System on Google
Glass for Assisting Social Interactions
Bappaditya Mandal∗, Chia Shue Ching, Liyuan Li, Vijay Ramaseshan
Chandrasekhar, Cheston Tan Yin Chet and Lim Joo Hwee
Visual Computing Department, Institute for Infocomm Research, Singapore
Email address: (∗Contact author: Bappaditya Mandal);
{scchia, lyli, vijay, cheston-tan,"
c7f752eea91bf5495a4f6e6a67f14800ec246d08,EXPLORING THE TRANSFER LEARNING ASPECT OF DEEP NEURAL NETWORKS IN FACIAL INFORMATION PROCESSING,"EXPLORING THE TRANSFER
LEARNING ASPECT OF DEEP
NEURAL NETWORKS IN FACIAL
INFORMATION PROCESSING
A DISSERTATION SUBMITTED TO THE UNIVERSITY OF MANCHESTER
FOR THE DEGREE OF MASTER OF SCIENCE
IN THE FACULTY OF ENGINEERING AND PHYSICAL SCIENCES
Crefeda Faviola Rodrigues
School of Computer Science"
c751a00dd5357e2e390e5af2626b5fbb6223ea1c,Behavioral and neural indices of affective coloring for neutral social stimuli,"Social Cognitive and Affective Neuroscience, 2018, 310–320
doi: 10.1093/scan/nsy011
Advance Access Publication Date: 13 February 2018
Original article
Behavioral and neural indices of affective coloring for
neutral social stimuli
Daniel W. Grupe, Stacey M. Schaefer, Regina C. Lapate, Andrew J. Schoen,
Lauren K. Gresham, Jeanette A. Mumford, and Richard J. Davidson
Center for Healthy Minds and Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin-
Madison, Madison, WI 53706, USA
Correspondence should be addressed to Daniel W. Grupe, Center for Healthy Minds and Waisman Laboratory for Brain Imaging and Behavior, University
of Wisconsin-Madison, 625 West Washington Ave, Madison, WI 53703, USA. E-mail:"
c7f63fc2ff20513c6dc233ec3419417b43b39209,Human Detection from Aerial Imagery for Automatic Counting of Shellfish Gatherers,"Human Detection from Aerial Imagery for Automatic Counting of
Shellfish gatherers
Mathieu Laroze, Luc Courtrai and Sébastien Lefèvre
Univ. Bretagne-Sud, UMR 6074 IRISA
{mathieu.laroze, luc.courtrai,
F-56000, Vannes, France
Keywords:
Human Detection, Image Stitching, Aerial Imagery, Image Mosaicing, Patch Classification, Object Detection"
c719a718073128a985c957cdfa3f298706a180e6,Comparative Evaluations of Selected Tracking-by-Detection Approaches,"Comparative Evaluations of Selected
Tracking-by-Detection Approaches
Alhayat Ali Mekonnen, Frédéric Lerasle
To cite this version:
Alhayat Ali Mekonnen, Frédéric Lerasle. Comparative Evaluations of Selected Tracking-by-Detection
Approaches. IEEE Transactions on Circuits and Systems for Video Technology, Institute of Electrical
nd Electronics Engineers, 2018, <10.1109/TCSVT.2018.2817609>. <hal-01815850>
HAL Id: hal-01815850
https://hal.laas.fr/hal-01815850
Submitted on 14 Jun 2018
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
c726ea46544968335f1e51be633f15d0cc0f0311,Generalized feature learning and indexing for object localization and recognition,"Generalized Feature Learning and Indexing for Object Localization and
Recognition
Ning Zhou∗
UNC, Charlotte
Anelia Angelova∗
Google Inc
Jianping Fan
UNC, Charlotte"
c7c405b6fc95ff2ccf2cb5b59942db4343558fc4,Pseudo 2 D Hidden Markov Model Based Face Recognition System Using Singular Values Decomposition Coefficients,"Pseudo 2D Hidden Markov Model Based Face Recognition System Using Singular
Values Decomposition Coefficients
Mukundhan Srinivasan
Department of Electronics & Communication Engineering
Alpha College of Engineering
Chennai, TN India
Sabarigirish Vijayakumar
Retail Domain
Tata Consultancy Services (TCS)
Chennai, TN India"
c72796c511e2282e4088b0652a4cce0e2da4296c,Hierarchical Discrete Distribution Decomposition for Match Density Estimation,"Hierarchical Discrete Distribution Decomposition for Match Density Estimation
Zhichao Yin
Trevor Darrell
UC Berkeley
Fisher Yu"
c7774fd600630684cc1d6be8313e2935bb198880,Adapting Hausdorff Metrics to Face Detection Systems: A Scale-Normalized Hausdorff Distance Approach,"Adapting Hausdorff metrics to face detection
systems: a scale-normalized Hausdorff distance
pproach
Pablo Suau
Departamento de Ciencia de la Computaci´on e Inteligencia Artificial
Universidad de Alicante, Ap. de correos 99, 03080, Alicante (Spain)"
c757f6ee46208c1c26572265803068f8d837c384,Thermal imaging systems for real-time applications in smart cities,"Aalborg Universitet
Thermal Imaging Systems for Real-Time Applications in Smart Cities
Gade, Rikke; Moeslund, Thomas B.; Nielsen, Søren Zebitz; Skov-Petersen, Hans; Andersen,
Hans Jørgen; Basselbjerg, Kent; Dam, Hans Thorhauge; Jensen, Ole B.; Jørgensen, Anders;
Lahrmann, Harry Spaabæk; Madsen, Tanja Kidholm Osmann; Skouboe, Esben Bala; Povey,
Bo Ø.
Published in:
International Journal of Computer Applications in Technology
DOI (link to publication from Publisher):
Publication date:
Document Version
Accepted author manuscript, peer reviewed version
Link to publication from Aalborg University
Citation for published version (APA):
Gade, R., Moeslund, T. B., Nielsen, S. Z., Skov-Petersen, H., Andersen, H. J., Basselbjerg, K., ... Povey, B. Ø.
(2016). Thermal Imaging Systems for Real-Time Applications in Smart Cities. International Journal of Computer
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nd it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.
? Users may download and print one copy of any publication from the public portal for the purpose of private study or research."
c7742e63579cfea8655606ec6bd9047140efe96a,D and Pseudo-2d Hidden Markov Models for Image Analysis. Theoretical Introduction 1d and Pseudo-2d Hidden Markov Models for Image Analysis. Theoretical Introduction,"
D and Pseudo-D Hidden Markov Models
for Image Analysis.
Theoretical Introduction
ephane Marchand-Maillet - Multimedia Communications
Email:
Phone: + .	...	 - Fax: + .	...
Date: November , 

Technical Report RR-		-	 Part A
Condential: No
ecom’s research is partially supported by its industrial members:
Ascom, Cegetel, France Telecom, Hitachi, IBM France, Motorola,
Swisscom, Texas Instruments, and Thomson CSF.
Multimedia Communications
Institut EURECOM  BP 
	.
	 Sophia Antipolis  France
T.R. RR-		-	 Part A  November , 
"
c7d7cf88d2e9f3194aec2121eb19dbfed170dba8,Unconstrained Gaze Estimation Using Random Forest Regression Voting,"Unconstrained Gaze Estimation Using Random Forest
Regression Voting
Amine Kacete, Renaud Séguier, Michel Collobert, Jérôme Royan
To cite this version:
Amine Kacete, Renaud Séguier, Michel Collobert, Jérôme Royan. Unconstrained Gaze Estimation
Using Random Forest Regression Voting. Springer. ACCV 13th Asian Conference on Computer
Vision, Nov 2016, Taipei, Taiwan. <http://www.accv2016.org/>. <hal-01393591>
HAL Id: hal-01393591
https://hal.archives-ouvertes.fr/hal-01393591
Submitted on 7 Nov 2016
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
c77c094faf7b1a4e293609a0909c7c50b468675a,Satyam: Democratizing Groundtruth for Machine Vision,"SATYAM: DEMOCRATIZING GROUNDTRUTH FOR MACHINE VISION
Hang Qiu?, Krishna Chintalapudi†, Ramesh Govindan?"
c7de0c85432ad17a284b5b97c4f36c23f506d9d1,RANSAC-Based Training Data Selection for Speaker State Recognition,"INTERSPEECH 2011
RANSAC-based Training Data Selection for Speaker State Recognition
Elif Bozkurt1, Engin Erzin1, C¸ i˘gdem Ero˘glu Erdem2, A.Tanju Erdem3
Multimedia, Vision and Graphics Laboratory, Koc¸ University, Istanbul, Turkey
Department of Electrical and Electronics Engineering, Bahc¸es¸ehir University, Istanbul, Turkey
Department of Electrical and Computer Engineering, ¨Ozye˘gin University, Istanbul, Turkey
ebozkurt,"
c70ad19c90491e2de8de686b6a49f9bbe44692c0,Seeing with Humans: Gaze-Assisted Neural Image Captioning,"Seeing with Humans: Gaze-Assisted
Neural Image Captioning
Yusuke Sugano and Andreas Bulling"
c74a42afeae520ff6ab280d17bccf0d082ba8de5,The Concept of Comprehensive Data Analysis from Ultra-Wideband Subsystem for Smart City Positioning Purposes,"Preprints (www.preprints.org)  |  NOT PEER-REVIEWED  |  Posted: 25 October 2018                   doi:10.20944/preprints201810.0609.v1
Article
The Concept of Comprehensive Data Analysis from
Ultra-Wideband Subsystem for Smart City
Positioning Purposes
Damian Grzechca *, Krzysztof Hanzel and Krzysztof Paszek
Faculty of Automatic Control, Electronics and Computer Science,
Silesian University of Technology Gliwice, Poland;
*  Correspondence: Tel.: +48-32-237-2717"
c7c8d150ece08b12e3abdb6224000c07a6ce7d47,DeMeshNet: Blind Face Inpainting for Deep MeshFace Verification,"DeMeshNet: Blind Face Inpainting for Deep MeshFace Verification
National Laboratory of Pattern Recognition, CASIA
Center for Research on Intelligent Perception and Computing, CASIA
Shu Zhang Ran He Tieniu Tan"
c737e65d7e8696f5a2878ac623c61aeff434f92d,The influences of face inversion and facial expression on sensitivity to eye contact in high-functioning adults with autism spectrum disorders.,"J Autism Dev Disord (2013) 43:2536–2548
DOI 10.1007/s10803-013-1802-2
O R I G I N A L P A P E R
The Influences of Face Inversion and Facial Expression
on Sensitivity to Eye Contact in High-Functioning Adults
with Autism Spectrum Disorders
Mark D. Vida • Daphne Maurer • Andrew J. Calder •
Gillian Rhodes • Jennifer A. Walsh •
Matthew V. Pachai • M. D. Rutherford
Published online: 8 March 2013
Ó Springer Science+Business Media New York 2013"
c7fde641178549bbd1860144138bffdc9e800540,Deep CNN Framework for Audio Event Recognition using Weakly Labeled Web Data,"Deep CNN Framework for Audio Event
Recognition using Weakly Labeled Web Data
Anurag Kumar, Student Member, IEEE, and Bhiksha Raj, Fellow, IEEE"
c7ecb2ca791fe23c182a06e7700c4e41f5ffa79d,A Review of Sentiment Analysis in Spanish Una Revisión Sobre el Análisis de Sentimientos en Español,"DOI: http://dx.doi.org/10.18180/tecciencia.2017.22.5
A Review of Sentiment Analysis in Spanish
Una Revisión Sobre el Análisis de Sentimientos en Español
Carlos Henríquez Miranda1*, Jaime Guzmán2
Universidad Autónoma, Barranquilla, Colombia
Universitario Nacional de Colombia, Bogotá, Colombia
Received: 11 Dec 2015
Accepted: 6 Sep 2016
Available Online: 7 Dec 2016"
9452d029f5d140aece06619b6fd8e47b070cacd1,Urban classification by pixel and object-based approaches for very high resolution imagery,"FACULTY OF ENGINEERING AND SUSTAINABLE DEVELOPMENT
Department of Industrial Development, IT and Land Management
Urban classification by pixel and object-based
pproaches for very high resolution imagery
Fadi Ali
Student thesis, Advanced level (Master degree, one year), 15 HE
Geomatics
Master Programme in Geomatics
Supervisor: Dr. Julia Åhlén
Examiner: Prof. Dr. Bin Jiang
Assistant examiner: Ding Ma"
940ab36a8b2cdf6cb6a08093bd382ad375717942,Human violence recognition and detection in surveillance videos,"Human Violence Recognition and Detection in Surveillance Videos
Piotr Bilinski
nd Francois Bremond
INRIA Sophia Antipolis, STARS team
004 Route des Lucioles, BP93, 06902 Sophia Antipolis, France"
949079cc466e875df1ee6bd6590052ba382a35cf,0 Large-Scale Face Image Retrieval :,"We are IntechOpen,
the world’s leading publisher of
Open Access books
Built by scientists, for scientists
,800
16,000
Open access books available
International  authors and editors
Downloads
Our authors are among the
Countries delivered to
TOP 1%
2.2%
most cited scientists
Contributors from top 500 universities
Selection of our books indexed in the Book Citation Index
in Web of Science™ Core Collection (BKCI)
Interested in publishing with us?
Contact
Numbers displayed above are based on latest data collected."
942bb63e78d9edfe3b8d0a4bf9a3511c736a6930,"Implementing Efficient, Portable Computations for Machine Learning","Implementing Efficient, Portable Computations for Machine
Learning
Matthew Walter Moskewicz
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2017-37
http://www2.eecs.berkeley.edu/Pubs/TechRpts/2017/EECS-2017-37.html
May 9, 2017"
948af4b04b4a9ae4bff2777ffbcb29d5bfeeb494,Face Recognition From Single Sample Per Person by Learning of Generic Discriminant Vectors,"Available online at www.sciencedirect.com
Procedia Engineering   41  ( 2012 )  465 – 472
International Symposium on Robotics and Intelligent Sensors 2012 (IRIS 2012)
Face Recognition From Single Sample Per Person by Learning of
Generic Discriminant Vectors
Fadhlan Hafiza*, Amir A. Shafieb, Yasir Mohd Mustafahb
Faculty of Electrical Engineering, University of Technology MARA, Shah Alam, 40450 Selangor, Malaysia
Faculty of Engineering, International Islamic University, Jalan Gombak, 53100 Kuala Lumpur, Malaysia"
947c973846f2c5f8f42225c1108810bcdb4a7015,Grounded language understanding for manipulation instructions using GAN-based classification,"GROUNDED LANGUAGE UNDERSTANDING FOR MANIPULATION INSTRUCTIONS
USING GAN-BASED CLASSIFICATION
Komei Sugiura and Hisashi Kawai
National Institute of Information and Communications Technology, Japan"
94a11b601af77f0ad46338afd0fa4ccbab909e82,"Title of dissertation : EFFICIENT SENSING , SUMMARIZATION AND CLASSIFICATION OF VIDEOS",
94eeae23786e128c0635f305ba7eebbb89af0023,On the Emergence of Invariance and Disentangling in Deep Representations,"Journal of Machine Learning Research 18 (2018) 1-34
Submitted 01/17; Revised 4/18; Published 6/18
Emergence of Invariance and Disentanglement
in Deep Representations∗
Alessandro Achille
Department of Computer Science
University of California
Los Angeles, CA 90095, USA
Stefano Soatto
Department of Computer Science
University of California
Los Angeles, CA 90095, USA
Editor: Yoshua Bengio"
9434524669777d281a8a7358f20181c9e157942e,VSEM: An open library for visual semantics representation,"Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pages 187–192,
Sofia, Bulgaria, August 4-9 2013. c(cid:13)2013 Association for Computational Linguistics"
942dd0a7f975a3400bd6bf37e3a06bb50e70297c,A Weighted Discrete KNN Method for Mandarin Speech and Emotion Recognition,"We are IntechOpen,
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944592fcb4ab1d254ea55bd32960b6482556a1e6,The VidTIMIT Database,"The VidTIMIT Database
Conrad Sanderson (*)
IDIAP–Com 02-06
August 2002
D a l l e M o l l e
I n s t i t u t e
f o r P e r c e p t u a l A r t i f i c i a l
Intelligence • P.O.Box 592 •
Martigny • Valais • Switzerland
phone +41 − 27 − 721 77 11
+41 − 27 − 721 77 12
e-mail
internet http://www.idiap.ch"
943b1b92b5bdee0b5770418c645a4a17bded1ccf,MX-LSTM: Mixing Tracklets and Vislets to Jointly Forecast Trajectories and Head Poses,"MX-LSTM: mixing tracklets and vislets to jointly forecast
trajectories and head poses
Irtiza Hasan1,2, Francesco Setti1, Theodore Tsesmelis1,2,3, Alessio Del Bue3,
Fabio Galasso2, and Marco Cristani1
University of Verona (UNIVR)
OSRAM GmbH
Istituto Italiano di Tecnologia (IIT)"
946a3294007cd69769639cc0ff7a24c07eb9f29c,CAN: Composite Appearance Network and a Novel Evaluation Metric for Person Tracking,"CAN: Composite Appearance Network and a
Novel Evaluation Metric for Person Tracking
Neeti Narayan, Nishant Sankaran, Srirangaraj Setlur, Senior Member IEEE, and Venu
Govindaraju, Life Fellow, IEEE"
948853c269cf97251ba5082db0481ce6f96cf886,Efficient Distributed Training of Vehicle Vision Systems,"Efficient Distributed Training of Vehicle Vision Systems
Sung-Li Chiang
Xinlei Pan
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2016-195
http://www2.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-195.html
December 11, 2016"
9499b8367a84fccb3651a95e4391d6e17fd92ec5,Face Recognition Issues in a Border Control Environment,"Face Recognition Issues in a Border Control
Environment
Marijana Kosmerlj, Tom Fladsrud, Erik Hjelm˚as, and Einar Snekkenes
Department of Computer Science and Media Technology
NISlab
Gjøvik University College
P. O. Box 191, N-2802 Gjøvik, Norway"
94686d5df14875ed800a9f710bfa43ba4eb19b75,OCCLUSION HANDLING FOR PEDESTRIAN TRACKING USING PARTIAL OBJECT TEMPLATE-BASED COMPONENT PARTICLE FILTER,"IADIS International Journal on Computer Science and Information Systems
Vol. 8, No. 2, pp. 40-50
ISSN: 1646-3692
OCCLUSION HANDLING FOR PEDESTRIAN
TRACKING USING PARTIAL OBJECT
TEMPLATE-BASED COMPONENT PARTICLE
FILTER
Daw-Tung Lin. Department of Computer Science and Information Engineering, National Taipei
University, Taiwan.
Yen-Hsiang Chang. Department of Computer Science and Information Engineering, National Taipei
University, Taiwan."
944faf7f14f1bead911aeec30cc80c861442b610,Action Tubelet Detector for Spatio-Temporal Action Localization,"Action Tubelet Detector for Spatio-Temporal Action Localization
Vicky Kalogeiton1,2
Philippe Weinzaepfel3
Vittorio Ferrari2
Cordelia Schmid1"
94780b00dc2807ec507ae91500b622ec7a8ddb12,Selective Feature Connection Mechanism: Concatenating Multi-layer CNN Features with a Feature Selector,"Selective Feature Connection Mechanism:
Concatenating Multi-layer CNN Features with a Feature Selector
Chen Du1,2, Chunheng Wang1, Cunzhao Shi1, Baihua Xiao1
Institute of Automation, Chinese Academy of Sciences(CASIA)
University of Chinese Academy of Sciences(UCAS)
{duchen2016, chunheng.wang, cunzhao.shi,"
940a675de8a48b54bac6b420f551529d2bc53b99,"Advances , Challenges , and Opportunities in Automatic Facial Expression Recognition","Advances, Challenges, and Opportunities in
Automatic Facial Expression Recognition
Brais Martinez and Michel F. Valstar"
94ded2328a91b678447657b9344a1423ed538374,Tool for Semiautomatic Labeling of Moving Objects in Video Sequences: TSLAB,"Sensors 2015, 15, 15159-15178; doi:10.3390/s150715159
OPEN ACCESS
sensors
ISSN 1424-8220
www.mdpi.com/journal/sensors
Article
Tool for Semiautomatic Labeling of Moving Objects in Video
Sequences: TSLAB
Carlos Cuevas *, Eva María Yáñez and Narciso García
Grupo de Tratamiento de Imágenes, Universidad Politécnica de Madrid (UPM), E-28040 Madrid,
Spain; E-Mails: (E.M.Y.); (N.G.)
* Author to whom correspondence should be addressed; E-Mail:
Tel.: +34-913367353.
Academic Editor: Vittorio M.N. Passaro
Received: 23 March 2015 / Accepted: 18 June 2015 / Published: 29 June 2015"
9432e1157f252ee626511b2270126436b0e80b73,A set theoretic approach to object-based image restoration,"Image Processing: Algorithms and Systems IV, edited by Edward R. Dougherty,
Jaakko T. Astola, Karen O. Egiazarian, Proc. of SPIE-IS&T Electronic Imaging,
SPIE Vol. 5672 © 2005 SPIE and IS&T · 0277-786X/05/$15"
940865fc3f7ee5b386c4188c231eb6590db874e9,Security and Surveillance System for Drivers Based on User Profile and learning systems for Face Recognition,"Network Protocols and Algorithms
ISSN  1943-3581
015,  Vol.  7,  No.  1
Security and Surveillance System for Drivers based on
User Profile and Learning Systems for Face
Recognition
Loubna Cherrat
Mathematic and Application Laboratory, FSTT of Tangier
Tangier (Morocco)
Tel: 06-64-43-39-18    E-mail:
Mostafa Ezziyyani
Mathematic and Application Laboratory, FSTT of Tangier
Tangier (Morocco)
Tel: 06-61-63-03-01    E-mail:
Annas EL Mouden
Mathematic and Application Laboratory, FSTT of Tangier
Tangier (Morocco)
Tel: 06-66-63-73-63    E-mail:
Mohammed Hassar
Mathematic and Application Laboratory, FSTT of Tangier"
9458642e7645bfd865911140ee8413e2f5f9fcd6,Efficient Multiple People Tracking Using Minimum Cost Arborescences,"Ef‌f‌icient Multiple People Tracking Using
Minimum Cost Arborescences
Roberto Henschel1, Laura Leal-Taix´e2, Bodo Rosenhahn1
Institut f¨ur Informationsverarbeitung, Leibniz Universit¨at Hannover,
Institute of Geodesy and Photogrammetry, ETH Zurich,"
947f2d465df60ec49f441f02733edbeb81dde2f2,Fast Object Localization Using a CNN Feature Map Based Multi-Scale Search,"Fast Object Localization Using a CNN Feature
Map Based Multi-Scale Search
Hyungtae Lee1, Heesung Kwon1, Archith J. Bency2,
nd William D. Nothwang1
U.S. Army Research Laboratory, Adelphi, MD, USA
University of California, Santa Barbara, CA, USA"
941166547968081463398c9eb041f00eb04304f7,Structure-Preserving Sparse Decomposition for Facial Expression Analysis,"Structure-Preserving Sparse Decomposition for
Facial Expression Analysis
Sima Taheri, Student Member, IEEE, Qiang Qiu, Student Member, IEEE, and Rama Chellappa, Fellow, IEEE"
947399fef66bd8c536c6f784a0501b34e4e094bf,Towards Recovery of Conditional Vectors from Conditional Generative Adversarial Networks,"Towards Recovery of Conditional Vectors from
Conditional Generative Adversarial Networks
Sihao Ding
Andreas Wallin
{sihao.ding,"
b3e2bd3f89e49833d45c30af7d5c923489b4d5fc,Fast Approximate kNN Graph Construction for High Dimensional Data via Recursive Lanczos Bisection,"Fast Approximate kNN Graph Construction for High
Dimensional Data via Recursive Lanczos Bisection∗
Jie Chen†
Haw-ren Fang†
Yousef Saad†
October 2, 2008"
b34e7a2218abd5894525a60ed4f106cb9c3dc1e8,UNDERSTANDING GROUNDED LANGUAGE LEARNING AGENTS,"Under review as a conference paper at ICLR 2018
UNDERSTANDING GROUNDED LANGUAGE LEARNING
AGENTS
Anonymous authors
Paper under double-blind review"
b308706f194e273e19731711db6986863b2db798,"UAV, do you see me? Establishing mutual attention between an uninstrumented human and an outdoor UAV in flight","UAV, Do You See Me? Establishing Mutual Attention Between an
Uninstrumented Human and an Outdoor UAV In Flight
Mani Monajjemi, Jake Bruce, Seyed Abbas Sadat, Jens Wawerla and Richard Vaughan∗"
b340f275518aa5dd2c3663eed951045a5b8b0ab1,Visual inference of human emotion and behaviour,"Visual Inference of Human Emotion and Behaviour
Shaogang Gong
Caifeng Shan
Tao Xiang
Dept of Computer Science
Queen Mary College, London
Dept of Computer Science
Queen Mary College, London
Dept of Computer Science
Queen Mary College, London
England, UK
England, UK
England, UK"
b3d8705d46a1d63b40a76bbcf8822b2e90b3b9ad,Efficient Labelling of Pedestrian Supervisions,"Electronic Letters on Computer Vision and Image Analysis 15(1):77-99, 2016
Efficient Labelling of Pedestrian Supervisions
Kyaw Kyaw Htike
School of Information Technology, UCSI University, Kuala Lumpur, Malaysia
Received 7th Mar 2016; accepted 26th Jun 2016"
b3e1e5718bb2313fb888255d63e9f6ad0a2dbdd6,Laser – visual – inertial odometry and mapping with high robustness and low drift,"Received: 10 November 2017 | Revised: 23 March 2018 | Accepted: 9 July 2018
DOI: 10.1002/rob.21809
R E G U L A R A R T I C L E
Laser–visual–inertial odometry and mapping with high
robustness and low drift
Ji Zhang
| Sanjiv Singh
Kaarta, Inc., Pittsburgh, Pennsylvania
Correspondence
Ji Zhang, Kaarta, Inc., Pittsburgh, PA 15213.
Email:"
b34487edb8d47c0101d514b8cb63148d80deee54,Utility of Satellite and Aerial Images for Quantification of Canopy Cover and Infilling Rates of the Invasive Woody Species Honey Mesquite (Prosopis Glandulosa) on Rangeland,"Remote Sens. 2012, 4, 1947-1962; doi:10.3390/rs4071947
OPEN ACCESS
ISSN 2072-4292
www.mdpi.com/journal/remotesensing
Article
Utility of Satellite and Aerial Images for Quantification of
Canopy Cover and Infilling Rates of the Invasive Woody Species
Honey Mesquite (Prosopis Glandulosa) on Rangeland
Mustafa Mirik * and R. James Ansley
Texas AgriLife Research, P.O. Box 1658, 11708 Hwy 70 South, Vernon, TX 76385, USA;
E-Mail:
*  Author to whom correspondence should be addressed; E-Mail:
Tel.: +1-940-552-9941; Fax: +1-940-552-2317.
Received: 9 May 2012; in revised form: 5 June 2012 / Accepted: 25 June 2012 /
Published: 29 June 2012"
b38e5da11281be44c82d184079d762c9d526ba2e,UNDERSTANDING GROUNDED LANGUAGE LEARNING AGENTS,"Under review as a conference paper at ICLR 2018
UNDERSTANDING GROUNDED LANGUAGE LEARNING
AGENTS
Anonymous authors
Paper under double-blind review"
b331ca23aed90394c05f06701f90afd550131fe3,Double regularized matrix factorization for image classification and clustering,"Zhou et al. EURASIP Journal on Image and Video Processing  (2018) 2018:49
https://doi.org/10.1186/s13640-018-0287-5
EURASIP Journal on Image
nd Video Processing
R ES EAR CH
Double regularized matrix factorization for
image classification and clustering
Wei Zhou1*
, Chengdong Wu2, Jianzhong Wang3,4, Xiaosheng Yu2 and Yugen Yi5
Open Access"
b32f7c045801dea27d58e47917badb5b075ab0cf,Motion and appearance based MultiTask Learning network for autonomous driving,"Motion and appearance based Multi-Task Learning
network for autonomous driving
Anonymous Author(s)
Affiliation
Address
email"
b336f946d34cb427452517f503ada4bbe0181d3c,Diagnosing Error in Temporal Action Detectors,"Diagnosing Error in Temporal Action Detectors
Humam Alwassel, Fabian Caba Heilbron, Victor Escorcia, and Bernard
Ghanem
King Abdullah University of Science and Technology (KAUST), Saudi Arabia
http://www.humamalwassel.com/publication/detad/
{humam.alwassel, fabian.caba, victor.escorcia,"
b3d936c0d82f9b2032949af685a10708c6856d2c,Deep Learning From Noisy Image Labels With Quality Embedding,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
Deep Learning from Noisy Image Labels with
Quality Embedding
Jiangchao Yao, Jiajie Wang,
Ivor Tsang, Ya Zhang, Jun Sun, Chengqi Zhang, Rui Zhang"
b32631f456397462b3530757f3a73a2ccc362342,Discriminant Tensor Dictionary Learning with Neighbor Uncorrelation for Image Set Based Classification,Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
b32cf547a764a4efa475e9c99a72a5db36eeced6,Mimicry of ingroup and outgroup emotional expressions,"UvA-DARE (Digital Academic Repository)
Mimicry of ingroup and outgroup emotional expressions
Sachisthal, M.S.M.; Sauter, D.A.; Fischer, A.H.
Published in:
Comprehensive Results in Social Psychology
0.1080/23743603.2017.1298355
Link to publication
Citation for published version (APA):
Sachisthal, M. S. M., Sauter, D. A., & Fischer, A. H. (2016). Mimicry of ingroup and outgroup emotional
expressions. Comprehensive Results in Social Psychology, 1(1-3), 86-105. DOI:
0.1080/23743603.2017.1298355
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b31f37fd71b7b45e6fd8978960e271a7db1ee212,DICTING IMAGE ROTATIONS,"Published as a conference paper at ICLR 2018
UNSUPERVISED REPRESENTATION LEARNING BY PRE-
DICTING IMAGE ROTATIONS
Spyros Gidaris, Praveer Singh, Nikos Komodakis
University Paris-Est, LIGM
Ecole des Ponts ParisTech"
b3f0a87043f7843b79744ec19dc0b93324d055d5,Improvements to Tracking Pedestrians in Video Streams Using a Pre-trained Convolutional Neural Network,"Western University
Electronic Thesis and Dissertation Repository
August 2016
Improvements to Tracking Pedestrians in Video
Streams Using a Pre-trained Convolutional Neural
Network
Marjan Ramin
The University of Western Ontario
Supervisor
Dr. Jagath Samarabandu
The University of Western Ontario
Graduate Program in Electrical and Computer Engineering
A thesis submitted in partial fulfillment of the requirements for the degree in Master of Engineering Science
© Marjan Ramin 2016
Follow this and additional works at: https://ir.lib.uwo.ca/etd
Part of the Computer Engineering Commons
Recommended Citation
Ramin, Marjan, ""Improvements to Tracking Pedestrians in Video Streams Using a Pre-trained Convolutional Neural Network"" (2016).
Electronic Thesis and Dissertation Repository. 3886.
https://ir.lib.uwo.ca/etd/3886"
b3655bcc6f491ae995c652c7f51e1b9b3a36d39c,User authentication based on foot motion,"Noname manuscript No.
(will be inserted by the editor)
User Authentication Based on Foot Motion
Davrondzhon Gafurov, Patrick Bours and Einar Snekkenes
Received: date / Accepted: date"
b3eb61c3542e0c6bafb4c1acd05cffc0970faa85,Region-Based Image Retrieval Revisited,"Region-Based Image Retrieval Revisited
Ryota Hinami
(cid:140)e University of Tokyo
Yusuke Matsui
National Institute of Infomatics
National Institute of Infomatics
Shin’ichi Satoh"
b3f7c772acc8bc42291e09f7a2b081024a172564,"A novel approach for performance parameter estimation of face recognition based on clustering , shape and corner detection","www.ijmer.com            Vol. 3, Issue. 5, Sep - Oct. 2013 pp-3225-3230                 ISSN: 2249-6645
International Journal of Modern Engineering Research (IJMER)
A novel approach for performance parameter estimation of face
recognition based on clustering, shape and corner detection
.Smt.Minj Salen Kujur , 2.Prof. Prashant Jain,
Department of Electronics & Communication Engineering college Jabalpur"
b3adc7617dff08d7427142837a326b95d2e83969,A Panoramic View of Performance,"Comp. by: BVijayalakshmi Stage: Galleys ChapterID: 0000883562 Date:27/1/09 Time:17:57:10
Evaluation of Gait Recognition
, ZONGYI LIU
SUDEEP SARKAR
Computer Science and Engineering, University of
South Florida, Tampa, FL, USA
Amazon.com, Seattle, WA, USA
Synonyms
Gait recognition; Progress in gait recognition
Definition
Gait recognition refers to automated vision methods
that use video of human gait to recognize or to identify
person. Evaluation of gait recognition refers to the
enchmarking of progress in the design of gait recog-
nition algorithms on standard, common, datasets.
Introduction
Design of biometric algorithms and evaluation of per-
formance goes hand in hand. It is important to con-
stantly evaluate and analyze progress being at various
levels of biometrics design. This evaluation can be of"
b3c60b642a1c64699ed069e3740a0edeabf1922c,Max-Margin Object Detection,"Max-Margin Object Detection
Davis E. King"
b33b88a5fa5d4f20c24dd0e5f3b3529b7545c9e6,Object Detection in Real Images,"SCHOOL OF COMPUTER ENGINEERING
PhD Confirmation Report
Object Detection in Real Images
Submitted by: Dilip Kumar Prasad
Research Student (PhD)
School of Computer Engineering
E-mail:
Supervisor:     Dr. Maylor K. H. Leung
Associate Professor,
School of Computer Engineering
E-mail:
August 2010"
b30bdbad88c72938c476f1ea6827d8b10c300da4,Supervised Mixed Norm Autoencoder for Kinship Verification in Unconstrained Videos,"Supervised Mixed Norm Autoencoder for Kinship
Verification in Unconstrained Videos
Naman Kohli, Student Member, IEEE, Daksha Yadav, Student Member, IEEE, Mayank Vatsa,
Senior Member, IEEE, Richa Singh, Senior Member, IEEE, and Afzel Noore, Senior Member, IEEE."
b3be6c26e00671fa8b18587409e656d5bbecdcf7,Investigation of multimodal template-free biometric techniques and associated exception handling,"Kent Academic Repository
Full text document (pdf)
Citation for published version
Aldosary, Saad  (2015) Investigation of Multimodal Template-Free Biometric Techniques and
Associated Exception Handling.   Doctor of Philosophy (PhD) thesis, University of Kent.
Link to record in KAR
http://kar.kent.ac.uk/54805/
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UNSPECIFIED
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b32f15264a50d5619526d80a908b848bbcded5be,J-MOD2: Joint Monocular Obstacle Detection and Depth Estimation,"J-MOD2: Joint Monocular Obstacle Detection and Depth Estimation
Michele Mancini1, Gabriele Costante1, Paolo Valigi1 and Thomas A. Ciarfuglia1"
b375db63742f8a67c2a7d663f23774aedccc84e5,Brain-Inspired Classroom Occupancy Monitoring on a Low-Power Mobile Platform,"Brain-inspired Classroom Occupancy
Monitoring on a Low-Power Mobile Platform
Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy
Francesco Conti∗, Antonio Pullini† and Luca Benini∗†
Integrated Systems Laboratory, ETH Zurich, Switzerland"
b348d5c7ac93d1148265284d71234e200c9c5f02,GibbsNet: Iterative Adversarial Inference for Deep Graphical Models,"GibbsNet: Iterative Adversarial Inference for Deep
Graphical Models
Alex Lamb
MILA, Universite de Montreal
Yaroslav Ganin
MILA, Universite de Montreal
R Devon Hjelm
MILA, Universite de Montreal
Joseph Paul Cohen
MILA, Universite de Montreal
Institute for Reproducible Research
Aaron Courville
MILA, Universite de Montreal
CIFAR
Yoshua Bengio
MILA, Universite de Montreal
CIFAR"
b362b812ececef21100d7a702447fcf5ab6d4715,Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer,"Understanding and Improving Interpolation in
Autoencoders via an Adversarial Regularizer
David Berthelot∗
Google Brain
Colin Raffel∗
Google Brain
Aurko Roy
Google Brain
Ian Goodfellow
Google Brain"
b3cbbcf67aea4ae28a01bdc5f0c75580f869d82b,RAPID DETERMINATION OF AGE CLASSIFICATION BY TIMOTHY H BALL A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY,"RAPID DETERMINATION OF AGE
CLASSIFICATION
TIMOTHY H BALL
A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE
REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
COMPUTER SCIENCE
UNIVERSITY OF RHODE ISLAND"
b3cb91a08be4117d6efe57251061b62417867de9,Label propagation approach for predicting missing biographic labels in face-based biometric records,"T. Swearingen and A. Ross. ""A label propagation approach for predicting missing biographic labels in
A Label Propagation Approach for
Predicting Missing Biographic Labels
in Face-Based Biometric Records
Thomas Swearingen and Arun Ross"
3d8c8acb8c59e9f23f048f44a23f36ffd791cdf5,Visual tracking over multiple temporal scales,"Khan, Muhammad Haris (2015) Visual tracking over
multiple temporal scales. PhD thesis, University of
Nottingham.
Access from the University of Nottingham repository:
http://eprints.nottingham.ac.uk/33056/1/Thesis.pdf
Copyright and reuse:
The Nottingham ePrints service makes this work by researchers of the University of
Nottingham available open access under the following conditions.
This article is made available under the University of Nottingham End User licence and may
e reused according to the conditions of the licence.  For more details see:
http://eprints.nottingham.ac.uk/end_user_agreement.pdf
For more information, please contact"
3d94f81cf4c3a7307e1a976dc6cb7bf38068a381,Data-Dependent Label Distribution Learning for Age Estimation,"Data-Dependent Label Distribution Learning
for Age Estimation
Zhouzhou He, Xi Li, Zhongfei Zhang, Fei Wu, Xin Geng, Yaqing Zhang, Ming-Hsuan Yang, and Yueting Zhuang"
3dec830b2514e82c714162622b3077966660112f,Statistical Evaluation of Face Recognition Techniques under Variable Environmental Constraints,"International Journal of Statistics and Probability; Vol. 4, No. 4; 2015
ISSN 1927-7032      E-ISSN 1927-7040
Published by Canadian Center of Science and Education
Statistical Evaluation of Face Recognition Techniques under Variable
Environmental Constraints
Louis Asiedu1, Atinuke O. Adebanji2, Francis Oduro3
& Felix O. Mettle4
Department of Statistics, University of Ghana, Legon-Accra, Ghana
Department of Mathematics, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
Department  of  Mathematics,  Kwame  Nkrumah  University  of  Science  and  Technology,  Kumasi,  Ghana
Department of Statistics, University of Ghana, Legon-Accra, Ghana
Correspondence:  Louis  Asiedu,  Department  of  Statistics,  University  of  Ghana,  Legon-Accra,  Ghana.  Tel:
33-543-426-707. E-mail:
Received: August 1, 2015      Accepted: August 19, 2015      Online Published: October 9, 2015
doi:10.5539/ijsp.v4n4p93                    URL: http://dx.doi.org/10.5539/ijsp.v4n4p93"
3daafe6389d877fe15d8823cdf5ac15fd919676f,Human Action Localization with Sparse Spatial Supervision,"Human Action Localization
with Sparse Spatial Supervision
Philippe Weinzaepfel, Xavier Martin, and Cordelia Schmid, Fellow, IEEE"
3d5187a957cc90f4143e6302786d65dbedf7d9bb,Stacking With Auxiliary Features for Visual Question Answering,"To Appear In Proceedings of the 16th Annual Conference of the North American
Chapter of the Association for Computational Linguistics: Human Language
Technologies 2018."
3d3a7225b441d669a0e6133546db4cef18884360,Video Liveness for Citizen Journalism: Attacks and Defenses,The final version of
3dcebd4a1d66313dcd043f71162d677761b07a0d,Local binary pattern domain local appearance face recognition,"Yerel Đkili Örüntü Ortamında Yerel Görünüme Dayalı Yüz Tanıma
Local Binary Pattern Domain Local Appearance Face Recognition
Hazım K. Ekenel1, Mika Fischer1, Erkin Tekeli2, Rainer Stiefelhagen1, Aytül Erçil2
Institut für Theorestische Informatik, Universität Karlsruhe (TH), Karlsruhe, Germany
Faculty of Engineering and Natural Sciences, Sabancı University, Đstanbul, Turkey
Özetçe
Bu bildiride, ayrık kosinüs dönüşümü tabanlı yerel görünüme
dayalı  yüz  tanıma  algoritması  ile  yüz  imgelerinin  yerel  ikili
örüntüye  (YĐÖ)  dayalı  betimlemesini  birleştiren  hızlı  bir  yüz
tanıma  algoritması  sunulmuştur.  Bu  tümleştirmedeki  amaç,
yerel  ikili  örüntünün  dayanıklı  imge  betimleme  yeteneği  ile
yrık  kosinüs  dönüşümünün  derli-toplu  veri  betimleme
yeteneğinden  yararlanmaktır.  Önerilen  yaklaşımda,  yerel
görünümün  modellenmesinden  önce  girdi  yüz  imgesi  yerel
ikili  örüntü  ile  betimlenmiştir.  Elde  edilen  YĐÖ  betimlemesi,
irbirleri  ile  örtüşmeyen  bloklara  ayrılmış  ve  her  blok
üzerinde  yerel  özniteliklerin  çıkartımı  için  ayrık  kosinüs
dönüşümü uygulanmıştır.  Çıkartımı  yapılan  yerel  öznitelikler
daha  sonra  arka  arkaya  eklenerek  global  öznitelik  vektörü
oluşturulmuştur.  Önerilen  algoritma,  CMU  PIE  ve  FRGC"
3dba6c86541aad3ec8f54c55d57eca9aa98f4ed2,PAC-Bayesian Majority Vote for Late Classifier Fusion,"PAC-Bayesian Majority Vote for Late Classifier Fusion∗
Aix-Marseille Univ., LIF-QARMA, CNRS, UMR 7279, F-13013, Marseille, France
Emilie Morvant
St´ephane Ayache
Amaury Habrard
Univ. of St-Etienne, Lab. Hubert Curien, CNRS, UMR 5516, F-42000, St-Etienne, France
May 2, 2014"
3df5e17e87144b1e84b5ab9467bc2c2f233b66c7,Convolutional Architecture Exploration for Action Recognition and Image Classification,"Convolutional Architecture Exploration for
Action Recognition and Image Classification
JT Turner∗1,2, David Aha1, Leslie Smith1, and Kalyan Moy Gupta2
Knexus Research Corporation;
74 Waterfront Street Suite 310; National Harbor, MD 20745
Navy Center for Applied Research in Artificial Intelligence;
Naval Research Laboratory (Code 5514); Washington, DC 20375"
3ddb88f9294e962dfd9c99c5864b2b8690b56d90,Multimodal Image Alignment Through a Multiscale Chain of Neural Networks with Application to Remote Sensing,"Multimodal image alignment
through a multiscale chain of neural networks
with application to remote sensing
Armand Zampieri1, Guillaume Charpiat2, Nicolas Girard1, and Yuliya
Tarabalka1
TITANE team, INRIA, Universit´e Cˆote d’Azur, France
TAU team, INRIA, LRI, Universit´e Paris-Sud, France"
3d74d4177f5c1444b73221c12f359e858625a691,Composite-ISA Cores : Enabling Multi-ISA Heterogeneity Using a Single ISA,"ISCA 2018 Submission #283
Confidential Draft: DO NOT DISTRIBUTE
Composite-ISA Cores: Enabling Multi-ISA Heterogeneity
Using a Single ISA"
3df7401906ae315e6aef3b4f13126de64b894a54,Robust learning of discriminative projection for multicategory classification on the Stiefel manifold,"Robust Learning of Discriminative Projection for Multicategory Classification on
the Stiefel Manifold
Duc-Son Pham and Svetha Venkatesh
Dept. of Computing, Curtin University of Technology
GPO Box U1987, Perth, WA 6845, Australia"
3d21b7b4f48e614bc2f2b87eb110aa329b7d66d8,Recognizing Human Actions by Using Effective Codebooks and Tracking,"Recognizing Human Actions by using Effective
Codebooks and Tracking
Lamberto Ballan, Lorenzo Seidenari, Giuseppe Serra, Marco Bertini and Alberto
Del Bimbo"
3d91ba69bfbb2ba018419342d279f2d7571530f6,Qualitative Tracking Performance Evaluation without Ground-Truth,"Qualitative Tracking Performance Evaluation without Ground-Truth∗
Dept. of Computer Science and Engineering
Dept. of Computer Science and Engineering
Jihun Hamm
Bohyung Han
POSTECH, Korea"
3d88180732d63a4babf3a4b1a82dd7fdf27a7520,"Facial expression, size, and clutter: Inferences from movie structure to emotion judgments and back.","23Attention, Perception, &Psychophysics ISSN 1943-3921Volume 78Number 3 Atten Percept Psychophys (2016)78:891-901DOI 10.3758/s13414-015-1003-5Facial expression, size, and clutter:Inferences from movie structure to emotionjudgments and backJames E. Cutting & Kacie L. Armstrong"
3d67aa108e65e636158abc0f31b703af3d31baa6,Decorrelating Semantic Visual Attributes by Resisting the Urge to Share,"Decorrelating Semantic Visual Attributes by Resisting the Urge
Supplementary material for CVPR 2014 submission ID 0824
to Share
In this document, we provide supplementary material for our CVPR 2014 submission “Decorrelating Semantic
Visual Attributes by Resisting the Urge to Share”(Paper ID 0824). Sec 1 gives additional details for our experi-
mental setup (Sec 4 of the paper). Sec 1.1 lists the groups used in all three datasets in our experiments. Sec 1.2
discusses the details of the image descriptors used for each dataset. Sec 2 discusses how attributes are localized
for our experiments in Sec 4.1 in the paper. Sec 3 discusses how it is posible to set parameters that generalize well
to novel test sets, using only training data. Sec 4 discusses the details of the optimization of our formulation (Eq 4
in the paper).
Datasets
.1 Groups
(see para on Semantic groups in Sec 4 in the paper)
Fig 1, 2 and 3 show the attribute groups used in our experiments on the CUB, AwA and aPY datasets
respectively. The 28 CUB groups come pre-specified with the dataset [6]. The groups on AwA match exactly the
groups specified in [5]. Those on aPY also match the groups outlined in [5] on the 25 attributes (see paper) used
in our experiments (aPY-25). In each figure, attribute groups are enclosed in shaded boxes, and phrases in larger
font labeling the boxes indicate the rationale for the grouping.
.2 Features
(see also Sec 3.2 and para on Features in Sec 4 in the paper)"
3da9a9091cfa8f4bf625829faf7a4c35a8fe91e0,Working memory network alterations in high-functioning adolescents with an autism spectrum disorder.,"PDF hosted at the Radboud Repository of the Radboud University
Nijmegen
The following full text is a publisher's version.
For additional information about this publication click this link.
http://hdl.handle.net/2066/183247
Please be advised that this information was generated on 2018-05-20 and may be subject to
hange."
3db588f1e58c1207685771d8015fa9427d731a53,An automatic 3D expression recognition framework based on sparse representation of conformal images,"An Automatic 3D Expression Recognition Framework based on Sparse
Representation of Conformal Images
Wei Zeng, Huibin Li, Liming Chen, Jean-Marie Morvan, Xianfeng David Gu"
3d42aedd347f927a6bce28d0fa509c6d2132c11f,3D Hand Pose Detection in Egocentric RGB-D Images,"International Journal of Computer Vision manuscript No.
(will be inserted by the editor)
D Hand Pose Detection in Egocentric RGB-D Images
Gr´egory Rogez · J. S. Supanˇciˇc III · Maryam Khademi ·
J. M. M. Montiel · Deva Ramanan
Received: date / Accepted: date"
3d1382fa43c31e594ed2d84dda9984b1db047b0e,Compositional Memory for Visual Question Answering,"Compositional Memory for Visual Question Answering
Aiwen Jiang1,2
Fang Wang2
Fatih Porikli2
Yi Li∗ 2,3
NICTA and ANU
{fang.wang,
Toyota Research Institute North America
feature as the first word to initialize the sequential learning.
While the use of holistic approach is straightforward and
onvenient, it is, however, debatably problematic. For ex-
mple, in the VQA problems many answers are directly re-
lated to the contents of some image regions. Therefore, it
is dubious if the holistic features are rich enough to provide
the information only available at regions. Also, it may hin-
der the exploration of finer-grained local features for VQA.
In this paper we propose a Compositional Memory for
n end-to-end training framework. Our approach takes the
dvantage of the recent progresses in image captioning [3,
], natural language processing [5], and computer vision to"
3de3c479164312ab3a1795ee84f20c16632c04c4,Scalable Deep Learning Logo Detection,"Scalable Deep Learning Logo Detection
Hang Su∗, Shaogang Gong†, Xiatian Zhu‡
† Queen Mary University of London ‡ Vision Semantics Ltd."
3df26d623b15b2e27a19f5cfef33e36112f07675,"Multiple Sensor Fusion for Detection, Classification and Tracking of Moving Objects in Driving Environments. (Fusion multi-capteur pour la detection, classification et suivi d'objets mobiles en environnement routier)","Multiple Sensor Fusion for Detection, Classification and
Tracking of Moving Objects in Driving Environments
R. Omar Chavez-Garcia
To cite this version:
R. Omar Chavez-Garcia. Multiple Sensor Fusion for Detection, Classification and Tracking of Moving
Objects in Driving Environments. Robotics [cs.RO]. Université de Grenoble, 2014. English. <tel-
01082021>
HAL Id: tel-01082021
https://hal.archives-ouvertes.fr/tel-01082021
Submitted on 12 Nov 2014
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
3dc3f0b64ef80f573e3a5f96e456e52ee980b877,MAXIMUM LIKELIHOOD TRAINING OF THE EMBEDDED HMM FOR FACE DETECTION AND RECOGNITION Ara V. Ne an and Monson H. Hayes III Center for Signal and Image Processing School of Electrical and Computer Engineering,"AX
U 
E
D TRA

G F TE EBEDDED  FR FACE
DETECT
 AD RECG
T

Aa V. e(cid:12)a ad  . aye 



Cee f Siga ad 

age ceig
Sch f Eecica ad C
	e Egieeig
Gegia 
i	e f Techgy Aaa GA 30332
faa"
3dd4d719b2185f7c7f92cc97f3b5a65990fcd5dd,Ensemble of Hankel Matrices for Face Emotion Recognition,"Ensemble of Hankel Matrices for
Face Emotion Recognition
Liliana Lo Presti and Marco La Cascia
DICGIM, Universit´a degli Studi di Palermo,
V.le delle Scienze, Ed. 6, 90128 Palermo, Italy,
DRAFT
To appear in ICIAP 2015"
3d97f739ae76c8db1146da4aaeb0dc1ef3d31c33,Données multimodales pour l ’ analyse d ’ image,"UNIVERSITÉDEGRENOBLENoattribuéparlabibliothèqueTHÈSEpourobtenirlegradedeDOCTEURDEL’UNIVERSITÉDEGRENOBLESpécialité:MathématiquesetInformatiquepréparéeauLaboratoireJeanKuntzmanndanslecadredel’ÉcoleDoctoraleMathématiques,SciencesetTechnologiesdel’Information,InformatiqueprésentéeetsoutenuepubliquementparMatthieuGuillauminle27septembre2010ExploitingMultimodalDataforImageUnderstandingDonnéesmultimodalespourl’analysed’imageDirecteursdethèse:CordeliaSchmidetJakobVerbeekJURYM.ÉricGaussierUniversitéJosephFourierPrésidentM.AntonioTorralbaMassachusettsInstituteofTechnologyRapporteurMmeTinneTuytelaarsKatholiekeUniversiteitLeuvenRapporteurM.MarkEveringhamUniversityofLeedsExaminateurMmeCordeliaSchmidINRIAGrenobleExaminatriceM.JakobVerbeekINRIAGrenobleExaminateur"
3d42e17266475e5d34a32103d879b13de2366561,The Global Dimensionality of Face Space,"Proc.4thIEEEInt’lConf.AutomaticFace&GestureRecognition,Grenoble,France,pp264–270
The Global Dimensionality of Face Space
(cid:3)
http://venezia.rockefeller.edu/
The Rockefeller University
Penio S. Penev
Laboratory of Computational Neuroscience
Lawrence Sirovich
Laboratory for Applied Mathematics
Mount Sinai School of Medicine
(cid:13) IEEE2000
230 York Avenue, New York, NY 10021
One Gustave L. Levy Place, New York, NY 10029"
3d7fce66c1880f4b29171e415cfad57d8b96ced2,Exploiting Ambiguities in the Analysis of Cumulative Matching Curves for Person Re-identification,
3dad8d0b5185503b3281bdeda10b9904ea19da50,Using probabilistic graphical models to detect dynamic objects for mobile robots by Daniek Brink,"Using probabilistic graphical models to detect
dynamic objects for mobile robots
Daniek Brink
Dissertation presented for the degree of Doctor of Engineering
in the Faculty of Engineering at Stellenbosch University
Promoter:
Co-promoter:
Dr C.E. van Daalen
Prof. B.M. Herbst
December 2016"
3d5a1be4c1595b4805a35414dfb55716e3bf80d8,Hidden Two-Stream Convolutional Networks for Action Recognition,"Hidden Two-Stream Convolutional Networks for
Action Recognition
Yi Zhu, Zhenzhong Lan, Shawn Newsam, Alexander G. Hauptmann"
3d740c4f2246ce8e63d0eacc2cc1a5c31259e9ee,Discovering Attribute Shades of Meaning with the Crowd,"http://dx.doi.org/10.1007/s11263-014-0798-1
Discovering Attribute Shades of Meaning with the Crowd
Adriana Kovashka · Kristen Grauman
Received: date / Accepted: date"
3d5b8127ce57279f9fd77d3a24d8034b485163a4,System ( tm ) for Image and Vision Computing Manuscript Draft Manuscript Number : IMAVIS-D16-00270 R 2 Title : Extended three-dimensional rotation invariant local binary patterns,"Elsevier Editorial System(tm) for Image and
Vision Computing
Manuscript Draft
Manuscript Number: IMAVIS-D-16-00270R2
Title: Extended three-dimensional rotation invariant local binary
patterns
Article Type: Full Length Article
Keywords: Local binary patterns (LBP); Three-dimensions; Rotation
invariance; Texture classification
Corresponding Author: Mr. Leonardo Citraro, MSc.
Corresponding Author's Institution: University of Southampton
First Author: Leonardo Citraro, MSc.
Order of Authors: Leonardo Citraro, MSc.; Sasan Mahmoodi, Professor, Phd;
Angela Darekar, Phd; Brigitte Vollmer, Professor, Phd"
3dc78b41ed926b88c9cc4d40c6c5250bfafad74a,A pilot study for mood-based classification of TV programmes,"Research & Development
White Paper
WHP 231
September 2012
A Pilot Study for
Mood-based Classification of TV Programmes
Jana Eggink, Penelope Allen, Denise Bland
BRITISH BROADCASTING CORPORATION"
3dffacda086689c1bcb01a8dad4557a4e92b8205,Multiple Object Tracking: A Literature Review,"Multiple Object Tracking: A Literature Review
Wenhan Luo, Junliang Xing, Anton Milan, Xiaoqin Zhang, Wei Liu, Xiaowei Zhao and Tae-Kyun Kim"
3dcc51a37f2e5e91d77ff00f18178484c4e938cb,Excitation Dropout: Encouraging Plasticity,"Under review as a conference paper at ICLR 2019
EXCITATION DROPOUT: ENCOURAGING PLASTICITY
IN DEEP NEURAL NETWORKS
Anonymous authors
Paper under double-blind review"
3d85cf942efda695347c7d95485fcd1e6796ee3a,Generating Photo-Realistic Training Data to Improve Face Recognition Accuracy,"Generating Photo-Realistic Training Data to Improve
Face Recognition Accuracy
Daniel S´aez Trigueros, Li Meng
School of Engineering and Technology
University of Hertfordshire
Hatfield AL10 9AB, UK
Margaret Hartnett
GBG plc
London E14 9QD, UK"
3dbb2ca6942eb49538d92823fe22c7475e866ca1,Institutionen för systemteknik Department of Electrical Engineering Examensarbete Autonomous Morphometrics using Depth Cameras for Object Classification and Identification,"Institutionen för systemteknik
Department of Electrical Engineering
Examensarbete
Autonomous Morphometrics using Depth Cameras for
Object Classification and Identification
Examensarbete utfört i Datorseende
vid Tekniska högskolan vid Linköpings universitet
Felix Björkeson
LiTH-ISY-EX--13/4680--SE
Linköping 2013
Department of Electrical Engineering
Linköpings universitet
SE-581 83 Linköping, Sweden
Linköpings tekniska högskola
Linköpings universitet
581 83 Linköping"
3d9d1f8075ebdd03f86b4e40b9a5d08447ade8d3,COMPARISON OF ILLUMINATION NORMALIZATION METHODS FOR FACE RECOGNITION,"COMPARISON OF ILLUMINATION NORMALIZATION METHODS FOR
FACE RECOGNITION(cid:3)
Mauricio Villegas Santamar·(cid:17)a and Roberto Paredes Palacios
Instituto Tecnol·ogico de Inform·atica
Universidad Polit·ecnica de Valencia
Camino de Vera s/n, 46022 Valencia (Spain)"
ef5531711a69ed687637c48930261769465457f0,Studio2Shop: from studio photo shoots to fashion articles,"Studio2Shop: from studio photo shoots to fashion articles
Julia Lasserre1, Katharina Rasch1 and Roland Vollgraf
Zalando Research, Muehlenstr. 25, 10243 Berlin, Germany
Keywords:
omputer vision, deep learning, fashion, item recognition, street-to-shop"
ef73e91b47c0043febed130896f610982fb89976,Layer-Structured 3D Scene Inference via View Synthesis,"Layer-structured 3D Scene Inference
via View Synthesis
Shubham Tulsiani1∗, Richard Tucker2, Noah Snavely2
University of California, Berkeley 2Google"
ef75007cd6e5b990d09e7f3c4ba119be6c2546fb,Lecture 20: Object Recognition 20.1 Introduction 20.2.1 Neocognitron,"Chapter 20
Lecture 20: Object recognition
0.1 Introduction
In its simplest form, the problem of recognition is posed as a binary classification task, namely distin-
guishing between a single object class and background class. Such a classification task can be turned
into a detector by sliding it across the image (or image pyramid), and classifying each local window.
Classifier based methods have defined their own family of object models. Driven by advances in
machine learning, a common practice became to through a bunch of features into the last published
lgorithm. However, soon became clear that such an approach, in which the research gave up into trying
to have a well defined physical model of the object, hold a lot of promise. In many cases, the use of a
specific classifier has driven the choice of the object representation and not the contrary. In classifier-
ased models, the preferred representations are driven by efficiency constraints and by the characteristics
of the classifier (e.g., additive models, SVMs, neural networks, etc.).
0.2 Neural networks
Although neural networks can be trained in other settings than a purely discriminative framework, some
of the first classifier based approaches used neural networks to build the classification function. Many
urrent approaches, despite of having a different inspiration, still follow an architecture motivated by
neural networks.
0.2.1 Neocognitron
The Neocognitron, developed by Fukushima in the 80 [8], consisted on a multilayered network with"
ef52f1e2b52fd84a7e22226ed67132c6ce47b829,Online Eye Status Detection in the Wild with Convolutional Neural Networks,
ef2084979a3191403c1b8b48f503d06f346afb8f,Une méthode de reconnaissance des expressions du visage basée sur la perception,"Une m´ethode de reconnaissance des expressions du
visage bas´ee sur la perception
Rizwan Khan, Alexandre Meyer, Hubert Konik, Saida Bouakaz
To cite this version:
Rizwan Khan, Alexandre Meyer, Hubert Konik, Saida Bouakaz. Une m´ethode de reconnais-
sance des expressions du visage bas´ee sur la perception. RFIA 2012 (Reconnaissance des
Formes et Intelligence Artificielle), Jan 2012, Lyon, France. pp.978-2-9539515-2-3, 2012. <hal-
00660976>
HAL Id: hal-00660976
https://hal.archives-ouvertes.fr/hal-00660976
Submitted on 19 Jan 2012
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,"
ef247c194162f76eb8d44b1f83c25a4002ab69a6,An Effective Profile Based Video Browsing System for e-Learning.,"An Effective Profile Based Video Browsing System for e-
Learning
S. C. Premaratne, D. D. Karunaratna and K. P. Hewagamage
University of Colombo School of Computing, Sri Lanka"
ef48f1d8ec88dabbf7253cb1c8a224cb95f604af,Survey on Video Analysis of Human Walking Motion,"International Journal of Signal Processing, Image Processing and Pattern Recognition
Vol.7, No.3 (2014), pp.99-122
http://dx.doi.org/10.14257/ijsip.2014.7.3.10
Survey on Video Analysis of Human Walking Motion
S. Nissi Paul and Y. Jayanta Singh
Dept. Computer Science Engineering and information Technology
Don Boco College of Engineering and Technology, Assam Don Bosco University
Guwahati, Assam - India"
ef42f6584d696513605b424752bd9fed6ed98fa0,Facial Motion Analysis using Clustered Shortest Path Tree Registration,"Author manuscript, published in ""The 1st International Workshop on Machine Learning for Vision-based Motion Analysis - MLVMA’08,
Marseille : France (2008)"""
ef4b5bcaad4c36d7baa7bc166bd1712634c7ad71,Towards Spatio-temporal Face Alignment in Unconstrained Conditions,
ef999ab2f7b37f46445a3457bf6c0f5fd7b5689d,Improving face verification in photo albums by combining facial recognition and metadata with cross-matching,"Calhoun: The NPS Institutional Archive
DSpace Repository
Theses and Dissertations
. Thesis and Dissertation Collection, all items
017-12
Improving face verification in photo albums by
ombining facial recognition and metadata
with cross-matching
Bouthour, Khoubeib
Monterey, California: Naval Postgraduate School
http://hdl.handle.net/10945/56868
Downloaded from NPS Archive: Calhoun"
effd46389bc45dfdbf32f00eddeef6cf5f0f7947,Face Verification Using Kernel Principle Component Analysis,"International Journal of Engineering Research & Technology (IJERT)
ISSN: 2278-0181
Vol. 2 Issue 1, January- 2013
Face Verification Using Kernel Principle Component Analysis
Manjupriya,
U.G Student,
Dept. of ECE
Chithra. C .K
U.G Student,
Dept. of ECE
Divya. M
U.G Student,
Dept. of ECE
Karthikeyan.V
Assistant Professor,
Dept. of ECE"
efa65394d0ec5a16ecd57075951016502c541c0d,The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers,"The Gap of Semantic Parsing: A Survey on Automatic
Math Word Problem Solvers
Dongxiang Zhang, Lei Wang, Nuo Xu, Bing Tian Dai and Heng Tao Shen"
efd308393b573e5410455960fe551160e1525f49,Tracking Persons-of-Interest via Unsupervised Representation Adaptation,"Tracking Persons-of-Interest via
Unsupervised Representation Adaptation
Shun Zhang, Jia-Bin Huang, Jongwoo Lim, Yihong Gong, Jinjun Wang,
Narendra Ahuja, and Ming-Hsuan Yang"
ef3ccf4c7d4b240ad0fbcc29f4e6be9334db9a18,IJERA ) ISSN : 2248-9622 Recent Trends in Mobile & Cloud Computing ( NCRMC-08,"International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622
Recent Trends in Mobile & Cloud Computing
(NCRMC- 08th & 09th October 2015)
RESEARCH ARTICLE
OPEN ACCESS
Emotion recognition from geometric facial patterns
Krupali Joshi, Pradeep Narwade
Electronics and Telecommunication, Ksiet, Hingoli, (M S) India
Email- Email–"
efe208a03e2f75ddcebf8bb0f10b1c0bea4824be,A data set for evaluating the performance of multi-class multi-object video tracking,"A data set for evaluating the performance of multi-class multi-object
video tracking
Avishek Chakrabortya, Victor Stamatescua, Sebastien C. Wongb, Grant Wigleya, David Kearneya
Computational Learning Systems Laboratory, School of Information Technology and Mathematical
Sciences, University of South Australia, Mawson Lakes, SA, Australia; bDefence Science and
Technology Group, Edinburgh, SA, Australia"
efbe52289f71eca9a0aaa8a5362f73334fa6b23c,EAI Endorsed Transactions on Context-aware Systems and Applications,"EAI Endorsed Transactions
on Context-aware Systems and Applications
Research Article
Face recognition based on LDA in manifold subspace
Hung Phuoc Truong1, Tue-Minh Dinh Vo1 and Thai Hoang Le1, *
Faculty of Information Technology, University of Science – Vietnam National University Ho Chi Minh city, 227 Nguyen
Van Cu street, HCMc, Vietnam"
efa08283656714911acff2d5022f26904e451113,Active Object Localization in Visual Situations,"Active Object Localization in Visual Situations
Max H. Quinn, Anthony D. Rhodes, and Melanie Mitchell"
efef00465e1b2f4003e838e50f9c8fa1c8ffaf3e,SceneNet: A Perceptual Ontology for Scene Understanding,"SceneNet: A Perceptual Ontology for Scene
Understanding
Ilan Kadar and Ohad Ben-Shahar
Ben-Gurion University of the Negev"
ef61e43a1cce95afdc0696879085e834b981d5de,Real time multi-object tracking using multiple cameras Semester Project,"CVLab: Computer Vision Laboratory
School of Computer and Communication Sciences
Ecole Polytechnique Fédérale de Lausanne
http://cvlab.epfl.ch/
Real time multi-object tracking
using multiple cameras
Semester Project
Michalis Zervos
Supervisor  Professor Pascal Fua
Teaching Assistant  Horesh Ben Shitrit
Spring Semester
June 2012"
ef4ecb76413a05c96eac4c743d2c2a3886f2ae07,Modeling the importance of faces in natural images,"Modeling the Importance of Faces in Natural Images
Jin B.a, Yildirim G.a, Lau C.a, Shaji A.a, Ortiz Segovia M.b and S¨usstrunk S.a
EPFL, Lausanne, Switzerland;
Oc´e, Paris, France"
ef032afa4bdb18b328ffcc60e2dc5229cc1939bc,Attribute-enhanced metric learning for face retrieval,"Fang and Yuan EURASIP Journal on Image and Video
Processing  (2018) 2018:44
https://doi.org/10.1186/s13640-018-0282-x
EURASIP Journal on Image
nd Video Processing
RESEARCH
Open Access
Attribute-enhanced metric learning for
face retrieval
Yuchun Fang*
nd Qiulong Yuan"
ef230e3df720abf2983ba6b347c9d46283e4b690,QUIS-CAMPI: an annotated multi-biometrics data feed from surveillance scenarios,"Page 1 of 20
QUIS-CAMPI: An Annotated Multi-biometrics Data Feed From
Surveillance Scenarios
João Neves1,*, Juan Moreno2, Hugo Proença3
IT - Instituto de Telecomunicações, University of Beira Interior
Department of Computer Science, University of Beira Interior
IT - Instituto de Telecomunicações, University of Beira Interior"
ef66ed8d8db41f67048d077fd4b772c8ba748090,Reservoir Computing Hardware with Cellular Automata,"Reservoir Computing Hardware with
Cellular Automata
Alejandro Mor´an, Christiam F. Frasser and Josep L. Rossell´o
Electronic Engineering Group, Physics Department, Universitat de les Illes Balears,
Spain.
E-mail:
June 22, 2018"
ef473c96dde98e2015b2d135a17a2d734319649a,Playlist Generation using Facial Expression Analysis and Task Extraction,"Pobrane z czasopisma Annales AI- Informatica http://ai.annales.umcs.pl
Data: 04/05/2018 16:53:32
U M CS"
97692960a11d4316880fb229cca699293e133945,An efficient multi-resolution SVM network approach for object detection in aerial images,"015 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING, SEPT. 17–20, 2015, BOSTON, USA
AN EFFICIENT MULTI-RESOLUTION SVM NETWORK APPROACH FOR OBJECT
DETECTION IN AERIAL IMAGES
J. Pasquet(cid:63)†
M. Chaumont∗†
G. Subsol †
M. Derras(cid:63)
LIRMM, Universit´e de Montpellier / CNRS, France
(cid:63) Berger Levrault, Lab`ege, France
Universit´e de Nˆımes, France"
97b8249914e6b4f8757d22da51e8347995a40637,"Large-Scale Vehicle Detection, Indexing, and Search in Urban Surveillance Videos","Large-Scale Vehicle Detection, Indexing,
nd Search in Urban Surveillance Videos
Rogerio Schmidt Feris, Associate Member, IEEE, Behjat Siddiquie, James Petterson,
Yun Zhai, Associate Member, IEEE, Ankur Datta, Lisa M. Brown, Senior Member, IEEE, and
Sharath Pankanti, Fellow, IEEE"
978c6e9c01dd6fa64dd0e357bc750345b06ff6bb,Query-Driven Approach to Face Clustering and Tagging,"Query-Driven Approach to Face
Clustering and Tagging
Liyan Zhang, Xikui Wang, Dmitri V. Kalashnikov, Sharad Mehrotra, and Deva Ramanan"
979f63114a30d60c5c06d4c9c18c8249c3a63099,Synthetically Trained Neural Networks for Learning Human-Readable Plans from Real-World Demonstrations,"Synthetically Trained Neural Networks for Learning
Human-Readable Plans from Real-World Demonstrations
Jonathan Tremblay
Thang To
Artem Molchanov†
Stephen Tyree
Jan Kautz
Stan Birchfield"
97d9c57576a573955c1b21b63f5b5ae44438e973,Human Action Recognition Based Sparse Representation,
97a25752c91538ae0d9d1f5db5ae97e2719b528f,Finding the traces of behavioral and cognitive processes in big data and naturally occurring datasets,"Behav Res
DOI 10.3758/s13428-017-0874-x
Finding the traces of behavioral and cognitive processes in big
data and naturally occurring datasets
Alexandra Paxton 1,2
& Thomas L. Griffiths 1,3
# The Author(s) 2017. This article is an open access publication"
97a0aba4e9a95db17c3d4367f59aad1f02e04b55,How far did we get in face spoofing detection?,"This manuscript is a preprint version. The final version of this paper is
vailable in Engineering Applications of Artificial Intelligence, vol. 72,
pp. 368-381, 2018. DOI: 10.1016/j.engappai.2018.04.013
How far did we get in face spoofing detection?
Luiz Souza, Luciano Oliveira, Mauricio Pamplona
IVISION Lab, Federal University of Bahia
Joao Papa
RECOGNA Lab, S˜ao Paulo State University"
97ede92a6a3579f9fc8ad7c179eaaf37b3966e5a,Bicycle tracking using ellipse extraction,"Bicycle Tracking Using Ellipse Extraction
Tohid Ardeshiri, Fredrik Larsson, Fredrik Gustafsson, Thomas B. Sch¨on, Michael Felsberg
Department of Electrical Engineering
Link¨oping University
Link¨oping, Sweden
e-mail: {tohid, larsson, fredrik, schon,"
9755554b13103df634f9b1ef50a147dd02eab02f,How Transferable Are CNN-Based Features for Age and Gender Classification?,"How Transferable are CNN-based Features for
Age and Gender Classification?
Gökhan Özbulak1, Yusuf Aytar2 and Hazım Kemal Ekenel1"
970e571305ed9dde9308e559694044e204d6e2ad,Learning Finer-class Networks for Universal Representations,"GIRARD ET AL.: FINER-CLASS NETWORKS
Learning Finer-class Networks for Universal
Representations
Julien Girard12
Youssef Tamaazousti123
Hervé Le Borgne2
Céline Hudelot3
Both authors contributed equally.
CEA LIST
Vision Laboratory,
Gif-sur-Yvette, France.
CentraleSupélec,
MICS Laboratory,
Châtenay-Malabry, France."
973e3d9bc0879210c9fad145a902afca07370b86,From Emotion Recognition to Website Customizations,"(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 7, No. 7, 2016
From Emotion Recognition to Website
Customizations
O.B.  Efremides
School  of  Web  Media
Bahrain  Polytechnic
Isa  Town,  Kingdom  of  Bahrain"
97865d31b5e771cf4162bc9eae7de6991ceb8bbf,Face and Gender Classification in Crowd Video,"Face and Gender Classification in Crowd Video
Priyanka Verma
IIIT-D-MTech-CS-GEN-13-100
July 16, 2015
Indraprastha Institute of Information Technology
New Delhi
Thesis Advisors
Dr. Richa Singh
Dr. Mayank Vatsa
Submitted in partial fulfillment of the requirements
for the Degree of M.Tech. in Computer Science
(cid:13) Verma, 2015
Keywords : Face Recognition, Gender Classification, Crowd database"
9728c3e32f57b54dea94fa9737c8f300de5cc468,Imbalanced Malware Images Classification: a CNN based Approach,"Imbalanced Malware Images Classification: a CNN
ased Approach
Songqing Yue
University of Wisconsin"
9715aba0688195b2019d510ae3fd8da2e40f6e20,Evaluation of color spaces for person re-identification,"1st International Conference on Pattern Recognition (ICPR 2012)
November 11-15, 2012. Tsukuba, Japan
978-4-9906441-1-6 ©2012 IAPR"
97bcf007516cb70d8cb17b7de6452aa06c4b9c76,GABAergic neurotransmission alterations in autism spectrum disorders,"Neurotransmitter 2015; 2: e1052. doi: 10.14800/nt.1052; © 2015 by Carla V Sesarini
http://www.smartscitech.com/index.php/nt
REVIEW
GABAergic neurotransmission alterations in autism spectrum
disorders
Carla V Sesarini
Instituto  de  Ciencias  Básicas  y  Medicina  Experimental  (ICBME),  Instituto  Universitario  del  Hospital  Italiano  de  Buenos  Aires
(HIBA), Potosi 4240 (C1199ACL), CABA, Argentina
Correspondence: Carla V Sesarini
E-mail:
Received: October 04, 2015
Published online: November 09, 2015
Autism  spectrum  disorders  (ASDs)  are  a  group  of  complex  disorders  of  neurodevelopment  characterized  by
difficulties in social interaction, verbal and nonverbal communication, and repetitive behaviors. In ASD, deficits
in social cognition and related cognitive functions would be the resultant of reduced synchronization between
rain regions. A possible explanation for ASDs is the disturbance of the delicate balance between excitation and
inhibition  in  the  developing  brain  which  may  have  profound  impact  in  neurobehavioral  phenotypes.  At  least
some forms of autism would be caused by a disproportionately high level of excitation (or weaker inhibition) in
neural circuits that mediate language and social behavior (local circuits). A more excitable cortex (more weakly
inhibited)  is  functionally  more  poorly  differentiated  and  could  lead  to  broad  ranging  abnormalities  in"
97e6633c9865dba296a36a5e80e3d66109b43b0c,New learning paradigms for real-world environment perception,"New learning paradigms for real-world environment
perception
Alexander Gepperth
To cite this version:
Alexander Gepperth. New learning paradigms for real-world environment perception. Machine Learn-
ing [cs.LG]. Université Pierre
Marie Curie, 2016. <tel-01418147>
HAL Id: tel-01418147
https://hal.archives-ouvertes.fr/tel-01418147
Submitted on 16 Dec 2016
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
97104def2b92b430c02f595d7802f9ba23b74cc7,DispSegNet: Leveraging Semantics for End-to-End Learning of Disparity Estimation from Stereo Imagery,"DispSegNet: Leveraging Semantics for End-to-End Learning of
Disparity Estimation from Stereo Imagery
Junming Zhang1, Katherine A. Skinner2, Ram Vasudevan3 and Matthew Johnson-Roberson4"
97e7810f21a145caddc7e5168b59f0ab8894f669,Technical Report : Learning to Rank using High-Order Information,"Technical Report: Learning to Rank using
High-Order Information
Puneet K. Dokania1, Aseem Behl2, C. V. Jawahar2, and M. Pawan Kumar1
Ecole Centrale de Paris1, INRIA Saclay1, IIIT Hyderabad - India2"
97cf04eaf1fc0ac4de0f5ad4a510d57ce12544f5,"Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond","manuscript No.
(will be inserted by the editor)
Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge,
Deep Architectures, and Beyond
Dimitrios Kollias (cid:63) · Panagiotis Tzirakis † · Mihalis A. Nicolaou ∗ · Athanasios
Papaioannou(cid:107) · Guoying Zhao1 · Bj¨orn Schuller2 · Irene Kotsia3 · Stefanos
Zafeiriou4"
9709d362a15414b062efa9cf4a212469af803a7a,Holistic Multi-modal Memory Network for Movie Question Answering,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
Holistic Multi-modal Memory Network
for Movie Question Answering
Anran Wang, Anh Tuan Luu, Chuan-Sheng Foo, Hongyuan Zhu, Yi Tay, Vijay Chandrasekhar"
970e723404885e94e77780766b39ee951dd7abb3,Multimodal Learning of Geometry-Preserving Binary Codes for Semantic Image Retrieval,"IEICE TRANS. INF. & SYST., VOL.E100–D, NO.4 APRIL 2017
INVITED PAPER SpecialSectiononAward-winningPapers
Multimodal Learning of Geometry-Preserving Binary Codes for
Semantic Image Retrieval
Go IRIE†a), Hiroyuki ARAI†, Members, and Yukinobu TANIGUCHI††, Senior Member
SUMMARY
This paper presents an unsupervised approach to feature
inary coding for ef‌f‌icient semantic image retrieval. Although the majority
of the existing methods aim to preserve neighborhood structures of the fea-
ture space, semantically similar images are not always in such neighbors
ut are rather distributed in non-linear low-dimensional manifolds. More-
over, images are rarely alone on the Internet and are often surrounded by
text data such as tags, attributes, and captions, which tend to carry rich se-
mantic information about the images. On the basis of these observations,
the approach presented in this paper aims at learning binary codes for se-
mantic image retrieval using multimodal information sources while pre-
serving the essential low-dimensional structures of the data distributions in
the Hamming space. Specifically, after finding the low-dimensional struc-
tures of the data by using an unsupervised sparse coding technique, our
pproach learns a set of linear projections for binary coding by solving an"
9782c3827fb5563e6230e8c3bd72dc456a557da6,Colour Spaces Study for Skin Colour Detection in Face Recognition Systems,"COLOUR SPACES STUDY FOR SKIN COLOUR DETECTION IN
FACE RECOGNITION SYSTEMS
Jose M. Chaves-González, Miguel A. Vega-Rodríguez
Juan A. Gómez-Pulido and Juan M. Sánchez-Pérez
Univ. Extremadura, Dept. Technologies of Computers and Communications,
Escuela Politécnica, Campus Universitario s/n, 10071, Cáceres,Spain
Keywords:
Face detection, colour spaces, YUV, YIQ, RGB, YCbCr, HSV, skin colour detection."
9729ff547b6882b49898c1f5abb69646edf77e71,Two Kinds of Statistics for Better Face Recognition,"Two Kinds of Statistics for Better Face Recognition
Manuel Günther, Marco K. Müller and Rolf P. Würtz
Institut für Neuroinformatik, Ruhr-Universität, 44780 Bochum, Germany"
97470d68743f338070f32fbb75b7c8a3cb4c045d,Combining experts for improved face verification performance *,"Combining experts for improved face verification performance*
Vitomir Štruc, France Mihelič, Nikola Pavešić
Fakulteta za elektrotehniko, Univerza v Ljubljani
Tržaška 25, 1000 Ljubljana, Slovenija
Večmodalni pristop k  razpoznavanju
obrazov
Samodejno  razpoznavanje  (avtentikacija/identifikacija)
obrazov  predstavlja  eno  najaktivnejših  raziskovalnih
področij biometrije. Avtentikacija oz. identifikacija oseb
razpoznavanjem  obrazov  ponuja  možen  način
povečanja  varnosti  pri  različnih  dejavnostih,  (npr.  pri
elektronskem  poslovanju  na  medmrežju,  pri  bančnih
storitvah  ali  pri  vstopu  v  določene  prostore,  stavbe  in
države).  Ponuja  univerzalen
in  nevsiljiv  način
razpoznavanja  oseb,  ki  pa  trenutno  še  ni  dovolj
zanesljiv.  Kot  možna  rešitev  problema  zanesljivosti
razpoznavanja  se  v  literaturi  vse  pogosteje  pojavljajo
večmodalni pristopi, v katerih se razpoznavanje izvede
na  podlagi  večjega  števila  postopkov  razpoznavanja"
9794d69194ac772c3e92ee1f322a36feb3c16239,HAUSDORFF ARTMAP FOR HUMAN FACE RECOGNITION,"HAUSDORFF ARTMAP FOR HUMAN FACE RECOGNITION
ARIT THAMMANO AND CHONGKOLNEE RUNGRUANG
Faculty of Information Technology
King Mongkut’s Institute of Technology Ladkrabang,
Bangkok, 10520 Thailand
later
received
identification  has
encompasses
ll  of"
9727c74a09aad74abd67ff1d2dff083cc73d4a2e,Visual Focus of Attention in Non-calibrated Environments using Gaze Estimation,"Int J Comput Vis
DOI 10.1007/s11263-013-0691-3
Visual Focus of Attention in Non-calibrated Environments using
Gaze Estimation
Stylianos Asteriadis · Kostas Karpouzis ·
Stefanos Kollias
Received: 24 May 2012 / Accepted: 2 December 2013
© Springer Science+Business Media New York 2013"
97032b13f1371c8a813802ade7558e816d25c73f,Total Recall Final Report,"Total Recall Final Report
Peter Collingbourne, Nakul Durve, Khilan Gudka, Steve Lovegrove, Jiefei Ma, Sadegh Shahrbaf
Supervisor: Professor Duncan Gillies
January 11, 2006"
9717bd66ad50aedabaea0f3af784c7ba9643b686,TransFlow: Unsupervised Motion Flow by Joint Geometric and Pixel-level Estimation,"TransFlow: Unsupervised Motion Flow by Joint
Geometric and Pixel-level Estimation
Stefano Alletto*, Davide Abati, Simone Calderara, Rita Cucchiara
University of Modena and Reggio Emilia
Via P. Vivarelli 10, Modena, Italy
Luca Rigazio*
Panasonic Silicon Valley Laboratory
0900 North Tantau Avenue, Suite 200, Cupertino, CA, USA"
5da139fc43216c86d779938d1c219b950dd82a4c,A Generalized Multiple Instance Learning Algorithm for Iterative Distillation and Cross-Granular Propagation of Video Annotations,"-4244-1437-7/07/$20.00 ©2007 IEEE
II - 205
ICIP 2007"
5d7f8eb73b6a84eb1d27d1138965eb7aef7ba5cf,Robust Registration of Dynamic Facial Sequences,"Robust Registration of Dynamic Facial Sequences
Evangelos Sariyanidi, Hatice Gunes, and Andrea Cavallaro"
5dcfb84ab3f5d5f1dd02f59e45154c9710de97b2,On the Latent Variable Interpretation in Sum-Product Networks,"On the Latent Variable Interpretation in
Sum-Product Networks
Robert Peharz, Robert Gens, Franz Pernkopf, Senior Member, IEEE, and Pedro Domingos"
5d233e6f23b1c306cf62af49ce66faac2078f967,Optimal Geometrical Set for Automated Marker Placement to Virtualized Real-Time Facial Emotions,"RESEARCH ARTICLE
Optimal Geometrical Set for Automated
Marker Placement to Virtualized Real-Time
Facial Emotions
Vasanthan Maruthapillai, Murugappan Murugappan*
School of Mechatronic Engineering, Universiti Malaysia Perlis, 02600, Ulu Pauh, Arau, Perlis, West Malaysia"
5d09d5257139b563bd3149cfd5e6f9eae3c34776,Pattern recognition with composite correlation fi lters designed with multi-objective combinatorial optimization,"Optics Communications 338 (2015) 77–89
Contents lists available at ScienceDirect
Optics Communications
journal homepage: www.elsevier.com/locate/optcom
Pattern recognition with composite correlation filters designed with
multi-objective combinatorial optimization
Victor H. Diaz-Ramirez a,n, Andres Cuevas a, Vitaly Kober b, Leonardo Trujillo c,
Abdul Awwal d
Instituto Politécnico Nacional – CITEDI, Ave. del Parque 1310, Mesade Otay, Tijuana B.C. 22510, México
Department of Computer Science, CICESE, Carretera Ensenada-Tijuana 3918, Ensenada B.C. 22860, México
Instituto Tecnológico de Tijuana, Blvd. Industrial y Ave. ITR TijuanaS/N, Mesa de Otay, Tijuana B.C. 22500, México
d National Ignition Facility, Lawrence Livermore National Laboratory, Livermore, CA 94551, USA
r t i c l e i n f o
b s t r a c t
Article history:
Received 12 July 2014
Accepted 16 November 2014
Available online 23 October 2014
Keywords:
Object recognition"
5df11c59e3b47189486445f5833675bf08359bfe,Influence of Image Classification Accuracy on Saliency Map Estimation,"IET Research Journals
Brief Paper
Influence of Image Classification Accuracy
on Saliency Map Estimation
Taiki Oyama1 Takao Yamanaka1
Department of Information & Communication Sciences, Sophia University, 7-1 Kioi-cho, Chiyoda-ku, Tokyo, 102-0094, Japan
* E-mail:
ISSN 1751-8644
doi: 0000000000
www.ietdl.org"
5d80149e005894ab57f47e667f3e060e247d8e43,Lip reading using CNN and LSTM,"Lip reading using CNN and LSTM
Amit Garg
Jonathan Noyola
Sameep Bagadia"
5d165ff5b0b389e32809c17838a2afc218a91d62,Object Detectors Emerge in Deep Scene CNNs,"Published as a conference paper at ICLR 2015
OBJECT DETECTORS EMERGE IN DEEP SCENE CNNS
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, Antonio Torralba
Computer Science and Artificial Intelligence Laboratory, MIT"
5d25caad6e551ca7b39c66f1f79998bfe6c71ceb,5 D Facial Analysis via Bio-Inspired Active Appearance Model and Support Vector Machine for Forensic Application,"(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 8, No.7, 2017
.5 D Facial Analysis via Bio-Inspired Active
Appearance Model and Support Vector Machine for
Forensic Application
Siti Norul Huda Sheikh Abdullah
Faculty of Information Science and
Technology,
Universiti Kebangsaan Malaysia,
Bandar Baru Bangi, Malaysia
Mohammed Hasan Abdulameer
Department of Computer Science,
Faculty of Education for Women,
University of Kufa,
Nazri Ahmad Zamani
Digital Forensics lab,
CyberSecurity Malaysia,
Seri Kembangan,
Malaysia
Khairul Akram Zainol Ariffin"
5d90f06bb70a0a3dced62413346235c02b1aa086,Learning Multiple Layers of Features from Tiny Images,"Learning Multiple Layers of Features from Tiny Images
Alex Krizhevsky
April 8, 2009"
5da740682f080a70a30dc46b0fc66616884463ec,Real-Time Head Pose Estimation Using Multi-variate RVM on Faces in the Wild,"Real-Time Head Pose Estimation Using
Multi-Variate RVM on Faces in the Wild
Mohamed Selim, Alain Pagani, Didier Stricker
Augmented Vision Research Group,
German Research Center for Artificial Intelligence (DFKI),
Tripstaddterstr. 122, 67663 Kaiserslautern, Germany
Technical University of Kaiserslautern
http://www.av.dfki.de"
5db46dda9f0f08220d49a5db1204f149bd4f6a4a,Engaging Image Captioning Via Personality,"ENGAGING IMAGE CAPTIONING VIA PERSONALITY
Kurt Shuster, Samuel Humeau, Hexiang Hu, Antoine Bordes, Jason Weston
Facebook AI Research"
5d1608e03ab9c529d0b05631f9d2a3afcbf1c3e3,Sparsity and Robustness in Face Recognition,"Sparsity and Robustness in Face Recognition
John Wright, Arvind Ganesh, Allen Yang, Zihan Zhou, and Yi Ma
Background. This note concerns the use of techniques for sparse signal representation and sparse
from the paper [WYG+09], which showed how, under certain technical conditions, one could cast
the face recognition problem as one of seeking a sparse representation of a given input face image
in terms of a “dictionary” of training images and images of individual pixels. To be more precise,
the method of [WYG+09] assumes access to a suf‌f‌icient number of well-aligned training images of
each of the k subjects. These images are stacked as the columns of matrices A1, . . . , Ak. Given a
new test image y, also well aligned, but possibly subject to illumination variation or occlusion, the
method of [WYG+09] seeks to represent y as a sparse linear combination of the database as whole.
Writing A = [A1 | ··· | Ak], this approach solves
(cid:107)x(cid:107)1 + (cid:107)e(cid:107)1
subj. to Ax + e = y.
minimize
the identity of the test image y the index whose sparse coef‌f‌icients minimize the residual:
ˆi = arg min
(cid:107)y − Aixi − e(cid:107)2.
This approach demonstrated successful results in laboratory settings (fixed pose, varying illumi-
nation, moderate occlusion) in [WYG+09], and was extended to more realistic settings (involving
moderate pose and misalignemnt) in [WWG+11]. For the sake of clarity, we repeat the above"
5dc60d250e648f96a799db986a336ad344577405,SIFTing the Relevant from the Irrelevant: Automatically Detecting Objects in Training Images,"SIFTing the Relevant from the Irrelevant:
Automatically Detecting Objects in Training Images
Edmond Zhang, Michael Mayo
Department of Computer Science
The University of Waikato
Hamilton, New Zealand
{ez1,"
5d7f9e1463b596eb5d77865a8b1a0e149215303b,A Hidden Markov Model-based Approach for Face Detection and Recognition a Hidden Markov Model-based Approach for Face Detection and Recognition,"AHiddenMarkovModel-BasedApproach
forFaceDetectionandRecognition
ATHESIS
Presentedto
TheAcademicFaculty
AraNe(cid:12)an
InPartialFul(cid:12)llment
oftheRequirementsfortheDegreeof
DoctorofPhilosophyinElectricalEngineering
GeorgiaInstituteofTechnology
August,
"
5d4af8c9321168f9ba7a501f33fb019fa2deaa22,Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems,"Examining Gender and Race Bias
in Two Hundred Sentiment Analysis Systems
Svetlana Kiritchenko and Saif M. Mohammad
National Research Council Canada"
5da0224590d91defe8c75db0ab5e12d50b6ab6f3,NMTPY: A Flexible Toolkit for Advanced Neural Machine Translation Systems,"NMTPY: A FLEXIBLE TOOLKIT FOR ADVANCED
NEURAL MACHINE TRANSLATION SYSTEMS
Ozan Caglayan, Mercedes García-Martínez, Adrien Bardet, Walid Aransa,
Fethi Bougares, Loïc Barrault
Laboratoire d’Informatique de l’Université du Maine (LIUM)
Language and Speech Technology (LST) Team
Le Mans, France"
5d04bd7104f08f7fb91967613ffc519c27641e99,Bound to Lose: Physical Incapacitation Increases the Conceptualized Size of an Antagonist in Men,"Bound to Lose: Physical Incapacitation Increases the
Conceptualized Size of an Antagonist in Men
Daniel M. T. Fessler*, Colin Holbrook
Department of Anthropology and Center for Behavior, Evolution, and Culture, University of California Los Angeles, Los Angeles, California, United States of America"
5d0e11844f1a210f16025e990de938f6732672ab,Distance to Center of Mass Encoding for Instance Segmentation,"Distance to Center of Mass Encoding for Instance Segmentation
Thomio Watanabe
University of Sao Paulo
Denis Wolf
University of Sao Paulo"
5da43ff9c246ae37d9006bba3406009cb4fb1dcf,"Lifelong Machine Learning November , 2016","Lifelong Machine Learning
November, 2016
Zhiyuan Chen and Bing Liu
Draft : This is an early draft of the book.
Zhiyuan Chen and Bing Liu. Lifelong Machine Learning.
Morgan & Claypool Publishers, Nov 2016.
LifelongMachineLearningZhiyuan ChenBing Liu"
5d5cd6fa5c41eb9d3d2bab3359b3e5eb60ae194e,Face Recognition Algorithms,"Face Recognition Algorithms
Proyecto Fin de Carrera
June 16, 2010
Ion Marqu´es
Supervisor:
Manuel Gra˜na"
5d185d82832acd430981ffed3de055db34e3c653,A Fuzzy Reasoning Model for Recognition of Facial Expressions,"A Fuzzy Reasoning Model for Recognition
of Facial Expressions
Oleg Starostenko1, Renan Contreras1, Vicente Alarcón Aquino1, Leticia Flores Pulido1,
Jorge Rodríguez Asomoza1, Oleg Sergiyenko2, and Vira Tyrsa3
Research Center CENTIA, Department of Computing, Electronics and Mechatronics,
Universidad de las Américas, 72820, Puebla, Mexico
{oleg.starostenko; renan.contrerasgz; vicente.alarcon; leticia.florespo;
Engineering Institute, Autonomous University of Baja California, Blvd. Benito Juárez,
Insurgentes Este, 21280, Mexicali, Baja California, Mexico
Universidad Politécnica de Baja California, Mexicali, Baja California, Mexico"
5d14cc415a93e6f3a625ed7794e1fdcf99ea5713,Predicting Face Recognition Performance Using Image Quality,"Predicting Face Recognition Performance Using
Image Quality
Abhishek Dutta, Raymond Veldhuis, Senior Member, IEEE and Luuk Spreeuwers,"
5db4fe0ce9e9227042144758cf6c4c2de2042435,Recognition of Facial Expression Using Haar Wavelet Transform,"INTERNATIONAL JOURNAL OF ELECTRICAL AND ELECTRONIC SYSTEMS RESEARCH, VOL.3, JUNE 2010
Recognition of Facial Expression Using Haar
Wavelet Transform
M. Satiyan,  M.Hariharan,  R.Nagarajan
paper
features
investigates"
5da2ae30e5ee22d00f87ebba8cd44a6d55c6855e,"When facial expressions do and do not signal minds: The role of face inversion, expression dynamism, and emotion type.","This is an Open Access document downloaded from ORCA, Cardiff University's institutional
repository: http://orca.cf.ac.uk/111659/
This is the author’s version of a work that was submitted to / accepted for publication.
Citation for final published version:
Krumhuber, Eva G, Lai, Yukun, Rosin, Paul and Hugenberg, Kurt 2018. When facial expressions
Publishers page:
Please note:
Changes made as a result of publishing processes such as copy-editing, formatting and page
numbers may not be reflected in this version. For the definitive version of this publication, please
refer to the published source. You are advised to consult the publisher’s version if you wish to cite
this paper.
This version is being made available in accordance with publisher policies. See
http://orca.cf.ac.uk/policies.html for usage policies. Copyright and moral rights for publications
made available in ORCA are retained by the copyright holders."
5d7de2eb2ee99798bfb2e50ed5169e3b8a35469a,Design of a Three-Dimensional Face Recognition System,"The Open Automation and Control Systems Journal, 2015, 7, 587-590
Design of a Three-Dimensional Face Recognition System
Send Orders for Reprints to
Open Access
Wang Xuechun* and Wang Zhaoping
School of Information Engineering, Huanghe Science and Technology College, Zhengzhou, Henan, 450006, P.R. China"
5dc003a75a302761778cb1c15d796e3d90dd9322,Bayesian Fisher's Discriminant for Functional Data,"Bayesian Fisher’s Discriminant for Functional Data
Yao-Hsiang Yang ∗, Lu-Hung Chen†, Chieh-Chih Wang‡, and Chu-Song Chen §
December 10, 2014"
5d197c8cd34473eb6cde6b65ced1be82a3a1ed14,A Face Image Database for Evaluating Out-of-Focus Blur,"0AFaceImageDatabaseforEvaluatingOut-of-FocusBlurQiHan,QiongLiandXiamuNiuHarbinInstituteofTechnologyChina1.IntroductionFacerecognitionisoneofthemostpopularresearchfieldsofcomputervisionandmachinelearning(Tores(2004);Zhaoetal.(2003)).Alongwithinvestigationoffacerecognitionalgorithmsandsystems,manyfaceimagedatabaseshavebeencollected(Gross(2005)).Facedatabasesareimportantfortheadvancementoftheresearchfield.Becauseofthenonrigidityandcomplex3Dstructureofface,manyfactorsinfluencetheperformanceoffacedetectionandrecognitionalgorithmssuchaspose,expression,age,brightness,contrast,noise,blurandetc.Someearlyfacedatabasesgatheredunderstrictlycontrolledenvironment(Belhumeuretal.(1997);Samaria&Harter(1994);Turk&Pentland(1991))onlyallowslightexpressionvariation.Toinvestigatetherelationshipsbetweenalgorithms’performanceandtheabovefactors,morefacedatabaseswithlargerscaleandvariouscharacterswerebuiltinthepastyears(Bailly-Bailliereetal.(2003);Flynnetal.(2003);Gaoetal.(2008);Georghiadesetal.(2001);Hallinan(1995);Phillipsetal.(2000);Simetal.(2003)).Forinstance,The""CAS-PEAL"",""FERET"",""CMUPIE"",and""YaleB""databasesincludevariousposes(Gaoetal.(2008);Georghiadesetal.(2001);Phillipsetal.(2000);Simetal.(2003));The""HarvardRL"",""CMUPIE""and""YaleB""databasesinvolvemorethan40differentconditionsinillumination(Georghiadesetal.(2001);Hallinan(1995);Simetal.(2003));Andthe""BANCA"",and""NDHID""databasescontainover10timesgathering(Bailly-Bailliereetal.(2003);Flynnetal.(2003)).Thesedatabaseshelpresearcherstoevaluateandimprovetheiralgorithmsaboutfacedetection,recognition,andotherpurposes.Blurisnotthemostimportantbutstillanotablefactoraffectingtheperformanceofabiometricsystem(Fronthaleretal.(2006);Zamanietal.(2007)).Themainreasonsleadingblurconsistinout-of-focusofcameraandmotionofobject,andtheout-of-focusblurismoresignificantintheapplicationenvironmentoffacerecognition(Eskicioglu&Fisher(1995);Kimetal.(1998);Tanakaetal.(2007);Yitzhaky&Kopeika(1996)).Toinvestigatetheinfluenceofbluronafacerecognitionsystem,afaceimagedatabasewithdifferentconditionsofclarityandefficientblurevaluatingalgorithmsareneeded.Thischapterintroducesanewfacedatabasebuiltforthepurposeofblurevaluation.Theapplicationenvironmentsoffacerecognitionareanalyzedfirstly,thenaimagegatheringschemeisdesigned.Twotypicalgatheringfacilitiesareusedandthefocusstatusaredividedinto11steps.Further,theblurassessmentalgorithmsaresummarizedandthecomparisonbetweenthemisraisedonthevarious-claritydatabase.The7www.intechopen.com"
7ae0212d6bf8a067b468f2a78054c64ea6a577ce,Human Face Processing Techniques With Application To Large Scale Video Indexing,"Human Face Processing Techniques
With Application To
Large Scale Video Indexing
LE DINH DUY
DOCTOR OF
PHILOSOPHY
Department of Informatics,
School of Multidisciplinary Sciences,
The Graduate University for Advanced Studies (SOKENDAI)
006 (School Ｙear)
September 2006"
7ace44190729927e5cb0dd5d363fcae966fe13f7,A bag-of-features approach based on Hue-SIFT descriptor for nude detection,"7th European Signal Processing Conference (EUSIPCO 2009)
Glasgow, Scotland, August 24-28, 2009
A BAG-OF-FEATURES APPROACH BASED ON
HUE-SIFT DESCRIPTOR FOR NUDE DETECTION
Ana P. B. Lopes1,2, Sandra E. F. de Avila1, Anderson N. A. Peixoto1
Rodrigo S. Oliveira1 and Arnaldo de A. Ara´ujo1
Computer Science Department – Federal University of Minas Gerais
Av. Antˆonio Carlos, 6627, Pampulha, CEP 31270–901, Belo Horizonte, MG, Brazil
Exact and Technological Sciences Department – State University of Santa Cruz
Rodovia Ilh´eus-Itabuna, km 16 – Pavilh˜ao Jorge Amado, CEP 45600-000, Ilh´eus, BA, Brazil"
7a0fb972e524cb9115cae655e24f2ae0cfe448e0,Facial Expression Classification Using RBF AND Back-Propagation Neural Networks,"Facial Expression Classification Using RBF AND Back-Propagation Neural Networks
R.Q.Feitosa1,2,
M.M.B.Vellasco1,2,
D.T.Oliveira1,
D.V.Andrade1,
S.A.R.S.Maffra1
– Catholic University of Rio de Janeiro, Brazil
Department of Electric Engineering
– State University of Rio de Janeiro, Brazil
Department of Computer Engineering
e-mail: [raul, -rio.br, [diogo,"
7aa062c6c90dba866273f5edd413075b90077b51,Minimizing Separability : A Comparative Analysis of Illumination Compensation Techniques in Face Recognition,"I.J. Information Technology and Computer Science, 2017, 5, 40-51
Published Online May 2017 in MECS (http://www.mecs-press.org/)
DOI: 10.5815/ijitcs.2017.05.06
Minimizing Separability: A Comparative Analysis
of Illumination Compensation Techniques in Face
Recognition
Chollette C. Olisah
Department of Computer Science and IT, Baze University, Abuja, Nigeria
E-mail:"
7a7a04a02e807d7c9cb90ab8442ac428904d2415,Automated Latent Fingerprint Recognition,"Automated Latent Fingerprint Recognition
Kai Cao and Anil K. Jain, Fellow, IEEE"
7a0c93d7cc75187aebb1a42772c79839c8f681c5,Détection de marqueurs affectifs et attentionnels de personnes âgées en interaction avec un robot. (Audio-visual detection of emotional (laugh and smile) and attentional markers for elderly people in social interaction with a robot),"Détection de marqueurs affectifs et attentionnels de
personnes âgées en interaction avec un robot
Fan Yang
To cite this version:
Fan Yang. Détection de marqueurs affectifs et attentionnels de personnes âgées en interaction
vec un robot.
Intelligence artificielle [cs.AI]. Université Paris-Saclay, 2015. Français. <NNT :
015SACLS081>. <tel-01280505>
HAL Id: tel-01280505
https://tel.archives-ouvertes.fr/tel-01280505
Submitted on 29 Feb 2016
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,"
7a1828e181e3c8bd014c7e5fc1bcc417f122c18c,Face Perception and Test Reliabilities in Congenital Prosopagnosia in Seven Tests,"i-Perception
January-February 2016: 1–37
! The Author(s) 2016
DOI: 10.1177/2041669515625797
ipe.sagepub.com
Article
Face Perception and Test
Reliabilities in Congenital
Prosopagnosia in Seven Tests
Janina Esins
Department of Human Perception, Cognition and Action, Max Planck
Institute for Biological Cybernetics, Tu¨bingen, Germany
Johannes Schultz
Department of Psychology, Durham University, Durham, UK
Claudia Stemper
Institute of Human Genetics, Westfa¨lische Wilhelms-Universita¨t
Mu¨nster, Mu¨nster, Germany
Ingo Kennerknecht
Institute of Human Genetics, Westfa¨lische Wilhelms-Universita¨t
Mu¨nster, Mu¨nster, Germany"
7a9ef491914d515bb5570aa9b3a261d42d430b86,"Excuse Me, Do I Know You From Somewhere? Unaware Facial Recognition Using Brain-Computer Interfaces","Proceedings of the 50th Hawaii International Conference on System Sciences | 2017
Excuse Me, Do I Know You From Somewhere? Unaware Facial Recognition
Using Brain-Computer Interfaces
Christopher Bellman
Miguel Vargas Martin
Shane MacDonald
University of Ontario Institute
University of Ontario Institute
University of Ontario Institute
of Technology
of Technology
of Technology
Ruba Alomari
Ramiro Liscano
University of Ontario Institute of Technology
University of Ontario Institute of Technology"
7aa4c16a8e1481629f16167dea313fe9256abb42,Multi-task learning for face identification and attribute estimation,"978-1-5090-4117-6/17/$31.00 ©2017 IEEE
ICASSP 2017"
7a97de9460d679efa5a5b4c6f0b0a5ef68b56b3b,Constrained Joint Cascade Regression Framework for Simultaneous Facial Action Unit Recognition and Facial Landmark Detection,"nd Face shape relationship2)AU relationship3)Face shape patternUpdate facial landmark locationsUpdate AU activation probabilitiesAU activation probabilitiesCurrent landmark locationsFigure1.Constrainedjointcascaderegressionframeworkforsi-multaneousfacialactionunitrecognitionandlandmarkdetection.wouldenablethemachineunderstandingofhumanfacialbehavior,intent,emotionetc.Facialactionunitrecognitionandfaciallandmarkdetec-tionarerelatedtasks,buttheyareseldomlyexploitedjointlyintheliteratures.Forexample,thefaceshapedefinedbythelandmarklocationsareconsideredaseffectivefeaturesforAUrecognition.But,thelandmarklocationinforma-tionisusuallyextractedbeforehandwithfaciallandmarkdetectionalgorithms.Ontheotherhand,theActionUnitinformationisrarelyutilizedintheliteraturetohelpfaciallandmarkdetection,eventhoughthefacialmusclemove-mentsandtheactivationofspecificfacialactionunitcancausetheappearanceandshapechangesofthefacewhichsignificantlyaffectfaciallandmarkdetection.Themutualinformationandintertwinedrelationshipamongfacialac-tionunitrecognitionandfaciallandmarkdetectionshouldbeutilizedtoboosttheperformancesofbothtasks.Cascaderegressionframeworkhasbeenshowntobeaneffectivemethodforfacealignmentrecently[19][13].Itstartsfromaninitialfaceshape(e.g.meanface)anditit-erativelyupdatesthefaciallandmarklocationsbasedonthelocalappearancefeaturesuntilconvergence.Severalregres-sionmodelshavebeenappliedtolearnthemappingfromthelocalappearancefeaturestothefaceshapeupdate.Toleveragethesuccessofthecascaderegressionframe-workandtoachievethegoalofjointfacialactionunit13400"
7a776f080b270c8759b2b4fe601682276d1b2eb4,Multi-target Tracking with Sparse Group Features and Position Using Discrete-Continuous Optimization,"Multi-Target Tracking with Sparse Group
Features and Position using Discrete-Continuous
Optimization
Billy Peralta (1) and Alvaro Soto (2)
(1)Universidad Cat´olica de Temuco, (2)Pontificia Universidad Cat´olica de Chile"
7ad77b6e727795a12fdacd1f328f4f904471233f,Supervised Local Descriptor Learning for Human Action Recognition,"Supervised Local Descriptor Learning
for Human Action Recognition
Xiantong Zhen, Feng Zheng, Ling Shao, Senior Member, IEEE, Xianbin Cao, Senior Member, IEEE, and Dan Xu"
7a88d33b3e23a2cdf1e8a2b848c73a12a34ba88c,TUB-IRML at MediaEval 2014 Violent Scenes Detection Task: Violence Modeling through Feature Space Partitioning,"TUB-IRML at MediaEval 2014 Violent Scenes Detection
Task: Violence Modeling through Feature Space
Partitioning
Esra Acar, Sahin Albayrak
DAI Laboratory, Technische Universität Berlin
Ernst-Reuter-Platz 7, TEL 14, 10587 Berlin, Germany"
7afb39dcf74b5d9ae082ae33ebf3ce04dceb0cf8,Evaluation of Face R,"Recognition Techniques for Application
Evaluation of Face R
Enrique G. O
Brian C. Becker
University of Centr
Carnegie Mellon Univ
versity
000 Central Flori
5000 Forbes Av
Pittsburgh, PA 152
Orlando, FL 32
ker.com
http://www.enriqueg
http://www.briancbec"
7af6d86139aa86cb5897904563a9f67c016a176d,Performance of Correlation Filters in Facial Recognition,"Performance of Correlation Filters in Facial
Recognition
Everardo Santiago-Ramirez, J.A. Gonzalez-Fraga, and J.I. Ascencio-Lopez
Facultad de Ciencias, Universidad Autónoma de Baja California, Km. 103, Carretera Tijuana-
Ensenada, Ensenada, Baja California C. P. 22860"
7a81967598c2c0b3b3771c1af943efb1defd4482,Do We Need More Training Data?,"Do We Need More Training Data?
Xiangxin Zhu · Carl Vondrick · Charless C. Fowlkes · Deva Ramanan"
7a7a53b05e22305b2963c05ac89830e099146767,Assessing fish abundance from underwater video using deep neural networks,"Assessing fish abundance from underwater video
using deep neural networks
Ranju Mandal∗, Rod M. Connolly†, Thomas A. Schlacher‡ and Bela Stantic∗
School of ICT, Griffith Sciences, Griffith University, QLD 4222, Australia
Australian Rivers Institute - Coast & Estuaries and
School of Environment and Science, Griffith University, QLD 4222, Australia
School of Science and Engineering, University of the Sunshine Coast, QLD 4558, Australia
{r.mandal, r.connolly,"
7a9fe5781220cca6ca600833015f200a9c03d50e,Teaching Machines to Describe Images via Natural Language Feedback,"Teaching Machines to Describe Images via Natural
Language Feedback
Huan Ling1, Sanja Fidler1,2
University of Toronto1, Vector Institute2"
7ab9035ec3871bbeadf1095afbe1ff9d9cb25480,DLBP and SVD Fusion for 3 D Face Recognition Using Range Image,"Computer Science and Information Technology 5(2): 61-65, 2017
DOI: 10.13189/csit.2017.050203
http://www.hrpub.org
DLBP and SVD Fusion for 3D Face Recognition Using
Range Image
El Mahdi Barrah, Rachid Ahdid, Said Safi, Abdessamad Malaoui∗
Interdisciplinary Laboratory of Research in Sciences and Technologies (LIRST), Sultan Moulay Slimane University, Bni Mellal, Morocco
Copyright c(cid:13)2017 by authors, all rights reserved. Authors agree that this article remains permanently
open access under the terms of the Creative Commons Attribution License 4.0 International License"
7a540e0e2049a8f0118be2eab9a2ec5f57e022c9,Deep Learning Methods for Classification with Limited Training Data Seminar Report : Spring 2017,"Deep Learning Methods for Classification with
Limited Training Data
Seminar Report : Spring 2017
submitted by
Aviral Kumar
(140070031)
under the guidance of
Prof. Sunita Sarawagi
Department of Computer Science and Engineering
Indian Institute of Technology Bombay
April, 2017"
7a4f3d17672ecd89e4ad0d4f3a9257352a055d9b,A Novel Data-driven Image Annotation Method,"A Novel Data-driven Image Annotation Method
Guiguang Ding, Jianmin Wang, Na Xu"
7a8ffddbceb14a5b5fb5fc52c47a4db366e47f80,The Rosario Dataset: Multisensor Data for Localization and Mapping in Agricultural Environments,"The Rosario Dataset: Multisensor Data
for Localization and Mapping in
Agricultural Environments
Journal Title
XX(X):1–8
©The Author(s) 2016
Reprints and permission:
sagepub.co.uk/journalsPermissions.nav
DOI: 10.1177/ToBeAssigned
www.sagepub.com/
Taih ´u Pire1, Mart´ın Mujica1, Javier Civera2 and Ernesto Kofman1"
7ab41d2fb37079d20db5e25fd6e71755673f82f0,Building Emotional Machines: Recognizing Image Emotions Through Deep Neural Networks,"Building Emotional Machines: Recognizing Image
Emotions through Deep Neural Networks
Hye-Rin Kim, Yeong-Seok Kim, Seon Joo Kim, In-Kwon Lee"
7ad7897740e701eae455457ea74ac10f8b307bed,Random Subspace Two-dimensional LDA for Face Recognition,"Random Subspace Two-dimensional LDA for Face Recognition*
Garrett Bingham1"
7a0ae1abc9b999ce7dfdd7879f5c29b1992f254b,Fusion of Silhouette Based Gait Features for Gait Recognition,"International Journal of Engineering and Technical Research (IJETR)
ISSN: 2321-0869, Volume-2, Issue-8, August 2014
Fusion of Silhouette Based Gait Features for Gait
Recognition
Mohan Kumar H P,  Nagendraswamy H S"
7acc05ae92823c12b28d6ad73cb2a7707ccb6c7b,Single view-based 3D face reconstruction robust to self-occlusion,"Lee et al. EURASIP Journal on Advances in Signal Processing 2012, 2012:176
http://asp.eurasipjournals.com/content/2012/1/176
R ES EAR CH
Open Access
Single view-based 3D face reconstruction robust
to self-occlusion
Youn Joo Lee1, Sung Joo Lee2, Kang Ryoung Park3, Jaeik Jo1 and Jaihie Kim1*"
7a82d83f818cdc4ac714e468446bc2499ff9caa7,Object Referring in Videos with Language and Human Gaze,"Object Referring in Videos with Language and Human Gaze
Arun Balajee Vasudevan1, Dengxin Dai1, Luc Van Gool1,2
ETH Zurich1
KU Leuven 2"
a949b8700ca6ba96ee40f75dfee1410c5bbdb3db,Instance-Weighted Transfer Learning of Active Appearance Models,"Instance-weighted Transfer Learning of Active Appearance Models
Daniel Haase, Erik Rodner, and Joachim Denzler
Computer Vision Group, Friedrich Schiller University of Jena, Germany
Ernst-Abbe-Platz 2-4, 07743 Jena, Germany"
a94c3091be2090df6144bd121e41e7dfa96ec0e9,Enhanced visual functioning in autism: an ALE meta-analysis.,"Enhanced Visual Functioning in Autism:
An ALE Meta-Analysis
Fabienne Samson,1 Laurent Mottron,1 Isabelle Soulie` res,1,2
nd Thomas A. Zeffiro2
Centre d’Excellence en Troubles Envahissants du De´veloppement de l’Universite´ de Montre´al
Neural Systems Group, Massachusetts General Hospital, Boston, Massachusetts
(CETEDUM), Montre´al, QC, Canada"
a92b5234b8b73e06709dd48ec5f0ec357c1aabed,Disjoint Multi-task Learning Between Heterogeneous Human-Centric Tasks,
a93781e6db8c03668f277676d901905ef44ae49f,Recent Data Sets on Object Manipulation: A Survey.,"Recent Datasets on Object Manipulation: A Survey
Yongqiang Huang, Matteo Bianchi, Minas Liarokapis and Yu Sun"
a91d0ebc1255d6de1c4588767b3b5e1fc630e56f,FBI-HH-M-345 / 10 eTRIMS Scene Interpretation Datasets,"Universit¨at Hamburg
Technical Report FBI-HH-M-345/10
eTRIMS Scene Interpretation
Datasets
Johannes Hartz
Patrick Koopmann
Arne Kreutzmann
Kasim Terzi´c
{hartz | koopmann |
informatik.uni-hamburg.de
November 15, 2010"
a9978df0b4df4d7b04bc4e9464c67f9ff7c31d3d,FROM TRADITIONAL TO INTERACTIVE PLAYSPACES FROM TRADITIONAL TO INTERACTIVE PLAYSPACES FROM TRADITIONAL TO INTERACTIVE PLAYSPACES Automatic Analysis of Player Behavior in the Interactive Tag Playground,"FROM TRADITIONAL TO
FROM TRADITIONAL TO
INTERACTIVE PLAYSPACES
INTERACTIVE PLAYSPACES
Automatic Analysis of Player Behavior in the
Interactive Tag Playground
CTIT Ph.D. Thesis Series No. 16-386
ISSN: 1381-3617
Alejandro Moreno"
a9d3547ab16a9cc936bf5991bf8fb475eadce931,Face Recognition using DWT with HMM,"Eng. & Tech. Journal, Vol.30, No.1, 2012
Face Recognition using DWT with HMM
Dr. Eyad I. Abbas
Department of Electrical Engineering, University of Technology/ Baghdad
Hameed R. Farhan
Department of Electrical Engineering, Engineering College, University of Kerbala/ Kerbala
Received on: 19/6/2011 & Accepted on: 3/11/2011"
a90226c41b79f8b06007609f39f82757073641e2,Β-vae: Learning Basic Visual Concepts with a Constrained Variational Framework,"Under review as a conference paper at ICLR 2017
β-VAE: LEARNING BASIC VISUAL CONCEPTS WITH A
CONSTRAINED VARIATIONAL FRAMEWORK
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot,
Matthew Botvinick, Shakir Mohamed, Alexander Lerchner
Google DeepMind
{irinah,lmatthey,arkap,cpburgess,glorotx,"
a975f1aea5dbb748955da0e17eef8d2270a49f25,Object Recognition Sensory Data Acquisition Light Intensity Images,"OBJECT RECOGNITION
Object recognition is a subproblem of the more general
problem of perception, and can be defined as follows. Given
scene consisting of one or more objects, can we identify
nd localize those objects that are sufficiently visible to
the sensory system? It is generally assumed that a de-
scription of each object to be recognized is available to the
omputer and can be used to facilitate the task of iden-
tification and localization. These descriptions can either
e model-based or appearance-based, or a combination of
oth. Model-based object representation is based on geo-
metric features, whereas appearance-based representation
uses a large set of images for training but does not require
ny insight into the geometric structure of the objects. Ob-
ject recognition is a key component of many intelligent vi-
sion systems, such as those used in hand-eye coordination
for bin picking, inspection, and mobile robotics.
Various types of object recognition problems can be
stated based on the dimensionality of their spatial descrip-
tion: (1) recognition of a 2-D object from a single 2-D im-"
a9f03e4bb90addab234423994bfd8c25854484ea,OBJECT BASED IMAGE RETRIEVAL USING LBP AND FUZZY CLUSTERING METHOD,"Volume1, Issue 3, 15 May- 15 August 2015
International Journal In Applied Studies And
Production Management
ISSN 2394-840X
OBJECT BASED IMAGE RETRIEVAL USING LBP AND FUZZY
CLUSTERING METHOD
Jiwanjot kaur Bhinder
Department of Computer Science & Engg, RIMT(IET) Mandigobindgarh
Kirti joshi
Department of Computer Science & Engg, RIMT(IET) Mandigobindgarh"
a9453721f35f364e176a5aaa7bdb622f72fbcaec,Learning Articulated Motion Models from Visual and Lingual Signals,"Learning Articulated Motion Models from Visual and Lingual Signals
Zhengyang Wu
Georgia Tech
Atlanta, GA 30332
Mohit Bansal
TTI-Chicago
Chicago, IL 60637
Matthew R. Walter
TTI-Chicago
Chicago, IL 60637"
a9286519e12675302b1d7d2fe0ca3cc4dc7d17f6,Learning to Succeed while Teaching to Fail: Privacy in Closed Machine Learning Systems,"Learning to Succeed while Teaching to Fail:
Privacy in Closed Machine Learning Systems
Jure Sokoli´c, Qiang Qiu, Miguel R. D. Rodrigues, and Guillermo Sapiro"
a9eb6e436cfcbded5a9f4b82f6b914c7f390adbd,A Model for Facial Emotion Inference Based on Planar Dynamic Emotional Surfaces,"(IJARAI) International Journal of Advanced Research in Artificial Intelligence,
Vol. 5, No.6, 2016
A Model for Facial Emotion Inference Based on
Planar Dynamic Emotional Surfaces
Ruivo,  J.  P.  P.
Escola  Polit´ecnica
Negreiros,  T.
Escola  Polit´ecnica
Barretto,  M.  R.  P.
Escola  Polit´ecnica
Tinen,  B.
Escola  Polit´ecnica
Universidade de S˜ao Paulo
Universidade de S˜ao Paulo
Universidade de S˜ao Paulo
Universidade de S˜ao Paulo
S˜ao Paulo, Brazil
S˜ao Paulo, Brazil
S˜ao Paulo, Brazil
S˜ao Paulo, Brazil"
a937c7cbb38d26dbc1d2769780cde6e0c7cc5d90,Offline Generation of High Quality Background Subtraction Data,"Offline Generation of High Quality Background
Subtraction Data
Etienne Grossmann, Amit Kale, Christopher Jaynes and Sen-ching Samson Cheung
UK Center for Visualization and Virtual Environments


	

		
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*+	
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Quality Street; Lexington KY 40507-1464, USA"
a94b832facb57ea37b18927b13d2dd4c5fa3a9ea,Domain transfer convolutional attribute embedding,"April 3, 2018
Journal of Experimental & Theoretical Artificial Intelligence
To appear in the Journal of Experimental & Theoretical Artificial Intelligence
Vol. 00, No. 00, Month 20XX, 1–23
Domain transfer convolutional attribute embedding
Fang Sua ∗ , Jing-Yan Wangb
School of Economics and Management, Shaanxi University of Science & Technology, Xi’an,
New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
ShaanXi Province, P.R.C, 710021
(v5.0 released July 2015)
In this paper, we study the problem of transfer learning with the attribute data. In the trans-
fer learning problem, we want to leverage the data of the auxiliary and the target domains
to build an effective model for the classification problem in the target domain. Meanwhile,
the attributes are naturally stable cross different domains. This strongly motives us to learn
effective domain transfer attribute representations. To this end, we proposed to embed the
ttributes of the data to a common space by using the powerful convolutional neural net-
work (CNN) model. The convolutional representations of the data points are mapped to the
orresponding attributes so that they can be effective embedding of the attributes. We also
represent the data of different domains by a domain-independent CNN, ant a domain-specific
CNN, and combine their outputs with the attribute embedding to build the classification"
a9f5acdcf1fbc9563aaad943cbe1c195b796aa62,Learning Fashion By Simulated Human Supervision,"Learning Fashion By Simulated Human Supervision
Eli Alshan Sharon Alpert Assaf Neuberger Nathaniel Bubis Eduard Oks
{alshan, alperts, neuberg, bubis,
Amazon Lab126"
a9f63dcae167630b0c6ba4131897539151217e2b,Testing a Method for Statistical Image Classification in Image Retrieval,"Testing a Method for Statistical Image
Classification in Image Retrieval
Christoph Rasche, Constantin Vertan
Laboratorul de Analiza si Prelucrarea Imaginilor
Universitatea Politehnica din Bucuresti
Bucuresti 061071, RO"
a9c120de41679fe336e2779f3e6fe4b04945cb3a,A Robust Multilinear Model Learning Framework for 3D Faces,"A Robust Multilinear Model Learning Framework for 3D Faces∗
Timo Bolkart
Stefanie Wuhrer
Saarland University, Germany
Inria Grenoble Rhˆone-Alpes, France"
a98316980b126f90514f33214dde51813693fe0d,Collaborations on YouTube: From Unsupervised Detection to the Impact on Video and Channel Popularity,"Collaborations on YouTube: From Unsupervised Detection to the
Impact on Video and Channel Popularity
Christian Koch, Moritz Lode, Denny Stohr, Amr Rizk, Ralf Steinmetz
Multimedia Communications Lab (KOM), Technische Universität Darmstadt, Germany
E-Mail: {Christian.Koch | Denny.Stohr | Amr.Rizk |"
a97f3d2313affd35c889c57f2ebe21e7ba2b5bbb,Real-Time Semantic Mapping for Autonomous Off-Road Navigation,"Real-time Semantic Mapping for Autonomous
Off-Road Navigation
Daniel Maturana, Po-Wei Chou, Masashi Uenoyama and Sebastian Scherer"
a9e1bddb0fdc862859b90d03e20b34d4cfdf4b93,Automatic Determination of Body Condition Score of Dairy Cows from 3D Images,"Automatic Determination
of Body Condition Score of Dairy
Cows from 3D Images
Processing and pattern recognition
in images from a time-of-flight camera
M A R I L Y N   K R U K O W S K I
Master of Science Thesis
Stockholm, Sweden 2009"
a921dbe7659e4a75c1ac53f5bdc1fcdc6caa5933,Title Detection based low frame rate human tracking,"Title
Detection based low frame rate human tracking
Author(s)
Wang, L; Yung, NHC
Citation
The 20th International Conference on Pattern Recognition (ICPR
010), Istanbul, Turkey, 23-26 August 2010. In Proceedings of
0th ICPR, 2010, p. 3529-3532
Issued Date
http://hdl.handle.net/10722/137721
Rights
International Conference on Pattern Recognition. Copyright ©
IEEE, Computer Society."
a9e8c08c69b8752a8afad752ec26a2b3807ede50,"PersEmoN: A Deep Network for Joint Analysis of Apparent Personality, Emotion and Their Relationship","SUBMITTED TO IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
PersEmoN: A Deep Network for Joint Analysis of
Apparent Personality, Emotion and Their
Relationship
Le Zhang, Songyou Peng, and Stefan Winkler, Fellow, IEEE"
a9d6d62f4f3f12ed565e5d75f8c4b7a202a3d809,Action and intention recognition of pedestrians in urban traffic,"Action and intention recognition of pedestrians in urban traffic
Dimitrios Varytimidis1, Fernando Alonso-Fernandez1, Boris Duran2 and Cristofer Englund1,2∗"
a9d2c96cead937e53e614abb9fd051574a55c77a,Ensembling Visual Explanations for,"In Proceedings of the NIPS 2017 workshop on Visually-Grounded Interaction and
Language (ViGIL), December 2017."
a9e53a7533c9c743b57b6668c11be0c73525f188,Enhanced Feature Sets for Face Recognition with varying Lighting Conditions and Noise,"Enhanced Feature Sets for Face Recognition with varying Lighting Conditions and Noise  ISSN  2278 – 3806
Enhanced Feature Sets for Face Recognition with
varying Lighting Conditions and Noise
Final ME (CSE), 2Head of Department of Computer Science and Engineering
S.Vishnupriya1  Dr.k.Lakshmi2
Periyar Maniammai University, Thanjavur, Tamilnadu, India."
a9e28863c7fb963b40a379c5a4e0da00eb031933,A Corpus of Natural Language for Visual Reasoning,"Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Short Papers), pages 217–223
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Short Papers), pages 217–223
Vancouver, Canada, July 30 - August 4, 2017. c(cid:13)2017 Association for Computational Linguistics
Vancouver, Canada, July 30 - August 4, 2017. c(cid:13)2017 Association for Computational Linguistics
https://doi.org/10.18653/v1/P17-2034
https://doi.org/10.18653/v1/P17-2034"
a9791544baa14520379d47afd02e2e7353df87e5,The Need for Careful Data Collection for Pattern Recognition in Digital Pathology,"Technical Note
The Need for Careful Data Collection for Pattern Recognition in
Digital Pathology
Raphaël Marée1
Department of Electrical Engineering and Computer Science, Montefiore Institute, University of Liège, 4000 Liège, Belgium
Received: 08 December 2016
Accepted: 15 March 2017
Published: 10 April 2017"
a91caf771905ddff8cb271f04e7ede1a8b6d529b,Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training,"Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via
Adversarial Training
Department of Biomedical Engineering
Department of Computer Science
Faisal Mahmood1 Richard Chen2 Nicholas J. Durr1
Johns Hopkins University (JHU)
{faisalm, rchen40,"
a91fd02ed2231ead51078e3e1f055d8be7828d02,The Robust Manifold Defense: Adversarial Training using Generative Models,"The Robust Manifold Defense:
Adversarial Training using Generative Models
Andrew Ilyas
Ajil Jalal
Eirini Asteri
MIT EECS
UT Austin
UT Austin
Constantinos Daskalakis
Alexandros G. Dimakis
MIT EECS
UT Austin
December 27, 2017
Problems worthy of attack,
prove their worth by fighting back."
a9ad8f6c6bf110485921b17f9790241b1548487c,Automatic Skin Tone Extraction for Visagism Applications,
a9adb6dcccab2d45828e11a6f152530ba8066de6,Aydınlanma Alt-uzaylarına dayalı Gürbüz Yüz Tanıma Illumination Subspaces based Robust Face Recognition,"Aydınlanma Alt-uzaylarına dayalı Gürbüz Yüz Tanıma
Illumination Subspaces based Robust Face Recognition
D. Kern, H.K. Ekenel, R. Stiefelhagen
Interactive Systems Labs, Universität Karlsruhe (TH)
76131 Karlsruhe, Almanya
web: http://isl.ira.uka.de/face_recognition
Özetçe
yönlerine
ydınlanma
kaynaklanan
sonra,  yüz  uzayı
Bu çalışmada aydınlanma alt-uzaylarına dayalı bir yüz tanıma
sistemi  sunulmuştur.  Bu  sistemde,
ilk  olarak,  baskın
ydınlanma yönleri, bir topaklandırma algoritması kullanılarak
öğrenilmiştir.  Topaklandırma  algoritması  sonucu  önden,  sağ
ve  sol  yanlardan  olmak  üzere  üç  baskın  aydınlanma  yönü
gözlemlenmiştir.  Baskın
karar
-yüzün  görünümündeki"
a93eef69847a623e0c3273bb3f1c7d86987abb6f,Ney Log-Linear Mixture Models for Patch-Based Object Recognition vorgelegt,"Diplomarbeit im Fach Informatik
Rheinisch-Westf¨alische Technische Hochschule Aachen
Lehrstuhl f¨ur Informatik 6
Prof. Dr.-Ing. H. Ney
Log-Linear Mixture Models for
Patch-Based Object Recognition
vorgelegt von:
Tobias Weyand
Matrikelnummer 238060
Gutachter:
Prof. Dr.-Ing. H. Ney
Prof. Dr. rer. nat. T. Seidl
Betreuer:
Dipl.-Inform. T. Deselaers"
a94aac3caccebd82413dd05707ef8bf525dc46b9,Evaluation of the UR 3 D algorithm using the FRGC v 2 data set,"Evaluation of the UR3D algorithm using the FRGC v2 data set
G. Passalis, I.A. Kakadiaris, T. Theoharis, G. Toderici and N. Murtuza
Visual Computing Lab, Dept. of Computer Science, Univ. of Houston, Houston, TX 77204, USA"
df9768bedaada944d283af357234a0250bc46523,An Overview of Deep Learning Based Methods for Unsupervised and Semi-Supervised Anomaly Detection in Videos,"An overview of deep learning based methods for
unsupervised and semi-supervised anomaly
detection in videos
B Ravi Kiran, Dilip Mathew Thomas, Ranjith Parakkal"
df5fe0c195eea34ddc8d80efedb25f1b9034d07d,Robust modified Active Shape Model for automatic facial landmark annotation of frontal faces,"Robust Modified Active Shape Model for Automatic Facial Landmark
Annotation of Frontal Faces
Keshav Seshadri and Marios Savvides"
dfa53c8a4d66ab6219d6f00219ca7ed3909832fd,Probabilistic Neural Network with Complex Exponential Activation Functions in Image Recognition using Deep Learning Framework,"Probabilistic Neural Network with Complex
Exponential Activation Functions in Image
Recognition using Deep Learning Framework
Andrey V. Savchenko
these  drawbacks  by  replacing"
dfecaedeaf618041a5498cd3f0942c15302e75c3,A recursive framework for expression recognition: from web images to deep models to game dataset,"Noname manuscript No.
(will be inserted by the editor)
A Recursive Framework for Expression Recognition: From
Web Images to Deep Models to Game Dataset
Wei Li · Christina Tsangouri · Farnaz Abtahi · Zhigang Zhu
Received: date / Accepted: date"
df310591dfba9672252d693bc87da73c246749c9,Fusion of Holistic and Part Based Features for Gender Classification in the Wild,"Fusion of Holistic and Part Based Features
for Gender Classification in the Wild
Modesto Castrill´on-Santana(B), Javier Lorenzo-Navarro,
nd Enrique Ram´on-Balmaseda
Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
http://berlioz.dis.ulpgc.es/roc-siani"
dfc784c860795f4f9aa704b7655f6d1321018980,Unsupervised Co-Activity Detection from Multiple Videos Using Absorbing Markov Chain,"Unsupervised Co-activity Detection from
Multiple Videos using Absorbing Markov Chain
Donghun Yeo, Bohyung Han, Joon Hee Han
Department of Computer Science and Engineering, POSTECH, Korea"
dfcae2db863381ea8047f78f65b25146e696115a,Deep Learning Applications for Autonomous Driving,"Thesis for the degree of Licentiate of Engineering
Deep Learning Applications
for Autonomous Driving
LUCA CALTAGIRONE
Department of Mechanics and Maritime Sciences
CHALMERS UNIVERSITY OF TECHNOLOGY
G¨oteborg, Sweden 2018"
df06d165b107318e270e59f15fdfe7792a49baaa,Seeing the Wood for the Trees: Reliable Localization in Urban and Natural Environments,"Seeing the Wood for the Trees: Reliable Localization
in Urban and Natural Environments
Georgi Tinchev, Simona Nobili and Maurice Fallon"
dff612c198dc50a7bef5a9cd48da5da1f893fa72,A fast stereo-based multi-person tracking using an approximated likelihood map for overlapping silhouette templates,"A Fast Stereo-Based Multi-Person Tracking
using an Approximated Likelihood Map
for Overlapping Silhouette Templates
Junji Satake
Jun Miura
Department of Computer Science and Engineering
Toyohashi University of Technology
Email: {satake,
Toyohashi, Japan"
df999184b1bb5691cd260b2b77df7ef00c0fe7b1,On Latent Distributions Without Finite Mean in Generative Models,"On Latent Distributions Without Finite Mean in
Generative Models
Damian Le´sniak∗
Igor Sieradzki∗
Jagiellonian University
Igor Podolak"
dfbc3a6a629433f24f4e06fdfe8389f83afa7094,Learning OpenCV,"Learning OpenCV
Gary Bradski and Adrian Kaehler
Beijing  ·  Cambridge  ·  Farnham  ·  Köln  ·  Sebastopol  ·  Taipei  ·  Tokyo"
dfe7700ed053d4788ecea4a18431806581e03291,Grammatical facial expression recognition using customized deep neural network architecture,"Grammatical facial expression recognition using customized
deep neural network architecture
Devesh Walawalkar"
df8aee8aef6f0c71f968979318dafcd53da04bdc,Bending the Curve: Improving the ROC Curve Through Error Redistribution,"Bending the Curve: Improving the ROC Curve Through Error
Redistribution
Oran Richman
Department of Electrical Engineering Technion Haifa, Israel
Shie Mannor
Department of Electrical Engineering Technion Haifa, Israel
September 21, 2018"
dffb64ac066bbcfe6aea6b11408b5ea62a40e9fb,"A New Face Recognition Scheme for Faces with Expressions , Glasses and Rotation","International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING &
ISSN 0976 - 6375(Online), Volume 5, Issue 4, April (2014), pp. 11-23 © IAEME
TECHNOLOGY (IJCET)
ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 5, Issue 4, April (2014), pp. 11-23
© IAEME: www.iaeme.com/ijcet.asp
Journal Impact Factor (2014): 8.5328 (Calculated by GISI)
www.jifactor.com
IJCET
© I A E M E
A NEW FACE RECOGNITION SCHEME FOR FACES WITH EXPRESSIONS,
GLASSES AND ROTATION
Walaa M Abdel-Hafiez1,    Mohamed Heshmat2,    Moheb Girgis3,    Seham Elaw4
, 2, 4Faculty of Science, Mathematical and Computer Science Department,
Sohag University, 82524, Sohag, Egypt
3Faculty of Science, Department of Computer Science,
Minia University, El-Minia, Egypt"
df50e6e2ad60825167c6b3e641eb5cda0f3dc505,Theoretical vs. empirical discriminability: the application of ROC methods to eyewitness identification,"Wixted and Mickes Cognitive Research: Principles and Implications  (2018) 3:9
https://doi.org/10.1186/s41235-018-0093-8
Cognitive Research: Principles
nd Implications
TU T O R I A L R E V I EW
Theoretical vs. empirical discriminability:
the application of ROC methods to
eyewitness identification
John T. Wixted1* and Laura Mickes2
Open Access"
df4525d7d99f7237c864adbcb2dab30d8f7447e0,Kernel Cross-View Collaborative Representation based Classification for Person Re-Identification,"Kernel Cross-View Collaborative Representation based Classification for Person
Re-Identification
Raphael Prates and William Robson Schwartz
Universidade Federal de Minas Gerais, Brazil
6627, Av. Pres. Antˆonio Carlos - Pampulha, Belo Horizonte - MG, 31270-901"
df5094b2e8cf7e3bde3943ca7a56eb879b8e34ab,A Concatenated Residual Convolutional Network for Image Deblurring,"A Concatenated Residual Convolutional Network for Image Deblurring
Li Si-Yao1, Dongwei Ren2, Zijian Hu1, Junfeng Li1, Qian Yin1*, Ping Guo1,3
Beijing Normal University 2 Tianjin University 3 Beijing Institute of Technology
{rendongweihit,
{lijunfeng,"
df674dc0fc813c2a6d539e892bfc74f9a761fbc8,An Image Mining System for Gender Classification & Age Prediction Based on Facial Features,"IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 10, Issue 6 (May. - Jun. 2013), PP 21-29
www.iosrjournals.org
An Image Mining System for Gender Classification & Age
Prediction Based on Facial Features
1.Ms.Dhanashri Shirkey  , 2Prof.Dr.S.R.Gupta,
M.E(Scholar),Department Computer Science & Engineering, PRMIT & R, Badnera
Asstt.Prof. Department Computer Science & Engineering, PRMIT & R, Badnera"
df28cd627afe6d20eb198b8406ff25ece340653d,The Acquisition of Sign Language by Deaf Children with Autism Spectrum Disorder,"The Acquisition of Sign
Language by Deaf Children
with Autism Spectrum
Disorder
Aaron Shield and Richard P. Meier
Introduction
Autism spectrum disorder (ASD) consists of a set of neurobiological
developmental disorders characterized by communicative and social deficits
s well as repetitive, stereotyped behaviors.1 In this chapter, we use the
terms ‘ASD’ and ‘autism’ interchangeably; although ‘autism’ is not a clinical
term, it is the term popularly used to refer to the range of disorders found
in ASD.
The language deficits of hearing children with autism are well docu-
mented, and can range from the very mild in highly fluent speakers to the
very severe in children with a total absence of productive spoken language.
For those children who do acquire speech, the most common  characteristics
of autistic language include echolalia (echoing the utterances of others),
pronoun reversal, idiosyncratic language use and neologisms (the creation
of new words), difficulty with pragmatics (problems interpreting the use
of language in context and the non-literal use of language), and abnormal"
dfbf49ed66a9e48671964872c84f75d7f916c131,Supplementary Material for Sparsity Invariant CNNs,"Supplementary Material for
Sparsity Invariant CNNs
Jonas Uhrig(cid:63),1,2 Nick Schneider(cid:63),1,3
Lukas Schneider1,4
Uwe Franke1
Thomas Brox2 Andreas Geiger4,5
Daimler R&D Sindelfingen
University of Freiburg
KIT Karlsruhe
ETH Z¨urich
5MPI T¨ubingen
. Convergence Analysis
We find that Sparse Convolutions converge much faster than standard convolutions for most input-output-combinations,
especially for those on Synthia with irregularly sparse depth input, as considered in Section 5.1 of the main paper. In Figure
, we show the mean average error in meters on our validation subset of Synthia over the process of training with identical
solver settings (Adam with momentum terms of β1 = 0.9, β2 = 0.999 and delta 1e−8). We chose for each variant the
maximal learning rate which still causes the network to converge (which turned out to be 1e−3 for all three variants). We
find that Sparse Convolutions indeed train much faster and much smoother compared to both ConvNet variants, most likely
aused by the explicit ignoring of invalid regions in the update step. Interestingly, the ConvNet variant with concatenated
visibility mask in the input converges smoother than the variant with only sparse depth in the input, however, additionally"
df90850f1c153bfab691b985bfe536a5544e438b,"Face Tracking Algorithm Robust to Pose , Illumination and Face Expression Changes : a 3 D Parametric Model Approach","FACE TRACKING ALGORITHM ROBUST TO POSE,
ILLUMINATION AND FACE EXPRESSION CHANGES: A 3D
PARAMETRIC MODEL APPROACH
Marco Anisetti, Valerio Bellandi
University of Milan - Department of Information Technology
via Bramante 65 - 26013, Crema (CR), Italy
Luigi Arnone, Fabrizio Beverina
STMicroelectronics - Advanced System Technology Group
via Olivetti 5 - 20041, Agrate Brianza, Italy
Keywords:
Face tracking, expression changes, FACS, illumination changes."
dfe2d36ca249876e5ab5500f155e3a5094dbc170,Application of common sense computing for the development of a novel knowledge-based opinion mining engine,"Application of Common Sense Computing for
the Development of a Novel Knowledge-Based
Opinion Mining Engine
A thesis submitted in accordance with the requirements of
the University of Stirling for the degree of Doctor of Philosophy
Erik Cambria
Principal Supervisor: Amir Hussain (University of Stirling, UK)
Additional Supervisor: Catherine Havasi (MIT Media Laboratory, USA)
Industrial Supervisor: Chris Eckl (Sitekit Solutions Ltd, UK)
Department of Computing Science & Mathematics
University of Stirling, Scotland, UK
December 2011"
dfa80e52b0489bc2585339ad3351626dee1a8395,Human Action Forecasting by Learning Task Grammars,"Human Action Forecasting by Learning Task Grammars
Tengda Han
Jue Wang
Anoop Cherian
Stephen Gould"
dfcb4773543ee6fbc7d5319b646e0d6168ffa116,Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks,"Unifying Variational Autoencoders and Generative Adversarial Networks
Adversarial Variational Bayes:
Lars Mescheder 1
Sebastian Nowozin 2
Andreas Geiger 1 3"
df353e3a46cca8c1ef274994f5a6dcb580231726,Data-driven fundamental models for pedestrian movements THÈSE,"POUR L'OBTENTION DU GRADE DE DOCTEUR ÈS SCIENCESacceptée sur proposition du jury:Prof. P. Frossard, président du juryProf. M. Bierlaire, directeur de thèseProf. H. Mahmassani, rapporteurProf. S. Hoogendoorn, rapporteurProf. N. Geroliminis, rapporteurData-driven fundamental models for pedestrian movementsTHÈSE NO 7613 (2017)ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNEPRÉSENTÉE LE 5 MAI 2017À LA FACULTÉ DE L'ENVIRONNEMENT NATUREL, ARCHITECTURAL ET CONSTRUITLABORATOIRE TRANSPORT ET MOBILITÉPROGRAMME DOCTORAL EN GÉNIE ÉLECTRIQUE Suisse2017PARMarija NIKOLIĆ"
df577a89830be69c1bfb196e925df3055cafc0ed,"Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions","Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions
Bichen Wu, Alvin Wan∗, Xiangyu Yue∗, Peter Jin, Sicheng Zhao,
Noah Golmant, Amir Gholaminejad, Joseph Gonzalez, Kurt Keutzer
UC Berkeley"
df372ccb1b3220f877e0db8c21c603e57897bfce,Human Detection Based on a Probabilistic Assembly of Robust Part Detectors,"Some Interesting Papers at ECCV 2004
Tim Cootes∗
June 11, 2004
The European Conference on Computer Vision took place in Prague 11-15 May 2004. There were 41 presentations
nd about 149 posters, selected from 550 submissions.
This document summarises a few that caught my attention. There are probably quite a few very interesting or
worthy papers that I’ve failed to include, due to my failing memory and lack of judgement.
Simultaneous Object Recognition and Segmentation by Image Exploration
V. Ferraria and T. Tuytelaars and L. Van Gool [7]
• Extends part based object detection to find heavily occluded things in cluttered scenes
• Looks for matches of af‌f‌ine invariant patches on model and image
• Takes each putative match and attempts to extend it, the idea being that if one part of the object matches
t a particular point in the image, then nearby object structure should be found at a predictable position.
• The additional matches so generated are then pruned using consistancy constraints
• Iterating gradually extends matches over most of visible object area, and suppresses most of false matches
• Impressive results, demonstrating system works even with considerable clutter and occlusion
Recognition by Probabalistic Hypothesis Construction
P. Moreels and M. Maire and P. Perona [13]
• Extension of Perona’s work, simplifying model.
Human Detection Based on a Probabalistic Assembly of Robust Part Detectors"
dfd18b71f5c53ec2a95fcbe327cf7710da3b4851,Robust Submodular Maximization: A Non-Uniform Partitioning Approach,"Robust Submodular Maximization:
A Non-Uniform Partitioning Approach
Ilija Bogunovic 1 Slobodan Mitrovi´c 2 Jonathan Scarlett 1 Volkan Cevher 1"
df3b560a5d6c8cc5fa1477d3a89245a0d3b60715,Human tracking with multiple parallel metrics,"Human tracking with multiple parallel metrics
P. M. Birch*, W. Hassan, R. C. D. Young, C.R. Chatwin
Dept. of Engineering and Design, University of Sussex, Falmer, UK, BN1 9QT
Keywords: HOG, Correlation, Tracking"
df3178e77fa6655bbcae65c00b732ef240d99fa5,SOFT-SLAM : Computationally Efficient Stereo Visual SLAM for Autonomous UAVs,"SOFT-SLAM: Computationally Ef‌f‌icient Stereo Visual
SLAM for Autonomous UAVs
Igor Cviˇsi´c
University of Zagreb
Josip ´Cesi´c
University of Zagreb
Faculty of Electrical Engineering and Computing
Faculty of Electrical Engineering and Computing
HR-10000, Zagreb, Croatia
HR-10000, Zagreb, Croatia
Ivan Markovi´c
University of Zagreb
Ivan Petrovi´c
University of Zagreb
Faculty of Electrical Engineering and Computing
Faculty of Electrical Engineering and Computing
HR-10000, Zagreb, Croatia
HR-10000, Zagreb, Croatia"
ca48c87bbba1a07cf126cf3bb04a53d1758d66f5,Semantic Segmentation of Highly Structured and Weakly Structured Images. (Segmentation sémantique d'images fortement structurées et faiblement structurées),"Semantic Segmentation of Highly Structured and
Weakly Structured Images
Raghu Deep Gadde
To cite this version:
Raghu Deep Gadde. Semantic Segmentation of Highly Structured and Weakly Structured Images.
Signal and Image Processing. Université Paris-Est, 2017. English. <NNT : 2017PESC1083>. <tel-
01743925>
HAL Id: tel-01743925
https://pastel.archives-ouvertes.fr/tel-01743925
Submitted on 26 Mar 2018
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
cace0a45de20f12df44e4aa07c07ccdd0278b787,Object segmentation in depth maps with one user click and a synthetically trained fully convolutional network,"Object segmentation in depth maps
with one user click and a synthetically
trained fully convolutional network
Matthieu Grard1,2, Romain Br´egier1,3,
Florian Sella1, Emmanuel Dellandr´ea2, and Liming Chen2
Sil´eane, 17 rue Descartes, F-42000 St ´Etienne, France
Universit´e de Lyon, CNRS, ´Ecole Centrale de Lyon
LIRIS UMR5205, F-69134 Lyon, France
Univ. Grenoble Alpes, Inria, CNRS
Grenoble INP, LIG, F-38000 Grenoble, France"
ca400e0c7a739ce5555b2e3eccccbcea65e71b11,Neural Mechanisms of Emotion Regulation in Autism Spectrum Disorder.,"J Autism Dev Disord
DOI 10.1007/s10803-015-2359-z
S I : E M O T I O N R E G U L A T I O N A N D P S Y C H I A T R I C C O M O R B I D I T Y I N A S D
Neural Mechanisms of Emotion Regulation in Autism Spectrum
Disorder
J. Anthony Richey • Cara R. Damiano • Antoinette Sabatino • Alison Rittenberg •
Chris Petty • Josh Bizzell • James Voyvodic • Aaron S. Heller • Marika C. Coffman •
Moria Smoski • Richard J. Davidson • Gabriel S. Dichter
Ó Springer Science+Business Media New York 2015
ccount of"
ca37eda56b9ee53610c66951ee7ca66a35d0a846,Semantic Concept Discovery for Large-Scale Zero-Shot Event Detection,"Semantic Concept Discovery for Large-Scale Zero-Shot Event Detection
Xiaojun Chang1,2, Yi Yang1, Alexander G. Hauptmann2, Eric P. Xing3 and Yao-Liang Yu3∗
Centre for Quantum Computation and Intelligent Systems, University of Technology Sydney.
Language Technologies Institute, Carnegie Mellon University.
Machine Learning Department, Carnegie Mellon University.
{cxj273, {alex, epxing,"
ca581cd5bd0cecf346f2bc47f4b67bfee31b9da1,"Providing Fairness in Heterogeneous Multicores with a Predictive, Adaptive Scheduler","Providing Fairness in Heterogeneous Multicores with a Predictive, Adaptive
Scheduler
Saeid Barati
University of Chicago
Henry Hoffmann
University of Chicago"
cad7845e9668884caf4842b14983ec0e45bbbc75,Urban Tracker: Multiple object tracking in urban mixed traffic,"Urban Tracker: Multiple Object Tracking in Urban Mixed Traffic
Jean-Philippe Jodoin, Guillaume-Alexandre Bilodeau
LITIV lab., Dept. of computer & software eng.
´Ecole Polytechnique de Montr´eal
Montr´eal, QC, Canada
Nicolas Saunier
Dept. of civil, geo. and mining eng.
´Ecole Polytechnique de Montr´eal
Montr´eal, QC, Canada"
cacd51221c592012bf2d9e4894178c1c1fa307ca,Face and Expression Recognition Techniques : A Review,"ISSN: 2277-3754
ISO 9001:2008 Certified
International Journal of Engineering and Innovative Technology (IJEIT)
Volume 4, Issue 11, May 2015
Face and Expression Recognition Techniques: A
Review
Advanced Communication & Signal Processing Laboratory, Department of Electronics & Communication
engineering, Government College of Engineering Kannur, Kerala, India.
Rishin C. K, Aswani Pookkudi, A. Ranjith Ram"
cae87d5a724507e06f6d8178cfbec043db854fe3,Bayesian Nonparametric Latent Feature Models,"Bayesian Nonparametric Latent Feature Models
Kurt Miller
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2011-78
http://www.eecs.berkeley.edu/Pubs/TechRpts/2011/EECS-2011-78.html
June 28, 2011"
cad24ba99c7b6834faf6f5be820dd65f1a755b29,"Understanding hand-object manipulation by modeling the contextual relationship between actions, grasp types and object attributes","Understanding hand-object
manipulation by modeling the
ontextual relationship between actions,
grasp types and object attributes
Minjie Cai1, Kris M. Kitani2 and Yoichi Sato1
Journal Title
XX(X):1–14
(cid:13)The Author(s) 2016
Reprints and permission:
sagepub.co.uk/journalsPermissions.nav
DOI: 10.1177/ToBeAssigned
www.sagepub.com/"
cae68d5af3c3c6167aa9b2247ca27efe75992ccd,Face recognition: handling data misalignments implicitly by fusion of sparse representations,"Received on 10th February 2014
Revised on 8th May 2014
Accepted on 10th June 2014
doi: 10.1049/iet-cvi.2014.0039
www.ietdl.org
ISSN 1751-9632
Face recognition: handling data misalignments
implicitly by fusion of sparse representations
Hugo Proença, João Neves, Juan Briceño
Department of Computer Science, Instituto de Telecomunicações, University of Beira Interior, 6200 Covilhã, Portugal
E-mail:"
ca0363d29e790f80f924cedaf93cb42308365b3d,Facial Expression Recognition in Image Sequences Using Geometric Deformation Features and Support Vector Machines,"Facial Expression Recognition in Image Sequences
using Geometric Deformation Features and Support
Vector Machines
Irene Kotsiay and Ioannis Pitasy,Senior Member IEEE
yAristotle University of Thessaloniki
Department of Informatics
Box 451
54124 Thessaloniki, Greece
email:"
cad52d74c1a21043f851ae14c924ac689e197d1f,From Ego to Nos-Vision: Detecting Social Relationships in First-Person Views,"From Ego to Nos-vision:
Detecting Social Relationships in First-Person Views
Stefano Alletto, Giuseppe Serra, Simone Calderara, Francesco Solera and Rita Cucchiara
Universit`a degli Studi di Modena e Reggio Emilia
Via Vignolese 905, 41125 Modena - Italy"
ca7be47d26abdc6fd3f229e9827484e91dbd7752,ADAPTED FUSION SCHEMES FOR MULTIMODAL BIOMETRIC AUTHENTICATION,"UNIVERSIDAD POLIT´ECNICA DE MADRID
ESCUELA T´ECNICA SUPERIOR DE INGENIEROS DE TELECOMUNICACI ´ON
DEPARTAMENTO DE SE ˜NALES, SISTEMAS Y RADIOCOMUNICACIONES
ADAPTED FUSION SCHEMES
FOR MULTIMODAL BIOMETRIC
AUTHENTICATION
–TESIS DOCTORAL–
ESQUEMAS ADAPTADOS DE FUSI ´ON PARA
AUTENTICACI ´ON BIOM ´ETRICA MULTIMODAL
Author: Juli´an Fi´errez Aguilar
(Ingeniero de Telecomunicaci´on, UPM)
A Thesis submitted for the degree of:
Doctor of Philosophy
Madrid, May 2006"
ca228b42de0f0311f4826292a2996d3348ca43fe,Modeling and Inferring Human Intents and Latent Functional Objects for Trajectory Prediction,"FOR REVIEW: IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Modeling and Inferring Human Intents and Latent
Functional Objects for Trajectory Prediction
Dan Xie, Tianmin Shu, Sinisa Todorovic and Song-Chun Zhu"
ca754b826476b3e4083a0a6fbac3ac39b494fd43,Supporting data-driven I/O on GPUs using GPUfs,"Supporting data-driven I/O on GPUs using GPUfs
Sagi Shahar
Mark Silberstein
Technion - Israel Institute of Technology
Technion - Israel Institute of Technology
Computations on large data sets necessarily involve file
ccesses, but current GPUs cannot access a host file system
directly because they lack file system access support. There-
fore, an application developer needs to coordinate GPU ac-
esses to secondary storage via explicit application-level
management code running on a CPU. This code performs
file accesses on GPU’s behalf and manages low level data
transfers to/from GPU memory. Furthermore, all the data
that a GPU may need must be resident in the GPU mem-
ory prior to computations, and it is the responsibility of a
GPU developer to ensure that this is the case. As a result, all
the potential GPU accesses to data must be known before the
GPU execution starts. This requirement impedes the use of
GPUs to run data processing algorithms with irregular data
ccess pattern on large datasets."
cabe652bb3b150f35db9db1434cec69f081c4a60,Towards Scene Understanding : Deep and Layered Recognition and Heuristic Parsing of Objects,"Towards Scene Understanding: Deep and Layered Recognition
nd Heuristic Parsing of Objects
Dissertation Submitted to
Xi’an Jiaotong University
In partial fulfillment of the requirement
for the degree of
Doctor of Engineering Science
Yang Wu
(Control Science and Engineering)
Supervisor: Prof. Nanning Zheng
May 2010"
ca6b2b75db9ff8444744df9149601a4ef2beefd4,MirBot: A Multimodal Interactive Image Retrieval System,"MirBot: A multimodal interactive
image retrieval system
Antonio Pertusa, Antonio-Javier Gallego, and Marisa Bernabeu
DLSI, University of Alicante
http://www.dlsi.ua.es"
cac3bf3ceba79e6a6c8e51eb44c6862b81661f85,Learning Data-Driven Representations for Robust Monocular Computer Vision Applications,"Learning Data-Driven Representations for
Robust Monocular Computer Vision
Applications
Dissertation
der Mathematisch-Naturwissenschaftlichen Fakultät
der Eberhard Karls Universität Tübingen
zur Erlangung des Grades eines
Doktors der Naturwissenschaften
(Dr. rer.-nat.)
Dipl.-math. Christian Joachim Herdtweck
vorgelegt von
us Stuttgart
Tübingen"
ca1accedbc1c698c03e9835d9f960c40e9d3ef51,Implementing a Cloud Platform for Autonomous Driving,"Implementing a Cloud Platform for Autonomous Driving
Shaoshan Liu, Jie Tang, Chao Wang, Quan Wang, and Jean-Luc Gaudiot, Fellow, IEEE
unified
driving
support
utonomous
infrastructure"
ca494a2f20c267210a677ed9c509c4570f420fdf,Learning to Globally Edit Images with Textual Description,"Learning to Globally Edit Images
with Textual Description
Hai Wang † Jason D. Williams ‡ Sing Bing Kang §"
ca8dc160836a87055579ea9bc4fc379a95f3156a,Face Recognition Based on Nonlinear DCT Discriminant Feature Extraction Using Improved Kernel DCV,"IEICE TRANS. INF. & SYST., VOL.E92–D, NO.12 DECEMBER 2009
LETTER
Face Recognition Based on Nonlinear DCT Discriminant Feature
Extraction Using Improved Kernel DCV
Sheng LI†, Student Member, Yong-fang YAO†, Xiao-yuan JING†a), Heng CHANG†, Shi-qiang GAO†,
David ZHANG††, and Jing-yu YANG†††, Nonmembers
SUMMARY
This letter proposes a nonlinear DCT discriminant feature
extraction approach for face recognition. The proposed approach first se-
lects appropriate DCT frequency bands according to their levels of nonlin-
ear discrimination. Then, this approach extracts nonlinear discriminant fea-
tures from the selected DCT bands by presenting a new kernel discriminant
method, i.e. the improved kernel discriminative common vector (KDCV)
method. Experiments on the public FERET database show that this new
pproach is more effective than several related methods.
key words: DCT frequency bands selection, the improved KDCV, nonlinear
DCT feature extraction, face recognition
Introduction
Discrete cosine transform (DCT) is a widely used im-
ge processing technique [1], and discriminant analysis is"
214db8a5872f7be48cdb8876e0233efecdcb6061,Semantic-Aware Co-Indexing for Image Retrieval,"Semantic-aware Co-indexing for Image Retrieval
Shiliang Zhang2, Ming Yang1, Xiaoyu Wang1, Yuanqing Lin1, Qi Tian2
NEC Laboratories America, Inc.
Dept. of CS, Univ. of Texas at San Antonio
Cupertino, CA 95014
San Antonio, TX 78249"
21f2f1693fdb6478a8f3306f377f1ce7df6e036e,3D Face Recognition,"Chapter 1
D Face Recognition
Berk G¨okberk, Albert Ali Salah, Ne¸se Aly¨uz, Lale Akarun
.1 Introduction
Face is the natural assertion of identity: We show our face as proof of who we
re. Due to this widely accepted cultural convention, face is the most widely
ccepted biometric modality.
Face recognition has been a specialty of human vision: Something humans
re so good at that even a days-old baby can track and recognize faces.
Computer vision has long strived to imitate the success of human vision and in
most cases, has come nowhere near its performance. However, the recent Face
Recognition Vendor Test (FRVT06), has shown that automatic algorithms
have caught up with the performance of humans in face recognition [73].
How has this increase in performance come about? This can partly be
ttributed to the advances in 3D face recognition in the last decade. 3D face
recognition has important advantages over 2D; it makes use of shape and
texture channels simultaneously, where the texture channel carries 2D image
information. However, it is registered with the shape channel, and intensity
an now be associated with shape attributes such as the surface normal.
The shape channel does not suffer from certain problems that the texture"
21b0b2f5df87318912d58d3b843da363a4fb91c3,"Distributed and Higher-Order Graphical Models: towards Segmentation, Tracking, Matching and 3D Model Inference Defended by","ECOLECENTRALEPARISPHDTHESIStoobtainthetitleofDoctorofEcoleCentraleParisSpecialty:APPLIEDMATHEMATICSDistributedandHigher-OrderGraphicalModels:towardsSegmentation,Tracking,Matchingand3DModelInferenceDefendedbyChaohuiWANGpreparedatEcoleCentraleParis,MASlaboratorydefendedonSeptember29,2011JURYChairman:Prof.HenriMAITRE-TélécomParisTechReviewers:Prof.MichaelJ.BLACK-MaxPlanckInstituteforIntelligentSystemsProf.PhilipH.S.TORR-OxfordBrookesUniversityAdvisor:Prof.NikosPARAGIOS-EcoleCentraleParisExaminers:Prof.PatrickBOUTHEMY-INRIA-RennesProf.VladimirKOLMOGOROV-InstituteofScienceandTechnologyAustriaProf.DimitrisSAMARAS-StonyBrookUniversity"
21ac5d1c34675bf6056d2670f9fa3dde530b1716,ALB at SemEval-2018 Task 10: A System for Capturing Discriminative Attributes,"Proceedings of the 12th International Workshop on Semantic Evaluation (SemEval-2018), pages 963–967
New Orleans, Louisiana, June 5–6, 2018. ©2018 Association for Computational Linguistics"
218595e1979007ccd6b1bc5a30a3484841c0eafa,Discovering Beautiful Attributes for Aesthetic Image Analysis,"Noname manuscript No.
(will be inserted by the editor)
Discovering beautiful attributes for aesthetic image analysis
Luca Marchesotti · Naila Murray · Florent Perronnin
Received: date / Accepted: date"
21262e01039e5994114b4c102fc80e9afa3f1bde,Pedestrian Detection and Tracking in Thermal Images from Aerial MPEG Videos,
2118b1ce0c2551e75d30fb6ba24482e50b319a90,Ensemble Projection for Semi-supervised Image Classification,"Ensemble Projection for Semi-supervised Image Classification
Dengxin Dai
Computer Vision Lab, ETH Zurich
Luc Van Gool
Computer Vision Lab, ETH Zurich"
21ef129c063bad970b309a24a6a18cbcdfb3aff5,Individual and Inter-related Action Unit Detection in Videos for Affect Recognition,"POUR L'OBTENTION DU GRADE DE DOCTEUR ÈS SCIENCESacceptée sur proposition du jury:Dr J.-M. Vesin, président du juryProf. J.-Ph. Thiran, Prof. D. Sander, directeurs de thèseProf. M. F. Valstar, rapporteurProf. H. K. Ekenel, rapporteurDr S. Marcel, rapporteurIndividual and Inter-related Action Unit Detection in Videos for Affect RecognitionTHÈSE NO 6837 (2016)ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNEPRÉSENTÉE LE 19 FÉVRIER 2016À LA FACULTÉ DES SCIENCES ET TECHNIQUES DE L'INGÉNIEURLABORATOIRE DE TRAITEMENT DES SIGNAUX 5PROGRAMME DOCTORAL EN GÉNIE ÉLECTRIQUE Suisse2016PARAnıl YÜCE"
21d7130230162af2a4cc1b9375bfe9b37dbbd499,Origami: A 803-GOp/s/W Convolutional Network Accelerator,"ARXIV PREPRINT
Origami: A 803 GOp/s/W
Convolutional Network Accelerator
Lukas Cavigelli, Student Member, IEEE, and Luca Benini, Fellow, IEEE
these algorithms is essential"
218b2c5c9d011eb4432be4728b54e39f366354c1,Enhancing Training Collections for Image Annotation: An Instance-Weighted Mixture Modeling Approach,"Enhancing Training Collections for Image
Annotation: An Instance-Weighted Mixture
Modeling Approach
Neela Sawant, Student Member, IEEE, James Z. Wang, Senior Member, IEEE, Jia Li, Senior Member, IEEE."
21a9f713a664374bfdcca6f4c8f267b85e63ad7a,Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions,"FINDING STRUCTURE WITH RANDOMNESS:
PROBABILISTIC ALGORITHMS FOR CONSTRUCTING
APPROXIMATE MATRIX DECOMPOSITIONS
N. HALKO
, P. G. MARTINSSON
, AND J. A. TROPP"
21a1654b856cf0c64e60e58258669b374cb05539,"You Only Look Once: Unified, Real-Time Object Detection","You Only Look Once:
Unified, Real-Time Object Detection
Joseph Redmon∗, Santosh Divvala∗†, Ross Girshick¶, Ali Farhadi∗†
University of Washington∗, Allen Institute for AI†, Facebook AI Research¶
http://pjreddie.com/yolo/"
2170636d5d31eb461618b5da10f4473c67e74e73,Person Re-identification by Multi-Channel Parts-Based CNN with Improved Triplet Loss Function,"Person Re-Identification by Multi-Channel Parts-Based CNN with Improved
Triplet Loss Function
De Cheng, Yihong Gong, Sanping Zhou, Jinjun Wang, Nanning Zheng
Institute of Artificial Intelligence and Robotics
Xi’an Jiaotong University,Xi’an, Shaanxi, P.R. China"
2198a3d3342442d6ed6608f2e2b0687f644b67d6,Dynamic High Resolution Deformable Articulated Tracking,"Dynamic High Resolution
Deformable Articulated Tracking
Aaron Walsman
Weilin Wan
Tanner Schmidt
Dieter Fox
Paul G. Allen School of Computer Science and Engineering University of Washington"
21967faefa55857c6a09f9fe52a10a394757d59c,Emotion Recognition Ability Test Using JACFEE Photos: A Validity/Reliability Study of a War Veterans' Sample and Their Offspring,"RESEARCH ARTICLE
Emotion Recognition Ability Test Using
JACFEE Photos: A Validity/Reliability Study of
War Veterans' Sample and Their Offspring
Ivone Castro-Vale1,5*, Milton Severo2,3, Davide Carvalho4,5, Rui Mota-Cardoso1
Medical Psychology Unit, Department of Clinical Neurosciences and Mental Health, Faculty of Medicine,
University of Porto, Porto, Portugal, 2 Department of Clinical Epidemiology, Predictive Medicine and Public
Health, Faculty of Medicine, University of Porto, Porto, Portugal, 3 Department of Medical Education and
Simulation, Faculty of Medicine, University of Porto, Porto, Portugal, 4 Department of Endocrinology,
Diabetes and Metabolism, Centro Hospitalar Sāo Joāo, Faculty of Medicine, University of Porto, Porto,
Portugal, 5 Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal
11111"
21241d07840e3cc30feda59642571a9b459c817b,Biometrics via Oculomotor Plant Characteristics: Impact of Parameters in Oculomotor Plant Model,"This is a pre-print. Final version of the paper will be available at ACM digital library.
Biometrics via Oculomotor Plant Characteristics:
Impact of Parameters in Oculomotor Plant Model
OLEG KOMOGORTSEV, COREY HOLLAND, ALEX KARPOV, AND LARRY R. PRICE Texas State University
This  paper  proposes  and  evaluates  a  novel  biometric  approach  utilizing  the  internal,  non-visible,  anatomical  structure  of  the  human  eye.  The
proposed method estimates the anatomical properties of the human oculomotor plant from the measurable properties of human eye movements,
utilizing  a  two-dimensional  linear  homeomorphic  model  of  the  oculomotor  plant.  The  derived  properties  are  evaluated  within  a  biometric
framework to determine their efficacy in both verification and identification scenarios. The results suggest that the physical properties derived from
the oculomotor plant model are capable of achieving 20.3% equal error rate and 65.7% rank-1 identification rate on high-resolution equipment
involving 32 subjects, with biometric samples taken over four recording sessions; or 22.2% equal error rate and 12.6% rank-1 identification rate on
low-resolution equipment involving 172 subjects, with biometric samples taken over two recording sessions.
Categories  and  Subject  Descriptors:  I.2.10  [Artificial  Intelligence]:  Vision  and  Scene  Understanding—Modeling  and  recovery  of  physical
ttributes; I.5.1 [Pattern Recognition]: Models—Structural; I.6.4 [Simulation and Modeling]: Model Validation and Analysis
General Terms: Biometrics
Additional Key Words and Phrases: Human oculomotor system, biological system modeling, mathematical model, security and protection.
ACM Reference Format:
Komogortsev,  O.,  Holland,  C.,  Karpov,  A.,  and  Price,  L.  R.  2014.  Oculomotor  Plant  Characteristics:  Biometric  Performance  Evaluation.  ACM
Trans. Appl. Percept. 2, 3, Article 1 (May 2014), 13 pages.
DOI:http://dx.doi.org/10.1145/0000000.0000000
INTRODUCTION"
210cc7fb206355a795b1c50ac8d17445e4546747,Nonparametric Belief Propagation and Facial Appearance Estimation,"m a s s a c h u s e t t s   i n s t i t u t e   o f
t e c h n o l o g y   — a r t i f i c i a l   i n t e l l i g e n c e   l a b o r a t o r y
Nonparametric Belief
Propagation and Facial
Appearance Estimation
Erik B. Sudderth, Alexander T. Ihler,
William T. Freeman and Alan S. Willsky
AI Memo 2002-020
December 2002
© 2 0 0 2   m a s s a c h u s e t t s   i n s t i t u t e   o f
t e c h n o l o g y, c a m b r i d g e , m a   0 2 1 3 9   u s a   —   w w w. a i . m i t . e d u"
213a579af9e4f57f071b884aa872651372b661fd,Automatic and Efficient Human Pose Estimation for Sign Language Videos,"Int J Comput Vis
DOI 10.1007/s11263-013-0672-6
Automatic and Efficient Human Pose Estimation for Sign
Language Videos
James Charles · Tomas Pfister · Mark Everingham ·
Andrew Zisserman
Received: 4 February 2013 / Accepted: 29 October 2013
© Springer Science+Business Media New York 2013"
2129304075990cd2f3317ea67a2acf52b7d7a3e2,Face Recognition and Detection through Similarity Measurements,"International Journal of Computer Applications (0975 – 8887)
Volume 174 – No.3, September 2017
Face Recognition and Detection through Similarity
Measurements
Irfan Bashir
M.Tech( CSE) Schoral
SMVDU, Kakryal Katra, Jummu"
2168ec12eff5c3d1ff09d0f3c13d6df5b5061164,Face recognition with salient local gradient orientation binary patterns,"978-1-4244-5654-3/09/$26.00 ©2009 IEEE
ICIP 2009"
2114b25727a21275e88e30dad0423752f6047dae,Generic Visual Recognition on Non-Uniform Distributions Based on AdaBoost Codebooks,"Int'l Conf. IP, Comp. Vision, and Pattern Recognition |  IPCV'11  |
Generic Visual Recognition on Non-Uniform
Distributions Based on AdaBoost Codebooks
Mohammad Mahdi Dehshibi, Member IEEE
Faculty of Electrical, Computer, and IT, Islamic Azad
University  Qazvin Branch, Qazvin, Iran
Seyed Meysam Alavi
Islamic Azad University  Hamedan Branch,
Hamedan, Iran
in  several
rather
further
improve
introducing
tasks  are  conducted
to  demonstrate
lustering,  but
locality-sensitive  hashing
the  proposed  method  can"
21b16df93f0fab4864816f35ccb3207778a51952,Recognition of Static Gestures Applied to Brazilian Sign Language (Libras),"Recognition of Static Gestures applied to Brazilian Sign Language (Libras)
Igor L. O. Bastos
Math Institute
Michele F. Angelo, Angelo C. Loula
Department of Technology, Department of Exact Sciences
Federal University of Bahia (UFBA),
State University of Feira de Santana (UEFS)
Salvador, Brazil
Feira de Santana, Brazil"
2186944cd69dc4fe680dcea50e45c15905eac2e6,Modular PCA and Probabilistic Similarity Measure for Robust Face Recognition,"Modular PCA and Probabilistic Similarity Measure
for Robust Face Recognition
Kwanyong Lee1 and Hyeyoung Park2
Korea National Open University, Seoul, Korea
School of Electrical Engineering and Computer Science
Kyungpook National University, Daegu, Korea"
21160be824d97e66fcd7d55ed502b36d3e691c1f,Assessment of Deep Convolutional Neural Networks for Road Surface Classification,"Assessment of Deep Convolutional Neural
Networks for Road Surface Classification
Marcus Nolte, Nikita Kister and Markus Maurer
Institute of Control Engineering
Technische Universit¨at Braunschweig
Email: {nolte,
Braunschweig, Germany"
21679eb7e953bd132803703c27dcd56484d497e6,"utism , oxytocin and interoception","Neuroscience  and  Biobehavioral  Reviews  47  (2014)  410–430
Contents  lists  available  at  ScienceDirect
Neuroscience
Biobehavioral
Reviews
j o u r n a l  h  o m  e p a  g e :  w w w . e l s e v i e r . c o m / l o c a t e / n e u b i o r e v
Review
Autism,   oxytocin   and   interoception
E.   Quattrocki∗,   Karl   Friston 1
The  Wellcome  Trust  Centre  for  Neuroimaging,  UCL,  12  Queen  Square,  London  WC1N  3BG,  UK
Article  history:
Received  5  February  2014
Received  in  revised  form  23  July  2014
Accepted  20  September  2014
Available  online  30  September  2014
Keywords:
Autism
Oxytocin
Interoception
Bayesian  predictive  coding"
21e666abe02b1cab090825199471db7f744aa424,A REAL-TIME FACE RECOGNITION SYSTEM USING MULTIPLE MEAN FACES AND I DUAL MODE FISHEFWACES,"A REAL-TIME FACE RECOGNITION SYSTEM USING MULTIPLE MEAN FACES AND
DUAL MODE FISHEFWACES
Jongmoo Choi, Sanghoon Lee, Chilgee Lee, Juneho Yi
School of Electrical and Computer Engineering
S ungkyunkw an University
00, ChunChundong, Jangan-guy Suwon 440-746, Korea
Cjmchoi, armofgod, cslee, jhyi}"
2151b0aec991bfc609d46d10107d4622150c806f,Improving the Effectiveness of Local Feature Detection,"Improving the Effectiveness of Local
Feature Detection
Shoaib Ehsan
A thesis submitted for the degree of
Doctor of Philosophy
t the
School of Computer Science and Electronic Engineering
University of Essex
May 2012"
216c61796c6ead27b1042046e1d95a2038624d26,Vehicle Re-identification Using Quadruple Directional Deep Learning Features,"Vehicle Re-identification Using Quadruple
Directional Deep Learning Features
Jianqing Zhu, Huanqiang Zeng, Jingchang Huang, Shengcai Liao, Zhen Lei, Canhui Cai and LiXin Zheng"
216081f7e3ac058b2bad7609676193f50da24db9,Misleading first impressions: different for different facial images of the same person.,"http://pss.sagepub.com/
Misleading First Impressions: Different for Different Facial Images of the Same Person
Alexander Todorov and Jenny M. Porter
2014 25: 1404 originally published online 27 May 2014
DOI: 10.1177/0956797614532474
The online version of this article can be found at:
http://pss.sagepub.com/content/25/7/1404
Published by:
http://www.sagepublications.com
On behalf of:
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http://www.sagepub.com/journalsPermissions.nav"
2155739f578e33449546f45a0b4cf64dbd614025,FaceReader Methodology Note what is facereader ?,"FaceReader
Methodology Note
what is facereader?
FaceReader™ is a program for facial analysis. It can detect
facial expressions. FaceReader has been trained to classify
expressions in one of the following categories: happy,
sad, angry, surprised, scared, disgusted, and neutral. These
emotional categories have been described by Ekman [1]
s the basic or universal emotions. In addition to these
asic emotions, contempt can be classified as expression,
just like the other emotions [2]. Obviously, facial expres-
sions vary in intensity and are often a mixture of emo-
tions. In addition, there is quite a lot of interpersonal
variation.
Figure 1. Analyzing facial expressions with FaceReader.
FaceReader has been trained to classify the expressions
mentioned above. It is not possible to add expressions to
the software yourself. Please contact Noldus Information
Technology if you are interested in the classification of
other expressions."
21913787b7ed62773926a287b60308d1960e6966,LR-CNN for fine-grained classification with varying resolution,"LR-CNN FOR FINE-GRAINED CLASSIFICATION WITH VARYING RESOLUTION
M. Chevalier(1,2), N. Thome(1), M. Cord(1), J. Fournier(2), G. Henaff(2), E. Dusch(2)
(1) Sorbonne Universit´es, UPMC Univ Paris 06, LIP6, 4 place Jussieu 75005 Paris, France
(2) Thales Optronique S.A.S., 2 avenue Gay-Lussac, 78990 Elancourt, France"
21ff1d20dd7b3e6b1ea02036c0176d200ec5626d,Loss Max-Pooling for Semantic Image Segmentation,"Loss Max-Pooling for Semantic Image Segmentation
Samuel Rota Bul`o(cid:63),†
Gerhard Neuhold†
Peter Kontschieder†
Mapillary - Graz, Austria -
(cid:63)FBK - Trento, Italy -"
21e5a220734de4d6509996223c48778559f77358,Gender Identification from Facial Image using Compound Local Binary Pattern ( CLBP,"International Journal of Computer Applications (0975 – 8887)
Volume 100– No.3, August 2014
Gender Identification from Facial Image using
Compound Local Binary Pattern (CLBP)
Emam Hossain
Ahsanullah University of
Science and Technology,
Dhaka, Bangladesh
Shayla Azad Bhuyan
BRAC University, Dhaka,
Bangladesh
Faisal Ahmed
Islamic University of
Technology, Dhaka,
Bangladesh
robust
feature  descriptor"
72c37e7507439fc6de2b8385d8842f0805b095b6,A Study of Filtering Approaches for Sliding Window Pedestrian Detection,"A Study of Filtering Approaches for Sliding
Window Pedestrian Detection
Artur Jord˜ao Lima Correia, Victor Hugo Cunha de Melo, William Robson Schwartz
Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
Email:"
72ecaff8b57023f9fbf8b5b2588f3c7019010ca7,Facial Keypoints Detection,"Facial Keypoints Detection
Shenghao Shi"
72944b4266523effe97708bff89e1d57d6aebf50,Multisensory Versus Unisensory Integration : Contrasting Modes in the Superior Colliculus,"A Multi-Sensory, Automated and Accelerated
Sensory Integration Program
The Research
Below are several published research reports that document the efficacy of  a
singular program such as auditory therapy or visual therapy alone as well as the
use of multi-sensory programs using one or more sensory programs together.
This is only a sample of the volumes of research that has been done.
Multisensory integration of cross-modal stimulus combinations yielded responses
that  were  significantly  greater  than  those  evoked  by  the  best  component
stimulus.  J  Neurophysiol  97:  3193–3205,  2007.  doi:10.1152/jn.00018.2007.
Multisensory  Versus  Unisensory  Integration:  Contrasting  Modes  in  the  Superior
Colliculus, Juan Carlos Alvarado, J. William Vaughan, Terrence R. Stanford, and
Barry E. Stein
Department  of  Neurobiology  and  Anatomy,  Wake  Forest  University  School  of
Medicine, Winston-Salem, North Carolina
When  sound  and  touch  were  activated  simultaneously,  the  activation  of  the
uditory  cortex  was  strongest.  Auditory  information  in  conjunction  with  tactile
input  assists  with  making  tactile  decisions.  Tactile  and  auditory  stimulation
simultaneously  and  individually  may  positively  impact  neuroplastic  changes  in
individuals  with  neurological  deficits  or  impairments.  Used  singularly,  sound"
727d03100d4a8e12620acd7b1d1972bbee54f0e6,von Mises-Fisher Mixture Model-based Deep learning: Application to Face Verification,"von Mises-Fisher Mixture Model-based Deep
learning: Application to Face Verification
Md. Abul Hasnat, Julien Bohn´e, Jonathan Milgram, St´ephane Gentric and Liming Chen"
72c32e48a82b2ef2da26c78b4b8c8b4ec888d062,Impact of the GSM and CDMA Mobile Phone Networks on the Strength of Speech Evidence in Forensic Voice Comparison,"rnal of  F o rensic
ISSN: 2157-7145
Journal of Forensic Research
Nair BBT et al, J Forensic Res 2016, 7:2
DOI: 10.4172/2157-7145.1000324
Research Article
Impact of the GSM and CDMA Mobile Phone Networks on the Strength of
Speech Evidence in Forensic Voice Comparison
Open Access
Balamurali B T Nair1,2*, Esam A S Alzqhoul1,2 and Bernard J Guillemin1,2
Forensic and Biometrics Research Group (FaB), The University of Auckland, New Zealand
Department of Electrical and Computer Engineering, The University of Auckland, New Zealand
Corresponding author: Balamurali B T Nair, Ph D., The University of Auckland, New Zealand, E-mail:
Received date: Nov 22, 2015;Accepted date: May 11, 2016; Published date: May 25, 2016
Copyright: © 2016 Nair BBT, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and source are credited."
72c248c8d3bd76e2a31963aad7286b8d06ab7f8e,Looking outside of the Box: Object Detection and Localization with Multi-scale Patterns.,"Looking outside of the Box:
Object Detection and Localization with
Multi-scale Patterns
Eshed Ohn-Bar, Student Member, IEEE, and Mohan Manubhai Trivedi, Fellow, IEEE"
727c8c696c6acc04e57b6c3541613702c22c6f0f,Optimal discrete wavelet transform (DWT) features for face recognition,"010 Asia Pacific Conference on Circuits and Systems (APCCAS 2010)
6 - 9 December 2010, Kuala Lumpur, Malaysia
Optimal Discrete Wavelet Transform (DWT)
Features for Face Recognition
Paul Nicholl
School of Electronics, Electrical
Engineering & Computer Science
Queen’s Univ., Northern Ireland
Email:
Afandi Ahmad
Abbes Amira
JEC, Faculty of. Elec. and Electronic Eng.
Univ. Tun Hussein Onn Malaysia
NIBEC, Faculty of Comp. and Eng.
Univ. of Ulster, Jordanstown Campus
Johor, Malaysia
Email:
Northern Ireland
Email:"
72ef87fb1a49f0e386f123a6b4f5566f51a3a47d,Minimizing Latency for Secure Coded Computing Using Secret Sharing via Staircase Codes,"Minimizing Latency for Secure Coded Computing
Using Secret Sharing via Staircase Codes
Rawad Bitar, Parimal Parag, and Salim El Rouayheb"
729a30040132909cda0eab2c6c4ba60d6d1941b5,Image-based Plant Species Identification with Deep Convolutional Neural Networks,"Image-based Plant Species Identification with Deep
Convolutional Neural Networks
Mario Lasseck
Museum für Naturkunde Berlin, Germany"
729a9d35bc291cc7117b924219bef89a864ce62c,Recognizing Material Properties from Images.,"Recognizing Material Properties from Images
Gabriel Schwartz and Ko Nishino, Senior Member, IEEE"
7214d9356398aa39923c69650bcf761d4ab6307f,Improving Spatial Saliency Using Affinity Model and Temporal Motion,"Int'l Conf. IP, Comp. Vision, and Pattern Recognition |  IPCV'15  |
Improving Spatial Saliency Using
Affinity Model and Temporal Motion
Dept. of Computer and Communications Engineering, Kangwon National University
Manbae Kim
Chunchon, Gangwondo, Republic of Korea
E-mail:"
72a1ecfcd5f0b022fef49cab72bb476e41dea40e,Bag-of-features representations using spatial visual vocabularies for object classification,"BAG-OF-FEATURES REPRESENTATIONS USING SPATIAL VISUAL VOCABULARIES FOR
OBJECT CLASSIFICATION
Rene Grzeszick, Leonard Rothacker, Gernot A. Fink
TU Dortmund
Email: {rene.grzeszick, leonard.rothacker,
Department of Computer Science"
725597072c76dad5caa92b7baa6e1c761addc300,Deep adversarial neural decoding,"Deep adversarial neural decoding
Ya˘gmur Güçlütürk*, Umut Güçlü*,
Katja Seeliger, Sander Bosch,
Rob van Lier, Marcel van Gerven,
Radboud University, Donders Institute for Brain, Cognition and Behaviour
Nijmegen, the Netherlands
*Equal contribution"
728f656a4e8525112618badc15b2cf04669f3bc0,Learning structure-from-motionfrom motion,"Learning structure-from-motion from motion
Cl´ement Pinard1,2, Laure Chevalley2, Antoine Manzanera1, and David Filliat1
ENSTA ParisTech
Computer Science and System Engineering Department
{clement.pinard, antoine.manzanera,
Palaiseau, France
Parrot, Paris, France"
727ecf8c839c9b5f7b6c7afffe219e8b270e7e15,AND THE COMMITTEE ON GRADUATE STUDIES OF STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY,"LEVERAGING GEO-REFERENCED DIGITAL PHOTOGRAPHS
A DISSERTATION
SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE
AND THE COMMITTEE ON GRADUATE STUDIES
OF STANFORD UNIVERSITY
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
Mor Naaman
July 2005"
7249b263d0a84d2d9d03f2f7b378778d129f9af9,Research Statement Research Focus,"RESEARCH STATEMENT
Ryan Farrell
In recent years, the topic of object detection/recognition has rapidly gained in popularity and is now
perhaps the most actively researched topic in computer vision. Object detection algorithms are becoming
prevalent in consumer devices such as digital cameras (real-time face detection) and automobiles (pedestrian
detection systems for collision avoidance are already available and will be a standard feature on new cars
within a few years). Object recognition technology is quickly becoming widespread in smartphone apps;
examples include Google Goggles, Amazon Flow and Leafsnap. I believe we are at a ‘tipping point’ towards
the impending ubiquity of computer vision, specifically object recognition, in our everyday lives.
RESEARCH FOCUS
My research in object recognition focuses specifically on Fine-grained Visual Categorization (sometimes
bbreviated FGVC). For many years, computer vision has focused on classifying an object in several basic-
level categories such as person, car, frog, or piano. At the opposing end of the categorization spectrum
(see Figure ) is biometric identification - recognizing individuals within a population (e.g. face recognition or
recognizing individual whales by unique fluke patterns). Between these two extremes lie what are called entry-
nd subordinate-level categories. Entry-level categories include penguin, owl, etc.; people generally use these
more specific labels instead of simply saying “bird” (the basic-level category). Subordinate-level categories
re highly specific. Continuing with the example of birds, categorizing at the subordinate-level would require
differentiating two quite similar species (such as the Red-breasted and White-breasted Nuthatches). Fine-
grained recognition addresses this situation where categories are distinguised by very subtle differences."
724a493411b7c5a904445406d3037df4a22b6c89,Training of Convolutional Networks on Multiple Heterogeneous Datasets for Street Scene Semantic Segmentation,"Training of Convolutional Networks on Multiple Heterogeneous
Datasets for Street Scene Semantic Segmentation
Panagiotis Meletis and Gijs Dubbelman"
722364dc6205074a75a0caf3c1b4d2f27f5eb794,Uncertainty Constrained Robotic Exploration: An Integrated Exploration Planner,"An Extended Treatment of Uncertainty Constrained Robotic
Exploration: An Integrated Exploration Planner
Alexander Ivanov1 and Mark Campbell2"
72a00953f3f60a792de019a948174bf680cd6c9f,Understanding the role of facial asymmetry in human face identification,"Stat Comput (2007) 17:57–70
DOI 10.1007/s11222-006-9004-9
Understanding the role of facial asymmetry in human face
identification
Sinjini Mitra · Nicole A. Lazar · Yanxi Liu
Received: May 2005 / Accepted: September 2006 / Published online: 30 January 2007
C(cid:1) Springer Science + Business Media, LLC 2007"
72f8df596eb9bb3a8c8206329083c42e70fcd9fd,Will People Like Your Image?,
72edc24c67c34b5f2c98086a689bf0f3591e393d,An Introduction to Image Synthesis with Generative Adversarial Nets,"An Introduction to Image Synthesis with
Generative Adversarial Nets
He Huang, Phillip S. Yu and Changhu Wang"
725a45ad75caf0112d649253f8a69793b1f00e80,LIFEisGAME : An approach to the utilization of serious games for therapy for children with ASD,"LIFEisGAME: An approach to the utilization of serious
games for therapy for children with ASD
Tiago Fernandes1,5, Samanta Alves2, José Miranda3,5, Cristina Queirós2, Verónica
Instituto de Telecomunicações, Lisboa, Portugal,
Faculdade de Psicologia da Universidade do Porto, Porto, Portugal,
Instituto Politécnico da Guarda, Porto, Portugal,
Faculdade de Ciências da Universidade do Porto, Porto, Portugal,
5 Faculdade de Engenharia da Universidade do Porto, Porto, Portugal,
Orvalho1,4"
72a79f351d4ae03ff940ff920898e41ce960f58e,Author's Personal Copy Backtracking: Retrospective Multi-target Tracking,"(This is a sample cover image for this issue. The actual cover is not yet available at this time.)
This article appeared in a journal published by Elsevier. The attached
opy is furnished to the author for internal non-commercial research
nd education use, including for instruction at the authors institution
nd sharing with colleagues.
Other uses, including reproduction and distribution, or selling or
licensing copies, or posting to personal, institutional or third party
websites are prohibited.
In most cases authors are permitted to post their version of the
rticle (e.g. in Word or Tex form) to their personal website or
institutional repository. Authors requiring further information
regarding Elsevier’s archiving and manuscript policies are
encouraged to visit:
http://www.elsevier.com/copyright"
721fbc63a647239158bf817311d1c084455398e9,Shape-based automatic detection of a large number of 3D facial landmarks,"Shape-based Automatic Detection of a Large Number of 3D Facial Landmarks
Syed Zulqarnain Gilani, Faisal Shafait, Ajmal Mian
School of Computer Science and Software Engineering,The University of Western Australia.
Figure 3: Histogram of mean localization error for 18 landmarks on 4,007
scans of FRGCv2 dataset (18× 4007 Landmarks).
Mean Localization Error(mm)
Neutral
Non−Neutral
Neutral
Level−1
Level−2
Level−3
Level−4
Figure 1: Our algorithm automatically detects an arbitrarily large number of
facial landmarks by establishing dense correspondences between 3D faces.
The figure shows 85 landmarks detected (red) on neutral and extreme anger
expression of a subject from BU3DFE database [3]. The ground truth is
represented by blue dots.
2202
Mean Localization Error(mm)"
7227c1d8f53279041822266c7a6d0ac128e96d05,Multi-view People Tracking via Hierarchical Trajectory Composition,"Multi-view People Tracking via Hierarchical Trajectory Composition∗
Yuanlu Xu1, Xiaobai Liu2, Yang Liu1 and Song-Chun Zhu1
Dept. Computer Science and Statistics, University of California, Los Angeles (UCLA)
Dept. Computer Science, San Diego State University (SDSU)"
722221f6c696b4a7cc094748aaad8158990ec41e,3D facial expression recognition: A perspective on promises and challenges,"D Facial Expression Recognition:
A Perspective on Promises and Challenges
T. Fang, X. Zhao, O. Ocegueda, S.K. Shah and I.A. Kakadiaris*"
72f4c415b5f3ecf63380b6985c95c5af2ba72632,Activity Recognition on a Large Scale in Short Videos - Moments in Time Dataset,"ACTIVITY RECOGNITION ON A LARGE SCALE IN
SHORT VIDEOS - MOMENTS IN TIME DATASET
Ankit Parag Shah* ∗
Harini Kesavamoorthy*
Poorva Rane*
Pramati Kalwad*
Alexander Hauptmann
Florian Metze"
7276a3ffa0941524083ac0fa9f0129746bca65d7,Multi-scale Deep Learning Architectures for Person Re-identification,"Multi-scale Deep Learning Architectures for Person Re-identification
Xuelin Qian1 Yanwei Fu2,5,* Yu-Gang Jiang1,3 Tao Xiang4 Xiangyang Xue1,2
Shanghai Key Lab of Intelligent Info. Processing, School of Computer Science, Fudan University;
School of Data Science, Fudan University; 3Tencent AI Lab;
Queen Mary University of London; 5University of Technology Sydney;"
72591a75469321074b072daff80477d8911c3af3,Group Component Analysis for Multiblock Data: Common and Individual Feature Extraction,"Group Component Analysis for Multi-block Data:
Common and Individual Feature Extraction
Guoxu Zhou, Andrzej Cichocki Fellow, IEEE, Yu Zhang, and Danilo Mandic Fellow, IEEE"
72a6044a0108e0f8f1e68cd70ada46c81a416324,Improved Training of Generative Adversarial Networks Using Representative Features,"Improved Training of Generative Adversarial Networks
using Representative Features
Duhyeon Bang 1 Hyunjung Shim 1"
721d9c387ed382988fce6fa864446fed5fb23173,Assessing Facial Expressions in Virtual Reality Environments,
72a87f509817b3369f2accd7024b2e4b30a1f588,Fault diagnosis of a railway device using semi-supervised independent factor analysis with mixing constraints,"Fault diagnosis of a railway device using semi-supervised
independent factor analysis with mixing constraints
Etienne Côme, Latifa Oukhellou, Thierry Denoeux, Patrice Aknin
To cite this version:
Etienne Côme, Latifa Oukhellou, Thierry Denoeux, Patrice Aknin. Fault diagnosis of a railway device
using semi-supervised independent factor analysis with mixing constraints. Pattern Analysis and
Applications, Springer Verlag, 2012, 15 (3), pp.313-326. <hal-00750589>
HAL Id: hal-00750589
https://hal.archives-ouvertes.fr/hal-00750589
Submitted on 11 Nov 2012
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
728a8c4ed6b5565a250bd1e0587293a6a97f515b,Arguing Machines: Human Supervision of Black Box AI Systems That Make Life-Critical Decisions,"Arguing Machines: Human Supervision of Black Box
AI Systems That Make Life-Critical Decisions
Lex Fridman*
Li Ding
Massachusetts Institute of Technology (MIT)
Benedikt Jenik
Bryan Reimer
Figure 1: “Arguing machines” framework that adds a secondary system to a primary “black box” AI system that makes life-
ritical decisions and uses disagreement between the two as a signal to seek human supervision. We demonstrate that this can
e a powerful way to reduce overall system error."
72ef0ac03d3043bf664ca7c21abafc4191b24557,Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection,"Towards Safe Autonomous Driving: Capture Uncertainty in the Deep
Neural Network For Lidar 3D Vehicle Detection
Di Feng1, Lars Rosenbaum1, Klaus Dietmayer2"
7278f4c361f960b2e54275c5efd98535f9ccaded,Image Based Recognition of Dynamic Traffic Situations by Evaluating the Exterior Surrounding and Interior Space of Vehicles,"IMAGE BASED RECOGNITION OF DYNAMIC TRAFFIC SITUATIONS BY
EVALUATING THE EXTERIOR SURROUNDING AND INTERIOR SPACE OF VEHICLES
Photogrammetry & Remote Sensing, Technische Universitaet Muenchen, Germany - (alexander.hanel, ludwig.hoegner,
BMW Research & Technology, Muenchen, Germany -
A. Hanela, H. Klödenb, L. Hoegnera, U. Stillaa
KEY WORDS: vehicle camera system, crowd sourced data, image analysis, machine learning, object detection, illumination recogni-
tion, traffic situation recognition"
7238e8f34c662af74b81de80fa6c01838a06b349,Combining SVMS for face class modeling,"COMBINING SVMS FOR FACE CLASS MODELING
Julien Meynet, Vlad Popovici, Matteo Sorci and Jean-Philippe Thiran
Ecole Polytechnique F·ed·erale de Lausanne (EPFL)
Signal Processing Institute
CH-1015 Lausanne, Switzerland
http://itswww.ep(cid:3).ch"
72367499712525a9f82d0a1d844e2a76d749304d,Multi-layer Biometric System for the Port of Los Angeles Final Report,"Multi-Layer Biometric System for the Port of
Los Angeles
Final Report
METRANS Project 11-19
July 2012
Principal Investigator: Xiaolong Wu
Associate Professor
Co-Principal Investigator: Burkhard Englert
Professor
California State University Long Beach
Department of Computer Engineering and Computer Science
250 Bellflower Boulevard
Long Beach, CA 90840
Tel: (562) 985-5291
Fax: (562) 985-7823
Email:"
72c0c8deb9ea6f59fde4f5043bff67366b86bd66,Age progression in Human Faces : A Survey,"Age progression in Human Faces : A Survey
Narayanan Ramanathan, Rama Chellappa and Soma Biswas"
7252b7146697e7a7e33bddb5c49f18af2d0fc9db,Learning to See : Genetic and Environmental Influences on Visual Development,"Copyright
James Albert Bednar"
72cebd7d046080899703ed3cd96e3019a9f60f13,Interpreting Visual Question Answering Models,"Towards Transparent AI Systems:
Interpreting Visual Question Answering Models
Yash Goyal, Akrit Mohapatra, Devi Parikh, Dhruv Batra
{ygoyal, akrit, parikh,
Virginia Tech"
72d067a6e1fd447ef512262248ad5f73823a3842,Probabilistic Models for 3D Urban Scene Understanding from Movable Platforms,"Probabilistic Models for
D Urban Scene Understanding
from Movable Platforms
Dissertation
Dipl.-Inform. Andreas Geiger"
034050422f90938a43e9cfd292187aef124fef61,Race recognition from face images using Weber local descriptor,"Paper 1569528513
IWSSIP 2012, 11-13 April 2012, Vienna, Austria
. INTRODUCTION"
034f7d5b3878f8b2db92a7cb7f12edcd5681eca7,FAST Pre-Filtering-Based Real Time Road Sign Detection for Low-Cost Vehicle Localization,"Article
FAST Pre-Filtering-Based Real Time Road Sign
Detection for Low-Cost Vehicle Localization
Kyoungtaek Choi 1, Jae Kyu Suhr 2
Department of Electronic Engineering, Korea National University of Transportation, 50 Daehak-ro,
nd Ho Gi Jung 1,*
Chungju-si 27469, Korea;
School of Intelligent Mechatronics Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu,
Seoul 05006, Korea;
* Correspondence: Tel. +82-43-841-5366
Received: 11 September 2018; Accepted: 16 October 2018; Published: 22 October 2018"
03a9affced96b1554b6208ff148617fdfde6712c,Error Detector Facial Landmarks Checker 2 . Face Normalization Facial Proportions Extractor Facial Landmarks Extractor Noise Mask Similarity Computation System Forensic Examiner,"Repositorio Institucional de la Universidad Autónoma de Madrid
https://repositorio.uam.es
Esta es la versión de autor de la comunicación de congreso publicada en:
This is an author produced version of a paper published in:
013 International Workshop on Biometrics and Forensics (IWBF). IEEE, 2013
DOI:    http://dx.doi.org/10.1109/IWBF.2013.6547306
Copyright:  © 2013 IEEE
El acceso a la versión del editor puede requerir la suscripción del recurso
Access to the published version may require subscription"
038277dbfcd767b0a0899de42d3277b5b253cc8e,TR-IIS-14-003 Review and Implementation of High-Dimensional Local Binary Patterns and Its Application to Face Recognition,"TR-IIS-14-003
Review and Implementation of
High-Dimensional Local Binary
Patterns and Its Application to
Face Recognition
Bor-Chun Chen, Chu-Song Chen, Winston Hsu
July. 24,    2014    ||    Technical Report No. TR-IIS-14-003
http://www.iis.sinica.edu.tw/page/library/TechReport/tr2014/tr14.html"
037e17ac0272b4db0d4761067dbf0ee56d91e6dd,A New Multi-modal Dataset for Human Affect Analysis,"A New Multi-Modal Dataset for Human Affect
Analysis
nonymous for review
nonymous for review"
03f4c0fe190e5e451d51310bca61c704b39dcac8,CHEAVD: a Chinese natural emotional audio–visual database,"J Ambient Intell Human Comput
DOI 10.1007/s12652-016-0406-z
O R I G I N A L R E S E A R C H
CHEAVD: a Chinese natural emotional audio–visual database
Ya Li1
• Jianhua Tao1,2,3
• Linlin Chao1
• Wei Bao1,4
• Yazhu Liu1,4
Received: 30 March 2016 / Accepted: 22 August 2016
Ó Springer-Verlag Berlin Heidelberg 2016"
0328b975b11fd4153010535d1a06cf618f9b0c91,Evaluating the complexity of databases for person identification and verification,"IDIAP
Martigny - Valais - Suisse
EvaluatingtheComplexity
ofDatabasesforPerson
Identificationand
Verification
G.Thimm,S.Ben-Yacoub,J.Luettin
IDIAP{RR	-

revisedinJanuary
,

submittedforpublication
PDalleMolle
Institute
forPerceptualArtificial
Intelligence(cid:15)P.O.Box	(cid:15)
Martigny(cid:15)Valais(cid:15)Switzerland
phone+
(cid:0)(cid:0)



+
(cid:0)(cid:0)


internethttp://www.idiap.ch"
032825000c03b8ab4c207e1af4daeb1f225eb025,A Novel Approach for Human Face Detection in Color Images Using Skin Color and Golden Ratio,"J. Appl. Environ. Biol. Sci., 7(10)159-164, 2017
ISSN: 2090-4274
© 2017, TextRoad Publication
Journal of Applied Environmental
nd Biological Sciences
www.textroad.com
A Novel Approach for Human Face Detection in Color Images Using Skin
Color and Golden Ratio
Faizan Ullah*1, Dilawar Shah1, Sabir Shah1, Abdus Salam2, Shujaat Ali1
Department of Computer Science, Bacha Khan University, Charsadda, KPK, Pakistan1
Department of Computer Science, Abdul WaliKhan University, Mardan, KPK, Pakistan2
Received: May 9, 2017
Accepted: August 2, 2017"
0322e69172f54b95ae6a90eb3af91d3daa5e36ea,Face Classification using Adjusted Histogram in Grayscale,"Face Classification using Adjusted Histogram in
Grayscale
Weenakorn Ieosanurak, and Watcharin Klongdee"
036a8cb922a30d766b0fc0ba5954098a1d2a09f5,Learning Similarities for Rigid and Non-rigid Object Detection,"Learning Similarities for Rigid and Non-Rigid Object Detection
Asako Kanezaki
The Univ. of Tokyo
Emanuele Rodol`a
TU Munich
Daniel Cremers
TU Munich
Tatsuya Harada
The Univ. of Tokyo"
0321d183932ff1353d59e418847ff5bf8f5df5d2,Geolocation Estimation of Photos Using a Hierarchical Model and Scene Classification,"Geolocation Estimation of Photos using a
Hierarchical Model and Scene Classification
Eric M¨uller-Budack1,2[0000−0002−6802−1241], Kader
Pustu-Iren1[0000−0003−2891−9783], and Ralph Ewerth1,2[0000−0003−0918−6297]
Leibniz Information Centre for Science and Technology (TIB), Hannover, Germany
L3S Research Center, Leibniz Universit¨at Hannover, Germany"
0313f71c856b0732d7e7b46755ca77eede77f2f7,Fast computation of the performance evaluation of biometric systems: Application to multibiometrics,"Fast Computation of the Performance Evaluation of
Biometric Systems: Application to Multibiometrics
Romain Giot∗, Mohamad El-Abed, Christophe Rosenberger
GREYC Laboratory
ENSICAEN - University of Caen Basse Normandie - CNRS
6 Boulevard Mar´echal Juin, 14000 Caen Cedex - France"
034f7fcf5a393ac3307ac3609c2b971df6efaff6,Can Synthetic Data Handle Unconstrained Gaze Estimation?,"Can Synthetic Data Handle Unconstrained Gaze
Estimation ?
Amine Kacete, Renaud Séguier, Michel Collobert, Jérôme Royan
To cite this version:
Amine Kacete, Renaud Séguier, Michel Collobert, Jérôme Royan. Can Synthetic Data Handle Uncon-
strained Gaze Estimation ?. Conférence Nationale sur les Applications Pratiques de l’Intelligence Ar-
tificielle, Jul 2017, Caen, France. Conférence Nationale sur les Applications Pratiques de l’Intelligence
Artificielle. <hal-01561526>
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03650399cbf53d916d10a507852c9e94a02ee13f,3D faces in motion: Fully automatic registration and statistical analysis,"D Faces in Motion: Fully Automatic Registration and Statistical Analysis
Timo Bolkarta,∗, Stefanie Wuhrera
Saarland University, Saarbr¨ucken, Germany"
03de6b2a3c81b26eecbec2705173da3dba25ecbb,FineTag: Multi-attribute Classification at Fine-grained Level in Images,"FineTag: Multi-attribute Classification at
Fine-grained Level in Images
Roshanak Zakizadeh, Michele Sasdelli, Yu Qian and Eduard Vazquez
Cortexica Vision Systems, London, UK"
03a65d274dc6caea94f6ab344e0b4969575327e3,CrowdHuman: A Benchmark for Detecting Human in a Crowd,"CrowdHuman: A Benchmark for Detecting Human in a Crowd
Shuai Shao∗ Zijian Zhao∗ Boxun Li
Tete Xiao Gang Yu Xiangyu Zhang
Jian Sun
{shaoshuai, zhaozijian, liboxun, xtt, yugang, zhangxiangyu,
Megvii Inc. (Face++)"
035ef7b25991b0f7ea841a2270ed053198aab09e,"Retrieval of Images with Objects of Specific Size, Location, and Spatial Configuration","Retrieval of images with objects of specific size, location and spatial configuration
Niloufar Pourian
B.S. Manjunath
Department of Electrical and Computer Engineering
University of California, Santa Barbara, United States"
0332ae32aeaf8fdd8cae59a608dc8ea14c6e3136,Large Scale 3D Morphable Models,"Int J Comput Vis
DOI 10.1007/s11263-017-1009-7
Large Scale 3D Morphable Models
James Booth1
Stefanos Zafeiriou1
· Anastasios Roussos1,3 · Allan Ponniah2 · David Dunaway2 ·
Received: 15 March 2016 / Accepted: 24 March 2017
© The Author(s) 2017. This article is an open access publication"
03a2235fea70317461222fac05e38ee35ead9711,Implementation of a Computer Vision Algorithm for Onboard Detection of Unmanned Aircraft submitted by Lukáš Bauer,"Implementation of a Computer Vision Algorithm for Onboard Detection
Bachelor’s Thesis Review
of Unmanned Aircraft
submitted by Luk´aˇs Bauer
The bachelor’s thesis of Luk´aˇs Bauer is concerned with vision-based algorithms for the
detection and localization of UAVs in images from an on-board UAV camera. This is
n interesting problem with several applications. The first set of goals included studying
different methods for the real-time detection and localization of UAVs, selecting a method
for implementation from the candidates, and then integrating and optimizing the selected
method as a Robot Operating System (ROS), which operates on the embedded computer
of the UAV. The second set of goals included testing the method on simulated and real-
word data, evaluating the precision and the computational speed of the implementation on
the embedded device, and preparing the system for integration into a formation-control
lgorithm.
The goals of the thesis were only partially fulfilled. The thesis includes a study of the
literature and describes several approaches for object detection. Luk´aˇs Bauer chose the
YOLOv2 visual detector and implemented this detector as a ROS node operating on-
oard of a UAV. The YOLO detector is a state-of-the-art convolutional neural network
that is tuned for real-time object detection. The configuration file and the pre-trained
weight file for the YOLO detector are available online. For the given problem it was"
033fde43e6ff235fd560435bc060d5ffd14fb827,Pose Estimation and Tracking of Eating Persons in Real-life Settings,"ASCI { IPA { SIKS tracks, ICT.OPEN, Veldhoven, November 14{15, 2011
Pose Estimation and Tracking of Eating Persons in Real-life Settings
Lu Zhang
EWI-TUDelft
Laurens van der Maaten
EWI-TUDelft
Nicole Koenderink
Wageningen UR, FBR
Franck Golbach
Wageningen UR, FBR
Emile Hendriks
EWI-TUDelft"
0365ea467c169134e858bb668a8e19bd251019e7,Orthogonal Neighborhood Preserving Projections: A Projection-Based Dimensionality Reduction Technique,"Orthogonal Neighborhood Preserving Projections: A
projection-based dimensionality reduction technique ∗
E. Kokiopoulou †
Y. Saad‡
March 21, 2006"
03ea398fcefc53a1bd041346c895aadcffed0261,Learning an Alphabet of Shape and Appearance for Multi-Class Object Detection,"Int J Comput Vis
DOI 10.1007/s11263-008-0139-3
Learning an Alphabet of Shape and Appearance for Multi-Class
Object Detection
Andreas Opelt · Axel Pinz · Andrew Zisserman
Received: 28 February 2007 / Accepted: 4 April 2008
© The Author(s) 2008"
033e3fe75da26d8d3dd3cb0f99640181655e6746,From generic to specific deep representations for visual recognition,"Factors of Transferability for a Generic ConvNet Representation
Hossein Azizpour, Ali Sharif Razavian, Josephine Sullivan, Atsuto Maki, Stefan Carlsson
{azizpour, razavian, sullivan, atsuto,
Computer Vision and Active Perception (CVAP), Royal Institute of Technology (KTH), Stockholm, SE-10044 Sweden
Evidence is mounting that Convolutional Networks (ConvNets) are the most effective representation learning method for visual
recognition tasks. In the common scenario, a ConvNet is trained on a large labeled dataset (source) and the feed-forward units
ctivation of the trained network, at a certain layer of the network, is used as a generic representation of an input image for a
task with relatively smaller training set (target). Recent studies have shown this form of representation transfer to be suitable for a
wide range of target visual recognition tasks. This paper introduces and investigates several factors affecting the transferability of
such representations. It includes parameters for training of the source ConvNet such as its architecture, distribution of the training
data, etc. and also the parameters of feature extraction such as layer of the trained ConvNet, dimensionality reduction, etc. Then,
y optimizing these factors, we show that significant improvements can be achieved on various (17) visual recognition tasks. We
further show that these visual recognition tasks can be categorically ordered based on their distance from the source task such that
correlation between the performance of tasks and their distance from the source task w.r.t. the proposed factors is observed.
Index Terms—Convolutional Neural Networks, Transfer Learning, Representation Learning, Deep Learning, Visual Recognition
I. INTRODUCTION
C ONVOLUTIONAL NETWORKS (ConvNets) trace back
to the early works on digit and character recognition
[11], [23]. Prior to 2012, though, in computer vision field,
neural networks were more renowned for their propensity to"
03a8f53058127798bc2bc0245d21e78354f6c93b,Max-margin additive classifiers for detection,"Max-Margin Additive Classifiers for Detection
Subhransu Maji and Alexander C. Berg
Sam Hare
VGG Reading Group
October 30, 2009"
03f14159718cb495ca50786f278f8518c0d8c8c9,Can subspace based learning approach perform on makeup face recognition?,"015 IEEE International Conference on Control System, Computing and Engineering, Nov 27 – Nov 29, 2015 Penang, Malaysia
015 IEEE International Conference on Control System,
Computing and Engineering (ICCSCE2015)
Technical Session 1A – DAY 1 – 27th Nov 2015
Time: 3.00 pm – 4.30 pm
Venue: Jintan
Topic: Signal and Image Processing
.00 pm – 3.15pm
.15 pm – 3.30pm
.30 pm – 3.45pm
.45 pm – 4.00pm
.00 pm – 4.15pm
.15 pm – 4.30pm
.30 pm – 4.45pm
A 01 ID3
Can  Subspace  Based  Learning  Approach  Perform  on  Makeup  Face
Recognition?
Khor Ean Yee, Pang Ying Han, Ooi Shih Yin and Wee Kuok Kwee
A 02 ID35
Performance  Evaluation  of  HOG  and  Gabor  Features  for  Vision-based"
034addac4637121e953511301ef3a3226a9e75fd,Implied Feedback: Learning Nuances of User Behavior in Image Search,"Implied Feedback: Learning Nuances of User Behavior in Image Search
Devi Parikh
Virginia Tech"
032c1e19a59cdbeb3fb741a812980f52c1461ce1,"Mining textural knowledge in biological images: Applications, methods and trends","Computational and Structural Biotechnology Journal 15 (2017) 56–67
j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / c s b j
Mining textural knowledge in biological images: Applications, methods
nd trends
Santa Di Cataldo*, Elisa Ficarra
Dept. of Computer and Control Engineering, Politecnico di Torino, Cso Duca degli Abruzzi 24, Torino 10129, Italy
A R T I C L E
I N F O
A B S T R A C T
Article history:
Received 25 August 2016
Received in revised form 14 November 2016
Accepted 15 November 2016
Available online 24 November 2016
Keywords:
Textural analysis
Bioimaging
Textural features extraction
Texture classification
Feature encoding"
03b98b4a2c0b7cc7dae7724b5fe623a43eaf877b,Acume: A new visualization tool for understanding facial expression and gesture data,"Acume: A Novel Visualization Tool for Understanding Facial
Expression and Gesture Data"
031055c241b92d66b6984643eb9e05fd605f24e2,Multi-fold MIL Training for Weakly Supervised Object Localization,"Multi-fold MIL Training for Weakly Supervised Object Localization
Ramazan Gokberk Cinbis
Jakob Verbeek Cordelia Schmid
Inria∗"
03c56c176ec6377dddb6a96c7b2e95408db65a7a,A Novel Geometric Framework on Gram Matrix Trajectories for Human Behavior Understanding,"A Novel Geometric Framework on Gram Matrix
Trajectories for Human Behavior Understanding
Anis Kacem, Mohamed Daoudi, Boulbaba Ben Amor, Stefano Berretti, and Juan Carlos Alvarez-Paiva"
0306a275e80d11d65c4261b8f3d45317a49c1bf7,Optimal Architecture for Deep Neural Networks with Heterogeneous Sensitivity,"Optimal Architecture for Deep Neural Networks
with Heterogeneous Sensitivity
Hyunjoong Cho, Jinhyeok Jang, Chanhyeok Lee, and Seungjoon Yang"
03701e66eda54d5ab1dc36a3a6d165389be0ce79,Improved Principal Component Regression for Face Recognition Under Illumination Variations,"Improved Principal Component Regression for Face
Recognition Under Illumination Variations
Shih-Ming Huang and Jar-Ferr Yang, Fellow, IEEE"
0394040749195937e535af4dda134206aa830258,Geodesic entropic graphs for dimension and entropy estimation in manifold learning,"Geodesic Entropic Graphs for Dimension and
Entropy Estimation in Manifold Learning
Jose A. Costa and Alfred O. Hero III
December 16, 2003"
038ce930a02d38fb30d15aac654ec95640fe5cb0,Approximate structured output learning for Constrained Local Models with application to real-time facial feature detection and tracking on low-power devices,"Approximate Structured Output Learning for Constrained Local
Models with Application to Real-time Facial Feature Detection and
Tracking on Low-power Devices
Shuai Zheng, Paul Sturgess and Philip H. S. Torr"
03ac1c694bc84a27621da6bfe73ea9f7210c6d45,Chapter 1 Introduction to information security foundations and applications,"Chapter 1
Introduction to information security
foundations and applications
Ali Ismail Awad1,2
.1 Background
Information security has extended to include several research directions like user
uthentication and authorization, network security, hardware security, software secu-
rity, and data cryptography. Information security has become a crucial need for
protecting almost all information transaction applications. Security is considered as
n important science discipline whose many multifaceted complexities deserve the
synergy of the computer science and engineering communities.
Recently, due to the proliferation of Information and Communication Tech-
nologies, information security has started to cover emerging topics such as cloud
omputing security, smart cities’ security and privacy, healthcare and telemedicine,
the Internet-of-Things (IoT) security [1], the Internet-of-Vehicles security, and sev-
eral types of wireless sensor networks security [2,3]. In addition, information security
has extended further to cover not only technical security problems but also social and
organizational security challenges [4,5].
Traditional systems’ development approaches were focusing on the system’s
usability where security was left to the last stage with less priority. However, the"
03889b0e8063532ae56d36dd9c54c3784a69e4d4,Learning to Play Guess Who? and Inventing a Grounded Language as a Consequence,"Learning to Play Guess Who? and Inventing a
Grounded Language as a Consequence
Emilio Jorge1, Mikael Kågebäck2, and Emil Gustavsson1
Fraunhofer-Chalmers Centre , Göteborg, Sweden ,
Computer Science & Engineering , Chalmers University of Technology , Göteborg, Sweden ,"
03adcf58d947a412f3904a79f2ab51cfdf0e838a,Video-based face recognition : a survey,"World Journal of Science and Technology 2012, 2(4):136-139
ISSN: 2231 – 2587
Available Online: www.worldjournalofscience.com
_________________________________________________________________
Proceedings of ""Conference on Advances in Communication and Computing (NCACC'12)”
Held at R.C.Patel Institute of Technology, Shirpur, Dist. Dhule,Maharastra,India.
April 21, 2012
Video-based face recognition: a survey
Shailaja A Patil1 and Pramod J Deore2
Department of Electronics and Telecommunication, R.C.Patel Institute of Technology,Shirpur,Dist.Dhule.Maharashtra,India."
035c606bc6a05e2018e57859737877043673b7b9,Fine-Grained Image Classification by Exploring Bipartite-Graph Labels,"Fine-grained Image Classification by Exploring Bipartite-Graph Labels
Feng Zhou
NEC Labs
Yuanqing Lin
NEC Labs
www.f-zhou.com"
03faf0afc44e0e2462dfbfd83ab2e2e7b3874d3e,Gesture control interface for immersive panoramic displays,"Multimed Tools Appl
DOI 10.1007/s11042-013-1605-7
Gesture control interface for immersive
panoramic displays
Marcel Alcoverro· Xavier Suau· Josep R. Morros·
Adolfo López-Méndez· Albert Gil·
Javier Ruiz-Hidalgo· Josep R. Casas
© Springer Science+Business Media New York 2013"
03e83659f0fc98dd03c354a2cc7a90d585ff9cf5,Face Recognition Using Holistic Features and Within Class Scatter-Based PCA,"GSTF JOURNAL ON COMPUTING, VOL. 3, NO. 2, JUNE 2013
(cid:2)(cid:3)(cid:4)(cid:5)(cid:1)(cid:6)(cid:7)(cid:8)(cid:9)(cid:10)(cid:7)(cid:11)(cid:8)(cid:12)(cid:13)(cid:7)(cid:11)(cid:14)(cid:1)(cid:15)(cid:13)(cid:16)(cid:10)(cid:7)(cid:11)(cid:14)(cid:1)(cid:13)(cid:7)(cid:1)(cid:17)(cid:13)(cid:18)(cid:19)(cid:16)(cid:8)(cid:12)(cid:7)(cid:20)(cid:1)(cid:21)(cid:15)(cid:13)(cid:17)(cid:22)(cid:23)(cid:1)(cid:24)(cid:13)(cid:14)(cid:25)(cid:1)(cid:26)(cid:1)(cid:27)(cid:13)(cid:25)(cid:1)(cid:28)(cid:23)(cid:1)(cid:15)(cid:16)(cid:14)(cid:29)(cid:1)(cid:28)(cid:30)(cid:31)(cid:26)
DOI 10.7603/s40601-013-0002-4
Face Recognition Using Holistic Features and
Within Class Scatter-Based PCA
I Gede Pasek Suta Wijaya, Non-Member, IEEE, Keiichi Uchimura, Non-Member, IEEE,
Gou Koutaki, Non-Member, IEEE"
0393723dff4c00262c1daf34c26d27fa6fc52ab6,Pedestrian detection in outdoor images using color and gradients,"Pedestrian Detection in Outdoor Images using Color and Gradients
Marcel H¨aselich
Michael Klostermann
Dietrich Paulus
Active Vision Group, University of Koblenz-Landau, 56070 Koblenz, Germany
{mhaeselich, michaelk,"
0315c68902edca77d2c15cfc1f1335d55343c715,Towards optimal distortion-based visual privacy filters,"TOWARDS OPTIMAL DISTORTION-BASED VISUAL PRIVACY FILTERS
Pavel Korshunov and Touradj Ebrahimi
Multimedia Signal Processing Group, EPFL, Lausanne, Switzerland"
032a1c95388fb5c6e6016dd8597149be40bc9d4d,Finding action tubes,"Finding Action Tubes
Georgia Gkioxari
UC Berkeley
Jitendra Malik
UC Berkeley"
030ff7012b92b805a60976f8dbd6a08c1cecebe6,DCAN: Dual Channel-Wise Alignment Networks for Unsupervised Scene Adaptation,
030646f4fc694ffea5d4f77203cbbc5d02aae797,Cognitive Deep Machine Can Train Itself,"Technical Report: NIPG 2016
COGNITIVE DEEP MACHINE CAN TRAIN ITSELF
A. L˝orincz, M. Csákvári, Á. Fóthi, Z. Á. Milacski, A. Sárkány & Z. T˝osér
Department of Software Technology and Methodology
Faculty of Informatics, Eötvös Loránd University
Pázmány Péter sétány 1/C, Budapest, Hungary, H-1117"
0313924b600ebb8f608705d96c06b133b3b9627a,Deciphering the Crowd: Modeling and Identification of Pedestrian Group Motion,"Sensors 2013, 13, 875-897; doi:10.3390/s130100875
OPEN ACCESS
sensors
ISSN 1424-8220
www.mdpi.com/journal/sensors
Article
Deciphering the Crowd: Modeling and Identification of
Pedestrian Group Motion
Zeynep Y¨ucel *, Francesco Zanlungo, Tetsushi Ikeda, Takahiro Miyashita and Norihiro Hagita
Intelligent Robotics and Communication Laboratories, Advanced Telecommunications Research
Institute International, Kyoto 619-0288, Japan; E-Mails: (F.Z.); (T.I.);
(T.M.); (N.H.)
* Author to whom correspondence should be addressed; E-Mail:
Tel.: +81-774-95-1405.
Received: 14 December 2012; in revised form: 20 December 2012 / Accepted: 4 January 2013 /
Published: 14 January 2013"
038b8b2b629a8ba1e2ad6f9319e16b68e83e518a,Assessing Water Stress of Desert Tamarugo Trees Using in situ Data and Very High Spatial Resolution Remote Sensing,"Remote Sens. 2013, 5, 5064-5088; doi:10.3390/rs5105064
OPEN ACCESS
ISSN 2072-4292
www.mdpi.com/journal/remotesensing
Article
Assessing Water Stress of Desert Tamarugo Trees Using in situ
Roberto O. Chávez 1,*, Jan G. P. W. Clevers 1, Martin Herold 1, Edmundo Acevedo 2
nd Mauricio Ortiz 2,3
6700 AA Wageningen, The Netherlands; E-Mails: (J.G.P.W.C.);
(M.H.)
Laboratorio de Relación Suelo-Agua-Planta, Facultad de Ciencias Agronómicas,
Universidad de Chile, Casilla 1004, Santiago, Chile; E-Mail: (E.A.);
(M.O.)
Centro de Estudios Avanzados en Fruticultura (CEAF), Conicyt-Regional R08I1001,
Av. Salamanca s/n, Rengo, Chile
*  Author to whom correspondence should be addressed; E-Mails: or
Tel.: +31-317-481-552; Fax: +31-317-419-000.
Received: 24 July 2013; in revised form: 12 September 2013 / Accepted: 9 October 2013 /
Published: 15 October 2013"
03ae36b2ed0215b15c5bc7d42fbe20b1491e551a,Learning scene-specific pedestrian detectors without real data,"Learning Scene-Specific Pedestrian Detectors without Real Data
Hironori Hattori1, Vishnu Naresh Boddeti2, Kris Kitani2, Takeo Kanade2
Sony Corporation. 2Carnegie Mellon University.
Figure 1: Overview: For every grid location, geometrically correct renderings of pedestrian are synthetically generated using known scene information
such as camera calibration parameters, obstacles (red), walls (blue) and walkable areas (green). All location-specific pedestrian detectors are trained
jointly to learn a smoothly varying appearance model. Multiple scene-and-location-specific detectors are run in parallel at every grid location.
Consider the scenario in which a new surveillance system is installed
in a novel location and an image-based pedestrian detector must be trained
without access to real scene-specific pedestrian data. A similar situation
may arise when a new imaging system (i.e., a custom camera with unique
lens distortion) has been designed and must be able to detect pedestrians
without the expensive process of collecting data with the new imaging de-
vice. One can use a generic pedestrian detection algorithm trained over co-
pious amounts of real data to work robustly across many scenes. However,
generic models are not always best-suited for detection in specific scenes.
In many surveillance scenarios, it is more important to have a customized
pedestrian detection model that is optimized for a single scene. Optimiz-
ing for a single scene however often requires a labor intensive process of
ollecting labeled data – drawing bounding boxes of pedestrians taken with
particular camera in a specific scene. The process also takes time, as"
036fac2b87cf04c3d93e8a59da618d56a483a97d,Query Adaptive Late Fusion for Image Retrieval,"MANUSCRIPT
Query Adaptive Late Fusion for Image Retrieval
Zhongdao Wang, Liang Zheng, Shengjin Wang"
03df507b31691baeb7343d3eb70d048943e2d4f4,Exploring the Use of Local Descriptors for Fish Recognition in LifeCLEF 2015,"Exploring the use of local descriptors for fish
recognition in LifeCLEF 2015
Jorge Cabrera-G´amez, Modesto Castrill´on-Santana, Antonio Dom´ınguez-Brito,
Daniel Hern´andez-Sosa, Josep Isern-Gonz´alez, and Javier Lorenzo-Navarro
Universidad de Las Palmas de Gran Canaria
SIANI
Spain
http://berlioz.dis.ulpgc.es/roc-siani"
03f6d738f9b916f80ce22c3ba605a0fa4d7830c1,Automated Reconstruction of Evolving Curvilinear Tree Structures,"POUR L'OBTENTION DU GRADE DE DOCTEUR ÈS SCIENCESacceptée sur proposition du jury:Prof. P. Dillenbourg, président du juryProf. P. Fua, directeur de thèseDr F. Moreno-Noguer, rapporteurDr R. Sznitman, rapporteurProf. S. Süsstrunk, rapporteuseAutomated Reconstruction of Evolving Curvilinear Tree StructuresTHÈSE NO 6930 (2016)ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNEPRÉSENTÉE LE 18 MARS 2016À LA FACULTÉ INFORMATIQUE ET COMMUNICATIONSLABORATOIRE DE VISION PAR ORDINATEURPROGRAMME DOCTORAL EN INFORMATIQUE ET COMMUNICATIONS Suisse2016PAR(cid:51)(cid:85)(cid:93)(cid:72)(cid:80)(cid:92)(cid:86)(cid:227)(cid:68)(cid:90)(cid:3)(cid:53)(cid:68)(cid:73)(cid:68)(cid:227)(cid:3)(cid:42)(cid:226)(cid:50)(cid:58)(cid:36)(cid:38)(cid:46)(cid:44)"
03161081b47eba967fd3e663c57ec2f99f66eebd,Face and Facial Feature Localization,"Face and facial feature localization
Paola Campadelli?, Raffaella Lanzarotti??, Giuseppe Lipori, and Eleonora Salvi
Dipartimento di Scienze dell’Informazione
Universit(cid:30)a degli Studi di Milano
Via Comelico, 39/41 - 20135 Milano, Italy
fcampadelli, lanzarotti,
http://homes.dsi.unimi.it/(cid:24)campadel/LAIV/"
03f3bde03f83c3ff4f346d761fde4ce031dd4c69,Deep Models Calibration with Bayesian Neural Networks,"Under review as a conference paper at ICLR 2019
DEEP MODELS CALIBRATION WITH BAYESIAN NEURAL
NETWORKS
Anonymous authors
Paper under double-blind review"
cbca355c5467f501d37b919d8b2a17dcb39d3ef9,Super-resolution of Very Low Resolution Faces from Videos,"CANSIZOGLU, JONES: SUPER-RESOLUTION OF VERY LR FACES FROM VIDEOS
Super-resolution of Very Low-Resolution
Faces from Videos
Esra Ataer-Cansizoglu
Michael Jones
Mitsubishi Electric Research Labs
(MERL)
Cambridge, MA, USA"
cba90ec61155a233fee33b529401e65d9481213a,Houdini: Fooling Deep Structured Prediction Models,"Houdini: Fooling Deep Structured Prediction Models
Moustapha Cisse
Facebook AI Research
Natalia Neverova*
Facebook AI Research"
cb1214e42fa81977bc21f4b3c8e194a9b68278f5,Visually Aligned Word Embeddings for Improving Zero-shot Learning,"Qiao et al.: Visually Aligned Word Embeddings. Appearing in Proc. British Mach. Vis. Conf. 2017
Visually Aligned Word Embeddings for Improving
Zero-shot Learning
School of Computer Science, University of
Adelaide, Australia
Ruizhi Qiao
Lingqiao Liu
Chunhua Shen
Anton van den Hengel"
cbe4eac494fa7bc0bcd958d67393f097e0cb17db,A Multi-task Learning Approach for Image Captioning,Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18)
cb84229e005645e8623a866d3d7956c197f85e11,Disambiguating Visual Verbs,"Disambiguating Visual Verbs
Spandana Gella, Frank Keller, and Mirella Lapata"
cbef6ee44bb87f54d80ea2ca7d1266bc624d9d07,Deep Graphical Feature Learning for Face Sketch Synthesis,Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
cb8b2db657cd6b6ccac13b56e2ca62b7d88eda68,Log Hyperbolic Cosine Loss Improves Varia-,"Under review as a conference paper at ICLR 2019
LOG HYPERBOLIC COSINE LOSS IMPROVES VARIA-
TIONAL AUTO-ENCODER
Anonymous authors
Paper under double-blind review"
cb8567f074573a0d66d50e75b5a91df283ccd503,Large Margin Learning in Set-to-Set Similarity Comparison for Person Reidentification,"Large Margin Learning in Set to Set Similarity
Comparison for Person Re-identification
Sanping Zhou, Jinjun Wang, Rui Shi, Qiqi Hou, Yihong Gong, Nanning Zheng"
cbdca5e0f1fd3fd745430497d372a2a30b7bb0c5,Towards Distributed Coevolutionary GANs,"Towards Distributed Coevolutionary GANs
Abdullah Al-Dujaili, Tom Schmiedlechner, Erik Hemberg and Una-May O’Reilly
CSAIL, MIT, USA"
cb7bbede1c2eae831dd73440f439955c4310837f,Cross-Cultural and Cultural-Specific Production and Perception of Facial Expressions of Emotion in the Wild,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
Cross-Cultural and Cultural-Specific Production
nd Perception of Facial Expressions of Emotion
in the Wild
Ramprakash Srinivasan, Aleix M. Martinez"
cb6be69c67b0b15ebbda89a126f4dd62a4d32958,Igure Qa : a N a Nnotated F Igure D Ataset for V Isual R Easoning,"Workshop track - ICLR 2018
FIGUREQA: AN ANNOTATED FIGURE DATASET FOR
VISUAL REASONING
Samira Ebrahimi Kahou1∗, Vincent Michalski2∗†, Adam Atkinson1,
Ákos Kádár3†, Adam Trischler1, Yoshua Bengio3
Microsoft Research Montréal
Université de Montréal, MILA
Tilburg University"
cb96c819f20f05ad0d85bba91f86795162f63445,Noisy Ocular Recognition Based on Three Convolutional Neural Networks,"Article
Noisy Ocular Recognition Based on Three
Convolutional Neural Networks
Min Beom Lee, Hyung Gil Hong and Kang Ryoung Park *
Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu,
Seoul 100-715, Korea; (M.B.L.); (H.G.H.)
* Correspondence: Tel.: +82-10-3111-7022; Fax: +82-2-2277-8735
Received: 18 October 2017; Accepted: 14 December 2017; Published: 17 December 2017"
cb4418b5bddaaceb92caea9e72c8cc528ce4e3cc,Generative Semantic Manipulation with Contrasting GAN,"Generative Semantic Manipulation with Contrasting
Xiaodan Liang, Hao Zhang, Eric P. Xing
Carnegie Mellon University and Petuum Inc.
{xiaodan1, hao,"
cb11a150fc245958799e763069a6ae3080814d40,3d Face Recognition from Range Image,
cb94ea16f12bde2de91d3cf3fac03a20b02611b1,Element-wise Bilinear Interaction for Sentence Matching,"Proceedings of the 7th Joint Conference on Lexical and Computational Semantics (*SEM), pages 107–112
New Orleans, June 5-6, 2018. c(cid:13)2018 Association for Computational Linguistics"
cb4f0656ce177161667759b46e20aec5488550fa,Learning with single view . . .,"Washington University in St. Louis
School of Engineering and Applied Science
Department of Computer Science and Engineering
Dissertation Examination Committee:
Kilian Q. Weinberger, Chair
John Blitzer
John Cunningham
Tao Ju
Robert Pless
Bill Smart
Learning with Single View Co-training and Marginalized Dropout
Minmin Chen
A dissertation presented to the Graduate School of Arts and Sciences
of Washington University in partial fulfillment of the
requirements for the degree of
Doctor of Philosophy
May 2013
Saint Louis, Missouri"
cb30c1370885033bc833bc7ef90a25ee0900c461,FaceOff: Anonymizing Videos in the Operating Rooms,"FaceOff: Anonymizing Videos in the Operating
Rooms
Evangello Flouty1, Odysseas Zisimopoulos1, and Danail Stoyanov1,2
Wellcome / ESPRC Centre for Interventional and Surgical Sciences, London,
Digital Surgery, London, United Kingdom
United Kingdom"
cb658e9e0823dc7afe66b593307b230cc2747790,Nouveau modèle pour la datation automatique de photographies à partir de caractéristiques visuelles,"Nouveau modèle pour la datation
utomatique de photographies
à partir de caractéristiques visuelles1
Paul MARTIN* — Antoine DOUCET** — Frédéric JURIE*
* Laboratoire GREYC [UMR 6072], Université de Caen Normandie, FRANCE 14032
{paul.martin ;
** Laboratoire L3i, Université de La Rochelle, FRANCE 17042
RÉSUMÉ. Nous présentons, dans cet article, une méthode de datation de photographies par
l’usage du contenu visuel de celles-ci. Nous nous sommes inspirés de travaux récents de la
vision par ordinateur. Nous avons amélioré la méthode de classification utilisée dans ces tra-
vaux en dépassant une limite intrinsèque de leur approche. En effet, ils considèrent la datation
d’images comme un problème de classification multi-classes, pour lequel une classe repré-
sente un ensemble d’années, mais ignorant l’ordre relatif sous-jacent à l’information tempo-
relle. Dans leur approche soit une prédiction est bonne (période valide) soit elle est mauvaise
(période invalide) mais aucune différence n’est faite entre se tromper d’une décennie ou de
plusieurs. Nos travaux, s’appuient sur des avancées récentes en classification ordinale. Nous
onsidérons les dates comme des attributs à la fois ordonnés et relatifs et nous proposons un
adre spécifique pour les manipuler."
cb76c7002b9a813405359b2bc2a29756bf426bb4,Combination of In Vogue Algorithms for Human Detection and Tracking,"International Journal of Computer Applications (0975 – 8887)
National Conference “Electronics, Signals, Communication and Optimization"" (NCESCO 2015)
Combination of In Vogue Algorithms for Human
Detection and Tracking
Bhuvanarjun K.M.
PG Scholar, Signal Processing,
Siddaganga Institute of Technology,
Tumakuru, Karnataka (India)."
cbe859d151466315a050a6925d54a8d3dbad591f,Gaze shifts as dynamical random sampling,"GAZE SHIFTS AS DYNAMICAL RANDOM SAMPLING
Giuseppe Boccignone
Mario Ferraro
Dipartimento di Scienze dell’Informazione
Universit´a di Milano
Via Comelico 39/41
0135 Milano, Italy"
cbae3eaf926aede9bec7ce2e28c35c1c50b1b43f,Fast RGB-D people tracking for service robots,"Noname manuscript No.
(will be inserted by the editor)
Fast RGB-D People Tracking for Service Robots
Matteo Munaro · Emanuele Menegatti
Received: date / Accepted: date"
cb1018a7eadc1b4f686c15d2abd2723d1e6eeec3,Image-based people detection,"Image-based people detection
Tommi Tikkanen
(78748P)
Valvoja: Arto Visala
AS-0.3100 Automaatio- ja systeemitekniikan seminaari
Syksy 2013"
cb38b4a5e517b4bcb00efbb361f4bdcbcf1dca2c,Learning towards Minimum Hyperspherical Energy,"Learning towards Minimum Hyperspherical Energy
Weiyang Liu1,*, Rongmei Lin2,*, Zhen Liu1,*, Lixin Liu3,*, Zhiding Yu4, Bo Dai1,5, Le Song1,6
Georgia Institute of Technology 2Emory University
South China University of Technology 4NVIDIA 5Google Brain 6Ant Financial"
cbd1a47356d8dc1c46d569d55eb803945193f9b0,SurfConv: Bridging 3D and 2D Convolution for RGBD Images,"SurfConv: Bridging 3D and 2D Convolution for RGBD Images
Hang Chu1,2 Wei-Chiu Ma3 Kaustav Kundu1,2 Raquel Urtasun1,2,3 Sanja Fidler1,2
University of Toronto 2Vector Institute 3Uber ATG
{chuhang1122, kkundu,
{weichiu,"
cb3d38cd18c99aca9c2a228aeb4998f394c7b1b3,Impairments in facial affect recognition associated with autism spectrum disorders: a meta-analysis.,"# Cambridge University Press 2014
doi:10.1017/S0954579414000479
Impairments in facial affect recognition associated with autism
spectrum disorders: A meta-analysis
LEAH M. LOZIER, JOHN W. VANMETER, AND ABIGAIL A. MARSH
Georgetown University"
cb34481714bc7194ac108a1568d34e120f256405,Audio Visual Scene-Aware Dialog (AVSD) Challenge at DSTC7,"Audio Visual Scene-Aware Dialog (AVSD) Challenge at DSTC7
Huda Alamri∗†, Vincent Cartillier∗, Raphael Gontijo Lopes∗, Abhishek Das∗, Jue Wang†,
Irfan Essa∗, Dhruv Batra∗, Devi Parikh∗,
Anoop Cherian†, Tim K. Marks†, Chiori Hori†
School of Interactive Computing, Georgia Tech
Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA, USA"
cb4fc4d49783f2049c48a062169f04eb744443ec,Paying More Attention to Saliency: Image Captioning with Saliency and Context Attention,"Paying More Attention to Saliency: Image Captioning with
Saliency and Context Attention
MARCELLA CORNIA, University of Modena and Reggio Emilia
LORENZO BARALDI, University of Modena and Reggio Emilia
GIUSEPPE SERRA, University of Udine
RITA CUCCHIARA, University of Modena and Reggio Emilia
Image captioning has been recently gaining a lot of attention thanks to the impressive achievements shown by
deep captioning architectures, which combine Convolutional Neural Networks to extract image representations,
nd Recurrent Neural Networks to generate the corresponding captions. At the same time, a significant research
effort has been dedicated to the development of saliency prediction models, which can predict human eye
fixations. Even though saliency information could be useful to condition an image captioning architecture, by
providing an indication of what is salient and what is not, research is still struggling to incorporate these two
techniques. In this work, we propose an image captioning approach in which a generative recurrent neural
network can focus on different parts of the input image during the generation of the caption, by exploiting
the conditioning given by a saliency prediction model on which parts of the image are salient and which are
ontextual. We show, through extensive quantitative and qualitative experiments on large scale datasets, that
our model achieves superior performances with respect to captioning baselines with and without saliency,
nd to different state of the art approaches combining saliency and captioning.
CCS Concepts: • Computing methodologies → Scene understanding; Natural language generation;
Additional Key Words and Phrases: saliency, visual saliency prediction, image captioning, deep learning."
a8eeace37181dd87d5125c213add6e15fdd9d9f7,Approximate Fisher Kernels of Non-iid Image Models for Image Categorization,"Approximate Fisher Kernels of non-iid Image
Models for Image Categorization
Ramazan Gokberk Cinbis, Jakob Verbeek, and Cordelia Schmid, Fellow, IEEE"
a8d41c63462da7dbddf4094eddaa0bb6d72d0fdc,A Semantic-based Method for Visualizing Large Image Collections.,"A Semantic-based Method for
Visualizing Large Image Collections
Xiao Xie, Xiwen Cai, Junpei Zhou, Nan Cao, Yingcai Wu"
a8c58660bf2ee1fddc3ef05ce52c42775eb0b2b7,Multitype Activity Recognition in Robot-Centric Scenarios,"Multi-Type Activity Recognition in Robot-Centric Scenarios
Ilaria Gori1, J. K. Aggarwal1, Larry Matthies2, and M. S. Ryoo3"
a8ed00afc46064b18a6bcc7aa282e554891eacf2,Underwater image restoration: super-resolution and deblurring via sparse representation and denoising by means of marine snow removal,"Underwater Image Restoration:
Super-resolution and Deblurring via Sparse Representation and
Denoising by Means of Marine Snow Removal
Dissertation
Erlangung des akademischen Grades
Doktor-Ingenieur (Dr.-Ing)
der Fakultät für Informatik und Elektrotechnik
der Universität Rostock
vorgelegt von
Fahimeh Farhadifard
geb. am 05.11.1985 in Mashhad/Iran
us Rostock
Rostock, den 27. Oktober 2017"
a856449c724f958dbb2f0629228d26a322153ba3,Face Mask Extraction in Video Sequence,"Face Mask Extraction in Video Sequence
Yujiang Wang 1 · Bingnan Luo 1 · Jie Shen 1 · Maja Pantic 1"
a870a4672dcdc53f8f85828cb64da50e51f05348,AUDIO-VISUAL PERCEPTION OF HUMANS FOR A HUMANOID ROBOT,"AUDIO-VISUAL PERCEPTION OF HUMANS FOR A HUMANOID ROBOT
Kai Nickel, Hazim K. Ekenel, Michael Voit, Rainer Stiefelhagen
Interactive Systems Labs
Universit¨at Karlsruhe (TH)"
a8788ce65d01018a0e1b4cdaf6466f495e68f7e3,A Probabilistic Retrieval Model for Word Spotting based on Direct Attribute Prediction,"A Probabilistic Retrieval Model
for Word Spotting based on
Direct Attribute Prediction
Eugen Rusakov, Leonard Rothacker, Hyunho Mo, and Gernot A. Fink
Department of Computer Science
TU Dortmund University
4221 Dortmund, Germany
Email:{eugen.rusakov, leonard.rothacker, hyunho.mo,"
a8a30a8c50d9c4bb8e6d2dd84bc5b8b7f2c84dd8,This is a repository copy of Modelling of Orthogonal Craniofacial Profiles,"This is a repository copy of Modelling of Orthogonal Craniofacial Profiles.
White Rose Research Online URL for this paper:
http://eprints.whiterose.ac.uk/131767/
Version: Published Version
Article:
Dai, Hang, Pears, Nicholas Edwin orcid.org/0000-0001-9513-5634 and Duncan, Christian
(2017) Modelling of Orthogonal Craniofacial Profiles. Journal of Imaging. ISSN 2313-433X
https://doi.org/10.3390/jimaging3040055
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emailing including the URL of the record and the reason for the withdrawal request.
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a85007fe09e96e13cc30992ba19d71cae75634c6,BlazeIt : An OptimizingQuery Engine for Video at Scale Extended Abstract,BlazeIt: An Optimizing Query Engine for Video at Scale
a83fc450c124b7e640adc762e95e3bb6b423b310,Deep Face Feature for Face Alignment and Reconstruction,"Deep Face Feature for Face Alignment
Boyi Jiang, Juyong Zhang, Bailin Deng, Yudong Guo and Ligang Liu"
a887937b813f267507203d4faef1013043cf09d0,Automatic Process to Build a Contextualized Detector,"AUTOMATIC PROCESS TO BUILD A CONTEXTUALIZED
DETECTOR
Thierry Chesnais1, Nicolas Allezard1, Yoann Dhome1 and Thierry Chateau2
CEA, LIST, Vision and Content Engineering Laboratory, Point Courrier 94, F-91191 Gif-sur-Yvette, France
Lasmea, UMR6602, CNRS, Blaise Pascal University, Clermont-Ferrand, France
{thierry.chesnais, nicolas.allezard,
video surveillance, object detection, pedestrian detection, semi-supervised learning, oracle.
Keywords:"
a8d5f1416760dbd8d2993358938f85c7ab04e85a,Robust Multi-person Tracking for Real-Time Intelligent Video Surveillance,"Robust Multi-person Tracking for
Real-Time Intelligent Video Surveillance
We propose a novel multiple-object tracking algorithm
for  real-time  intelligent  video  surveillance.  We  adopt
particle filtering as our tracking framework. Background
modeling and subtraction are used to generate a region of
interest.  A  two-step  pedestrian  detection  is  employed  to
reduce  the  computation  time  of  the  algorithm,  and  an
iterative  particle  repropagation  method  is  proposed  to
enhance  its  tracking  accuracy.  A  matching  score  for
greedy data association is proposed to assign the detection
results  of  the  two-step  pedestrian  detector  to  trackers.
Various  experimental  results  demonstrate  that  the
proposed algorithm tracks multiple objects accurately and
precisely in real time.
Keywords:  Multiple-object  tracking,  particle  filter,
ackground  modeling,  pedestrian  detection,  real-time
pplications, video surveillance applications.
Manuscript received May 22, 2014; revised Jan. 1, 2015; accepted Jan. 17, 2015.
This work was partly supported by the ICT R&D program of the MSIP, Rep. of Korea (No."
a8bf49021017e19df051a3efb7337d93ea263e37,Deep Multiple Instance Hashing for Object-based Image Retrieval,"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
queryRetrieval ResultFigure1:Queryingaboutmultipleobjects.toryresults.Inparticular,theerrorsinthesegmentationstepwillbepropagatedtotheindex,anditisdifficulttofindop-timalhand-craftedfeaturesforindexcreation.Ontheotherhand,mostexistingobject-basedimageretrievalapproachesconcentrateonqueryingbyasingleobject.However,usersmayalsowanttoqueryaboutmultipleobjects,asillustratedinFig.1:intheupperexampletheuserisinterestedinpho-toscontainingbothhumanandhorses,asinthequery;inthelowercasetheusermaywanttowriteapoliticalcommentaryaboutObamaandPutinbutthereisnogroupphotoofthemathand.Duetothelackofabilitytodetectvariousobjects,previousapproacheswillincurpoorperformancewhentheyaredirectlyusedtocopewithmulti-objectqueries.Recentadvancesindeeplearninghaveprovedthatconvo-lutionalneuralnetworks(CNNs)trainedend-to-endcanlearnpowerfulfeaturerepresentations.Intermsofobjectdetec-tion,anumberoftechniquesbasedondeepCNNshavebeenproposed[Renetal.,2015;Liuetal.,2016]andachievedgoodresultsonsomehigh-qualitypublicimagedatasetslikeImageNet,PASCALVOCandMSCOCO.However,theseapproachesnecessitatelarge-scaletrainingdatawithlabelsofobjectlocationstolearnmodelstoharvest“objectness”,whereasthelabelingworkisverytediousandexpensive.Itisoftenthecasethatweonlyhaveimage-levelobjectlabelswithoutobjectlocations,i.e.weaklabelsforobjectdetection"
a81d396c9210282d461f9f08b7b9794b096ecdfe,FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising,"FFDNet: Toward a Fast and Flexible Solution for
CNN based Image Denoising
Kai Zhang, Wangmeng Zuo, Senior Member, IEEE, and Lei Zhang, Fellow, IEEE"
a8a45d083e5535812f991afb686b4e36ea9f0717,Face Recognition Using Gradient Derivative Local Binary Patterns,"International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 7 (2017) pp. 1316-1323
© Research India Publications.  http://www.ripublication.com
Face Recognition Using Gradient Derivative Local Binary Patterns"
a87ab836771164adb95d6744027e62e05f47fd96,Understanding human-human interactions: a survey,"Understanding human-human interactions: a survey
Alexandros Stergiou
Department of Information and Computing Sciences, Utrecht University,Buys Ballotgebouw, Princetonplein 5, Utrecht, 3584CC, Netherlands
Department of Information and Computing Sciences, Utrecht University,Buys Ballotgebouw, Princetonplein 5, Utrecht, 3584CC, Netherlands
Ronald Poppe1"
a825364ada04007221577d305cbc42d413239f03,LaFiDa - A Laserscanner Multi-Fisheye Camera Dataset,"Article
LaFiDa—A Laserscanner Multi-Fisheye Camera Dataset
Steffen Urban * and Boris Jutzi *
Institute of Photogrammetry and Remote Sensing (IPF) Karlsruhe Institute of Technology (KIT), Englerstr. 7,
76131 Karlsruhe, Germany
* Correspondence: (S.U.); (B.J.)
Academic Editors: Hassan Ugail, Lihua You and Gonzalo Pajares Martinsanz
Received: 22 September 2016; Accepted: 4 January 2017; Published: 17 January 2017"
a8e5d204549fcf93c5bea88b0f99a2e4da9648e7,Neuropeptidergic regulation of affiliative behavior and social bonding in animals,"www.elsevier.com/locate/yhbeh
Neuropeptidergic regulation of affiliative behavior and
social bonding in animals
Miranda M. Lim 1, Larry J. Young ⁎
Center for Behavioral Neuroscience, Department of Psychiatry and Behavioral Sciences, and 954 Gatewood Road Yerkes National Primate Research Center,
Emory University, Atlanta, GA 30322, USA
Received 16 May 2006; revised 26 June 2006; accepted 27 June 2006
Available online 4 August 2006"
a8420e7fa53b81b8069ced8d9c743c141e2fc432,Real-Time Multiple Object Tracking - A Study on the Importance of Speed,"Real-TimeMultipleObjectTrackingAStudyontheImportanceofSpeedSAMUELMURRAYMaster’sProgramme,MachineLearningDate:September28,2017Supervisor:KevinSmithExaminer:HedvigKjellströmPrincipal:HelmutPrendinger,NationalInstituteofInformatics,TokyoSwedishtitle:IdentifieringavrörligaobjektirealtidSchoolofComputerScienceandCommunication"
a81769a36c9ed7b6146a408eb253eb8e0d3ad41e,Super-Fine Attributes with Crowd Prototyping.,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
Super-Fine Attributes
with Crowd Prototyping
Daniel Martinho-Corbishley, Mark S. Nixon and John N. Carter"
a88640045d13fc0207ac816b0bb532e42bcccf36,Simultaneously Learning Neighborship and Projection Matrix for Supervised Dimensionality Reduction,"ARXIV VERSION
Simultaneously Learning Neighborship and
Projection Matrix for Supervised
Dimensionality Reduction
Yanwei Pang, Senior Member, IEEE, Bo Zhou, and Feiping Nie, Senior Member, IEEE"
a8948941f7a24c09cd7c26f3635d8571c7998570,Face recognition of Pose and Illumination changes using Extended ASM and Robust sparse,"IOSR Journal of Dental and Medical Sciences (IOSR-JDMS)
e-ISSN: 2279-0853, p-ISSN: 2279-0861.Volume 13, Issue 3 Ver. VI. (Mar. 2014), PP 49-54
www.iosrjournals.org
Face recognition of Pose and Illumination changes using
Extended ASM and Robust sparse coding
Arulmurugan R1, Laxmi Priya M.R2
(Information Technology, Bannari Amman Institute of Technology, India)
(Information Technology, Bannari Amman Institute of Technology, India)"
a8c776ad5f4e0ccf9a3e6c5073e8951bb11d187a,Spontaneous Inference of Personality Traits and Effects on Memory for Online Profiles,"Spontaneous Inference of Personality Traits and Effects on Memory for
Online Profiles
Kristin Stecher & Scott Counts
University of Washington & Microsoft Research
Department of Psychology, 351525, Seattle, WA, 98195
One Microsoft Way, Redmond, WA, 98052"
a8638a07465fe388ae5da0e8a68e62a4ee322d68,How to predict the global instantaneous feeling induced by a facial picture,"How to predict the global instantaneous feeling induced
y a facial picture?
Arnaud Lienhard, Patricia Ladret, Alice Caplier
To cite this version:
Arnaud Lienhard, Patricia Ladret, Alice Caplier. How to predict the global instantaneous
feeling induced by a facial picture?. Signal Processing: Image Communication, Elsevier, 2015,
pp.1-30. .
HAL Id: hal-01198718
https://hal.archives-ouvertes.fr/hal-01198718
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´emanant des ´etablissements d’enseignement et de"
a8d3dc5c68032c60ebbe3b547ac948d7cf8dd1d8,Multi-Label Zero-Shot Learning via Concept Embedding,"Multi-Label Zero-Shot Learning via Concept
Embedding
Ubai Sandouk and Ke Chen"
8c6c0783d90e4591a407a239bf6684960b72f34e,Knowledge Management in a Large Organization : a Practical Case Study,"SESSION
KNOWLEDGE ENGINEERING AND
MANAGEMENT + KNOWLEDGE ACQUISITION
Chair(s)
Int'l Conf. Information and Knowledge Engineering | IKE'13 |1"
8c6db488bebf3fb2c50d951c4a9f002a531f9b61,Modeling Multimodal Dynamic Spatiotemporal Graphs,"Modeling Multimodal Dynamic Spatiotemporal Graphs
Boris Ivanovic
Marco Pavone"
8c7f4c11b0c9e8edf62a0f5e6cf0dd9d2da431fa,Dataset Augmentation for Pose and Lighting Invariant Face Recognition,"Dataset Augmentation for Pose and Lighting
Invariant Face Recognition
Daniel Crispell∗, Octavian Biris∗, Nate Crosswhite†, Jeffrey Byrne†, Joseph L. Mundy∗
Vision Systems, Inc.
Systems and Technology Research"
8c2c4e3db2de3305b1d25368695c258fea6a6076,The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems,"IEEE 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, Hawaii, USA, November 2018
The highD Dataset: A Drone Dataset of Naturalistic Vehicle
Trajectories on German Highways for Validation of Highly
Automated Driving Systems
Robert Krajewski, Julian Bock, Laurent Kloeker and Lutz Eckstein
Figure 1.   Example of a recorded highway including bounding boxes and labels of detected vehicles. The color of the bounding boxes indicates the class of
the detected object (car: yellow, truck: green). Every vehicle is assigned a unique id for tracking and its speed is estimated over time."
8cc07ae9510854ec6e79190cc150f9f1fe98a238,Using Deep Learning to Challenge Safety Standard for Highly Autonomous Machines in Agriculture,"Article
Using Deep Learning to Challenge Safety Standard
for Highly Autonomous Machines in Agriculture
Kim Arild Steen *,†, Peter Christiansen †, Henrik Karstoft and Rasmus Nyholm Jørgensen
Department of Engineering, Aarhus University, Finlandsgade 22 8200 Aarhus N, Denmark;
(P.C.); (H.K.); (R.N.J.)
* Correspondence: Tel.: +45-3116-8628
These authors contributed equally to this work.
Academic Editors: Francisco Rovira-Más and Gonzalo Pajares Martinsanz
Received: 18 December 2015; Accepted: 2 February 2016; Published: 15 February 2016"
8c13f2900264b5cf65591e65f11e3f4a35408b48,A GENERIC FACE REPRESENTATION APPROACH FOR LOCAL APPEARANCE BASED FACE VERIFICATION,"A GENERIC FACE REPRESENTATION APPROACH FOR
LOCAL APPEARANCE BASED FACE VERIFICATION
Hazim Kemal Ekenel, Rainer Stiefelhagen
Interactive Systems Labs, Universität Karlsruhe (TH)
76131 Karlsruhe, Germany
{ekenel,
web: http://isl.ira.uka.de/face_recognition/"
8cb4349f7d4b04a2e98b727524d3699bad50de1c,SOCIAL GAME EPITOME VERSUS AUTOMATIC VISUAL ANALYSIS Paper ID ***,"SOCIAL GAME EPITOME VERSUS AUTOMATIC VISUAL ANALYSIS
Paper ID ***"
8ca29760334b7bdeaa7ad7ae4ff54c3b24420dd2,Analysis of Dynamic Characteristics of Spontaneous Facial Expressions,"Analysis of Dynamic Characteristics of Spontaneous Facial Expressions
Masashi Komori  Yoshitaro Onishi
Division of Information and Computer Sciences, Osaka Electro-Communication University,
8-8 Hatsucho, Neyagawa, Osaka, 572-8530, JAPAN"
8c955f3827a27e92b6858497284a9559d2d0623a,"Tom 53 ( 67 ) , Fascicola 1-2 , 2008 Facial Expression Recognition under Noisy Environment Using Gabor Filters","Buletinul Ştiinţific al Universităţii ""Politehnica"" din Timişoara
Seria ELECTRONICĂ şi TELECOMUNICAŢII
TRANSACTIONS on ELECTRONICS and COMMUNICATIONS
Tom 53(67), Fascicola 1-2, 2008
Facial Expression Recognition under Noisy Environment
Using Gabor Filters
Ioan Buciu1, I. Nafornita2, I. Pitas3"
8c244417db2082f4d5897548e72ef304ae886e52,Tree Based Space Partition of Trajectory Pattern Mining For Frequent Item Sets,"Australian Journal of Basic and Applied Sciences, 10(2) Special 2016, Pages: 250-261
Australian Journal of Basic and Applied Sciences
AUSTRALIAN JOURNAL OF BASIC AND
AUSTRALIAN JOURNAL OF BASIC AND
APPLIED SCIENCES
ISSN:1991-8178        EISSN: 2309-8414
Journal home page: www.ajbasweb.com
Tree  Based  Space  Partition  of  Trajectory  Pattern  Mining  For  Frequent
Tree  Based  Space  Partition  of  Trajectory  Pattern  Mining  For  Frequent
Tree  Based  Space  Partition  of  Trajectory  Pattern  Mining  For  Frequent
Item Sets
nd Engineering  , Alagappa University, Tamil Nadu, India.
P.Geetha and 2 E. Ramaraj
Ph.D scholar, Alagappa University.
Department of Computer Science and Engineering
Address For Correspondence:
P.Geetha, Ph.D scholar, Alagappa University.
Ph.D scholar, Alagappa University.
A R T I C L E   I N F O
Article history:"
8c5fa29c9bcab3d518fdf355e9da62fb0b58905e,Exploiting Semantics in Adversarial Training for Image-Level Domain Adaptation,"Exploiting Semantics in Adversarial Training for
Image-Level Domain Adaptation
st Pierluigi Zama Ramirez
University of Bologna
nd Alessio Tonioni
University of Bologna
rd Luigi Di Stefano
University of Bologna"
8c0f38c7c07c631d0b5414a84dda2992bdc4514f,Development of an Efficient Face Recognition System Based on Linear and Nonlinear Algorithms,"Development of an Efficient Face Recognition System based on Linear and Nonlinear Algorithms
{tag}                                                 {/tag}
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 134
Number 7
Year of Publication: 2016
Authors:
Filani Araoluwa S., Adetunmbi Adebayo O.
10.5120/ijca2016907932
{bibtex}2016907932.bib{/bibtex}"
8c5852530abaefcdce805d1e339677351c6ec7fe,Lernen situationsunabhängiger Personenerkennung,"{ HAUPTBEITRAG / SITUATIONSUNABHÄNGIGE PERSONENERKENNUNG
Lernen situationsunabhängiger
Personenerkennung
Marco K. Müller · Michael Tremer
Christian Bodenstein · Rolf P. Würtz
Einleitung
In den vergangenen 25 Jahren hat sich automati-
sche Gesichtserkennung von einem akademischen
Projekt zu einer reifen Technik entwickelt. Bei der
Frage, ob es sich auf zwei Fotos um die gleiche Per-
son handelt, sind kommerzielle Systeme inzwischen
sogar Menschen überlegen [6]. Dies ist nicht mit der
Erkennung von bekannten Personen zu verwech-
seln, die der Mensch in sehr vielen verschiedenen
Situationen auch nach vielen Jahren wiedererkennen
kann.
Es ist eine zentrale Aufgabe des Computersehens,
ekannte Objekte in Bildern wiederzuerkennen. Dies
ist schwierig, weil dasselbe Objekt in verschiede-
nen Situationen sehr verschiedene Bilder erzeugt."
8cb6daba2cb1e208e809633133adfee0183b8dd2,Know Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models,"Know Before You Do: Anticipating Maneuvers
via Learning Temporal Driving Models
Ashesh Jain, Hema S Koppula, Bharad Raghavan, Shane Soh, Ashutosh Saxena
Cornell University and Stanford University"
8c63bf3b7b35d4ba6309cf19a181f6aaf8b83435,Sustained striatal activity predicts eudaimonic well-being and cortisol output.,"http://pss.sagepub.com/
Aaron S. Heller, Carien M. van Reekum, Stacey M. Schaefer, Regina C. Lapate, Barry T. Radler, Carol D. Ryff and Richard
Sustained Striatal Activity Predicts Eudaimonic Well-Being and Cortisol Output
J. Davidson
2013 24: 2191 originally published online 20 September 2013
DOI: 10.1177/0956797613490744
The online version of this article can be found at:
http://pss.sagepub.com/content/24/11/2191
Published by:
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8c3c699f568ee825eefc4dc44b71c8b0bc592cca,Binary Multi-View Clustering.,"Binary Multi-View Clustering
Zheng Zhang†, Li Liu†, Fumin Shen, Heng Tao Shen, Ling Shao*"
8c305b2d8982135da852cb005dcf602a02f04118,FAST RADIOMETRY GUIDED FUSION OF DISPARITY IMAGES,"FAST RADIOMETRY GUIDED FUSION OF DISPARITY IMAGES
Stephan Schmida, Dieter Fritschb
Institute for Photogrammetry, University of Stuttgart -
Daimler AG -
Commission III, WG III/1
KEY WORDS: Disparity fusion, multi-view stereo, real-time stereo, machine vision, photo-consistency"
8ce9b7b52d05701d5ef4a573095db66ce60a7e1c,Structured Sparse Subspace Clustering: A Joint Affinity Learning and Subspace Clustering Framework,"Structured Sparse Subspace Clustering: A Joint
Affinity Learning and Subspace Clustering
Framework
Chun-Guang Li, Chong You, and Ren´e Vidal"
8ce4a669358dadbfac69ff0d313af042aeb94de1,Pedestrian intention recognition using Latent-dynamic Conditional Random Fields,"Pedestrian Intention Recognition using Latent-dynamic Conditional
Random Fields
Andreas Th. Schulz1 and Rainer Stiefelhagen2"
8cf3b70f9247be23de9cc42272464e0363acb426,Multi-label classification of a real-world image dataset,"Multi-label classification of a real-world
image dataset
Nikita Uvarov
Applied Computer Science
Submission date: June 2017
Supervisor:
Co-supervisor:
Rune Hjelsvold, IDI
Faouzi Alaya Cheikh, IDI
Norwegian University of Science and Technology
Department of Computer Science"
8c30b154811453b6a1017bb27e3becefde44f689,Bibliometric profile of the global scientific research on autism spectrum disorders,"Sweileh et al. SpringerPlus  (2016) 5:1480
DOI 10.1186/s40064-016-3165-6
RESEARCH
Bibliometric profile of the global
scientific research on autism
spectrum disorders
Waleed M. Sweileh1*, Samah W. Al‑Jabi2, Ansam F. Sawalha1 and Sa’ed H. Zyoud2
Open Access"
8c9b29d6df5601d0bc9f6164d99a26a1cb1e256d,Two-Dimensional Joint Bayesian Method for Face Verification,"J Inf Process Syst, Vol.12, No.3, pp.381~391, September 2016
ISSN 1976-913X (Print)
ISSN 2092-805X (Electronic)
Two-Dimensional Joint Bayesian Method
for Face Verification
Sunghyu Han*, Il-Yong Lee**, and Jung-Ho Ahn***"
8cc23e554d98522b377d227dc78e9382a0ed35e5,"Bootstrap, Review, Decode: Using Out-of-Domain Textual Data to Improve Image Captioning","Bootstrap, Review, Decode: Using Out-of-Domain Textual Data
to Improve Image Captioning
Wenhu Chen
RWTH Aachen
Aurelien Lucchi
ETH Zurich
Thomas Hofmann
ETH Zurich"
b1a40665eb4c0a4c6978e84539ece1c0f406b8f5,Human Visual System Based Framework For Gender Recognition,"Human Visual System Based Framework For Gender Recognition
Cherinet G. Zewdie1,2 and Hubert Konik1,2
Universit´e de Lyon, CNRS, 37 Rue du Repos, 69007 Lyon, France
Universit´e Jean Monnet, Laboratoire Hubert Curien, UMR5516, 42000 Saint-Etienne, France
Keywords:
Human Visual System, Gender Recognition, Salient Region, HVS Inspired Gender Recognition, Local Binary
Pattern."
b183914d0b16647a41f0bfd4af64bf94a83a2b14,Extensible video surveillance software with simultaneous event detection for low and high density crowd analysis,"Extensible Video Surveillance Software with
Simultaneous Event Detection for Low and High
Density Crowd Analysis
Anuruddha L. Hettiarachchi, Heshani O. Thathsarani, Pamuditha U. Wickramasinghe,
Dilranjan S. Wickramasuriya and Ranga Rodrigo
Department of Electronic and Telecommunication Engineering, University of Moratuwa, Sri Lanka
Email: 090184v, 090518c, 090560v, 090561b,"
b14b672e09b5b2d984295dfafb05604492bfaec5,Apprentissage de Modèles pour la Classification et la Recherche d ’ Images Learning Image Classification and Retrieval Models,LearningImageClassificationandRetrievalModelsThomasMensink
b18f94c5296a9cebe9e779d50d193fd180f78ed9,Forecasting Interactive Dynamics of Pedestrians with Fictitious Play,"Forecasting Interactive Dynamics of Pedestrians with Fictitious Play
Wei-Chiu Ma1 De-An Huang2 Namhoon Lee3 Kris M. Kitani4
Stanford
Oxford"
b166ce267ddb705e6ed855c6b679ec699d62e9cb,Sample group and misplaced atom dictionary learning for face recognition,"Turk J Elec Eng & Comp Sci
(2017) 25: 4421 { 4430
⃝ T (cid:127)UB_ITAK
doi:10.3906/elk-1702-49
Sample group and misplaced atom dictionary learning for face recognition
Meng WANG1;2, Zhengping HU1;(cid:3)
, Zhe Sun1, Mei ZHU2, Mei SUN2
Department of Information Science & Engineering, Faculty of Electronics & Communication, Yanshan University,
Department of Physics & Electronics Engineering, Faculty of Electronics & Communication, Taishan University,
Qinhuangdao, P.R. China
Tai’an, P.R. China
Received: 04.02.2017
(cid:15)
Accepted/Published Online: 01.06.2017
(cid:15)
Final Version: 05.10.2017"
b1e27fade89e973f4087ed9a243981b0e713b22c,Functional neuroanatomy and the rationale for using EEG biofeedback for clients with Asperger's syndrome.,"Appl Psychophysiol Biofeedback (2010) 35:39–61
DOI 10.1007/s10484-009-9095-0
Functional Neuroanatomy and the Rationale for Using EEG
Biofeedback for Clients with Asperger’s Syndrome
Lynda Thompson Æ Michael Thompson Æ
Andrea Reid
Published online: 1 July 2009
Ó Springer Science+Business Media, LLC 2009
nd Oberman"
b1edff56936e5d306e51479142b98cc2414c1a56,Human-Centered Autonomous Vehicle Systems: Principles of Effective Shared Autonomy,"Human-Centered Autonomous Vehicle Systems:
Principles of E(cid:128)ective Shared Autonomy
Massachuse(cid:138)s Institute of Technology (MIT)
Lex Fridman
Figure 1: Principles of shared autonomy used for the design and development of the Human-Centered Autonomous Vehicle."
b18efa91e9893ae5fdfcaf880bae5c569fab4d18,Visual Scanning of Dynamic Affective Stimuli in Autism Spectrum Disorders,"Georgia State University
ScholarWorks Georgia State University
Psychology Dissertations
Department of Psychology
8-1-2012
Visual Scanning of Dynamic Affective Stimuli in
Autism Spectrum Disorders
Susan M. McManus
Georgia State University
Follow this and additional works at: http://scholarworks.gsu.edu/psych_diss
Recommended Citation
McManus, Susan M., ""Visual Scanning of Dynamic Affective Stimuli in Autism Spectrum Disorders."" Dissertation, Georgia State
University, 2012.
http://scholarworks.gsu.edu/psych_diss/105
This Dissertation is brought to you for free and open access by the Department of Psychology at ScholarWorks Georgia State University. It has been
ccepted for inclusion in Psychology Dissertations by an authorized administrator of ScholarWorks Georgia State University. For more information,
please contact"
b19e83eda4a602abc5a8ef57467c5f47f493848d,Heat Kernel Based Local Binary Pattern for Face Representation,"JOURNAL OF LATEX CLASS FILES
Heat Kernel Based Local Binary Pattern for
Face Representation
Xi Li†, Weiming Hu†, Zhongfei Zhang‡, Hanzi Wang§"
b13499d60e7be1d593ec91fc952b9c32ce62bd57,Gambit : A Robust Chess-Playing Robotic System,"Gambit: A Robust Chess-Playing Robotic System
Cynthia Matuszek, Brian Mayton, Roberto Aimi, Marc Peter Deisenroth, Liefeng Bo,
Robert Chu, Mike Kung, Louis LeGrand, Joshua R. Smith, Dieter Fox"
b147baa55e7b6456419d1518ccc9fd16f5ba2db2,An Study on Re-identification in RGB-D Imagery,"An study on re-identification in RGB-D imagery
J. Lorenzo-Navarro and M. Castrill´on-Santana and D. Hern´andez-Sosa (cid:63)
Instituto Universitario SIANI
Universidad de Las Palmas de Gran Canaria
Campus de Tafira, 35017 Las Palmas, SPAIN"
b13254c2c9ca90f57e385d34abc7fe78d74e5222,Real-Time Multi-object Tracking with Occlusion and Stationary Objects Handling for Conveying Systems,"Real-time Multi-Object Tracking with Occlusion and
Stationary Objects Handling for Conveying Systems
Adel Benamara, Serge Miguet, Mihaela Scuturici
To cite this version:
Adel Benamara, Serge Miguet, Mihaela Scuturici. Real-time Multi-Object Tracking with Occlu-
sion and Stationary Objects Handling for Conveying Systems. 12th International Symposium
on Visual Computing (ISVC’16), Dec 2016, Las Vegas, NV, United States. .
HAL Id: hal-01385529
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HAL is a multi-disciplinary open access
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scientifiques de niveau recherche, publi´es ou non,
´emanant des ´etablissements d’enseignement et de"
b19f24ec92388513d1516d71292559417c776006,CAUSALGAN: LEARNING CAUSAL IMPLICIT GENER-,"Under review as a conference paper at ICLR 2018
CAUSALGAN: LEARNING CAUSAL IMPLICIT GENER-
ATIVE MODELS WITH ADVERSARIAL TRAINING
Anonymous authors
Paper under double-blind review"
b1451721864e836069fa299a64595d1655793757,Criteria Sliders: Learning Continuous Database Criteria via Interactive Ranking,"Criteria Sliders: Learning Continuous
Database Criteria via Interactive Ranking
James Tompkin,1∗ Kwang In Kim,2∗ Hanspeter Pfister,3 and Christian Theobalt4
Brown University 2University of Bath
Harvard University 4Max Planck Institute for Informatics"
b17b20c3a3804482a1af3be897758d4f3be26677,Self-calibrating 3D context for retrieving people with luggage,"Self-Calibrating 3D Context for Retrieving People with Luggage
Johannes Schels∗ , Joerg Liebelt∗
EADS Innovation Works
M¨unchen, Germany
Rainer Lienhart
University of Augsburg
Augsburg, Germany"
b1290dff343ae4980e3e853055ad9a5b9116238b,"Gender Classification Based on Fusion of Different Spatial Scale Features Selected by Mutual Information From Histogram of LBP, Intensity, and Shape","Gender Classification Based on Fusion of Different
Spatial Scale Features Selected by Mutual Information
From Histogram of LBP, Intensity, and Shape
Juan E. Tapia, Graduate Student Member, IEEE, and Claudio A. Perez, Senior Member, IEEE"
b1ffa7a926e129f8dccdd6f258fea034cbee9160,Minimizing hallucination in histogram of Oriented Gradients,"Minimizing hallucination in Histogram of Oriented Gradients
Sławomir B ˛ak Michał Koperski
INRIA Sophia Antipolis, STARS group
François Brémond
004, route des Lucioles, BP93
06902 Sophia Antipolis Cedex - France
Javier Ortiz"
b16ff1331f961b2067c9464c491b7cbe90694758,Automatic plankton image classification combining multiple view features via multiple kernel learning,"Zheng et al. BMC Bioinformatics 2017, 18(Suppl 16):570
DOI 10.1186/s12859-017-1954-8
RESEARCH
Open Access
Automatic plankton image classification
ombining multiple view features via multiple
kernel learning
Haiyong Zheng1, Ruchen Wang1, Zhibin Yu1, Nan Wang1, Zhaorui Gu1 and Bing Zheng2*
From 16th International Conference on Bioinformatics (InCoB 2017)
Shenzhen, China. 20-22 September 2017
including phytoplankton and zooplankton, are the main source of food for organisms in the"
b1d89015f9b16515735d4140c84b0bacbbef19ac,Too Far to See? Not Really!—Pedestrian Detection With Scale-Aware Localization Policy,"Too Far to See? Not Really!
— Pedestrian Detection with Scale-aware
Localization Policy
Xiaowei Zhang, Li Cheng, Bo Li, and Hai-Miao Hu"
b196f95a4274533b7f931a509eaf5507358945f9,Transformation-Invariant Analysis of Visual Signals with Parametric Models,"POUR L'OBTENTION DU GRADE DE DOCTEUR ÈS SCIENCESacceptée sur proposition du jury:Prof. P. Vandergheynst, président du juryProf. P. Frossard, directeur de thèseProf. D. Kressner, rapporteur Dr G. Peyré, rapporteur Prof. M. B. Wakin, rapporteurTransformation-Invariant Analysis of Visual Signals with Parametric ModelsTHÈSE NO 5844 (2013)ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNEPRÉSENTÉE LE 4 OCTOBRE 2013 À LA  FACULTÉ DES SCIENCES ET TECHNIQUES DE L'INGÉNIEURLABORATOIRE DE TRAITEMENT DES SIGNAUX 4PROGRAMME DOCTORAL EN GÉNIE ÉLECTRIQUESuisse2013PARElif VURAL"
b11e97d5a12046ded77bc4dc0f762ac3c34e65cb,BLUR AND ILLUMINATION INVARIANT ROBUST FACE RECOGNITION USING SUPPORT VECTOR MACHINE ( SVM ),"Vetri--International Journal of Computer Science information and Engg., Technologies ISSN 2277-4408 || 01032014-011
BLUR AND ILLUMINATION INVARIANT ROBUST
FACE RECOGNITION USING SUPPORT VECTOR
MACHINE (SVM)
A.Vetri Selvi1 , N.Priyalakshmi2, S.Reshmi3
, G.Nandhini4,
1, 2, 3  UG Scholars, Department of Information Technology, Sri Ramakrishna Engineering College, Coimbatore, India.
4  Assistant  Professor,  Department  of  Information  Technology,  Sri  Ramakrishna  Engineering  College,  Coimbatore,
India."
b1898f8fe31147779a841f56795a776db6699efd,Automatic Detection of the Optimal Acceptance Threshold in a Face Verification System,"Automatic Detection of the Optimal Acceptance
Threshold in a Face Verification System
Raquel Montes Diez, Cristina Conde, and Enrique Cabello
Universidad Rey Juan Carlos (ESCET),
http://frav.escet.urjc.es
C/Tulipán s/n,
8933 Móstoles, Spain"
b1ffd13e8f68401a603eea9806bc37e396a3c77d,Face Generation with Conditional Generative Adversarial Networks,"Face Generation with Conditional Generative Adversarial Networks
Xuwen Cao, Subramanya Rao Dulloor, Marcella Cindy Prasetio"
b1ec55cbf2e9a6785e1f1f2fc060e4171ec88b4b,Implicit Discrimination of Basic Facial Expressions of Positive/Negative Emotion in Fragile X Syndrome and Autism Spectrum Disorder.,"015, Vol. 120, No. 4, 328–345
EAAIDD
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Implicit Discrimination of Basic Facial Expressions of
Positive/Negative Emotion in Fragile X Syndrome and
Autism Spectrum Disorder
Hayley Crawford, Joanna Moss, Giles M. Anderson, Chris Oliver, and Joseph P. McCleery"
b12431e61172443c534ea523a4d7407e847b5c5b,Yüz Tanımaya Dayalı Kişi Bazlı Test Otomasyonu,"Y¨uz Tanımaya Dayalı Ki¸si Bazlı Test
Otomasyonu
Alphan C¸ amlı1, Damla G¨ulen1, Nihat ¨Uk1, and Anıl G¨undo˘gdu1
Siemens A.S., Istanbul 34870, Turkey"
b15a06d701f0a7f508e3355a09d0016de3d92a6d,Facial contrast is a cue for perceiving health from the face.,"Running head:  FACIAL CONTRAST LOOKS HEALTHY
Facial contrast is a cue for perceiving health from the face
Richard Russell1, Aurélie Porcheron2,3, Jennifer R. Sweda1, Alex L. Jones1, Emmanuelle
Mauger2, Frederique Morizot2
Gettysburg College, Gettysburg, PA, USA
CHANEL Recherche et Technologie, Chanel PB
Université Grenoble Alpes
Author Note
Richard Russell, Jennifer R. Sweda, and Alex L. Jones, Department of Psychology,
Gettysburg College.  Aurélie Porcheron, Emmanuelle Mauger, and Frederique Morizot,
CHANEL Recherche et Technologie, Chanel PB.  Aurélie Porcheron, Laboratoire de
Psychologie et NeuroCognition, Université Grenoble Alpes.
Corresponding author:  Richard Russell, Department of Psychology, Box 407, Gettysburg
College, Gettysburg, PA 17325, USA.  Email:
This is a prepublication copy.  This article may not exactly replicate the authoritative document
published in the APA journal. It is not the copy of record.  The authoritative document can be
found through this DOI: http://psycnet.apa.org/doi/10.1037/xhp0000219"
b172c7073b7c2e032e7489cff66188605f13f57c,A Framework towards Domain Specific Video Summarization,"A Framework towards Domain Specific Video Summarization
Vishal Kaushal
IIT Bombay
Sandeep Subramanian
IIT Bombay
Suraj Kothawade
IIT Bombay
Rishabh Iyer
Microsoft Corporation
Ganesh Ramakrishnan
IIT Bombay"
b1fe9715f3f49980785db00066cb9062990a58ac,Study of the Changing Trends in Facial Expression Recognition,"Study of the Changing Trends in Facial Expression Recognition
{tag}                                                                   {/tag}
International Journal of Computer Applications
© 2011 by IJCA Journal
Number 5 - Article 3
Year of Publication: 2011
Authors:
Dr. S. Ravi
Mahima S
10.5120/2509-3397"
6e50c32f7244e3556eb879f24b7de8410f3177f6,Visual Social Relationship Recognition,"manuscript No.
(will be inserted by the editor)
Visual Social Relationship Recognition
Junnan Li · Yongkang Wong · Qi Zhao · Mohan S. Kankanhalli"
6e44ddb54edbb80d5bb8f2ca3b36e40c486b9daf,Evolutionary 3 D Mapping Using the GPU Calculating the psi similarity function for 2 D images,"Evolutionary 3D Mapping Using the GPU
Calculating the psi similarity function for 2D images
Diana Cristina Albu
May 7, 2007
Submitted to the School of Engineering and Sciences
in partial fulfillment of the requirements for the degree of
Bachelor of Science in Electrical Engineering and Computer Science
Jacobs University Bremen
Supervisor: Andreas Birk"
6ee1f57cbf7daa37576efca7e7d24040a5c94ee2,Aalborg Universitet Multimodal Neural Network for Overhead Person Re-identification,"Aalborg Universitet
Multimodal Neural Network for Overhead Person Re-identification
Lejbølle, Aske Rasch; Nasrollahi, Kamal; Krogh, Benjamin; Moeslund, Thomas B.
Published in:
6th International Conference of the Biometrics Special Interest Group
DOI (link to publication from Publisher):
0.23919/BIOSIG.2017.8053514
Publication date:
Document Version
Accepted author manuscript, peer reviewed version
Link to publication from Aalborg University
Citation for published version (APA):
Lejbølle, A. R., Nasrollahi, K., Krogh, B., & Moeslund, T. B. (2017). Multimodal Neural Network for Overhead
Person Re-identification. In 16th International Conference of the Biometrics Special Interest Group IEEE.
https://doi.org/10.23919/BIOSIG.2017.8053514
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6eb7ae81554ad4db92ee6b578f47be659c8b9cbd,Audio phrases for audio event recognition,"AUDIO PHRASES FOR AUDIO EVENT RECOGNITION
Huy Phan(cid:63)†, Lars Hertel(cid:63), Marco Maass(cid:63), Radoslaw Mazur(cid:63), and Alfred Mertins(cid:63)
Graduate School for Computing in Medicine and Life Sciences, University of L¨ubeck, Germany
(cid:63)Institute for Signal Processing, University of L¨ubeck, Germany
Email: {phan, hertel, maass, mazur,"
6ee3fbc4768f578601d42b1596aaf2b0cfa1d40a,Human Detection and Identification by Robots Using Thermal and Visual Information in Domestic Environments,"J Intell Robot Syst (2012) 66:223–243
DOI 10.1007/s10846-011-9612-2
Human Detection and Identification by Robots
Using Thermal and Visual Information
in Domestic Environments
Mauricio Correa · Gabriel Hermosilla ·
Rodrigo Verschae · Javier Ruiz-del-Solar
Received: 11 December 2010 / Accepted: 30 May 2011 / Published online: 12 July 2011
© Springer Science+Business Media B.V. 2011"
6e99832e265999194aa88958d892db62afbd7ac9,Is Combinational Strategy Better For Image Memorability Prediction,"Is Combinational Strategy Better For Image
Memorability Prediction
Wenting Zhu"
6e35585eb37ee8a1de60a10a56a3183af480e214,"The YLI-MED Corpus: Characteristics, Procedures, and Plans",
6ed22b934e382c6f72402747d51aa50994cfd97b,Customized expression recognition for performance-driven cutout character animation,"Customized Expression Recognition for Performance-Driven
Cutout Character Animation
Xiang Yu†
NEC Laboratories America
Jianchao Yang‡ Wilmot Li§
Snapchat"
6ebc601bba067af64b57d59039319650168df4c9,CNN-based Real-time Dense Face Reconstruction with Inverse-rendered Photo-realistic Face Images.,"CNN-based Real-time Dense Face Reconstruction
with Inverse-rendered Photo-realistic Face Images
Yudong Guo, Juyong Zhang†, Jianfei Cai, Boyi Jiang and Jianmin Zheng"
6e1802874ead801a7e1072aa870681aa2f555f35,Exploring Feature Descritors for Face Recognition,"­4244­0728­1/07/$20.00 ©2007 IEEE
I ­ 629
ICASSP 2007
*22+),)164,7+616DAIK??AIIB=B=?AHA?CEJE=CHEJDCHA=JOHAEAI.EIDAHB=?A -*/ 4A?AJO?=*E=HO2=JJAH*22+),)"
6ed738ff03fd9042965abdfaa3ed8322de15c116,K-MEAP: Generating Specified K Clusters with Multiple Exemplars by Efficient Affinity Propagation,"This document is downloaded from DR-NTU, Nanyang Technological
University Library, Singapore.
Title
K-MEAP: Generating Specified K Clusters with Multiple
Exemplars by Efficient Affinity Propagation
Author(s) Wang, Yangtao; Chen, Lihui
Citation
Wang, Y & Chen, L. (2014). K-MEAP: Generating
Specified K Clusters with Multiple Exemplars by Efficient
Affinity Propagation. 2014 IEEE International Conference
on Data Mining (ICDM), 1091-1096.
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6e261b9e539ecd03d76063f893d59c6eafb6ed43,On the Use of External Face Features for Identity Verification,"On the Use of External Face Features for
Identity Verification
`Agata Lapedriza1, David Masip2 and Jordi Vitri`a1
Computer Vision Center (CVC), Computer Science Dept.
Universitat Aut`onoma de Barcelona
Bellaterra, Spain, 08193.
{agata,
Department of Applied Mathematics and Analysis (MAiA)
University of Barcelona (UB)
Edifici Hist`oric Gran Via de les Corts Catalanes 585, Barcelona 08007, Spain."
6e9862146e7e081429da233960ba0dfa9e400fc2,Cross-Spectral Cross-Resolution Face Recognition in Videos,"Cross-Spectral Cross-Resolution Face Recognition in
Videos
Student Name: Nikita Gupta
Roll Number: 2013068
Student Name: Sanchit Gupta
Roll Number: 2013088
BTP report submitted in partial fulfillment of the requirements
for the Degree of B.Tech. in Computer Science & Engineering
on 24th April 2017
BTP Track: Research
BTP Advisor
Dr. Richa Singh
Dr. Mayank Vatsa
Indraprastha Institute of Information Technology
New Delhi"
6e6f516ed1ce0089d4f6d113797d711d5997802b,Face Analysis and Recognition in Mobile Devices,"Face Analysis and Recognition in Mobile Devices Valencia, November 2008 Departamento de Sistemas Informáticos y ComputaciónUniversidad Politécnica de ValenciaMauricio Villegas Santamaría"
6ee2173c06c84cae6aae2912a4439ec956ecc3bb,Zero-shot Sim-to-Real Transfer with Modular Priors,"Zero-shot Sim-to-Real Transfer with Modular Priors
Robert Lee1,2, Serena Mou1,2, Vibhavari Dasagi2, Jake Bruce2, J¨urgen Leitner1,2, Niko S¨underhauf1,2"
6eabb24b358dea9c66ad28d2efc2c9c469e115c4,On the use of dynamic features in face biometrics: recent advances and challenges,"(will be inserted by the editor)
On the Use of Dynamic Features in Face Biometrics: Recent Advances
nd Challenges
Abdenour Hadid · Jean-Luc Dugelay · Matti Pietik¨ainen
Received: DD month YYYY / Revised: DD month YYYY / Accepted: DD month YYYY / Published: DD month YYYY"
6ee64c19efa89f955011531cde03822c2d1787b8,Table S1: Review of existing facial expression databases that are often used in social psycholgy,"Table S1: Review of existing facial expression databases that are often used in social
psycholgy.
Author
database
Expressions1
Format
Short summary
GEMEP Corpus
Mind Reading: the
interactive
guide
to emotions
udio
video
record-
Videos
nger,
muse-
dmiration,
ment,"
6e5a9fe3937fddf09a5c2604a13b9692d30db176,Relative Depth Order Estimation Using Multi-scale Densely Connected Convolutional Networks,"RELATIVE DEPTH ORDER ESTIMATION
Relative Depth Order Estimation Using Multi-scale
Densely Connected Convolutional Networks
Ruoxi Deng, Tianqi Zhao, Chunhua Shen, Shengjun Liu"
6eba25166fe461dc388805cc2452d49f5d1cdadd,"ALBANIE, VEDALDI: LEARNING GRIMACES BY WATCHING TV 1 Learning Grimaces by Watching TV","Pages 122.1-122.12
DOI: https://dx.doi.org/10.5244/C.30.122"
6edb41364802b0fdd1e3e98d644fe78b1ecbbe45,Understanding Image and Text Simultaneously: a Dual Vision-Language Machine Comprehension Task,"Understanding Image and Text Simultaneously: a Dual Vision-Language
Machine Comprehension Task
Nan Ding
Google
Sebastian Goodman
Google
Fei Sha
Google
Radu Soricut
Google"
6e1b85aabb132ed741381fdf00909475d16cd3ba,"Motor, emotional and cognitive empathic abilities in children with autism and conduct disorder","Motor, Emotional and Cognitive Empathic Abilities
in Children with Autism and Conduct Disorder
Danielle M.A. Bons1,2
+31 (0)488 – 469 611
Nanda N.J. Rommelse1,2
+31 (0)24 351 2222
Floor E. Scheepers1
Jan K. Buitelaar1,2
Karakter child- and adolescent psychiatry
University Centre Nijmegen, Zetten-Tiel
Department of Psychiatry UMC St. Radboud
P.O. Box 9101, 6500HB Nijmegen, The
P.O. Box 104, 6670AC Zetten, The Netherlands
the  studies"
6e80caed3f2ac86db775bd5e7d64925b00f1a0ca,Social interaction contexts bias the perceived expressions of interactants.,"City Research Online
City, University of London Institutional Repository
Citation: Gray, K., Barber, L., Murphy, J. & Cook, R. (2017). Social interaction contexts
0.1037/emo0000257
This is the accepted version of the paper.
This version of the publication may differ from the final published
version.
Permanent repository link:  http://openaccess.city.ac.uk/16315/
Link to published version: http://dx.doi.org/10.1037/emo0000257
Copyright and reuse: City Research Online aims to make research
outputs of City, University of London available to a wider audience.
Copyright and Moral Rights remain with the author(s) and/or copyright
holders. URLs from City Research Online may be freely distributed and
linked to.
City Research Online:            http://openaccess.city.ac.uk/"
6e911227e893d0eecb363015754824bf4366bdb7,Wasserstein Divergence for GANs,"Wasserstein Divergence for GANs
Jiqing Wu1, Zhiwu Huang1, Janine Thoma1, Dinesh Acharya1, and
Luc Van Gool1,2
Computer Vision Lab, ETH Zurich, Switzerland
VISICS, KU Leuven, Belgium"
6e75fcf384b31ea2108a81d868fbb886f39cd188,Sparse Coding on Symmetric Positive Definite Manifolds Using Bregman Divergences,"Sparse Coding on Symmetric Positive Definite Manifolds
using Bregman Divergences
Mehrtash Harandi, Richard Hartley, Brian Lovell, Conrad Sanderson"
6e74a055a70c69c287a34d86ce8b159456cf4420,Institutionen För Systemteknik Pose Recognition for Tracker Initialization Using 3d Models Pose Recognition for Tracker Initialization Using 3d Models,"Institutionen för systemteknik
Department of Electrical Engineering
Examensarbete
Pose Recognition for Tracker Initialization Using
D Models
Examensarbete utfört i Bildbehandling
vid Tekniska högskolan i Linköping
Martin Berg
LiTH-ISY-EX--07/4076--SE
Linköping 2008
Department of Electrical Engineering
Linköpings universitet
SE-581 83 Linköping, Sweden
Linköpings tekniska högskola
Linköpings universitet
581 83 Linköping"
6e7cfcefe82471a6aca78b59be0285467ce37b8b,Déjà Vu: an empirical evaluation of the memorization properties of ConvNets,"D´ej`a Vu: an empirical evaluation of the
memorization properties of ConvNets
Alexandre Sablayrolles†,(cid:63), Matthijs Douze†, Cordelia Schmid(cid:63),
nd Herv´e J´egou†
Facebook AI Research
(cid:63)Inria
September 19, 2018"
6e7d799497b94954dc4232d840628c3a00263e42,Aalborg Universitet Deep Multimodal Pain Recognition : A Database and Comparison of Spatio-Temporal Visual,"Aalborg Universitet
Deep Multimodal Pain Recognition: A Database and Comparison of Spatio-Temporal
Visual Modalities
Haque, Mohammad Ahsanul; Nasrollahi, Kamal; Moeslund, Thomas B.; B. Bautista, Ruben;
Laursen, Christian B.; Escalera, Sergio; Irani, Ramin; Andersen, Ole Kæseler; Spaich, Erika
Geraldina; Kulkarni, Kaustubh; Bellantonio, Marco; Anbarjafari, Gholamreza; Noroozi,
Fatemeh
Published in:
Proc. of the 13th IEEE Conf. on Automatic Face and Gesture Recognition
Publication date:
Link to publication from Aalborg University
Citation for published version (APA):
Haque, M. A., Nasrollahi, K., Moeslund, T. B., B. Bautista, R., Laursen, C. B., Escalera, S., ... Noroozi, F. (2018).
Deep Multimodal Pain Recognition: A Database and Comparison of Spatio-Temporal Visual Modalities. In Proc.
of the 13th IEEE Conf. on Automatic Face and Gesture Recognition (pp. 1). IEEE.
General rights
Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners
nd it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.
? Users may download and print one copy of any publication from the public portal for the purpose of private study or research.
? You may not further distribute the material or use it for any profit-making activity or commercial gain"
6ef1996563835b4dfb7fda1d14abe01c8bd24a05,Nonparametric Part Transfer for Fine-Grained Recognition,"Nonparametric Part Transfer for Fine-grained Recognition
Christoph G¨oring, Erik Rodner, Alexander Freytag, and Joachim Denzler∗
Computer Vision Group, Friedrich Schiller University Jena
www.inf-cv.uni-jena.de"
6e7b2afb4daf1fe50a62faf75018ff81c24ee526,Discriminant Analysis Based Feature Extraction,"SubmittedtoCVPR'		DiscriminantAnalysisbasedFeatureExtraction
W.Zhao
CenterforAutomationResearch
UniversityofMaryland
CollegePark,MD-
nantAnalysishaveachievedquiteasuccessinprac-"
6e7248f33be3f6b44d6089b7039a5c2d84acaed0,Object cosegmentation using deep Siamese network,"Object cosegmentation using deep Siamese network
Prerana Mukherjee∗, Brejesh Lall∗ and Snehith Lattupally∗
Dept of EE, IIT Delhi, India.
Email: {eez138300, brejesh, eet152695"
6ef0b43cf897f527540c29cae0618aabb7329072,Parallel Algorithms for Nearest Neighbor Search Problems in High Dimensions,"PARALLEL ALGORITHMS FOR NEAREST NEIGHBOR SEARCH
PROBLEMS IN HIGH DIMENSIONS.
BO XIAO∗ AND GEORGE BIROS†"
6e82ce9897093ce4f5fa795887273992489c380d,Face recognition using Eigensurface on Kinect depth-maps,"Int'l Conf. IP, Comp. Vision, and Pattern Recognition |  IPCV'16  |
Face recognition using Eigensurface on Kinect depth-maps
Marcelo Romero1, Cesar Flores1, Vianney Muñoz1 and Luis Carlos Altamirano2
Universidad Autónoma del Estado de México1 and Benemérita Universidad Autónoma de Puebla2"
6e0288b874320b1b6461016fde8b215c3ba46b90,Recognising activities by jointly modelling actions and their effects,"This thesis has been submitted in fulfilment of the requirements for a postgraduate degree
(e.g. PhD, MPhil, DClinPsychol) at the University of Edinburgh. Please note the following
terms and conditions of use:
This work is protected by copyright and other intellectual property rights, which are
retained by the thesis author, unless otherwise stated.
A copy can be downloaded for personal non-commercial research or study, without
prior permission or charge.
This thesis cannot be reproduced or quoted extensively from without first obtaining
permission in writing from the author.
The content must not be changed in any way or sold commercially in any format or
medium without the formal permission of the author.
When referring to this work, full bibliographic details including the author, title,
warding institution and date of the thesis must be given."
6e198f6cc4199e1c4173944e3df6f39a302cf787,MORPH-II : Inconsistencies and Cleaning Whitepaper,"MORPH-II: Inconsistencies and Cleaning Whitepaper
Participants: G. Bingham, B. Yip, M. Ferguson, and C. Nansalo
Mentors: C. Chen, Y. Wang, and T. Kling
NSF-REU Site at UNC Wilmington, Summer 2017"
6e0a05d87b3cc7e16b4b2870ca24cf5e806c0a94,RANDOM GRAPHS FOR STRUCTURE DISCOVERY IN HIGH-DIMENSIONAL DATA,"RANDOM GRAPHS FOR STRUCTURE
DISCOVERY IN HIGH-DIMENSIONAL DATA
Jos¶e Ant¶onio O. Costa
A dissertation submitted in partial fulflllment
of the requirements for the degree of
Doctor of Philosophy
(Electrical Engineering: Systems)
in The University of Michigan
Doctoral Committee:
Professor Alfred O. Hero III, Chair
Professor Jefirey A. Fessler
Professor Susan A. Murphy
Professor David L. Neuhofi"
6e604946a0a51911db0e887378ba1ae103dcfb9e,Detection and Classification of a Moving Object in a Video Stream,"Proc. of the Intl. Conf. on Advances in Computing and Information Technology-- ACIT 2014
Copyright © Institute of Research Engineers and Doctors. All rights reserved.
ISBN: 978-981-07-8859-9 doi: 10.3850/ 978-981-07-8859-9_23
Detection and Classification of a Moving Object
in a Video Stream
Asim R. Aldhaheri and Eran A. Edirisinghe"
6e93fd7400585f5df57b5343699cb7cda20cfcc2,Comparing a novel model based on the transferable belief model with humans during the recognition of partially occluded facial expressions.,"http://journalofvision.org/9/2/22/
Comparing a novel model based on the transferable
elief model with humans during the recognition of
partially occluded facial expressions
Zakia Hammal
Martin Arguin
Frédéric Gosselin
Département de Psychologie, Université de Montréal,
Canada
Département de Psychologie, Université de Montréal,
Canada
Département de Psychologie, Université de Montréal,
Canada
Humans recognize basic facial expressions effortlessly. Yet, despite a considerable amount of research, this task remains
elusive for computer vision systems. Here, we compared the behavior of one of the best computer models of facial
expression recognition (Z. Hammal, L. Couvreur, A. Caplier, & M. Rombaut, 2007) with the behavior of human observers
during the M. Smith, G. Cottrell, F. Gosselin, and P. G. Schyns (2005) facial expression recognition task performed on
stimuli randomly sampled using Gaussian apertures. The modelVwhich we had to significantly modify in order to give the
bility to deal with partially occluded stimuliVclassifies the six basic facial expressions (Happiness, Fear, Sadness,
Surprise, Anger, and Disgust) plus Neutral from static images based on the permanent facial feature deformations and the"
6e9680fe35a752590ad2d750ba1aa2b387cba135,Low-Shot Learning with Large-Scale Diffusion,"Low-shot learning with large-scale diffusion
Matthijs Douze†, Arthur Szlam†, Bharath Hariharan†∗, Herv´e J´egou†
Facebook AI Research
*Cornell University"
6e5363af2bfb7d1b2bd13feb41c2688bd0cf12b3,Detection of US Traffic Signs,"Aalborg Universitet
Detection of U.S. Traffic Signs
Møgelmose, Andreas; Liu, Dongran; Trivedi, Mohan M.
Published in:
I E E E Transactions on Intelligent Transportation Systems
DOI (link to publication from Publisher):
0.1109/TITS.2015.2433019
Publication date:
Document Version
Accepted author manuscript, peer reviewed version
Link to publication from Aalborg University
Citation for published version (APA):
Møgelmose, A., Liu, D., & Trivedi, M. M. (2015). Detection of U.S. Traffic Signs. I E E E Transactions on
Intelligent Transportation Systems, 16(6), 3116 - 3125. https://doi.org/10.1109/TITS.2015.2433019
General rights
Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners
nd it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.
? Users may download and print one copy of any publication from the public portal for the purpose of private study or research.
? You may not further distribute the material or use it for any profit-making activity or commercial gain
? You may freely distribute the URL identifying the publication in the public portal ?"
6ee8a94ccba10062172e5b31ee097c846821a822,How to solve classification and regression problems on high-dimensional data with a supervised extension of slow feature analysis,"Submitted 3/13; Revised 10/13; Published 12/13
How to Solve Classification and Regression Problems on
High-Dimensional Data with a Supervised
Extension of Slow Feature Analysis
Alberto N. Escalante-B.
Laurenz Wiskott
Institut f¨ur Neuroinformatik
Ruhr-Universit¨at Bochum
Bochum D-44801, Germany
Editor: David Dunson"
0199150ccad6479eac9d693a7cc0406935d877a8,Towards Real-Time Accurate Object Detection in Both Images and Videos Based on Dual Refinement.,"Towards Real-Time Accurate Object Detection in
Both Images and Videos Based on Dual Refinement
Xingyu Chen, Junzhi Yu, Senior Member, IEEE, Shihan Kong, Zhengxing Wu, and Li Wen Member, IEEE"
01a152e7ca6accce4fa52e29b27feb76418583fb,Tracking Multiple High-Density Homogeneous Targets,"IEEE TRANSACTION ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. X, NO. X, XXXX
Tracking multiple high-density homogeneous targets
Fabio Poiesi and Andrea Cavallaro"
01bef320b83ac4405b3fc5b1cff788c124109fb9,Translating Head Motion into Attention - Towards Processing of Student's Body-Language.,"de Lausanne
RLC D1 740, CH-1015
Lausanne
de Lausanne
RLC D1 740, CH-1015
Lausanne
de Lausanne
RLC D1 740, CH-1015
Lausanne
Translating Head Motion into Attention - Towards
Processing of Student’s Body-Language
Mirko Raca
CHILI Laboratory
Łukasz Kidzi´nski
CHILI Laboratory
Pierre Dillenbourg
CHILI Laboratory
École polytechnique fédérale
École polytechnique fédérale
École polytechnique fédérale"
01b5d63b60bcc35aa8bead42ea52a517f879bfc9,Solving Uncalibrated Photometric Stereo Using Total Variation,"Noname manuscript No.
(will be inserted by the editor)
Solving Uncalibrated Photometric Stereo using Total
Variation
Yvain Qu´eau · Fran¸cois Lauze · Jean-Denis Durou
the date of receipt and acceptance should be inserted later"
014b4335d055679bc680a6ceb6f1a264d8ce8a4a,Are You Sure You Want To Do That? Classification with Verification,"Are You Sure You Want To Do That?
Classification with Verification
Harris Chan∗
Atef Chaudhury∗
Kevin Shen∗"
01c4cf9c7c08f0ad3f386d88725da564f3c54679,Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV),"Interpretability Beyond Feature Attribution:
Quantitative Testing with Concept Activation Vectors (TCAV)
Been Kim Martin Wattenberg Justin Gilmer Carrie Cai James Wexler
Fernanda Viegas Rory Sayres"
01d785bb989850019001a418a16202fd7502ac14,Hierarchical object detection and tracking with an Implicit Shape Model,"Hierarchical object detection and tracking with an Implicit Shape
Model
K. Jüngling1, S. Becker1, and M. Arens1
Object Recognition, Fraunhofer IOSB, Ettlingen, Germany"
01591390da1356b871aefe11d4bb92c1df5ba082,Online Appendix to : Content-based Image Retrieval,"Content-based Image Retrieval
Haoran Wang,
Yuanbin Wang,
This is a project report for the graduate capstone course of Search Engine Architecture at NYU. In this
project, we implemented a basic image search engine based on mere image content. In other words, we use
n image to search other similar images. We don’t take into consideration the text information related to
images, but just the image content. As the past few years have seen amazingly successful applications of
deep learning to images, we employ convolutional neural networks as our tool to extract content (features)
from images. We chose this topic for our project specifically because information (image) retrieval is highly
relevant to this course and also because we would love to explore the use of deep learning for content-based
image retrieval. Our results show that our image search engine can correctly retrieve similar images given
n query image, which further proves the use of deep learning as image feature extractor in this case. Next,
we will briefly describe the details of the project in the following sections.
Additional Key Words and Phrases: Image retrieval, deep learning, image content, search engine, informa-
tion retrieval, search engine architecture, capstone project
ACM Reference Format:
Haoran Wang, Yuanbin Wang, 2017. Content-based Image Retrieval ACM Trans. Embedd. Comput. Syst. V,
N, Article 1 (May 2017), 8 pages.
DOI:http://dx.doi.org/10.1145/0000000.0000000
. INTRODUCTION"
016473c5b809ff55304a2923c36eaf58f02f02e4,DensePose: Dense Human Pose Estimation In The Wild,"DensePose: Dense Human Pose Estimation In The Wild
Rıza Alp G¨uler∗
Natalia Neverova
Iasonas Kokkinos
INRIA-CentraleSup´elec
Facebook AI Research
Facebook AI Research
Figure 1: Dense pose estimation aims at mapping all human pixels of an RGB image to the 3D surface of the human body.
spondences for 50K images, and train DensePose-RCNN to densely regress UV coordinates at multiple frames per second.
Right: Partitioning and UV parametrization of the body surface."
014e1186209e4f942f3b5ba29b6b039c8e99ad88,Social interactions: A first-person perspective,"Social Interactions: A First-Person
Perspective
Alireza Fathi, Jessica K. Hodgins, James M. Rehg
CVPR 2012
Bora Çelikkale"
014e3d0fa5248e6f4634dc237e2398160294edce,What does 2D geometric information really tell us about 3D face shape?,"Int J Comput Vis manuscript No.
(will be inserted by the editor)
What does 2D geometric information really tell us about
D face shape?
Anil Bas1 · William A. P. Smith1
Received: date / Accepted: date"
01e5eb25e262afa4289d39b964c837a22a32f5a2,Cricket activity detection,"Cricket Activity Detection
Ashok Kumar(11164)
Javesh Garg(11334)
March 1, 2014"
0155c2921f060a95c0eca8c64bf62a1eaac591e4,Spatiotemporal CNNs for Pornography Detection in Videos,"Spatiotemporal CNNs for Pornography
Detection in Videos
Murilo Varges da Silva1,2 and Aparecido Nilceu Marana3
UFSCar - Federal University of Sao Carlos, Sao Carlos, SP, Brazil
IFSP - Federal Institute of Education of Sao Paulo, Birigui, SP, Brazil
UNESP - Sao Paulo State University, Bauru, SP, Brazil"
0135747b4d3c9a2d983f7d0d9f4c39e094825149,Embedded wavelet-based face recognition under variable position,"Embedded wavelet-based face recognition under variable
position
Pascal Cotreta, Stéphane Chevobbea and Mehdi Darouicha
CEA, LIST, Laboratoire Adéquation Algorithme Architecture, Gif-sur-Yvette, F-91191 France"
016860404c0926dda53b9bf4745f3eb9708fa1d2,Iterative hypothesis testing for multi-object tracking in presence of features with variable reliability,"Iterative hypothesis testing for multi-object tracking in presence of
features with variable reliability
Amit Kumar K.C.1, Damien Delannay2 and Christophe De Vleeschouwer1
ISPGroup, ELEN Department, Universit´e catholique de Louvain, Belgium
{amit.kc,
Keemotion, Belgium"
0171bdeb1c6e333287be655c667cfba5edb89b76,Aggregated Residual Transformations for Deep Neural Networks,"Aggregated Residual Transformations for Deep Neural Networks
Saining Xie1
Ross Girshick2
Piotr Doll´ar2
Zhuowen Tu1
Kaiming He2
UC San Diego
Facebook AI Research"
0145dc4505041bf39efa70ea6d95cf392cfe7f19,Human action segmentation with hierarchical supervoxel consistency,"Human Action Segmentation with Hierarchical Supervoxel Consistency
Jiasen Lu1, Ran Xu1 Jason J. Corso2
Department of Computer Science and Engineering, SUNY at Buffalo. 2Department of EECS, University of Michigan.
Detailed analysis of human action, such as classification, detection and lo-
alization has received increasing attention from the community; datasets
like J-HMDB [1] have made it plausible to conduct studies analyzing the
impact that such deeper information has on the greater action understanding
problem. However, detailed automatic segmentation of human action has
omparatively been unexplored. In this paper, we introduce a hierarchical
MRF model to automatically segment human action boundaries in videos
“in-the-wild” (see Fig. 1).
We first propose a human motion saliency representation which incor-
porates two parts: foreground motion and human appearance information.
For foreground motion estimation, we propose a new motion saliency fea-
ture by using long-term trajectories to build a camera motion model, and
then measure the motion saliency via the deviation from the camera model.
For human appearance information, we use a DPM person detector trained
on PASCAL VOC 2007 and construct a saliency map by averaging the nor-
malized detection score of all the scale and all components.
Then, to segment the human action, we start by applying hierarchical"
013ae78fc6bd26a13799fe2e07a6ad363aca9ba7,Inspiring Computer Vision System Solutions,"Inspiring Computer Vision System Solutions
Julian Zilly 1 Amit Boyarski 2 Micael Carvalho 3 Amir Atapour Abarghouei 4 Konstantinos Amplianitis 5
Aleksandr Krasnov 6 Massimiliano Mancini 7 Hernán Gonzalez 8 Riccardo Spezialetti 9
Carlos Sampedro Pérez 10 Hao Li 11"
0181fec8e42d82bfb03dc8b82381bb329de00631,Discriminative Subspace Clustering,"Discriminative Subspace Clustering
Vasileios Zografos∗1, Liam Ellis†1, and Rudolf Mester‡1 2
CVL, Dept. of Electrical Engineering, Link¨oping University, Link¨oping, Sweden
VSI Lab, Computer Science Department, Goethe University, Frankfurt, Germany"
01bd864085b7ba7c9c85c593bd893a5cbb13b136,On Model-Based Analysis of Ear Biometrics,"On Model-Based Analysis of Ear Biometrics
Banafshe Arbab-Zavar, Mark S. Nixon, and David J. Hurley"
0183eff3a60f44bc6e4bcade37518f6470af3437,Human Identification Using Temporal Information Preserving Gait Template,"Human Identification Using Temporal
Information Preserving Gait Template
Chen Wang, Junping Zhang, IEEE Member, Liang Wang, IEEE Senior Member,
Jian Pu, and Xiaoru Yuan, IEEE Member"
014844a9e6ae39a101fb79f103aa047699f88246,Interpretable Counting for Visual Question Answering,"Under review as a conference paper at ICLR 2018
INTERPRETABLE COUNTING FOR VISUAL QUESTION
ANSWERING
Anonymous authors
Paper under double-blind review"
01959ef569f74c286956024866c1d107099199f7,VQA: Visual Question Answering,"VQA: Visual Question Answering
www.visualqa.org
Stanislaw Antol∗1, Aishwarya Agrawal∗1, Jiasen Lu, Margaret Mitchell,
Dhruv Batra, C. Lawrence Zitnick, Devi Parikh"
019a95631c49011330773e953194a0c73c61f3f0,Impairments in monkey and human face recognition in 2-year-old toddlers with Autism Spectrum Disorder and Developmental Delay.,"DOI: 10.1111/j.1467-7687.2006.00543.x
Blackwell Publishing Ltd
Face recognition in ASD
PAPER
Impairments in monkey and human face recognition in
-year-old toddlers with Autism Spectrum Disorder and
Developmental Delay
Katarzyna Chawarska and Fred Volkmar
Child Study Center, Yale University School of Medicine, New Haven, CT, USA"
0144b29bde2579e0a1b8ab3a38306c5621a5c30b,Top-Down Visual Saliency via Joint CRF and Dictionary Learning,"Top-Down Visual Saliency via Joint CRF and Dictionary Learning
Jimei Yang and Ming-Hsuan Yang
University of California at Merced"
017229c2df23c542b30c59f4a5eeb747e3d34729,Efficient Object Recognition using Convolution Neural Networks Theorem,"International Journal of Computer Applications (0975 – 8887)
Volume 161 – No 2, March 2017
Efficient Object Recognition using Convolution Neural
Networks Theorem
Aarushi Thakral
VIT University
Vellore
Tamil Nadu
Shaurya Shekhar
VIT University
Vellore
Tamil Nadu
to  overcome"
01b527641d1f23cddac932d9fe20b44ec39114a3,CS 341 Final Report : Towards Real-time Detection and Camera Triggering,"CS341 Final Report: Towards Real-time Detection and Camera Triggering
Yundong Zhang
Haomin Peng
Pan Hu"
01574e0d3be1b866471b83e39f7404c80d608e63,Application of Blind Deblurring Algorithm for Face Biometric,"Application of Blind Deblurring Algorithm for Face
International Journal of Computer Applications (0975 – 8887)
Volume 105 – No. 2, November 2014
F.Alaoui
Faculty of science
Chouaib doukkali El-
jadida
Biometric
A. Ghlaifan Abdo
Saleh
Faculty of science
Chouaib doukkali El-
jadida
V.Dembele
Faculty of science
Chouaib doukkali El-
jadida
A.Nassim
Faculty of science
Chouaib doukkali El-"
011c5bb510c9a4c24e2fc07e7464fa8493237058,Accelerating Nearest Neighbor Search on Manycore Systems,"Accelerating Nearest Neighbor Search on Manycore
Systems
Lawrence Cayton
Max Planck Institute
Tübingen, Germany"
010f0f4929e6a6644fb01f0e43820f91d0fad292,YFCC100M: the new data in multimedia research,"YFCC100M: The New Data in Multimedia Research
Bart Thomee
Yahoo Labs
San Francisco, CA, USA
Benjamin Elizalde∗
Mountain View, CA, USA
David A. Shamma
Yahoo Labs
San Francisco, CA, USA
Karl Ni†
In-Q-Tel
Menlo Park, CA, USA
Gerald Friedland
Berkeley, CA, USA
Douglas Poland
Livermore, CA, USA
Damian Borth‡
Kaiserslautern, Germany
Li-Jia Li§
Snapchat"
014b8df0180f33b9fea98f34ae611c6447d761d2,Facial feature tracking and expression recognition for sign language,"Facial Feature Tracking and Expression Recognition
for Sign Language
˙Ismail Arı
Computer Engineering
Bo˜gazic.i University
˙Istanbul, Turkey
Email:
Asli Uyar
Computer Engineering
Bo˜gazic.i University
˙Istanbul, Turkey
Email:
Lale Akarun
Computer Engineering
Bo˜gazic.i University
˙Istanbul, Turkey
Email:"
01381e2faeb26489ae9c24c84c42df33901cfe61,Multi-scale Deep Learning Architectures for Person Re-identification,"Multi-scale Deep Learning Architectures for Person Re-identification
Xuelin Qian1 Yanwei Fu2,5,* Yu-Gang Jiang1,3 Tao Xiang4 Xiangyang Xue1,2
Shanghai Key Lab of Intelligent Info. Processing, School of Computer Science, Fudan University;
School of Data Science, Fudan University; 3Tencent AI Lab;
Queen Mary University of London; 5University of Technology Sydney;"
01ababc0985143ad57320b0599fb2f581d79d3c2,Unobtrusive Low Cost Pupil Size Measurements using Web cameras,"Unobtrusive Low Cost Pupil Size Measurements using Web cameras
Sergios Petridis, Theodoros Giannakopoulos and Costantine D. Spyropoulos
National Center for Scientific Research ""Demokritos""
Unobtrusive every day health monitoring can be of important use for the elderly population. In
particular, pupil size may be a valuable source of information, since, apart from pathological
ases, it can reveal the emotional state, the fatigue and the ageing. To allow for unobtrusive
monitoring to gain acceptance, one should seek for ef‌f‌icient methods of monitoring using com-
mon low-cost hardware. This paper describes a method for monitoring pupil sizes using a
ommon web camera in real time. Our method works by first detecting the face and the eyes
rea. Subsequently, optimal iris and sclera location and radius, modelled as ellipses, are found
using ef‌f‌icient filtering. Finally, the pupil center and radius is estimated by optimal filtering
within the area of the iris. Experimental result show both the ef‌f‌iciency and the effectiveness
of our approach.
Keywords: video analysis, eye tracking, pupil size estimation, physiological measurements
Motivation
Unobtrusive every day health monitoring can be of im-
portant use for the elderly population.
In particular, pupil
size may be a valuable source of information, since, apart
from pathological cases, it can reveal the emotional state, the"
01915181692c821cc5a0a703047bd5b07c1f9af5,Cross-Caption Coreference Resolution for Automatic Image Understanding,"Proceedings of the Fourteenth Conference on Computational Natural Language Learning, pages 162–171,
Uppsala, Sweden, 15-16 July 2010. c(cid:13)2010 Association for Computational Linguistics"
01ece1dd9a0a2a7289d791625c6c7446d38584e7,A Comparative Analysis of Classification Algorithms Applied to M 5 AIE-Extracted Human Poses,"A Comparative Analysis of Classification Algorithms
Applied to M5AIE-Extracted Human Poses
Andr´e Brand˜ao, Leandro A. F. Fernandes, and Esteban Clua
MediaLab-UFF, Instituto de Computac¸˜ao, Universidade Federal Fluminense
Email:
CEP 24210-240 Niter´oi, RJ, Brazil"
01c9f0be6a300f385274b72a5463a650e51e300a,Support Vector Data Description based on PCA features for face detection,"SUPPORT VECTOR DATA DESCRIPTION BASED ON PCA FEATURES FOR FACE
DETECTION
Ver´onica Vilaplana and Ferran Marqu´es
phone: + (34)934011066, fax: + (34)934016447, email:
Jordi Girona, 1-3, 08034 Barcelona, SPAIN
Image Processing Group, Universitat Polit`ecnica de Catalunya
web: gps-tsc.upc.es/imatge"
01f42436042ddaa48998c87109cbe46cad6e7e52,Schedtask: a hardware-assisted task scheduler,"SchedTask: A Hardware-Assisted Task Scheduler
Prathmesh Kallurkar∗
Microarchitecture Research Lab
Intel Corporation
Smruti R. Sarangi
Department of Computer Science
Indian Institute of Technology Delhi"
013e0fe2d203eaa33a4b42d057688815116cc6bb,Recognizing Car Fluents from Video,"Recognizing Car Fluents from Video
Bo Li1,∗, Tianfu Wu2, Caiming Xiong3,∗ and Song-Chun Zhu2
Beijing Lab of Intelligent Information Technology, Beijing Institute of Technology
Department of Statistics, University of California, Los Angeles
Metamind Inc.
{tfwu,"
011e6146995d5d63c852bd776f782cc6f6e11b7b,Fast Training of Triplet-Based Deep Binary Embedding Networks,"Fast Training of Triplet-based Deep Binary Embedding Networks
Bohan Zhuang, Guosheng Lin, Chunhua Shen∗, Ian Reid
The University of Adelaide; and Australian Centre for Robotic Vision"
0136bf1d3747770a7fb4fcdeaf0b4b195815ed67,Weighted Fourier Series Representation and Its Application to Quantifying the Amount of Gray Matter,"Weighted Fourier Series Representation and
Its Application to Quantifying the Amount
of Gray Matter
Moo K. Chung*, Kim M. Dalton, Li Shen, Alan C. Evans, and Richard J. Davidson"
0113b302a49de15a1d41ca4750191979ad756d2f,Matching Faces with Textual Cues in Soccer Videos,"­4244­0367­7/06/$20.00 ©2006 IEEE
ICME 2006"
015d25f66514ce0a966300944201d45968a104ba,SIMPLE IMAGE DESCRIPTION GENERATOR VIA A LINEAR PHRASE-BASED MODEL Rémi Lebret,"SIMPLE IMAGE DESCRIPTION GENERATOR
VIA A LINEAR PHRASE-BASED MODEL
Rémi Lebret        Pedro H. O. Pinheiro
Ronan Collobert
Idiap-RR-22-2015
JUNE 2015
Idiap
Centre du Parc, Rue Marconi 19, P.O. Box 592, CH - 1920 Martigny
T +41 27 721 77 11  F +41 27 721 77 12   www.idiap.ch"
c6fdbdbbbc7642daae22df0b7812e78d0647afb3,Unsupervised feature learning with C-SVDDNet,"Unsupervised Feature Learning with C-SVDDNet
Dong Wang and Xiaoyang Tan"
c68ec931585847b37cde9f910f40b2091a662e83,A Comparative Evaluation of Dotted Raster-Stereography and Feature-Based Techniques for Automated Face Recognition,"(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 9, No. 6, 2018
A Comparative Evaluation of Dotted Raster-
Stereography and Feature-Based Techniques for
Automated Face Recognition
Muhammad Wasim
S. Talha Ahsan
Department of Computer Science
Department of Electrical Engineering
Usman Institute of Technology
Usman Institute of Technology
Karachi, Pakistan
Karachi, Pakistan
Lubaid Ahmed, Syed Faisal Ali,
Fauzan Saeed
Department of Computer Science
Usman Institute of Technology
Karachi, Pakistan
feature-based
system.  The"
c636cd6eba286357fe807c0ca4b02c3b9b7b5619,Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization,"Training Deep Networks with Synthetic Data:
Bridging the Reality Gap by Domain Randomization
Jonathan Tremblay∗
Aayush Prakash∗
David Acuna∗†
Mark Brophy∗
Varun Jampani
Cem Anil†
Thang To
Eric Cameracci
Shaad Boochoon
Stan Birchfield
NVIDIA
also University of Toronto
{jtremblay,aayushp,dacunamarrer,markb,vjampani,"
c694b397a3a0950cd20699a687fe6c8a3173b107,Explaining autism spectrum disorders: central coherence vs. predictive coding theories.,"J Neurophysiol 112: 2669 –2671, 2014.
First published May 28, 2014; doi:10.1152/jn.00242.2014.
Neuro Forum
Explaining autism spectrum disorders: central coherence vs. predictive coding
theories
Jason S. Chan and Marcus J. Naumer
Institute of Medical Psychology, Goethe-University, Frankfurt, Germany
Submitted 27 March 2014; accepted in final form 23 May 2014
Chan JS, Naumer MJ. Explaining autism spectrum disorders: central
oherence vs. predictive coding theories. J Neurophysiol 112: 2669–2671,
014. First published May 28, 2014; doi:10.1152/jn.00242.2014.—In this
rticle, we review a recent paper by Stevenson et al. (J Neurosci 34:
691–697, 2014). This paper illustrates the need to present different forms of
stimuli in order to characterize the perceptual abilities of people with autism
spectrum disorder (ASD). Furthermore, we will discuss their behavioral
results and offer an opposing viewpoint to the suggested neuronal drivers of
utism spectrum disorder; multisensory integration; temporal binding
window
THE DIFFERENCE in propagation time between an auditory and a
visual stimulus can be substantial, depending on the distance"
c6d6193c8f611331c8178c3857f9ef92607a4507,i i '-o A Study on Using Mid-Wave Infrared Images for Face Recognition,"Sensing Technologies for Global Health, Military Medicine, Disaster Response, and Environmental Monitoring II; and
Biometric Technology for Human Identification IX, edited by Sárka O. Southern, et al., Proc. of SPIE Vol. 8371, 83711K
© 2012 SPIE · CCC code: 0277-786X/12/$18 · doi: 10.1117/12.918899
Proc. of SPIE Vol. 8371  83711K-1
From: http://spiedigitallibrary.org/ on 04/30/2013 Terms of Use: http://spiedl.org/terms"
c6c3cee8adacff8a63ab84dc847141315e874400,Disentangling by Factorising,"Disentangling by Factorising
Hyunjik Kim 1 2 Andriy Mnih 1"
c6ecb8e20250e3fa8ef2a5edd8fad4131f3e874f,EigenBody : Analysis of body shape for gender from noisy images,"EigenBody: Analysis of body shape for gender from noisy images
Matthew Collins, Jianguo Zhang, Paul Miller, Hongbin Wang and Huiyu Zhou
Institute of Electronics Communications and Information Technology (ECIT)
Queens University Belfast"
c62c07de196e95eaaf614fb150a4fa4ce49588b4,SSR-Net: A Compact Soft Stagewise Regression Network for Age Estimation,Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18)
c62c4e5d8243da6bc1fde64097b2ab8971e6e51f,"A Unified Approach for Conventional Zero-Shot, Generalized Zero-Shot, and Few-Shot Learning","JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2017
A Unified approach for Conventional Zero-shot,
Generalized Zero-shot and Few-shot Learning
Shafin Rahman, Salman H. Khan and Fatih Porikli"
c65d7b7c111b1e6303b460ebd484c81537ecae80,Boosting Localized Features for Speaker and Speech Recognition,"Boosting Localized Features for Speaker and Speech Recognition
TH`ESE No 5212 (2011)
PR´ESENT´EE LE 6 OCTOBRE 2011
`A LA FACULT´E SCIENCES ET TECHNIQUES DE L’ING´ENIEUR
LABORATOIRE DE L’IDIAP
PROGRAMME DOCTORAL EN G´ENIE ´ELECTRIQUE
´ECOLE POLYTECHNIQUE F ´ED ´ERALE DE LAUSANNE
POUR L’OBTENTION DU GRADE DE DOCTEUR `ES SCIENCES
Anindya Roy
ccept ´ee sur proposition du jury :
Prof. Pierre Vandergheynst, pr ´esident du jury
Prof. Herv ´e Bourlard, directeur de th `ese
Dr. S ´ebastien Marcel, co-directeur de th `ese
Prof. Jan ˘Cernock´y, rapporteur
Dr. Nicholas Evans, rapporteur
Prof. Jean-Philippe Thiran, rapporteur
Lausanne, EPFL"
c607572fd2594ca83f732c9790fd590da9e69eb1,Comparative Evaluation of Deep Architectures for Face Recognition in Unconstrained Environment ( FRUE ),"Comparative Evaluation of Deep Architectures for Face
Recognition in Unconstrained Environment (FRUE)
Deeksha Gupta
Department of Computer Science and Applications,
MCM DAV College for Women, Chandigarh, (India)"
c6381f055793b406a1aa0375374348cf99502f13,Perception and Processing of Faces in the Human Brain Is Tuned to Typical Feature Locations.,"de Haas, Benjamin; Schwarzkopf, D. Samuel; Alvarez, Ivan; Lawson, Rebecca P.;
Henriksson, Linda; Kriegeskorte, Nikolaus; Rees, Geraint
Perception and processing of faces in the human brain is tuned to typical feature
locations
Published in:
JOURNAL OF NEUROSCIENCE
0.1523/JNEUROSCI.4131-14.2016
Published: 07/09/2016
Document Version
Publisher's PDF, also known as Version of record
Please cite the original version:
de Haas, B., Schwarzkopf, D. S., Alvarez, I., Lawson, R. P., Henriksson, L., Kriegeskorte, N., & Rees, G. (2016).
Perception and processing of faces in the human brain is tuned to typical feature locations. JOURNAL OF
NEUROSCIENCE, 36(36), 9289-9302. DOI: 10.1523/JNEUROSCI.4131-14.2016
This is an electronic reprint of the original article.This reprint may differ from the original in pagination and typographic detail.Powered by TCPDF (www.tcpdf.org)This material is protected by copyright and other intellectual property rights, andduplication or sale of all or part of any of the repository collections is not permitted, except that material maybe duplicated by you for your research use or educational purposes in electronic or print form. You mustobtain permission for any other use. Electronic or print copies may not be offered, whether for sale orotherwise to anyone who is not an authorised user."
c614450c9b1d89d5fda23a54dbf6a27a4b821ac0,Face Image Retrieval of Efficient Sparse Code words and Multiple Attribute in Binning Image,"Vol.60: e17160480, January-December 2017
http://dx.doi.org/10.1590/1678-4324-2017160480
ISSN 1678-4324 Online Edition
Engineering,Technology and Techniques
BRAZILIAN ARCHIVES OF
BIOLOGY AND TECHNOLOGY
A N   I N T E R N A T I O N A L   J O U R N A L
Face  Image  Retrieval  of  Efficient  Sparse  Code  words  and
Multiple Attribute in Binning Image
Suchitra S1*.
Srm Easwari Engineering College, Ramapuram, Bharathi Salai, Chennai, Tamil Nadu, India."
c6b7c2a485f13bb97283c367dc2811266a68ef64,Exploring the potential of combining time of flight and thermal infrared cameras for person detection,"Exploring the Potential of Combining Time of Flight and Thermal
Infrared Cameras for Person Detection
Wim Abbeloos and Toon Goedem´e
KU Leuven, Department of Electrical Engineering, EAVISE
Leuven, Belgium
Keywords:
Time of Flight : Range Image : 2.5D : Thermal Infrared : Thermopile Array : Calibration : Camera : Sensor :
Data Fusion : Measurement Errors : Scattering : Multi-path Interference."
c6dab0aba7045f078313a4186cd507ff8eb8ce32,Atypical disengagement from faces and its modulation by the control of eye fixation in children with autism spectrum disorder.,"BIROn - Birkbeck Institutional Research Online
Enabling open access to Birkbeck’s published research output
Atypical disengagement from faces and its modulation
y the control of eye fixation in children with Autism
Spectrum Disorder
Journal Article
http://eprints.bbk.ac.uk/4677
Version: Accepted (Refereed)
Citation:
© 2011 Springer
Publisher  version
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All articles available through Birkbeck ePrints are protected by intellectual property law, including
opyright law. Any  use made of the contents should comply with the relevant law.
______________________________________________________________
Kikuchi, Y.; Senju, A.; Akechi, H.; Tojo, Y.; Osanai, H.; Hasegawa, T.
(2011)
Atypical disengagement from faces and its modulation by the control of
eye fixation in children with Autism Spectrum Disorder
Deposit Guide"
c69f4ec3ba6ae4a627fcb5d4f58cd07b8c42e4e4,k-fold Subsampling based Sequential Backward Feature Elimination,
c6c086748474dcda06d773891848aa1472de3560,Activity Recognition Based on a Magnitude-Orientation Stream Network,"Activity Recognition based on a
Magnitude-Orientation Stream Network
Carlos Caetano, Victor H. C. de Melo, Jefersson A. dos Santos, William Robson Schwartz
Smart Surveillance Interest Group, Department of Computer Science
Universidade Federal de Minas Gerais, Belo Horizonte, Brazil"
c6badb2cc1191f9dd5e5bea7df75a76349176d01,Densely tracking sequences of 3D face scans,"Densely tracking sequences of 3D face scans
Huaxiong DING
Ecole Centrale de LYON
Liming Chen
Ecole Centrale de LYON"
c63f31a938944f55027808fb9afd4d6e0b8b645e,Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation,"Simultaneous Learning of Trees and Representations for Extreme Classification
nd Density Estimation
Yacine Jernite 1 Anna Choromanska 1 David Sontag 2"
c65407a1a25b3e47cbf21b50bd02c929f9cb5ed4,Sparse interacting Gaussian processes: Efficiency and optimality theorems of autonomous crowd navigation,"Sparse Interacting Gaussian Processes: Efficiency and Optimality Theorems of Autonomous
Crowd Navigation
Pete Trautman
Galois, Inc."
c693c578d783323d130d642bd04d391aac7e8f81,Semantic Pyramids for Gender and Action Recognition,"Semantic Pyramids for Gender and Action
Recognition
Fahad Shahbaz Khan, Joost van de Weijer, Rao Muhammad Anwer, Michael Felsberg, Carlo Gatta"
c6643a771521b19ffc0df035e806b03c2ec00782,Incorporating invariants in Mahalanobis distance based classifiers : Application to Face Recognition,"Incorporating invariants in Mahalanobis distance based classifiers: Application to
Face Recognition
Andrew M. Fraser
Portland State University and Los Alamos National Laboratory
Nicolas W. Hengartner, Kevin R. Vixie, and Brendt E. Wohlberg
Los Alamos National Laboratory
Los Alamos, NM 87545"
c6eb026d3a0081f4cb5cde16d3170f8ecf8ce706,Face Recognition: From Traditional to Deep Learning Methods,"Face Recognition: From Traditional to Deep
Learning Methods
Daniel S´aez Trigueros, Li Meng
School of Engineering and Technology
University of Hertfordshire
Hatfield AL10 9AB, UK
Margaret Hartnett
GBG plc
London E14 9QD, UK"
c6260f83e86dd4d1ece92e528422ecc6e36c13ef,Siamese networks for generating adversarial examples,"Siamese networks for generating adversarial examples
Mandar Kulkarni
Data Scientist
Schlumberger"
c610888cadcf2aa45e7367f43e42eaa7a586652e,Fast Convergence for Object Detection by Learning how to Combine Error Functions,"(cid:13) 2018 IEEE.
Personal use of this material is permitted. Permission from
IEEE must be obtained for all other uses, in any current or
future media, including reprinting/republishing this material
for advertising or promotional purposes, creating new
ollective works, for resale or redistribution to servers or
lists, or reuse of any copyrighted component of this work in
other works.
Accepted version."
c679fd4e29597c64e5921fad796183ae30db8396,LG ] 5 M ar 2 01 6 A Latent-Variable Grid Model,"A Latent-Variable Grid Model
Rajasekaran Masatran
Computer Science and Engineering, Indian Institute of Technology Madras
FREESHELL · ORG"
c600e985ae3af9143b41271abd040a1c1e89177e,Nonparametric Video Retrieval and Frame Classification using Tiny Videos,"Nonparametric Video Retrieval and Frame Classification using Tiny Videos
{tag}                                                        {/tag}
IJCA Proceedings on International Conference in
Recent trends in Computational Methods, Communication and Controls (ICON3C 2012)
© 2012 by IJCA Journal
ICON3C - Number 3
Year of Publication: 2012
Authors:
A. K.  M.  Shanawas Fathima
R.  Kanthavel
{bibtex}icon3c1024.bib{/bibtex}"
c6d5d47513d6a7a1b0b92b33efda3f2a866d34ad,Characterizing International Travel Behavior from Geotagged Photos: A Case Study of Flickr,"RESEARCH ARTICLE
Characterizing International Travel Behavior
from Geotagged Photos: A Case Study of
Flickr
Yihong Yuan*, Monica Medel
Department of Geography, Texas State University, San Marcos, Texas, 78666, United States of America"
c696c9bbe27434cb6279223a79b17535cd6e88c8,Facial Expression Recognition with Pyramid Gabor Features and Complete Kernel Fisher Linear Discriminant Analysis,"International Journal of Information Technology    Vol.11   No.9  2005
Discriminant Analysis
Facial Expression Recognition with Pyramid Gabor
Features and Complete Kernel Fisher Linear
Duan-Duan Yang1, Lian-Wen Jin1, Jun-Xun Yin1, Li-Xin Zhen2, Jian-Cheng Huang2
School of Electronic and Information Engineering, South China
University of Technology, Guangzhou, 510640, P.R.China
{ddyang,
Motorola China Research Center, Shanghai, 210000, P.R.China
{Li-Xin.Zhen,"
f66bc143d85d2b1d9aafec20f598a21d2b90b0c0,SEEING 3 D OBJECTS IN A SINGLE 2 D IMAGE,"Accepted for publication in the Proceedings of the 12th International Conference of Computer Vision, 2009
Seeing 3D Objects in a Single 2D Image
Diego Rother
Johns Hopkins University"
f663ad5467721159263c1cde261231312893f45d,UvA-DARE ( Digital Academic Repository ) Gaze Embeddings for Zero-Shot Image Classification,"UvA-DARE (Digital Academic Repository)
Gaze Embeddings for Zero-Shot Image Classification
Karessli, N.; Akata, Z.; Schiele, B.; Bulling, A.
Published in:
0th IEEE Conference on Computer Vision and Pattern Recognition
0.1109/CVPR.2017.679
Link to publication
Citation for published version (APA):
Karessli, N., Akata, Z., Schiele, B., & Bulling, A. (2017). Gaze Embeddings for Zero-Shot Image Classification. In
0th IEEE Conference on Computer Vision and Pattern Recognition: CVPR 2017 : 21-26 July 2016, Honolulu,
Hawaii : proceedings (pp. 6412-6421). Piscataway, NJ: IEEE. DOI: 10.1109/CVPR.2017.679
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Download date: 18 Nov 2018"
f672d6352a5864caab5a5a286fbc1ce042b55c16,Stabilizing GAN Training with Multiple Random Projections,"Under review as a conference paper at ICLR 2018
Stabilizing GAN Training with
Multiple Random Projections
Anonymous authors
Paper under double-blind review"
f67afec4226aba674e786698b39b85b124945ddd,Spatial Variational Auto-Encoding via Matrix-Variate Normal Distributions,"Spatial Variational Auto-Encoding via Matrix-Variate
Normal Distributions
Zhengyang Wang
School of Electrical Engineering
nd Computer Science
Washington State University
Pullman, WA 99163
Hao Yuan
School of Electrical Engineering
nd Computer Science
Washington State University
Pullman, WA 99163
Shuiwang Ji
School of Electrical Engineering
nd Computer Science
Washington State University
Pullman, WA 99163"
f6d8b67755af67d469fc1b68c52d4c9910eae0e8,Chapter 2 Sentiment Analysis Using Social Multimedia,"Chapter 2
Sentiment Analysis Using Social
Multimedia
Jianbo Yuan, Quanzeng You and Jiebo Luo"
f6066a3428a60904b39dccfd4cb7ec522b4a69f4,Active shape model-based user identification for an intelligent wheelchair,"Int. J. Advanced Mechatronic Systems, Vol. 1, No. 4, 2009
Active shape model-based user identification for an
intelligent wheelchair
P. Jia* and H. Hu
School of Computer Science and Electronic Engineering,
University of Essex,
Colchester, CO4 3SQ, UK
E-mail:
*Corresponding author
E-mail:"
f68f20868a6c46c2150ca70f412dc4b53e6a03c2,Differential Evolution to Optimize Hidden Markov Models Training: Application to Facial Expression Recognition,"Differential Evolution to Optimize
Hidden Markov Models Training:
Application to Facial Expression
Recognition
Khadoudja Ghanem, Amer Draa, Elvis Vyumvuhore and
Ars`ene Simbabawe
MISC Laboratory, Constantine 2 University, Constantine, Algeria
The base system in this paper uses Hidden Markov
Models (HMMs) to model dynamic relationships among
facial features in facial behavior interpretation and un-
derstanding field. The input of HMMs is a new set
of derived features from geometrical distances obtained
from detected and automatically tracked facial points.
Numerical data representation which is in the form of
multi-time series is transformed to a symbolic repre-
sentation in order to reduce dimensionality, extract the
most pertinent information and give a meaningful repre-
sentation to humans. The main problem of the use of
HMMs is that the training is generally trapped in local
minima, so we used the Differential Evolution (DE)"
f6fa97fbfa07691bc9ff28caf93d0998a767a5c1,K2-means for Fast and Accurate Large Scale Clustering,"k2-means for fast and accurate large scale clustering
Eirikur Agustsson
Computer Vision Lab
D-ITET
ETH Zurich
Radu Timofte
Computer Vision Lab
D-ITET
ETH Zurich
Luc Van Gool
ESAT, KU Leuven
D-ITET, ETH Zurich"
f614f9ba33554cfd1a474be03520319b51651a35,Cardiac interoceptive learning is modulated by emotional valence perceived from facial expressions,"Social Cognitive and Affective Neuroscience, 2018, 677–686
doi: 10.1093/scan/nsy042
Advance Access Publication Date: 6 April 2018
Original article
Cardiac interoceptive learning is modulated by
emotional valence perceived from facial expressions
Amanda C. Marshall, Antje Gentsch, Lena Schro¨ der, and
Simone Schu¨ tz-Bosbach
General and Experimental Psychology Unit, Department of Psychology, Ludwig-Maximilians University
Munich, D-80802 Munich, Germany
Correspondence should be addressed to Amanda C. Marshall, General and Experimental Psychology Unit, Department of Psychology, Ludwig-
Maximilians-University Munich, Leopoldstr. 13, D-80802 Munich, Germany. E-mail:"
f614b449ee2fd45974214014c109d993aab73343,A Mathematical Motivation for Complex-Valued Convolutional Networks,"A Mathematical Motivation for
Complex-valued Convolutional Networks
Joan Bruna, Soumith Chintala, Yann LeCun, Serkan Piantino, Arthur Szlam, Mark Tygert
Facebook Artificial Intelligence Research, 1 Facebook Way, Menlo Park, California 94025
Keywords: deep learning, neural networks, harmonic analysis"
f61fa90e4e60aab8366831d4a8a7bed980b76073,An Integrated Soft Computing Approach to a Multi-biometric Security Model,"AN INTEGRATED SOFT COMPUTING
APPROACH TO A MULTI-BIOMETRIC
SECURITY MODEL
A THESIS
SUBMITTED TO
DAYALBAGH EDUCATIONAL INSTITUTE
TOWARDS FULFILMENT OF THE REQUIREMENTS
FOR THE AWARD OF
DOCTOR OF PHILOSOPHY
PHYSICS AND COMPUTER SCIENCE
PREM SEWAK SUDHISH
DEPARTMENT OF PHYSICS AND COMPUTER SCIENCE
DAYALBAGH EDUCATIONAL INSTITUTE"
f636c087091847bd4ccd6d196ada6c0894b52d88,Rate-Accuracy Trade-Off in Video Classification with Deep Convolutional Neural Networks,"Rate-Accuracy Trade-Off In Video Classification
With Deep Convolutional Neural Networks
Mohammad Jubran, Alhabib Abbas, Aaron Chadha and Yiannis Andreopoulos, Senior Member, IEEE"
f6cf2108ec9d0f59124454d88045173aa328bd2e,Robust User Identification Based on Facial Action Units Unaffected by Users' Emotions,"Robust user identification based on facial action units
unaffected by users’ emotions
Ricardo Buettner
Aalen University, Germany"
f6ba16aee3c40b69dc88c947ae59811104b1bd49,Skeletal Tracking using Microsoft Kinect,"Skeletal Tracking using Microsoft Kinect
Abhishek Kar
Advisors: Dr. Amitabha Mukerjee & Dr. Prithwijit Guha
Department of Computer Science and Engineering, IIT Kanpur"
f6a050314881488d0f6653c0c6883937a722eff5,Retina Recognition Based on Fractal Dimension,"IJCSNS International Journal of Computer Science and Network Security, VOL.9 No.10, October 2009
Retina Recognition Based on Fractal Dimension
S.Sukumaran, 1 Dr. M.Punithavalli 2
S.G. Lecturer in Computer Science, Erode Arts College,
Erode – 638 009. India.
Director, Sri Ramakrishna College of Arts & Science for Women
Coimbatore - 641 044. India.
other  problems  the  best  methodology  can  be  used  is
retina based biometric technology for the authentication
to be more accurate and secure as it does not variant and
provide a permanent and unique personal identification.
The retina of a normal human though small (11 mm) and
sometimes  problematic  to  image,  it  has  the  great
mathematical advantage that its pattern variability among
different  persons  is  enormous.  It  is  an  internal  (yet
externally  visible)  organ  of  the  eye,  the  retina  is  well
protected from the environment, and stable over time [6].
The  paper  is  organized  as  follows.  The  next
section  describes  the  fundamentals  of  fractals,  followed
y  the  section  describing  the  evaluation  of  fractal"
f6cf220b8ef17e0a4bef0ff5aadc40eec9653159,Automated System for interpreting Non-verbal Communication in Video Conferencing,"Chetana Gavankar et al / International Journal on Computer Science and Engineering Vol.2(1), 2010, 22-27
Automated System for interpreting Non-verbal
Communication in Video Conferencing
Chetana Gavankar
Senior Lecturer,
Department of Information Technology
Cummins College of Engineering for Women
Karve Nagar, Pune - 411052
for  more  effective"
f6684367e7925cd90fb8974640d41823191c7cff,CNN-based Pore Detection and Description for High-Resolution Fingerprint Recognition,"Automatic Dataset Annotation to Learn CNN Pore
Description for Fingerprint Recognition
Gabriel Dahia
Maur´ıcio Pamplona Segundo
Department of Computer Science, Federal University of Bahia"
f64fb2e5cfa86fe68e9c239557f440c412d02274,Fast Face Detection using MLP and FFT,"Fast Face Detection using MLP and FFT
S(cid:0) Ben(cid:1)Yacoub(cid:2) B(cid:0) Fasel and J(cid:0) L(cid:3)uttin
IDIAP
CP 	(cid:2) 
	 Martigny
Switzerland
email(cid:0) sby(cid:1)idiap(cid:2)ch"
f6567671cc9d204c1dd1322e9c49d2053ed734c5,A REVIEW OF VISION BASED HAND GESTURES RECOGNITION,"International Journal of Information Technology and Knowledge Management
July-December 2009, Volume 2, No. 2, pp. 405-410
A REVIEW OF VISION BASED HAND GESTURES RECOGNITION
G. R. S. Murthy & R. S. Jadon
With the ever-increasing diffusion of computers into the society, it is widely believed that present popular mode of interactions
with computers (mouse and keyboard) will become a bottleneck in the effective utilization of information flow between the
omputers and the human. Vision based Gesture recognition has the potential to be a natural and powerful tool supporting
efficient and intuitive interaction between the human and the computer. Visual interpretation of hand gestures can help in
chieving the ease and naturalness desired for Human Computer Interaction (HCI). This has motivated many researchers in
omputer vision-based analysis and interpretation of hand gestures as a very active research area. We surveyed the literature
on  visual  interpretation  of  hand  gestures  in  the  context  of  its  role  in  HCI  and  various  seminal  works  of  researchers  are
emphasized. The purpose of this review is to introduce the field of gesture recognition as a mechanism for interaction with
omputers.
Keywords: Hand Gesture Recognition, Human Computer Interaction, Computer Vision.
. INTRODUCTION
With  the  development  of  information  technology  in  our
society,  we  can  expect  that  computer  systems  to  a  larger
extent  will  be  embedded  into  our  environment.  These
environments will impose needs for new types of human-
omputer-interaction,  with  interfaces  that  are  natural  and"
f6ce34d6e4e445cc2c8a9b8ba624e971dd4144ca,Cross-Label Suppression: A Discriminative and Fast Dictionary Learning With Group Regularization,"Cross-label Suppression: A Discriminative and Fast
Dictionary Learning with Group Regularization
Xiudong Wang and Yuantao Gu∗
April 24, 2017"
f621f4cdb4158f72f12fdd55564bcdce322dce7c,Recursive Coarse-to-Fine Localization for Fast Object Detection,"International Journal of Control and Automation
Vol.7, No.1 (2014), pp.235-242
http://dx.doi.org/10.14257/ijca.2014.7.1.20
Recursive Coarse-to-Fine Localization for Fast Object Detection
Quy Nguyen Trung, Dung Phan,Soo Hyung Kim, In Seop Na*
nd Hyung Jeong Yang
School of Electronics and Computer Engineering Chonnam National University
77 Yongbong-ro, Gwangju, 500-757 South Korea"
f6785ffe6fe2c30887637a61061a64f4d6725979,BAR: Bayesian Activity Recognition using variational inference,"BAR: Bayesian Activity Recognition using variational
inference
Ranganath Krishnan
Mahesh Subedar
Omesh Tickoo
Intel Labs
Hillsboro, OR (USA)"
f6c70635241968a6d5fd5e03cde6907022091d64,Measuring Deformations and Illumination Changes in Images with Applications to Face Recognition,
f6742010372210d06e531e7df7df9c01a185e241,Dimensional Affect and Expression in Natural and Mediated Interaction,"Dimensional Affect and Expression in
Natural and Mediated Interaction
Michael J. Lyons
Ritsumeikan, University
Kyoto, Japan
October, 2007"
f6488c7741eaddf6cbbd0b0728c48fd8ee1c00a4,Super-Resolution of Face Images Using Kernel PCA-Based Prior,"[3] C. L. Yang, L. M. Po, and W. H. Lam, “A fast H.264 intra prediction
lgorithm using macroblock properties,” in Proc. IEEE ICIP’04, Oct.
004, vol. 1, pp. 461–464.
[4] F. Pan, X. Lin, S. Rahardja, and K. P. Lim et al., “Fast mode decision
lgorithm for intraprediction in H.264/AVC video coding,” IEEE Trans.
Circuits Syst. Video Technol., vol. 15, no. 7, pp. 813–822, Jul. 2005.
[5] P. Yin, H. C. Tourapis, A. M. Tourapis, and J. Boyce, “Fast mode de-
ision and motion estimation for JVT/H.264,” in Proc. IEEE ICIP’03,
Sept. 2003, vol. 3, pp. 853–856.
[6] X. Jing and L. P. Chau, “Fast approach for H.264 inter mode decision,”
Electron. Lett., vol. 40, no. 17, pp. 1051–1052, Aug. 2004.
[7] D. Wu, F. Pan, K. P. Lim, and S. Wu et al., “Fast intermode decision in
H.264/AVC video coding,” IEEE Trans. Circuits Syst. Video Technol.,
vol. 15, no. 7, pp. 953–958, Jul. 2005.
[8] L. Yang, K. Yu, J. Li, and S. Li, “An effective variable block-size early
termination algorithm for H.264 video coding,” IEEE Trans. Circuits
Syst. Video Technol., vol. 15, no. 6, pp. 784–788, Jun. 2005.
[9] H.264/AVC Reference Software JM9.5 [Online]. Available: http://
iphome.hhi.de/suehring/tml/
[10] Y. H. Moon, G. Y. Kim, and J. H. Kim, “An improved early"
f6ce7e947f1cfe75abda61f018c3ca7e38fceb20,NLE@MediaEval'17: Combining Cross-Media Similarity and Embeddings for Retrieving Diverse Social Images,"NLE MediaEval’17: Combining Cross-Media Similarity and
Embeddings for Retrieving Diverse Social Images
Jean-Michel Renders and Gabriela Csurka
Naver Labs Europe, Meylan, France"
f676cdb17ccd5b65f44ce67677d8646b48d8939e,Occupancy Networks: Learning 3D Reconstruction in Function Space,"Occupancy Networks: Learning 3D Reconstruction in Function Space
Lars Mescheder1 Michael Oechsle1,2 Michael Niemeyer1
Sebastian Nowozin3† Andreas Geiger1
Autonomous Vision Group, MPI for Intelligent Systems and University of T¨ubingen
ETAS GmbH, Stuttgart
Google AI Berlin"
f60070d3a4d333aa1436e4c372b1feb5b316a7ba,Face Recognition via Centralized Coordinate Learning,"Face Recognition via Centralized Coordinate
Learning
Xianbiao Qi, Lei Zhang"
7dba0e39bb059103e10fb81bce2fe831f520fb38,Articulated human pose estimation in natural images,"Articulated Human Pose Estimation
in Natural Images
Samuel Alan Johnson
Submitted in accordance with the requirements
for the degree of Doctor of Philosophy.
The University of Leeds
School of Computing
October 2012"
7d057676c9ba7b313adf0b191f64eb26ac2f9dd6,Variability in postnatal sex hormones due to the use of oral contraception and the phase of menstrual cycle influenced brain,"SEX DIFFERENCES AND THE ROLE OF SEX
HORMONES IN FACE DEVELOPMENT AND FACE
PROCESSING
Klára Marečková, MSc.
Thesis submitted to the University of Nottingham for the degree of
Doctor of Philosophy
JULY 2013"
7d98dcd15e28bcc57c9c59b7401fa4a5fdaa632b,FACE APPEARANCE FACTORIZATION FOR EXPRESSION ANALYSIS AND SYNTHESIS,"FACE APPEARANCE FACTORIZATION FOR EXPRESSION ANALYSIS AND SYNTHESIS
Bouchra Abboud, Franck Davoine
Heudiasyc Laboratory, CNRS, University of Technology of Compi`egne.
BP 20529, 60205 COMPIEGNE Cedex, FRANCE.
E-mail:"
7d7f60e41dd9cb84ac5754d59e5a8b418fc7a685,Generator Based On Deep Neural Networks,"Image Caption Generator Based On Deep Neural Networks
Jianhui Chen
CPSC 503
CS Department
Wenqiang Dong
CPSC 503
CS Department
Minchen Li
CPSC 540
CS Department"
7d3dd33950f4a1be56eb88c0791263b3e3a6deee,Object Counts! Bringing Explicit Detections Back into Image Captioning,"Object Counts! Bringing Explicit Detections Back into Image Captioning
Josiah Wang, Pranava Madhyastha and Lucia Specia
{j.k.wang, p.madhyastha,
Department of Computer Science
University of Sheffield, UK"
7db00be42ded44f87f23661c49913f9d64107983,TROPE R HCRAESE R PAID I 2 D FACE RECOGNITION : AN EXPERIMENTAL AND REPRODUCIBLE RESEARCH SURVEY,"D FACE RECOGNITION: AN
EXPERIMENTAL AND REPRODUCIBLE
RESEARCH SURVEY
Manuel Günther        Laurent El Shafey
Sébastien Marcel
Idiap-RR-13-2017
APRIL 2017
Centre du Parc, Rue Marconi 19, P.O. Box 592, CH - 1920 Martigny
T +41 27 721 77 11  F +41 27 721 77 12   www.idiap.ch"
7dab6fbf42f82f0f5730fc902f72c3fb628ef2f0,An Unsupervised Approach to Solving Inverse Problems using Generative Adversarial Networks,"An Unsupervised Approach to Solving Inverse
Problems using Generative Adversarial Networks
Rushil Anirudh
Center for Applied Scientific Computing
Lawrence Livermore National Laboratory
Jayaraman J. Thiagarajan
Center for Applied Scientific Computing
Lawrence Livermore National Laboratory
Bhavya Kailkhura
Timo Bremer
Center for Applied Scientific Computing
Lawrence Livermore National Laboratory
Center for Applied Scientific Computing
Lawrence Livermore National Laboratory"
7de028e5c878b56057559bfbd57f1ce6482ec282,An Architecture for Agile Machine Learning in Real-Time Applications,"An Architecture for Agile Machine Learning
in Real-Time Applications
Johann Schleier-Smith
San Francisco, CA 94111
if(we) Inc.
848 Battery St."
7d96ecb24b3d8595267365cfc9878d46280f1e8c,End-to-end steering angle prediction and object detection using convolutional neural networks,"End-to-end steering angle prediction and
object detection using convolutional
neural networks
Øyvind Kjeldstad Grimnes
Master of Science in Computer Science
Submission date: June 2017
Supervisor:
Frank Lindseth, IDI
Norwegian University of Science and Technology
Department of Computer Science"
7d92d82eae23fe872e8d29116ae22cbd0b15abce,Joint Image Clustering and Labeling by Matrix Factorization,"Joint Image Clustering and Labeling
y Matrix Factorization
Seunghoon Hong, Jonghyun Choi, Jan Feyereisl, Bohyung Han, Larry S. Davis"
7d7024c8c97c972f81c0789a3d282470c1737652,Spatio-Temporal View Invariant Human Pose Recovery in Cluttered Scenes,"Thesis Proposal
Spatio-Temporal View Invariant Human
Pose Recovery in Cluttered Scenes
PhD Student
Xavier P´erez Sala
Advisor
Cecilio Angulo Bah´on
Co-Advisor
Sergio Escalera Guerrero
Universitat Polit`ecnica de Catalunya
(UPC)
PhD in Artificial Intelligence
June 14, 2012"
7ddd2eb46fcc7cb36974b5d105e84071c3e40572,On design and optimization of face verification systems that are smart-card based,"DOI 10.1007/s00138-009-0187-x
ORIGINAL PAPER
On design and optimization of face verification systems
that are smart-card based
Thirimachos Bourlai · Josef Kittler · Kieron Messer
Received: 19 November 2007 / Revised: 26 October 2008 / Accepted: 11 January 2009 / Published online: 10 February 2009
© Springer-Verlag 2009"
7dfedb083fadb6822c07be82233588c31f37317c,FPGA-based IP cores implementation for face recognition using dynamic partial reconfiguration,"J Real-Time Image Proc (2013) 8:327–340
DOI 10.1007/s11554-011-0221-x
S P E C I A L I S S U E
FPGA-based IP cores implementation for face
recognition using dynamic partial reconfiguration
Afandi Ahmad • Abbes Amira • Paul Nicholl •
Benjamin Krill
Received: 8 October 2010 / Accepted: 22 August 2011 / Published online: 14 September 2011
Ó Springer-Verlag 2011"
7d3f6dd220bec883a44596ddec9b1f0ed4f6aca2,Linear Regression for Face Recognition,"Linear Regression for Face Recognition
Imran Naseem,
Roberto Togneri, Senior Member, IEEE, and
Mohammed Bennamoun"
7d84c9322368e0df02b2ee3649c368b10e99cc80,Wavelet – Based Face Recognition Schemes 99 Wavelet – Based Face Recognition Schemes,"Wavelet–Based Face Recognition Schemes
Wavelet–Based Face Recognition Schemes
Sabah A. Jassim
University of Buckingham, Buckingham MK18 1EG
United Kingdom"
7d5a83495c4eff62c98c3fd27d0992850611b2bd,Enhanced Performance of Consensus Fault-tolerant Schemes for Decentralized 363 Unmanned Autonomous Vehicle System —,"Proceedings of the Pakistan Academy of Sciences:
A. Physical and Computational Sciences 53 (4): 363–372 (2016)
Copyright © Pakistan Academy of Sciences
ISSN: 2518-4245 (print), 2518-4253 (online)
Pakistan Academy of Sciences
Research Article
Enhanced Performance of Consensus Fault-tolerant Schemes for
Decentralized Unmanned Autonomous Vehicle System
Naeem Khan*, Aitzaz Ali, and Wasi Ullah
Campus, Pakistan
*Electrical Engineering Department, University of Engineering and Technology Peshawar, Bannu"
7d8b467a524bfd4d65d6e9481dd56a1fb381723a,Research on Robust Local Feature Extraction Method for Human Detection,"Waseda University Doctoral Dissertation
Research on Robust Local Feature Extraction
Method for Human Detection
TANG, Shaopeng
Graduate School of Information, Production and Systems
Waseda University
Feb. 2011"
7d42c27163619fb259992ba518f165dd13eca11c,Face Recognition Using Completed Local Ternary Pattern ( CLTP ) Texture Descriptor,"International Journal of Electrical and Computer Engineering (IJECE)
Vol. 7, No. 3, June 2017, pp. 1594~1601
ISSN: 2088-8708, DOI: 10.11591/ijece.v7i3.pp1594-1601
      1594
Face Recognition Using Completed Local Ternary Pattern
(CLTP) Texture Descriptor
Taha H. Rassem1, Nasrin M. Makbol2, Sam Yin Yee3
,3 Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, Kuantan, Malaysia
IBM Centre of Excellence, Universiti Malaysia Pahang, Kuantan, Malaysia
School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Penang, Malaysia
Article Info
Article history:
Received Feb 21, 2017
Revised Apr 21, 2017
Accepted May 6, 2017
Keyword:
Face recognition
Completed  local binary pattern
(CLBP)
Completed  local ternary"
7da069849066a55d219b9e5b043fb4e07dee2652,Image-based Family Verification in the wild,"Máster Universitario en Ingeniería Computacional y Sistemas
Inteligentes
Master Thesis
Image-based Family Verification in the wild
Oscar Serradilla Casado
Director:
Fadi Dornaika
Co-director:
Ignacio Arganda Carreras"
7d6132a884d2b154059c461e107c7a8c41603ef7,Exploring Multi-Branch and High-Level Semantic Networks for Improving Pedestrian Detection,"Exploring Multi-Branch and High-Level Semantic
Networks for Improving Pedestrian Detection
Jiale Cao, Yanwei Pang, Senior Member, IEEE, and Xuelong Li, Fellow, IEEE"
7dffe7498c67e9451db2d04bb8408f376ae86992,LEAR-INRIA submission for the THUMOS workshop,"LEAR-INRIA submission for the THUMOS workshop
Heng Wang and Cordelia Schmid
LEAR, INRIA, France"
7d73adcee255469aadc5e926066f71c93f51a1a5,Face alignment by deep convolutional network with adaptive learning rate,"978-1-4799-9988-0/16/$31.00 ©2016 IEEE
ICASSP 2016"
7d9dbef9bacf1257e942121f82c3f411f2a78fff,Machine Learning Performance on Face Expression Recognition using Filtered Backprojection in DCT-PCA Domain,"Machine Learning Performance on Face Expression Recognition
using Filtered Backprojection in DCT-PCA Domain.
Ongalo Pheobe1, Huang DongJun2 and Richard Rimiru3
1 School of Information Science and Engineering, Central South University
Changsha, Hunan, 410083, PR China
School of Information Science and Engineering, Central South University
Changsha, Hunan, 410083, PR China
School of Information Science and Engineering, Central South University
Changsha, Hunan, 410083, PR China"
7d7cfc8dc71967f93c2b5ec611747e63c06e1aa1,Crowd Counting and Profiling : Methodology and Evaluation,"Crowd Counting and Profiling: Methodology
nd Evaluation
Chen Change Loy, Ke Chen, Shaogang Gong, and Tao Xiang"
7dd654ac5e775fa1fa585e257565455ae8832caf,Deep Pictorial Gaze Estimation,"Deep Pictorial Gaze Estimation
Seonwook Park, Adrian Spurr, and Otmar Hilliges
AIT Lab, Department of Computer Science, ETH Zurich"
7d841607ce29ff4a75734ffbf569431425d8342f,Bimodal 2D-3D face recognition using a two-stage fusion strategy,"Bimodal 2D-3D face recognition using a two-stage fusion
strategy
Amel AISSAOUI1 and Jean MARTINET2
University of Science and Technologies
Houari Boumediene
Algiers, Algeria
Email:
CRIStAL
Lille 1 University
Villeneuve d’Ascq, France
Email:"
7dcd927b3651d9230f074d19a9b65d1c96fc4604,How to Train a CAT: Learning Canonical Appearance Transformations for Direct Visual Localization Under Illumination Change,"How to Train a CAT: Learning Canonical
Appearance Transformations for Direct Visual
Localization Under Illumination Change
Lee Clement1 and Jonathan Kelly1"
7dfb22b8762575420f914305c5616515bd1b4aba,Design and Implementation of Real-Time Vehicular Camera for Driver Assistance and Traffic Congestion Estimation,"Sensors 2015, 15, 20204-20231; doi:10.3390/s150820204
OPEN ACCESS
sensors
ISSN 1424-8220
www.mdpi.com/journal/sensors
Article
Design and Implementation of Real-Time Vehicular Camera for
Driver Assistance and Traffic Congestion Estimation
Sanghyun Son and Yunju Baek *
School of Computer Science and Engineering, Pusan National University, Busan 609735, Korea;
E-Mail:
*  Author to whom correspondence should be addressed; E-Mail:
Tel.: +82-10-2937-2599; Fax: +82-51-583-2873.
Academic Editor: Leonhard M. Reindl
Received: 16 June 2015 / Accepted: 10 August 2015 / Published: 18 August 2015"
7dce05b7765541b3fb49a144fb39db331c14fdd1,Modélisation et suivi des déformations faciales : applications à la description des expressions du visage dans le contexte de la langue des signes,"Modélisation et suivi des déformations faciales :
pplications à la description des expressions du visage
dans le contexte de la langue des signes
Hugo Mercier
To cite this version:
Hugo Mercier. Modélisation et suivi des déformations faciales : applications à la description des
expressions du visage dans le contexte de la langue des signes.
Interface homme-machine [cs.HC].
Université Paul Sabatier - Toulouse III, 2007. Français. <tel-00185084>
HAL Id: tel-00185084
https://tel.archives-ouvertes.fr/tel-00185084
Submitted on 5 Nov 2007
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents"
7df103807902f45824329ab9b2a558b8baf950b2,Precise Localization in High-Definition Road Maps for Urban Regions,"Precise Localization in High-Definition Road Maps for Urban Regions
Fabian Poggenhans1, Niels Ole Salscheider1 and Christoph Stiller2"
7d30939e2d6f8b980910f4eeca5338d072f5ecb6,Pylon Model for Semantic Segmentation,"A Pylon Model for Semantic Segmentation
Victor Lempitsky
Andrea Vedaldi
Visual Geometry Group, University of Oxford∗
Andrew Zisserman"
7dbc14895390babea6f283fb9153bae94eab727f,Segmentation de scènes extérieures à partir d ’ ensembles d ’ étiquettes à granularité et sémantique variables,"Segmentation de sc`enes ext´erieures `a partir d’ensembles
d’´etiquettes `a granularit´e et s´emantique variables
Damien Fourure, R´emi Emonet, Elisa Fromont, Damien Muselet, Alain
Tremeau, Christian Wolf
To cite this version:
Damien Fourure, R´emi Emonet, Elisa Fromont, Damien Muselet, Alain Tremeau, et al.. Seg-
mentation de sc`enes ext´erieures `a partir d’ensembles d’´etiquettes `a granularit´e et s´emantique
variables. RFIA 2016, Jun 2016, Clermont Ferrand, France. <hal-01318461>
HAL Id: hal-01318461
https://hal.archives-ouvertes.fr/hal-01318461
Submitted on 20 May 2016
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,"
7d35fe4f4a932d7598d94d64b72cfa7e6a70286d,Experimental Evaluation of Multiplicative Kernel SVM Classifiers for Multi-Class Detection,"Experimental Evaluation of Multiplicative Kernel
SVM Classifiers for Multi-Class Detection
Valentina Zadrija
Mireo d.d.
Zagreb, Croatia
Email:
Siniˇsa ˇSegvi´c
Faculty of Electrical Engineering and Computing
University of Zagreb
Zagreb, Croatia
Email:"
7d8354627468f1cb236c9f6f42c317c9c09f0c85,A DCT-based Multimanifold face recognition method using single sample per person,"The 8th Symposium on Advances in Science and Technology (8thSASTech), Mashhad, Iran. 8thSASTech.khi.ac.ir
A DCT-based Multimanifold face recognition method
using single sample per person
Mehrasa Nabipour1, Ali Aghagolzadeh2, Homayun Motameni1
- Department of Computer Eng., Faculty of Eng., Sari Islamic Azad University, Sari, Iran
- Faculty of Electrical and Computer Eng., Babol University of Technology, Babol, Iran."
7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22,Labeled Faces in the Wild : A Survey,"Labeled Faces in the Wild: A Survey
Erik Learned-Miller, Gary Huang, Aruni RoyChowdhury, Haoxiang Li, Gang Hua"
7d39d69b23424446f0400ef603b2e3e22d0309d6,"YOLO9000: Better, Faster, Stronger","YOLO9000:
Better, Faster, Stronger
Joseph Redmon∗†, Ali Farhadi∗†
University of Washington∗, Allen Institute for AI†
http://pjreddie.com/yolo9000/"
2d1f710ba593833cdb0b63880f60146504cf1dc5,Linguistically-driven Framework for Computationally Efficient and Scalable Sign Recognition,"Linguistically-driven Framework for Computationally
Efficient and Scalable Sign Recognition
Dimitris Metaxas*, Mark Dilsizian*, Carol Neidle**
*Rutgers University, **Boston University
*Rutgers University, CBIM, Department of Computer Science, 617 Bowser Road, Piscataway, NJ  08854
**Boston University Linguistics, 621 Commonwealth Ave., Boston, MA  02215"
2d1b8f60f2724efd6c9344870fb60e8525157d70,Parallel Multiscale Autoregressive Density Estimation,"Parallel Multiscale Autoregressive Density Estimation
Scott Reed 1 A¨aron van den Oord 1 Nal Kalchbrenner 1 Sergio G´omez Colmenarejo 1 Ziyu Wang 1
Yutian Chen 1 Dan Belov 1 Nando de Freitas 1"
2d919473cf43e2522b2366271b778ce6ce7dc75c,Appearance-Based Re-identification of Humans in Low-Resolution Videos Using Means of Covariance Descriptors,"Appearance-based Re-Identification of Humans in Low-Resolution Videos
using Means of Covariance Descriptors
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB
J¨urgen Metzler
76131 Karlsruhe, Germany"
2df4d05119fe3fbf1f8112b3ad901c33728b498a,A regularization scheme for structured output problems : an application to facial landmark detection,"Facial landmark detection using structured output deep
neural networks
Soufiane Belharbi ∗1, Cl´ement Chatelain∗1, Romain H´erault∗1, and S´ebastien
Adam∗2
LITIS EA 4108, INSA de Rouen, Saint ´Etienne du Rouvray 76800, France
LITIS EA 4108, UFR des Sciences, Universit´e de Rouen, France.
September 24, 2015"
2d6d4899c892346a9bc8902481212d7553f1bda4,Neural Face Editing with Intrinsic Image Disentangling SUPPLEMENTARY MATERIAL,"Neural Face Editing with Intrinsic Image Disentangling
SUPPLEMENTARY MATERIAL
Zhixin Shu1 Ersin Yumer2 Sunil Hadap2 Kalyan Sunkavalli2 Eli Shechtman 2 Dimitris Samaras1,3
Stony Brook University 2Adobe Research 3 CentraleSup´elec, Universit´e Paris-Saclay
. Implementation: more details
In this section, we provide more details regarding
the implementation of the rendering layers fshading and
fimage-formation as described in the paper.
.1. Shading Layer
The shading layer is rendered with a spherical harmonics
illumination representation [6, 2, 7, 1].
where
= c3n2
z − c5
= 2c1nxnz
= c1n2
x − c1n2
The forward process is described by equations (3),(4),
nd (5) in the main paper. We now provide the backward
process, i.e., the partial derivatives ∂Si"
2d950a3e8c74efb08197bfcb9c64a6b111f6bcd9,DENSE MATCHING COMPARISON BETWEEN CENSUS AND A CONVOLUTIONAL NEURAL NETWORK ALGORITHM FOR PLANT RECONSTRUCTION,"DENSE MATCHING COMPARISON BETWEEN CENSUS AND A CONVOLUTIONAL
NEURAL NETWORK ALGORITHM FOR PLANT RECONSTRUCTION
Y. Xia, J. Tian, P. d’Angelo, P. Reinartz
German Aerospace Center (DLR), Remote Sensing Technology Institute, 82234 Wessling, Germany
(Yuanxin.Xia, Jiaojiao.Tian, Pablo.Angelo,
Commission II, WG II/2
KEY WORDS: Dense Matching, Plants, 3D Modelling, Semi-Global Matching, Census, Convolutional Neural Networks"
2dc4b472fe257522a106dc1246b758113be60b59,Image Co-segmentation by Incorporating Color Reward Strategy and Active Contours Model,"Image Co-segmentation by Incorporating Color
Reward Strategy and Active Contours Model
Fanman Meng, Hongliang Li, Senior Member, IEEE, Guanghui Liu,
King Ngi Ngan, Fellow, IEEE"
2d072cd43de8d17ce3198fae4469c498f97c6277,Random Cascaded-Regression Copse for Robust Facial Landmark Detection,"Random Cascaded-Regression Copse for Robust
Facial Landmark Detection
Zhen-Hua Feng, Student Member, IEEE, Patrik Huber, Josef Kittler, Life Member, IEEE, William Christmas,
nd Xiao-Jun Wu"
2d3d4883350a48708cdc0c260479110e5eed965a,Leveraging Visual Question Answering for Image-Caption Ranking,"Leveraging Visual Question Answering for
Image-Caption Ranking
Xiao Lin Devi Parikh
Virginia Tech"
2d54dc50bbc1a0a63b6f1000bc255f88d57a7a63,It's All Fun and Games until Someone Annotates: Video Games with a Purpose for Linguistic Annotation,Submitted 10/2013; Revised 03/2014; Revised 08/2014; Published 10/2014. c(cid:13)2014 Association for Computational Linguistics.
2d8eff4b085b57788e2f4485c81eb80910f94da0,The impact of organizational performance on the emergence of Asian American leaders.,"Journal of Applied Psychology
The Impact of Organizational Performance on the
Emergence of Asian American Leaders
Seval Gündemir, Andrew M. Carton, and Astrid C. Homan
Online First Publication, September 24, 2018. http://dx.doi.org/10.1037/apl0000347
CITATION
Gündemir, S., Carton, A. M., & Homan, A. C. (2018, September 24). The Impact of Organizational
Performance on the Emergence of Asian American Leaders. Journal of Applied Psychology.
Advance online publication. http://dx.doi.org/10.1037/apl0000347"
2d9e30680a17bbdf83ce27287bc1901da7198f74,"Symmetry, probability, and recognition in face space.","Symmetry, probability, and recognition in face space
Lawrence Sirovich1 and Marsha Meytlis
Laboratory of Applied Mathematics, Mount Sinai School of Medicine, 1 Gustave L. Levy Place, New York, NY 10029
Communicated by Mitchell J. Feigenbaum, The Rockefeller University, New York, NY, December 12, 2008 (received for review June 9, 2008)
The essential midline symmetry of human faces is shown to play a
key role in facial coding and recognition. This also has deep and
important connections with recent explorations of the organiza-
tion of primate cortex, as well as human psychophysical experi-
ments. Evidence is presented that the dimension of face recogni-
tion space for human faces is dramatically lower than previous
estimates. One result of the present development is the construc-
tion of a probability distribution in face space that produces an
interesting and realistic range of (synthetic) faces. Another is a
recognition algorithm that by reasonable criteria is nearly 100%
ccurate.
face dimension 兩 probability distributions 兩 face recognition
Visual space may be conveniently regarded as a tableau of
gray levels assigned to a square of O(105) pixels. Human
faces belong to a subspace of this high-dimensional space, and
the representation/identification of faces termed the Rogue’s"
2d690c63b00e68782666ebf86ac0756fad100a18,Multiple-view face hallucination by a novel regression analysis in tensor space,"The International Arab Journal of Information Technology, Vol. 13, No. 6, November 2016
Multiple-View Face Hallucination by a Novel
Regression Analysis in Tensor Space
Faculty of Engineering and Technology, Panyapiwat Institute of Management, Thailand
Parinya Sanguansat"
2d84c0d96332bb4fbd8acced98e726aabbf15591,Investigating the Role of Saliency for Face Recognition,"UNIVERSITY OF CALIFORNIA
RIVERSIDE
Investigating the Role of Saliency for Face Recognition
A Dissertation submitted in partial satisfaction
of the requirements for the degree of
Doctor of Philosophy
Electrical Engineering
Ramya Malur Srinivasan
March 2015
Dissertation Committee:
Professor Amit K Roy-Chowdhury, Chairperson
Professor Ertem Tuncel
Professor Conrad Rudolph
Professor Tamar Shinar"
2d120c8c74bc029a14fb0726ef103c873a5090eb,Real-Time Gender Classification by Face Eman,"(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 7, No. 3, 2016
Real-Time Gender Classification by Face
Eman Fares Al Mashagba
Computer Sciences Department
Zarqa University
Zarqa, Jordan"
2d7d8c468bdf123b50ea473fe78a178bfc50724c,Evaluating multi-modal deep learning systems with microworlds,"Research proposal: Evaluating multi-modal deep
learning systems with micro-worlds
Alexander Kuhnle
University of Cambridge (United Kingdom)
6th November 2016"
2d88e7922d9f046ace0234f9f96f570ee848a5b5,Building Better Detection with Privileged Information,"Building Better Detection with Privileged Information
Z. Berkay Celik
Department of CSE
The Pennsylvania State
University
Patrick McDaniel
Department of CSE
The Pennsylvania State
University
Rauf Izmailov
Applied Communication
Sciences
Basking Ridge, NJ, US
Nicolas Papernot
Department of CSE
The Pennsylvania State
University
Ananthram Swami
Army Research
Laboratory"
2dfc48168c0de9e6c7135293c95b7d794fcfbbbf,Query-Driven Locally Adaptive Fisher Faces and Expert-Model for Face Recognition,"-4244-1437-7/07/$20.00 ©2007 IEEE
I - 141
ICIP 2007"
2d27e2d8188743c4e3ca30fda5c25e70775f03e8,FollowMe: Person following and gesture recognition with a quadrocopter,"FollowMe: Person Following and
Gesture Recognition with a Quadrocopter
Tayyab Naseer*, J¨urgen Sturm†, and Daniel Cremers†
*Department of Computer Science, University of Freiburg, Germany
Department of Computer Science, Technical University of Munich, Germany"
2d12efd5aef4c180ecfaf65184eb7b56e5a40329,Object Recognition Based on Image Features : A Survey,"D Object Recognition Based on Image Features: A
International Journal of Computer and Information Technology (ISSN: 2279 – 0764)
Volume 03 – Issue 03, May 2014
Survey
Dept. of Information Systems, Faculty of Computers and
Khaled Alhamzi
Information, Mansoura University
Mansoura, Egypt
Kalhamzi {at} yahoo.com
Mohammed Elmogy
Dept. of Information Technology, Faculty of Computers and
Information, Mansoura University
Mansoura, Egypt
Dept. of Information Systems, Faculty of Computers and
Sherif Barakat
Information, Mansoura University
Mansoura, Egypt"
2d71e0464a55ef2f424017ce91a6bcc6fd83f6c3,A Survey on : Image Process using Two-Stage Crawler,"International Journal of Computer Applications (0975 – 8887)
National Conference on Advancements in Computer & Information Technology (NCACIT-2016)
A Survey on: Image Process using Two- Stage Crawler
Nilesh Wani
Assistant Professor
SPPU, Pune
Department of Computer Engg
Department of Computer Engg
Department of Computer Engg
Dipak Bodade
BE Student
SPPU, Pune
Savita Gunjal
BE Student
SPPU, Pune
Varsha Mahadik
BE Student
Department of Computer Engg
SPPU, Pune
dditional"
2df6aa6dd2683fb35f7dcd8536d7b67fb72ede12,What drives social in-group biases in face recognition memory? ERP evidence from the own-gender bias.,"doi:10.1093/scan/nst024
SCAN (2014) 9, 580^590
What drives social in-group biases in face recognition
memory? ERP evidence from the own-gender bias
Nicole Wolff,1,2 Kathleen Kemter,1 Stefan R. Schweinberger,1,2 and Holger Wiese1,2
Friedrich Schiller University of Jena, Jena, Germany and 2DFG Research Unit Person Perception, Friedrich Schiller University of Jena,
Jena, Germany
It is well established that memory is more accurate for own-relative to other-race faces (own-race bias), which has been suggested to result from larger
perceptual expertise for own-race faces. Previous studies also demonstrated better memory for own-relative to other-gender faces, which is less likely to
result from differences in perceptual expertise, and rather may be related to social in-group vs out-group categorization. We examined neural correlates
of the own-gender bias using event-related potentials (ERP). In a recognition memory experiment, both female and male participants remembered faces
of their respective own gender more accurately compared with other-gender faces. ERPs during learning yielded significant differences between the
subsequent memory effects (subsequently remembered – subsequently forgotten)
for own-gender compared with other-gender faces in the
occipito-temporal P2 and the central N200, whereas neither later subsequent memory effects nor ERP old/new effects at test reflected a neural
orrelate of the own-gender bias. We conclude that the own-gender bias is mainly related to study phase processes, which is in line with sociocognitive
ccounts.
Keywords: own-gender bias; ERP; face processing; Dm effect; old/new effect
INTRODUCTION
Although humans can recognize a massive number of previously en-"
2d9a49666bd72e7ba06579d9411ceb2df5205466,3D Face Mesh Modeling from Range Images for 3D Face Recognition,"-4244-1437-7/07/$20.00 ©2007 IEEE
IV - 509
ICIP 2007"
2dc8364433bde6041f38792cd01a6e80a55b1471,Multi-target tracking by discriminative analysis on Riemannian manifold,"MULTI-TARGET TRACKING BY DISCRIMINATIVE
ANALYSIS ON RIEMANNIAN MANIFOLD
Slawomir Bak, Duc Phu Chau, Julien Badie, Etienne Corvee, François
Bremond, Monique Thonnat
To cite this version:
Slawomir Bak, Duc Phu Chau, Julien Badie, Etienne Corvee, François Bremond, et al.. MULTI-
TARGET TRACKING BY DISCRIMINATIVE ANALYSIS ON RIEMANNIAN MANIFOLD. ICIP
- International Conference on Image Processing - 2012, Sep 2012, Orlando, United States.
Computer Society, 1, pp.1-4, 2012, People re-identification and tracking from multiple cameras. <hal-
00703633>
HAL Id: hal-00703633
https://hal.inria.fr/hal-00703633
Submitted on 7 Jun 2012
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est"
2d8d089d368f2982748fde93a959cf5944873673,Visually Guided Spatial Relation Extraction from Text,"Proceedings of NAACL-HLT 2018, pages 788–794
New Orleans, Louisiana, June 1 - 6, 2018. c(cid:13)2018 Association for Computational Linguistics"
2d0dfa8779aefa1a9a89a1b400188fa9114b4c0a,Functional Map of the World,"Functional Map of the World
Gordon Christie1
Neil Fendley1
The Johns Hopkins University Applied Physics Laboratory
James Wilson2
Ryan Mukherjee1
DigitalGlobe"
2db0d42192618d0c7419321fac06b887d96dea53,Image Set Classification for Low Resolution Surveillance,"Image Set Classification
for Low Resolution Surveillance
Uzair Nadeem, Syed Afaq Ali Shah, Mohammed Bennamoun, Roberto Togneri
nd Ferdous Sohel"
2d6130f043e69849fc0443bb489c5d21f933eddd,Deep Recurrent Convolutional Networks for Video-based Person Re-identification: An End-to-End Approach,"Noname manuscript No.
(will be inserted by the editor)
Deep Recurrent Convolutional Networks for Video-based Person
Re-identification: An End-to-End Approach
Lin Wu · Chunhua Shen · Anton van den Hengel"
2d42b5915ca18fdc5fa3542bad48981c65f0452b,Generalization and Equilibrium in Generative Adversarial Nets (GANs),"Generalization and Equilibrium in Generative Adversarial Nets
(GANs)
Sanjeev Arora∗
Rong Ge †
Yingyu Liang‡
Tengyu Ma§
Yi Zhang¶"
2d51b52b3eeae8877d1a76ca564a35b8e5051c9d,AU recognition on 3D faces based on an extended statistical facial feature model,"AU Recognition on 3D Faces Based On An Extended Statistical Facial
Feature Model
Xi Zhao, Emmanuel Dellandr´ea, Liming Chen and Dimitris Samaras"
2d15a7546c16d5821ffa8f769eb7ec18e435e64d,Recognition in Terra Incognita,"Recognition in Terra Incognita
Sara Beery, Grant Van Horn, and Pietro Perona
Caltech"
2d22a60e69ebdb3fde056adcf4f6a08ccdb6106f,Robust Facial Expression Recognition Mr,"IJREAT International Journal of Research in Engineering & Advanced Technology, Volume 2, Issue 2, Apr-May, 2014
ISSN: 2320 – 8791 (Impact Factor: 1.479)
www.ijreat.org
Robust Facial Expression Recognition
Mr. Mukund Kumar1, Ms. D. Udaya2
, 2Computer Science and Engineering, Dr. Pauls Engineering College,Villupuram"
2d05e768c64628c034db858b7154c6cbd580b2d5,FACIAL EXPRESSION RECOGNITION : Machine Learning using C # Author : Neda Firoz,"Neda Firoz et al, International Journal of Computer Science and Mobile Computing, Vol.4 Issue.8, August- 2015, pg. 431-446
Available Online at www.ijcsmc.com
International Journal of Computer Science and Mobile Computing
A Monthly Journal of Computer Science and Information Technology
IJCSMC, Vol. 4, Issue. 8, August 2015, pg.431 – 446
RESEARCH ARTICLE
ISSN 2320–088X
FACIAL EXPRESSION RECOGNITION:
Machine Learning using C#
Author: Neda Firoz
Advisor: Dr. Prashant Ankur Jain"
2df731a01db3caf45105c40ac266f76fe1871470,Affective issues in adaptive educational environments,"Neapolis University
HEPHAESTUS Repository
School of Information Sciences
http://hephaestus.nup.ac.cy
Book chapters
Affective Issues in Adaptive Educational Environments
Leontidis, Makis
IGI Global
http://hdl.handle.net/11728/6301
Downloaded from HEPHAESTUS Repository, Neapolis University institutional repository"
2d146cc0908c931d87f6e6e5d08b117c30a69b8d,Learning Assignment Order of Instances for the Constrained K-Means Clustering Algorithm,"Learning Assignment Order of Instances for
Constrained K-means Clustering Algorithm
Yi Hong, and Sam Kwong, Senior Member, IEEE
Yi Hong is with the department of computer science, at the City University of Hong Kong,
Tat Chee Avenue, Kowloon, Hong Kong.
December 9, 2007
DRAFT"
2da845c75bf9ff02bd27b6e2ceb4732e89b05fad,Linear Support Tensor Machine With LSK Channels: Pedestrian Detection in Thermal Infrared Images,"Linear Support Tensor Machine:
Pedestrian Detection in Thermal Infrared Images
Sujoy Kumar Biswas, Student Member, IEEE, Peyman Milanfar, Fellow, IEEE"
2d95cf1df9701de410792997205c71208bde98d9,Visual-Inertial based autonomous navigation of an Unmanned Aerial Vehicle in GPS-Denied environments,"FACULDADE DE ENGENHARIA DA UNIVERSIDADE DO PORTO
Visual-Inertial based autonomous
navigation of an Unmanned Aerial
Vehicle in GPS-Denied environments
Francisco de Babo Martins
EEC0035 - PREPARAÇÃO DA DISSERTAÇÃO
Mestrado Integrado em Engenharia Electrotécnica e de Computadores
Supervisor: Luís Teixeira
February 18, 2015"
2d532fd0636fd49dd893c9dff7fe615f974ec826,Causal Inference in Nonverbal Dyadic Communication with Relevant Interval Selection and Granger Causality,"Causal Inference in Nonverbal Dyadic Communication with Relevant
Interval Selection and Granger Causality
Lea M¨uller1, Maha Shadaydeh1∗, Martin Th¨ummel1, Thomas Kessler2, Dana Schneider2 and Joachim
Denzler1,3
Computer Vision Group, Friedrich Schiller University of Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany
Department of Social Psychology, Friedrich Schiller University of Jena, Humboldtstrasse 26, 07743 Jena, Germany
Michael Stifel Center, Ernst-Abbe-Platz 2, 07743 Jena, Germany
Keywords:
Nonverbal emotional communication, Granger causality, maximally coherent intervals"
ecdf8e5393eead0b63c5bc4fbe426db5a70574eb,Linear Subspace Learning for Facial Expression Analysis,"Linear Subspace Learning for
Facial Expression Analysis
Caifeng Shan
Philips Research
The Netherlands
. Introduction
Facial  expression,  resulting  from  movements  of  the  facial  muscles,  is  one  of  the  most
powerful, natural, and immediate means for human beings to communicate their emotions
nd intentions. Some examples of facial expressions are shown in Fig. 1. Darwin (1872) was
the  first  to  describe  in  detail  the  specific  facial  expressions  associated  with  emotions  in
nimals  and  humans;  he  argued  that  all  mammals  show  emotions  reliably  in  their  faces.
Psychological  studies  (Mehrabian,  1968;  Ambady  &  Rosenthal,  1992)  indicate  that  facial
expressions, with other non-verbal cues, play a major and fundamental role in face-to-face
ommunication.
Fig. 1. Facial expressions of George W. Bush.
Machine  analysis  of  facial  expressions,  enabling  computers  to  analyze  and  interpret  facial
expressions  as  humans  do,  has  many  important  applications  including  intelligent  human-
omputer  interaction,  computer  animation,  surveillance  and  security,  medical  diagnosis,
law  enforcement,  and  awareness  system  (Shan,  2007).  Driven  by  its  potential  applications
nd  theoretical  interests  of  cognitive  and  psychological  scientists,  automatic  facial"
ec4af4a6e89d61c05dcdf89f7f5d0a404bed4027,Bodily action penetrates affective perception.,"Bodily action penetrates affective
perception
Carlo Fantoni, Sara Rigutti and Walter Gerbino
Department of Life Sciences, Psychology Unit “Gaetano Kanizsa,” University of Trieste, Trieste, Italy"
ec1f3f1f1d9af3046650c3a30f95a7f2f0a78390,What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?,"IJCV VISI manuscript No.
(will be inserted by the editor)
What Makes Good Synthetic Training Data for Learning
Disparity and Optical Flow Estimation?
Nikolaus Mayer · Eddy Ilg · Philipp Fischer · Caner Hazirbas · Daniel
Cremers · Alexey Dosovitskiy · Thomas Brox
Received: date / Accepted: date"
ec7a545ba99542b2b74340d2e863590e4f450bb7,Sparse Subspace Clustering by Orthogonal Matching Pursuit,"Sparse Subspace Clustering by Orthogonal Matching Pursuit
Center for Imaging Science, Johns Hopkins University, Baltimore, MD, 21218, USA
Chong You
nd Ren´e Vidal"
ecf2ba5ea183a6be63b57543a19dd41e8017daaf,Cooperative Learning of Energy-Based Model and Latent Variable Model via MCMC Teaching,"Cooperative Learning of Energy-Based Model and Latent Variable Model via
MCMC Teaching
Jianwen Xie 1,2, Yang Lu 1,3, Ruiqi Gao 1, Ying Nian Wu 1
Department of Statistics, University of California, Los Angeles, USA
Hikvision Research America
Amazon RSML (Retail System Machine Learning) Group"
ec6855acd0871d3e000872a5dd89db97c1554e18,Contrasting emotion processing and executive functioning in attention-deficit/hyperactivity disorder and bipolar disorder.,"016, Vol. 130, No. 5, 531–543
0735-7044/16/$12.00
© 2016 American Psychological Association
http://dx.doi.org/10.1037/bne0000158
Contrasting Emotion Processing and Executive Functioning in
Attention-Deficit/Hyperactivity Disorder and Bipolar Disorder
Stephen Soncin, Donald C. Brien, and Brian C. Coe
Queen’s University
Queen’s University and Hotel Dieu Hospital, Kingston,
Alina Marin
Ontario, Canada
Douglas P. Munoz
Queen’s University
Attention-deficit/hyperactivity disorder (ADHD) and bipolar disorder (BD) are highly comorbid and
share executive function and emotion processing deficits, complicating diagnoses despite distinct clinical
features. We compared performance on an oculomotor task that assessed these processes to capture subtle
differences between ADHD and BD. The interaction between emotion processing and executive func-
tioning may be informative because, although these processes overlap anatomically, certain regions that
re compromised in each network are different in ADHD and BD. Adults, aged 18 – 62, with ADHD (n ⫽
2), BD (n ⫽ 20), and healthy controls (n ⫽ 21) performed an interleaved pro- and antisaccade task"
ecbb05ad1d5ffc9bad3c33a8cb800d3a6d034159,Generative Sensing: Transforming Unreliable Sensor Data for Reliable Recognition,
ec196dbede1772541bb9df2efeda655d377b291e,Fast Disparity Estimation Using Dense Networks,"Fast Disparity Estimation using Dense Networks*
Rowel Atienza1"
ec89c5f2f5acce23b0d05736cd9f32d4ca6dc382,Body Actions Change the Appearance of Facial Expressions,"Body Actions Change the Appearance of Facial
Expressions
Carlo Fantoni1,2*, Walter Gerbino1
Department of Life Sciences, Psychology Unit ‘‘Gaetano Kanizsa’’, University of Trieste, Trieste, Italy, 2 Center for Neuroscience and Cognitive Istituto
Italiano di Tecnologia, Rovereto, Italy"
ecbaa92c289f4f5ff9a57b19a2725036a92311f5,Focused Evaluation for Image Description with Binary Forced-Choice Tasks,"Proceedings of the 5th Workshop on Vision and Language, pages 19–28,
Berlin, Germany, August 12 2016. c(cid:13)2016 Association for Computational Linguistics"
ec2027c2dd93e4ee8316cc0b3069e8abfdcc2ecf,Latent Variable PixelCNNs for Natural Image Modeling,"Latent Variable PixelCNNs for Natural Image Modeling
Alexander Kolesnikov 1 Christoph H. Lampert 1"
ec91c6d6235f31c751b03489d7b1d472dfc9da26,Face Database Retrieval Using Pseudo 2D Hidden Markov Models,"Face Database Retrieval Using Pseudo 2D Hidden Markov Models
Fraunhofer Institute for Media Communication IMK
Stefan Eickeler
Schloss Birlinghoven
53754 Sankt Augustin, Germany"
ec1e03ec72186224b93b2611ff873656ed4d2f74,D Reconstruction of “ Inthe-Wild ” Faces in Images and Videos,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
D Reconstruction of “In-the-Wild” Faces in
Images and Videos
James Booth, Anastasios Roussos, Evangelos Ververas, Epameinondas Anton-
kos, Stylianos Ploumpis, Yannis Panagakis, and Stefanos Zafeiriou"
ec90d333588421764dff55658a73bbd3ea3016d2,Protocol for Systematic Literature Review of Face Recognition in Uncontrolled Environment,"Research Article
Protocol for Systematic Literature Review of Face
Recognition in Uncontrolled Environment
Faizan Ullah, Sabir Shah, Dilawar Shah, Abdusalam, Shujaat Ali
Department of Computer Science, Bacha Khan University, Charsadda, KPK, Pakistan"
ecb927634e8a734710a3b3734e1bffccac7b7061,Action Search: Spotting Actions in Videos and Its Application to Temporal Action Localization,"Action Search: Spotting Actions in Videos and
Its Application to Temporal Action Localization
Humam Alwassel, Fabian Caba Heilbron, and Bernard Ghanem
King Abdullah University of Science and Technology (KAUST), Saudi Arabia
http://www.humamalwassel.com/publication/action-search/"
ecc09ab9c61dc3a3a15f55332f63bccbf443f291,Cross-Domain Deep Face Matching for Real Banking Security Systems,"Cross-Domain Deep Face Matching for Real
Banking Security Systems
Johnatan S. Oliveira1,∗, Gustavo B. Souza2,∗, Anderson R. Rocha3, Fl´avio E. Deus1 and Aparecido N. Marana4
Department of Electrical Engineering, University of Bras´ılia (UnB), Bras´ılia, Brazil.
Department of Computing, Federal University of S˜ao Carlos (UFSCar), S˜ao Carlos, Brazil.
Institute of Computing, University of Campinas (Unicamp), Campinas, Brazil.
Department of Computing, S˜ao Paulo State University (Unesp), Bauru, Brazil.
E-mails: {jow,
Equal contributors."
ece31d41b4da5457d570c04d22f19fcd026776b6,Learning Deep Disentangled Embeddings with the F-Statistic Loss,"Learning Deep Disentangled Embeddings
With the F-Statistic Loss
Karl Ridgeway
University of Colorado
Boulder, Colorado
Department of Computer Science
Department of Computer Science
Michael C. Mozer
University of Colorado
Boulder, Colorado"
ec5f36e9a1086401695e7b9fc1bb8a0ee4df7fe1,Safe Reinforcement Learning with Model Uncertainty Estimates,"Safe Reinforcement Learning with Model Uncertainty Estimates
Bj¨orn L¨utjens, Michael Everett, Jonathan P. How"
ec25f39fa6b4ef4529981a1ae051086e93642d27,Deformable Part Models are Convolutional Neural Networks Tech report,"Deformable Part Models are Convolutional Neural Networks
Tech report
Ross Girshick Forrest Iandola Trevor Darrell
Jitendra Malik
UC Berkeley"
ec9e8d69b67bcb2814b538091fa288b6bdbb990f,GURLS : a Toolbox for Regularized Least Squares Learning,"Computer Science and ArtificialIntelligence LaboratoryTechnical Reportmassachusetts institute of technology, cambridge, ma 02139 usa — www.csail.mit.eduMIT-CSAIL-TR-2012-003CBCL-306January 31, 2012GURLS: a Toolbox for Regularized Least Squares LearningAndrea Tacchetti, Pavan S. Mallapragada, Matteo Santoro, and Lorenzo Rosasco"
ec27b3d1548909d3f55246a388d768cf51e65802,Face Mask Extraction in Video Sequence,"Face Mask Extraction in Video Sequence
Yujiang Wang 1 · Bingnan Luo 1 · Jie Shen 1 · Maja Pantic 1"
ecab74350042a494110ee2f452bb96a89bc146f6,OBLIQUE AERIAL IMAGERY – A REVIEW,"Remondino, Gerke
Oblique Aerial Imagery – A Review
Fabio Remondino, Trento
Markus Gerke, Twente"
ec7d418ddf95d231b2afc70ed8c94d0764abec61,Knowledge Transfer Using Latent Variable Models,"Copyright
Ayan Acharya"
ec0104286c96707f57df26b4f0a4f49b774c486b,An Ensemble CNN2ELM for Age Estimation,"An Ensemble CNN2ELM for Age Estimation
Mingxing Duan , Kenli Li, Senior Member, IEEE, and Keqin Li, Fellow, IEEE"
ecd0a2e55f456b69243d1278fee15d8dbfc98c28,Heterogeneous Multicores : When Slower is Faster,"Heterogeneous Multicores: When Slower is Faster
Tomas Hruby
Herbert Bos
The Network Institute, VU University Amsterdam
Andrew S. Tanenbaum"
ecf4690ddd3ad26f9cd1749d16ef1aa06d391f92,Does Exposure to Hostile Environments Predict Enhanced Emotion Detection?,"PDF hosted at the Radboud Repository of the Radboud University
Nijmegen
The following full text is a publisher's version.
For additional information about this publication click this link.
http://hdl.handle.net/2066/191999
Please be advised that this information was generated on 2018-06-28 and may be subject to
hange."
ec1223c8fc16751dd577d3418f61d44a139c7dc3,Group Influences on Engaging Self-Control: Children Delay Gratification and Value It More When Their In-Group Delays and Their Out-Group Doesn't.,"RUNNING HEAD: GROUP INFLUENCES ON SELF-CONTROL
Group Influences on Engaging Self-control: Children Delay Gratification and Value It More
When Their In-Group Delays and Their Out-Group Doesn’t
Sabine Doebel* and Yuko Munakata
Department of Psychology and Neuroscience, University of Colorado Boulder
*Corresponding author"
ec12f805a48004a90e0057c7b844d8119cb21b4a,Distance-Based Descriptors and Their Application in the Task of Object Detection,"Distance-Based Descriptors and Their
Application in the Task of Object Detection
Radovan Fusek(B) and Eduard Sojka
Department of Computer Science, Technical University of Ostrava, FEECS,
7. Listopadu 15, 708 33 Ostrava-Poruba, Czech Republic"
ec33350d7c4dd7a3b35bf6d7cf799af4ac1796a0,Learning towards Minimum Hyperspherical Energy,"Learning towards Minimum Hyperspherical Energy
Weiyang Liu1,*, Rongmei Lin2,*, Zhen Liu1,*, Lixin Liu3,*, Zhiding Yu4, Bo Dai1,5, Le Song1,6
Georgia Institute of Technology 2Emory University
South China University of Technology 4NVIDIA 5Google Brain 6Ant Financial"
376ea595a6ff5b876367654833de1e1778bacd1e,Examensarbete på avancerad nivå Independent degree project second cycle,"Bilingualism and ambiguous emotional cues  1
Examensarbete på avancerad nivå
Independent degree project  second cycle
Psychology
Major subject
Title
Bilingualism and Children's Attention to Facial Expressions that Conflict with Lexical
Content
Amani Asad"
375993fd5f94c7b02169ff0d71a74d1b84262dfc,Parallel Application Library for Object Recognition,"Parallel Application Library for Object Recognition
Bor-Yiing Su
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2012-199
http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-199.html
September 27, 2012"
3771e8a09bc448af076a02ec7e0780d8435ed86a,Synchronous Adversarial Feature Learning for LiDAR based Loop Closure Detection,
37347e4c1b35196761fc1620e451738f880f0392,Exemplar-based human action pose correction and tagging,"Exemplar-Based Human Action Pose Correction and Tagging
Wei Shen
Ke Deng
Xiang Bai
Huazhong Univ. of Sci.&Tech.
Microsoft Corporation
Huazhong Univ. of Sci.&Tech.
Tommer Leyvand
Microsoft Corporation
Baining Guo
Zhuowen Tu
Microsoft Research Asia
Microsoft Research Asia & UCLA"
372bc106c61e7eb004835e85bbfee997409f176a,Coupled Generative Adversarial Networks,"Coupled Generative Adversarial Networks
Mitsubishi Electric Research Labs (MERL),
Mitsubishi Electric Research Labs (MERL),
Ming-Yu Liu
Oncel Tuzel"
3799e75b49d5b29d2a2a68c934d58ab4984e215c,Scale-invariant sampling for supervised image segmentation,"1st International Conference on Pattern Recognition (ICPR 2012)
November 11-15, 2012. Tsukuba, Japan
978-4-9906441-0-9 ©2012 ICPR"
37a95a78bee34bb26a64c7ec30f7bd0496e072f1,The Focus-Aspect-Polarity Model for Predicting Subjective Noun Attributes in Images,"The Focus-Aspect-Polarity Model
for Predicting Subjective Noun Attributes in Images
Tushar Karayil1
DFKI, Germany
Philipp Blandfort1
DFKI and TUK, Germany
J¨orn Hees
DFKI, Germany
Andreas Dengel
DFKI, Germany"
37c42f0a0e2e97a74113e1a1e1a79b04e0c64244,Covariance Pooling For Facial Expression Recognition,"Covariance Pooling for Facial Expression Recognition
Computer Vision Lab, ETH Zurich, Switzerland
VISICS, KU Leuven, Belgium
Dinesh Acharya†, Zhiwu Huang†, Danda Pani Paudel†, Luc Van Gool†‡
{acharyad, zhiwu.huang, paudel,"
376b73334bd9aebed1fbb69c4ed3848ec0826b6c,Online non-rigid structure-from-motion based on a keyframe representation of history,"Online Non-rigid Structure-from-motion based on a
keyframe representation of history
Simon Donn´e, Ljubomir Jovanov, Bart Goossens, Wilfried Philips, Aleksandra Piˇzurica
Department of Telecommunications and Information Processing (TELIN)
{Simon.Donne, Ljubomir.Jovanov, Bart.Goossens, Wilfried.Philips,
Ghent University
Ghent, Belgium"
376d9030b796cc6f0d1280aa2ff2c268eeae33b9,Tomofaces: Eigenfaces extended to videos of speakers,"-4244-1484-9/08/$25.00 ©2008 IEEE
ICASSP 2008"
37a4eb74f9c9d6333864dbe1e0803d30c2e4db7c,An Evaluation of Deep CNN Baselines for Scene-Independent Person Re-Identification,"An Evaluation of Deep CNN Baselines for
Scene-Independent Person Re-Identification
Paul Marchwica, Michael Jamieson, Parthipan Siva
Senstar Corporation
Waterloo, Canada
{Paul.Marchwica, Mike.Jamieson,
the art"
37c014854655980870122143d16ecf585c7b8980,Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning,"Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning
Crowd-Robot Interaction:
Changan Chen, Yuejiang Liu, Sven Kreiss, Alexandre Alahi"
37838a832838ff3211b358bc51ba5105b9d82e89,The Complete Gabor-Fisher Classifier for Robust Face Recognition,"EURASIP JOURNAL ON ADVANCES IS SIGNAL PROCESSING
The Complete Gabor-Fisher Classifier for Robust
Face Recognition
Vitomir ˇStruc and Nikola Paveˇsi´c"
372fb32569ced35eaf3740a29890bec2be1869fa,Mu rhythm suppression is associated with the classification of emotion in faces.,"Running head: MU RHYTHM MODULATION BY CLASSIFICATION OF EMOTION  1
Mu rhythm suppression is associated with the classification of emotion in faces
Matthew R. Moore1, Elizabeth A. Franz1
Department of Psychology, University of Otago, Dunedin, New Zealand
Corresponding authors:
Matthew Moore & Liz Franz
Phone:  +64 (3) 479 5269; Fax:  +64 (3) 479 8335
Department of Psychology
University of Otago
PO Box 56
Dunedin, New Zealand"
379ac913e976db227ff00c7e085fc97f5c8fb247,Learning Object Categories From Internet Image Searches,"I N V I T E D
P A P E R
Learning Object Categories
From Internet Image Searches
This paper shows how the results returned by an image search engine
an be used to construct models from Internet images and
use them for object recognition.
By Rob Fergus, Li Fei-Fei, Pietro Perona, Member IEEE, and Andrew Zisserman"
377a1be5113f38297716c4bb951ebef7a93f949a,Facial emotion recognition with anisotropic inhibited Gabor energy histograms,"Dear Faculty, IGERT Fellows, IGERT Associates and Students,
You are cordially invited to attend a Seminar presented by Albert Cruz. Please
plan to attend.
Albert Cruz
IGERT Fellow
Electrical Engineering
Date: Friday, October 11, 2013
Location: Bourns A265
Time: 11:00am
Facial  emotion  recognition  with  anisotropic
inhibited gabor energy histograms"
37c1a8e302a450206580f95a2abceb5585be33e8,Title of Document : LONGITUDINAL SINGLE-UNIT RECORDING IN THE MACAQUE FACE PATCH SYSTEM : IDENTITY AND PLASTICITY IN THE ANTERIOR FUNDUS FACE PATCH,"Title of Document:
Directed By:"
37381718559f767fc496cc34ceb98ff18bc7d3e1,Harnessing Synthesized Abstraction Images to Improve Facial Attribute Recognition,Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18)
370b5757a5379b15e30d619e4d3fb9e8e13f3256,Labeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments,"Labeled Faces in the Wild: A Database for Studying
Face Recognition in Unconstrained Environments
Gary B. Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller"
375435fb0da220a65ac9e82275a880e1b9f0a557,From Pixels to Response Maps: Discriminative Image Filtering for Face Alignment in the Wild,"This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
From Pixels to Response Maps: Discriminative Image
Filtering for Face Alignment in the Wild
Akshay Asthana, Stefanos Zafeiriou, Georgios Tzimiropou-
los, Shiyang Cheng and Maja Pantic"
378d1d5c16d816df8ffd79e9e8bf2056a0c66838,Scaling Up a Metric Learning Algorithm for Image Recognition and Representation,"Scaling Up a Metric Learning Algorithm for
Image Recognition and Representation
Adrian Perez-Suay and Francesc J. Ferri
Dept. Inform`atica, Universitat de Val`encia. Spain"
372bf2716c53e353be6c3f027493f1a40edb6640,MINE: Mutual Information Neural Estimation,"Mutual Information Neural Estimation
Mohamed Ishmael Belghazi 1 Aristide Baratin 1 2 Sai Rajeswar 1 Sherjil Ozair 1 Yoshua Bengio 1 3 4
Aaron Courville 1 3 R Devon Hjelm 1 4"
37a1c00a1f5b78eac269ed335c6610b91c4be76c,A multi-level data fusion approach for gradually upgrading theperformances of identity veri cation systems,"Amulti-leveldatafusionapproachforgraduallyupgradingthe
performancesofidentityveri(cid:12)cationsystems
Patrick Verlindea,b, Pascal Druytsa, G´erard Cholletb and Marc Acheroya
Royal Military Academy/Signal and Image Centre, Brussels, Belgium
CNRS URA-820, Ecole Nationale Sup´erieure de T´el´ecommunications/TSI Department, Paris, France"
377513421f9470f34c77bf64dc2c798eb50143d0,Pivoting strategy for fast LU decomposition of sparse block matrices,"PIVOTING STRATEGY FOR FAST LU
DECOMPOSITION OF SPARSE BLOCK MATRICES
Lukas Polok
Brno University of Technology,
Faculty of Information Technology,
IT4Innovations Centre of Excellence
Pavel Smrz
Brno University of Technology,
Faculty of Information Technology,
IT4Innovations Centre of Excellence
Bozetechova 1/2, Brno 61266, Czech Republic
Bozetechova 1/2, Brno 61266, Czech Republic"
372a8bf0ef757c08551d41e40cb7a485527b6cd7,Unsupervised Video Hashing by Exploiting Spatio-Temporal Feature,"Unsupervised Video Hashing by Exploiting
Spatio-Temporal Feature
Chao Ma, Yun Gu, Wei Liu, and Jie Yang(cid:63)
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong
University, Shanghai, China."
37b207d2c4a82a57f80e96353f79ecd71320a854,Person Search with Natural Language Description,"Person Search with Natural Language Description
Shuang Li1 Tong Xiao1 Hongsheng Li1∗ Bolei Zhou2 Dayu Yue3 Xiaogang Wang1 ∗
The Chinese University of Hong Kong 2Massachuate Institute of Technology 3SenseTime Group Limited"
37f2e03c7cbec9ffc35eac51578e7e8fdfee3d4e,Co-operative Pedestrians Group Tracking in Crowded Scenes Using an MST Approach,"WACV 2015 Submission #394. CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE.
Co-operative Pedestrians Group Tracking in Crowded Scenes using an MST
Approach
Anonymous WACV submission
Paper ID 394"
37278ffce3a0fe2c2bbf6232e805dd3f5267eba3,Can we still avoid automatic face detection?,"Can we still avoid automatic face detection?
Michael J. Wilber1,2
Vitaly Shmatikov1,2
Serge Belongie1,2
Department of Computer Science, Cornell University 2 Cornell Tech"
3773e5d195f796b0b7df1fca6e0d1466ad84b5e7,Learning from Time Series in the Presence of Noise: Unsupervised and Semi-Supervised Approaches,"UNIVERSITY OF CALIFORNIA
RIVERSIDE
Learning from Time Series in the Presence of Noise: Unsupervised and Semi-Supervised
Approaches
A Dissertation submitted in partial satisfaction
of the requirements for the degree of
Doctor of Philosophy
Computer Science
Dragomir Dimitrov Yankov
March 2008
Dissertation Committee:
Dr. Eamonn Keogh, Chairperson
Dr. Stefano Lonardi
Dr. Vassilis Tsotras"
3765df816dc5a061bc261e190acc8bdd9d47bec0,Presentation and validation of the Radboud Faces Database,"This article was downloaded by: [Radboud University Nijmegen]
On: 24 November 2010
Access details: Access Details: [subscription number 907172236]
Publisher Psychology Press
Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-
1 Mortimer Street, London W1T 3JH, UK
Cognition & Emotion
Publication details, including instructions for authors and subscription information:
http://www.informaworld.com/smpp/title~content=t713682755
Presentation and validation of the Radboud Faces Database
Oliver Langnera; Ron Dotscha; Gijsbert Bijlstraa; Daniel H. J. Wigboldusa; Skyler T. Hawkb; Ad van
Knippenberga
Radboud University Nijmegen, Nijmegen, The Netherlands b University of Amsterdam, Amsterdam,
The Netherlands
Online publication date: 22 November 2010
To cite this Article Langner, Oliver , Dotsch, Ron , Bijlstra, Gijsbert , Wigboldus, Daniel H. J. , Hawk, Skyler T. and van
Knippenberg, Ad(2010) 'Presentation and validation of the Radboud Faces Database', Cognition & Emotion, 24: 8, 1377 —
To link to this Article: DOI: 10.1080/02699930903485076
URL: http://dx.doi.org/10.1080/02699930903485076
PLEASE SCROLL DOWN FOR ARTICLE"
3752dc15fada54abc0af866273d03a28f4dc8975,A VARIATIONAL FRAMEWORK FOR PEDESTRIAN SEGMENTATION IN CLUTTERED SCENES USING BAG OF OPTICAL FLOWS AND SHAPE PRIORS,"A VARIATIONAL FRAMEWORK FOR PEDESTRIAN
SEGMENTATION IN CLUTTERED SCENES USING
BAG OF OPTICAL FLOWS AND SHAPE PRIORS
Gagan Bansal
A thesis submitted to The Johns Hopkins University in conformity with the requirements
for the degree of Master of Science.
Baltimore, Maryland
January, 2009
(cid:176) Gagan Bansal 2009
All rights reserved"
3726b82007512a15a530fd1adad57af58a9abb62,Teaching Compositionality to CNNs,"Teaching Compositionality to CNNs∗
Austin Stone
Yi Liu
Huayan Wang
D. Scott Phoenix
Michael Stark
Dileep George
Vicarious FPC, San Francisco, CA, USA
{austin, huayan, michael, yi, scott,"
370ed90971eca7ad84c67d8804f97e02ff6fd5b4,"The Socio-Moral Image Database (SMID): A novel stimulus set for the study of social, moral and affective processes","RESEARCH ARTICLE
The Socio-Moral Image Database (SMID): A
novel stimulus set for the study of social,
moral and affective processes
Damien L. Crone1*, Stefan Bode1, Carsten Murawski2, Simon M. Laham1
Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, Australia,
Department of Finance, University of Melbourne, Melbourne, Australia"
37d6cde8be756b70d22262f1acc3442a0c6aa7ea,Kernel learning approaches for image classification,"Kernel Learning Approaches for
Image Classification
Dissertation
zur Erlangung des akademischen Grades
Doctor rerum naturalium (Dr.rer.nat)
n der Naturwissenschaftlich-Technischen Fakult¨at I
der Universit¨at des Saarlandes, Saarbr¨ucken
vorgelegt von Dipl.-Inform.
Peter Vincent Gehler
0. Juni 2009"
37c4541037b67e8f4c538b285efe80aa251a49b9,Tracking as a Whole: Multi-Target Tracking by Modeling Group Behavior With Sequential Detection,"Tracking as a Whole: Multi-Target Tracking
y Modeling Group Behavior With
Sequential Detection
Yuan Yuan, Senior Member, IEEE, Yuwei Lu, and Qi Wang, Senior Member, IEEE"
374c7a2898180723f3f3980cbcb31c8e8eb5d7af,Facial Expression Recognition in Videos using a Novel Multi-Class Support Vector Machines Variant,"FACIAL EXPRESSION RECOGNITION IN VIDEOS USING A NOVEL MULTI-CLASS
SUPPORT VECTOR MACHINES VARIANT
Irene Kotsiay, Nikolaos Nikolaidisy and Ioannis Pitasy
yAristotle University of Thessaloniki
Department of Informatics
Box 451, 54124 Thessaloniki, Greece"
370277791a0708b7c93deb21da172e025b558643,"Fusing LIDAR, camera and semantic information: A context-based approach for pedestrian detection","Fusing LIDAR, camera and semantic information:
context-based approach for pedestrian detection
Cristiano Premebida and Urbano Nunes
The final version is available at: http://ijr.sagepub.com/content/32/3.toc
This is a pre-print version."
37992120053b50b2f92eaa1949273bf828a54b50,Face Recognition Techniques-An evaluation Study,"Int. J. Advanced Networking and Applications
Volume: 6 Issue: 4 Pages: 2393-2397       (2015) ISSN: 0975-0290
Face Recognition Techniques - An evaluation
Department of Management Information System, Applied Science University, 166-11391, Jordan
Study
Dr.Asmahan M Altaher
Email:
Keywords"
374227c5d1858960ad1130059337b8c417a7ef15,The effect of the proportion of mismatching trials and task orientation on the confidence-accuracy relationship in unfamiliar face matching.,"Journal of Experimental Psychology:
Applied
The Effect of the Proportion of Mismatching Trials and
Task Orientation on the Confidence–Accuracy
Relationship in Unfamiliar Face Matching
Rachel G. Stephens, Carolyn Semmler, and James D. Sauer
Online First Publication, August 14, 2017. http://dx.doi.org/10.1037/xap0000130
CITATION
Stephens, R. G., Semmler, C., & Sauer, J. D. (2017, August 14). The Effect of the Proportion of
Mismatching Trials and Task Orientation on the Confidence–Accuracy Relationship in Unfamiliar
Face Matching. Journal of Experimental Psychology: Applied. Advance online publication.
http://dx.doi.org/10.1037/xap0000130"
f9f08511f77c29ff948e146434dfb23608d3deb5,Question Answering Using Match-LSTM and Answer Pointer,"Question Answering Using Match-LSTM and Answer Pointer
Annie Hu, Cindy Wang, and Brandon Yang
{anniehu, ciwang,
CodaLab: anniehu
March 21, 2016
Introduction
Machine comprehension of text is a significant problem in natural language processing today –
in this project, we tackle machine reading comprehension as applied to question answering. Our
goal is: given a question and a context paragraph, to extract from the paragraph the answer to
the question.
As an oracle, on the dataset we used, humans score over 86.8% accuracy (EM) on the test
set for this task, while the best models only achieve roughly 75%. Existing approaches to this
extractive Question Answering problem typically involve an encoding layer that encodes the
question and paragraph into a sequence, some additional layer that accounts for interaction
etween the question and paragraph, and a final decoding layer that extracts the answer from
the paragraph [2][3][4][7]. In this paper, we will follow a similar structure, using LSTMs in our
encoding and decoding layers, and calculating attention as our interaction layer.
Dataset
The dataset used is the recently released Stanford Question Answering Dataset (SQuAD)[1].
The context paragraphs are extracted from Wikipedia, while questions and answers are human-"
f9028b47a4755a7349108b1dc281f13add5c6c12,Atypical gaze patterns in children and adults with autism spectrum disorders dissociated from developmental changes in gaze behaviour,"Downloaded from
http://rspb.royalsocietypublishing.org/
on June 9, 2017
Proc. R. Soc. B
doi:10.1098/rspb.2010.0587
Published online
Atypical gaze patterns in children and
dults with autism spectrum disorders
dissociated from developmental changes
in gaze behaviour
Tamami Nakano1,2, Kyoko Tanaka3, Yuuki Endo1, Yui Yamane1,
Takahiro Yamamoto4, Yoshiaki Nakano4, Haruhisa Ohta2,5,
Nobumasa Kato2,5 and Shigeru Kitazawa1,2,*
Department of Neurophysiology, and 3Department of Pediatrics, Juntendo University
School of Medicine, Tokyo, Japan
CREST, JST, Saitama, Japan
Japanese Institute for Education and Treatment, Tokyo, Japan
5Department of Psychiatry, Showa University School of Medicine, Tokyo, Japan
Eye tracking has been used to investigate gaze behaviours in individuals with autism spectrum disorder
(ASD). However, traditional analysis has yet to find behavioural characteristics shared by both children"
f935225e7811858fe9ef6b5fd3fdd59aec9abd1a,Spatiotemporal dynamics and connectivity pattern differences between centrally and peripherally presented faces,"www.elsevier.com/locate/ynimg
Spatiotemporal dynamics and connectivity pattern differences
etween centrally and peripherally presented faces
Lichan Liu and Andreas A. Ioannides*
Laboratory for Human Brain Dynamics, RIKEN Brain Science Institute (BSI), 2-1 Hirosawa, Wakoshi, Saitama, 351-0198, Japan
Received 4 May 2005; revised 26 January 2006; accepted 6 February 2006
Available online 24 March 2006
Most neuroimaging studies on face processing used centrally presented
images with a relatively large visual field. Images presented in this way
ctivate widespread striate and extrastriate areas and make it difficult
to study spatiotemporal dynamics and connectivity pattern differences
from various parts of the visual field. Here we studied magneto-
encephalographic responses in humans to centrally and peripherally
presented faces for testing the hypothesis that processing of visual
stimuli with facial expressions of emotions depends on where the
stimuli are presented in the visual field. Using our tomographic and
statistical parametric mapping analyses, we identified occipitotemporal
reas activated by face stimuli more than by control conditions. V1/V2
ctivity was significantly stronger for lower than central and upper
visual field presentation. Fusiform activity, however, was significantly"
f96970f75b0f37787a47073bf7d02111f45abe83,3 D Face Recognition Performance under Adversarial Conditions,
f997a71f1e54d044184240b38d9dc680b3bbbbc0,Deep Cross Modal Learning for Caricature Verification and Identification(CaVINet),"Deep Cross Modal Learning for Caricature Verification and
Identification(CaVINet)
https://lsaiml.github.io/CaVINet/
Jatin Garg∗
Indian Institute of Technology Ropar
Himanshu Tolani∗
Indian Institute of Technology Ropar
Skand Vishwanath Peri∗
Indian Institute of Technology Ropar
Narayanan C Krishnan
Indian Institute of Technology Ropar"
f95616b1593467f5b11689582d934da34e6ad1ee,Interactive Language Acquisition with One-shot Visual Concept Learning through a Conversational Game,"Interactive Language Acquisition with One-shot Visual Concept Learning
through a Conversational Game
Haichao Zhang†, Haonan Yu†, and Wei Xu †§
§ National Engineering Laboratory for Deep Learning Technology and Applications, Beijing China
Baidu Research - Institue of Deep Learning, Sunnyvale USA"
f9255703f0a89c9ca2e9256595a0526829ff4402,On the Importance of Visual Context for Data Augmentation in Scene Understanding,"On the Importance of Visual Context for Data
Augmentation in Scene Understanding
Nikita Dvornik, Julien Mairal, Senior Member, IEEE, and Cordelia Schmid, Fellow, IEEE"
f94feceb5b725c6b303b758a0e5e90215b0174d3,Learning Non-maximum Suppression,"Learning non-maximum suppression
Jan Hosang
Rodrigo Benenson
Bernt Schiele
Max Planck Institut für Informatik
Saarbrücken, Germany"
f95ba7673789d1b4118d30e360a5a37fd75d3961,Face Recognition using Modified Generalized Hough Transform and Gradient Distance Descriptor,"Face Recognition using Modified Generalized Hough Transform
nd Gradient Distance Descriptor
Marian Moise, Xue Dong Yang and Richard Dosselmann
Department of Computer Science, University of Regina, 3737 Wascana Parkway, Regina, Saskatchewan, Canada
Keywords:
Face Recognition, Generalized Hough Transform, Image Descriptors."
f9784db8ff805439f0a6b6e15aeaf892dba47ca0,"Comparing the performance of Emotion-Recognition Implementations in OpenCV , Cognitive Services , and Google Vision APIs","Comparing the performance of Emotion-Recognition Implementations
in OpenCV, Cognitive Services, and Google Vision APIs
LUIS ANTONIO BELTRÁN PRIETO, ZUZANA KOMÍNKOVÁ OPLATKOVÁ
Department of Informatics and Artificial Intelligence
Tomas Bata University in Zlín
Nad Stráněmi 4511, 76005, Zlín
CZECH REPUBLIC"
f9d1f12070e5267afc60828002137af949ff1544,Maximum Entropy Binary Encoding for Face Template Protection,"Maximum Entropy Binary Encoding for Face Template Protection
Rohit Kumar Pandey
Yingbo Zhou
Bhargava Urala Kota
Venu Govindaraju
University at Buffalo, SUNY
{rpandey, yingbozh, buralako,"
f98a975642972ce24e42e6957f63be556c11dd31,Dynamic Obstacle Detection of Road Scenes using Equi-Height Mosaicking Image,"Electronic Letters on Computer Vision and Image Analysis 13(2):13-14, 2014
Dynamic Obstacle Detection of Road Scenes
using Equi-Height Mosaicking Image
Min Woo Park and Soon Ki Jung
School of Computer Science and Engineering, Kyungpook National University,
80 Daehak-ro, Bukgu, Daegu, Republic of Korea
Advisor/s: Soon Ki Jung
Date and location of PhD thesis defense: 3 December 2013, Kyungpook National University
Received 30 January 2014; accepted 25 May 2014"
f95ba9aab699394954f96c1419d3b1894b847e41,A Face Recognition Algorithm using Eigenphases and Histogram Equalization,"A Face Recognition Algorithm using
Eigenphases and Histogram Equalization
Kelsey Ramirez-Gutierrez, Daniel Cruz-Perez, Jesús Olivares-Mercado,
Mariko Nakano-Miyatake, and Hector Perez-Meana"
f9bee6e61833c0323c9175402b73442d27ab9eb8,D Human Poses Estimation from a Single 2 D Silhouette,
f95f5e43f34e1bfb425b6491fc09558c44d2973d,Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation,"Soft Layer-Specific Multi-Task Summarization
with Entailment and Question Generation
Han Guo∗
Ramakanth Pasunuru∗
UNC Chapel Hill
{hanguo, ram,
Mohit Bansal"
f984a9bb5c6e7b8a055b810bff468d7f8d80a7ff,Face identification by using fusing Photographic and Thermal Images,"www.jchps.com                                                                    Journal of Chemical and Pharmaceutical Sciences
Face identification by using fusing Photographic and Thermal Images
M. Parisa Beham, 2M.R.H. Prasanna, 2SM.Mansoor Roomi and 1H. Jebina
ISSN: 0974-2115
Vickram College of Engineering, Tamilnadu, India.
Thiagarajar College of Engineering, Tamilnadu, India.
*Corresponding Author:E-Mail"
f9fdc63934841a0c4d8d29fdea80e1972ffcfe1e,Pedestrian Using Catadioptric Sensor 12,"Journal of Theoretical and Applied Information Technology
0th April 2018. Vol.96. No 8
© 2005 – ongoing  JATIT & LLS
ISSN: 1992-8645                                                         www.jatit.org                                                        E-ISSN: 1817-3195
PEDESTRIAN USING CATADIOPTRIC SENSOR
2BOUI MAROUANE, 2HADJ-ABDELKADER HICHAM, 2ABABSA FAKHR-EDDINE,
ABOUYAKHF EL HOUSSINE
LIMIARF University Mohammed V-Rabat
IBISC, University of Evry, France
E-mail:"
f96b3122f66c01cb78643d7e1b412e1bae16f2c4,Affective Robots : Evaluation of Automatic Emotion Recognition Approaches on a Humanoid Robot towards Emotionally Intelligent Machines,"World Academy of Science, Engineering and Technology
International Journal of Mechanical and Mechatronics Engineering
Vol:12, No:6, 2018
Affective Robots: Evaluation of Automatic Emotion
Recognition Approaches on a Humanoid Robot
towards Emotionally Intelligent Machines
Silvia Santano Guill´en, Luigi Lo Iacono, Christian Meder"
f98cbf32989387733529fa4fc943f0a7e97b5c07,To Know and To Learn - About the Integration of Knowledge Representation and Deep Learning for Fine-Grained Visual Categorization,
f92ceb6875f614bbccb25e4b11ca55353773890f,Saliency Detection via Boundary Prior and Center Prior,"International Robotics & Automation Journal
Saliency Detection via Boundary Prior and Center Prior"
f8796b8e8246ce41efb2904c053fe0ea2868e373,A Variational U-Net for Conditional Appearance and Shape Generation,"A Variational U-Net for Conditional Appearance and Shape Generation
Patrick Esser∗, Ekaterina Sutter∗, Bj¨orn Ommer
Heidelberg Collaboratory for Image Processing
IWR, Heidelberg University, Germany"
f884a67187929e7dda66091c13867ed0a8a36d01,Weighted-Fusion-Based Representation Classifiers for Hyperspectral Imagery,"Remote Sens. 2015, 7, 14806-14826; doi:10.3390/rs71114806
OPEN ACCESS
ISSN 2072-4292
www.mdpi.com/journal/remotesensing
Article
Weighted-Fusion-Based Representation Classifiers for
Hyperspectral Imagery
Bing Peng 1, Wei Li 1,*, Xiaoming Xie 1,*, Qian Du 2 and Kui Liu 3
College of Information Science and Technology, Beijing University of Chemical Technology,
Beijing 100029, China; E-Mail:
Department of Electrical and Computer Engineering, Mississippi State University, Starkville,
MS 39762, USA; E-Mail:
Intelligent Fusion Technology, Germantown, MD 20876, USA; E-Mail:
*  Authors to whom correspondence should be addressed; E-Mails: (W.L.);
(X.X.); Tel.: +86-010-6443-3717 (W.L.); +86-010-6441-3467 (X.X.).
Academic Editors: Magaly Koch and Prasad S. Thenkabail
Received: 17 June 2015 / Accepted: 30 October 2015 / Published: 6 November 2015"
f8eedcca6263062b6bab11ead255f719452f1c81,Motion in action : optical flow estimation and action localization in videos. (Le mouvement en action : estimation du flot optique et localisation d'actions dans les vidéos),"Motion in action : optical flow estimation and action
localization in videos
Philippe Weinzaepfel
To cite this version:
Philippe Weinzaepfel. Motion in action : optical flow estimation and action localization in videos.
Computer Vision and Pattern Recognition [cs.CV]. Université Grenoble Alpes, 2016. English. <NNT :
016GREAM013>. <tel-01407258>
HAL Id: tel-01407258
https://tel.archives-ouvertes.fr/tel-01407258
Submitted on 1 Dec 2016
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
f89e5a8800b318fa03289b5cc67df54b956875b4,Do GANs actually learn the distribution? An empirical study,"Do GANs actually learn the distribution? An empirical study
Sanjeev Arora
Yi Zhang
July 4, 2017"
f881d2a04de838c8950a279e1ed8c0f9886452af,Multi-Stage Variational Auto-Encoders for Coarse-to-Fine Image Generation,"Multi-Stage Variational Auto-Encoders for
Coarse-to-Fine Image Generation
Lei Cai
Hongyang Gao
Washington State University
Washington State University
Pullman, WA 99164
Pullman, WA 99164
Shuiwang Ji
Washington State University
Pullman, WA 99164"
f86d8385a6170b98e434a121fb7d12facb2c8426,Frank-Wolfe Algorithm for Exemplar Selection,"Frank-Wolfe Algorithm for Exemplar Selection
Gary Cheng
UC Berkeley
Armin Askari
UC Berkeley
Laurent El Ghaoui
Kannan Ramchandran
UC Berkeley
UC Berkeley"
f8d68084931f296abfb5a1c4cd971f0b0294eaa4,UNCONDITIONAL GENERATIVE MODELS,"Published as a conference paper at ICLR 2018
LATENT CONSTRAINTS:
LEARNING TO GENERATE CONDITIONALLY FROM
UNCONDITIONAL GENERATIVE MODELS
Jesse Engel
Google Brain
San Francisco, CA, USA
Matthew D. Hoffman
Google Inc.
San Francisco, CA, USA
Adam Roberts
Google Brain
San Francisco, CA, USA"
f8ed5f2c71e1a647a82677df24e70cc46d2f12a8,Artificial Neural Network Design and Parameter Optimization for Facial Expressions Recognition,"International Journal of Scientific & Engineering Research, Volume 2, Issue 12, December-2011                                                                                         1
ISSN 2229-5518
Artificial Neural Network Design and Parameter
Optimization for Facial Expressions Recognition
Ammar A. Alzaydi"
f8e0910d50d7dffe7097b6263c6bb3952b1de336,50 Years of Test (Un)fairness: Lessons for Machine Learning,"50 Years of Test (Un)fairness: Lessons for Machine Learning
Ben Hutchinson and Margaret Mitchell"
f8ec92f6d009b588ddfbb47a518dd5e73855547d,Extreme Learning Machine Ensemble Using Bagging for Facial Expression Recognition,"J Inf Process Syst, Vol.10, No.3, pp.443~458, September 2014
ISSN 1976-913X (Print)
ISSN 2092-805X (Electronic)
Extreme Learning Machine Ensemble Using
Bagging for Facial Expression Recognition
Deepak Ghimire* and Joonwhoan Lee*"
f8facd9691b9b41392aef606aea5e6bc2e7d489c,Recognition of Facial Expressions by Cortical Multi-scale Line and Edge Coding,"Recognition of facial expressions by cortical
multi-scale line and edge coding
R.J.R. de Sousa, J.M.F. Rodrigues and J.M.H. du Buf
Vision Laboratory, Institute for Systems and Robotics (ISR/IST),
University of the Algarve (ISE and FCT), 8005-135 Faro, Portugal"
f8ec2079838520fcb9394574bdd956ac9d3d5832,Visual Dynamics: Stochastic Future Generation via Layered Cross Convolutional Networks,"Visual Dynamics: Stochastic Future Generation
via Layered Cross Convolutional Networks
Tianfan Xue*, Jiajun Wu*, Katherine L. Bouman, and William T. Freeman"
f86c65bc2753ae71826a0dafbf46a75d22fb5b5b,Fearful Faces do Not Lead to Faster Attentional Deployment in Individuals with Elevated Psychopathic Traits,"J Psychopathol Behav Assess (2017) 39:596–604
DOI 10.1007/s10862-017-9614-x
Fearful Faces do Not Lead to Faster Attentional Deployment
in Individuals with Elevated Psychopathic Traits
Sylco S. Hoppenbrouwers 1 & Jaap Munneke 2,3 & Karen A. Kooiman 4 & Bethany Little 4 &
Craig S. Neumann 5 & Jan Theeuwes 4
Published online: 30 June 2017
# The Author(s) 2017. This article is an open access publication"
f8ea0f76f2044168040fcd0a9e81072c88cde4a4,Nonlinear Feature Extraction using Multilayer Perceptron based Alternating Regression for Classification and Multiple-output Regression Problems,
f8b26b2ec62cf76f58f95938233bc22ae1902144,UvA-DARE ( Digital Academic Repository ) Visual Tracking : An Experimental Survey Smeulders,"UvA-DARE (Digital Academic Repository)
Visual Tracking: An Experimental Survey
Smeulders, A.W.M.; Chu, D.M.; Cucchiara, R.; Calderara, S.; Dehghan, A.; Shah, M.
Published in:
IEEE Transactions on Pattern Analysis and Machine Intelligence
0.1109/TPAMI.2013.230
Link to publication
Citation for published version (APA):
Smeulders, A. W. M., Chu, D. M., Cucchiara, R., Calderara, S., Dehghan, A., & Shah, M. (2014). Visual
Tracking: An Experimental Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(7),
442-1468. DOI: 10.1109/TPAMI.2013.230
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f8f872044be2918de442ba26a30336d80d200c42,Facial Emotion Recognition Techniques : A Survey,"IJSRD - International Journal for Scientific Research & Development| Vol. 3, Issue 03, 2015 | ISSN (online): 2321-0613
Facial Emotion Recognition Techniques: A Survey
Namita Rathore1 Rohit Miri2
,2Department of Computer Science and Engineering
,2Dr C V Raman Institute of Science and Technology
defense
systems,
surveillance"
f8a5bc2bd26790d474a1f6cc246b2ba0bcde9464,"KDEF-PT: Valence, Emotional Intensity, Familiarity and Attractiveness Ratings of Angry, Neutral, and Happy Faces","ORIGINAL RESEARCH
published: 19 December 2017
doi: 10.3389/fpsyg.2017.02181
KDEF-PT: Valence, Emotional
Intensity, Familiarity and
Attractiveness Ratings of Angry,
Neutral, and Happy Faces
Margarida V. Garrido* and Marília Prada
Instituto Universitário de Lisboa (ISCTE-IUL), CIS – IUL, Lisboa, Portugal
The Karolinska Directed Emotional Faces (KDEF)
is one of the most widely used
human facial expressions database. Almost a decade after the original validation study
(Goeleven et al., 2008), we present subjective rating norms for a sub-set of 210 pictures
which depict 70 models (half female) each displaying an angry, happy and neutral facial
expressions. Our main goals were to provide an additional and updated validation
to this database, using a sample from a different nationality (N = 155 Portuguese
students, M = 23.73 years old, SD = 7.24) and to extend the number of subjective
dimensions used to evaluate each image. Specifically, participants reported emotional
labeling (forced-choice task) and evaluated the emotional intensity and valence of the
expression, as well as the attractiveness and familiarity of the model (7-points rating"
f81f5da2a1e4eb80b465b8dffca4c9e583a8a8a6,"Rapid Object Detection Systems , Utilising Deep Learning and Unmanned Aerial Systems ( Uas ) for Civil Engineering Applications","RAPID OBJECT DETECTION SYSTEMS, UTILISING DEEP LEARNING AND
UNMANNED AERIAL SYSTEMS (UAS) FOR CIVIL ENGINEERING APPLICATIONS
UCL Department of Civil, Environmental & Geomatic Engineering, Gower Street, London, WC1E 6BT – (david.griffiths.16,
David Griffiths*, Jan Boehm
Commission II, WG II/6
KEY WORDS: Object detection, Deep Learning, Unmanned Aerial Systems, Railway, Rapid"
f865248065b8d6bcbce4a4053b73e4de2080ba23,Efficient object detection for high resolution images,"Efficient Object Detection for High Resolution Images
Yongxi Lu1 and Tara Javidi1"
f879556115284946637992191563849e840789d1,Geometry Guided Adversarial Facial Expression Synthesis,"Geometry Guided Adversarial Facial Expression Synthesis
Lingxiao Song1,2
Zhihe Lu1,3 Ran He1,2,3
Zhenan Sun1,2
Tieniu Tan1,2,3
National Laboratory of Pattern Recognition, CASIA
Center for Research on Intelligent Perception and Computing, CASIA
Center for Excellence in Brain Science and Intelligence Technology, CAS"
f827b596b4099b0490ab46a9dd2922db2b708963,Pathologies of Neural Models Make Interpretation Difficult,"Pathologies of Neural Models Make Interpretations Difficult
Shi Feng1 Eric Wallace1 Alvin Grissom II2 Mohit Iyyer3,4
Pedro Rodriguez1 Jordan Boyd-Graber1
University of Maryland 2Ursinus College
UMass Amherst 4Allen Institute for Artificial Intelligence"
f8106b414d81df11ef2e9c26dd83f812711eec35,Inferring Analogous Attributes : Large-Scale Transfer of Category-Specific Attribute Classifiers,"Inferring Analogous Attributes:
Large-Scale Transfer of Category-Specific Attribute Classifiers
Chao-Yeh Chen and Kristen Grauman"
f809f9e5a03817d238718723a7b4ac04abcd3f12,Highly Efficient 8-bit Low Precision Inference,"Under review as a conference paper at ICLR 2019
HIGHLY EFFICIENT 8-BIT LOW PRECISION INFERENCE
OF CONVOLUTIONAL NEURAL NETWORKS
Anonymous authors
Paper under double-blind review"
f8d5ab79943ca03d6ce1a24605435054ea9bd105,IR–videostream rendering based on high-level object information,"IR–videostream rendering
ased on high-level object information
Stefan Becker, Wolfgang H¨ubner, and Michael Arens
Fraunhofer IOSB
Fraunhofer Institute for Optronics, System Technologies, and Image Exploitation
Gutleuthausstr. 1, 76275 Ettlingen, Germany"
e3e66ee37d08752dc19c3e3b212f869c8ff19f71,Effective Interaction-aware Trajectory Prediction using Temporal Convolutional Neural Networks,"Effective Interaction-aware Trajectory Prediction using
Temporal Convolutional Neural Networks
Noha Radwan
Wolfram Burgard"
e39f9565903a9701657ce3ade94c37d8a12f702e,Audio-Visual Scene Analysis with Self-Supervised Multisensory Features,"Audio-Visual Scene Analysis with
Self-Supervised Multisensory Features
Andrew Owens Alexei A. Efros
UC Berkeley"
e3bbdd6efc906f6ae17e5b1d62497420991b977d,Visual Explanation by High-Level Abduction: On Answer-Set Programming Driven Reasoning About Moving Objects,"Visual Explanation by High-Level Abduction
On Answer-Set Programming Driven Reasoning about Moving Objects
Jakob Suchan1, Mehul Bhatt1,2, Przemysław Wał˛ega3, and Carl Schultz4
Cognitive Vision – www.cognitive-vision.org
EASE CRC – http://ease-crc.org
HCC Lab., University of Bremen, Germany, 2MPI Lab., Örebro University, Sweden
University of Warsaw, Poland, and 4Aarhus University, Denmark"
e37681d2fb9f65ed14170bb556cf64156f055167,Tracking by Prediction: A Deep Generative Model for Mutli-person Localisation and Tracking,"Tracking by Prediction: A Deep Generative Model for Mutli-Person localisation
nd Tracking
Tharindu Fernando
Simon Denman
Sridha Sridharan
Clinton Fookes
Image and Video Research Laboratory, Queensland University of Technology (QUT), Australia
{t.warnakulasuriya, s.denman, s.sridharan,"
e38709a2ec162a6f2a2fa3b4b6463e752267b154,SUPER-RESOLUTION FOR FACE RECOGNITION BASED ON CORRELATED FEATURES AND NONLINEAR MAPPINGS,"978-1-4244-4296-6/10/$25.00 ©2010 IEEE
ICASSP 2010"
e3660a13fcd75cf876a6ce355c2c1a578cfb57cb,2DHMM-Based Face Recognition Method,"DHMM-BASED FACE RECOGNITION
METHOD
Janusz Bobulski1
Czestochowa University of Technology
Institute of Computer and Information Science
Dabrowskiego Street 73, 42-200 Czestochowa, Poland
Summary. So far many methods of recognizing the face arose, each has the merits
nd demerits. Among these methods are methods based on Hidden Markov models,
nd their advantage is the high ef‌f‌iciency. However, the traditional HMM uses one-
dimensional data, which is not a good solution for image processing, because the
images are two-dimensional. Transforming the image in a one-dimensional feature
vector, we remove some of the information that can be used for identification. The
rticle presents the full ergodic 2D-HMM and applied for face identification.
Introduction
Face recognition has great potentials in many applications dealing with unco-
operative subjects, in which the full power of face recognition being a passive
iometric technique can be implemented and utilised. Face recognition has
een an active area of research in image processing and computer vision due
to its extensive range of prospective applications relating to biometrics, infor-
mation security, video surveillance, law enforcement, identity authentication,"
e312e7657cb98cf03d3b2bf8b21b0ff75fbd4613,No 272 2 D Articulated Human Pose Estimation and Retrieval in ( Almost ) Unconstrained Still Images,"ETH Zurich, D-ITET, BIWI
Technical Report No 272
D Articulated Human Pose Estimation and Retrieval in (Almost)
Unconstrained Still Images
M. Eichner, M. Marin-Jimenez, A. Zisserman, V. Ferrari"
e3f63d12be07c743e7590957f4ed38b06cd98aba,A Novel Approach to Face Detection Algorithm,"A Novel Approach to Face Detection Algorithm
{tag}                                                                       {/tag}
International Journal of Computer Applications
© 2011 by IJCA Journal
Number 2 - Article 4
Year of Publication: 2011
Authors:
Pritam Singh
A.S. Thoke
Kesari Verma
10.5120/3537-4836"
e378ce25579f3676ca50c8f6454e92a886b9e4d7,Robust Video Super-Resolution with Learned Temporal Dynamics,"Robust Video Super-Resolution with Learned Temporal Dynamics
Ding Liu1 Zhaowen Wang2 Yuchen Fan1 Xianming Liu3
Zhangyang Wang4 Shiyu Chang5 Thomas Huang1
University of Illinois at Urbana-Champaign 2Adobe Research
Facebook 4Texas A&M University 5IBM Research"
e38c93bb8f7ee103eba4b78443d94f55a63bdf08,Extracting Pathlets From Weak Tracking Data ∗,"Extracting Pathlets From Weak Tracking Data∗
Kevin Streib
James W. Davis
Dept. of Computer Science and Engineering
Ohio State University, Columbus, OH 43210"
e39af9fb267c9deb81f9c73bbd71f5674b4358c0,Conceptualizing and Measuring Well-Being Using Statistical Semantics and Numerical Rating Scales,"Conceptualizing and Measuring Well-Being Using Statistical Semantics and Numerical
Rating Scales
Kjell, Oscar
Published: 2018-03-01
Document Version
Publisher's PDF, also known as Version of record
Link to publication
Citation for published version (APA):
Kjell, O. (2018). Conceptualizing and Measuring Well-Being Using Statistical Semantics and Numerical Rating
Scales Lund
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LUND UNIVERSITYPO Box 117221 00 Lund+46 46-222 00 00"
e3144f39f473e238374dd4005c8b83e19764ae9e,Hybrid Learning of Optical Flow and Next Frame Prediction to Boost Optical Flow in the Wild,"Next-Flow: Hybrid Multi-Tasking with Next-Frame Prediction to Boost
Optical-Flow Estimation in the Wild
Nima Sedaghat
University of Freiburg
Germany"
e3c420b29b8590442decd330ef70494c2209f149,Learning a Part-Based Pedestrian Detector in a Virtual World,"Learning a Part-based Pedestrian Detector in Virtual
World
Jiaolong Xu, David V´azquez, Antonio M. L´opez Member, IEEE, Javier Mar´ın and Daniel Ponsa"
e33b1833b2d0cd7b0450b22b96a567a59c9e4685,Attribute Discovery via Predictable Discriminative Binary Codes,"Attribute Discovery via
Predictable Discriminative Binary Codes
Mohammad Rastegari†
Ali Farhadi‡
David Forsyth†
University of Illinois at Urbana Champagin
Carnegi Mellon University
http://vision.ri.cmu.edu/projects/dbc/dbc.html"
e36e8e1fb9abec16f0430bf69070bb318197d3d6,SUNIL BHARAMGOUDAR DETECTION OF FREE SPACE/OBSTACLES IN FRONT OF THE EGO CAR USING STEREO CAMERA IN URBAN SCENES,"SUNIL BHARAMGOUDAR
DETECTION OF FREE SPACE/OBSTACLES IN FRONT OF THE
EGO CAR USING STEREO CAMERA IN URBAN SCENES
Master of Science thesis
Examiner:  prof.  Jose  L.  Martinez
Lastra
Examiner and topic approved by the
Faculty Council of the Faculty of
Engineering Sciences
on 3rd Sept 2014"
e3e44385a71a52fd483c58eb3cdf8d03960c0b70,A Hierarchical Graphical Model for Recognizing Human Actions and Interactions in Video,"Copyright
Sangho Park"
e3c5c5623af4b1a1f719cac24850dcaa6a304bd5,Training Effective Node Classifiers for Cascade Classification,"ppearing in Int. J. Comput. Vis.; content may change prior to final publication.
Training Effective Node Classifiers for Cascade
Classification
Chunhua Shen · Peng Wang · Sakrapee Paisitkriangkrai ·
Anton van den Hengel
December 2012"
e393a038d520a073b9835df7a3ff104ad610c552,Automatic temporal segment detection via bilateral long short-term memory recurrent neural networks,"Automatic temporal segment
detection via bilateral long short-
term memory recurrent neural
networks
Bo Sun
Siming Cao
Jun He
Lejun Yu
Liandong Li
Bo Sun, Siming Cao, Jun He, Lejun Yu, Liandong Li, “Automatic temporal segment
detection via bilateral long short-term memory recurrent neural networks,” J.
Electron. Imaging 26(2), 020501 (2017), doi: 10.1117/1.JEI.26.2.020501.
Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 03/03/2017 Terms of Use: http://spiedigitallibrary.org/ss/termsofuse.aspx"
e3b0caa1ff9067665e349a2480b057e2afdbc41f,Interactive Effects of Obvious and Ambiguous Social Categories on Perceptions of Leadership: When Double-Minority Status May Be Beneficial.,"702373 PSPXXX10.1177/0146167217702373Personality and Social Psychology BulletinWilson et al.
research-article2017
Article
Interactive Effects of Obvious and
Ambiguous Social Categories on
Perceptions of Leadership: When
Double-Minority Status May
Be Beneficial
Personality and Social
Psychology Bulletin
017, Vol. 43(6) 888 –900
© 2017 by the Society for Personality
nd Social Psychology, Inc
Reprints and permissions:
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DOI: 10.1177/0146167217702373
https://doi.org/10.1177/0146167217702373
journals.sagepub.com/home/pspb
John Paul Wilson1, Jessica D. Remedios2, and Nicholas O. Rule3"
e39d1345a5aef8a5ee32c0a774de877b903de50c,Unsupervised Learning of Semantics of Object Detections for Scene Categorization,"Unsupervised Learning of Semantics of Object
Detections for Scene Categorization
Grégoire Mesnil, Salah Rifai, Antoine Bordes, Xavier Glorot, Yoshua Bengio
nd Pascal Vincent"
e309632d479b8f59e615d0f3c4bc69938361d187,Deep Learning for Imbalance Data Classification using Class Expert Generative Adversarial Network,"Deep Learning for Imbalance Data Classification using Class Expert
Generative Adversarial Network
Fannya, Tjeng Wawan Cenggoroa,b
Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia 11480
Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia 11480"
e33bc0cd79d92d6868989a29c3ab06b75f808590,Deep Nets: What have they ever done for Vision?,"Noname manuscript No.
(will be inserted by the editor)
Deep Nets: What have they ever done for Vision?
Alan L. Yuille · Chenxi Liu
Received: date / Accepted: date"
e3f2e09894e49b2d0134cebd07d1f2fcbb22d97e,Bilingual corpus for AVASR using multiple sensors and depth information,"ISCA Archive
http://www.isca-speech.org/archive
Auditory-Visual Speech Processing
(AVSP) 2011
Volterra, Italy
September 1-2, 2011"
e3917d6935586b90baae18d938295e5b089b5c62,Face localization and authentication using color and depth images,"Face Localization and Authentication
Using Color and Depth Images
Filareti Tsalakanidou, Sotiris Malassiotis, and Michael G. Strintzis, Fellow, IEEE"
e3b92cc14f2c33bfdc07b794292a30384f8d0ad1,Local Segmentation for Pedestrian Tracking in Dense Crowds,"Local Segmentation for Pedestrian Tracking in
Dense Crowds
Clement Creusot
Toshiba RDC, Kawasaki, Japan,
http://clementcreusot.com/pedestrian"
e3f2e337d4470545398cc6753a54c21debf9c37b,Potential Contrast – A New Image Quality Measure,"Potential Contrast – A New Image Quality Measure
Arie Shaus, Shira Faigenbaum-Golovin, Barak Sober, Eli Turkel, Eli Piasetzky; Tel Aviv University; Tel Aviv, Israel"
32103c021e4d52f9915c3236669de5da973fb13f,Affective Image Content Analysis: A Comprehensive Survey,Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18)
323cd51bc18c700fa88044dd24ae663a7eabaa68,Utilizing student activity patterns to predict performance,"Casey and Azcona International Journal of Educational Technology
in Higher Education  (2017) 14:4
DOI 10.1186/s41239-017-0044-3
R ES EAR CH A R T I C LE
Utilizing student activity patterns to predict
performance
Kevin Casey1* and David Azcona2
Open Access
* Correspondence:
Maynooth University, Maynooth,
Ireland
Full list of author information is
vailable at the end of the article"
327f3d65a380f70bc39fe99c7ad55d76a5f7fff4,A data-synthesis-driven method for detecting and extracting vague cognitive regions,"International Journal of Geographical Information
Science
ISSN: 1365-8816 (Print) 1362-3087 (Online) Journal homepage: http://www.tandfonline.com/loi/tgis20
A data-synthesis-driven method for detecting and
extracting vague cognitive regions
Song Gao, Krzysztof Janowicz, Daniel R. Montello, Yingjie Hu, Jiue-An Yang,
Grant McKenzie, Yiting Ju, Li Gong, Benjamin Adams & Bo Yan
To cite this article: Song Gao, Krzysztof Janowicz, Daniel R. Montello, Yingjie Hu, Jiue-An
Yang, Grant McKenzie, Yiting Ju, Li Gong, Benjamin Adams & Bo Yan (2017): A data-synthesis-
driven method for detecting and extracting vague cognitive regions, International Journal of
Geographical Information Science, DOI: 10.1080/13658816.2016.1273357
To link to this article:  http://dx.doi.org/10.1080/13658816.2016.1273357
Published online: 08 Jan 2017.
Submit your article to this journal
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Full Terms & Conditions of access and use can be found at
http://www.tandfonline.com/action/journalInformation?journalCode=tgis20
Download by: [UC Santa Barbara Library]
Date: 09 January 2017, At: 09:44"
324cf94743359df3ada2f86ee8cd3bb6dccae695,FERA 2015-Second Facial Expression Recognition and Analysis Challenge,"FG 2015
FG 2015 Submission. CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE.
FG 2015
FERA 2015 - Second Facial Expression Recognition and Analysis
Challenge
Anonymous FG 2015 submission
– DO NOT DISTRIBUTE –"
32743e72cdb481b7a30a3d81a96569dcbea4e409,Ultra Power-Efficient CNN Domain Specific Accelerator with 9.3TOPS/Watt for Mobile and Embedded Applications,"Ultra Power-Efficient CNN Domain Specific Accelerator with 9.3TOPS/Watt for
Mobile and Embedded Applications
Baohua Sun,
Lin Yang,
Patrick Dong, Wenhan Zhang,
Gyrfalcon Technology Inc.
Jason Dong, Charles Young
900 McCarthy Blvd. Milpitas, CA 95035"
321bd4d5d80abb1bae675a48583f872af3919172,Entropy-weighted feature-fusion method for head-pose estimation,"Wang et al. EURASIP Journal on Image and Video Processing  (2016) 2016:44
DOI 10.1186/s13640-016-0152-3
EURASIP Journal on Image
nd Video Processing
R EV I E W
Entropy-weighted feature-fusion method
for head-pose estimation
Xiao-Meng Wang*, Kang Liu and Xu Qian
Open Access"
326ac7776f000c43ff05fbbbef444a55984346e1,"Ear biometrics: a survey of detection, feature extraction and recognition methods","Ear Biometrics: A Survey of Detection,
Feature Extraction and Recognition
Methods
Anika Pflug, Christoph Busch ∗
July 2, 2012
The possibility of identifying people by the shape of their outer ear was first
discovered by the French criminologist Bertillon, and refined by the American
police of‌f‌icer Iannarelli, who proposed a first ear recognition system based
on only seven features.
The detailed structure of the ear is not only unique, but also permanent,
s the appearance of the ear does not change over the course of a human
life. Additionally, the acquisition of ear images does not necessarily require
person’s cooperation but is nevertheless considered to be non-intrusive by
most people.
Because of these qualities, the interest in ear recognition systems has grown
significantly in recent years. In this survey, we categorize and summarize ap-
proaches to ear detection and recognition in 2D and 3D images. Then, we
provide an outlook over possible future research in the field of ear recogni-
tion, in the context of smart surveillance and forensic image analysis, which
we consider to be the most important application of ear recognition charac-"
320d6e79fa184dcf24c30232ab762d29f7e3d9c6,Face recognition using Hidden Markov Models,"Face recognition using Hidden Markov Models
Johan Stephen Simeon Ballot
Thesis presented at the University of Stellenbosch
in partial fulfilment of the requirements for the
degree of
Master of Science in Electronic Engineering with Computer
Science
Department of Electrical & Electronic Engineering
University of Stellenbosch
Private Bag X1, 7602 Matieland, South Africa
Study leaders:
Prof. J.A. du Preez
Prof. B.M. Herbst
April 2005"
3233340bbb770187cc870f01755a658d3ae92396,A Classification approach towards Unsupervised Learning of Visual Representations,"A Classification approach towards Unsupervised Learning of Visual
Representations
Aditya Vora
Johnson Controls Inc."
32bebe84ffbd4fd81f0e5bb30dbc90774aa3b14b,Segmentation Results Stimuli Final Saliency Map Ground Truth Constructed Graph CCA,"Noname manuscript No.
(will be inserted by the editor)
Hierarchical Cellular Automata for Visual Saliency
Yao Qin* · Mengyang Feng* · Huchuan Lu · Garrison W. Cottrell
Received: date / Accepted: date"
324d82129642f84838be71bd7401f38c80fb87d7,Adaptive Mixtures of Factor Analyzers,"Adaptive Mixtures of Factor Analyzers
Heysem Kayaa,∗, Albert Ali Salaha
Department of Computer Engineering
Bo˘gazi¸ci University, 34342, Bebek, ˙Istanbul"
32d6ee09bd8f1a7c42708d6dd8a5fb85ac4e08bc,Non-Interfering Effects of Active Post-Encoding Tasks on Episodic Memory Consolidation in Humans,"ORIGINAL RESEARCH
published: 29 March 2017
doi: 10.3389/fnbeh.2017.00054
Non-Interfering Effects of Active
Post-Encoding Tasks on Episodic
Memory Consolidation in Humans
Samarth Varma 1*, Atsuko Takashima 1,2, Sander Krewinkel 1, Maaike van Kooten 1,
Lily Fu 1, W. Pieter Medendorp 1, Roy P. C. Kessels 1 and Sander M. Daselaar 1
Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands, 2Department of
Neurobiology of Language, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
So far, studies that investigated interference effects of post-learning processes on
episodic memory consolidation in humans have used tasks involving only complex and
meaningful information. Such tasks require reallocation of general or encoding-specific
resources away from consolidation-relevant activities. The possibility that interference
an be elicited using a task that heavily taxes our limited brain resources, but has
low semantic and hippocampal related long-term memory processing demands, has
never been tested. We address this question by investigating whether consolidation
ould persist in parallel with an active, encoding-irrelevant, minimally semantic task,
regardless of its high resource demands for cognitive processing. We distinguish the
impact of such a task on consolidation based on whether it engages resources that"
32bd968e6cf31e69ee5fca14d3eadeec7f4187c6,Monocular Pedestrian Detection: Survey and Experiments,"Monocular Pedestrian Detection:
Survey and Experiments
Markus Enzweiler, Student Member, IEEE, and Dariu M. Gavrila"
325b048ecd5b4d14dce32f92bff093cd744aa7f8,Multi-Image Graph Cut Clothing Segmentation for Recognizing People Anonymous CVPR submission Paper ID 2670,"#2670
CVPR 2008 Submission #2670. CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE.
#2670
Multi-Image Graph Cut Clothing Segmentation for Recognizing People
Anonymous CVPR submission
Paper ID 2670"
32b9be86de4f82c5a43da2a1a0a892515da8910d,Robust False Positive Detection for Real-Time Multi-target Tracking,"Robust False Positive Detection for Real-Time
Multi-Target Tracking
Henrik Brauer, Christos Grecos, and Kai von Luck
University of the West of Scotland
University of Applied Sciences Hamburg"
32ef19e90e7834ec09ef19fcef7cd2aa6eff85a9,Modeling Natural Images Using Gated MRFs,"JOURNAL OF PAMI, VOL. ?, NO. ?, JANUARY 20??
Modeling Natural Images Using Gated MRFs
Marc’Aurelio Ranzato, Volodymyr Mnih, Joshua M. Susskind, Geoffrey E. Hinton"
32f0c95cee39eba143452d6a0fe93283575257e6,Generative Adversarial Networks for Extreme Learned Image Compression,"GENERATIVE ADVERSARIAL NETWORKS FOR
EXTREME LEARNED IMAGE COMPRESSION
Eirikur Agustsson∗, Michael Tschannen∗, Fabian Mentzer∗, Radu Timofte & Luc Van Gool
{aeirikur, mentzerf, radu.timofte,
ETH Zurich"
322ff387087134ac776a4270cd55e7f3334edeb2,"Image Features Detection , Description and Matching","Image Features Detection, Description
nd Matching
M. Hassaballah, Aly Amin Abdelmgeid and Hammam A. Alshazly"
325c9f6f848407a22b86e3253cb7f29fac19e40c,Change Detection in Crowded Underwater Scenes - Via an Extended Gaussian Switch Model Combined with a Flux Tensor Pre-segmentation,"Change Detection in Crowded Underwater Scenes
via an Extended Gaussian Switch Model combined with a Flux Tensor
Pre-Segmentation
Martin Radolko1,2, Fahimeh Farhadifard1,2 and Uwe von Lukas1,2
Institute for Computer Science, University Rostock, Rostock, Germany
Fraunhofer Institute for Computer Fraphics Research IGD , Rostock, Germany
{Martin.Radolko,
Keywords:
Change Detection, Background Subtraction, Video Segmentation, Video Segregation, Underwater Segmenta-
tion, Flux Tensor"
3265c7799f9d14e29de37b1e37aec4330cd1d747,Class-Specific Binary Correlograms for Object Recognition,"Class-Specific Binary Correlograms for Object
Recognition
Jaume Amores1, Nicu Sebe2, Petia Radeva3
IMEDIA Research Group, INRIA, France
Univ. of Amsterdam, The Netherlands
Computer Vision Center, UAB, Spain"
325000c2ebe4fcfd08946aef91aee8bec22026a5,Multi-Label Learning With Fused Multimodal Bi-Relational Graph,"Multi-Label Learning With Fused
Multimodal Bi-Relational Graph
Jiejun Xu, Vignesh Jagadeesh, and B. S. Manjunath, Fellow, IEEE"
3274a13562029f36e2f0fad3270e3ecb9ca013bd,Real-time UAV Target Tracking System Based on Optical Flow and Particle Filter Integration,"Real-time UAV Target Tracking System Based on Optical Flow and
Particle Filter Integration
WESAM ASKAR
Electrical Engineering
Military Tech. College
EGYPT
OSAMA ELMOWAFY
Computer Engineering
New Cairo Academy
ALIAA YOUSSIF
Computer Engineering
Helwan University
GAMAL ELNASHAR
Electrical Engineering
Military Tech. College
EGYPT
EGYPT
EGYPT"
32f7e1d7fa62b48bedc3fcfc9d18fccc4074d347,Hierarchical Sparse and Collaborative Low-Rank representation for emotion recognition,"HIERARCHICAL SPARSE AND COLLABORATIVE LOW-RANK REPRESENTATION FOR
EMOTION RECOGNITION
Xiang Xiang, Minh Dao, Gregory D. Hager, Trac D. Tran
Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA
{xxiang, minh.dao, ghager1,"
326df1b94624b7958cff0f7e3d16e612ea9d7e4d,Similarity rank correlation for face recognition under unenrolled pose,"Similarity Rank Correlation for Face
Recognition Under Unenrolled Pose
Marco K. M¨uller, Alexander Heinrichs, Andreas H.J. Tewes,
Achim Sch¨afer, and Rolf P. W¨urtz
Institut f¨ur Neuroinformatik, Ruhr-Universit¨at, D–44780 Bochum, Germany"
323fabb6cb4e74518fd4c7ad6ea5a1b2674e63d3,Object recognition based on radial basis function neural networks: Experiments with RGB-D camera embedded on mobile robots,"Object Recognition Based on Radial Basis Function
Neural Networks: experiments with RGB-D camera
embedded on mobile robots
Saeed Gholami Shahbandi
LISA - University of Angers
Philippe Lucidarme
LISA - University of Angers
62 av. Notre Dame du Lac, 49000 Angers, France
62 av. Notre Dame du Lac, 49000 Angers, France"
320ea4748b1f7e808eabedbedb75cce660122d26,"Detecting Avocados to Zucchinis: What Have We Done, and Where Are We Going?","Detecting avocados to zucchinis: what have we done, and where are we going?
Olga Russakovsky1, Jia Deng1, Zhiheng Huang1, Alexander C. Berg2, Li Fei-Fei1
Stanford University1 , UNC Chapel Hill2"
32d8194269faf6ae505a8d7937a3423e4830187e,Big Five Personality Recognition from Multiple Text Genres,"Big Five Personality Recognition from
Multiple Text Genres
Vitor Garcia dos Santos, Ivandr´e Paraboni, and Barbara Barbosa Claudino Silva
University of S˜ao Paulo, School of Arts, Sciences and Humanities, S˜ao Paulo, Brazil"
32728e1eb1da13686b69cc0bd7cce55a5c963cdd,Automatic Facial Emotion Recognition Method Based on Eye Region Changes,"Automatic Facial Emotion Recognition Method Based on Eye
Region Changes
Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
Mina Navraan
Nasrollah Moghadam Charkari*
Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
Muharram Mansoorizadeh
Faculty of Electrical and Computer Engineering, Bu-Ali Sina University, Hamadan, Iran
Received: 19/Apr/2015            Revised: 19/Mar/2016            Accepted: 19/Apr/2016"
32deaec54b9860bb4b81c8c9a64d11b0eea382b8,Large-Scale Image Segmentation with Convolutional Networks,"POUR L'OBTENTION DU GRADE DE DOCTEUR ÈS SCIENCESacceptée sur proposition du jury:Prof. J.-Ph. Thiran, président du juryProf. H. Bourlard, Dr R. Collobert, directeurs de thèseDr C. Schmid, rapporteuseProf. R. Fergus, rapporteurDr M. Salzmann, rapporteurLarge-Scale Image Segmentation with Convolutional NetworksTHÈSE NO 7571 (2017)ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNEPRÉSENTÉE LE 17 FÉVRIER 2017À LA FACULTÉ DES SCIENCES ET TECHNIQUES DE L'INGÉNIEURLABORATOIRE DE L'IDIAPPROGRAMME DOCTORAL EN GÉNIE ÉLECTRIQUE Suisse2017PARPedro Henrique OLIVEIRA PINHEIRO"
32cde90437ab5a70cf003ea36f66f2de0e24b3ab,The Cityscapes Dataset for Semantic Urban Scene Understanding,"The Cityscapes Dataset for Semantic Urban Scene Understanding
Marius Cordts1,2
Markus Enzweiler1
Mohamed Omran3
Rodrigo Benenson3
Sebastian Ramos1,4
Timo Rehfeld1,2
Uwe Franke1
Stefan Roth2
Bernt Schiele3
Daimler AG R&D, 2TU Darmstadt, 3MPI Informatics, 4TU Dresden
www.cityscapes-dataset.net
train/val – fine annotation – 3475 images
train – coarse annotation – 20 000 images
test – fine annotation – 1525 images"
3295ec2e52cd83cec75fc7c7064a843756b4d1ee,An Efficient Pedestrian Detection Approach Using a Novel Split Function of Hough Forests,"Regular Paper
Journal of Computing Science and Engineering,
Vol. 8, No. 4, December 2014, pp. 207-214
An Efficient Pedestrian Detection Approach Using a Novel Split
Function of Hough Forests
Trung Dung Do, Thi Ly Vu, Van Huan Nguyen, Hakil Kim*, and Chongho Lee
School of Information and Communication Engineering, Inha University, Incheon, Korea
{dotrungdung, vuthily, {hikim,"
32c3dcf95da2e54d1635f06f5714c5099cc6034e,A dataset for benchmarking vision-based localization at intersections,"A dataset for benchmarking vision-based
localization at intersections
supplemental material for “Vision-Based Localization at Intersections using Digital Maps”
Augusto L. Ballardini, Daniele Cattaneo, and Domenico G. Sorrenti"
322c063e97cd26f75191ae908f09a41c534eba90,Improving Image Classification Using Semantic Attributes,"Noname manuscript No.
(will be inserted by the editor)
Improving Image Classification using Semantic Attributes
Yu Su · Fr´ed´eric Jurie
Received: date / Accepted: date"
323d6d93b059372bbe26a86bad1b9d94b076f50e,(A) Vision for 2050 - Context-Based Image Understanding for a Human-Robot Soccer Match,"Electronic Communications of the EASST
Volume 62 (2013)
Specification, Transformation, Navigation
Special Issue dedicated to Bernd Krieg-Br¨uckner
on the Occasion of his 60th Birthday
(A) Vision for 2050 – Context-Based Image Understanding for a
Human-Robot Soccer Match
Udo Frese, Tim Laue, Oliver Birbach, and Thomas R¨ofer
9 pages
Guest Editors: Till Mossakowski, Markus Roggenbach, Lutz Schr¨oder
Managing Editors: Tiziana Margaria, Julia Padberg, Gabriele Taentzer
ISSN 1863-2122"
32a6f6aa50ce2a631bf4de7432f830b29b6b05f2,Through the eyes of a child: preschoolers' identification of emotional expressions from the child affective facial expression (CAFE) set.,"Cognition and Emotion
ISSN: 0269-9931 (Print) 1464-0600 (Online) Journal homepage: http://www.tandfonline.com/loi/pcem20
Through the eyes of a child: preschoolers’
identification of emotional expressions from the
hild affective facial expression (CAFE) set
Vanessa LoBue, Lewis Baker & Cat Thrasher
To cite this article: Vanessa LoBue, Lewis Baker & Cat Thrasher (2017): Through the eyes of a
hild: preschoolers’ identification of emotional expressions from the child affective facial expression
(CAFE) set, Cognition and Emotion, DOI: 10.1080/02699931.2017.1365046
To link to this article:  http://dx.doi.org/10.1080/02699931.2017.1365046
Published online: 10 Aug 2017.
Submit your article to this journal
View related articles
View Crossmark data
Full Terms & Conditions of access and use can be found at
http://www.tandfonline.com/action/journalInformation?journalCode=pcem20
Download by: [173.56.101.121]
Date: 10 August 2017, At: 05:46"
32925200665a1bbb4fc8131cd192cb34c2d7d9e3,An Active Appearance Model with a Derivative-Free Optimization,"MVA2009 IAPR Conference on Machine Vision Applications, May 20-22, 2009, Yokohama, JAPAN
An Active Appearance Model with a Derivative-Free
Optimization
Jixia ZHANG‡, Franck DAVOINE†, Chunhong PAN‡
CNRS†, Institute of Automation of the Chinese Academy of Sciences‡
95, Zhongguancun Dong Lu, PO Box 2728 − Beijing 100190 − PR China
LIAMA Sino-French IT Lab."
321fbbe7da848b602f376219ed9aed6a7f4b7f57,Effective use of frequent itemset mining for image classification,"Effective Use of Frequent Itemset Mining for
Image Classification
Basura Fernando1, Elisa Fromont2, and Tinne Tuytelaars1
KU Leuven, ESAT-PSI, IBBT (Belgium)
University of Saint-Etienne(France)"
32df63d395b5462a8a4a3c3574ae7916b0cd4d1d,Facial expression recognition using ensemble of classifiers,"978-1-4577-0539-7/11/$26.00 ©2011 IEEE
ICASSP 2011"
fbc0e33bed3bba7eff9bed2c31f33da793120929,Efficient Online Segmentation for Sparse 3 D Laser Scans,"PFG 0000 / 0, 0000 – 0001
Stuttgart, 00 0000
Article
Efficient Online Segmentation for Sparse 3D Laser
Scans
IGOR BOGOSLAVSKYI & CYRILL STACHNISS, Bonn
Keywords: Segmentation, 3D laser, online, range image, sparse data, point cloud
Summary: The ability to extract individual objects in the scene is key for a large number of
utonomous navigation systems such as mobile robots or autonomous cars. Such systems
navigating in dynamic environments need to be aware of objects that may change or move. In
most perception cues, a pre-segmentation of the current image or laser scan into individual
objects is the first processing step before a further analysis is performed. In this paper, we present
n effective method that first removes the ground from the scan and then segments the 3D data in
range image representation into different objects. A key focus of our work is a fast execution
with several hundred Hertz. Our implementation has small computational demands so that it can
run online on most mobile systems. We explicitly avoid the computation of the 3D point cloud
nd operate directly on a 2.5D range image, which enables a fast segmentation for each 3D scan.
This approach can furthermore handle sparse 3D data well, which is important for scanners such
s the new Velodyne VLP-16 scanner. We implemented our approach in C++ and ROS,
thoroughly tested it using different 3D scanners, and will release the source code of our"
fb3af250a2ff85145519fea9ece7187452d02a50,The WILDTRACK Multi-Camera Person Dataset,"The WILDTRACK Multi-Camera Person
Dataset
Tatjana Chavdarova1, Pierre Baqu´e2, St´ephane Bouquet2,
Andrii Maksai2, Cijo Jose1, Louis Lettry3,
Pascal Fua2, Luc Van Gool3 and Fran¸cois Fleuret1
Machine Learning group, Idiap Research Institute & ´Ecole
Polytechnique F´ed´erale de Lausanne
CVLab, ´Ecole Polytechnique F´ed´erale de Lausanne
Computer Vision Lab, ETH Zurich"
fbd781143a3f4c9d03c227cfbd1f528d658195ce,A Gender Recognition Experiment on the CASIA Gait Database Dealing with Its Imbalanced Nature.,"A GENDER RECOGNITION EXPERIMENT ON THE CASIA GAIT
DATABASE DEALING WITH ITS IMBALANCED NATURE
Ra´ul Mart´ın-F´elez, Ram´on A. Mollineda and J. Salvador S´anchez
Institute of New Imaging Technologies (INIT) and Dept. Llenguatges i Sistemes Inform`atics
Universitat Jaume I. Av. Sos Baynat s/n, 12071, Castell´o de la Plana, Spain
{martinr, mollined,
Keywords:
Gender recognition, Gait analysis, Class imbalance problem, Human silhouette, Appearance-based method."
fbbc2978758fe459f05d749a0fbd1ef3a3ba3c6c,The TUM VI Benchmark for Evaluating Visual-Inertial Odometry,"The TUM VI Benchmark for Evaluating Visual-Inertial Odometry
David Schubert∗, Thore Goll∗, Nikolaus Demmel∗, Vladyslav Usenko∗, J¨org St¨uckler and Daniel Cremers"
fbb304770d33f44006d134906481208ad087ce63,Visual Self-Localization with Tiny Images,"Visual Self-Localization with Tiny Images
Marius Hofmeister, Sara Erhard and Andreas Zell
University of T¨ubingen, Department of Computer Science, Sand 1, 72076 T¨ubingen"
fb671ce90db5e3092d1f1bfa78c5bdcc06db5d21,Learning to Detect Objects with Minimal Supervision THÈSE,"Learning to Detect Objects with Minimal Supervision
THÈSE NO 5310 (2012)
PRÉSENTÉE LE 8 MARS 2012
À LA  FACULTÉ INFORMATIQUE ET COMMUNICATIONS
LABORATOIRE DE VISION PAR ORDINATEUR
PROGRAMME DOCTORAL EN INFORMATIQUE, COMMUNICATIONS ET INFORMATION
ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE
POUR L'OBTENTION DU GRADE DE DOCTEUR ÈS SCIENCES
Karim ALI
cceptée sur proposition du jury:
Prof. M. Pauly, président du jury
Prof. P. Fua, Dr F. Fleuret, directeurs de thèse
Dr D. Hasler, rapporteur
Prof. M. Seeger, rapporteur
Prof. A. Trouvé, rapporteur
Suisse"
fbd7d591e6eecb9a947e377d5b1a865a9f86a11f,Consensual and Privacy-Preserving Sharing of Multi-Subject and Interdependent Data,"Consensual and Privacy-Preserving Sharing of
Multi-Subject and Interdependent Data
Alexandra-Mihaela Olteanu
EPFL, UNIL–HEC Lausanne
K´evin Huguenin
UNIL–HEC Lausanne
Italo Dacosta
Jean-Pierre Hubaux"
fbbccf0454c84bea1fd5c5a1dcd9fd7bba301a44,Face detection using gradient vector flow,"Proceedings of  the Second International Conference on Machine Learning and Cybernetics, Wan, 2-5 November 2003
FACE DETECTION USING GRADIENT VECTOR FLOW
MAYANK VATSA,  RICHA SINCH, P. GUPTA
Department of Computer Science & Engineering Indian Institute of Technology Kanpur
Kanpur INDIA, 208016
E-MAIL: (mayankv, richas, pg} cse.iitk.ac.in"
fb710e9d897b7c1fd5275a0bcfa22711c5768990,A Graphical Model for Rapid Obstacle Image-Map Estimation from Unmanned Surface Vehicles,"A graphical model for rapid obstacle image-map
estimation from unmanned surface vehicles
Matej Kristan1,2, Janez Perˇs1, Vildana Suliˇc1, Stanislav Kovaˇciˇc1
Faculty of computer and information science University of Ljubljana, 2Faculty of
electrical engineering University of Ljubljana"
fb4545782d9df65d484009558e1824538030bbb1,"Learning Visual Patterns: Imposing Order on Objects, Trajectories and Networks",
fbf196d83a41d57dfe577b3a54b1b7fa06666e3b,Extreme Learning Machine for Large-Scale Action Recognition,"Extreme Learning Machine for Large-Scale
Action Recognition
G¨ul Varol and Albert Ali Salah
Department of Computer Engineering, Bo˘gazi¸ci University, Turkey"
fb95fb1e0bf99347a69f76c9fd65e039024e73b7,Photograph Based Pair-matching Recognition of Human Faces,"World Academy of Science, Engineering and Technology
International Journal of Computer and Information Engineering
Vol:5, No:12, 2011
Photograph Base
sed Pair-matching Recogn
gnition of
Human Faces
Min Y
n Yao, Kota Aoki, and Hiroshi Nagahashi
(cid:1)"
fb5a370a4d6364887ae334923335e8c48aed0f78,Biometrics at the Frontiers: Assessing the impact on Society,"T E C H N I C A L   R E P O R T   S E R I E S
Biometrics at the Frontiers:
Assessing the Impact on
Society
For the European Parliament
Committee on Citizens' Freedoms and Rights,
Justice and Home Affairs (LIBE)
EUR 21585 EN
Institute for
Prospective
Technological Studies"
fbc93b13b8a6a5e4ed11310ce4da3be0b7541da8,Real-time Pedestrian Detection in a Truck's Blind Spot Camera,"Real-time pedestrian detection in a truck’s blind spot camera
Kristof Van Beeck1,2 and Toon Goedem´e1,2
EAVISE, Campus De Nayer - KU Leuven, J. De Nayerlaan 5, 2860 Sint-Katelijne-Waver, Belgium
ESAT-PSI, KU Leuven, Kasteel Arenbergpark 10, 3100 Heverlee, Belgium
{kristof.vanbeeck,
Keywords:
Pedestrian detection, Tracking, Real-time, Computer vision, Active safety systems"
fb76adeff0309ff4c8de4d0b413a8e3a637774d0,client2vec: Towards Systematic Baselines for Banking Applications,"lient2vec: Towards Systematic Baselines for Banking
Applications
Leonardo Baldassini
BBVA Data & Analytics
Jose Antonio Rodr´ıguez Serrano
BBVA Data & Analytics"
fb66546a16751810754430286fe4c636e4411ca4,Complementary feature sets for optimal face recognition,"Singh et al. EURASIP Journal on Image and Video Processing 2014, 2014:35
http://jivp.eurasipjournals.com/content/2014/1/35
R ES EAR CH
Complementary feature sets for optimal face
recognition
Chandan Singh1, Neerja Mittal2* and Ekta Walia3
Open Access"
fbd047862ea869973ecf8fc35ae090ca00ff06d8,Literature review of fingerprint quality assessment and its evaluation,"A Literature Review of Fingerprint Quality Assessment
nd Its Evaluation
Zhigang Yao, Jean-Marie Le Bars, Christophe Charrier, Christophe
Rosenberger
To cite this version:
Zhigang Yao, Jean-Marie Le Bars, Christophe Charrier, Christophe Rosenberger. A Literature Review
of Fingerprint Quality Assessment and Its Evaluation.
IET journal on Biometrics, 2016. <hal-
01269240>
HAL Id: hal-01269240
https://hal.archives-ouvertes.fr/hal-01269240
Submitted on 5 Feb 2016
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
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broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents"
fb82681ac5d3487bd8e52dbb3d1fa220eeac855e,"Summary of Research on Informant Accuracy in Network Data, 11 and on the Reverse Small World Problem","CONNECTIONS
VOLUME IV, NUMBER 2
Summer 1981
CONTENTS
NETWORK NOTEBOOK
MEETING CALENDAR
RESEARCH REPORTS
Social Networks :
A Beginner's Bookshelf
Linton C . Freeman (California-Irvine)
Summary of Research on Informant Accuracy in Network Data,
nd on the Reverse Small World Problem
H . Russell Bernard (Florida), Peter D . Killworth (Cambridge)
& Lee Sailer (Pittsburgh)
Russell's Paradox (Part II)
Linton C . Freeman (California-Irvine)
Goedel's Spoof :
A Reply to Freeman
Peter D . Killworth (Cambridge) & H . Russell Bernard (Florida)
The Norwegian Connection :"
fba464cb8e3eff455fe80e8fb6d3547768efba2f,Survey Paper on Emotion Recognition,"International Journal of Engineering and Applied Sciences (IJEAS)
ISSN: 2394-3661, Volume-3, Issue-2, February 2016
Survey Paper on Emotion Recognition
Prachi Shukla, Sandeep Patil"
fb1732a1476798c42a0123aaf127036bf8daef09,LightDenseYOLO: A Fast and Accurate Marker Tracker for Autonomous UAV Landing by Visible Light Camera Sensor on Drone,"Article
LightDenseYOLO: A Fast and Accurate Marker
Tracker for Autonomous UAV Landing by Visible
Light Camera Sensor on Drone
Phong Ha Nguyen, Muhammad Arsalan, Ja Hyung Koo, Rizwan Ali Naqvi, Noi Quang Truong
nd Kang Ryoung Park *
Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul
00-715, Korea; (P.H.N.); (M.A.); (J.H.K.);
(R.A.N.); (N.Q.T.)
* Correspondence: Tel.: +82-10-3111-7022; Fax: +82-2-2277-8735
Received: 3 May 2018; Accepted: 22 May 2018; Published: 24 May 2018"
fbd169c0f700fcca26a74c79bb46c5ca2d37648a,FACIAL FEATURE BASED HEAD TRACKING AND POSE ESTIMATION,"FACIAL FEATURE BASED HEAD
TRACKING AND POSE ESTIMATION
Jari Hannuksela"
fb2379346def4846ac24bc41349e7cac7c1e7243,ClusterNet: 3D Instance Segmentation in RGB-D Images,"ClusterNet: 3D Instance Segmentation in RGB-D Images
Lin Shao, Ye Tian, and Jeannette Bohg"
fb748a6953e72ad6d508109f8d809c25570ff07b,"The ""Eye Avoidance"" Hypothesis of Autism Face Processing.","NIH Public Access
Author Manuscript
J Autism Dev Disord. Author manuscript; available in PMC 2015 April 23.
The “eye avoidance” hypothesis of autism face processing
James W. Tanaka1 and Andrew Sung2
Department of Psychology, University of Victoria, British Columbia
Department of Special Education and Leadership Studies, University of Victoria, British
Columbia"
fbf20dc3367864462d7630aad81c436e50d1cd60,Iterative Bayesian Learning for Crowdsourced Regression,"Iterative Bayesian Learning for Crowdsourced Regression
Jungseul Ok∗, Sewoong Oh∗, Yunhun Jang †, Jinwoo Shin†, and Yung Yi†
October 9, 2018"
fbd17af24e86fe487e28f99ba3e402dd6cfcd16a,Research Statement: towards Detailed Recognition of Visual Categories,"Research Statement: Towards Detailed Recognition of Visual Categories
Subhransu Maji
As humans, we have a remarkable ability to perceive the world around us in minute detail purely
from the light that is reflected off it – we can estimate material and metric properties of objects, localize
people in images, describe what they are doing, and even identify them. Automatic methods for such
detailed recognition of images are essential for most human-centric applications and large scale analysis
of the content of media collections for market research, advertisement, and social studies. For example,
in order to shop for shoes in an on-line catalogue, a system should be able to understand the style of a
shoe, the length of its heels, or the shininess of its material. In order to support visual demographics
nalysis for advertisement, a system should be able to not only identify the people in a scene, but also
to understand what kind (style and brand) of clothes they are wearing, whether they are wearing any
ccessories, and so on.
Despite several successes, such detailed recognition is beyond the current computer vision systems.
This is a challenging task, and to make progress we have to make advances on several fronts. We need
etter representations of visual categories that can enable fine-grained reasoning about their properties,
s well as machine learning methods that can leverage ‘big-data’ to learn such representations. In order
to enable benchmarks for evaluating recognition tasks and to guide learning and inference in models
that solve challenging problems, we need to develop better ways of human-computer interaction. My
research touches upon several such themes in the intersection of computer vision, machine learning, and
human-computer interaction including:"
fb17e9cab49665863f360d5f9e61e6048a7e1b28,Reconstruction-Based Pairwise Depth Dataset for Depth Image Enhancement Using CNN,"Reconstruction-based Pairwise Depth Dataset for
Depth Image Enhancement Using CNN
Junho Jeon and Seungyong Lee
POSTECH
{zwitterion27,"
fbd287db41bff55956b9faf4a226a39efe96e6da,Radboud University Object Localization and Path Prediction Using Radar and Other Sources Towards Autonomous Shipping,"Master Thesis
Computer Science
Radboud University
Prediction Using Radar and Other
Sources Towards Autonomous
Object Localization and Path
Shipping
Author:
Koen Vijverberg, BSc
Supervisor:
Prof. Dr. Elena Marchiori
External supervisor:
Fons de Leeuw, BSc
Second supervisor:
Prof. Dr. Tom Heskes
August 23, 2018"
fb9ad920809669c1b1455cc26dbd900d8e719e61,3 D Gaze Estimation from Remote RGB-D Sensors THÈSE,"D Gaze Estimation from Remote RGB-D Sensors
THÈSE NO 6680 (2015)
PRÉSENTÉE LE 9 OCTOBRE 2015
À LA FACULTÉ DES SCIENCES ET TECHNIQUES DE L'INGÉNIEUR
LABORATOIRE DE L'IDIAP
PROGRAMME DOCTORAL EN GÉNIE ÉLECTRIQUE
ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE
POUR L'OBTENTION DU GRADE DE DOCTEUR ÈS SCIENCES
Kenneth Alberto FUNES MORA
cceptée sur proposition du jury:
Prof. K. Aminian, président du jury
Dr J.-M. Odobez,   directeur de thèse
Prof. L.-Ph. Morency, rapporteur
Prof. D. Witzner Hansen, rapporteur
Dr R. Boulic, rapporteur
Suisse"
fbb6e707c8a5f189d8ad416597e23671b884448b,Altered gaze following during live interaction in infants at risk for autism: an eye tracking study,"Thorup et al. Molecular Autism  (2016) 7:12
DOI 10.1186/s13229-016-0069-9
R ES EAR CH
Altered gaze following during live
interaction in infants at risk for autism:
n eye tracking study
Emilia Thorup1*, Pär Nyström1, Gustaf Gredebäck1, Sven Bölte3,2, Terje Falck-Ytter3,1 and The EASE Team
Open Access"
fbaceba60619d9f76f7acf6e639669cd6150049e,Automatic Semantic Content Removal by Learning to Neglect.,"QIN, WEI, MANDUCHI: AUTOMATIC SEMANTIC CONTENT REMOVAL
Automatic Semantic Content Removal
y Learning to Neglect
University of California, Santa Cruz
Santa Cruz, CA, USA
Siyang Qin
Jiahui Wei
Roberto Manduchi"
fb4c3b2f893baa1fbf8d16da2e09aa9868c61a7a,Decoupled Weight Decay Regularization,"Under review as a conference paper at ICLR 2019
DECOUPLED WEIGHT DECAY REGULARIZATION
Anonymous authors
Paper under double-blind review"
fb04a8cb4b573d6b565a5b0c369d775e6bfb04f1,LOOKING AT PEOPLE USING PARTIAL LEAST SQUARES by William Robson Schwartz,
fbb9cdd699baf86e9d616b259ada02449c2322ca,Active Testing: An Efficient and Robust Framework for Estimating Accuracy,"Active Testing: An Efficient and Robust Framework for Estimating Accuracy.
Phuc Nguyen 1 Deva Ramanan 2 Charless Fowlkes 1"
fb2ac6befd22fde3e6cff180d3431f02c81bc32a,Genetic optimisation of illumination compensation methods in cascade for face recognition,"Genetic optimisation of illumination
ompensation methods in cascade for face
recognition
C.A. Perez, L.E. Castillo, L.A. Cament, P.A. Este´vez and
C.M. Held
Face detection and recognition depend strongly on illumination con-
ditions. In this reported work, genetic algorithms are used to optimise
parameters of the modified local normalisation and self-quotient image
methods in cascade for illumination compensation to improve face rec-
ognition. The main novelty of the proposed method is that it applies to
non-homogeneous as well as homogeneous illumination conditions.
The results are compared to those of the best illumination compen-
sation methods published in the literature, obtaining 100% recognition
on faces with non-homogeneous illumination and significantly better
results than other methods with homogeneous illumination.
Introduction: Face detection and face recognition are important
several applications such as security, human–machine interfaces, auto-
matic driver monitoring, biometric identification, video search, etc. [1–3].
Facial images are dramatically changed by lighting variations and there-
fore may cause performance degradation both in face detection and in rec-"
fbb4f4959756798aabba8034cb3167756b191811,Supervised Infinite Feature Selection,"Supervised Infinite Feature Selection
Sadegh Eskandari A∗1 and Emre Akbas B†2
Department of Computer Science, University of Guilan, Rasht,
Department of Computer Engineering, Middle East Technical
Unviersity, Ankara 06800, Turkey
August 22, 2017"
9f7c1b794805be34bc2091e02c382c5461e0bcb4,On-board real-time tracking of pedestrians on a UAV,"On-board real-time tracking of pedestrians on a UAV
Floris De Smedt, Dries Hulens, and Toon Goedem´e
ESAT-PSI-VISICS, KU Leuven, Belgium"
9f483933bcc872771707dcf0acb1382411ffee94,Which Facial Expressions Can Reveal Your Gender? A Study With 3D Faces,"IN SUBMISSION TO IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
Which Facial Expressions Can Reveal Your
Gender? A Study With 3D Faces
Baiqiang XIA"
9fa1be81d31fba07a1bde0275b9d35c528f4d0b8,Identifying Persons by Pictorial and Contextual Cues,"Identifying Persons by Pictorial and
Contextual Cues
Nicholas Leonard Pi¨el
Thesis submitted for the degree of Master of Science
Supervisor:
Prof. dr. Theo Gevers
April 2009"
9f22e0749405dfc3e3211474b933aa7514722e4b,Theory of mind - not emotion recognition - mediates the relationship between executive functions and social functioning in patients with schizophrenia.,"© Medicinska naklada - Zagreb, Croatia
Original paper
THEORY OF MIND - NOT EMOTION RECOGNITION -
MEDIATES THE RELATIONSHIP BETWEEN EXECUTIVE
FUNCTIONS AND SOCIAL FUNCTIONING IN PATIENTS
WITH SCHIZOPHRENIA
Michal Hajdúk1,2, Dana Kraj(cid:254)ovi(cid:254)ová2, Miroslava Zimányiová2, Viera Ko(cid:284)ínková2,
Anton Heretik1 & Ján Pe(cid:254)e(cid:278)ák2
Department of Psychology, Faculty of Arts, Comenius University, Bratislava, Slovak Republic
Clinic of Psychiatry, Faculty of Medicine, Comenius University, Bratislava, Slovak Republic
received: 9.8.2017;
revised: 15.3.2018;
ccepted: 17.7.2018
SUMMARY
Background:  Dysfunction  of  social-cognitive  abilities  is  one  of  the  hallmark  features  of  schizophrenia  and  is  associated  with
neurocognition and social functioning. The Green and Nuechterlein model proposed that social cognition mediates the relationship
etween neurocognition and functional outcome. We tested this hypothesis in schizophrenia patients in the everyday clinical setting.
Subjects and methods: Social cognition, executive function and social functioning were assessed in a group of 43 patients with
schizophrenia or schizoaffective disorder using a range of measures.
Results:  Theory  of  mind  was  associated  with  executive  functions  and  social  functioning.  Results  of  our  mediation  analysis"
9fede7e3fac47a4206a643c4647834e5680f2a8f,Results from a Real-time Stereo-based Pedestrian Detection System on a Moving Vehicle,"Results from a Real-time Stereo-based Pedestrian Detection System on
Moving Vehicle
Max Bajracharya, Baback Moghaddam, Andrew Howard, Shane Brennan, Larry H. Matthies"
9fc37eccb3d12329f208cb7d3a509024e182a100,Mel-cepstral feature extraction methods for image representation,Downloaded From: https://www.spiedigitallibrary.org/journals/Optical-Engineering on 9/28/2017 Terms of Use: https://spiedigitallibrary.spie.org/ss/TermsOfUse.aspx
9f094341bea610a10346f072bf865cb550a1f1c1,Recognition and volume estimation of food intake using a mobile device,"Recognition and Volume Estimation of Food Intake using a Mobile Device
Manika Puri Zhiwei Zhu Qian Yu Ajay Divakaran Harpreet Sawhney
Sarnoff Corporation
01 Washington Rd,
Princeton, NJ, 08540
{mpuri, zzhu, qyu, adivakaran,"
9f91fd3e9621b88769ecc330f362a591876f948f,Bicycle Detection Based On Multi-feature and Multi-frame Fusion in low-resolution traffic videos,"Bicycle Detection Based On Multi-feature and
Multi-frame Fusion in low-resolution traffic videos
Yicheng Zhang, Student Member, IEEE, and Qiang Ling, Senior Member, IEEE
Some other methods like using MSC-HOG method for
detection [12] or detecting tires of bicycles in videos [13]
lso can get good results, but they are either time consuming
or high quality videos required. Some new methods, such the
method based on HOG features with ROI in [14], try to use
more advanced hardware device like GPU to finish the great
mount of computation.
In summary, there are three major defects in the available
icycle detection methods based on image processing. First,
they require fine features for detection, which are hard to
extract, particularly for traffic videos with low-resolution.
Second, the processing time under these methods is usually
long and may not meet
the requirement of the real-time
detection. Last, they make the bicycle detection decision by the
information in a single frame, which may lead to misjudgment,
especially in the case of strong noise or light changing."
9f545b9006970f7626b7b121c5c3c66204f1ba40,Improving Pairwise Ranking for Multi-label Image Classification,"Image courtesy of ReflectedSerendipityon Flickr.catflowervaseOutputInputPredictionPairwise Ranking(cat, 0.99)(flower, 0.82)(vase, 0.68) (window, 0.52)(ladder, 0.21)(horse, 0.02)(person, 0.01)Ranked List…DecisionPer-classThresholdEstimationFigure1:Ranking-basedmulti-labelclassificationemploysatwo-stepprocess:labelpredictionthatproducesarankedlistoflabelconfidencescores,andlabeldecisionthatdeter-mineswhichlabelstoincludeintheoutput.Weproposeanewpairwiserankinglossfunctionandaper-classthresh-oldestimationmethodinaunifiedframework,improvingexistingranking-basedapproachesinaprincipledmanner.labeldependency[1,25],labelsparsity[10,12,27],andlabelnoise[33,39].Motivatedbythesuccessofdeepcon-volutionalneuralnetworks(CNNs)[13,23],otherrecentapproachescombinerepresentationlearningandmulti-labellearningintoanend-to-endtrainablesystem[29].Recently,Westonetal.[32]proposedanideatoapplypairwiserankingtotheimageclassificationproblem.Theirmainideaisthat,whilewhatwecarethemostaboutiscorrectlyidentifyingpositivelabels,itisequallyimportantfortheclassifiertomake“sensible”mistakes.Specifically,evenwhenaclassifierfailstoidentifypositivelabels,itshouldatleastassignhigherrankstothepositivelabelsthantomostofthenegativelabels.Extendingthisidea,Gongetal.[9]appliedthepairwiserankingapproachtotrainaCNNandreportedthestate-of-the-artresultontheNUS-WIDEmulti-labelimageannotationtask[7].WhilethepairwiserankingapproachinWestonetal.[32]andGongetal.[9]providesflexibilitytotrainavarietyoflearningmachines,withgoodempiricalperformanceonreal-worldproblems,wearguethatithastwoimportantdrawbackswhenappliedtomulti-labelclassification.First,asweshowinSection3,thehingelossfunctionusedinWestonetal.[32]andGongetal.[9]isnon-smoothand1"
9f7aaa10067f8e10f87169be1a3d0c153d979157,Extended Bag-Of-Words Formalism For Image Classification,"EXTENDED BAG-OF-WORDS FORMALISM FOR
IMAGE CLASSIFICATION
Sandra Avila
To cite this version:
Sandra Avila. EXTENDED BAG-OF-WORDS FORMALISM FOR IMAGE CLASSIFICATION.
Computer Vision and Pattern Recognition [cs.CV]. Université Pierre et Marie Curie - Paris VI, 2013.
English. <tel-00958547>
HAL Id: tel-00958547
https://tel.archives-ouvertes.fr/tel-00958547
Submitted on 12 Mar 2014
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
9fb1bd7d98a2fa79e1b9cb21b865ec7af0c1283f,Not All Distraction Is Bad: Working Memory Vulnerability to Implicit Socioemotional Distraction Correlates with Negative Symptoms and Functional Impairment in Psychosis,"Hindawi Publishing Corporation
Schizophrenia Research and Treatment
Volume 2014, Article ID 320948, 6 pages
http://dx.doi.org/10.1155/2014/320948
Clinical Study
Not All Distraction Is Bad: Working Memory Vulnerability
to Implicit Socioemotional Distraction Correlates with Negative
Symptoms and Functional Impairment in Psychosis
Quintino R. Mano,1,2,3 Gregory G. Brown,1,2,3 Heline Mirzakhanian,1,2,3
Khalima Bolden,1,2,3 Kristen S. Cadenhead,1,2,3 and Gregory A. Light1,2,3
San Diego Veterans Affairs Healthcare System, San Diego, CA 92161, USA
VISN-22 Mental Illness, Research, Education and Clinical Center (MIRECC), VA San Diego Healthcare System,
San Diego, CA 92161, USA
Department of Psychiatry, University of California, San Diego, School of Medicine, San Diego, CA, USA
Correspondence should be addressed to Gregory G. Brown;
Received 31 July 2013; Revised 26 November 2013; Accepted 15 December 2013; Published 27 February 2014
Academic Editor: Steven J. Siegel
Copyright © 2014 Quintino R. Mano et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
ited."
9f986e3f5b4a4fad6bc579e3b752809db819576e,Generalized Exponential Bidirectional Fuzzy Associative Memory with Fuzzy Cardinality-Based Similarity Measures Applied to Face Recognition,"Tendˆencias em Matem´atica Aplicada e Computacional, 19, N. 2 (2018), 221-233
© 2018 Sociedade Brasileira de Matem´atica Aplicada e Computacional
www.scielo.br/tema
doi: 10.5540/tema.2018.019.02.0221
Generalized Exponential Bidirectional Fuzzy
Associative Memory with Fuzzy Cardinality-Based Similarity Measures
Applied to Face Recognition
A.C. SOUZA* and M.E. VALLE
Received on April 12, 2017 / Accepted on November 27, 2017"
9f889c81bdb1d791e22c5f455baf32829b5b788b,The GRODE metrics: Exploring the performance of group detection approaches,"Exploring the Performance of Group Detection Approaches
The GRODE Metrics:
Francesco Setti
ISTC - CNR
via alla Cascata 56/C, I-38121 Trento"
9fed3f4967bcdaebd1c321fee18fed865020a641,Objektsensitive Verfolgung und Klassifikation von Fußgängern mit verteilten Multi-Sensor-Trägern,"Forschungsberichte aus der
Industriellen Informationstechnik 11
Johannes Pallauf
Objektsensitive Verfolgung und
Klassifi kation von Fußgängern
mit verteilten Multi-Sensor-Trägern"
9f96b12427e65eb00ca6cacd9839f21892ee55d9,Feature Learning for Scene Flow Estimation from LIDAR,"Feature Learning for
Scene Flow Estimation from LIDAR
Arash K. Ushani
Computer Science and Engineering
University of Michigan Ann Arbor
United States
Ryan M. Eustice
Naval Architecture and Marine Engineering
University of Michigan Ann Arbor
United States"
9f1319162974cb4d6125e8c6c52878ebc48eb8a7,Loss factors for learning Boosting ensembles from imbalanced data,"Loss Factors for Learning Boosting Ensembles
from Imbalanced Data
Roghayeh Soleymani∗, Eric Granger∗, Giorgio Fumera†
Laboratoire d’imagerie, de vision et d’intelligence artificielle, École de technologie supérieure,
Université du Québec, Montreal, Canada,
Dept. of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy,
Email:
Email:"
9fcb23adc3e0b2e0e81df0ed30abbdef0c3bb16d,Motion Guided LIDAR-camera Autocalibration and Accelerated Depth Super Resolution,"Motion Guided LIDAR-camera Autocalibration and Accelerated
Depth Super Resolution
Juan Castorena, Gint Puskorius and Gaurav Pandey ∗"
9fb1d7cbf1baf5f347d159410d22912fcee1fdb1,Face Detection using Ferns,"FACE DETECTION USING FERNS
Venkatesh Bala Subburaman        Sébastien Marcel
Idiap-Com-01-2011
DECEMBER 2011
Centre du Parc, Rue Marconi 19, P.O. Box 592, CH - 1920 Martigny
T +41 27 721 77 11  F +41 27 721 77 12   www.idiap.ch"
9fbe2611b1e2a49199fdee96c2083da625ba57df,Leveraging Multi-Modal Sensing for Mobile Health: A Case Review in Chronic Pain,"J-STSP-PCSPHT-00370-2015.R1
Leveraging Multi-Modal Sensing for Mobile
Health: a Case Review in Chronic Pain
Min S. H. Aung, Faisal Alquaddoomi, Andy Hsieh, Mashfiqui Rabbi, Longqi Yang, J.P. Pollak,
Tanzeem Choudhury, and Deborah Estrin
(cid:3)"
9f2328931be1cf81a9ea14b35f3373b6122f0714,Motion-Based Object Segmentation Based on Dense RGB-D Scene Flow,"Motion-based Object Segmentation based on Dense RGB-D Scene Flow
Lin Shao, Parth Shah∗, Vikranth Dwaracherla∗, and Jeannette Bohg"
9ff45d69168eedc6538dc684d94460ab4522480b,Efficiently comparing face images using a modified Hausdorff distance,"Efficiently comparing face images using a modified
Hausdorff distance
Y. Gao"
9fb3f13fa1085dbf0c89158640979dad70047121,Comparison between Speech Parameters for Forensic Voice Comparison Using Mobile Phone Speech,"Comparison between Speech Parameters for Forensic Voice Comparison Using
Esam A. S. Alzqhoul 1, 2, Balamurali B. T. Nair 1, 2, Bernard J. Guillemin 1, 2
Mobile Phone Speech
Forensic and Biometrics Research Group (FaB), The University of Auckland, New Zealand
Department of Electrical and Computer Engineering, The University of Auckland, New Zealand"
4f051022de100241e5a4ba8a7514db9167eabf6e,Face Parsing via a Fully-Convolutional Continuous CRF Neural Network,"Face Parsing via a Fully-Convolutional Continuous
CRF Neural Network
Lei Zhou, Zhi Liu, Senior Member, IEEE, Xiangjian He, Senior Member, IEEE"
4fec8a97d6d87713c5c00f369fc1373fba4377e3,Training Sources 3 D Normalized Pose Space 2 D Normalized Pose Space KD-Tree Input Image 2 D Pose Estimation 3 D Pose Reconstruction Retrieved 3 D Nearest Neighbours Motion Capture Dataset Annotated 2,"SUBMITTED TO COMPUTER VISION AND IMAGE UNDERSTANDING.
A Dual-Source Approach for 3D Human Pose
Estimation from a Single Image
Umar Iqbal*, Andreas Doering*, Hashim Yasin, Björn Krüger, Andreas Weber, and Juergen Gall"
4fcc8e5d78c166b40e5b6ee439edc9092811c159,Recent advances in biometric person authentication,"RECENT ADVANCES IN BIOMETRIC PERSON AUTHENTICATION
J.-L. Dugelay1   J.-C. Junqua2    C. Kotropoulos3(cid:39)     R. Kuhn2     F.  Perronnin1     I. Pitas3
Institut EURECOM, Multimedia Communications Dept., 2229, Route des Cretes-B.P. 193, F-06904 Sophia Antipolis Cedex, France
Panasonic Speech Technology Lab, Suite #202, 3888 State Street, Santa Barbara, CA 93105, U.S.A.
{dugelay,
Dept. of Informatics, Aristotle Univ. of Thessaloniki, Box 451, GR-540 06 Thessaloniki, Greece
{jcj,
{costas,"
4f591e243a8f38ee3152300bbf42899ac5aae0a5,Understanding Higher-Order Shape via 3D Shape Attributes,"SUBMITTED TO TPAMI
Understanding Higher-Order Shape
via 3D Shape Attributes
David F. Fouhey, Abhinav Gupta, Andrew Zisserman"
4fc609df4e17b5854e3b7f4371e5f4192608eda5,3D Face Recognition Benchmarks on the Bosphorus Database with Focus on Facial Expressions,"D Face Recognition Benchmarks on the
Bosphorus Database with Focus on Facial
Expressions
Nes¸e Aly¨uz1, Berk G¨okberk2, Hamdi Dibeklio˘glu1, Arman Savran3, Albert Ali
Salah4, Lale Akarun1, B¨ulent Sankur3"
4f77c682f133d5010762556ebf512533524da071,Deep Learning of Appearance Models for Online Object Tracking,"Deep Learning of Appearance Models for Online
Object Tracking
Mengyao Zhai, Mehrsan Javan Roshtkhari, Greg Mori"
4fc044f55a3875108dc592198d8999ea51f08d77,Cloudlets: at the leading edge of mobile-cloud convergence,"014 6th  International  Conference  on
Mobile Computing, Applications
nd Services (MobiCASE)
Cloudlets:
t the Leading Edge of  Mobile-Cloud
Convergence
(Invited
Paper)
Mahadev Satyanarayanant
, Zhuo Chent, Kiryong Hat, Wenlu Hut, Wolfgang Richtert, Padmanabhan Pillai+
tCamegie Mellon University
nd +Intel Labs"
4f46dba09e075b2e7dfae1ba2a71e8e21b46e88d,Genetic CNN,"Genetic CNN
Center for Imaging Science, The Johns Hopkins University, Baltimore, MD, USA
Lingxi Xie, Alan Yuille"
4ff1ac4e66bc545275c83bc2ef1f3fb8bc207aec,An MPCA / LDA Based Dimensionality Reduction Algorithm for Face Recognition,"Charles Darwin University
An MPCA/LDA Based Dimensionality Reduction Algorithm for Face Recognition
Huang, Jun; Su, Kehua; El-Den, Jamal; Hu, Tao; Li, Junlong
Published in:
Mathematical Problems in Engineering
0.1155/2014/393265
Published: 01/01/2014
Document Version
Publisher's PDF, also known as Version of record
Link to publication
Citation for published version (APA):
Huang, J., Su, K., El-Den, J., Hu, T., & Li, J. (2014). An MPCA/LDA Based Dimensionality Reduction Algorithm
for Face Recognition. Mathematical Problems in Engineering, 2014, 1-12. DOI: 10.1155/2014/393265
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Take down policy"
4f7e4b1b74955b54c434bdf76c47fb1e96db74e0,Naive Bayes Image Classification: Beyond Nearest Neighbors,"Naive Bayes Image Classification:
Beyond Nearest Neighbors
Radu Timofte1, Tinne Tuytelaars1, and Luc Van Gool1,2
ESAT-VISICS /IBBT, Catholic University of Leuven, Belgium
D-ITET, ETH Zurich, Switzerland"
4fb11a58d5a3ffc0bb6d4ade334a366b4a431b02,The Role of Minimal Complexity Functions in Unsupervised Learning of Semantic Mappings,
4f4f646b65921850b7812a376fc2ac5ff806b1c8,Joint Stem Detection and Crop-Weed Classification for Plant-Specific Treatment in Precision Farming,"Joint Stem Detection and Crop-Weed Classification
for Plant-specific Treatment in Precision Farming
Philipp Lottes
Jens Behley
Nived Chebrolu
Andres Milioto
Cyrill Stachniss"
4f41f7a2f1f5eb5f26d47aeb168dbeb0f9ed453f,A Graph Transduction Game for Multi-target Tracking,"A Graph Transduction Game for Multi-target
Tracking
Tewodros Mulugeta Dagnew∗, Dalia Coppi†, Marcello Pelillo∗, Rita Cucchiara†
DAIS - Ca´ Foscari University
Venezia, Italy
Email:
DIEF - University of Modena and Reggio Emilia
Email:
Modena, Italy"
4fc936102e2b5247473ea2dd94c514e320375abb,Guess Where? Actor-Supervision for Spatiotemporal Action Localization,"Guess Where? Actor-Supervision for Spatiotemporal Action Localization
Victor Escorcia1∗
Cuong D. Dao1
Mihir Jain3
KAUST1, University of Amsterdam2, Qualcomm Technologies, Inc.3
Bernard Ghanem1
Cees Snoek2∗"
4fdeb5d59b218ecba0f72dc3c42f38a086417c0f,InformatIon theoretIc combInatIon of classIfIers wIth applIcatIon to face DetectIon,"InformatIon theoretIc combInatIon
of classIfIers wIth applIcatIon to face DetectIon
THÈSE NO 3951 (2007)
PRÉSENTÉE LE 23 NOvEMBRE 2007
À LA FACULTÉ DES SCIENCES ET TECHNIQUES DE L'INGÉNIEUR
LABORATOIRE DE TRAITEMENT DES SIGNAUX 5
PROGRAMME DOCTORAL EN INFORMATIQUE, COMMUNICATIONS ET INFORMATION
ÉCOLE POLyTECHNIQUE FÉDÉRALE DE LAUSANNE
POUR L'OBTENTION DU GRADE DE DOCTEUR ÈS SCIENCES
Julien MEyNET
DEA signal, image, parole, télécoms, Institut national polytechnique de Grenoble, France
et de nationalité française
cceptée sur proposition du jury:
Prof. H. Bourlard, président du jury
Prof. J.-Ph. Thiran, directeur de thèse
Prof. A. Billard, rapporteur
Prof. H. Bunke, rapporteur
Prof. J. Kittler, rapporteur
Suisse"
4fa6a688f350831503d158f8f618c58d1e06bc5d,A Semi-supervised Framework for Image Captioning,"Bootstrap, Review, Decode: Using Out-of-Domain Textual Data
to Improve Image Captioning
Wenhu Chen
RWTH Aachen
Aurelien Lucchi
ETH Zurich
Thomas Hofmann
ETH Zurich"
4f5e5fea12c44a5be7107748320e6d66192b7acb,Automatic approach-avoidance tendencies as a candidate intermediate phenotype for depression: Associations with childhood trauma and the 5-HTTLPR transporter polymorphism,"RESEARCH ARTICLE
Automatic approach-avoidance tendencies as
candidate intermediate phenotype for
depression: Associations with childhood
trauma and the 5-HTTLPR transporter
polymorphism
Pascal Fleurkens1*, Agnes van Minnen1,2, Eni S. Becker1, Iris van Oostrom3,
Anne Speckens3, Mike Rinck1, Janna N. Vrijsen3,4
Behavioural Science Institute, Radboud University Nijmegen, Nijmegen, The Netherlands,
Psychotrauma Expertise Centrum (PSYTREC), Bilthoven, The Netherlands, 3 Department of Psychiatry,
Radboud University Medical Centre, Nijmegen, The Netherlands, 4 Pro Persona: Institution for Integrated
Mental Health Care, Nijmegen, The Netherlands"
4f77a37753c03886ca9c9349723ec3bbfe4ee967,"Localizing Facial Keypoints with Global Descriptor Search, Neighbour Alignment and Locally Linear Models","Localizing Facial Keypoints with Global Descriptor Search,
Neighbour Alignment and Locally Linear Models
Md. Kamrul Hasan1, Christopher Pal1 and Sharon Moalem2
´Ecole Polytechnique de Montr´eal, Universit´e de Montr´eal
University of Toronto and Recognyz Systems Technologies
lso focused on emotion recognition in the wild [9]."
4f4f920eb43399d8d05b42808e45b56bdd36a929,A Novel Method for 3 D Image Segmentation with Fusion of Two Images using Color K-means Algorithm,"International Journal of Computer Applications (0975 – 8887)
Volume 123 – No.4, August 2015
A Novel Method for 3D Image Segmentation with Fusion
of Two Images using Color K-means Algorithm
Neelam Kushwah
Dept. of CSE
ITM Universe
Gwalior
Priusha Narwariya
Dept. of CSE
ITM Universe
Gwalior"
4f0aedbd0b5cb5939449da41579c93b98048fcdc,Robust classification using structured sparse representation,"Robust Classification using Structured Sparse Representation
Center for Imaging Science, Johns Hopkins University, Baltimore MD 21218, USA
Ehsan Elhamifar Ren´e Vidal"
4f10b81f822091ce2142e33f0578940da1e25ad3,"Indoor Mobile Robotics at Grima, PUC","Noname manuscript No.
(will be inserted by the editor)
Indoor Mobile Robotics at Grima, PUC
L. Caro · J. Correa · P. Espinace · D.
Maturana · R. Mitnik · S. Montabone · S.
Pszcz´o(cid:32)lkowski · D. Langdon · A. Araneda ·
D. Mery · M. Torres · A. Soto
Received: date / Accepted: date"
4f1765b1c352093718ccc59f420ead44a4bb267b,Reduced memory region based deep Convolutional Neural Network detection,"Reduced Memory Region Based Deep
Convolutional Neural Network Detection
Denis Tom´e, Luca Bondi, Luca Baroffio, Stefano Tubaro
Dipartimento di Elettronica, Informazione e Bioingegneria
Politecnico di Milano
Milano, Italy
Emanuele Plebani, Danilo Pau
Advanced System Technology
STMicroelectronics
Agrate Brianza, Italy"
4f892475be26333ddf1b72c21f0c9c4ca129bd80,Mobile Cloud Computing for Biometric Applications,"Singidunum University
Belgrade, Serbia
Mobile Cloud Computing for Biometric Applications
Milos Stojmenovic
Department of Informatics and Computation"
4f4c067e684252cf5549f60036829a89b2f35fc8,Sentic Avatar: Multimodal Affective Conversational Agent with Common Sense,"Sentic Avatar: Multimodal Affective
Conversational Agent with Common Sense
Erik Cambria1, Isabelle Hupont2,
Amir Hussain1, Eva Cerezo3, and Sandra Baldassarri3
University of Stirling, Stirling, UK
Aragon Institute of Technology, Zaragoza, Spain
University of Zaragoza, Zaragoza, Spain
http://cs.stir.ac.uk/~eca/sentics"
4f606761ce65399ef4ff24cd503ec09cf53562e9,"A System View of the Recognition and Interpretation of Observed Human Shape, Pose and Action","Copyright © 2015 David W. Arathorn
A System View of the Recognition and Interpretation of
Observed Human Shape, Pose and Action
David W. Arathorn
Dept of Electrical and Computer Engineering
(formerly of Center for Computational Biology)
Montana State University-Bozeman
General Intelligence Corporation
Bozeman, MT"
4f5708dfbdf03b370fe6be9a6e260d6e124ca09a,Face Recognition using Symbolic KDA in the framework of Symbolic Data Analysis,"Face Recognition using Symbolic KDA in the framework of Symbolic
Data Analysis"
4f863543407143a62e1bb053d435a947886ba619,Distributed deep learning on edge-devices: Feasibility via adaptive compression,"Distributed deep learning on edge-devices:
feasibility via adaptive compression
Corentin Hardy
Technicolor, Inria
Rennes, France
Erwan Le Merrer
Technicolor
Rennes, France
Bruno Sericola
Inria
Rennes, France"
4fe0c6c83d998a0660bc5280c8ab6e61df9df887,FACE IMAGE NORMALIZATION AND EXPRESSION / POSE VALIDATION FOR THE ANALYSIS OF MACHINE READABLE TRAVEL DOCUMENTS,"FACE IMAGE NORMALIZATION AND
EXPRESSION/POSE VALIDATION FOR THE
ANALYSIS OF MACHINE READABLE TRAVEL
DOCUMENTS
Markus Storer1, Martin Urschler1, Horst Bischof1,
Josef A. Birchbauer2"
4ff7f5928f96ddc877b4b8675cc41cc08f4bd561,Recent Advance in Content-based Image Retrieval: A Literature Survey,"Recent Advance in Content-based Image
Retrieval: A Literature Survey
Wengang Zhou, Houqiang Li, and Qi Tian Fellow, IEEE"
4fec382efed4e08a36fafa3710b97f0b20de1ebe,BINARIZED REPRESENTATION ENTROPY (BRE) REGULARIZATION,"Published as a conference paper at ICLR 2018
IMPROVING GAN TRAINING VIA
BINARIZED REPRESENTATION ENTROPY (BRE)
REGULARIZATION
Yanshuai Cao, Gavin Weiguang Ding, Kry Yik-Chau Lui, Ruitong Huang
Borealis AI
Canada"
4f00f5fe9d762009f524fb97555088769b96328c,Eye Gaze Tracking System Using . Net,"IJSART - Volume 3 Issue 5 –MAY 2017                                                                                                ISSN [ONLINE]: 2395-1052
Eye Gaze Tracking System Using .Net
Madhu M Nayak1, Usha Rani J2, Anandhi G3
Department of CSE
, 2, 3Assistant Professor,GSSIETW, Mysuru"
4fb569af589d89f11d84d4b828459231345cc301,Exploring Linear Relationship in Feature Map Subspace for ConvNets Compression,"Exploring Linear Relationship in Feature Map
Subspace for ConvNets Compression
Dong Wang1, Lei Zhou1, Xueni Zhang1, Xiao Bai1, and Jun Zhou2
Beihang University 2Grif‌f‌ith University"
4f48c8653cd38cd18f08924c9304bc02ed7ea492,Learning from Noisy Labels with Distillation,
4f8bd3519a6e8a05db9e35b027c0c65c91d2ff62,Brain Oxytocin is a Main Regulator of Prosocial Behaviour - Link to Psychopathology,"We are IntechOpen,
the world’s leading publisher of
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4f618cbf19917ce5b8703adbc14e15b0bf0d35cc,Multi-View Dynamic Facial Action Unit Detection,"Multi-View Dynamic Facial Action Unit Detection
Andr´es Romero
Juan Le´on
Pablo Arbel´aez
Universidad de los Andes"
4f15b1e750007465024181dd002dfc6d1baa48c9,Face Recognition and Computer Graphics for Modelling Expressive Faces in 3d Face Recognition and Computer Graphics for Modeling Expressive Faces in 3d,"Face  Recognition  and Computer Graphics for
Modelling  Expressive  Faces in 3D
Tufool  Al-Nuaimi
Submitted to  the Department  of Electrical  Engineering  and Computer Science
in Partial Fulfillment of the Requirements  for the Degree  of
Master of Engineering  in Electrical  Engineering  and Computer Science
t the Massachusetts  Institute  of Technology
May  26,  2006
Copyright 2006  Tufool  AI-Nuaimi.  All  rights reserved.
The author  hereby grants to  M.I.T. permission to reproduce  and
distribute  publicly paper  and electronic  copies  of this thesis
nd to grant  others the right to  do so.
Author
Certified by_
Accepted by_  _
Tufool  Al-Nuaimi
Department  of  Electrical  Engineering and Computer Science
-Ma  26,  2006
Judith Barry
Supervisor"
9e2120e48d497b373c53563275c3786c11749883,Topological and metric robot localization through computer vision techniques,"Topological and metric robot localization through computer vision
techniques
A. C. Murillo, J. J. Guerrero and C. Sag¨u´es
DIIS - I3A, University of Zaragoza, Spain"
9ea37d031a8f112292c0d0f8d731b837462714e9,Face Recognition: From Traditional to Deep Learning Methods,"Face Recognition: From Traditional to Deep
Learning Methods
Daniel S´aez Trigueros, Li Meng
School of Engineering and Technology
University of Hertfordshire
Hatfield AL10 9AB, UK
Margaret Hartnett
GBG plc
London E14 9QD, UK"
9ef2b2db11ed117521424c275c3ce1b5c696b9b3,Robust Face Alignment Using a Mixture of Invariant Experts,"Robust Face Alignment Using a Mixture of Invariant Experts
Oncel Tuzel†
Salil Tambe‡∗
Tim K. Marks†
Intel Corporation
Mitsubishi Electric Research Labs (MERL)
{oncel,"
9ee4d3c173c41ffb6f5aa3c40951aefe3da11d5b,Forming A Random Field via Stochastic Cliques: From Random Graphs to Fully Connected Random Fields,"Forming A Random Field via Stochastic
Cliques: From Random Graphs to Fully
Connected Random Fields
M. J. Shafiee, A. Wong and P. Fieguth"
9ebe5d78163a91239f10c453d76082dfa329851d,Teacher's Perception in the Classroom,"Teachers’ Perception in the Classroom
¨Omer S¨umer1
Patricia Goldberg1
Kathleen St¨urmer1
Tina Seidel3
Peter Gerjets2 Ulrich Trautwein1
Enkelejda Kasneci1
University of T¨ubingen, Germany
Leibniz-Institut f¨ur Wissensmedien, Germany
Technical University of Munich, Germany"
9eeada49fc2cba846b4dad1012ba8a7ee78a8bb7,A New Facial Expression Recognition Method Based on Local Gabor Filter Bank and PCA plus LDA,"Hong-Bo Deng, Lian-Wen Jin, Li-Xin Zhen, Jian-Cheng Huang
A New Facial Expression Recognition Method Based on Local Gabor Filter Bank and PCA plus LDA
A New Facial Expression Recognition Method Based on
Local Gabor Filter Bank and PCA plus LDA
Hong-Bo Deng1, Lian-Wen Jin1, Li-Xin Zhen2, Jian-Cheng Huang2
School of Electronic and Information Engineering, South China
University of Technology, Guangzhou, 510640, P.R.China
Motorola China Research Center, Shanghai, 210000, P.R.China
{hbdeng,
{Li-Xin.Zhen,"
9e92d847ba169146ceb8b3b7cfdc47925e5a53e9,The LIUM-AVS database : a corpus to test lip segmentation and speechreading systems in natural conditions,"The LIUM-AVS database : a corpus to test lip segmentation
nd speechreading systems in natural conditions
Philippe Daubias, Paul Del´eglise
Laboratoire d’Informatique de l’Universit´e du Maine
Le Mans, France"
9ed3e04586f311b1e2b5ded9c9c4bfeeecf27f0c,Understanding rapid category detection via multiply degraded images.,"http://journalofvision.org/9/6/19/
Understanding rapid category detection via multiply
degraded images
Chetan Nandakumar
Vision Science Graduate Program,
University of California, Berkeley, Berkeley, CA, USA
Jitendra Malik
Department of Electrical Engineering and
Computer Science, University of California,
Berkeley, Berkeley, CA, USA
Rapid category detection, as discovered by S. Thorpe, D. Fize, and C. Marlot (1996), demonstrated that the human visual
system can detect object categories in natural images in as little as 150 ms. To gain insight into this phenomenon and to
determine its relevance to naturally occurring conditions, we degrade the stimulus set along various image dimensions and
investigate the effects on perception. To investigate how well modern-day computer vision algorithms cope with
degradations, we conduct an analog of this same experiment with state-of-the-art object recognition algorithms. We
discover that rapid category detection in humans is quite robust to naturally occurring degradations and is mediated by a
non-linear interaction of visual features. In contrast, modern-day object recognition algorithms are not as robust.
Keywords: rapid category detection, degraded images, object recognition, eye tracking
Citation: Nandakumar, C., & Malik, J. (2009). Understanding rapid category detection via multiply degraded images. Journal
of Vision, 9(6):19, 1–8, http://journalofvision.org/9/6/19/, doi:10.1167/9.6.19."
9eb111f6990d1494a3904f22be9836c202efd7d1,Exploiting workload similarities for efficient scheduling in diverse asymmetric chip multiprocessing Research,Exploiting workload similarities for efficient scheduling in diverse asymmetric chip multiprocessing                Dani Shaket
9e194413f10ea3385f063de87e15287072ac357b,An a-contrario Approach for Face Matching,"An a-contrario Approach for Face Matching
Luis D. Di Martino, Javier Preciozzi, Federico Lecumberry and Alicia Fern´andez
Instituto de Ingener´ıa El´ectrica, Universidad de la Rep´ublica, Montevideo, Uruguay
Keywords:
Face Recognition, Face Matching, a-contrario, STASM, LBP, Extended LBP, Chi-Square."
9e5acdda54481104aaf19974dca6382ed5ff21ed,Automatic localization of facial landmarks from expressive images of high complexity,"Yulia Gizatdinova and Veikko Surakka
Automatic localization of facial
landmarks from expressive images
of high complexity
DEPARTMENT OF COMPUTER SCIENCES
UNIVERSITY OF TAMPERE
D‐2008‐9
TAMPERE 2008"
9e759860762d40505f25d6fc5c4f4c1f6500d68b,Elastic Net Hypergraph Learning for Image Clustering and Semi-Supervised Classification,"Elastic Net Hypergraph Learning for Image
Clustering and Semi-supervised Classification
Qingshan Liu, Seninor Member, IEEE, Yubao Sun, Cantian Wang, Tongliang Liu and Dacheng Tao, Fellow, IEEE"
9e8637a5419fec97f162153569ec4fc53579c21e,Segmentation and Normalization of Human Ears Using Cascaded Pose Regression,"Segmentation and Normalization of Human Ears
using Cascaded Pose Regression
Anika Pflug and Christoph Busch
University of Applied Sciences Darmstadt - CASED,
Haardtring 100,
64295 Darmstadt, Germany
http://www.h-da.de"
9e1712ac91c7a882070a8e2740ed476d59d6d5d4,Expressive image manipulations for a variety of visual representations. (Manipulations d'image expressives pour une variété de représentations visuelles),"Expressive image manipulations for a variety of visual
representations
Adrien Bousseau
To cite this version:
Adrien Bousseau. Expressive image manipulations for a variety of visual representations. Human-
Computer Interaction [cs.HC]. Université Joseph-Fourier - Grenoble I, 2009. English. <tel-00429151>
HAL Id: tel-00429151
https://tel.archives-ouvertes.fr/tel-00429151
Submitted on 31 Oct 2009
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de
recherche français ou étrangers, des laboratoires"
9e071a5c3b8d85627c61bbbd303f919afc64ac3b,Evaluation of LBP and HOG Descriptors for Clothing Attribute Description,"Evaluation of LBP and HOG Descriptors for
Clothing Attribute Description
J. Lorenzo(cid:63), M. Castrill´on, E. Ram´on, and D. Freire
Instituto Universitario SIANI
Campus Universitario de Tafira
5017 Las Palmas - SPAIN
Universidad de Las Palmas de Gran Canaria"
9e8dd40aea9204ad670b312a46ba807bfc0c61ce,Distribution-sensitive learning for imbalanced datasets Citation,"Distribution-sensitive learning for imbalanced datasets
The MIT Faculty has made this article openly available. Please share
how this access benefits you.  Your story matters.
Citation
As Published
Publisher
Version
Accessed
Citable Link
Terms of Use
Detailed Terms
Song, Yale, Louis-Philippe Morency, and Randall Davis.
“Distribution-Sensitive Learning for Imbalanced Datasets.” 2013
0th IEEE International Conference and Workshops on
Automatic Face and Gesture Recognition (FG) (n.d.).
http://dx.doi.org/10.1109/FG.2013.6553715
Institute of Electrical and Electronics Engineers (IEEE)
Author's final manuscript
Fri Jan 08 19:33:51 EST 2016
http://hdl.handle.net/1721.1/86107"
9edd7c738171b0f36b65ae771711c38ed1dc38ad,Long-Term Multi-Cue Tracking of Hands in Vehicles,"Long-Term Multi-Cue Tracking of Hands in Vehicles
Akshay Rangesh, Eshed Ohn-Bar, and Mohan Manubhai Trivedi, Fellow, IEEE"
9e9052256442f4e254663ea55c87303c85310df9,On Attribute-assisted Reranking for Image Search,"International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 4 Issue 10, October 2015
Review On Attribute-assisted Reranking for
Image Search
Waghmare Supriya, Wavhal Archana, Patil Nital, Tapkir Yogita, Prof. Yogesh Thorat"
9e0285debd4b0ba7769b389181bd3e0fd7a02af6,From Face Images and Attributes to Attributes,"From face images and attributes to attributes
Robert Torfason, Eirikur Agustsson, Rasmus Rothe, Radu Timofte
Computer Vision Laboratory, ETH Zurich, Switzerland"
9e4b052844d154c3431120ec27e78813b637b4fc,Local gradient pattern-A novel feature representation for facial expression recognition,"Journal of AI and Data Mining
Vol. 2, No .1, 2014, 33-38.
Local gradient pattern - A novel feature representation for facial
expression recognition
M. Shahidul Islam
Department of Computer Science, School of Applied Statistics, National Institute of Development Administration, Bangkok, Thailand.
Received 23 April 2013; accepted 16 June 2013
*Corresponding author: (M.Shahidul Islam)"
9e6c15150179ce848402e89bd245831d9935f4f9,Bi-modal Face Recognition - How combining 2D and 3D Clues Can Increase the Precision,"Bi-modal face recognition
How combining 2D and 3D clues can increase the precision
Amel Aissaoui1, Jean Martinet2
USTHB, Algeria
Lille 1 University, France
issaoui
Keywords:
Face recognition, multimodal, 2D, 3D, LBP, RGB-depth."
9e263d429c3b87aae2653b6fb925b32b63c172cd,Enhanced image and video representation for visual recognition,"Enhanced image and video representation for visual
recognition
Mihir Jain
To cite this version:
Mihir Jain. Enhanced image and video representation for visual recognition. Computer Vision
nd Pattern Recognition [cs.CV]. Universit´e Rennes 1, 2014. English. <tel-00996793>
HAL Id: tel-00996793
https://tel.archives-ouvertes.fr/tel-00996793
Submitted on 27 May 2014
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,
´emanant des ´etablissements d’enseignement et de
recherche fran¸cais ou ´etrangers, des laboratoires"
9ee5218a2a74fafbc4227f6c7c587b72e141bd33,Iris Compression and Recognition using Spherical Geometry Image,"(IJARAI) International Journal of Advanced Research in Artificial Intelligence,
Vol. 4, No.6, 2015
Iris Compression and Recognition using Spherical
Geometry Image
College of Computers and Information Technology  University of Tabuk  Tabuk, KSA
Rabab M. Ramadan
in  3D  domain  to  test"
9ea7205ef136f207123cd6b54e15075835ae0049,Self-supervised language grounding by active sensing combined with Internet acquired images and text,"http://www.diva-portal.org
Postprint
This is the accepted version of a paper presented at Fourth International Workshop on Recognition and
Action for Scene Understanding (REACTS2017), August 25, 2017, Ystad, Sweden.
Citation for the original published paper:
Abedin, M R., Bensch, S., Hellström, T. (2017)
Self-supervised language grounding by active sensing combined with Internet acquired images
nd text.
In: Jorge Dias George Azzopardi, Rebeca Marf (ed.), Proceedings of the Fourth International
Workshop on Recognition and Action for Scene Understanding (REACTS2017) (pp. 71-83).
Málaga: REACTS
N.B. When citing this work, cite the original published paper.
Permanent link to this version:
http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-138290"
9e9c600919332dcabbd32bbe81a00d1e47449193,Automatic 3D face verification from range data,"- 1330-7803-7663-3/03/$17.00 ©2003 IEEEThis paper was originally published in the Proceedings of the 2003 IEEEInternational Conference on Acoustics, Speech, & Signal Processing,April 6-10, 2003, Hong Kong (cancelled). Reprinted with permission.(cid:224)"
9e594ae4f549e0d838f497de31a5b597a6826d55,Recognition of Emotion from Facial Expressions with Direct or Averted Eye Gaze and Varying Expression Intensities in Children with Autism Disorder and Typically Developing Children,"Hindawi Publishing Corporation
Autism Research and Treatment
Volume 2014, Article ID 816137, 11 pages
http://dx.doi.org/10.1155/2014/816137
Research Article
Recognition of Emotion from Facial Expressions with Direct or
Averted Eye Gaze and Varying Expression Intensities in Children
with Autism Disorder and Typically Developing Children
Dina Tell,1 Denise Davidson,2 and Linda A. Camras3
Department of Health Promotion, Loyola University Chicago, Marcella Niehoff School of Nursing, 2160 S. First Avenue,
Maywood, IL 60153, USA
Department of Psychology, Loyola University Chicago, 1032 W. Sheridan Road, Chicago, IL 60660, USA
Department of Psychology, DePaul University, 2219 N. Kenmore Avenue, Chicago, IL 60614, USA
Correspondence should be addressed to Denise Davidson;
Received 8 November 2013; Revised 7 February 2014; Accepted 12 February 2014; Published 3 April 2014
Academic Editor: Geraldine Dawson
Copyright © 2014 Dina Tell et al. This is an open access article distributed under the Creative Commons Attribution License, which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Eye gaze direction and expression intensity effects on emotion recognition in children with autism disorder and typically developing
hildren were investigated. Children with autism disorder and typically developing children identified happy and angry expressions"
9ea73660fccc4da51c7bc6eb6eedabcce7b5cead,Talking head detection by likelihood-ratio test,"Talking Head Detection by Likelihood-Ratio Test†
Carl Quillen, Kara Greenfield, and William Campbell
MIT Lincoln Laboratory,
Lexington MA 02420, USA"
7697295ee6fc817296bed816ac5cae97644c2d5b,Detecting and Recognizing Human-Object Interactions,"Detecting and Recognizing Human-Object Interactions
Georgia Gkioxari Ross Girshick
Piotr Doll´ar Kaiming He
Facebook AI Research (FAIR)"
76bcd8cce40892aee1bc46de17a3b803373172d9,A 58 . 6 mW 30 fps Real-Time Programmable Multi-Object Detection Accelerator with Deformable Parts Models on Full HD 1920,"A 58.6mW 30fps Real-Time Programmable Multi-Object
Detection Accelerator with Deformable Parts Models on
Full HD 1920×1080 Videos
The MIT Faculty has made this article openly available. Please share
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Suleiman, Amr, Zhang, Zhengdong, and Sze, Vivienne. ""A 58.6
mW 30 Frames/s Real-Time Programmable Multiobject
Detection Accelerator With Deformable Parts Models on Full HD
920×1080 Videos."" IEEE Journal of Solid State Circuits, 52
(March 2017): 844-855.© 2017 Institute of Electrical and
Electronics Engineers (IEEE)
https://doi.org/10.1109/JSSC.2017.2648820"
7608953ef5c7a882bd2e7e7053a600e543748233,Robust 3D Face Recognition by Local Shape Difference Boosting,"Robust 3D Face Recognition
y Local Shape Difference Boosting
Yueming Wang, Jianzhuang Liu, Senior Member, IEEE, and Xiaoou Tang, Fellow, IEEE"
76bfa74a6311db5d84bad2a7a941f30dd750d01c,Evidence That Emotion Mediates Social Attention in Rhesus Macaques,"Evidence That Emotion Mediates Social Attention in
Rhesus Macaques
Emily J. Bethell1*, Amanda Holmes2, Ann MacLarnon1, Stuart Semple1
Centre for Research in Evolutionary and Environmental Anthropology, University of Roehampton, London, United Kingdom, 2 Department of Psychology, University of
Roehampton, London, United Kingdom"
76ec5c774bb3fd04f9e68864a411286536a544c5,Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models,"LATENT CONSTRAINTS:
LEARNING TO GENERATE CONDITIONALLY FROM
UNCONDITIONAL GENERATIVE MODELS
Jesse Engel
Google Brain
San Francisco, CA, USA
Matthew D. Hoffman
Google Inc.
San Francisco, CA, USA
Adam Roberts
Google Brain
San Francisco, CA, USA"
76ff6a68d7a8dcc12b6ba68e914294f6720a466d,The red one!: On learning to refer to things based on discriminative properties,"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pages 213–218,
Berlin, Germany, August 7-12, 2016. c(cid:13)2016 Association for Computational Linguistics"
76ebe6d24ee69e3f853740fb75085a2118d40d51,ILLUMINANCE FLOW ( met een samenvatting in het Nederlands ) PROEFSCHRIFT ter verkrijging van de graad van doctor,"ILLUMINANCE FLOW
(met een samenvatting in het Nederlands)
PROEFSCHRIFT
ter verkrijging van de graad van doctor aan de Universiteit Utrecht op
gezag van de rector magnificus, prof.dr. J.C. Stoof, ingevolge het besluit
van het college voor promoties
in het openbaar te verdedigen op vrijdag 15 januari 2010
des middags te 4.15 uur
(Dan) Stefan Mikael Karlsson
geboren op 3 september 1978 te Stafsinge, Zweden"
76cb2ecc96f02b1d8a7a0d1681fbb55367a4b765,Learning Object States from Videos,"Learning Object States from Videos
Liang-Kang Huang
Katerina Fragkiadaki"
768cb0e32de3f1b5aebe04448aaec4c25586680c,Boosting Image Captioning with Attributes,"Under review as a conference paper at ICLR 2017
BOOSTING IMAGE CAPTIONING WITH ATTRIBUTES
Ting Yao, Yingwei Pan, Yehao Li, Zhaofan Qiu, Tao Mei
Microsoft Research Asia
{tiyao, v-yipan, v-yehl, v-zhqiu,"
766794580ddbb53f4e29f4912b8d373df7ba928b,Extracting Relevant Structures,"Chapter 5
Extracting relevant structures
A key problem in understanding auditory coding is to identify the acoustic features
that neurons at various levels of the system code. If we can map the relevant stimulus
features and trace how they change along the processing hierarchy, we can understand
the processing properties of the system.
A principled approach for extracting relevant features was proposed by Tishby
nd co-authors [Tishby et al., 1999], with the Information Bottleneck (IB) frame-
work. This powerful approach aims at identifying structures of a variable X that
have functional importance, by compressing X in a way that preserves information
bout another variable Y . In the context of the current problem it can be used to com-
press acoustic stimuli while preserving information about the distribution of neural
responses, and use it to identify the stimulus aspects to which system is sensitive.
Unfortunately, the IB approach is insu(cid:14)cient for characterizing the processing
that takes place in a brain region like the cortex. To understand the reason why,
onsider for example a case where one measures cortical activity in response to acous-
tic stimuli, and maps the acoustic features to which the cortical neurons respond.
Such an analysis does not characterize cortical processing but rather the processing
performed by the whole chain of processing stations that ends in the cortex. In fact,
many of the features that are revealed this way do not re(cid:13)ect cortical processing, but"
761de31c575bf30162b6e0d92a1800eb406e96b5,A Flexible Convolutional Solver with Application to Photorealistic Style Transfer,"A Flexible Convolutional Solver with Application to
Photorealistic Style Transfer
Gilles Puy and Patrick P´erez
(1) Artistic style
(2) Photorealistic style
(a) Input image
(b) Artistic transfer
(c) Photorealistic transfer
Figure 1. Given an input image (a), the proposed convolutional neural network yields fast artistic stylisation
results, e.g., (b) for style (1), and can be modified at runtime – hence without retraining – to yield photorealistic
stylisation results, e.g., (c) for style (2)."
76c018c6dfc81f61c3912c5ed442d9a72f64e467,Graphical Processing Unit Assisted Image Processing for Accelerated Eye Tracking,"Graphical Processing Unit Assisted Image Processing for
Accelerated Eye Tracking
Dissertation submitted by
Jean-Pierre Louis du Plessis
Student Number: 2006033415
to the
Department of Computer Science and Informatics
Faculty of Natural and Agricultural Sciences
University of the Free State, South Africa
Submitted in fulfilment of the requirements of the degree
Magister Scientiae
February 2015
Study Leader: Prof P.J. Blignaut"
76f73c884e4437a22afcba60193bbd7f35e64aaf,Title of dissertation : RESOURCE ALLOCATION IN COMPUTER VISION,
7671234c3726fda01b2842f85327624f0dda8ead,The data deluge: Challenges and opportunities of unlimited data in statistical signal processing,"978-1-4244-2354-5/09/$25.00 ©2009 IEEE
ICASSP 2009"
7689d23a22682c92bdf9a1df975fa2cdd24f1b87,MMD with Kernel Learning In practice we use finite samples from distributions to estimate,"MMD GAN: Towards Deeper Understanding of Moment Matching
Network
Chun-Liang Li
Committee: Barnab´as P´oczos and Pradeep Ravikumar
Tuesday 28th November, 2017"
7636f94ddce79f3dea375c56fbdaaa0f4d9854aa,Appl . Math . Inf . Sci . 6 No . 2 S pp . 403 S-408 S ( 2012 ) Robust Facial Expression Recognition Using a Smartphone Working against Illumination Variation,"Appl. Math. Inf. Sci. 6 No. 2S pp. 403S-408S (2012)
An International Journal
© 2012 NSP
Applied Mathematics & Information Sciences
Robust Facial Expression Recognition Using
Smartphone Working against Illumination Variation
2012 NSP
Natural Sciences Publishing Cor.
Kyoung-Sic Cho1, In-Ho Choi1 and Yong-Guk Kim1
Department of Computer Engineering, Sejong University, 98 Kunja-Dong, Kwangjin-Gu, Seoul, Korea
Corresponding author: Email:
Received June 22, 2010; Revised March 21, 2011; Accepted 11 June 2011
Published online: 1 January 2012"
7606a74de57f67257c77a8bb0295ff4593566040,Content-based Image Retrieval Using Constrained Independent Component Analysis : Facial Image Retrieval Based on Compound Queries,"We are IntechOpen,
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76d8b370d0a8fc63ead6ba657dd438d7155d659f,Modular Sensor Fusion for Semantic Segmentation,"(cid:13)2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any
urrent or future media, including reprinting/republishing this material for advertising or promotional purposes, creating
new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in
other works.
Please cite this paper as:
title
uthor
ooktitle = ""2018 {IEEE/RSJ} International Conference on Intelligent Robots
= ""Modular Sensor Fusion for Semantic Segmentation"",
= ""Blum, Hermann and Gawel, Abel and Siegwart, Roland and Cadena, Cesar"",
nd Systems ({IROS})"",
= 2018;"
76b11c281ac47fe6d95e124673a408ee9eb568e3,Real-time Multi View Face Detection and Pose Estimation Aishwarya,"International Journal of Latest Engineering and Management Research (IJLEMR)
ISSN: 2455-4847
www.ijlemr.com || Volume 02 - Issue 03 || March 2017 || PP. 59-71
REAL-TIME MULTI VIEW FACE DETECTION AND POSE
ESTIMATION
AISHWARYA.S1 , RATHNAPRIYA.K1, SUKANYA SARGUNAR.V2
U. G STUDENTS, DEPT OF CSE, ALPHA COLLEGE OF ENGINEERING, CHENNAI,
ASST PROF.DEPARTMENT OF CSE, ALPHA COLLEGE OF ENGINEERING, CHENNAI"
76f3450e50c20fca00dd6319df38503c5d7ebad0,Sujet de la thèse : Alignement élastique d ’ images pour la reconnaissance d ’ objet — Non-rigid image alignment for object recognition,"THÈSEDEDOCTORATprésentéeparOLIVIERDUCHENNEpourobtenirlegradedeDOCTEURDEL’ÉCOLENORMALESUPÉRIEUREDomaine:MATHÉMATIQUESAPPLIQUÉESSujetdelathèse:Alignementélastiqued’imagespourlareconnaissanced’objet—Non-rigidimagealignmentforobjectrecognitionThèseprésentéeetsoutenueàl’ENSUlmle29Novembre2012devantlejurycomposéde:JeanPonceProfesseur,DirecteurduDI,ENSUlmDirecteurdethèsePedroFelzenszwalbProfesseur,BrownUniversityRapporteurMartialHebertProfesseur,CarnegieMellonUniversityRapporteurFrancisBachDirecteurderecherche,ENSUlmÉxaminateurJitendraMalikProfesseur,UniversityofBerkeleyÉxaminateurCordeliaSchmidProfesseur,INPGrenobleÉxaminateurAndrewZissermanProfesseur,UniversityofOxfordÉxaminateurThèsepréparéeauseindel’équipeWILLOWdudépartementd’informatiquedel’ÉcoleNormaleSupérieure,Ulm.(INRIA/ENS/CNRSUMR8548)."
7638cb16631fbcdf621aaf392fec5108e6fa9f47,On Nonrigid Shape Similarity and Correspondence,"Alon Shtern and Ron Kimmel
November 25, 2013
trinsically symmetric halves of a human face were found by mapping the shape (left) to itself.
Textures from two faces (middle) were transferred to each half (right)."
763158cef9d1e4041f24fce4cf9d6a3b7a7f08ff,Hierarchical Modeling and Applications to Recognition Tasks,"Hierarchical Modeling and
Applications to Recognition Tasks
Thesis submitted for the degree of
”Doctor of Philosophy”
Alon Zweig
Submitted to the Senate of the Hebrew University
August / 2013"
76a0016ce19363ef8f7ba5c3964c4a0c29b608ca,ModaNet: A Large-scale Street Fashion Dataset with Polygon Annotations,"ModaNet: A Large-scale Street Fashion Dataset with Polygon
Annotations
Shuai Zheng
eBay Inc.
San Jose, California
M. Hadi Kiapour
eBay Inc.
San Francisco, California
Fan Yang
eBay Inc.
San Jose, California
Robinson Piramuthu
eBay Inc.
San Francisco, California"
76d9f5623d3a478677d3f519c6e061813e58e833,Fast Algorithms for the Generalized Foley-Sammon Discriminant Analysis,"FAST ALGORITHMS FOR THE GENERALIZED FOLEY-SAMMON
DISCRIMINANT ANALYSIS
LEI-HONG ZHANG∗, LI-ZHI LIAO† , AND MICHAEL K. NG‡"
76dc11b2f141314343d1601635f721fdeef86fdb,Weighted Decoding ECOC for Facial Action Unit Classification,"Weighted Decoding ECOC for Facial
Action Unit Classification
Terry Windeatt"
761db68bf6031545cc865a813f398cb4ee8f61a1,Feature to Feature Matching for LBP Based Face Recognition,"Feature to Feature Matching for LBP Based
Face Recognition
Ladislav Lenc1,2 and Pavel Kr´al1,2
Dept. of Computer Science & Engineering
Faculty of Applied Sciences
University of West Bohemia
Plzeˇn, Czech Republic
NTIS - New Technologies for the Information Society
Faculty of Applied Sciences
University of West Bohemia
Plzeˇn, Czech Republic"
76e2d7621019bd45a5851740bd2742afdcf62837,Real-Time Detection and Measurement of Eye Features from Color Images,"Article
Real-Time Detection and Measurement of Eye
Features from Color Images
Diana Borza 1, Adrian Sergiu Darabant 2 and Radu Danescu 1,*
Computer Science Department, Technical University of Cluj Napoca, 28 Memorandumului Street,
Cluj Napoca 400114, Romania;
Computer Science Department, Babes Bolyai University, 58-60 Teodor Mihali, C333, Cluj Napoca 400591,
Romania;
* Correspondence: Tel.: +40-740-502-223
Academic Editors: Changzhi Li, Roberto Gómez-García and José-María Muñoz-Ferreras
Received: 28 April 2016; Accepted: 14 July 2016; Published: 16 July 2016"
76b2732a8684babdfd95c655b2e1a1b79c3aeb9b,Face detection from few training examples,"978-1-4244-1764-3/08/$25.00 ©2008 IEEE
ICIP 2008
Authorized licensed use limited to: UNSW Library. Downloaded on June 12, 2009 at 01:20 from IEEE Xplore.  Restrictions apply."
76cd5e43df44e389483f23cb578a9015d1483d70,Face Verification from Depth using Privileged Information,"BORGHI ET AL.: FACE VERIFICATION FROM DEPTH
Face Verification from Depth using
Privileged Information
Department of Engineering
""Enzo Ferrari""
University of Modena and Reggio
Emilia
Modena, Italy
Guido Borghi
Stefano Pini
Filippo Grazioli
Roberto Vezzani
Rita Cucchiara"
06d6015a19ef180cb11d9f32feb9e6adfa2799f5,Learning image representations equivariant to ego-motion ( Supplementary material ),"Learning image representations equivariant to ego-motion
(Supplementary material)
Dinesh Jayaraman
UT Austin
Kristen Grauman
UT Austin
the optimal regularizer weight (for DRLIM, TEMPORAL
nd EQUIV) was selected from a logarithmic grid (steps
of 100.5). For EQUIV+DRLIM, the DRLIM loss regularizer
weight fixed for DRLIM was retained, and only the EQUIV
loss weight was cross-validated. The contrastive loss mar-
gin parameter δ in Eq (6) in DRLIM, TEMPORAL and EQUIV
were set uniformly to 1.0. Since no other part of these ob-
jectives (including the softmax classification loss) depends
on the scale of features,1 different choices of margins δ
in these methods lead to objective functions with equiva-
lent optima - the features are only scaled by a factor. For
EQUIV+DRLIM, we set the DRLIM and EQUIV margins re-
spectively to 1.0 and 0.1 to reflect the fact that the equiv-
riance maps Mg of Eq (5) applied to the representation"
06680961e99aadb366968e5f515da58864ecd784,Trends Research Enabler for Design Specifications Deliverable Trends Meta-deliverable 1 -state of the Art Security Classification : Pu Leading Partner Seram Meta Deliverable 1 -state of the Art,"Trends Research ENabler for Design Specifications
FP6-IST-2005-27916
Deliverable
TRENDS META-DELIVERABLE 1 - STATE OF THE ART
Security Classification :   PU
Leading partner
SERAM
Issue Date
03/09/2007
Version
Authors
Approved by
Final draft
Aranzazu BERECIARTUA, Carole BOUCHARD, Marin FERECATU, Guillaume LOGEROT, Loïs RIGOUSTE, Carlotta
VITALE
Carole Bouchard
03/09/2007
META DELIVERABLE 1 - STATE OF THE ART
This document presents a State Of the Art related to
most popular products, tools and methods"
0678a8abea82793993cd89383319da75f6dc4be3,ProNet: Learning to Propose Object-Specific Boxes for Cascaded Neural Networks,"ProNet: Learning to Propose Object-specific Boxes
for Cascaded Neural Networks
Chen Sun1,2 Manohar Paluri2 Ronan Collobert2 Ram Nevatia1
Lubomir Bourdev3
UC Berkeley
USC
{chensun,
Facebook AI Research
{mano,"
06262d14323f9e499b7c6e2a3dec76ad9877ba04,Real-Time Pose Estimation Piggybacked on Object Detection,"Real-Time Pose Estimation Piggybacked on Object Detection
Roman Jur´anek, Adam Herout, Mark´eta Dubsk´a, Pavel Zemˇc´ık
Brno University of Technology
Brno, Czech Republic"
06178edf24183e6fb9e3e265ff4f290a4f0934d2,A Cautionary Tail,"A Cautionary Tail
A Framework and Case Study for Testing Predictive Model Validity
Peter C. Casey
(cid:140)e Lab DC
Government of the
District of Columbia
350 Pennsylvania Avenue NW
Washington, D.C. 20004
350 Pennsylvania Avenue NW
350 Pennsylvania Avenue NW
David Yokum
(cid:140)e Lab DC
Government of the
District of Columbia
Washington, D.C.
Kevin H. Wilson
(cid:140)e Lab DC
Government of the
District of Columbia
Washington, D.C. 20004"
06cfc431b70ec6a6783284953a668984600e77e2,A Framework for Human Pose Estimation in Videos,"A Framework for Human Pose Estimation in
Videos
Dong Zhang and Mubarak Shah"
066454772e51df9142e99bfed1d83eae1c755ed1,Multi-Target Tracking by Online Learning a CRF Model of Appearance and Motion Patterns,"Int J Comput Vis
DOI 10.1007/s11263-013-0666-4
Multi-Target Tracking by Online Learning a CRF Model
of Appearance and Motion Patterns
Bo Yang · Ramakant Nevatia
Received: 21 December 2012 / Accepted: 7 October 2013
© Springer Science+Business Media New York 2013"
0697bd81844d54064d992d3229162fe8afcd82cb,User-driven mobile robot storyboarding: Learning image interest and saliency from pairwise image comparisons,"User-driven mobile robot storyboarding: Learning image interest and
saliency from pairwise image comparisons
Michael Burke1"
069ebb57ccca31ab68983e07044e65ce1a04174f,4D facial expression recognition,"011 IEEE International Conference on Computer Vision Workshops
978-1-4673-0063-6/11/$26.00 c(cid:13)2011 IEEE"
06da0e4ae21835f0d33cfbf66c8b73b58625c57b,Facial Keypoints Detection,"Facial Keypoints Detection
Shenghao Shi"
06cb0939ed5fb2b3398d54a7fcdb865fe53f414a,Bag-of-Words Image Representation: Key Ideas and Further Insight,"Chapter 2
Bag-of-Words Image Representation:
Key Ideas and Further Insight
Marc T. Law, Nicolas Thome and Matthieu Cord"
06262d6beeccf2784e4e36a995d5ee2ff73c8d11,Recognize Actions by Disentangling Components of Dynamics,"Recognize Actions by Disentangling Components of Dynamics
Yue Zhao1, Yuanjun Xiong1,2, and Dahua Lin1
CUHK - SenseTime Joint Lab, The Chinese University of Hong Kong 2Amazon Rekognition"
0602e312a532f9af85cfd0190a2f69f07f44b4fe,The influence of self and similarity on immediate affective impressions of people Ruys,"UvA-DARE (Digital Academic Repository)
The influence of self and similarity on immediate affective impressions of people
Ruys, K.I.
Link to publication
Citation for published version (APA):
Ruys, K. I. (2004). The influence of self and similarity on immediate affective impressions of people
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060797f33c242b568189be251f9735afdc4c9f22,Robust Deep-Learning-Based Road-Prediction for Augmented Reality Navigation Systems at Night,"Robust Deep-Learning-Based Road-Prediction
for Augmented Reality Navigation Systems
Matthias Limmer1*, Julian Forster1*, Dennis Baudach1, Florian Schüle2,
Roland Schweiger1 and Hendrik P.A. Lensch3"
06774cc8b0ab364866beaf3efda1b2d012a7bcf9,MobileNetV2: Inverted Residuals and Linear Bottlenecks,"MobileNetV2: Inverted Residuals and Linear Bottlenecks
Mark Sandler Andrew Howard Menglong Zhu Andrey Zhmoginov Liang-Chieh Chen
{sandler, howarda, menglong, azhmogin,
Google Inc."
061acf48912fe058ba7084dbbdbddc407f01263b,Detection and Orientation Estimation for Cyclists by Max Pooled Features,"Detection and Orientation Estimation for Cyclists
y Max Pooled Features"
068a7c7849cb6480def2e124ac5a45564e094b2a,Multi-Scale Learning for Low-Resolution Person Re-Identification,"Multi-scale learning for low-resolution person re-identification
Li, X; Zheng, WS; Wang, X; Xiang, T; Gong, S
© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be
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06d89147794d0889b2e031b0c6811423806f5ea0,Input Image Initialization Estimated Gaze Iterative Model Fitting 3,"A 3D Morphable Eye Region Model
for Gaze Estimation
Anonymous ECCV submission
Paper ID 93"
069c9b3c7cf82310d3e06831208aea15f6fdfc32,Power management for mobile games on asymmetric multi-cores,"Power Management for Mobile Games
on Asymmetric Multi-Cores
Anuj Pathania, Santiago Pagani, Muhammad Shafique, J¨org Henkel
Chair for Embedded Systems (CES), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Corresponding Author:"
066000d44d6691d27202896691f08b27117918b9,Vision-Based Analysis of Small Groups in Pedestrian Crowds,"Vision-based Analysis of Small Groups in
Pedestrian Crowds
Weina Ge, Robert T. Collins, Senior Member, IEEE, and R. Barry Ruback
E-mail:"
069c40a8ca5305c9a0734c1f6134eb19a678f4ab,LabelMe: A Database and Web-Based Tool for Image Annotation,"Int J Comput Vis (2008) 77: 157–173
DOI 10.1007/s11263-007-0090-8
LabelMe: A Database and Web-Based Tool for Image Annotation
Bryan C. Russell · Antonio Torralba ·
Kevin P. Murphy · William T. Freeman
Received: 6 September 2005 / Accepted: 11 September 2007 / Published online: 31 October 2007
© Springer Science+Business Media, LLC 2007"
068171535ac18a4b7b65be0748d483ce4c71a9a4,Event Specific Multimodal Pattern Mining with Image-Caption Pairs,"Event Specific Multimodal Pattern Mining with Image-Caption Pairs
Hongzhi Li∗
Columbia Univeristy
Joseph G. Ellis∗
Columbia Univeristy
Shih-Fu Chang
Columbia Univeristy"
061fb1b627554f52ff8f3ebb531e326767d845ec,Globally-optimal greedy algorithms for tracking a variable number of objects,"Globally-Optimal Greedy Algorithms for Tracking a Variable Number of
Objects
Hamed Pirsiavash Deva Ramanan Charless C. Fowlkes
Department of Computer Science, University of California, Irvine"
06fe63b34fcc8ff68b72b5835c4245d3f9b8a016,Learning semantic representations of objects and their parts,"Mach Learn
DOI 10.1007/s10994-013-5336-9
Learning semantic representations of objects
nd their parts
Grégoire Mesnil · Antoine Bordes · Jason Weston ·
Gal Chechik · Yoshua Bengio
Received: 24 May 2012 / Accepted: 26 February 2013
© The Author(s) 2013"
060820f110a72cbf02c14a6d1085bd6e1d994f6a,Fine-grained classification of pedestrians in video: Benchmark and state of the art,"Fine-Grained Classification of Pedestrians in Video: Benchmark and State of the Art
David Hall and Pietro Perona
California Institute of Technology.
The dataset was labelled with bounding boxes, tracks, pose and fine-
grained labels. To achieve this, crowdsourcing, using workers from Ama-
zon’s Mechanical Turk (MTURK) was used. A summary of the dataset’s
statistics can be found in Table 1.
Number of Frames Sent to MTURK
Number of Frames with at least 1 Pedestrian
Number of Bounding Box Labels
Number of Pose Labels
Number of Tracks
8,708
0,994
2,457
7,454
,222
Table 1: Dataset Statistics
A state-of-the-art algorithm for fine-grained classification was tested us-
ing the dataset. The results are reported as a useful performance baseline."
0645134e885259c7ddc97bd1136b009051444061,Point Distribution Models for Pose Robust Face Recognition: Advantages of Correcting Pose Parameters Over Warping Faces to Mean Shape,"Point Distribution Models for Pose Robust Face
Recognition: Advantages of Correcting Pose Parameters
Over Warping Faces to Mean Shape
Daniel Gonz´alez-Jim´enez and Jos´e Luis Alba-Castro⋆
Departamento de Teor´ıa de la Se˜nal y Comunicaciones
Universidad de Vigo (Spain)"
063a3be18cc27ba825bdfb821772f9f59038c207,The development of spontaneous facial responses to others’ emotions in infancy: An EMG study,"This is a repository copy of The development of spontaneous facial responses to others’
emotions in infancy. An EMG study.
White Rose Research Online URL for this paper:
http://eprints.whiterose.ac.uk/125231/
Version: Published Version
Article:
Kaiser, Jakob, Crespo-Llado, Maria Magdalena, Turati, Chiara et al. (1 more author)
(2017) The development of spontaneous facial responses to others’ emotions in infancy.
An EMG study. Scientific Reports. ISSN 2045-2322
https://doi.org/10.1038/s41598-017-17556-y
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06992ca951456bb88523f702f904dfd23eb27c53,Using Mobile Platform to Detect and Alerts Driver Fatigue,"International Journal of Computer Applications (0975 – 8887)
Volume 123 – No.8, August 2015
Using Mobile Platform to Detect and Alerts
Maysoon F. Abulkhair
Department of Information
Technology, Faculty of
Computing and Information
Technology, King Abdulaziz
University
B.P. 42808 Zip Code 21551-
Girl Section, Jeddah, Saudi
Arabia
Driver Fatigue
Hesham A. Salman
Department of Information
Systems
Faculty of Computing and
Information Technology King
Abdulaziz University
Lamiaa F. Ibrahim"
06e15d0d6f92a11bb5b46b5a3e0250cccc452c92,Diagnostic Features of Emotional Expressions Are Processed Preferentially,"Diagnostic Features of Emotional Expressions Are
Processed Preferentially
Elisa Scheller1, Christian Bu¨ chel2, Matthias Gamer2*
Department of Psychiatry and Psychotherapy, University Medical Center Freiburg, Freiburg, Germany, 2 Department of Systems Neuroscience, University Medical Center
Hamburg-Eppendorf, Hamburg, Germany"
06ad99f19cf9cb4a40741a789e4acbf4433c19ae,SenTion: A framework for Sensing Facial Expressions,"SenTion: A framework for Sensing Facial
Expressions
Rahul Islam∗, Karan Ahuja∗, Sandip Karmakar∗, Ferdous Barbhuiya∗ ∗IIIT Guwahati
{rahul.islam, karan.ahuja, sandip,"
06599d41a3256245aa0cb2e9e56b29459c2e2c69,VisualWord2Vec (Vis-W2V): Learning Visually Grounded Word Embeddings Using Abstract Scenes,Visual Word2Vec (vis-w2v): Learning Visually Grounded
065f05c9cb2a6080191851dd82cd9b439a77499a,Comparing Boosted Cascades to Deep Learning Architectures for Fast and Robust Coconut Tree Detection in Aerial Images,
06e7e99c1fdb1da60bc3ec0e2a5563d05b63fe32,WhittleSearch: Image search with relative attribute feedback,"WhittleSearch: Image Search with Relative Attribute Feedback
Adriana Kovashka, Devi Parikh and Kristen Grauman
(Supplementary Material)
Comparative Qualitative Search Results
We present three qualitative search results for human-generated feedback, in addition to those
shown in the paper. Each example shows one search iteration, where the 20 reference images are
randomly selected (rather than ones that match a keyword search, as the image examples in the
main paper illustrate). For each result, the first figure shows our method and the second figure
shows the binary feedback result for the corresponding target image. Note that for our method,
“more/less X” (where X is an attribute) means that the target image is more/less X than the
reference image which is shown.
Figures 1 and 2 show results for human-generated relative attribute and binary feedback, re-
spectively, when both methods are used to target the same “mental image” of a shoe shown in the
top left bubble. The top right grid of 20 images are the reference images displayed to the user, and
those outlined and annotated with constraints are the ones chosen by the user to give feedback.
The bottom row of images in either figure shows the top-ranked images after integrating the user’s
feedback into the scoring function, revealing the two methods’ respective performance. We see that
while both methods retrieve high-heeled shoes, only our method retrieves images that are as “open”
s the target image. This is because using the proposed approach, the user was able to comment
explicitly on the desired openness property."
06687e82ecc94f716d86d3e9f6bfbd30655c6631,CANDECOMP/PARAFAC Decomposition of High-Order Tensors Through Tensor Reshaping,"CANDECOMP/PARAFAC Decomposition of
High-order Tensors Through Tensor Reshaping
Anh Huy Phan∗, Petr Tichavsk´y and Andrzej Cichocki"
063f0e6afe13df9913617dbc2230ad4263a595bc,Loneliness and Hypervigilance to Social Cues in Females: An Eye-Tracking Study,"RESEARCH ARTICLE
Loneliness and Hypervigilance to Social Cues
in Females: An Eye-Tracking Study
Gerine M. A. Lodder1*, Ron H. J. Scholte1¤a, Ivar A. H. Clemens2, Rutger C. M. E. Engels1¤b,
Luc Goossens3, Maaike Verhagen1
Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands, 2 Donders Institute for
Brain, Cognition, and Behaviour, Radboud University, Nijmegen, The Netherlands, 3 Research Group
School Psychology and Child and Adolescent Development, KU Leuven, Leuven, Belgium
¤a Current address: Praktikon, Nijmegen, The Netherlands
¤b Current address: The Trimbos Institute, Netherlands Institute of Mental Health and Addiction, Utrecht,
The Netherlands"
0601416ade6707c689b44a5bb67dab58d5c27814,Feature Selection in Face Recognition: A Sparse Representation Perspective,"Feature Selection in Face Recognition: A Sparse
Representation Perspective
Allan Y. Yang
John Wright
Yi Ma
S. Shankar Sastry
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2007-99
http://www.eecs.berkeley.edu/Pubs/TechRpts/2007/EECS-2007-99.html
August 14, 2007"
06e768d74f076b251d53b0c86fc9910d7243bdc6,Effective and efficient visual description based on local binary patterns and gradient distribution for object recognition,"Effective and ef‌f‌icient visual description based on local
inary patterns and gradient distribution for object
recognition
Chao Zhu
To cite this version:
Chao Zhu. Effective and ef‌f‌icient visual description based on local binary patterns and gradient
distribution for object recognition. Other. Ecole Centrale de Lyon, 2012. English. <NNT :
012ECDL0005>. <tel-00755644>
HAL Id: tel-00755644
https://tel.archives-ouvertes.fr/tel-00755644
Submitted on 21 Nov 2012
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,"
06dee5ff4b41eadf5db5c6841d3441d388f08117,3D cascade of classifiers for open and closed eye detection in driver distraction monitoring,"D Cascade of Classifiers for
Open and Closed Eye Detection
in Driver Distraction Monitoring
Mahdi Rezaei and Reinhard Klette
The .enpeda.. Project, The University of Auckland
Tamaki Innovation Campus, Auckland, New Zealand"
06e959c88dcce05847a395dc404725dd0488003d,3D Pictorial Structures on RGB-D Data for Articulated Human Detection in Operating Rooms,"D Pictorial Structures on RGB-D Data for
Articulated Human Detection in Operating Rooms
Abdolrahim Kadkhodamohammadi, Afshin Gangi, Michel de Mathelin and Nicolas Padoy"
06de3eab314437cc3ed08c3db5171a79c1f684c6,Improving patch-based scene text script identification with ensembles of conjoined networks,"Boosting patch-based scene text script identification with
ensembles of conjoined networks
Lluis Gomez, Anguelos Nicolaou, Dimosthenis Karatzas
Computer Vision Center, Universitat Autonoma de Barcelona. Edifici O, Campus UAB, 08193 Bellaterra (Cerdanyola)
Barcelona, Spain. E-mail:"
06f251cceff93dac123e8568773295f2d0d702da,Player Tracking and Analysis of Basketball Plays,"Player Tracking and Analysis of Basketball Plays
Evan Cheshire, Cibele Halasz, and Jose Krause Perin"
06928aef986877d04e9ce642a3ac71b6f061c638,Monocular Simultaneous Localisation and Mapping for Road Vehicles,"Monocular Simultaneous Localisation
nd Mapping for Road Vehicles
Master’s thesis in Systems, Control and Mechatronics
MATHIAS ERNST, SAMUEL SCHEIDEGGER
Department of Signals and Systems
CHALMERS UNIVERSITY OF TECHNOLOGY
Gothenburg, Sweden 2015"
0690ba31424310a90028533218d0afd25a829c8d,Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs,"Published as a conference paper at ICLR 2015
SEMANTIC IMAGE SEGMENTATION WITH DEEP CON-
VOLUTIONAL NETS AND FULLY CONNECTED CRFS
Liang-Chieh Chen
Univ. of California, Los Angeles
George Papandreou ∗
Google Inc.
Iasonas Kokkinos
CentraleSup´elec and INRIA
Kevin Murphy
Google Inc.
Alan L. Yuille
Univ. of California, Los Angeles"
06a2a3c6d44ab5572df55ce34d9b1216bc685385,GANVO: Unsupervised Deep Monocular Visual Odometry and Depth Estimation with Generative Adversarial Networks,"GANVO: Unsupervised Deep Monocular Visual Odometry and Depth
Estimation with Generative Adversarial Networks
Yasin Almalioglu1, Muhamad Risqi U. Saputra1, Pedro P. B. de Gusmo1, Andrew Markham1, and Niki Trigoni1"
06d10f906ac9023b5566c70a2600384b8c1b24c3,Beautiful and Damned. Combined Effect of Content Quality and Social Ties on User Engagement,"Beautiful and damned. Combined effect of content quality and
social ties on user engagement
Luca M. Aiello
Nokia Bell Labs
Rossano Schifanella
University of Turin
Miriam Redi
Nokia Bell Labs
Stacey Svetlichnaya
Flickr
Frank Liu
Flickr
Simon Osindero
Flickr
Published in IEEE Transactions on Knowledge and Data Engi-
neering (Volume: PP, Issue: 99). Available at https://doi.org/10.1109/
TKDE.2017.2747552."
0628ffefb911d1446914098d7c38a094c92c8a70,An opportunistic prediction-based thread scheduling to maximize throughput/watt in AMPs,"An Opportunistic Prediction-based Thread
Scheduling to Maximize Throughput/Watt in AMPs
Arunachalam Annamalai, Rance Rodrigues, Israel Koren and Sandip Kundu
Department of Electrical and Computer Engineering, University of Massachusetts at Amherst
Email: {annamalai, rodrigues, koren,"
0683be899f3e04b8b55a501e8ffafc0484b44056,Using Deep Learning and Low-Cost RGB and Thermal Cameras to Detect Pedestrians in Aerial Images Captured by Multirotor UAV,"Article
Using Deep Learning and Low-Cost RGB and
Thermal Cameras to Detect Pedestrians in Aerial
Images Captured by Multirotor UAV
Diulhio Candido de Oliveira * ID and Marco Aurelio Wehrmeister ID
Computing Systems Engineering Laboratory (LESC), Federal University of Technology—Parana (UTFPR),
Curitiba 80230-901, Brazil;
* Correspondence: Tel.: +55-41-3310-4646
Received: 27 April 2018; Accepted: 3 July 2018; Published: 12 July 2018"
062c41dad67bb68fefd9ff0c5c4d296e796004dc,Temporal Generative Adversarial Nets with Singular Value Clipping,"Temporal Generative Adversarial Nets with Singular Value Clipping
Masaki Saito∗
Eiichi Matsumoto∗
Preferred Networks inc., Japan
{msaito, matsumoto,
Shunta Saito"
0612745dbd292fc0a548a16d39cd73e127faedde,Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models,"Noname manuscript No.
(will be inserted by the editor)
Flickr30k Entities: Collecting Region-to-Phrase Correspondences for
Richer Image-to-Sentence Models
Bryan A. Plummer · Liwei Wang · Chris M. Cervantes · Juan C. Caicedo · Julia
Hockenmaier · Svetlana Lazebnik
Received: date / Accepted: date"
06f7e0aee7fc5807ab862432a4e5ade2cda73c4b,Flowing ConvNets for Human Pose Estimation in Videos,"Flowing ConvNets for Human Pose Estimation in Videos
Tomas Pfister1, James Charles2 and Andrew Zisserman1
Objective & Contributions
Estimate 2D upper body joint positions (wrist, elbow, shoulder, head) with high accuracy in real-time
- A better ConvNet for general image (x,y) position regression
- Spatial fusion layers that learn an implicit spatial model between predicted positions
- Optical flow for propagating position predictions from neighbouring frames
. Regress a heatmap for each position
Heatmap
ConvNet
(fully convolutional)
56 x 256 x 3
64 x 64 x N
. Represent positions by Gaussians
k joints
Idea 1: Implicit ConvNet spatial model
. Add fusion layers to learn dependencies between predicted positions
onv1
5x5x128
pool 2x2"
060732e29971e4598b2b086620f66671d520a349,Contactless Interaction for Automotive Applications,"Contactless Interaction for Automotive Applications
Thomas Kopinski, Stefan Geisler, Uwe Handmann
Computer Science Institute, Hochschule Ruhr West, University of Applied Sciences"
06f969d3858b6d14425fcbe7ff12b72e213ee240,: CARDIAC ACQUISITION PLANE RECOGNITION 1 Recognizing cardiac magnetic resonance acquisition planes,"Recognizing cardiac magnetic resonance acquisition
planes
Jan Margeta, Antonio Criminisi, Daniel C. Lee, Nicholas Ayache
To cite this version:
Jan Margeta, Antonio Criminisi, Daniel C. Lee, Nicholas Ayache. Recognizing cardiac magnetic
resonance acquisition planes. MIUA - Medical Image Understanding and Analysis Conference
- 2014, Jul 2014, London, United Kingdom. 2014. <hal-01009952>
HAL Id: hal-01009952
https://hal.inria.fr/hal-01009952
Submitted on 19 Jun 2014
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,
´emanant des ´etablissements d’enseignement et de"
06ef2ba33ec911aa0102fb938b53bd3cc36a475f,Introducing FoxFaces: A 3-in-1 Head Dataset,
066d71fcd997033dce4ca58df924397dfe0b5fd1,Iranian Face Database and Evaluation with a New Detection Algorithm,"(cid:1)(cid:2)(cid:3)(cid:4)(cid:5)(cid:3)(cid:4)(cid:6)(cid:7)(cid:3)(cid:8)(cid:9)(cid:6)(cid:10)(cid:3)(cid:11)(cid:3)(cid:12)(cid:3)(cid:13)(cid:9)
(cid:3)(cid:4)(cid:14)(cid:6)(cid:15)(cid:16)(cid:3)(cid:17)(cid:18)(cid:3)(cid:11)(cid:5)(cid:19)(cid:4) (cid:20)(cid:5)(cid:11)(cid:21)(cid:6)(cid:3)(cid:6)(cid:22)(cid:9)(cid:20)(cid:6)(cid:10)(cid:9)(cid:11)(cid:9)(cid:8)(cid:11)(cid:5)(cid:19)(cid:4)(cid:6)(cid:23)(cid:17)(cid:24)(cid:19)(cid:2)(cid:5)(cid:11)(cid:21)(cid:25)
(cid:26)(cid:11)(cid:5)(cid:8)(cid:17)(cid:6)(cid:27)(cid:1)(cid:9)(cid:22)(cid:8)(cid:18)(cid:1)(cid:28)(cid:12)(cid:6)(cid:29)(cid:4)(cid:20)(cid:11)(cid:6)(cid:24)(cid:30)(cid:1)(cid:15)(cid:25)(cid:1)(cid:31)(cid:8)(cid:20)(cid:8) (cid:14)(cid:1)!(cid:8) (cid:8)(cid:6)(cid:4)(cid:1)""(cid:16)(cid:8)(cid:16)(cid:20)(cid:14)(cid:1)(cid:3)(cid:15)(cid:8)(cid:22)(cid:4)(cid:12)(cid:1)(cid:23)(cid:5)(cid:29)(cid:18)(cid:14)(cid:1)(cid:31)(cid:8)(cid:20)(cid:8) (cid:14)(cid:1)(cid:26)!(cid:9)(cid:13)(cid:14)(cid:1)#(cid:17)(cid:8)(cid:6)(cid:5)$(cid:1)(cid:17)(cid:4)(cid:5)%(cid:8)(cid:10)(cid:8)(cid:11)(cid:6)(cid:8)(cid:12)&(cid:30)(cid:8)(cid:16)(cid:15)(cid:15)(cid:21)(cid:27)(cid:15)(cid:17)
(cid:3)(cid:4)(cid:5)(cid:6)(cid:7)(cid:8)(cid:1)(cid:9)(cid:10)(cid:10)(cid:8)(cid:11)(cid:6)(cid:8)(cid:12)(cid:1)(cid:13)(cid:6)(cid:7)(cid:14) (cid:3)(cid:15)(cid:16)(cid:8)(cid:17)(cid:17)(cid:8)(cid:18)(cid:1)(cid:3)(cid:8)(cid:16)(cid:18)(cid:6)(cid:1)(cid:19)(cid:4)(cid:16)(cid:11)(cid:16)(cid:6)(cid:10)(cid:6)(cid:14)(cid:1)(cid:19)(cid:20)(cid:21)(cid:1)(cid:9)(cid:22)(cid:8)(cid:17)(cid:1)(cid:23)(cid:8)(cid:11)(cid:24)(cid:8)(cid:12)(cid:25)(cid:8)(cid:20)(cid:18)
(cid:23)(cid:12)(cid:13)(cid:11)(cid:2)(cid:3)(cid:8)(cid:11)$(cid:1)’(cid:16)(cid:6)(cid:11) ((cid:8)((cid:4)(cid:20)(cid:1)(cid:6)(cid:12)(cid:24)(cid:20)(cid:15)(cid:18))(cid:27)(cid:4)(cid:11)(cid:1)(cid:8)(cid:1)(cid:18)(cid:8)(cid:24)(cid:8)(cid:10)(cid:8)(cid:11)(cid:4)(cid:1)(cid:15)(cid:25)(cid:1)(cid:15)(cid:29)(cid:4)(cid:20)(cid:1)*(cid:14)+,,(cid:1)(cid:27)(cid:15)(cid:5)(cid:15)(cid:20)(cid:1)(cid:6)(cid:17)(cid:8)-(cid:4)(cid:11)(cid:1).(cid:4)(cid:1)(cid:27)(cid:15)(cid:5)(cid:5)(cid:4)(cid:27)(cid:24)(cid:4)(cid:18)(cid:1)(cid:25)(cid:20)(cid:15)(cid:17)(cid:1)+(cid:2)+(cid:1)(cid:18)(cid:6)(cid:25)(cid:25)(cid:4)(cid:20)(cid:4)(cid:12)(cid:24)(cid:1)(cid:16))(cid:17)(cid:8)(cid:12)(cid:1)(cid:25)(cid:8)(cid:27)(cid:4)(cid:11) (cid:6)(cid:12)(cid:1)(cid:8)-(cid:4)(cid:11)(cid:1)(cid:10)(cid:4)(cid:24).(cid:4)(cid:4)(cid:12)(cid:1)/
(cid:8)(cid:12)(cid:18) 01(cid:21)(cid:1)2(cid:4)(cid:1)(cid:12)(cid:8)(cid:17)(cid:4)(cid:18)(cid:1)(cid:24)(cid:16)(cid:6)(cid:11)(cid:1)(cid:18)(cid:8)(cid:24)(cid:8)(cid:10)(cid:8)(cid:11)(cid:4)(cid:1)(cid:24)(cid:16)(cid:4)(cid:1)(cid:26)(cid:20)(cid:8)(cid:12)(cid:6)(cid:8)(cid:12)(cid:1)3(cid:8)(cid:27)(cid:4)(cid:1)(cid:19)(cid:8)(cid:24)(cid:8)(cid:10)(cid:8)(cid:11)(cid:4)(cid:1)4(cid:26)3(cid:19)(cid:23)5(cid:21)(cid:1)’(cid:15)(cid:1)(cid:4)(cid:29)(cid:8)(cid:5))(cid:8)(cid:24)(cid:4)(cid:1)(cid:24)(cid:16)(cid:4)(cid:1)(cid:18)(cid:8)(cid:24)(cid:8)(cid:10)(cid:8)(cid:11)(cid:4)(cid:1)(cid:24)(cid:16)(cid:4)(cid:1)(cid:4)6((cid:4)(cid:20)(cid:6)(cid:17)(cid:4)(cid:12)(cid:24)(cid:8)(cid:5)(cid:1)(cid:20)(cid:4)(cid:11))(cid:5)(cid:24)(cid:1)(cid:15)(cid:25)(cid:1)(cid:8)(cid:1)(cid:12)(cid:4).(cid:1)(cid:25)(cid:8)(cid:27)(cid:6)(cid:8)(cid:5)(cid:1)
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((cid:4)(cid:20)(cid:11)(cid:15)(cid:12)9(cid:11)(cid:1) (cid:6)(cid:18)(cid:4)(cid:12)(cid:24)(cid:6)(cid:24)(cid:30)(cid:21)(cid:1) (cid:9)(cid:11)(cid:1) (cid:16))(cid:17)(cid:8)(cid:12)(cid:11)(cid:14)(cid:1) .(cid:4)(cid:1) (cid:8)(cid:20)(cid:4)(cid:1) (cid:8)(cid:10)(cid:5)(cid:4)(cid:1) (cid:24)(cid:15)(cid:1) (cid:27)(cid:8)(cid:24)(cid:4)-(cid:15)(cid:20)(cid:6)(cid:22)(cid:4)(cid:1) (cid:8)(cid:1)
((cid:4)(cid:20)(cid:11)(cid:15)(cid:12):(cid:11)(cid:1)(cid:8)-(cid:4)(cid:1)-(cid:20)(cid:15))((cid:1)(cid:25)(cid:20)(cid:15)(cid:17)(cid:1)(cid:8)(cid:1)((cid:4)(cid:20)(cid:11)(cid:15)(cid:12):(cid:11)(cid:1)(cid:25)(cid:8)(cid:27)(cid:4)(cid:1)(cid:6)(cid:17)(cid:8)-(cid:4)(cid:1)(cid:8)(cid:12)(cid:18)(cid:1)(cid:8)(cid:20)(cid:4)(cid:1)(cid:15)(cid:25)(cid:24)(cid:4)(cid:12)(cid:1)
(cid:8)(cid:10)(cid:5)(cid:4)(cid:1)(cid:24)(cid:15)(cid:1)(cid:10)(cid:4)(cid:1);)(cid:6)(cid:24)(cid:4)(cid:1)((cid:20)(cid:4)(cid:27)(cid:6)(cid:11)(cid:4)(cid:1)(cid:6)(cid:12)(cid:1)(cid:24)(cid:16)(cid:6)(cid:11)(cid:1)(cid:4)(cid:11)(cid:24)(cid:6)(cid:17)(cid:8)(cid:24)(cid:6)(cid:15)(cid:12)(cid:1)<(cid:2)=(cid:21)(cid:1)(cid:26)(cid:12)(cid:1)(cid:20)(cid:4)(cid:27)(cid:4)(cid:12)(cid:24)(cid:1)(cid:30)(cid:4)(cid:8)(cid:20)(cid:11)(cid:14)(cid:1)
(cid:25)(cid:8)(cid:27)(cid:4)(cid:1) (cid:20)(cid:4)(cid:27)(cid:15)-(cid:12)(cid:6)(cid:24)(cid:6)(cid:15)(cid:12)(cid:1) (cid:8)(cid:12)(cid:18)(cid:1) (cid:20)(cid:4)(cid:5)(cid:8)(cid:24)(cid:4)(cid:18)(cid:1) .(cid:15)(cid:20)(cid:7)(cid:11)(cid:1) (cid:16)(cid:8)(cid:29)(cid:4)(cid:1) (cid:20)(cid:4)(cid:27)(cid:4)(cid:6)(cid:29)(cid:4)(cid:18)(cid:1) (cid:11))(cid:10)(cid:11)(cid:24)(cid:8)(cid:12)(cid:24)(cid:6)(cid:8)(cid:5)(cid:1)
(cid:8)(cid:24)(cid:24)(cid:4)(cid:12)(cid:24)(cid:6)(cid:15)(cid:12)(cid:1) (cid:25)(cid:20)(cid:15)(cid:17)(cid:1) (cid:20)(cid:4)(cid:11)(cid:4)(cid:8)(cid:20)(cid:27)(cid:16)(cid:4)(cid:20)(cid:11)(cid:1) (cid:6)(cid:12)(cid:1) (cid:10)(cid:6)(cid:15)(cid:17)(cid:4)(cid:24)(cid:20)(cid:6)(cid:27)(cid:11)(cid:14)(cid:1) ((cid:8)(cid:24)(cid:24)(cid:4)(cid:20)(cid:12)(cid:1) (cid:20)(cid:4)(cid:27)(cid:15)-(cid:12)(cid:6)(cid:24)(cid:6)(cid:15)(cid:12)(cid:14)(cid:1)
(cid:8)(cid:12)(cid:18)(cid:1) (cid:27)(cid:15)(cid:17)()(cid:24)(cid:4)(cid:20) (cid:29)(cid:6)(cid:11)(cid:6)(cid:15)(cid:12)(cid:1) (cid:27)(cid:15)(cid:17)(cid:17))(cid:12)(cid:6)(cid:24)(cid:6)(cid:4)(cid:11)(cid:1) </(cid:14)(cid:1) *(cid:14)(cid:1) > (cid:8)(cid:12)(cid:18) 1=(cid:21)(cid:1) ’(cid:16)(cid:4)(cid:11)(cid:4)(cid:1)
(cid:27)(cid:15)(cid:17)(cid:17)(cid:15)(cid:12)(cid:1)(cid:6)(cid:12)(cid:24)(cid:4)(cid:20)(cid:4)(cid:11)(cid:24)(cid:11)(cid:1)(cid:8)(cid:17)(cid:15)(cid:12)-(cid:1)(cid:20)(cid:4)(cid:11)(cid:4)(cid:8)(cid:20)(cid:27)(cid:16)(cid:4)(cid:20)(cid:11)(cid:1)(cid:17)(cid:15)(cid:24)(cid:6)(cid:29)(cid:8)(cid:24)(cid:4)(cid:18)(cid:1))(cid:11)(cid:1)(cid:24)(cid:15)(cid:1)(cid:27)(cid:15)(cid:5)(cid:5)(cid:4)(cid:27)(cid:24)(cid:1)(cid:8)(cid:1)
(cid:18)(cid:8)(cid:24)(cid:8)(cid:10)(cid:8)(cid:11)(cid:4)(cid:1) (cid:15)(cid:25)(cid:1) (cid:25)(cid:8)(cid:27)(cid:6)(cid:8)(cid:5)(cid:1) (cid:6)(cid:17)(cid:8)-(cid:4)(cid:11)(cid:1) (cid:25)(cid:20)(cid:15)(cid:17)(cid:1) ((cid:4)(cid:15)((cid:5)(cid:4)(cid:1) (cid:6)(cid:12)(cid:1) (cid:18)(cid:6)(cid:25)(cid:25)(cid:4)(cid:20)(cid:4)(cid:12)(cid:24)(cid:1) (cid:8)-(cid:4)(cid:11)(cid:21) ’(cid:16)(cid:4)(cid:1)
(cid:18)(cid:8)(cid:24)(cid:8)(cid:10)(cid:8)(cid:11)(cid:4)(cid:1)(cid:6)(cid:11)(cid:1)(cid:6)(cid:12)(cid:24)(cid:4)(cid:12)(cid:18)(cid:4)(cid:18)(cid:1)(cid:25)(cid:15)(cid:20)(cid:1)(cid:18)(cid:6)(cid:11)(cid:24)(cid:20)(cid:6)(cid:10))(cid:24)(cid:6)(cid:15)(cid:12)(cid:1)(cid:24)(cid:15)(cid:1)(cid:20)(cid:4)(cid:11)(cid:4)(cid:8)(cid:20)(cid:27)(cid:16)(cid:4)(cid:20)(cid:11)(cid:21)
’(cid:16)(cid:4)(cid:20)(cid:4)(cid:1) (cid:8)(cid:20)(cid:4)(cid:1) (cid:17)(cid:8)(cid:12)(cid:30)(cid:1) ()(cid:10)(cid:5)(cid:6)(cid:27)(cid:8)(cid:5)(cid:5)(cid:30)(cid:1) (cid:8)(cid:29)(cid:8)(cid:6)(cid:5)(cid:8)(cid:10)(cid:5)(cid:4)(cid:1) (cid:18)(cid:8)(cid:24)(cid:8)(cid:10)(cid:8)(cid:11)(cid:4)(cid:11)(cid:1) (cid:25)(cid:15)(cid:20)(cid:1) (cid:25)(cid:8)(cid:27)(cid:4)(cid:1)"
069f2092c5d22e6d4c1e27c30e18dc63848fa3c3,A comparison of low-level features for visual attribute recognition,"Görsel Nitelik Ö˘grenmede Alt-Düzey Özniteliklerin
Kar¸sıla¸stırılması
A Comparison of Low-level Features for Visual
Attribute Recognition
Emine Gül DANACI
Bilgisayar Mühendisli˘gi Bölümü
Hacettepe Üniversitesi
Ankara, Türkiye
Nazlı ˙IK˙IZLER C˙INB˙I¸S
Bilgisayar Mühendisli˘gi Bölümü
Hacettepe Üniversitesi
Ankara, Türkiye
Özetçe —Görsel nitelik ö˘grenme ve kullanımı, son yıllarda
ilgisayarlı görü alanında sıklıkla ara¸stırılmaya ba¸slanmı¸s bir
konudur. Bu çalı¸smamızda, görsel nitelik ö˘grenmeye, hangi alt
düzey özniteliklerin daha anlamlı ve verimli sonuçlar verdi˘gini
ra¸stırmayı amaçlamaktayız. Bu kapsamda, renk ve ¸sekil bil-
gisini farklı detaylarda ele alan alt düzey özniteliklerin, nitelik
sınıflandırmaya katkısı ara¸stırılmı¸s, ve deneysel olarak de˘ger-
lendirilmi¸stir. Elde edilen sonuçlar, özellikle renk ve yerel ¸sekil"
06f146dfcde10915d6284981b6b84b85da75acd4,Scalable Face Image Retrieval Using Attribute-Enhanced Sparse Codewords,"Scalable Face Image Retrieval using
Attribute-Enhanced Sparse Codewords
Bor-Chun Chen, Yan-Ying Chen, Yin-Hsi Kuo, Winston H. Hsu"
06bd34951305d9f36eb29cf4532b25272da0e677,"A Fast and Accurate System for Face Detection, Identification, and Verification","JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
A Fast and Accurate System for Face Detection,
Identification, and Verification
Rajeev Ranjan, Ankan Bansal, Jingxiao Zheng, Hongyu Xu, Joshua Gleason, Boyu Lu, Anirudh Nanduri,
Jun-Cheng Chen, Carlos D. Castillo, Rama Chellappa"
069cadd9d8e52ad2715a3551012a06e506191626,Person re-identification using semantic color names and RankBoost,"Person Re-identification using Semantic Color Names and RankBoost
Cheng-Hao Kuo1, Sameh Khamis2∗, and Vinay Shet1
Imaging and Computer Vision, Siemens Corporation, Corporate Technology1, Princeton, NJ
University of Maryland2, College Park, MD"
064b797aa1da2000640e437cacb97256444dee82,Coarse-to-fine Face Alignment with Multi-Scale Local Patch Regression,"Coarse-to-fine Face Alignment with Multi-Scale Local Patch Regression
Zhiao Huang
Megvii Inc.
Erjin Zhou
Megvii Inc.
Zhimin Cao
Megvii Inc."
4b9b39bbdac95e24773789f1bb543149116cdc37,Region-Of-Interest Retrieval in Brain MR Images,"Technical Note PR-TN 2008/00905
Issued: 12/2008
Region-Of-Interest Retrieval in Brain MR
Images
D. Unay; A. Ekin
Philips Research Europe
Unclassified
 Koninklijke Philips Electronics N.V. 2008"
4b4106614c1d553365bad75d7866bff0de6056ed,Unconstrained Facial Images: Database for Face Recognition Under Real-World Conditions,"Unconstrained Facial Images: Database for Face
Recognition under Real-world Conditions⋆
Ladislav Lenc1,2 and Pavel Kr´al1,2
Dept. of Computer Science & Engineering
University of West Bohemia
Plzeˇn, Czech Republic
NTIS - New Technologies for the Information Society
University of West Bohemia
Plzeˇn, Czech Republic"
4be03fd3a76b07125cd39777a6875ee59d9889bd,Content-based analysis for accessing audiovisual archives: Alternatives for concept-based indexing and search,"CONTENT-BASED ANALYSIS FOR ACCESSING AUDIOVISUAL ARCHIVES:
ALTERNATIVES FOR CONCEPT-BASED INDEXING AND SEARCH
Tinne Tuytelaars
ESAT/PSI - IBBT
KU Leuven, Belgium"
4b60e45b6803e2e155f25a2270a28be9f8bec130,Attribute based object identification,"Attribute Based Object Identification
Yuyin Sun, Liefeng Bo and Dieter Fox"
4bb83b00e7b8eb27ad04d4bb80499e91fc471a07,Emotion related structures in large image databases,"Emotion Related Structures in Large Image Databases
Martin Solli
ITN, Linköping University
SE-60174 Norrköping, Sweden
Reiner Lenz
ITN, Linköping University
SE-60174 Norrköping, Sweden"
4b6ea82fa73d2137c884ad43f7865d88b24ff01d,How deep should be the depth of convolutional neural networks: a backyard dog case study,"How deep should be the depth of convolutional neural
networks: a backyard dog case study
Alexander N. Gorban, Evgeny M. Mirkes, Ivan Y. Tukin
University of Leicester, Leicester LE1 7RH, UK"
4b6eb9117c1b7833c8c6b95ecad427f8f994f023,Robust Depth-Based Person Re-Identification,"Robust Depth-based Person Re-identification
Ancong Wu, Wei-Shi Zheng, Jian-Huang Lai
Code is available at the project page:
http://isee.sysu.edu.cn/∼wuancong/ProjectDepthReID.htm
For reference of this work, please cite:
Ancong Wu, Wei-Shi Zheng,
Person Re-identification.
(DOI:10.1109/TIP.2017.2675201)
Jian-Huang Lai. Robust Depth-based
title={Robust Depth-based Person Re-identification},
uthor={Wu, Ancong and Zheng, Wei-Shi and Lai, Jianhuang},
(DOI:10.1109/TIP.2017.2675201)},
year={2017}"
4baf3b165489122a1f8b574240c2a7fa9b6a7a14,Composite Statistical Inference for Semantic Segmentation,"Composite Statistical Inference for Semantic Segmentation
Fuxin Li(1), Joao Carreira(2), Guy Lebanon(1), Cristian Sminchisescu(3)
(1) Georgia Institute of Technology. (2) ISR - University of Coimbra. (3) Lund University"
4b9374da18cbb300d943ddd7b43bbdc189b96778,IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments,"IDD: A Dataset for Exploring Problems of
Autonomous Navigation in Unconstrained Environments
Girish Varma1
Anbumani Subramanian2
Anoop Namboodiri1
IIIT Hyderabad"
4b7dc1e99b0b34022aec2bde1a13481f28f62030,Person Re-Identification Based on Weighted Indexing Structures,"Person Re-Identification based on Weighted
Indexing Structures
Cristianne R. S. Dutra, Matheus Castro Rocha, and William Robson Schwartz
Department of Computer Science, Universidade Federal de Minas Gerais
Belo Horizonte, Minas Gerais, Brazil, 31270-901
rocha"
4b04247c7f22410681b6aab053d9655cf7f3f888,Robust Face Recognition by Constrained Part-based Alignment,"Robust Face Recognition by Constrained Part-based
Alignment
Yuting Zhang, Kui Jia, Yueming Wang, Gang Pan, Tsung-Han Chan, Yi Ma"
4b57456642e1d21f2bda05aea586b7f419d309ce,Disposable Ties and the Urban Poor Author ( s ) :,"Disposable Ties and the Urban Poor
Author(s): Matthew Desmond
Reviewed work(s):
Source: American Journal of Sociology, Vol. 117, No. 5 (March 2012), pp. 1295-1335
Published by: The University of Chicago Press
Stable URL: http://www.jstor.org/stable/10.1086/663574 .
Accessed: 17/08/2012 17:34
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http://www.jstor.org"
4b89cf7197922ee9418ae93896586c990e0d2867,Unsupervised Discovery of Action Classes,"LATEX Author Guidelines for CVPR Proceedings
First Author
Institution1
Institution1 address"
4ba8be7f84b5eb9cf2078e5503b9ca55f5902add,Kinship Verification Using Facial Images by Robust Similarity Learning,"Publishing CorporationMathematical Problems in EngineeringVolume 2016, Article ID 4072323, 8 pageshttp://dx.doi.org/10.1155/2016/4072323"
4b7d5b17c0daa35f682417c32e80022c6645dc7f,Fine-Grained Object Recognition and Zero-Shot Learning in Remote Sensing Imagery,"Fine-Grained Object Recognition and Zero-Shot
Learning in Remote Sensing Imagery
Gencer Sumbul, Ramazan Gokberk Cinbis, and Selim Aksoy, Senior Member, IEEE
learning (ZSL)"
4b4a174f46ce03caf1ffa4addd074aaa70539f35,BlazeIt: Fast Exploratory Video Queries using Neural Networks.,"Fast Exploratory Video Queries using Neural Networks
BlazeIt:
Daniel Kang, Peter Bailis, Matei Zaharia
Stanford InfoLab"
4bc67489bbe634271f8fde73a851d7a59946ed36,Wide area motion capture using an array of consumer grade structured light depthsensors,"Mälardalen University
School of Innovation, Design and Engineering
Bachelor thesis in Computer science
Wide area motion capture using an array of
onsumer grade structured light depth
sensors
Author:
Karl Arvidsson
Supervisor:
Afshin Ameri
Examiner:
Baran Çürüklü
October 20, 2015"
4b69bbb6dc2959ea3d2e911ed45c6298dc531490,Deep Mixture of Experts via Shallow Embedding,"TAFE-Net: Task-Aware Feature Embeddings for
Efficient Learning and Inference
Xin Wang Fisher Yu Ruth Wang Trevor Darrell
EECS Department, UC Berkeley
Joseph E. Gonzalez"
4b8ecd100d6640fd7659be2715c86b7a9103240b,Quis-Campi: Extending in the Wild Biometric Recognition to Surveillance Environments,"Quis-Campi : Extending in the Wild Biometric
Recognition to Surveillance Environments
Jo˜ao C. Neves1, Gil Santos1(B), S´ılvio Filipe1, Emanuel Grancho1,
Silvio Barra2, Fabio Narducci3, and Hugo Proen¸ca1
Department of Computer Science, IT - Instituto de Telecomunica¸c˜oes,
University of Beira Interior, Covilh˜a, Portugal
DMI - Dipartimento di Matematica e Informatica,
University of Cagliari, Cagliary, Italy
DISTRA-MIT, University of Salerno, Fisciano, Salerno, Italy"
4b8ce1bfedb285d8d609d1059dd0183420d63671,Transductive Multi-View Zero-Shot Learning,"Transductive Multi-view Zero-Shot Learning
Yanwei Fu, Timothy M. Hospedales, Tao Xiang and Shaogang Gong"
4b90f2e4f421dd9198d4c52cd3371643acddf1f9,Detecting planar surface using a light-field camera with application to distinguishing real scenes from printed photos,"014 IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP)
978-1-4799-2893-4/14/$31.00 ©2014 IEEE
´Ecole Polytechnique F´ed´erale de Lausanne
School of Computer and Communication Sciences
AudioVisual Communications Laboratory
. INTRODUCTION
Alireza Ghasemi
Martin Vetterli"
4b5dd0a1b866f928734bc36afd597adca20a7ec1,Detector ensembles for face recognition in video surveillance,"Detector Ensembles for Face Recognition in Video Surveillance
Christophe Pagano, Eric Granger, Robert Sabourin and Dmitry O. Gorodnichy"
4b726a43e201f320b906e1f155aa27c32d43bbc6,Simultaneous Object Classification and Viewpoint Estimation using Deep Multi-task Convolutional Neural Network,
4b3c5b49fa099a77c98bad4b5299c6b4eb0a8f2e,Learning Deep Features for Discriminative Localization,"Learning Deep Features for Discriminative Localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, Antonio Torralba
Computer Science and Artificial Intelligence Laboratory, MIT"
4bde15a51413fafa04193e72c15e132e7716d8a6,Performance Study of Fusion in Multimodal Biometric Verification using Ear and Iris Features,"International Conference on Research Trends in Computer Technologies (ICRTCT - 2013)
Proceedings published in International Journal of Computer Applications® (IJCA) (0975 – 8887)
Performance Study of Fusion in Multimodal Biometric
Verification using Ear and Iris Features
Poornima.S
Department of IT, SSN College of Engineering
Chennai, India."
4b29795faf4bbd5a623bb74d39ac1561ace9828d,Combining Classifiers for Improved Multilabel Image Classification,"Combining Classifiers for Improved
Multilabel Image Classification
Martin Antenreiter, Ronald Ortner, and Peter Auer
University of Leoben, A-8700 Leoben, Austria"
4b0893bf71e4e13529cefb286c78b166a9491552,Estimating orientation in tracking individuals of flying swarms,"978-1-4799-9988-0/16/$31.00 ©2016 IEEE
ICASSP 2016"
4b1cdb96a39cf23139a21915ba9e3e0692de8563,Bridging the Gap Between Computational Photography and Visual Recognition,"Bridging the Gap Between Computational
Photography and Visual Recognition
Rosaura G. VidalMata*, Sreya Banerjee*, Brandon RichardWebster, Michael Albright, Pedro Davalos,
Scott McCloskey, Ben Miller, Asong Tambo, Sushobhan Ghosh, Sudarshan Nagesh, Ye Yuan, Yueyu Hu,
Junru Wu, Wenhan Yang, Xiaoshuai Zhang, Jiaying Liu, Zhangyang Wang, Hwann-Tzong Chen,
Tzu-Wei Huang, Wen-Chi Chin, Yi-Chun Li, Mahmoud Lababidi, Charles Otto, and Walter J. Scheirer"
4be63e7891180e28085d03bb992abbc5104ac446,Adapting a Pedestrian Detector by Boosting LDA Exemplar Classifiers,"Adapting a Pedestrian Detector by Boosting LDA Exemplar Classifiers
Jiaolong Xu1, David V´azquez1, Sebastian Ramos1, Antonio M. L´opez1,2 and Daniel Ponsa1,2
Computer Vision Center
Dept. of Computer Science
Autonomous University of Barcelona
08193 Bellaterra, Barcelona, Spain
{jiaolong, dvazquez, sramosp, antonio,"
4b2ce7064eeb014a480255c29bf147a441b3fa3f,Finding people in home environments with a mobile robot,"Finding People in Home Environments with a Mobile Robot
Michael Volkhardt and Horst-Michael Gross1"
4b4763303a15a4c6313bfb386756437f394a0129,Explicit Inductive Bias for Transfer Learning with Convolutional Networks,"Explicit Inductive Bias for Transfer Learning with Convolutional Networks
Xuhong LI 1 Yves GRANDVALET 1 Franck DAVOINE 1"
4b37efd3987c1e625b063a6998bd6b282c844915,End-to-end Convolutional Network for Saliency Prediction,"End-to-end Convolutional Network for Saliency Prediction
Junting Pan and Xavier Gir´o-i-Nieto
Universitat Politecnica de Catalunya (UPC)
Barcelona, Catalonia/Spain"
4ba503d8f173880d8e8402808f54b78b653e5d20,Accelerating Stochastic Gradient Descent via Online Learning to Sample,"Accelerating Stochastic Gradient Descent via Online
Learning to Sample
Guillaume Bouchard
Xerox Research Center Europe
Th´eo Trouillon
Xerox Research Center Europe
University of Grenoble - LIG
Julien Perez
Xerox Research Center Europe
Adrien Gaidon
Xerox Research Center Europe"
4b5eeea5dd8bd69331bd4bd4c66098b125888dea,Human Activity Recognition Using Conditional Random Fields and Privileged Information,"Human Activity Recognition Using Conditional
Random Fields and Privileged Information
DOCTORAL THESIS
submitted to
the designated by the General Assembly Composition of the
Department of Computer Science & Engineering Inquiry
Committee
Michalis Vrigkas
in partial fulfillment of the Requirements for the Degree of
DOCTOR OF PHILOSOPHY
February 2016"
4bfdbe2ffc6311c8a297355422d914cb666b358a,"On Boosting, Tug of War, and Lexicographic Programming","On Boosting, Tug of War, and Lexicographic
Programming
Shounak Datta, Sayak Nag, and Swagatam Das, Senior Member, IEEE"
4be10db13a9210e078d78a4a072c569d9bbd9939,"Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation","Seed, Expand and Constrain: Three Principles
for Weakly-Supervised Image Segmentation
Alexander Kolesnikov and Christoph H. Lampert
IST Austria"
4ba1cf65eb86aba729192d2f0fe2cd064ac346cf,One-shot person re-identification with a consumer depth camera,"One-Shot Person Re-Identification with a
Consumer Depth Camera
Matteo Munaro, Andrea Fossati, Alberto Basso, Emanuele Menegatti and Luc Van"
4b042eb64ddb8991c0e63fff02b1c51c378a8f58,Leveraging Massive User Contributions for Knowledge Extraction,"Chapter 16
Leveraging Massive User Contributions for
Knowledge Extraction
Spiros Nikolopoulos, Elisavet Chatzilari, Eirini Giannakidou,
Symeon Papadopoulos, Ioannis Kompatsiaris, and Athena Vakali"
81d5c4b49fe17aaa3af837745cafdedb066a067d,Automatic Adaptive Center of Pupil Detection Using Face Detection and CDF Analysis,"Automatic Adaptive Center of Pupil Detection
Using Face Detection and CDF Analysis
Mansour Asadifard, Jamshid Shanbezadeh"
819ff65fc6bdc9c4aea53b14e1431796c1b1d3d1,A voting method for stereo egomotion estimation,"Research Article
A voting method for stereo
egomotion estimation
International Journal of Advanced
Robotic Systems
May-June 2017: 1–16
ª The Author(s) 2017
DOI: 10.1177/1729881417710795
journals.sagepub.com/home/arx
Hugo Silva1, Alexandre Bernardino2 and Eduardo Silva1
image blur, pixel noise,"
811dff89b6d4657e5a0b8534e208baefd2204cee,Pseudo-Feature Generation for Imbalanced Data Analysis in Deep Learning,"Pseudo-Feature Generation for Imbalanced Data
Analysis in Deep Learning
Tomohiko Konno∗ and Michiaki Iwazume
AI Science Research and Development Promotion Center
National Institute of Information and Communications Technology, Tokyo Japan
Figure 1: The sketch of proposed method. Left: train deep neural networks. Center: extract features
from a layer, and then obtain multivariate probability distributions of the features, and then generate
pseudo-features of minority classes from the probability distributions, and then re-train the layers
elow the layer. Right: Put the retrained layers back to the original one. (It is the last classifier that is
re-trained and put back in the experiment.)"
8149c30a86e1a7db4b11965fe209fe0b75446a8c,Semi-supervised multiple instance learning based domain adaptation for object detection,"Semi-Supervised Multiple Instance Learning based
Domain Adaptation for Object Detection
Siemens Corporate Research
Siemens Corporate Research
Siemens Corporate Research
Amit Kale
Bangalore
Chhaya Methani
Bangalore
{chhaya.methani,
Rahul Thota
Bangalore
rahul.thota,"
81eb9fca9093f58eabb8850512f8f46fe2bb07a2,Sem-GAN: Semantically-Consistent Image-to-Image Translation,"Sem-GAN: Semantically-Consistent Image-to-Image Translation
Anoop Cherian
Alan Sullivan
Mitsubishi Electric Research Labs (MERL), Cambridge, MA
{cherian,"
81a51cd6ecd467abb1ef38c8e35bdf1885f96fe3,Deep Spatio-Temporal Random Fields for Efficient Video Segmentation,"Deep Spatio-Temporal Random Fields for Efficient Video Segmentation
Siddhartha Chandra1
Camille Couprie2
INRIA GALEN, Ecole CentraleSup´elec Paris
Iasonas Kokkinos2
Facebook AI Research, Paris"
81ff6d7f934f7134d93b2039d788b72f8593693c,Accelerating Convolutional Neural Network Systems,"Accelerating Convolutional
Neural Network Systems
Henry G.R. Gouk
This report is submitted in partial fulfillment of the requirements for the degree of
Bachelor of Computing and Mathematical Sciences with Honours (BCMS(Hons))
t The University of Waikato.
COMP520-14C (HAM)
© 2014 Henry G.R. Gouk"
81c3d1be0c69e9d3e13054969e4b67ee69a4e6f0,Dynamical Models for Neonatal Intensive Care Monitoring,"This thesis has been submitted in fulfilment of the requirements for a postgraduate degree
(e.g. PhD, MPhil, DClinPsychol) at the University of Edinburgh. Please note the following
terms and conditions of use:
This work is protected by copyright and other intellectual property rights, which are
retained by the thesis author, unless otherwise stated.
A copy can be downloaded for personal non-commercial research or study, without
prior permission or charge.
This thesis cannot be reproduced or quoted extensively from without first obtaining
permission in writing from the author.
The content must not be changed in any way or sold commercially in any format or
medium without the formal permission of the author.
When referring to this work, full bibliographic details including the author, title,
warding institution and date of the thesis must be given."
810eafc9e854ea9b1d7a9e9f755f8102310d5db6,Dynamic Multimodal Instance Segmentation Guided by Natural Language Queries,"Dynamic Multimodal Instance Segmentation
Guided by Natural Language Queries
Edgar Margffoy-Tuay, Juan C. P´erez, Emilio Botero, and Pablo Arbel´aez
{ea.margffoy10, jc.perez13, e.botero10,
Universidad de los Andes, Colombia"
816c8c8d0f02200f988625d4989a1b4b34d779c6,An Efficient Hybrid Face Recognition Algorithm Using PCA and GABOR Wavelets Regular Paper,
81695fbbbea2972d7ab1bfb1f3a6a0dbd3475c0f,Comparison of Face Recognition Neural Networks,"UNIVERSITY OF TARTU
FACULTY OF SCIENCE AND TECHNOLOGY
Institute of Computer Science
Computer Science
Zepp Uibo
Comparison of Face Recognition
Neural Networks
Bachelor's thesis (6 ECST)
Supervisor: Tambet Matiisen
Tartu 2016"
81a142c751bf0b23315fb6717bc467aa4fdfbc92,Pairwise Trajectory Representation for Action Recognition,"978-1-5090-4117-6/17/$31.00 ©2017 IEEE
ICASSP 2017"
81b337f2006cd6c8d5fd148a56cab3e5a662d40d,Improving Data Association by Joint Modeling of Pedestrian Trajectories and Groupings,"Improving Data Association by Joint Modeling
of Pedestrian Trajectories and Groupings
Stefano Pellegrini 1, Andreas Ess 1, and Luc Van Gool 1,2
Computer Vision Laboratory
ESAT-PSI / IBBT
ETH Zurich
KU Leuven"
816c1925de9e8557fa70ec67d0ff71a5059eb931,Person Re-identification by Articulated Appearance Matching,"Person Re-identification by Articulated
Appearance Matching
Dong Seon Cheng and Marco Cristani"
815b9ac1cc300187f649b2b65f6e34eb7a35d2e4,Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes,"Sensors 2015, 15, 9228-9250; doi:10.3390/s150409228
OPEN ACCESS
sensors
ISSN 1424-8220
www.mdpi.com/journal/sensors
Article
Visual Object Recognition with 3D-Aware Features in KITTI
Urban Scenes
J. Javier Yebes *, Luis M. Bergasa and Miguel Ángel García-Garrido
Department of Electronics, University of Alcalá, Alcalá de Henares 28871, Spain;
E-Mails: (L.M.B.), (M.Á.G.-G)
* Author to whom correspondence should be addressed; E-Mail:
Tel.: +34-918-856-807; Fax: +34-918-856-591.
Academic Editor: Felipe Jimenez
Received: 9 March 2015 / Accepted: 14 April 2015 / Published: 20 April 2015"
81bfe562e42f2eab3ae117c46c2e07b3d142dade,A Hajj And Umrah Location Classification System For Video Crowded Scenes,"A Hajj And Umrah Location Classification System For Video
Crowded Scenes
Hossam M. Zawbaa†
Salah A. Aly†‡
Adnan A. Gutub†
Center of Research Excellence in Hajj and Umrah, Umm Al-Qura University, Makkah, KSA
College of Computers and Information Systems, Umm Al-Qura University, Makkah, KSA"
81d327ec41c67728b15438bca86d10b72de1d88f,Visual Affordance and Function Understanding: A Survey,"JOURNAL OF LATEX CLASS FILES, VOL. 6, NO. 1, JULY 2018
Visual Affordance and Function Understanding:
A Survey
Mohammed Hassanin, Salman Khan, Murat Tahtali"
81cb69e401b3b51e49ec378dba4bc0c8e33448e1,Applying Domain Randomization to Synthetic Data for Object Category Detection,"Applying Domain Randomization to Synthetic
Data for Object Category Detection
Jo˜ao Borrego(cid:63), Atabak Dehban(cid:63), Rui Figueiredo, Plinio Moreno, Alexandre
Bernardino, and Jos´e Santos-Victor
{jborrego,adehban,ruifigueiredo,plinio,alex,jasv}
Instituto Superior T´ecnico"
814d091c973ff6033a83d4e44ab3b6a88cc1cb66,The EU-Emotion Stimulus Set: A validation study.,"Behav Res (2016) 48:567–576
DOI 10.3758/s13428-015-0601-4
The EU-Emotion Stimulus Set: A validation study
Helen O’Reilly 1,2 & Delia Pigat 1 & Shimrit Fridenson 5 & Steve Berggren 3,4 & Shahar Tal 5 &
Ofer Golan 5 & Sven Bölte 3,4 & Simon Baron-Cohen 1,6 & Daniel Lundqvist 3
Published online: 30 September 2015
# Psychonomic Society, Inc. 2015"
81ed28ea6cfe71bfc4cfc35c6695fa07dd7cc42e,"Deep Episodic Memory: Encoding, Recalling, and Predicting Episodic Experiences for Robot Action Execution","Deep Episodic Memory: Encoding, Recalling, and Predicting
Episodic Experiences for Robot Action Execution
Jonas Rothfuss∗†, Fabio Ferreira∗†, Eren Erdal Aksoy ‡, You Zhou† and Tamim Asfour†"
812725dc3968aaff6429ec7c3f44ba1ca2116013,Acoplamiento de micro multitudes para el desarrollo de videojuegos controlados por movimiento,"Acoplamiento de micro multitudes
para el desarrollo de videojuegos
ontrolados por movimiento
Iv´an Rivalcoba1, Krely Rodr´ıguez2, Oriam Degives1, Isaac Rudom´ın3
Tecnol´ogico de Monterrey, Campus Estado de M´exico,
M´exico
Tecnol´ogico de Minatitl´an,
Minatitl´an, Veracruz, M´exico
Barcelona Supercomputing Center
Barcelona, Espa˜na
Resumen. La simulaci´on de multitudes en tiempo real y los juegos controlados
por movimiento se han vuelto muy populares en los ´ultimos a˜nos. En conjunto
estas dos tecnolog´ıas proporcionan una mejor experiencia de juego en entornos
virtuales logrando escenas m´as realistas y vibrantes. Sin embargo, hasta ahora no
se ha explotado la interacci´on de m´ultiples jugadores con una gran multitud bajo
un entorno virtual. En este trabajo presentamos un sistema no intrusivo capaz
de simular multitudes virtuales acopladas en tiempo real con varios usuarios,
sentando con ello las bases para la creaci´on de juegos donde interact´uen muchos
jugadores con muchas personajes, para ello se realiza una detecci´on de personas
en una secuencia de v´ıdeo, nuestra contribuci´on consiste en utilizar patrones"
81e628a23e434762b1208045919af48dceb6c4d2,Attend and Rectify: A Gated Attention Mechanism for Fine-Grained Recovery,"Attend and Rectify: a Gated Attention
Mechanism for Fine-Grained Recovery
Pau Rodr´ıguez†, Josep M. Gonfaus‡, Guillem Cucurull†,
F. Xavier Roca†, Jordi Gonz`alez†
Computer Vision Center and Universitat Aut`onoma de Barcelona (UAB),
Campus UAB, 08193 Bellaterra, Catalonia Spain
Visual Tagging Services, Parc de Recerca, Campus UAB"
810d60ff5c0106de53a48fa2731eacf5ca2377b6,MultiQ: single sensor-based multi-quality multi-modal large-scale biometric score database and its performance evaluation,"Uddin et al. IPSJ Transactions on Computer Vision and
Applications  (2017) 9:18
DOI 10.1186/s41074-017-0029-0
IPSJ Transactions on Computer
Vision and Applications
TECHNICAL NOTE
Open Access
MultiQ: single sensor-based multi-quality
multi-modal large-scale biometric score
database and its performance evaluation
Md. Zasim Uddin*, Daigo Muramatsu, Takuhiro Kimura, Yasushi Makihara and Yasushi Yagi"
812d3f6975f4cb87e9905ef18696c5c779227634,A novel comparative deep learning framework for facial age estimation,"Abousaleh et al. EURASIP Journal on Image and Video
Processing  (2016) 2016:47
DOI 10.1186/s13640-016-0151-4
EURASIP Journal on Image
nd Video Processing
RESEARCH
Open Access
A novel comparative deep learning
framework for facial age estimation
Fatma S. Abousaleh1,2,3†, Tekoing Lim2†, Wen-Huang Cheng2, Neng-Hao Yu3*, M. Anwar Hossain4
nd Mohammed F. Alhamid4"
81f30bc57b84a6e5b71983b50bdea32f32bee285,"The more fine-grained, the better for transfer learning","The more fine-grained, the better for transfer learning
Anonymous Author(s)
Affiliation
Address
email"
81fc46dd71121cfafbb11455745ae62f6eca0b25,Joint Camera Pose Estimation and 3D Human Pose Estimation in a Multi-camera Setup,"Joint Camera Pose Estimation and 3D Human
Pose Estimation in a Multi-Camera Setup
Jens Puwein1, Luca Ballan1, Remo Ziegler2 and Marc Pollefeys1
Department of Computer Science, ETH Zurich, Switzerland
Vizrt"
81782b19656cd70fccc7cccab57db3967db8ad67,"OBJECT CLASSIFICATION VIA GEOMETRICAL , ZERNIKE AND LEGENDRE MOMENTS","Journal of Theoretical and Applied Information Technology
© 2005 - 2009 JATIT. All rights reserved.
www.jatit.org
OBJECT CLASSIFICATION VIA GEOMETRICAL, ZERNIKE
AND LEGENDRE MOMENTS
THAWAR ARIF, ZYAD SHAABAN, LALA KREKOR, SAMI BABA
Faculty of Information Technology, Applied Science University, Amman 11931, Jordan
E-mail:"
81eb804756f27d08f2d193d1074e58e1c5d263ca,Monocular 3D Human Pose Estimation Using Transfer Learning and Improved CNN Supervision,"Monocular 3D Human Pose Estimation Using Transfer Learning and Improved
CNN Supervision
Dushyant Mehta*, Helge Rhodin*, Dan Casass, Oleksandr Sotnychenko*, Weipeng Xu*, and Christian
Theobalt*
*Max Planck Institute For Informatics, Saarland Informatics Campus, Germany
sUniversidad Rey Juan Carlos, Spain"
815069f591122aa7b388615f944c17c7fa1eff14,Constrained Overcomplete Analysis Operator Learning for Cosparse Signal Modelling,"Constrained Overcomplete Analysis Operator
Learning for Cosparse Signal Modelling
Mehrdad Yaghoobi, Sangnam Nam, R´emi Gribonval and Mike E. Davies"
816eff5e92a6326a8ab50c4c50450a6d02047b5e,Fast Low-Rank Representation Using Frobenius,"fLRR: Fast Low-Rank Representation Using
Frobenius Norm
Haixian Zhang, Zhang Yi, and Xi Peng
Low Rank Representation (LRR) intends to find the representation
with lowest-rank of a given data set, which can be formulated as a
rank minimization problem. Since the rank operator is non-convex and
discontinuous, most of the recent works use the nuclear norm as a convex
relaxation. This letter theoretically shows that under some conditions,
Frobenius-norm-based optimization problem has an unique solution that
is also a solution of the original LRR optimization problem. In other
words, it is feasible to apply Frobenius-norm as a surrogate of the
nonconvex matrix rank function. This replacement will largely reduce the
time-costs for obtaining the lowest-rank solution. Experimental results
show that our method (i.e., fast Low Rank Representation, fLRR),
performs well in terms of accuracy and computation speed in image
lustering and motion segmentation compared with nuclear-norm-based
LRR algorithm.
Introduction: Given a data set X ∈ Rm×n(m < n) composed of column
vectors, let A be a data set composed of vectors with the same dimension
s those in X. Both X and A can be considered as matrices. A linear"
81b6de17391f44c07b2efe75a529aa200604ee48,Machine à Vecteurs Supports Multi-Noyau pour la détection de points caractéristiques du visage,"Machine à Vecteurs Supports Multi-Noyau pour la détection de points
aractéristiques du visage
Vincent Rapp1, Thibaud Senechal1, Kevin Bailly1, Lionel Prevost2
ISIR - CNRS UMR 7222
Université Pierre et Marie Curie, Paris
LAMIA - EA 4540
Université des Antilles et de la Guyanne
{rapp, senechal,
Résumé
Dans cet article, nous présentons une méthode robuste
et précise pour détecter 17 points caractéristiques du vi-
sage sur des images expressives. Une nouvelle architecture
multi-résolution basée sur les récents algorithmes multi-
noyau est introduite. Les patches de faibles résolutions
odent les informations globales du visage donnant lieu à
une détection grossière mais robuste du point désiré. Les
patches de grandes résolutions quant à eux utilisent les dé-
tails locaux afin d’affiner cette localisation. En combinant
une détection indépendante de points et des informations
priori sur les distributions de points, nous proposons"
819d1dcea397e6e671acf74adccdef5750550873,Representations for Visually Guided Actions,"Representations for Visually Guided Actions
Saurabh Gupta
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2018-104
http://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-104.html
August 8, 2018"
81c03eda1d175fbe351980ac4cffe42c5dec47b0,User observation & dataset collection for robot training,"User Observation & Dataset Collection for Robot Training
Caroline Pantofaru
Willow Garage, Inc.
Menlo Park, CA 94025
Categories and Subject Descriptors:
I.5.2 [Comput-
ing Methodologies]: Pattern Recognition - Design Method-
ology, H.1.2 [Information Systems]: Models and Principles -
User/Machine Systems
General Terms: Measurement
INTRODUCTION
Personal robots operate in human environments such as
homes and of‌f‌ices, co-habiting with people. To effectively
train robot algorithms for such scenarios, a large amount of
training data containing both people and the environment is
required. Collecting such data involves taking a robot into
new environments, observing and interacting with people.
So far, best practices for robot data collection have been
undefined. Fortunately, the human-robot interaction com-
munity has conducted field studies whose methodology can"
812a6ced985317b3b9429ef0455645a9744af6d1,No need for a social cue! A masked magician can also trick the audience in the vanishing ball illusion.,"Atten Percept Psychophys
DOI 10.3758/s13414-015-1036-9
No need for a social cue! A masked magician can also trick
the audience in the vanishing ball illusion
Cyril Thomas 1 & André Didierjean 1
# The Psychonomic Society, Inc. 2015"
8199803f476c12c7f6c0124d55d156b5d91314b6,The iNaturalist Species Classification and Detection Dataset,"The iNaturalist Species Classification and Detection Dataset
Grant Van Horn1 Oisin Mac Aodha1 Yang Song2 Yin Cui3 Chen Sun2
Alex Shepard4 Hartwig Adam2
Pietro Perona1
Serge Belongie3
Caltech
Google
Cornell Tech
iNaturalist"
816617fa6801fb2abd3d4475c459bf6e3221954d,3D human detection and tracking on a mobile platform for situation awareness,"D Human Detection and
Tracking on a Mobile Platform
for Situation Awareness
Niklas Beuter"
81ea29bde0216e41420c4591bebb800142fa3269,Learning Active Learning from Data,"Learning Active Learning from Data
Ksenia Konyushkova
Raphael Sznitman
University of Bern
Pascal Fua"
81b2a541d6c42679e946a5281b4b9dc603bc171c,Semi-supervised learning with committees: exploiting unlabeled data using ensemble learning algorithms,"Universit¨at Ulm | 89069 Ulm | Deutschland
Fakult¨at f¨ur Ingenieurwissenschaften und Informatik
Institut f¨ur Neuroinformatik
Direktor: Prof. Dr. G¨unther Palm
Semi-Supervised Learning with Committees:
Exploiting Unlabeled Data Using Ensemble
Learning Algorithms
Dissertation zur Erlangung des Doktorgrades
Doktor der Naturwissenschaften (Dr. rer. nat.)
der Fakult¨at f¨ur Ingenieurwissenschaften und Informatik
der Universit¨at Ulm
vorgelegt von
Mohamed Farouk Abdel Hady
us Kairo, ¨Agypten
Ulm, Deutschland"
81006fe4c4947d225b9fa17e6b98b8acb36a7692,A Dataset for Grasping and Manipulation using ROS,"A Dataset for Grasping and Manipulation using ROS
Matei Ciocarlie†, Gary Bradski†, Kaijen Hsiao† and Peter Brook†∗"
8134b052a9aedd573dd16649a611f68b48e30cb2,InverseFaceNet: Deep Monocular Inverse Face Rendering,"InverseFaceNet: Deep Single-Shot Inverse Face Rendering From A Single Image
Hyeongwoo Kim1
Justus Thies2
Max-Planck-Institute for Informatics
Michael Zollhöfer1
Christian Richardt3
University of Erlangen-Nuremberg 3 University of Bath
Christian Theobalt1
Ayush Tewari1
Figure 1. Our single-shot deep inverse face renderer InverseFaceNet obtains a high-quality geometry, reflectance and illumination estimate
from just a single input image. We jointly recover the face pose, shape, expression, reflectance and incident scene illumination. From left to
right: input photo, our estimated face model, its geometry, and the pointwise Euclidean error compared to Garrido et al. [14]."
8121824f4598d600e4cdb745cd2715e4655c9e88,A Taxonomy of Emerging Multilinear Discriminant Analysis Solutions for Biometric Signal Recognition 1,"Contents
A Taxonomy of Emerging Multilinear Discriminant Analysis Solutions
for Biometric Signal Recognition
Haiping Lu, K. N. Plataniotis and A. N. Venetsanopoulos
Introduction
.2 Multilinear basics
.3 Multilinear discriminant analysis
.5 Conclusions
Empirical Comparison of MLDA variants on Face Recognition
Appendix: Multilinear decompositions
References"
c03c16668426d8b069e75cb440686e12a9adbcd7,Deep Unsupervised Similarity Learning Using Partially Ordered Sets,"Deep Unsupervised Similarity Learning using Partially Ordered Sets
Miguel A. Bautista∗ , Artsiom Sanakoyeu∗ , Bj¨orn Ommer
Heidelberg Collaboratory for Image Processing
IWR, Heidelberg University, Germany"
c099a6dc8b9f731bbc9a29a920233257c13d9b00,Audio-visual Speech Recognition Workshop 2000 Final Report,"AUDIO-VISUAL SPEECH RECOGNITION
Chalapathy Neti IBM T. J. Watson Research Center, Yorktown Heights,
Gerasimos Potamianos IBM T. J. Watson Research Center, Yorktown Heights,
Juergen Luettin Institut Dalle Molle d’Intelligence Articielle Perceptive, Martigny,
Iain Matthews Carnegie Mellon University, Pittsburgh,
Herve Glotin Institut de la Communication Parl
ee, Grenoble; and
Institut Dalle Molle d’Intelligence Articielle Perceptive, Martigny,
Dimitra Vergyri Center for Language and Speech Processing, Baltimore,
June Sison University of California, Santa Cruz,
Azad Mashari University of Toronto, Toronto,
nd Jie Zhou The Johns Hopkins University, Baltimore
Workshop  Final Report
October 
, "
c0014e048a5d15ddfeffa075a1b819bcb93dd351,Simple and Efficient Visual Gaze Estimation,"Simple and Efficient Visual Gaze Estimation
Roberto Valenti
Nicu Sebe
Intelligent Systems Lab
Amsterdam
Kruislaan 403, 1018SJ
Amsterdam, The Netherlands
Theo Gevers"
c0c8d720658374cc1ffd6116554a615e846c74b5,Modeling Multimodal Clues in a Hybrid Deep Learning Framework for Video Classification,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
Modeling Multimodal Clues in a Hybrid Deep
Learning Framework for Video Classification
Yu-Gang Jiang, Zuxuan Wu, Jinhui Tang, Zechao Li, Xiangyang Xue, Shih-Fu Chang"
c03ef6e94808185c1080ac9b155ac3b159b4f1ec,Learning to Avoid Errors in GANs by Manipulating Input Spaces,"Learning to Avoid Errors in GANs by Manipulating
Input Spaces
Alexander B. Jung
TU Dortmund"
c0b3159ca4bc847913e82c9a4c1f96f14e0e52db,"An effective biometric discretization approach to extract highly discriminative, informative, and privacy-protective binary representation","Lim and Teoh EURASIP Journal on Advances in Signal Processing 2011, 2011:107
http://asp.eurasipjournals.com/content/2011/1/107
RESEARCH
Open Access
An effective biometric discretization approach to
extract highly discriminative, informative, and
privacy-protective binary representation
Meng-Hui Lim and Andrew Beng Jin Teoh*"
c0e9d06383442d89426808d723ca04586db91747,Cascaded SR-GAN for Scale-Adaptive Low Resolution Person Re-identification,Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18)
c05a7c72e679745deab9c9d7d481f7b5b9b36bdd,"Naval Postgraduate School Monterey, California Approved for Public Release; Distribution Is Unlimited Biometric Challenges for Future Deployments: a Study of the Impact of Geography, Climate, Culture, and Social Conditions on the Effective Collection of Biometrics","NPS-CS-11-005
NAVAL
POSTGRADUATE
SCHOOL
MONTEREY, CALIFORNIA
BIOMETRIC CHALLENGES FOR FUTURE DEPLOYMENTS:
A STUDY OF THE IMPACT OF GEOGRAPHY, CLIMATE, CULTURE,
AND SOCIAL CONDITIONS ON THE EFFECTIVE
COLLECTION OF BIOMETRICS
Paul C. Clark, Heather S. Gregg, with preface by Cynthia E. Irvine
April 2011
Approved for public release; distribution is unlimited"
c06447df3e50ec451240205cefa0708caee8ab8c,Picture it in your mind: generating high level visual representations from textual descriptions,"Picture It In Your Mind: Generating High Level Visual
Representations From Textual Descriptions
Fabio Carrara
ISTI-CNR
via G. Moruzzi, 1
56124 Pisa, Italy
Andrea Esuli
ISTI-CNR
via G. Moruzzi, 1
56124 Pisa, Italy
Tiziano Fagni
ISTI-CNR
via G. Moruzzi, 1
56124 Pisa, Italy
Fabrizio Falchi
ISTI-CNR
via G. Moruzzi, 1
56124 Pisa, Italy
Alejandro Moreo
Fernández"
c043f8924717a3023a869777d4c9bee33e607fb5,Emotion Separation Is Completed Early and It Depends on Visual Field Presentation,"Emotion Separation Is Completed Early and It Depends
on Visual Field Presentation
Lichan Liu1,2*, Andreas A. Ioannides1,2
Lab for Human Brain Dynamics, RIKEN Brain Science Institute, Wakoshi, Saitama, Japan, 2 Lab for Human Brain Dynamics, AAI Scientific Cultural Services Ltd., Nicosia,
Cyprus"
c08420b1bfa093e89e35e3b8d3a9e3e881f4f563,A Classification Framework for Large-Scale Face Recognition Systems,"Kent Academic Repository
Full text document (pdf)
Citation for published version
Zhou, Ziheng and Deravi, Farzin  (2009) A Classification Framework for Large-Scale Face Recognition
Systems.    In: 3rd IAPR/IEEE International Conference on Biometrics, 2-5 June, University of
Sassari, Italy.
https://doi.org/10.1007/978-3-642-01793-3_35
Link to record in KAR
http://kar.kent.ac.uk/23302/
Document Version
Author's Accepted Manuscript
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ontent is protected by copyright and in the absence of an open licence (eg Creative Commons), permissions
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Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the
published version of record.
Enquiries"
c02dbf756b9e9e2bed37cb7d295529397cad616a,Semantic Segmentation of RGBD Videos with Recurrent Fully Convolutional Neural Networks,"Semantic Segmentation of RGBD Videos with Recurrent Fully Convolutional
Neural Networks
Ekrem Emre Yurdakul, Y¨ucel Yemez
Computer Engineering Department, Koc¸ University
Istanbul, Turkey"
c0be23ae7f327f9415e583aee1936b9932c9b58b,Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data,"NetworkCNNimageslabelsFakeDatasetimages24132labelsTarget NetworkCNNimageslabelsOriginalDatasetFakeDatasetFig.1:Ontheleft,thetargetnetworkistrainedwithanoriginal(confidential)datasetandisservedpubliclyasanAPI,receivingimagesasinputandprovidingclasslabelsasoutput.Ontheright,itispresentedtheprocesstogetstolenlabelsandtocreateafakedataset:randomnaturalimagesaresenttotheAPIandthelabelsareobtained.Afterthat,thecopycatnetworkistrainedusingthisfakedataset.cloud-basedservicestocustomersallowingthemtooffertheirownmodelsasanAPI.Becauseoftheresourcesandmoneyinvestedincreatingthesemodels,itisinthebestinterestofthesecompaniestoprotectthem,i.e.,toavoidthatsomeoneelsecopythem.Someworkshavealreadyinvestigatedthepossibilityofcopyingmodelsbyqueryingthemasablack-box.In[1],forexample,theauthorsshowedhowtoperformmodelextractionattackstocopyanequivalentornear-equivalentmachinelearningmodel(decisiontree,logisticregression,SVM,andmultilayerperceptron),i.e.,onethatachievescloseto100%agreementonaninputspaceofinterest.In[2],theauthorsevaluatedtheprocessofcopyingaNaiveBayesandSVMclassifierinthecontextoftextclassification.Bothworksfocusedongeneralclassifiersandnotondeepneuralnetworksthatrequirelargeamountsofdatatobetrainedleavingthequestionofwhetherdeepmodelscanbeeasilycopied.Althoughthesecondusesdeeplearningtostealtheclassifiers,itdoesnottrytouseDNNstostealfromdeepmodels.Additionally,theseworksfocusoncopyingbyqueryingwithproblemdomaindata.Inrecentyears,researchershavebeenexploringsomeintriguingpropertiesofdeepneuralnetworks[3],[4].More©2018IEEE.Personaluseofthismaterialispermitted.PermissionfromIEEEmustbeobtainedforallotheruses,inanycurrentorfuturemedia,includingreprinting/republishingthismaterialforadvertisingorpromotionalpurposes,creatingnewcollectiveworks,forresaleorredistributiontoserversorlists,orreuseofanycopyrightedcomponentofthisworkinotherworks."
c08ef9ebf46e5a88c4ee1aa64dac104ddc07bee2,Classification of vehicles for urban traffic scenes,"Classification of Vehicles
for Urban Traffic Scenes
Norbert Erich Buch
Submitted in partial fulfilment of the requirements of
Kingston University for the degree of
Doctor of Philosophy
June, 2010
Collaborating partner:
Traffic Directorate at Transport for London"
c0afa514524a4cf4b1772c1738ceb6989bff1b71,Impact of Tone-mapping Algorithms on Subjective and Objective Face Recognition in HDR Images,"Impact of Tone-mapping Algorithms on Subjective and
Objective Face Recognition in HDR Images
Pavel Korshunov
MMSPG, EPFL
Marco V. Bernardo
Optics Center, UBI
Touradj Ebrahimi
MMSPG, EPFL
António M. G. Pinheiro
Optics Center, UBI"
c0a9f8c2b1ad60c9d42252c80ff8cea4a3a25c2c,Face Class Modeling Using Mixture of SVMs,"Face Class Modeling Using Mixture of SVMs
Julien Meynet1, Vlad Popovici, and Jean-Philippe Thiran
Signal Processing Institute, Swiss Federal Institute of Technology Lausanne,
CH-1015 Lausanne, Switzerland
http://itswww.epfl.ch"
c0d79e6077c47d6289ab89054f2b51653d887958,Action Search: Learning to Search for Human Activities in Untrimmed Videos,"Action Search: Spotting Actions in Videos and
Its Application to Temporal Action Localization
Humam Alwassel, Fabian Caba Heilbron, and Bernard Ghanem
King Abdullah University of Science and Technology (KAUST), Saudi Arabia
http://www.humamalwassel.com/publication/action-search/"
c08a826913a25792d0221cc419daf8f47ebbdf9a,FACE DETECTION AND RECOGNITION USING BACK PROPAGATION NEURAL NETWORK AND FOURIER,"Signal & Image Processing : An International Journal (SIPIJ) Vol.2, No.3, September 2011
FACE DETECTION AND RECOGNITION USING BACK
PROPAGATION NEURAL NETWORK AND FOURIER
GABOR FILTERS
Anissa Bouzalmat1, Naouar Belghini2, Arsalane Zarghili3 and Jamal Kharroubi4
Department of Computer Sciences, Sidi Mohamed Ben Abdellah University, Fez,
Department of Computer Sciences, Sidi Mohamed Ben Abdellah University, Fez,
Morocco
Department of Computer Sciences, Sidi Mohamed Ben Abdellah University, Fez,
Morocco
Department of Computer Sciences, Sidi Mohamed Ben Abdellah University, Fez,
Morocco
Morocco"
c04fec95a448f9b01dd4399b3a5a365f67448bdf,From Image Sequence to Frontal Image : Reconstruction of the Unknown Face A Forensic Case,"From Image Sequence to Frontal Image:
A Forensic Case
Christiaan van Dam"
c03fdf0f43f393e04743f9858d0950c28256560e,Feature extraction without learning in an analog Spatial Pooler memristive-CMOS circuit design of Hierarchical Temporal Memory,"Noname manuscript No.
(will be inserted by the editor)
Feature extraction without learning in an analog Spatial Pooler
memristive-CMOS circuit design of Hierarchical Temporal
Memory
Olga Krestinskaya · Alex Pappachen James
Received: date / Accepted: date"
c0992975eafd94746a4c18a0a2ca252044fcdc64,Unsupervised Adaptation for Deep,"Unsupervised Adaptation for Deep Stereo
Alessio Tonioni, Matteo Poggi, Stefano Mattoccia, Luigi Di Stefano
University of Bologna,
Department of Computer Science and Engineering (DISI)
Viale del Risorgimento 2, Bologna
{alessio.tonioni, matteo.poggi8, stefano.mattoccia,"
c0f17f99c44807762f2a386ac6579c364330e082,A Review on Deep Learning Techniques Applied to Semantic Segmentation,"A Review on Deep Learning Techniques
Applied to Semantic Segmentation
A. Garcia-Garcia, S. Orts-Escolano, S.O. Oprea, V. Villena-Martinez, and J. Garcia-Rodriguez"
c038beaa228aeec174e5bd52460f0de75e9cccbe,Temporal Segment Networks for Action Recognition in Videos,"Temporal Segment Networks for Action
Recognition in Videos
Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaoou Tang, and Luc Van Gool"
c0a0adb7f02d5509969e6107c914f7cc6e9ec881,Semantic Instance Segmentation via Deep Metric Learning,"Semantic Instance Segmentation via Deep Metric Learning
Alireza Fathi∗
Zbigniew Wojna∗
Vivek Rathod∗
Peng Wang†
Sergio Guadarrama∗
Kevin P. Murphy∗
Hyun Oh Song∗"
c038186138b76a625500ff84c9dadb18aae29f1c,Learning Implicit Transfer for Person Re-identification,"Learning Implicit Transfer
for Person Re-identi(cid:12)cation
Tamar Avraham, Ilya Gurvich, Michael Lindenbaum, and Shaul Markovitch
Computer science department, Technion - I.I.T., Haifa 32000, Israel."
c02847a04a99a5a6e784ab580907278ee3c12653,Fine Grained Video Classification for Endangered Bird Species Protection Non-Thesis MS Final Report,"Fine Grained Video Classification for
Endangered Bird Species Protection
Non-Thesis MS Final Report
Chenyu Wang
.  Introduction
.1 Background
This project is about detecting eagles in videos. Eagles are endangered species at the brim of
extinction since 1980s. With the bans of harmful pesticides, the number of eagles keep increasing.
However, recent studies on golden eagles’ activities in the vicinity of wind turbines have shown
significant number of turbine blade collisions with eagles as the major cause of eagles’ mortality. [1]
This project is a part of a larger research project to build an eagle detection and deterrent system
on wind turbine toward reducing eagles’ mortality. [2] The critical component of this study is a
omputer vision system for eagle detection in videos. The key requirement are that the system should
work in real time and detect eagles at a far distance from the camera (i.e. in low resolution).
There are three different bird species in my dataset - falcon, eagle and seagull. The reason for
involving only these three species is based on the real world situation. Wind turbines are always
installed near coast and mountain hill where falcons and seagulls will be the majority. So my model
will classify the minority eagles out of other bird species during the immigration season and protecting
them by using the deterrent system.
.2 Brief Approach"
c0d21722d83c126af4175add38ffc893a33ee01e,Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor,"Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor
Wongun Choi
NEC Laboratories America
0080 N. Wolfe Rd, Cupertino, CA, USA"
acee1e7700e9f084ff64805a2c67d16fe69e63a8,250 years Lambert surface: does it really exist?,"50 years Lambert surface: does it really
exist?
Institut f¨ur Lasertechnologien in der Medizin und Meßtechnik, Helmholtzstr.12, D-89081 Ulm,
Alwin Kienle∗ and Florian Foschum
Germany"
acd26d5b85e979d73101ac790bfdedf17bfe8ed1,Learning from PhotoShop Operation Videos : the PSOV Dataset,"Learning from PhotoShop Operation Videos: the PSOV
Dataset
Jingchun Cheng2(cid:63) Han-Kai Hsu1∗ Fang Chen3 Hailin Jin3 Shengjin Wang2‡
Ming-Hsuan Yang1
University of California, Merced 2Tsinghua University 3Adobe Research"
ac7f898ff5789914d423526c392ee61b979fdd8e,"Target Tracking with Kalman Filtering , KNN and LSTMs Dan Iter","Target Tracking with Kalman Filtering, KNN and LSTMs
Dan Iter
Jonathan Kuck
Philip Zhuang
December 17, 2016"
ac9feef881ed00a5a5e53bddb88f135a9cffe048,A general method for appearance-based people search based on textual queries,"A general method for appearance-based people
search based on textual queries
Riccardo Satta, Giorgio Fumera, and Fabio Roli
Dept. of Electrical and Electronic Engineering, University of Cagliari
Piazza d’Armi, 09123 Cagliari, Italy"
ac8441e30833a8e2a96a57c5e6fede5df81794af,Hierarchical Representation Learning for Kinship Verification,"IEEE TRANSACTIONS ON IMAGE PROCESSING
Hierarchical Representation Learning for Kinship
Verification
Naman Kohli, Student Member, IEEE, Mayank Vatsa, Senior Member, IEEE, Richa Singh, Senior Member, IEEE,
Afzel Noore, Senior Member, IEEE, and Angshul Majumdar, Senior Member, IEEE"
ac7c643794b9f55309f0d2041b12ae7bf845a52d,Graphical models for visual object recognition and tracking,"Graphical Models for Visual Object Recognition and Tracking
Erik B. Sudderth
B.S., Electrical Engineering, University of California at San Diego, 1999
S.M., Electrical Engineering and Computer Science, M.I.T., 2002
Submitted to the Department of Electrical Engineering and Computer Science
in partial fulfillment of the requirements for the degree of
Doctor of Philosophy
in Electrical Engineering and Computer Science
t the Massachusetts Institute of Technology
May, 2006
° 2006 Massachusetts Institute of Technology
All Rights Reserved.
Signature of Author:
Department of Electrical Engineering and Computer Science
May 26, 2006
Certified by:
Certified by:
Accepted by:
William T. Freeman
Professor of Electrical Engineering and Computer Science"
ac83b9ad20ecf63c7818ff1e43a99b4c626fac12,Accuracy and Security Evaluation of Multi-Factor Biometric Authentication,"Accuracy and Security Evaluation of Multi-Factor Biometric Authentication
Hisham Al-Assam, Harin Sellahewa, Sabah Jassim
Department of Applied Computing
University of Buckingham
Buckingham, MK18 1EG, United Kingdom
{hisham.al-assam, harin.sellahewa,"
aca232de87c4c61537c730ee59a8f7ebf5ecb14f,Ebgm Vs Subspace Projection for Face Recognition,"EBGM VS SUBSPACE PROJECTION FOR FACE RECOGNITION
Andreas Stergiou, Aristodemos Pnevmatikakis, Lazaros Polymenakos
9.5 Km Markopoulou Avenue, P.O. Box 68, Peania, Athens, Greece
Athens Information Technology
Keywords:
Human-Machine Interfaces, Computer Vision, Face Recognition."
ac71673da236ac440f647226dddd94aef3bd72b2,Pedestrian detection in thermal images using adaptive fuzzy C-means clustering and convolutional neural networks,"MVA2015 IAPR International Conference on Machine Vision Applications, May 18-22, 2015, Tokyo, JAPAN
Pedestrian Detection in Thermal Images Using Adaptive Fuzzy
C-Means Clustering and Convolutional Neural Networks
Vijay John
Zheng Liu
Toyota Technological Institute, Japan
Toyota Technological Institute, Japan
Seiichi Mita
Bin Qi
Toyota Technological Institute, Japan
Toyota Technological Institute, Japan"
acaa89fb6263aef7ad58a37d9cac79c8fcaa29ca,Person Re-identification in Identity Regression Space,"Noname manuscript No.
(will be inserted by the editor)
Person Re-Identification in Identity Regression Space
Hanxiao Wang · Xiatian Zhu · Shaogang Gong · Tao Xiang
Received: date / Accepted: date"
ac479607e6b44c69022a56b5847a055535ae63ed,Cross-domain fashion image retrieval,"Cross-domain fashion image retrieval
Bojana Gaji´c, Ramon Baldrich
Computer Vision Center
Universitat Autnoma de Barcelona
Edifici O. UAB. Bellaterra, Spain.
{bgajic,"
ac559873b288f3ac28ee8a38c0f3710ea3f986d9,Team DEEP-HRI Moments in Time Challenge 2018 Technical Report,"Team DEEP-HRI Moments in Time Challenge 2018 Technical Report
Chao Li, Zhi Hou, Jiaxu Chen, Yingjia Bu, Jiqiang Zhou, Qiaoyong Zhong, Di Xie and Shiliang Pu
Hikvision Research Institute"
ac8e09128e1e48a2eae5fa90f252ada689f6eae7,Leolani: A Reference Machine with a Theory of Mind for Social Communication,"Leolani: a reference machine with a theory of
mind for social communication
Piek Vossen, Selene Baez, Lenka Baj˘ceti´c, and Bram Kraaijeveld
VU University Amsterdam, Computational Lexicology and Terminology Lab, De
Boelelaan 1105, 1081HV Amsterdam, The Netherlands
www.cltl.nl"
acc37d228f6cb2205497df81532c582ed71dd9fe,Deep Ordinal Ranking for Multi-Category Diagnosis of Alzheimer's Disease using Hippocampal MRI data,"Deep Ordinal Ranking for Multi-Category Diagnosis of Alzheimer’s
Disease using Hippocampal MRI data
Hongming Li, Mohamad Habes, Yong Fan
nd for the Alzheimer's Disease Neuroimaging Initiative*
Section for Biomedical Image Analysis (SBIA), Center for Biomedical Image Computing and
Analytics (CBICA), Department of Radiology, Perelman School of Medicine, University of
Pennsylvania, Philadelphia, PA, 19104, USA
*Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database
(adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or
provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found
t: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf"
ac0d88ca5f75a4a80da90365c28fa26f1a26d4c4,MOT16: A Benchmark for Multi-Object Tracking,"MOT16: A Benchmark for Multi-Object Tracking
Anton Milan∗, Laura Leal-Taix´e∗, Ian Reid, Stefan Roth, and Konrad Schindler"
ac43a881c0ab8af00ccfca7cb7558c7f816ded1a,Multi-Step Prediction of Occupancy Grid Maps with Recurrent Neural Networks,"Multi-Step Prediction of Occupancy Grid Maps with Recurrent Neural Networks
Nima Mohajerin and Mohsen Rohani∗
Huawei Noah’s Ark, Markham, ON, Canada
{nima.mohajerin,"
ac26166857e55fd5c64ae7194a169ff4e473eb8b,Personalized Age Progression with Bi-Level Aging Dictionary Learning,"Personalized Age Progression with Bi-level
Aging Dictionary Learning
Xiangbo Shu, Jinhui Tang, Senior Member, IEEE, Zechao Li, Hanjiang Lai, Liyan Zhang
nd Shuicheng Yan, Fellow, IEEE"
ac5d0705a9ddba29151fd539c668ba2c0d16deb6,RED-Net: A Recurrent Encoder–Decoder Network for Video-Based Face Alignment,"IJCV special issue (Best papers of ECCV 2016) manuscript No.
(will be inserted by the editor)
RED-Net:
A Recurrent Encoder-Decoder Network for Video-based Face Alignment
Xi Peng · Rogerio S. Feris · Xiaoyu Wang · Dimitris N. Metaxas
Submitted: April 19 2017 / Revised: December 12 2017"
ac5c93b789bdd557b90ce77221f1c01ead63041f,Robust People Detection using Computer Vision Spring Term 2013,"Autonomous Systems Lab
Prof. Roland Siegwart
Master-Thesis
Robust People Detection
using Computer Vision
Spring Term 2013
Supervised by:
Jerome Maye
Paul Beardsley
Author:
Endri Dibra"
acb83d68345fe9a6eb9840c6e1ff0e41fa373229,"Kernel methods in computer vision: object localization, clustering, and taxonomy discovery","Kernel Methods in Computer Vision:
Object Localization, Clustering,
nd Taxonomy Discovery
vorgelegt von
Matthew Brian Blaschko, M.S.
us La Jolla
Von der Fakult¨at IV - Elektrotechnik und Informatik
der Technischen Universit¨at Berlin
zur Erlangung des akademischen Grades
Doktor der Naturwissenschaften
Dr. rer. nat.
genehmigte Dissertation
Promotionsausschuß:
Vorsitzender: Prof. Dr. O. Hellwich
Berichter: Prof. Dr. T. Hofmann
Berichter: Prof. Dr. K.-R. M¨uller
Berichter: Prof. Dr. B. Sch¨olkopf
Tag der wissenschaftlichen Aussprache: 23.03.2009
Berlin 2009"
ac20730c27b023ac8f75c3a62d001f8a3eb918dd,Sharing big display : développement des technologies et métaphores d'interactions nouvelles pour le partage collaboratif d'affichage en groupe ouvert. (Sharing big display : technology development and new interaction metaphors for collaborative sharing display in open group),"Sharing big display : développement des technologies et
métaphores d’interactions nouvelles pour le partage
ollaboratif d’affichage en groupe ouvert
Damien Marion
To cite this version:
Damien Marion. Sharing big display : développement des technologies et métaphores d’interactions
nouvelles pour le partage collaboratif d’affichage en groupe ouvert. Interface homme-machine [cs.HC].
Université de Bordeaux, 2017. Français. <NNT : 2017BORD0924>. <tel-01738673>
HAL Id: tel-01738673
https://tel.archives-ouvertes.fr/tel-01738673
Submitted on 20 Mar 2018
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
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L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,"
ac2a2c655ad1b3eea3f222b213a774aa25a403c0,Self-Learning of Feature Regions for Image Recognition,"Journal of Computer Sciences and Applications, 2015, Vol. 3, No. 1, 1-10
Available online at http://pubs.sciepub.com/jcsa/3/1/1
© Science and Education Publishing
DOI:10.12691/jcsa-3-1-1
Self-Learning of Feature Regions for Image Recognition
Satoru Yokota1,*, Jiang Li1, Yuichi Ogishima1, Hiromasa Kubo1, Hakaru Tamukoh2,*, Masatoshi Sekine1
Graduate School of Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan
Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu, Japan
*Corresponding author:
Received January 09, 2015; Revised January 19, 2015; Accepted January 22, 2015"
ac2e3a889fc46ca72f9a2cdedbdd6f3d4e9e2627,Age detection from a single image using multitask neural networks : An overview and design proposal,"Research School of Engineering
ANU College of Engineering and Computer Science
ASSIGNMENT COVER SHEET
THIS COVER SHEET SHOULD BE ATTACHED TO THE FRONT OF
YOUR ASSIGNMENT WHEN IT IS SUBMITTED
STUDENT NAME: Kiarie Ndegwa
STUDENT ID NUMBER: u4742829
COURSE NAME: Special Topics in Computer Science.
COURSE CODE: COMP6470
DUE DATE: 5pm 27th May 2016
Submission of this assignment constitutes a declaration that:
• No part of this work has been copied from any other person’s work except where
due acknowledgement is made in the text; and
• No part of this work has been written by any other person except where such
ollaboration has been authorised by the course lecturer concerned, and
• No part of this work has been submitted for assessment in another course in this or
nother part of the University except where authorised by the lecturer(s) concerned.
Student Name: Kiarie Ndegwa
Date: 27th May 2016"
ac6a9f80d850b544a2cbfdde7002ad5e25c05ac6,Privacy-Protected Facial Biometric Verification Using Fuzzy Forest Learning,"Privacy-Protected Facial Biometric Verification
Using Fuzzy Forest Learning
Richard Jiang, Ahmed Bouridane, Senior Member, IEEE, Danny Crookes, Senior Member, IEEE,
M. Emre Celebi, Senior Member, IEEE, and Hua-Liang Wei"
ac12ba5bf81de83991210b4cd95b4ad048317681,Combining Deep Facial and Ambient Features for First Impression Estimation,"Combining Deep Facial and Ambient Features
for First Impression Estimation
Furkan G¨urpınar1, Heysem Kaya2, Albert Ali Salah3
Program of Computational Science and Engineering, Bo˘gazi¸ci University,
Bebek, Istanbul, Turkey
Department of Computer Engineering, Namık Kemal University,
C¸ orlu, Tekirda˘g, Turkey
Department of Computer Engineering, Bo˘gazi¸ci University,
Bebek, Istanbul, Turkey"
acf13c52c86a3b38642ba0c6cbcd1b771778965c,NAACL HLT 2018 Generalization in the Age of Deep Learning Proceedings of the Workshop,"NAACLHLT2018GeneralizationintheAgeofDeepLearningProceedingsoftheWorkshopJune5,2018NewOrleans,Louisiana"
aca8c4a62ed6e590889f1e859d7bc79311fa6f4d,Beyond Universal Saliency: Personalized Saliency Prediction with Multi-task CNN,"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
Semantic labels Observer A Observer B Observer C Figure1:AnillustrationofPSMdataset.Ourdatasetprovidesbotheyefixationsofdifferentsubjectsandsemanticlabels.Duetothelargeamountofobjectsinourdataset,foreachimage,wedidn’tful-lysegmentitandonlylabelledobjectsthatcoveratleastthreegazepointsfromeachindividual.AnotabledifferencebetweenPSManditspredecessorsisthateachsubjectslooks4timesonPSMdatatoderivesolidfixationgroundtruthmaps.Bothcommonalityanddis-tinctivenessexistforPSMsviewedbydifferentparticipant.ThismotivatesustomodelPSMbasedonUSM.recognizingheterogeneityacrossindividuals.ExamplesinFig.1illustratethatwhilemultipleobjectsaredeemedhigh-lysalientwithinthesameimage(eg,humanface(firstrow),text(lasttowrows)andobjectof(highcolorcontrast),differ-entindividualshaveverydifferentfixationpreferenceswhenviewingtheimage.Fortherestofthepaper,weusetermuniversalsaliencytodescribesalientregionsthatincurhighfixationsacrossallsubjectsandtermpersonalizedsaliencytodescribetheheterogeneousones.Motivation.Infact,heterogeneityinsaliencypreferencehasbeenwidelyrecognizedinpsychology:”Interestingnessishighlysubjectiveandthereareindividualswhodidnotconsideranyimageinterestinginsomesequences”[Gyglietal.,2013].Therefore,onceweknowaperson’spersonal-izedinterestingnessovereachimage(personalizedsaliency),weshalldesigntailoredalgorithmstocatertohim/herneed-s.Forexample,intheapplicationofimageretargeting,thetextsonthetableinthefourthrowinFig.1shouldbepre-"
ac57b04359818c17d416ee53ae05a5f126eca4db,Detection and classification of the behavior of people in an intelligent building by camera,"Detection and classification of the behavior of people in an
intelligent building by camera
Henni Sid Ahmed1, Belbachir Mohamed Faouzi2, Jean Caelen3
Universite of sciences and technology USTO in Oran Algeria, laboratory LSSD, Faculty  genie
electrique, department electronique, BP 1505 el menouar Oran 31000 Algeria
Universite of sciences and technology USTO in Oran Algeria,  laboratory LSSD, Faculty genie
electrique, department electronique,  BP 1505 el menouar Oran 31000 Algeria
Universite Joseph Fourier, Grenoble, F , LIG   Grenoble computer laboratory ,domaine
universitaire BP 53, 220 rue de la   chimie 38041 Grenoble cedex 9  France
Emails: 1
Submitted: Apr. 10, 2013                 Accepted: July 30, 2013              Published: Sep. 3, 2013"
6acc92f30c7a141384b9b1bbec8dffe16b08a438,Improving Bag-of-Visual-Words Towards Effective Facial Expressive Image Classification,"Improving Bag-of-Visual-Words Towards Effective Facial Expressive
Image Classification
Dawood Al Chanti1 and Alice Caplier1
Univ. Grenoble Alpes, CNRS, Grenoble INP∗ , GIPSA-lab, 38000 Grenoble, France
Keywords:
BoVW, k-means++, Relative Conjunction Matrix, SIFT, Spatial Pyramids, TF.IDF."
6ae13c7dcd1d10d2dfe58546a49da09b0b471d68,Person-independent facial expression recognition based on compound local binary pattern (CLBP),"The International Arab Journal of Information Technology, Vol. 11, No. 2, March 2014                                                          195
Person-Independent Facial Expression Recognition
Based on Compound Local Binary Pattern (CLBP)
Department of Computer Science and Engineering, Islamic University of Technology, Bangladesh
Faisal Ahmed1, Hossain Bari2, and Emam Hossain3
2Samsung Bangladesh R & D Center Ltd, Bangladesh
3Department of Computer Science and Engineering, Ahsanullah University of Science and Technology,
Bangladesh"
6a553f7ef42000001f407e95f4955e7ddde46a83,A Dataset of Laryngeal Endoscopic Images with Comparative Study on Convolution Neural Network Based Semantic Segmentation,"IJCARS manuscript No.
(will be inserted by the editor)
A Dataset of Laryngeal Endoscopic Images with
Comparative Study on Convolution Neural Network
Based Semantic Segmentation
Max-Heinrich Laves · Jens Bicker · Lüder
A. Kahrs · Tobias Ortmaier
Received: date / Accepted: date"
6aa21d78af359853ee07288cfc8d047e914ce458,Facial Expression Recognition using Log-Euclidean Statistical Shape Models,"FACIAL EXPRESSION RECOGNITION USING
LOG-EUCLIDEAN STATISTICAL SHAPE MODELS
Bartlomiej W. Papiez, Bogdan J. Matuszewski, Lik-Kwan Shark and Wei Quan
Applied Digital Signal and Image Processing Research Centre, University of Central Lancashire, PR1 2HE Preston, U.K.
Keywords:
Facial expression representation, Facial expression recognition, Vectorial log-Euclidean statistics, Statistical
shape modelling."
6a69b790a7ec5a396607eb717da2b271a750faaa,Stacked Latent Attention for Multimodal Reasoning,"Stacked Latent Attention for Multimodal Reasoning
Haoqi Fan
Jiatong Zhou
Facebook Research
Facebook Research
Hacker Way
Hacker Way"
6a7ec333ccabd41b9d20f05c145b3377f6045f43,"C 2010 Zihan Zhou Face Recognition under Varying Illumination, Pose and Contiguous Occlusion",(cid:13) 2010 Zihan Zhou
6a41ba9db0affa701ea125e09a2fe7eb583e3ac9,Frontal imgelerden otomatik yüz tan ı ma Automatic face recognition from frontal images,"Frontal imgelerden otomatik yüz tanıma
Automatic face recognition from frontal images
Hasan Serhan Yavuz, Hakan Çevikalp, Rıfat Edizkan
Elektrik ve Elektronik Mühendisliği Bölümü
Eskişehir Osmangazi Üniversitesi
Eskişehir, Türkiye
fotoğraflanan
laboratuarımızda
Özetçe—Yüz tanıma basitçe kişilere ait olan yüz imgelerinden
kimlik  tespit  edilmesi  olarak  tanımlanabilir.  Bu  çalışmada,
sayısal  kamera
frontal
imgeler  kullanılarak  yüz  tanıma  yapılmıştır.  Otomatik  yüz
tanıma süreci sırasıyla yüz sezme, göz sezme, sezilen gözlerin orta
noktalarını  kullanarak  belirlenen  standart  bir  yüz  şablonuna
uyacak biçimde haritalama yapma ve sonrasında hizalanan yüz
imgelerini  sınıflandırma  basamaklarından  oluşur.  Literatürde
yüz  imgesi  hazırlama  süreci  genellikle  elle  yapılmaktadır.  Yüz
imgelerinin  tamamı  birebir  aynı  biçimde  kesildiği  için  çok
yüksek tanıma oranları elde edilir ancak bir otomatik yüz tanıma"
6a536aa4ecd6359d54a34aca7eff828e4df02730,Multimodal Observation and Interpretation of Subjects Engaged in Problem Solving,"Multimodal Observation and Interpretation of Subjects Engaged
in Problem Solving
Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP*, LIG,
Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP*, LIG,
(cid:140)omas Guntz
F-38000 Grenoble, France
Dominique Vaufreydaz
F-38000 Grenoble, France
Ra(cid:130)aella Balzarini
F-38000 Grenoble, France
James Crowley
F-38000 Grenoble, France
Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP*, LIG,
Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP*, LIG,"
6a4bb2a376068d8186272306c500ea332db0eae7,Gender Classification from Neutral and Expressive Faces,"Sixth International Conference on Machine Vision (ICMV 2013), edited by Antanas Verikas,
Branislav Vuksanovic, Jianhong Zhou, Proc. of SPIE Vol. 9067, 906723 · © 2013
SPIE · CCC code: 0277-786X/13/$18 · doi: 10.1117/12.2051041
Proc. of SPIE Vol. 9067  906723-1"
6a16b91b2db0a3164f62bfd956530a4206b23fea,A Method for Real-Time Eye Blink Detection and Its Application,"A Method for Real-Time Eye Blink Detection and Its Application
Chinnawat Devahasdin Na Ayudhya
Mahidol Wittayanusorn School
Puttamonton, Nakornpatom 73170, Thailand"
6a3a07deadcaaab42a0689fbe5879b5dfc3ede52,Learning to Estimate Pose by Watching Videos,"Learning to Estimate Pose by Watching Videos
Prabuddha Chakraborty and Vinay P. Namboodiri
Department of Computer Science and Engineering
IIT Kanpur
{prabudc, vinaypn}"
6a9c460952a96a04e12caa7bae07ae2f7df1238e,Exploiting scene context for on-line object tracking in unconstrained environments. (Exploitation du contexte de scène pour le suivi d'objet en ligne dans des environnements non contraints),"Exploiting scene context for on-line object tracking in
unconstrained environments
Salma Moujtahid
To cite this version:
Salma Moujtahid. Exploiting scene context for on-line object tracking in unconstrained environments.
Modeling and Simulation. Université de Lyon, 2016. English. <NNT : 2016LYSEI110>. <tel-
01783935>
HAL Id: tel-01783935
https://tel.archives-ouvertes.fr/tel-01783935
Submitted on 2 May 2018
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6a75ef6b36489cb59c61f21f3cd09c50ad5b2995,MVTec D2S: Densely Segmented Supermarket Dataset,"MVTec D2S: Densely Segmented Supermarket
Dataset
Patrick Follmann1,2[0000−0001−5400−2384], Tobias B¨ottger1,2[0000−0002−5404−8662],
Philipp H¨artinger1[0000−0002−7093−6280], Rebecca K¨onig1[0000−0002−4169−6759],
nd Markus Ulrich1[0000−0001−8457−5554]
MVTec Software GmbH, 80634 Munich, Germany
https://www.mvtec.com/research
Technical University of Munich, 80333 Munich, Germany"
6a14652508138fcf0aa8c518109165f65c88fd3f,Programming a humanoid robot in natural language : an experiment with description logics,"Programming a humanoid robot in natural language:
n experiment with description logics
Nicola Vitucci , Alessio Mauro Franchi, Giuseppina Gini
DEIB, Politecnico di Milano
Milano, Italy"
6a4dbd2db0e09a4f3aab3d2602db3176f825d8f9,Multi-Rate Gated Recurrent Convolutional Networks for Video-Based Pedestrian Re-Identification,"The Thirty-Second AAAI Conference
on Artificial Intelligence (AAAI-18)
Multi-Rate Gated Recurrent Convolutional Networks
for Video-Based Pedestrian Re-Identification
Zhihui Li,1 Lina Yao,2 Feiping Nie,3∗ Dingwen Zhang,4 Min Xu5
School of Computer Science and Engineering, University of New South Wales.
Beijing Etrol Technologies Co., Ltd.
Centre for OPTical Imagery Analysis and Learning, Northwestern Polytechnical University.
School of Automation, Northwestern Polytechnical University.
5School of Electrical and Data Engineering, University of Technology Sydney."
6af98f9843ba629ae1b0347e8b8d81a263f8d7f2,Does this recession make me look black? The effect of resource scarcity on the categorization of biracial faces.,"Short Report
Does This Recession Make Me Look Black?
The Effect of Resource Scarcity on the
Categorization of Biracial Faces
3(12) 1476 –1478
© The Author(s) 2012
Reprints and permission:
sagepub.com/journalsPermissions.nav
DOI: 10.1177/0956797612450892
http://pss.sagepub.com
Christopher D. Rodeheffer, Sarah E. Hill, and Charles G. Lord
Texas Christian University
Received 2/27/12; Revision accepted 5/10/12
Prosperity makes friends; adversity tries them.
—Publilius Syrus (Lyman, 1856, p. 73)
In-group  biases  are  a  ubiquitous  feature  of  human  social  life
(e.g., Brewer, 1979; Halevy, Bornstein, & Sagiv, 2008; Mullen,
Dovidio, Johnson, & Copper, 1992; Tajfel, 1982). One explana-
tion  offered  for  these  biases  is  that  they  arise  from  resource
ompetition between groups (e.g., Kurzban & Neuberg, 2005;"
6a951df76a56fc89e5df3fbba2e5699ccad4f199,Relative Pairwise Relationship Constrained Non-negative Matrix Factorisation,"IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Relative Pairwise Relationship Constrained
Non-negative Matrix Factorisation
Shuai Jiang, Kan Li, and Richard Yida Xu"
6a3cbe2bb27b2a7d32c358e0be4ed268f7d4455c,"Modeling Shape, Appearance and Self-Occlusions for Articulated Object Tracking","Modeling Shape, Appearance and Self-Occlusions
for Articulated Object Tracking
Yanchao Yang and Ganesh Sundaramoorthi"
6ae47c7793e2f0f684ae07357335c7cf338d66ef,Optimistic and pessimistic neural networks for object recognition,"published in: International Conference on Image Processing (ICIP) 2017
OPTIMISTIC AND PESSIMISTIC NEURAL NETWORKS FOR OBJECT RECOGNITION
Rene Grzeszick
Sebastian Sudholt
Gernot A. Fink
email:
TU Dortmund University, Germany"
6a55d6db1b31f44c9bb37b070fbf7c8f64a31f13,Aging and Emotion Recognition : An Examination of Stimulus and Attentional Mechanisms,"Cleveland State University
ETD Archive
Aging and Emotion Recognition: An Examination
of Stimulus and Attentional Mechanisms
Stephanie Nicole Sedall
Follow this and additional works at: http://engagedscholarship.csuohio.edu/etdarchive
Part of the Experimental Analysis of Behavior Commons
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uthorized administrator of For more information, please contact"
6a38c575733b0f7118970238e8f9b480522a2dbc,Random Forests Can Hash,"Accepted as a workshop contribution at ICLR 2015
RANDOM FORESTS CAN HASH
Qiang Qiu, Guillermo Sapiro, and Alex Bronstein
Duke University and Tel Aviv University
{qiang.qiu,"
6a1e5f4dbabf451122bf35228c8b25c79c7d235f,Learning to See the Invisible: End-to-End Trainable Amodal Instance Segmentation,"Learning to See the Invisible: End-to-End
Trainable Amodal Instance Segmentation
Patrick Follmann, Rebecca K¨onig, Philipp H¨artinger, Michael Klostermann
MVTec Software GmbH,
www.mvtec.com,"
6af35225cfd744b79577c126e553f549e5b5cdcc,Title Discriminative Hessian Eigenmaps for face recognition,"Title
Discriminative Hessian Eigenmaps for face recognition
Author(s)
Si, S; Tao, D; Chan, KP
Citation
The 2010 IEEE  International Conference on Acoustics, Speech
nd Signal Processing (ICASSP), Dallas, TX., 14-19 March 2010.
In IEEE International Conference on Acoustics, Speech and
Signal Processing Proceedings, 2010, p. 5586-5589
Issued Date
http://hdl.handle.net/10722/125723
Rights
IEEE International Conference on Acoustics, Speech and Signal
Processing Proceedings. Copyright © IEEE.; ©2010 IEEE.
Personal use of this material is permitted. However, permission
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omponent of this work in other works must be obtained from
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6a8a3b27a78c78bc80984fca29554de3269d34d3,Speeding-Up Object Detection Training for Robotics with FALKON,"Speeding-up Object Detection Training for Robotics with FALKON
Elisa Maiettini1,2,3, Giulia Pasquale1,2, Lorenzo Rosasco2,3 and Lorenzo Natale1"
6a27ffd788a0db64fef74e673786763c82902a26,Discriminative deep transfer metric learning for cross-scenario person re-identification,"Downloaded From: https://www.spiedigitallibrary.org/journals/Journal-of-Electronic-Imaging on 9/13/2018
Terms of Use: https://www.spiedigitallibrary.org/terms-of-use
Discriminativedeeptransfermetriclearningforcross-scenariopersonre-identificationTongguangNiXiaoqingGuHongyuanWangZhongbaoZhangShoubingChenCuiJinTongguangNi,XiaoqingGu,HongyuanWang,ZhongbaoZhang,ShoubingChen,CuiJin,“Discriminativedeeptransfermetriclearningforcross-scenariopersonre-identification,”J.Electron.Imaging27(4),043026(2018),doi:10.1117/1.JEI.27.4.043026."
6ada03f390f92704f3df1556846697c54c00f7da,of Problem Solving Human-Machine Cooperation in Large-Scale Multimedia Retrieval : A Survey,"Human-Machine Cooperation in Large-Scale
Multimedia Retrieval: A Survey
Kimiaki Shirahama,1 Marcin Grzegorzek,1 and Bipin Indurkhya2
University of Siegen, 2AGH University of Science and Technology
Correspondence:
Correspondence concerning this
rticle should be addressed to Kimiaki
Shirahama, Pattern Recognition Group,
University of Siegen, Hoelderlinstrasse 3,
57076 Siegen, Germany, or via email to
Keywords:
large-scale multimedia retrieval, human-
machine cooperation, machine-based
methods, human-based methods
Large-Scale Multimedia Retrieval (LSMR) is the task to fast analyze a large amount of multi-
media data like images or videos and accurately find the ones relevant to a certain semantic
meaning.  Although  LSMR  has  been  investigated  for  more  than  two  decades  in  the  fields
of multimedia processing and computer vision, a more interdisciplinary approach is neces-
sary to develop an LSMR system that is really meaningful for humans. To this end, this paper
ims to stimulate attention to the LSMR problem from diverse research fields. By explaining"
6a0b70abb9a81a96d4baa9b396deb9da4cc20f8f,Clustering through ranking on manifolds,"Clustering Through Ranking On Manifolds
Markus Breitenbach
Dept. of Computer Science; University of Colorado, Boulder, USA
Gregory Z. Grudic
Dept. of Computer Science; University of Colorado, Boulder, USA"
6a8a3c604591e7dd4346611c14dbef0c8ce9ba54,An Affect-Responsive Interactive Photo Frame,"ENTERFACE’10, JULY 12TH - AUGUST 6TH, AMSTERDAM, THE NETHERLANDS.
An Affect-Responsive Interactive Photo Frame
Hamdi Dibeklio˘glu, Ilkka Kosunen, Marcos Ortega Hortas, Albert Ali Salah, Petr Zuz´anek"
6a951a47aa545e08508b0b2c6a2bef45e154a3a9,DeepCoder: Semi-Parametric Variational Autoencoders for Automatic Facial Action Coding,"DeepCoder: Semi-parametric Variational Autoencoders
for Automatic Facial Action Coding
Dieu Linh Tran∗, Robert Walecki, Ognjen (Oggi) Rudovic*, Stefanos Eleftheriadis,
Bj¨orn Schuller and Maja Pantic
{linh.tran, r.walecki14, bjoern.schuller,"
6ac7fe3a292dc5e0f7d27e11b85ed8277905e9ba,Detecting Traffic Lights by Single Shot Detection,"Detecting Traffic Lights by Single Shot Detection
Julian M¨uller1 and Klaus Dietmayer1"
6ad32b70ee21b6fc16ff4caf7b4ada2aaf13cabc,Efficient Subwindow Search: A Branch and Bound Framework for Object Localization,"Efficient Subwindow Search: A Branch and Bound
Framework for Object Localization
Christoph H. Lampert, Matthew B. Blaschko, and Thomas Hofmann
n image of as low resolution as 320×240 contains more than
one billion rectangular subimages. In general, the number of
subimages grows quadratically with the number of image pix-
els, which makes it computationally too expensive to evaluate
the quality function exhaustively for all of these. Instead, one
typically uses heuristics to speed up the search that introduce
the risk of mispredicting the location of an object or even
missing it."
6aefe7460e1540438ffa63f7757c4750c844764d,Non-rigid Segmentation Using Sparse Low Dimensional Manifolds and Deep Belief Networks,"Non-rigid Segmentation using Sparse Low Dimensional Manifolds and
Deep Belief Networks ∗
Jacinto C. Nascimento
Instituto de Sistemas e Rob´otica
Instituto Superior T´ecnico, Portugal"
6afed8dc29bc568b58778f066dc44146cad5366c,Kernel Hebbian Algorithm for Single-Frame Super-Resolution,"Kernel Hebbian Algorithm for Single-Frame
Super-Resolution
Kwang In Kim1, Matthias O. Franz1, and Bernhard Sch¨olkopf1
Max Planck Institute f¨ur biologische Kybernetik
Spemannstr. 38, D-72076 T¨ubingen, Germany
{kimki, mof,
http://www.kyb.tuebingen.mpg.de/"
6a6280189ead63b2eec733b8e8ac507e830928fd,Face localization in color images with complex background,"Face localization in color images with complex
ackground
Paola Campadelli, Raffaella Lanzarotti, Giuseppe Lipori
Dipartimento di Scienze dell’Informazione
Universit(cid:30)a degli Studi di Milano
Via Comelico, 39/41 20135 Milano, Italy
fcampadelli, lanzarotti,"
6a52e6fce541126ff429f3c6d573bc774f5b8d89,Role of Facial Emotion in Social Correlation,"Role of Facial Emotion in Social Correlation
Pankaj Mishra, Rafik Hadfi, and Takayuki Ito
Department of Computer Science and Engineering
Nagoya Institute of Technology, Gokiso, Showa-ku, Nagoya, 466-8555 Japan
{pankaj.mishra,"
6a806978ca5cd593d0ccd8b3711b6ef2a163d810,Facial feature tracking for emotional dynamic analysis,"Facial feature tracking for Emotional Dynamic
Analysis
Thibaud Senechal1, Vincent Rapp1, and Lionel Prevost2
ISIR, CNRS UMR 7222
Univ. Pierre et Marie Curie, Paris
{rapp,
LAMIA, EA 4540
Univ. of Fr. West Indies & Guyana"
6a9c3011b5092daa1d0cacda23f20ca4ae74b902,Fast and Accurate Person Re-Identification with RMNet.,"Fast and Accurate Person Re-Identification
with RMNet
Evgeny Izutov
IOTG Computer Vision (ICV), Intel"
63e9deb375f260bdc0c4308410caca0755d307c5,A New 3D Object Pose Detection Method Using LIDAR Shape Set,"Article
A New 3D Object Pose Detection Method Using
LIDAR Shape Set
Jung-Un Kim and Hang-Bong Kang *
Department of Media Engineering, Catholic University of Korea, 43-1, Yeoggok 2-dong, Wonmmi-gu,
Bucheon-si, Gyeonggi-do 14662, Korea;
* Correspondence: Tel.: +82-2-2164-4599
Received: 7 February 2018; Accepted: 14 March 2018; Published: 16 March 2018"
634f698c05d640ab355e94a9a0cf9191891b3dcb,Video Face Recognition From A Single Still Image Using an Adaptive Appearance Model Tracker,"Video Face Recognition From A Single Still Image
Using an Adaptive Appearance Model Tracker
M. Ali Akber Dewan
E. Granger, R. Sabourin
G.-L. Marcialis, F. Roli
School of Computing and Information
Systems, Athabasca University
Department of Automated Production
Engineering, École de technologie supé-
Department of Electrical and Electronic
Engineering, University of Cagliari
Edmonton, Canada
rieure, Montreal, Canada
Cagliari, Italy"
63344dee49a1ab7e27ac34eefc30fb948a0bf9bb,Geometry and Illumination Modelling for Scene Understanding,"Geometry and Illumination Modelling for Scene Understanding
Principal Investigators: Jana Koˇseck´a and Dimitris Samaras
Project Summary The goal this proposal is to develop unified framework for reasoning about
objects, scenes and lighting from single and multiple views of indoors and outdoors environments.
We propose computational models for semantic parsing of scenes which incorporate information
bout the lighting and illumination to resolve the ambiguities of purely appearance based methods
nd develop class of models where partial geometry and semantic information aid the process of
recovery of illumination. The proposed work can be partitioned into three main research topics:
. Supervised approach for semantic parsing of object and non-object categories using photo-
metric, geometric and shadow cues.
. Closing the loop on estimation of Illumination using coarse object models and geometric
ontext.
. Object recognition, change detection, scene matching and 3D reconstruction with dramatic
hanges in illumination.
We propose to study the interactions between appearance, geometry and lighting in the context
of the problems outlined above and develop computational models which jointly consider these
spects. In some cases different models will serve as preprocessing stage for the follow up prob-
lems and in others they will interact jointly or in a feedback loop manner. For joint interactions
final inference for estimation of semantic categories and illumination will be formulated in Markov
Random field or Conditional Markov Random field using both photometric, geometric and illumi-"
631d21e51ca9100f1eca3c80dcf42db81cfc7e2b,Interactive Person Following and Gesture Recognition with a Flying Robot,"Interactive Person Following and
Gesture Recognition with a Flying Robot
Tayyab Naseer*, J¨urgen Sturm†, Wolfram Burgard*, and Daniel Cremers†
*Department of Computer Science, University of Freiburg, Germany
Department of Computer Science, Technical University of Munich, Germany"
63c7c0511e82172b6b60af21e56df68e2c6ab228,Target-based evaluation of face recognition technology for video surveillance applications,"Target-based evaluation of face recognition
technology for video surveillance applications
Dmitry Gorodnichy and Eric Granger"
63cbfc7bfabd1e234c779f8445ea775b74d8fbe8,Adequacy of the Gradient-Descent Method for Classifier Evasion Attacks,"Adequacy of the Gradient-Descent Method for
Classifier Evasion Attacks
Yi Han
School of Computing and Information Systems
University of Melbourne
Ben Rubinstein
School of Computing and Information Systems
University of Melbourne"
63f2c3e312d07c6452bdad0a8adef1b879950500,Multi-stage sampling with boosting cascades for pedestrian detection in images and videos,"Multi-stage Sampling with Boosting Cascades
for Pedestrian Detection in Images and Videos
Giovanni Gualdi, Andrea Prati, and Rita Cucchiara
University of Modena and Reggio Emilia(cid:2), Italy"
6321b0a9f210203a869cf38ca71d9b099943fb62,Kernels on Attributed Pointsets with Applications,"Kernels on Attributed Pointsets with Applications
Mehul Parsana
Sourangshu Bhattacharya
Chiranjib Bhattacharyya
K. R. Ramakrishnan"
63ed42249d7cbb21a4b0d42419d42b014ff114eb,Comprehensive Parameter Sweep for Learning-Based Detector on Traffic Lights,"Aalborg Universitet
Comprehensive Parameter Sweep for Learning-based Detector on Traffic Lights
Jensen, Morten Bornø; Philipsen, Mark Philip; Trivedi, Mohan M.; Moeslund, Thomas B.
Published in:
Advances in Visual Computing. ISVC 2016
DOI (link to publication from Publisher):
0.1007/978-3-319-50832-0_10
Publication date:
Document Version
Accepted author manuscript, peer reviewed version
Link to publication from Aalborg University
Citation for published version (APA):
Jensen, M. B., Philipsen, M. P., Trivedi, M. M., & Moeslund, T. B. (2016). Comprehensive Parameter Sweep for
Learning-based Detector on Traffic Lights. In Advances in Visual Computing. ISVC 2016: Lecture Notes in
Computer Science (LNCS) (Vol. 10073, pp. 92-100). Springer. DOI: 10.1007/978-3-319-50832-0_10
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? Users may download and print one copy of any publication from the public portal for the purpose of private study or research.
? You may not further distribute the material or use it for any profit-making activity or commercial gain"
63ebe80e020d902bc1fdc865c23a9ad7d1eac17a,Exploring the feasibility of subliminal priming on smartphones,"Exploring the Feasibility of Subliminal Priming on
Anonymised for blind review
Smartphones
Affiliation
City, Country
e-mail address"
637648198f9e91654ce27eaaa40512f2dc870fc1,Survey of Visual Question Answering: Datasets and Techniques,"Survey of Visual Question Answering: Datasets and Techniques
Akshay Kumar Gupta
Indian Institute of Technology Delhi"
63c022198cf9f084fe4a94aa6b240687f21d8b41,Consensus Message Passing for Layered Graphical Models,
635bea02dae6d4402b53eb3b31930b53ef00adc0,Unsupervised Feature Learning for Dense Correspondences Across Scenes,"Unsupervised Feature Learning for Dense Correspondences
cross Scenes
Chao Zhang, Chunhua Shen, Tingzhi Shen
v1 July 2014; v2 December 2014; v3 April 2015"
63a1efb1dc6d269b9f77aea5adad914c30e62686,Predicting Human Trajectories in Multi-Class Settings,"Predicting Human Trajectories in Multi-Class Settings
Karthik Raju (kraju444), Max Chang (mchang4)
December 17, 2016
Introduction
Related Work
In recent years, autonomous navigation has be-
ome of increasing prominence with companies like
Uber, Google, and Tesla building their state-of-the-
rt self-driving cars. There are other opportuni-
ties, however, for self-navigating vehicles in non-road
settings,
including social robots that can navigate
rowded spaces. Examples include driving through
malls, walking dogs through populated parks, or help-
ing blind pedestrians navigate around others.
In navigating crowded areas, autonomous agents
must adopt a set of common sense rules and social
onventions in interacting with other objects in the
same proximity. For example, when humans are de-
iding how to walk on a crowded sidewalk, they plan"
63db76fc3ab23beb921be682d70eb021cb6c4f16,How Polarized Have We Become? A Multimodal Classification of Trump Followers and Clinton Followers,
63c65e8584d2c3fb8833af772eb713f438cbdfe0,Exposing seam carving forgery under recompression attacks by hybrid large feature mining,"Cancún Center, Cancún, México, December 4-8, 2016
978-1-5090-4846-5/16/$31.00 ©2016 IEEE"
631483c15641c3652377f66c8380ff684f3e365c,Sync-DRAW: Automatic GIF Generation using Deep Recurrent Attentive Architectures,"Sync-DRAW: Automatic Video Generation using Deep Recurrent
A(cid:130)entive Architectures
Gaurav Mi(cid:138)al∗
Tanya Marwah∗
IIT Hyderabad
Vineeth N Balasubramanian
IIT Hyderabad"
632fa986bed53862d83918c2b71ab953fd70d6cc,What Face and Body Shapes Can Tell About Height,"GÜNEL ET AL.: WHAT FACE AND BODY SHAPES CAN TELL ABOUT HEIGHT
What Face and Body Shapes Can Tell
About Height
Semih Günel
Helge Rhodin
Pascal Fua
CVLab
EPFL,
Lausanne, Switzerland"
6388fb33b274d74a158aa3ed658a2d5d9e6553d2,Modeling scenes with local descriptors and latent aspects,"Modeling Scenes with Local Descriptors and Latent Aspects
P. Quelhasy, F. Monayy, J.-M. Odobezy, D. Gatica-Perezy, T. Tuytelaarsz, and L. Van Goolz
yIDIAP Research Institute, Martigny, Switzerland
zKatholieke Universiteit, Leuven, Belgium
fquelhas, monay, odobez, ftinne.tuytelaars,"
63c109946ffd401ee1195ed28f2fb87c2159e63d,Robust Facial Feature Localization Using Improved Active Shape Model and Gabor Filter,"MVA2011 IAPR Conference on Machine Vision Applications, June 13-15, 2011, Nara, JAPAN
Robust Facial Feature Localization using Improved Active Shape
Model and Gabor Filter
Hui-Yu Huang
Engineering, National Formosa University,
Taiwan
E-mail:"
63cf5fc2ee05eb9c6613043f585dba48c5561192,Prototype Selection for Classification in Standard and Generalized Dissimilarity Spaces,"Prototype Selection for
Classification in Standard
nd Generalized
Dissimilarity Spaces"
632931c2da6d558cca3c9e57cd27bca2dd36fdca,Secured access to terminals and teleservices using biometrics verification,"Secured access to terminals and teleservices using biometrics verification
G. Richard1, Y. Menguy, I. Guis, P. Lockwood
Matra Nortel Communications
Rue JP Timbaud, 78392 Bois d’Arcy, France."
63dbacac269c29b46b2b0bddbef828db025689dd,Deep Structure Inference Network for Facial Action Unit Recognition,"Deep Structure Inference Network for Facial Action Unit Recognition
Ciprian A. Corneanu1, Meysam Madadi2,3, Sergio Escalera1,2
Dept. Mathematics and Informatics, Universitat de Barcelona, Catalonia, Spain
Computer Vision Center, Edifici O, Campus UAB, 08193 Bellaterra (Barcelona), Catalonia, Spain
Dept. of Computer Science, Univ. Aut`onoma de Barcelona (UAB), 08193 Bellaterra, Catalonia, Spain"
634541661d976c4b82d590ef6d1f3457d2857b19,Advanced Techniques for Face Recognition under Challenging Environments,"AAllmmaa  MMaatteerr  SSttuuddiioorruumm  ––  UUnniivveerrssiittàà  ddii  BBoollooggnnaa
in cotutela con Università di Sassari
DOTTORATO DI RICERCA IN
INGEGNERIA ELETTRONICA, INFORMATICA E DELLE
TELECOMUNICAZIONI
Ciclo XXVI
Settore Concorsuale di afferenza: 09/H1
Settore Scientifico disciplinare: ING-INF/05
ADVANCED TECHNIQUES FOR FACE RECOGNITION
UNDER CHALLENGING ENVIRONMENTS
TITOLO TESI
YUNLIAN SUN
Presentata da:
Coordinatore Dottorato
ALESSANDRO VANELLI-CORALLI
Relatore
DAVIDE MALTONI
Relatore
MASSIMO TISTARELLI
Esame finale anno 2014"
6358b95b1c97df4f10f57a90913f672e44d2094b,Opponent colors for human detection,"Opponent Colors for Human Detection
Rao Muhammad Anwer, David V´azquez, and Antonio M. L´opez
Computer Vision Center and Computer Science Dpt.,
Universitat Aut`onoma de Barcelona
-- www.cvc.uab.es/adas
Edifici O, 08193 Bellaterra, Barcelona, Spain"
6374e5307c699da00875a858134653c16d5fd035,Audio-Visual Speech Modeling for Continuous Speech Recognition,"IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 2, NO. 3, SEPTEMBER 2000
Audio-Visual Speech Modeling for Continuous
Speech Recognition
Stéphane Dupont and Juergen Luettin"
6306ee4a2bab01890eacd74e55aedb207fed0353,Structure-Measure: A New Way to Evaluate Foreground Maps,"Structure-measure: A New Way to Evaluate Foreground Maps
Deng-Ping Fan1
Ming-Ming Cheng1 ∗
CCCE, Nankai University
Yun Liu1
Tao Li1
CRCV, UCF
Ali Borji2
http://dpfan.net/smeasure/"
6372262685162f3f11ef7ac1882c327e98564875,A Survey of Approaches for Curve Based Facial Surface Representations For Three-Dimensional Face Recognition,"A Survey of Approaches for Curve Based Facial Surface Representations
For Three-Dimensional Face Recognition
Aouragh Salima1,3, Sbaa Salim2, Taleb-Ahmed Abdelmalik3
Department of Electrical engineering, Kasdi Merbah University, Ouargla, Algeria.
Department of Electrical engineering, Mohamed Kheider University, Biskra, Algeria.
LAMIH UMR CNRS 8201 UVHC, University of Valenciennes and Hainaut Cambrésis, France."
63488398f397b55552f484409b86d812dacde99a,Learning Universal Multiview Age Estimator by Video Contexts,"Learning Universal Multi-view Age Estimator by Video Contexts
Zheng Song1, Bingbing Ni3, Dong Guo4, Terence Sim2, Shuicheng Yan1
Department of Electrical and Computer Engineering, 2 School of Computing, National University of Singapore;
{zheng.s,
Advanced Digital Sciences Center, Singapore; 4 Facebook"
6388c3f3559b61632942856bbede67b724542c9e,Multi-Target Tracking Using Hierarchical Convolutional Features and Motion Cues,"(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 8, No. 11, 2017
Multi-Target Tracking Using Hierarchical
Convolutional Features and Motion Cues
Heba Mahgoub, Khaled Mostafa, Khaled T. Wassif, Ibrahim Farag
Faculty of Computers and Information
Cairo University
Cairo, Egypt"
6332a99e1680db72ae1145d65fa0cccb37256828,Pose and Face Recovery via Spatio-temporal GrabCut Human Segmentation,"MASTER IN COMPUTER VISION AND ARTIFICIAL INTELLIGENCE
REPORT OF THE RESEARCH PROJECT
OPTION: COMPUTER VISION
Pose and Face Recovery via
Spatio-temporal GrabCut Human
Segmentation
Author: Antonio Hernández Vela
Date: 13/07/2010
Advisor: Sergio Escalera Guerrero"
636027f52ab111b2b22332ab2ec5346d03aac305,Unsupervised learning of foreground object detection,"Unsupervised learning of foreground object detection
Ioana Croitoru · Simion-Vlad Bogolin · Marius Leordeanu"
630ae5a734dc9dc9d46b05278f0bcb5a252f6998,FACIAL EXPRESSION RECOGNITION USING AAM ALGORITHM,"Generated by Foxit PDF Creator © Foxit Software
http://www.foxitsoftware.com   For evaluation only.
FACIAL EXPRESSION RECOGNITION USING AAM ALGORITHM
Thanh Nguyen  Duc, Tan Nguyen  Huu, Luy Nguyen  Tan*
Division of Automatic Control, Ho Chi Minh University of Technology, Vietnam
*National key lab for Digital Control & System Engineering, Vietnam"
63f38f60022ab78aa5e47bd84070547409ab3cc8,The Use of Semantic Human Description as a Soft Biometric,"The Use of Semantic Human Description as a Soft Biometric
Sina Samangooei
Baofeng Guo
Mark S. Nixon"
63b89e654124eb2b8edeeb82c6373bdcf228744e,Single-Image 3D Scene Parsing Using Geometric Commonsense,"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
image I3D	reconstructed	sceneFigure1:Single-view3DscenereconstructionusingGeometriccommonsense.Top:theworldisfullofcommonsenseovergeo-metricdimensions,e.g.,thatasedanisabout4.5meterslong.Bot-tom:exemplarresultoftheproposedmethod,includingsynthesizedimage(left),planarsegmentation(middle),anddepthmap(right).geometriccommonsensefor3Dsceneparsing.Suchapars-ingtaskaimstosegmentbothlow-levelsceneentities(e.g.,straightedges,semanticregions)andobject-levelsceneenti-ties(e.g.,human,vehicles)in2Dimages,andestimatetheirgeometricdimensionsinthe3Dworld[Hoiemetal.,2005;DelPeroetal.,2013;Liuetal.,2014;Wangetal.,2015a;Mottaghietal.,2016].Mostexisting3Dparsingalgo-rithms[Hoiemetal.,2008]aredesignedforaparticu-lartypeofscenecategories,e.g.,urban[Liuetal.,2014;Guptaetal.,2010],indoor[Wangetal.,2015b].Howev-er,apracticalAIsystem,e.g.,autonomousdriving,usuallyneedstodealwithawidevarietyofscenecategories.Oursolutiontotheabovechallengesismotivatedbythefactthatwehumanbeings,unconsciouslysometimes,uti-lizerichpriorknowledgeofthegeometricdimensionsofsceneentitiestounderstandthescenestructuresinimagesorvideos[Davisetal.,1993].Thisknowledgecanberoughlydividedintotwotypes:i)priordistributionsoverasingledi-mensionofobjects,e.g.,theheightofafemaleadultisabout1.75meters,orthatthelengthofasedanisabout4.5meters;ii)pair-wisecomparisonsbetweenthedimensionsofdifferentsceneentitiesatbothobject-level,e.g.,human,windows,ve-hicles,etc.,andpart-level,e.g.,straightedges,planarregions,etc.AsillustratedinFigure1,forexample,thewindowedgesonthesamefacadeareparalleltoeachotherandareorthog-onaltotheedgesontheground,abuildingishigherthanahuman,orthelengthofallsedansareroughlyequal.Theseu-naryandpair-wiseknowledge,onceacquired,arevalidacross"
630120d6cb9744f00d572d55701f90aff1951710,Analysing object detectors from the perspective of co-occurring object categories,"Analysing object detectors from the perspective of
o-occurring object categories
Csaba Nemes
Nokia Bell Labs
Budapest, Hungary
Sandor Jordan
Nokia Bell Labs
Budapest, Hungary
Email:
Email:"
63f2d3588994f19886e3131d8615f7375d41ef9c,Can low level image differences account for the ability of human observers to discriminate facial identity?,"http://journalofvision.org/8/15/5/
Can low level image differences account for the ability
of human observers to discriminate facial identity?
Danelle A. Wilbraham
Ohio State University, USA
James C. Christensen
Aleix M. Martinez
James T. Todd
Ohio State University, USA
Ohio State University, USA
Ohio State University, USA
A fundamental difficulty for image- or appearance-based models of face recognition is to distinguish variations in image
structure between two different individuals from those that can occur for a given individual due to changes in lighting, facial
expression, or pose. The research described in the present article was designed to examine how human observers are able
to cope with this problem. In two experiments, observers performed either a match-to-sample task (Experiment 1) or
same–different identity judgments (Experiment 2) for photographs of unfamiliar individuals. A key aspect of these studies is
that the matching or same stimulus pairs were never identical; that is to say, they always differed in terms of facial
expression or the pattern of illumination. In order to provide a quantitative assessment of appearance-based models, we
lso measured the optical differences for each pair of same or different images using a variety of possible distance metrics
ased on the pattern of pixel intensities or wavelet decompositions. These difference measures were then correlated with"
62dd66f9f4995cfdaafb479de50363ce0255b1bd,2 Feature Extraction Based on Wavelet Moments and Moment Invariants in Machine Vision Systems,"We are IntechOpen,
the world’s leading publisher of
Open Access books
Built by scientists, for scientists
,800
16,000
Open access books available
International  authors and editors
Downloads
Our authors are among the
Countries delivered to
TOP 1%
2.2%
most cited scientists
Contributors from top 500 universities
Selection of our books indexed in the Book Citation Index
in Web of Science™ Core Collection (BKCI)
Interested in publishing with us?
Contact
Numbers displayed above are based on latest data collected."
6211ba456908d605e85d102d63b106f1acb52186,Visual Interpretability forDeepLearning,"Zhang et al. / Front Inform Technol Electron Eng
in press
Frontiers of Information Technology & Electronic Engineering
www.jzus.zju.edu.cn; engineering.cae.cn; www.springerlink.com
ISSN 2095-9184 (print); ISSN 2095-9230 (online)
E-mail:
Visual Interpretability for Deep Learning∗
Quanshi Zhang and Song-Chun Zhu
(University of California, Los Angeles)
E-mail:"
6215c5713adeacbb33b9d1c4c739f2b0b50dd17f,PART-BASED 3 D FACE RECOGNITION UNDER POSE AND EXPRESSION VARIATIONS,"PART-BASED 3D FACE RECOGNITION UNDER POSE AND EXPRESSION
VARIATIONS
Hamdi Dibeklio˘glu
B.S, in Computer Engineering, Yeditepe University, 2006
Submitted to the Institute for Graduate Studies in
Science and Engineering in partial fulfillment of
the requirements for the degree of
Master of Science
Graduate Program in Computer Engineering
Bo˘gazi¸ci University"
62b90583723174220b26c92bd67f6c422ad75570,DNA-GAN: LEARNING DISENTANGLED REPRESEN-,"Under review as a conference paper at ICLR 2018
DNA-GAN: LEARNING DISENTANGLED REPRESEN-
TATIONS FROM MULTI-ATTRIBUTE IMAGES
Anonymous authors
Paper under double-blind review"
620c3f6605e528503a99a8832200d5afdf156c20,F2ID: a personal identification system using faces and fingerprints,"ndFingerprints
AnilJain,LinHong,andYatinKulkarni
DepartmentofComputerScience
MichiganStateUniversity
EastLansing,MI
FID:APersonalIdenti(cid:12)cationSystemUsingFaces
Keywords:Biometrics,Personalidenti(cid:12)cation,Minutiae,Fingerprintmatching,Face
recognition,Eigenface,Decisionfusion."
62a44dbadf36099ea4b76c0656adc2ad4c8ad734,Using Skin Melanin Layer for Facial Pore Identification in RGB Digital Images,"International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 8, August 2014)
Using Skin Melanin Layer for Facial Pore Identification in
RGB Digital Images
Carlos Villegas1, Joan Climent2, C. Rodrigo Villegas3
Engineering Department, Universidad Iberoamericana, Mexico City
ESSAI Department, Universitat Politecnica de Catalunya, Barcelona, Spain
Xin Cognicion Consulting, Mexico City"
627107c02c2df1366965f11678dd3c4fb14ac9b3,CONNECTING IMAGES AND NATURAL LANGUAGE A DISSERTATION SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE AND THE COMMITTEE ON GRADUATE STUDIES OF STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY,"CONNECTING IMAGES AND NATURAL LANGUAGE
A DISSERTATION
SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE
AND THE COMMITTEE ON GRADUATE STUDIES
OF STANFORD UNIVERSITY
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
Andrej Karpathy
August 2016"
6221e550baf4d6266434f897f45e5438b85d8207,Efficiency of Recognition Methods for Single Sample per Person Based Face Recognition,"We are IntechOpen,
the world’s leading publisher of
Open Access books
Built by scientists, for scientists
,800
16,000
Open access books available
International  authors and editors
Downloads
Our authors are among the
Countries delivered to
TOP 1%
2.2%
most cited scientists
Contributors from top 500 universities
Selection of our books indexed in the Book Citation Index
in Web of Science™ Core Collection (BKCI)
Interested in publishing with us?
Contact
Numbers displayed above are based on latest data collected."
624077c8c8c9306c12671870cacc0fb13ff20324,"Smart, Sparse Contours to Represent and Edit Images","Sparse, Smart Contours to Represent and Edit Images
Tali Dekel 1
Chuang Gan 2
Dilip Krishnan 1
Ce Liu 1 William T. Freeman 1,3
Google Research 2 MIT-Watson AI Lab 3 MIT-CSAIL
Figure 1. Our method produces high quality reconstructions of images from information along a small number of contours: a source
(512×512) image in (a) is reconstructed in (c) from gradient information stored at the set of colored contours in (b)2, which are less than
5% of the pixels. The model synthesizes hair texture, facial lines and shading even in regions where no input information is provided.
Our model allows for semantically intuitive editing in the contour domain. Top-right: a caricature-like result (e) is created by moving and
scaling some contours in (d). Bottom-right: hairs are synthesized by pasting a set of hair contours copied from a reference image. Edited
ontours are marked in green while the original contours in red."
6225e9c2a9ee47b4d3d58313a839f6e170b48525,Shape-Aware Matching of Implicit Surfaces Based on Thin Shell Energies,"SHAPE AWARE MATCHING OF IMPLICIT SURFACES BASED ON THIN SHELL
ENERGIES
JOS ´E A. IGLESIAS, MARTIN RUMPF, AND OTMAR SCHERZER"
6289d2c4c47d7101861153bfe78c92d16cf4581b,A Cross-Core Performance Model for Heterogeneous Many-Core Architectures,"A Cross-Core Performance Model for
Heterogeneous Many-Core Architectures
Rui Pinheiro, Nuno Roma, and Pedro Tom´as (cid:63)
INESC-ID, Instituto Superior T´ecnico, Universidade de Lisboa"
62d1b32d67e4a4b58a66cba91629aae5f7968962,Recurrent Neural Networks for Semantic Instance Segmentation,"Recurrent Neural Networks
for Semantic Instance Segmentation
Amaia Salvador1, M´ıriam Bellver2, V´ıctor Campos2, Manel Baradad1
Ferran Marques1 Jordi Torres2 and Xavier Giro-i-Nieto1
Universitat Polit`ecnica de Catalunya 2Barcelona Supercomputing Center"
6275aa21331a2712222b7ab2116e9589e21ae82c,Prediction of Manipulation Actions,"Noname manuscript No.
(will be inserted by the editor)
Prediction of Manipulation Actions
Cornelia Ferm¨uller · Fang Wang · Yezhou Yang · Konstantinos Zampogiannis · Yi
Zhang · Francisco Barranco · Michael Pfeiffer
the date of receipt and acceptance should be inserted later"
62e8010e2ac1523d3a3e7e1c13cb34e63e85ce04,Transfer Learning for Action Unit Recognition,"Transfer Learning for Action Unit Recognition
Yen Khye Lim1, Zukang Liao1, Stavros Petridis1 and Maja Pantic1,2"
627412bf4cf2706f6dc9530313ecf06bbc532cca,Human Pose Estimation from Video and Inertial Sensors,"Human Pose Estimation from Video and Inertial
Sensors
Von der Fakultät für Elektrotechnik und Informatik
der Gottfried Wilhelm Leibniz Universität Hannover
zur Erlangung des akademischen Grades
Doktor-Ingenieur
(abgekürzt: Dr.-Ing.)
genehmigte
Dissertation
Gerard Pons Moll
geboren am 25. Oktober 1984 in Barcelona."
621ea1f1e364262348135c803557e7b3454a804e,Generative spatiotemporal modeling of neutrophil behavior,"Accepted to 2018 IEEE International Symposium on Biomedical Imaging
Copyright ©2018 IEEE
Generative Spatiotemporal Modeling Of Neutrophil Behavior
Narita Pandhe(cid:63)
Balazs Rada†
Shannon Quinn(cid:63)
(cid:63) Department of Computer Science
Department of Infectious Diseases
University of Georgia"
6236962ce0d627fc23774f0680e77069b9667803,Fitting a Morphable Model to Pose and Shape of a Point Cloud,"Fitting a Morphable Model to Pose and Shape of a Point Cloud
David C. Schneider, Peter Eisert
Fraunhofer Heinrich Hertz Institute, Einsteinufer 37, 10587 Berlin, Germany"
626c12d6ccb1405c97beca496a3456edbf351643,Conditional Variance Penalties and Domain Shift Robustness,"Conditional Variance Penalties and Domain Shift Robustness
Christina Heinze-Deml & Nicolai Meinshausen
Seminar for Statistics
ETH Zurich
Zurich, Switzerland"
62cf8c07ca6c4c7817f6a5682eb2d7cde76198ae,Boosted Metric Learning for Efficient Identity-Based Face Retrieval,"NEGREL ET AL.: BOOSTED METRIC LEARNING FOR FACE RETRIEVAL
Boosted Metric Learning for Efficient
Identity-Based Face Retrieval
Romain Negrel
Alexis Lechervy
Frederic Jurie
GREYC, CNRS UMR 6072, ENSICAEN
Université de Caen Basse-Normandie
France"
622949b1aacd316c60a7034c44121c698a3fb6a4,Highway Driving Dataset for Semantic Video Segmentation,"KIM, YIM, AND KIM: HIGHWAY DRIVING DATASET
Highway Driving Dataset
for Semantic Video Segmentation
Byungju Kim
Junho Yim
Junmo Kim*
School of Electrical Engineering
Korea Advanced Institute of Science
nd Technology (KAIST),
South Korea"
623da0faea1f98f238936e34f361518829edfdf4,Digital geometry image analysis for medical diagnosis,"Digital Geometry Image Analysis for Medical Diagnosis
Jiandong Fang    Shiaofen Fang     Jeffrey Huang     Mihran Tuceryan
Department of Computer and Information Science
Indiana University Purdue University Indianapolis
723 W. Michigan St., SL 280
Indianapolis, IN 46202, USA
-317-274-9731"
62374b9e0e814e672db75c2c00f0023f58ef442c,Frontal Face Authentication Using Discriminating Grids with Morphological Feature Vectors,"Frontalfaceauthenticationusingdiscriminatinggridswith
morphologicalfeaturevectors
A.Tefas
C.Kotropoulos
I.Pitas
DepartmentofInformatics,AristotleUniversityofThessaloniki
Box
,Thessaloniki,GREECE
EDICSnumbers:-KNOWContentRecognitionandUnderstanding
-MODAMultimodalandMultimediaEnvironments
Anovelelasticgraphmatchingprocedurebasedonmultiscalemorphologicaloperations,thesocalled
morphologicaldynamiclinkarchitecture,isdevelopedforfrontalfaceauthentication.Fastalgorithms
forimplementingmathematicalmorphologyoperationsarepresented.Featureselectionbyemploying
linearprojectionalgorithmsisproposed.Discriminatorypowercoe(cid:14)cientsthatweighthematching
errorateachgridnodearederived.Theperformanceofmorphologicaldynamiclinkarchitecturein
frontalfaceauthenticationisevaluatedintermsofthereceiveroperatingcharacteristicontheMVTS
faceimagedatabase.Preliminaryresultsforfacerecognitionusingtheproposedtechniquearealso
presented.
Correspondingauthor:I.Pitas
DRAFT
September,
"
62c435bc714f13a373926e3b1914786592ed1fef,Classification Signboard Detection Face Detection Animal Detection Texture Detection GPS + IMU Depth Localization Module Central Control Speech,"MAVI: An Embedded Device to Assist Mobility of
Visually Impaired
Rajesh Kedia∗, Yoosuf K K∗, Pappireddy Dedeepya∗, Munib Fazal∗, Chetan Arora†, and M. Balakrishnan∗
Indian Institute of Technology Delhi
Indraprastha Institute of Information Technology Delhi
Contact Email:"
62070fbd22b2a4bba830668c2e9720ec4bff4171,Fast human detection using template matching for gradient images and aSC descriptors based on subtraction stereo,"978-1-4799-2341-0/13/$31.00 ©2013 IEEE
ICIP 2013"
62857147a6809063671614d62f43605130757b1c,Unsupervised Discriminant Projection Analysis for Feature Extraction,"Unsupervised Discriminant Projection Analysis for Feature Extraction
Jian Yang    David Zhang
Biometrics Centre, Department of
Computing, Hong Kong Polytechnic
University, Kowloon, Hong Kong
Zhong Jin    Jing-yu Yang
Department of Computer Science, Nanjing
University of Science and Technology,
Nanjing 210094, P. R. China"
62e0edcf5a2bab8163851ca1d6ce50d42c367660,Generalized two-dimensional linear discriminant analysis with regularization,"JOURNAL OF LATEX CLASS FILES, VOL.
, NO.
Generalized two-dimensional linear discriminant
nalysis with regularization
Chun-Na Li, Yuan-Hai Shao,Wei-Jie Chen, Zhen Wang and Nai-Yang Deng"
62e0380a86e92709fe2c64e6a71ed94d152c6643,Facial emotion recognition with expression energy,"Facial Emotion Recognition With Expression Energy
Albert Cruz
Center for Research in
Intelligent Systems
16 Winston Chung Hall
Bir Bhanu
Center for Research in
Intelligent Systems
16 Winston Chung Hall
Ninad Thakoor
Center for Research in
Intelligent Systems
16 Winston Chung Hall
Riverside, CA, 92521-0425,
Riverside, CA, 92521-0425,
Riverside, CA, 92521-0425,"
62d1a31b8acd2141d3a994f2d2ec7a3baf0e6dc4,Noise-resistant network: a deep-learning method for face recognition under noise,"Ding et al. EURASIP Journal on Image and Video Processing  (2017) 2017:43
DOI 10.1186/s13640-017-0188-z
EURASIP Journal on Image
nd Video Processing
R ES EAR CH
Noise-resistant network: a deep-learning
method for face recognition under noise
Yuanyuan Ding1,2, Yongbo Cheng1,2, Xiaoliu Cheng1, Baoqing Li1*, Xing You1 and Xiaobing Yuan1
Open Access"
62aeecbe5db3e4ed6b783f4b580157f4f1c8ba45,"Haar like and LBP based features for face , head and people detection in video sequences","Author manuscript, published in ""International Workshop on Behaviour Analysis and Video Understanding (ICVS 2011) (2011)"
6273b3491e94ea4dd1ce42b791d77bdc96ee73a8,"Evaluating Appearance Models for Recognition, Reacquisition, and Tracking","Evaluating Appearance Models for Recognition, Reacquisition, and Tracking
Doug Gray
Shane Brennan
Hai Tao
University of California, Santa Cruz
156 High St., Santa Cruz, CA 95064
{dgray, shanerb,"
626859fe8cafd25da13b19d44d8d9eb6f0918647,Activity Recognition Based on a Magnitude-Orientation Stream Network,"Activity Recognition based on a
Magnitude-Orientation Stream Network
Carlos Caetano, Victor H. C. de Melo, Jefersson A. dos Santos, William Robson Schwartz
Smart Surveillance Interest Group, Department of Computer Science
Universidade Federal de Minas Gerais, Belo Horizonte, Brazil"
62007c30f148334fb4d8975f80afe76e5aef8c7f,Eye In-Painting with Exemplar Generative Adversarial Networks,"Eye In-Painting with Exemplar Generative Adversarial Networks
Brian Dolhansky, Cristian Canton Ferrer
Facebook Inc.
Hacker Way, Menlo Park (CA), USA
{bdol,"
62f0d8446adee6a5e8102053a63a61af07ac4098,Facial point detection using convolutional neural network transferred from a heterogeneous task,"FACIAL POINT DETECTION USING CONVOLUTIONAL NEURAL NETWORK
TRANSFERRED FROM A HETEROGENEOUS TASK
Takayoshi Yamashita* Taro Watasue** Yuji Yamauchi* Hironobu Fujiyoshi*
**Tome R&D
*Chubu University,
200, Matsumoto-cho, Kasugai, AICHI"
62dccab9ab715f33761a5315746ed02e48eed2a0,A Short Note about Kinetics-600,"A Short Note about Kinetics-600
Jo˜ao Carreira
Eric Noland
Andras Banki-Horvath
Chloe Hillier
Andrew Zisserman"
62694828c716af44c300f9ec0c3236e98770d7cf,Identification of Action Units Related to Affective States in a Tutoring System for Mathematics,"Padrón-Rivera, G., Rebolledo-Mendez, G., Parra, P. P., & Huerta-Pacheco, N. S. (2016). Identification of Action Units Related to
Identification of  Action Units Related to Affective States in a Tutoring System
Gustavo Padrón-Rivera1, Genaro Rebolledo-Mendez1*, Pilar Pozos Parra2 and N. Sofia
Facultad de Estadística e Informática, Universidad Veracruzana, Mexico // 2Universidad Juárez Autónoma de
Tabasco, Mexico // // // //
for Mathematics
Huerta-Pacheco1
*Corresponding author"
62306db738cfddd39e1f12e22bce0f7615ce58b8,Face detection with colour segmentation and fuzzy template matching,"Proceedings of the 17th World Congress
The International Federation of Automatic Control
Seoul, Korea, July 6-11, 2008
978-1-1234-7890-2/08/$20.00 © 2008 IFAC
3773
0.3182/20080706-5-KR-1001.2215"
140eaf273eabe233f67257d1ae7ee44a8f21e502,Computational Maps in the Visual Cortex,"Risto Miikkulainen, James A. Bednar, Yoonsuck
Choe, and Joseph Sirosh
Computational Maps in the
Visual Cortex
February 6, 2005
Springer
Berlin Heidelberg NewYork
Hong Kong London
Milan Paris Tokyo"
147c33df99dd52502d65fe390ee45c585349b3b3,Pixel and Feature Level Based Domain Adaption for Object Detection in Autonomous Driving,"Pixel and Feature Level Based Domain Adaption
for Object Detection in Autonomous Driving
Yuhu Shan, Wen Feng Lu, Chee Meng Chew"
14fee990a372bcc4cb6dc024ab7fc4ecf09dba2b,Modeling Spatio-Temporal Human Track Structure for Action Localization,"Modeling Spatio-Temporal Human Track Structure for Action
Localization
Guilhem Ch´eron · Anton Osokin · Ivan Laptev · Cordelia Schmid"
14761b89152aa1fc280a33ea4d77b723df4e3864,Zero-Shot Learning via Visual Abstraction,
14a5feadd4209d21fa308e7a942967ea7c13b7b6,Content-based vehicle retrieval using 3D model and part information,"978-1-4673-0046-9/12/$26.00 ©2012 IEEE
ICASSP 2012"
1414d4880e368414cbbbbd215e8b0471f185aa03,Face Detection in Low-Resolution Color Images,"Face Detection in Low-resolution Color Images
Jun Zheng, Geovany A. Ramirez, and Olac Fuentes,
Computer Science Department,
University of Texas at El Paso,
El Paso, Texas, 79968, U.S.A.
No Institute Given"
14c988aa9086207b337dcc5611aad08422129b42,Human Relative Position Detection Based on Mutual Occlusion,"Human Relative Position Detection
Based on Mutual Occlusion
V´ıctor Borjas, Michal Drozdzal, Petia Radeva, and Jordi Vitri`a
Facultat de Matem`atiques & Centre de Visi`o per Computador,
Universitat de Barcelona,
Campus UAB"
14fed18d838bf6b89d98837837ff314e61ab7c60,Deep Learning with Differential Privacy,"A preliminary version of this paper appears in the proceedings of the 23rd ACM Conference on Computer and Communications Security
(CCS 2016). This is a full version.
Deep Learning with Differential Privacy
Martín Abadi∗
H. Brendan McMahan∗
October 25, 2016
Andy Chu∗
Ilya Mironov∗
Li Zhang∗
Ian Goodfellow†
Kunal Talwar∗"
149f3cc167b046dc790b1f4f1c48eeb31e898403,A study of vehicle detector generalization on U.S. highway,"Windsor Oceanico Hotel, Rio de Janeiro, Brazil, November 1-4, 2016
978-1-5090-1889-5/16/$31.00 ©2016 IEEE"
149e5e5eeea5a9015ab5ae755f62c45ef70fa79b,Hierarchical Convolutional Features for Visual Tracking,"Hierarchical Convolutional Features for Visual Tracking
Chao Ma
Jia-Bin Huang
Xiaokang Yang
Ming-Hsuan Yang
UC Merced"
14e428f2ff3dc5cf96e5742eedb156c1ea12ece1,Facial Expression Recognition Using Neural Network Trained with Zernike Moments,"Facial Expression Recognition Using Neural Network Trained with Zernike
Moments
Mohammed Saaidia
Dept. Génie-Electrique
Université M.C.M Souk-Ahras
Souk-Ahras, Algeria"
1436d72a51feefda3278068a164d263f6d845236,INTERACTIVE LEARNING A PERSON DETECTOR : FEWER CLICKS – LESS FRUSTRATION 1,"INTERACTIVE LEARNING A PERSON
DETECTOR:
FEWER CLICKS – LESS FRUSTRATION1
Peter M. Roth2, Helmut Grabner2, Christian Leistner2,
Martin Winter2, and Horst Bischof2"
14bf85bacf15241c500db72c145b0490a14addaa,Generalization in Holistic versus Analytic Processing of Faces,"Generalization in Holistic versus Analytic Processing of Faces
M. Bicego∗
A.A. Salah
DEIR - University of Sassari,
Centrum voor Wiskunde en Informatica,
via Torre Tonda, 34
07100, Sassari (Italy)
PNA4 - Signals and Images,
94079, Amsterdam (The Netherlands)
E. Grosso
M. Tistarelli
L. Akarun
DEIR - University of Sassari,
DAP - University of Sassari,
Perceptual Intelligence Lab.
via Torre Tonda, 34
07100, Sassari (Italy)
piazza Duomo, 6
07041, Alghero (Italy)
Bo˘gazic¸i University,"
14a01628169a3a060b6af5d5dcdeeb584b648abf,Semi-Supervised Multiresolution Classification Using Adaptive Graph Filtering With Application to Indirect Bridge Structural Health Monitoring,"Semi-Supervised Multiresolution Classification
Using Adaptive Graph Filtering with Application to
Indirect Bridge Structural Health Monitoring
Siheng Chen, Student Member, IEEE, Fernando Cerda, Piervincenzo Rizzo, Jacobo Bielak, James H. Garrett and
Jelena Kovaˇcevi´c, Fellow, IEEE"
1442319de86d171ce9595b20866ec865003e66fc,Vision-Based Fall Detection with Convolutional Neural Networks,"Vision-Based Fall Detection with Convolutional
Neural Networks
Adri´an Nu˜nez-Marcos1, Gorka Azkune1, Ignacio Arganda-Carreras234
DeustoTech - University of Deusto
Avenida de las Universidades, 24 - 48007, Bilbao, Spain
Dept. of Computer Science and Artificial Intelligence, Basque
Country University, San Sebastian, Spain
P. Manuel Lardizabal, 1 - 20018, San Sebastian, Spain
Ikerbasque, Basque Foundation for Science, Bilbao, Spain
Maria Diaz de Haro, 3 - 48013 Bilbao, Spain
Donostia International Physics Center (DIPC), San Sebastian, Spain
P. Manuel Lardizabal, 4 - 20018, San Sebastian, Spain"
148721b162dd355812fae94c8aaf365e5e2c3a79,"Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation","Vista: A Visually, Socially, and Temporally-aware Model
for Artistic Recommendation
Ruining He
UC San Diego
Chen Fang
Adobe Research
Zhaowen Wang
Adobe Research
Julian McAuley
UC San Diego"
140dbcb0be3ce7961ed551f129698e9ad4c9aa8c,Interactive Learning and its Role in Pervasive Robotics,"Interactive Learning and its Role in Pervasive Robotics
Cynthia Matuszek
Dieter Fox
Nicholas FitzGerald
Evan Herbst"
143e3ec5a5a11547da2d77a17d0ca7b1940280b5,"People detection, tracking and re-identification through a video camera network. (Détection, suivi et ré-identification de personnes à travers un réseau de caméra vidéo)","People detection, tracking and re-identification through
video camera network
Malik Souded
To cite this version:
Malik Souded. People detection, tracking and re-identification through a video camera network.
Other [cs.OH]. Université Nice Sophia Antipolis, 2013. English. <NNT : 2013NICE4152>. <tel-
00913072v2>
HAL Id: tel-00913072
https://tel.archives-ouvertes.fr/tel-00913072v2
Submitted on 29 Jan 2014
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
14b311b848b51b7b5345573a289b1cedcbb4d581,Instance Similarity Deep Hashing for Multi-Label Image Retrieval,"Instance Similarity Deep Hashing for Multi-Label
Image Retrieval
Zheng Zhang, Qin Zou, Qian Wang, Yuewei Lin, and Qingquan Li"
1419956b08f9ab398cd2100ddec74271ef5fa72c,Joint detection and online multi-object tracking,"Joint detection and online multi-object tracking
Hilke Kieritz, Wolfgang H¨ubner, and Michael Arens
Fraunhofer IOSB, Germany"
140c95e53c619eac594d70f6369f518adfea12ef,Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A,"Pushing the Frontiers of Unconstrained Face Detection and Recognition: IARPA Janus Benchmark A
Brendan F. Klare, Emma Taborsky , Austin Blanton , Jordan Cheney , Kristen Allen , Patrick Grother , Alan Mah , Anil K. Jain
The development of accurate and scalable unconstrained face recogni-
tion algorithms is a long term goal of the biometrics and computer vision
ommunities. The term “unconstrained” implies a system can perform suc-
essful identifications regardless of face image capture presentation (illumi-
nation, sensor, compression) or subject conditions (facial pose, expression,
occlusion). While automatic, as well as human, face identification in certain
scenarios may forever be elusive, such as when a face is heavily occluded or
aptured at very low resolutions, there still remains a large gap between au-
tomated systems and human performance on familiar faces. In order to close
this gap, large annotated sets of imagery are needed that are representative
of the end goals of unconstrained face recognition. This will help continue
to push the frontiers of unconstrained face detection and recognition, which
re the primary goals of the IARPA Janus program.
The current state of the art in unconstrained face recognition is high
ccuracy (roughly 99% true accept rate at a false accept rate of 1.0%) on
faces that can be detected with a commodity face detectors, but unknown
ccuracy on other faces. Despite the fact that face detection and recognition
research generally has advanced somewhat independently, the frontal face"
147b7998526ebbdf64b1662503b378d9f6456ccd,GENERATIVE ADVERSARIAL NETWORKS FOR IMAGE STEGANOGRAPHY,"Under review as a conference paper at ICLR 2017
GENERATIVE ADVERSARIAL NETWORKS FOR IMAGE
STEGANOGRAPHY
Denis Volkhonskiy2,3, Boris Borisenko3 and Evgeny Burnaev1,2,3
Skolkovo Institute of Science and Technology
The Institute for Information Transmission Problems RAS (Kharkevich Institute)
National Research University Higher School of Economics (HSE)"
14bf36c03e6fd5171794a2d56d18748b1ab3fd17,Challenges and design space of gaze-enabled public displays,"Challenges and Design Space of
Gaze-enabled Public Displays
Mohamed Khamis
LMU Munich
Munich, Germany
Florian Alt
LMU Munich
Munich, Germany
Andreas Bulling
Max Planck Institute for
Informatics
Saarbrücken, Germany
Permission to make digital or hard copies of part or all of this work for personal or
lassroom use is granted without fee provided that copies are not made or distributed
for profit or commercial advantage and that copies bear this notice and the full citation
on the first page. Copyrights for third-party components of this work must be honored.
For all other uses, contact the Owner/Author. Copyright is held by the owner/author(s).
Ubicomp/ISWC’16 Adjunct, September 12–16, 2016, Heidelberg, Germany.
ACM 978-1-4503-4462-3/16/09.
http://dx.doi.org/10.1145/2968219.2968342"
14f457bcb5c3e294919512b132bb171bdcaf5ec2,UNDERSTANDING HUMAN ACTIONS IN STILL IMAGES A DISSERTATION SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE AND THE COMMITTEE ON GRADUATE STUDIES OF STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY,"UNDERSTANDING HUMAN ACTIONS
IN STILL IMAGES
A DISSERTATION
SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE
AND THE COMMITTEE ON GRADUATE STUDIES
OF STANFORD UNIVERSITY
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
Bangpeng Yao
August 2013"
146879bd04a1ab25dce3484bc587e5f2ff1b1d91,Securing Certificate Revocation through Speaker Verification : the CertiVeR Project,"Securing Certificate Revocation through Speaker Verification:
the CertiVeR Project
Javier R. Saeta1, Javier Hernando2, Oscar Manso3, Manel Medina3
Biometric Technologies, S.L. Barcelona, Spain
TALP Research Center. Universitat Politècnica de Catalunya, Spain
SeMarket, S.A. Barcelona, Spain"
1450296fb936d666f2f11454cc8f0108e2306741,Learning to Discover Cross-Domain Relations with Generative Adversarial Networks,"Learning to Discover Cross-Domain Relations
with Generative Adversarial Networks
Taeksoo Kim 1 Moonsu Cha 1 Hyunsoo Kim 1 Jung Kwon Lee 1 Jiwon Kim 1"
142dcfc3c62b1f30a13f1f49c608be3e62033042,Adaptive region pooling for object detection,"Adaptive Region Pooling for Object Detection
Yi-Hsuan Tsai
UC Merced
Onur C. Hamsici
Qualcomm Research, San Diego
Ming-Hsuan Yang
UC Merced"
14aad0d391a9491eb122d5b6af6c325a0e090dc7,Development of an Efficient Face Recognition System based on Linear and Nonlinear Algorithms,"Development of an Efficient Face Recognition System based on Linear and Nonlinear Algorithms
{tag}                                                 {/tag}
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 134
Number 7
Year of Publication: 2016
Authors:
Filani Araoluwa S., Adetunmbi Adebayo O.
10.5120/ijca2016907932
{bibtex}2016907932.bib{/bibtex}"
146e6504d473b92e56108b7276d96aebaa58ccfc,Model and Part Fusion for Vehicle Retrieval,"International Journal of Research in Advent Technology, Vol.2, No.5, May 2014
E-ISSN: 2321-9637
D Model and Part Fusion for Vehicle Retrieval
M.Nagarasan1, T.N.Chitradevi2, S.Senthilnathan3
Department of computer science and engineering1,2, 3
Aditya institute of technology, Coimbatore.1, 3,Sri Ramakrishna Engineering College, Coimbatore2"
14fdec563788af3202ce71c021dd8b300ae33051,Social Influence Analysis based on Facial Emotions,"Social Influence Analysis based on Facial Emotions
Pankaj Mishra, Rafik Hadfi, and Takayuki Ito
Department of Computer Science and Engineering
Nagoya Institute of Technology, Gokiso, Showa-ku, Nagoya, 466-8555 Japan
{pankaj.mishra,"
143ac3b7338e240b106863d35177c4567ef9c1aa,Euclidean & Geodesic Distance between a Facial Feature Points in Two-Dimensional Face Recognition System,"Euclidean & Geodesic Distance between a Facial
Feature Points in Two-Dimensional Face
Recognition System
Rachid AHDID1, Khaddouj TAIFI1, Said SAFI1 and Bouzid MANAUT2"
143b54525bdda1f83965002616a4e7b5b9f523a3,A probabilistic patch based image representation using Conditional Random Field model for image classification,"A probabilistic patch based image representation using Conditional Random Field model
for image classification
Fariborz Taherkhani
Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, USA"
144ba4e9e64d4f9a5bb436c80c3c02b40e4092e0,Learning Video Features for Multi-label Classification,"Learning Video Features for Multi-Label
Classification
Shivam Garg[0000−0002−7213−3967]"
14860877a790d99296a990281b22e6b6a430b64f,Deep Over-sampling Framework for Classifying Imbalanced Data,"Deep Over-sampling Framework for Classifying
Imbalanced Data
Shin Ando1 and Chun Yuan Huang2
School of Management,
Tokyo University of Science,
-11-2 Fujimi, Chiyoda-ku, Tokyo, Japan
School of Management,
Tokyo University of Science,
-11-2 Fujimi, Chiyoda-ku, Tokyo, Japan"
1434c9140ba724c9a92f478781e890434e7a0215,The role of global and feature based information in gender classification of faces: a comparison of human performance and computational models,"April 11, 2005 16:8 00007
st Reading
(cid:13) World Scienti(cid:12)c Publishing Company
THE ROLE OF GLOBAL AND FEATURE BASED INFORMATION IN
GENDER CLASSIFICATION OF FACES: A COMPARISON OF HUMAN
PERFORMANCE AND COMPUTATIONAL MODELS
SAMARASENA BUCHALA(cid:3), NEIL DAVEYy, RAY J. FRANKz and MARTIN LOOMESx
School of Computer Science, University of Hertfordshire,
College Lane Hat(cid:12)eld, Herts. AL10 9AB, UK
TIM M.GALE
Department of Psychiatry, QEII Hospital,
Welwyn Garden City, Herts., AL7 4HQ, UK
Most computational models for gender classi(cid:12)cation use global information (the full face image) giv-
ing equal weight to the whole face area irrespective of the importance of the internal
features.
Here, we use a global and feature based representation of face images that includes both global
nd featural information. We use dimensionality reduction techniques and a support vector machine
lassi(cid:12)er and show that this method performs better than either global or feature based repre-
sentations alone. We also present results of human subjects performance on gender classi(cid:12)cation
task and evaluate how the di(cid:11)erent dimensionality reduction techniques compare with human sub-"
147fe6bfc76f30ccacc3620662511e452bc395f6,A Survey of Face Recognition Techniques,"Invited Paper
Journal of Information Processing Systems, Vol.5, No.2, June 2009  41
A Survey of Face Recognition Techniques
Rabia Jafri* and Hamid R. Arabnia*"
1467c4ab821c3b340abe05a1b13a19318ebbce98,Multitask and transfer learning for multi-aspect data,"Multitask and Transfer Learning for
Multi-Aspect Data
Bernardino Romera Paredes
A dissertation submitted in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy of University College London."
1451e7b11e66c86104f9391b80d9fb422fb11c01,Image privacy protection with secure JPEG transmorphing,"Research Article
Image privacy protection with secure JPEG
transmorphing
ISSN 1751-9675
Received on 30th December 2016
Revised 13th July 2017
Accepted on 11th August 2017
doi: 10.1049/iet-spr.2016.0756
www.ietdl.org
Lin Yuan1  , Touradj Ebrahimi1
Multimedia Signal Processing Group, Electrical Engineering Department, EPFL, Station 11, Lausanne, Switzerland
E-mail:"
146f6f6ed688c905fb6e346ad02332efd5464616,"Show, Attend and Tell: Neural Image Caption Generation with Visual Attention","Show, Attend and Tell: Neural Image Caption
Generation with Visual Attention
Kelvin Xu
Jimmy Lei Ba
Ryan Kiros
Kyunghyun Cho
Aaron Courville
Ruslan Salakhutdinov
Richard S. Zemel
Yoshua Bengio"
14e9eaa6ac23996e9a62060c8da90bdb7116ee37,Localization Recall Precision (LRP): A New Performance Metric for Object Detection,[cs.CV]  5 Jul 2018
14373c9fd08dee8f7195a88430121c69bbebbe1b,Head Pose Estimation Using Covariance of Oriented Gradients,"978-1-4244-4296-6/10/$25.00 ©2010 IEEE
ICASSP 2010"
1456f147381bf7c385225d854c2fb48c19eca285,LCAV-31: a dataset for light field object recognition,"Computational Imaging XII, edited by Charles A. Bouman, Ken D. Sauer, Proc. of SPIE-IS&T Electronic Imaging,
SPIE Vol. 9020, 902014 · © 2014 SPIE-IS&T · CCC code: 0277-786X/14/$18 · doi: 10.1117/12.2041097
Proc. of SPIE-IS&T/ Vol. 9020  902014-1"
1459d4d16088379c3748322ab0835f50300d9a38,Cross-Domain Visual Matching via Generalized Similarity Measure and Feature Learning,"Cross-Domain Visual Matching via Generalized
Similarity Measure and Feature Learning
Liang Lin, Guangrun Wang, Wangmeng Zuo, Xiangchu Feng, and Lei Zhang"
c5844de3fdf5e0069d08e235514863c8ef900eb7,A Study on Similarity Computations in Template Matching Technique for Identity Verification,"Lam S K et al. / (IJCSE) International Journal on Computer Science and Engineering
Vol. 02, No. 08, 2010, 2659-2665
A Study on Similarity Computations in Template
Matching Technique for Identity Verification
Lam, S. K., Yeong, C. Y., Yew, C. T., Chai, W. S., Suandi, S. A.
Intelligent Biometric Group, School of Electrical and Electronic Engineering
Engineering Campus, Universiti Sains Malaysia
4300 Nibong Tebal, Pulau Pinang, MALAYSIA
Email:"
c5e4467b5830d7dad4e940f0766ae728f22e38fc,Object recognition and localization,"Object recognition and localization
Badri Narayana Patro
Dept. of Electrical Engineering
Ganesh Boddupally
Dept. of Electrical Engineering"
c5d9ac2f52c9fc229890798b9d6e4d899b72c525,Image Enhancement Technique using Adaptive Multiscale Retinex for Face Recognition Systems,"Image Enhancement Technique using Adaptive
Multiscale Retinex for Face Recognition Systems
Khairul Anuar Ishak1, Salina Abdul Samad1
M. A. Hannan1 and Maizura Mohd Sani2
Dept. of Electrical, Electronics and Systems Engineering
Faculty of Engineering and Built Environment, University Kebangsaan Malaysia
3600, UKM Bangi, Selangor, Malaysia
Institute of Microengineering and Nanoelectronics, University Kebangsaan Malaysia
3600, UKM Bangi, Selangor, Malaysia"
c5ea084531212284ce3f1ca86a6209f0001de9d1,Audio-visual speech processing for multimedia localisation,"Audio-Visual Speech Processing for
Multimedia Localisation
Matthew Aaron Benatan
Submitted in accordance with the requirements
for the degree of Doctor of Philosophy
The University of Leeds
School of Computing
September 2016"
c50e498ede6f5216cffd0645e747ce67fae2096a,Low-Resolution Face Recognition in the Wild via Selective Knowledge Distillation,"Low-resolution Face Recognition in the Wild
via Selective Knowledge Distillation
Shiming Ge, Senior Member, IEEE, Shengwei Zhao, Chenyu Li and Jia Li, Senior Member, IEEE"
c50630e485d3c7785ea9e1f3bff35ea00e926a56,Deep Image Retrieval: Learning global representations for image search,"Deep Image Retrieval:
Learning global representations for image search
Albert Gordo, Jon Almaz´an, Jerome Revaud, and Diane Larlus
Computer Vision Group, Xerox Research Center Europe"
c5b9a96fcb07f538be3181922e5f1a24a7936783,"Autonomous drone cinematographer: Using artistic principles to create smooth, safe, occlusion-free trajectories for aerial filming","Autonomous drone cinematographer:
Using artistic principles to create smooth, safe,
occlusion-free trajectories for aerial filming
Rogerio Bonatti, Yanfu Zhang, Sanjiban Choudhury, Wenshan Wang, and Sebastian
Scherer"
c55a6c98887b3079647d0edb4778d81bab6708f6,Self-Similarity Representation of Faces for Kin Relationships,"HCTL Open International Journal of Technology Innovations and Research (IJTIR)
http://ijtir.hctl.org
Volume 16, July 2015
e-ISSN: 2321-1814, ISBN (Print): 978-1-943730-43-8
Self-Similarity Representation
of Faces for Kin
Relationships
Pratibha Chaskar1, Dr. Manjusha Deshmukh2"
c52bd60b737d17d749e6a3117d8faa3b3012865c,Face Detection Benchmark Database Data Set Location Description,"Face Detection
Summary
Resources for Face Detection
This web page contains information of face detection works. As a first step to encourage researchers to embark on this
topic, we also provide some sample code, scripts, and plots to develop face detection systems. Consequently, this page
does not contain all the detailed information that you may need. Nevertheless, we think it contains ""substantial""
information that you will find useful in developing face detection methods.
Reference
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Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 24, no. 1, pp. 34-58, 2002."
c55f3b1ab8f4de230a55a4702aeefeb6bc0bb68c,Maximally Stable Local Description for Scale Selection,"Maximally Stable Local Description for Scale
Selection
Gyuri Dork(cid:19)o and Cordelia Schmid
INRIA Rh^one-Alpes, 655 Avenue de l’Europe, 38334 Montbonnot, France"
c593c6080c75133191a27381a58cd07c97aa935b,Gender Classification Using a Min-Max Modular Support Vector Machine with Incorporating Prior Knowledge,"SUBMITTED TO IEEE TRANSACTIONS ON NEURAL NETWORKS
Gender Classification Using a Min-Max Modular
Support Vector Machine with Incorporating
Prior Knowledge
Hui-Cheng Lian and ∗Bao-Liang Lu, Senior Member, IEEE"
c5af99522e324b72c8a563a5d6b7c9a0101efb65,Exploring Human Vision Driven Features for Pedestrian Detection,"(cid:13) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any
urrent or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new
ollective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other
works."
c562e95b7906066be4210d00c4f6187475e6e13a,Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-Scale Lesion Database,"Deep Lesion Graphs in the Wild: Relationship Learning and Organization of
Significant Radiology Image Findings in a Diverse Large-scale Lesion Database
Imaging Biomarkers and Computer-Aided Diagnosis Laboratory
National Institutes of Health Clinical Center, 10 Center Drive, Bethesda, MD 20892
{ke.yan, xiaosong.wang, ling.zhang3, mohammad.bagheri,
Ke Yan, Xiaosong Wang, Le Lu, Ling Zhang, Adam P. Harrison
Mohammadhadi Bagheri, Ronald M. Summers
{lel,"
c542fa8c4cfaff6a8d8efa9678e42e1b9ead8aa9,griffith . edu . au Face Recognition using Ensemble String Matching,"Griffith Research Online
https://research-repository.griffith.edu.au
Face Recognition using Ensemble String
Matching
Author
Chen, Weiping, Gao, Yongsheng
Published
Journal Title
IEEE Transactions on Image Processing
https://doi.org/10.1109/TIP.2013.2277920
Copyright Statement
Copyright 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be
obtained for all other uses, in any current or future media, including reprinting/republishing this material
for advertising or promotional purposes, creating new collective works, for resale or redistribution to
servers or lists, or reuse of any copyrighted component of this work in other works.
Downloaded from
http://hdl.handle.net/10072/54416"
c5318c79bc1b880e8356211b837b684f1ee6e5c4,Acquiring Common Sense Spatial Knowledge Through Implicit Spatial Templates,"Acquiring Common Sense Spatial Knowledge through Implicit Spatial Templates
Department of Computer Science
Computer Vision Laboratory
Guillem Collell
KU Leuven
Luc Van Gool
ETH Zurich
Marie-Francine Moens
Department of Computer Science
KU Leuven"
c52aa6b9c7b89782f2316ce8ef2156fa06a3696d,Learning Semantic Part-Based Models from Google Images,"Learning Semantic Part-Based Models
from Google Images
Davide Modolo and Vittorio Ferrari"
c591cb28d12b7ee53af4e5c2050b74071527c248,The face of fear and anger: Facial width-to-height ratio biases recognition of angry and fearful expressions.,"The Face of Fear and Anger: Facial Width-to-Height
Ratio Biases Recognition of Angry and Fearful
Expressions
Jason C. Deska, E. Paige Lloyd, and Kurt Hugenberg
Online First Publication, May 11, 2017. http://dx.doi.org/10.1037/emo0000328
CITATION
Deska, J. C., Lloyd, E. P., & Hugenberg, K. (2017, May 11). The Face of Fear and Anger: Facial
online publication. http://dx.doi.org/10.1037/emo0000328"
c588c89a72f89eed29d42f34bfa5d4cffa530732,Attributes2Classname: A Discriminative Model for Attribute-Based Unsupervised Zero-Shot Learning,"Attributes2Classname: A discriminative model for attribute-based
unsupervised zero-shot learning
Berkan Demirel1,3, Ramazan Gokberk Cinbis2, Nazli Ikizler-Cinbis3
HAVELSAN Inc., 2Bilkent University, 3Hacettepe University"
c50c034d264083757eadeee5d0b94d933fe78544,Query by string word spotting based on character bi-gram indexing,"Query by String word spotting based on character
i-gram indexing
Computer Vision Center, Dept. Ci`encies de la Computaci´o
Universitat Aut`onoma de Barcelona, 08193 Bellaterra (Barcelona), Spain
Suman K. Ghosh and Ernest Valveny
Email:"
c528e6285ed170c9a838446c062c8dfbe31c546e,Real Time 3 D Head Pose Estimation : Recent Achievements and Future Challenges,"REAL TIME 3D HEAD POSE ESTIMATION:
RECENT ACHIEVEMENTS AND FUTURE CHALLENGES
Gabriele Fanelli, Juergen Gall, Luc Van Gool
Computer Vision Laboratory - ETH Zurich"
c5484a227f52e73481fdc453d74bd76675b8c943,REDUCED-GATE CONVOLUTIONAL LSTM DESIGN USING PREDICTIVE CODING FOR NEXT-FRAME VIDEO PREDICTION,"Under review as a conference paper at ICLR 2019
REDUCED-GATE CONVOLUTIONAL LSTM DESIGN
USING PREDICTIVE CODING FOR NEXT-FRAME VIDEO
PREDICTION
Anonymous authors
Paper under double-blind review"
c52f2a00fdbfb7fb10252796dbede6403e780da6,Input Convex Neural Networks,"Input Convex Neural Networks
Brandon Amos 1 Lei Xu 2 * J. Zico Kolter 1"
c5b05718963f4edff80456c441796e4199ad8d41,Sampling and Ontologically Pooling Web Images for Visual Concept Learning,"Sampling and Ontologically Pooling Web Images for
Visual Concept Learning
Shiai Zhu, Chong-Wah Ngo, and Yu-Gang Jiang"
c590c6c171392e9f66aab1bce337470c43b48f39,Emotion Recognition by Machine Learning Algorithms using Psychophysiological Signals,"Emotion Recognition by Machine Learning Algorithms using
Psychophysiological Signals
Eun-Hye Jang, 2Byoung-Jun Park, 3Sang-Hyeob Kim, 4Jin-Hun Sohn
, 2, 3 BT Convergence Technology Research Department, Electronics and Telecommunications
Research Institute, 138 Gajeongno, Yuseong-gu, Daejeon, 305-700, Republic of Korea,
*4Department of Psychology/Brain Research Institute, Chungnam National University 220,
Gung-dong, Yuseong-gu, Daejeon, 305-765, Republic of Korea,"
c5420ef59d7508d82e53671b0d623027eb58e6ed,Learning to Reweight Examples for Robust Deep Learning,"Learning to Reweight Examples for Robust Deep Learning
Mengye Ren 1 2 Wenyuan Zeng 1 2 Bin Yang 1 2 Raquel Urtasun 1 2"
c54e8c7a4f9c2ebd8787aecafa4cfdb35bfd49e0,Effective Use of Bidirectional Language Modeling for Medical Named Entity Recognition,"Effective Use of Bidirectional Language Modeling for
Medical Named Entity Recognition
Devendra Singh Sachan1,*, Pengtao Xie1, and Eric P Xing1
Petuum Inc, Pittsburgh, 15222, USA"
1fd54172f7388cd83ed78ff9165519296de5cf20,Changing the Image Memorability: From Basic Photo Editing to GANs,"Changing the Image Memorability: From Basic Photo Editing to GANs
Oleksii Sidorov
The Norwegian Colour and Visual Computing Laboratory, NTNU
Gjovik, Norway
Figure 1: Modification of memorability using the proposed algorithm. All the results were generated without any human intervention.
“What” and “how” to change were learned by the model from experimental data."
1f5b0caec136778decf7689cb090cb1911c2263f,Semantic Mapping for Orchard Environments by Merging Two-Sides Reconstructions of Tree Rows,"Semantic Mapping for Orchard Environments by
Merging Two-Sides Reconstructions of Tree Rows
Wenbo Dong
Pravakar Roy
Volkan Isler∗"
1ff057f2fb8258bd5359cded950a3627bd8ee1f4,Low-rank embedding for semisupervised face classification,"Low-Rank Embedding for Semisupervised Face Classification
Gaurav Srivastava, Ming Shao and Yun Fu∗"
1f7cd3343f4b6b0f936c94e3a45c477c014e2b5c,3D Human Pose Estimation on a Configurable Bed from a Pressure Image,"D Human Pose Estimation on a Configurable Bed from a Pressure Image
Henry M. Clever*, Ariel Kapusta, Daehyung Park, Zackory Erickson, Yash Chitalia, Charles C. Kemp"
1f8d539885f78e1a9d1314e952f3099e71676a5b,Audio-Visual Speaker Diarization Based on Spatiotemporal Bayesian Fusion,"Audio-Visual Speaker Diarization Based on
Spatiotemporal Bayesian Fusion
Israel D. Gebru, Sil`eye Ba, Xiaofei Li and Radu Horaud"
1f65cbc7894323a85f2964d05ae937070e70e43b,Eliminating Background-bias for Robust Person Re-identification,"Eliminating Background-bias for Robust Person Re-identification
Maoqing Tian1, Shuai Yi1, Hongsheng Li2, Shihua Li3,
Xuesen Zhang1, Jianping Shi1, Junjie Yan1, Xiaogang Wang2
SenseTime Research, 2 Chinese University of Hong Kong, 3 Shenzhen Municipal Public Security Bureau"
1fb2082d3f772933b586cca65af2099512b9c68b,Comparison of Spectral-Only and Spectral/Spatial Face Recognition for Personal Identity Verification,"Hindawi Publishing Corporation
EURASIP Journal on Advances in Signal Processing
Volume 2009, Article ID 943602, 6 pages
doi:10.1155/2009/943602
Research Article
Comparison of Spectral-Only and Spectral/Spatial Face
Recognition for Personal Identity Verification
Zhihong Pan,1 Glenn Healey,2 and Bruce Tromberg3
Galileo Group Inc., 100 Rialto Place Suite 737, Melbourne, FL 32901, USA
Department of Electrical Engineering and Computer Science, University of California, Irvine, CA 92697, USA
Beckman Laser Institute, 1002 East Health Sciences Road, Irvine, CA 92612, USA
Correspondence should be addressed to Zhihong Pan,
Received 29 September 2008; Revised 22 February 2009; Accepted 8 April 2009
Recommended by Kevin Bowyer
Face recognition based on spatial features has been widely used for personal identity verification for security-related applications.
Recently, near-infrared spectral reflectance properties of local facial regions have been shown to be suf‌f‌icient discriminants for
ccurate face recognition. In this paper, we compare the performance of the spectral method with face recognition using the
eigenface method on single-band images extracted from the same hyperspectral image set. We also consider methods that use
multiple original and PCA-transformed bands. Lastly, an innovative spectral eigenface method which uses both spatial and spectral
features is proposed to improve the quality of the spectral features and to reduce the expense of the computation. The algorithms"
1fbf9dddf0bd4c115e2256cbf5d3a3bcc5ad2060,Human Motion Prediction using Adaptable Neural Networks,"Human Motion Prediction using Adaptable Neural Networks
Yujiao Cheng*, Weiye Zhao*, Changliu Liu, and Masayoshi Tomizuka"
1fd2655fd69a44bd9ca0d855521247d5620d3f82,Multi-modal Geolocation Estimation Using Deep Neural Networks,"MULTI-MODAL GEOLOCATION ESTIMATION USING
DEEP NEURAL NETWORKS
Jesse M. Johns, Jeremiah Rounds & Michael J. Henry
Pacific Northwest National Laboratory
Richland, WA 99352, USA"
1f06651fc5ce2c10cb1f4ad53dd9c931ceb66448,Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360 ^\circ ∘ Panoramic Imagery,"Eliminating the Blind Spot: Adapting 3D Object
Detection and Monocular Depth Estimation to
60◦ Panoramic Imagery
Gr´egoire Payen de La Garanderie, Amir Atapour Abarghouei,
nd Toby P. Breckon
Department of Computer Science
Durham University"
1f8f0abfe4689aa93f2f6cc7ec4fd4c6adc2c2d6,Semantic Instance Segmentation with a Discriminative Loss Function,"Semantic Instance Segmentation with a Discriminative Loss Function
Bert De Brabandere∗
Davy Neven∗
ESAT-PSI, KU Leuven
Luc Van Gool"
1f8eefd6dd2f20fd78a67dfdfe33022c6f9981d6,Unsupervised Features for Facial Expression Intensity Estimation over Time,
1fee632cad6a3853cc43621ba50161f5dd6263e5,Gated classifiers: Boosting under high intra-class variation,"Gated Classifiers: Boosting under High Intra-Class Variation
Oscar Danielsson, Babak Rasolzadeh and Stefan Carlsson
School of Computer Science and Communication, Royal Inst. of Technology, Stockholm, Sweden
. Motivation
. Learning
. Experiments and Results
Combinations of weak classifiers that never occur together on any
example of the target class may generate false positives.
Using Gated Classifiers with AdaBoost requires only very small changes
to existing boosting code.
Synthetic data: Increasingly difficult datasets generated by adding more
omponents to Gaussian Mixture generating positive examples. A pool of
weak classifiers split the x- and y-axes into a set of intervals. By adding
first tautology and falsum classifiers and then NAND and XOR classifiers
we can better represent complex distributions.
Solution: Add new classifier
h = ¬ h1 ∧ h2
Problem: The outputs of h1 and
h2 are negatively correlated on
the target class, but positively"
1fed6a571d9f688e18960e560d9441f5c5e3e2bd,Scalable Active Learning for Multiclass Image Classification,"MITSUBISHI ELECTRIC RESEARCH LABORATORIES
http://www.merl.com
Scalable Active Learning for Multi-Class
Image Classification
Joshi, A.J.; Porikli, F.; Papanikolopoulos, N.
TR2012-026
January 2012"
1fcd7978c6956fd9a0d752ecc9f5ac1a1b2896e9,Impact of Face Registration Errors on Recognition,"Impact of Face Registration Errors on Recognition
E. Rentzeperis, A. Stergiou, A. Pnevmatikakis and L. Polymenakos
Athens Information Technology, Autonomic and Grid Computing,
Markopoulou Ave., 19002 Peania, Greece
{eren, aste, apne,
http://www.ait.edu.gr/research/RG1/overview.asp"
1f2551b2acfb6895e91e39ae36a51335893a849f,3D Face Recognition Using Face Feature Points Based on Parallel Stereo Vision,"D Face Recognition Using Face Feature Points Based on Parallel Stereo Vision
Li Wei, and Eung-Joo Lee
D Face Recognition Using Face Feature Points Based on Parallel
*1 Information Communication Engineering Department, Tongmyong University, Busan, Korean
*2, Corresponding author
Information Communication Engineering Department, Tongmyong University,
Stereo Vision
Li Wei, and *Eung-Joo Lee
Busan, Korean"
1fbccb842cf34697c98532d69b044463985f90ee,Constructing PCA Baseline Algorithms to Reevaluate ICA-Based Face-Recognition Performance,"IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 37, NO. 4, AUGUST 2007
Constructing PCA Baseline Algorithms to Reevaluate
ICA-Based Face-Recognition Performance
Jian Yang, David Zhang, and Jing-Yu Yang"
1f4aa1d14bb99e152dd1c7ac3cfd5afa8f6a012f,Learning Discriminative Part Detectors for Image Classification and Cosegmentation,"Learning Discriminative Part Detectors for Image Classification and
Cosegmentation
Jian Sun
Jean Ponce
Xi’an Jiaotong University, INRIA, ∗
´Ecole Normale Sup´erieure, *
This is a preliminary version accepted for publication to ICCV 2013"
1f6dd0ff2e8493b81e3699b520193198d4eed4e6,Shaogang Gong Part I Features and Representations 1 Discriminative Image Descriptors for Person Re-identification . . . . . 25 7 One-shot Person Re-identification with a Consumer Depth Camera . 163 List of Contributors the Re-identification Challenge,"Shaogang Gong
Marco Cristani
Shuicheng Yan
Chen Change Loy (Eds.)
PERSON RE-IDENTIFICATION
October 10, 2013
Springer"
1fff309330f85146134e49e0022ac61ac60506a9,Data-Driven Sparse Sensor Placement for Reconstruction,"Data-Driven Sparse Sensor Placement for Reconstruction
Krithika Manohar∗, Bingni W. Brunton, J. Nathan Kutz, and Steven L. Brunton
Corresponding author:"
1f5e47ad5490a63c7bea79000999b711055fbf2a,Aggregated Channels Network for Real-Time Pedestrian Detection,"Aggregated Channels Network for Real-Time Pedestrian Detection
Farzin Ghorban1,2, Javier Marín3, Yu Su2, Alessandro Colombo2, Anton Kummert1
Universität Wuppertal, 2Delphi Deutschland, 3Massachusetts Institute of Technology"
1f98daf89f9a3dba655f0a4eb4164118ea6226ef,"Parallel k-Means Image Segmentation Using Sort, Scan and Connected Components on a GPU","The original publication is available at: www.springerlink.com
Parallel k-Means Image Segmentation Using
Sort, Scan & Connected Components on a GPU
Michael Backer, Jan T¨unnermann, and B¨arbel Mertsching
GET Lab, University of Paderborn, Pohlweg 47-49, 33098 Paderborn, Germany
{backer, tuennermann,
http://getwww.upb.de"
1f82eebadc3ffa41820ad1a0f53770247fc96dcd,Using Trajectories derived by Dense Optical Flows as a Spatial Component in Background Subtraction,"Using Trajectories derived by Dense Optical Flows as a
Spatial Component in Background Subtraction
Martin Radolko
University of Rostock
nd Fraunhofer IGD
Joachim-Jungius 11
Rostock 18059
r.fraunhofer.de
Fahimeh Farhadifard
University of Rostock
nd Fraunhofer IGD
Joachim-Jungius 11
Rostock 18059
r.fraunhofer.de"
1f2f712253a68cd9f8172de19297e35cec7919dd,Vision System of Facial Robot SHFR- III for Human-robot Interaction,
1f436aa4e68274037fff44e6cfbcd0a1ee3f60df,Tell and Predict: Kernel Classifier Prediction for Unseen Visual Classes from Unstructured Text Descriptions,"Tell and Predict: Kernel Classifier Prediction for Unseen Visual Classes
from Unstructured Text Descriptions
Mohamed Elhoseiny, Ahmed Elgammal, Babak Saleh"
1fefc1d288a87fe218ba25024c4b2b6ef345738e,Self-ensembling for domain adaptation,"Self-ensembling for domain adaptation
French, G.
Mackiewicz, M.
Fisher, M.
November 6, 2017"
1fc249ec69b3e23856b42a4e591c59ac60d77118,Evaluation of a 3D-aided pose invariant 2D face recognition system,"Evaluation of a 3D-aided Pose Invariant 2D Face Recognition System
Xiang Xu, Ha A. Le, Pengfei Dou, Yuhang Wu, Ioannis A. Kakadiaris
{xxu18, hale4, pdou, ywu35,
Computational Biomedicine Lab
800 Calhoun Rd. Houston, TX, USA"
1fa9c5af78b3ca04476f4ee6910684dc19008f5e,Supplementary Material : Cross-Dataset Adaptation for Visual Question Answering,"Supplementary Material:
Cross-Dataset Adaptation for Visual Question Answering
Wei-Lun Chao∗
U. of Southern California
Los Angeles, CA
Hexiang Hu∗
Los Angeles, CA
U. of Southern California
U. of Southern California
Fei Sha
Los Angeles, CA
We provide contents omitted in the main text.
• Section 1: details on Name that dataset! (Sect. 3.2 of
the main text).
• Section 2: details on the proposed domain adaptation
lgorithm (Sect. 4.2 and 4.3 of the main text).
• Section 3: details on the experimental setup (Sect. 5.2
of the main text).
• Section 4: additional experimental results (Sect. 5.3
nd 5.4 of the main text)."
1f7cf2df2fa7719c9db3fe57a0f01d65f08a9a8f,How social exclusion modulates social information processing: A behavioural dissociation between facial expressions and gaze direction,"RESEARCH ARTICLE
How social exclusion modulates social
information processing: A behavioural
dissociation between facial expressions and
gaze direction
Francesco Bossi1,2*, Marcello Gallucci1,2, Paola Ricciardelli1,2
Department of Psychology, University of Milan – Bicocca, Milan, Italy, 2 NeuroMI: Milan Center for
Neuroscience, Milan, Italy"
1fd808c6fda9c530d00d7a588693244ce9f324a8,Face Recognition using Feed Forward Neural Network,"IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 5, Ver. II (Sep. – Oct. 2015), PP 61-65
www.iosrjournals.org
Face Recognition using Feed Forward Neural Network
Pooja Rani
(Department of computer science/ Punjabi university, India)"
1f0c7b93636f879bd5ef3dd915a02dcd813a053d,Interpreting Deep Visual Representations via Network Dissection,"Interpreting Deep Visual Representations via
Network Dissection
Bolei Zhou∗, David Bau∗, Aude Oliva, and Antonio Torralba"
1fdb84cd0ce04df3c4997059a5571e043dc32c8a,Identifying whether the mystery man or elimination lineup method is most effective for children,"Western University
Undergraduate Honors Theses
Winter 4-30-2014
Identifying whether the mystery man or
elimination lineup method is most effective for
hildren
Logan Ewanation
King's University College,
Psychology
Follow this and additional works at: https://ir.lib.uwo.ca/psychK_uht
Part of the Psychology Commons
Recommended Citation
Ewanation, Logan, ""Identifying whether the mystery man or elimination lineup method is most effective for children"" (2014).
Undergraduate Honors Theses. 8.
https://ir.lib.uwo.ca/psychK_uht/8
This Dissertation/Thesis is brought to you for free and open access by the Psychology at It has been accepted for inclusion in
Undergraduate Honors Theses by an authorized administrator of For more information, please contact"
1f5c409e9b6aec60003b5d4534373f9b07ff8443,Saliency Weighted Features for Person Re-identification,"Saliency Weighted Features for Person
Re-Identification
Niki Martinel, Christian Micheloni and Gian Luca Foresti
Department of Mathematics and Computer Science
University of Udine - 33100, Udine, Italy"
1f9ae272bb4151817866511bd970bffb22981a49,An Iterative Regression Approach for Face Pose Estimation from RGB Images,"An Iterative Regression Approach for Face Pose Estima-
tion from RGB Images
Wenye He
This paper presents a iterative optimization method, explicit shape regression, for face pose
detection and localization. The regression function is learnt to find out the entire facial shape
nd minimize the alignment errors. A cascaded learning framework is employed to enhance
shape constraint during detection. A combination of a two-level boosted regression, shape
performance. In this paper, we have explain the advantage of ESR for deformable object like
face pose estimation and reveal its generic applications of the method. In the experiment,
we compare the results with different work and demonstrate the accuracy and robustness in
different scenarios.
Introduction
Pose estimation is an important problem in computer vision, and has enabled many practical ap-
plication from face expression 1 to activity tracking 2. Researchers design a new algorithm called
explicit shape regression (ESR) to find out face alignment from a picture 3. Figure 1 shows how
the system uses ESR to learn a shape of a human face image. A simple way to identify a face is to
find out facial landmarks like eyes, nose, mouth and chin. The researchers define a face shape S
nd S is composed of Nf p facial landmarks. Therefore, they get S = [x1, y1, ..., xNf p, yNf p]T . The
objective of the researchers is to estimate a shape S of a face image. The way to know the accuracy"
1f18708439ba1dadd81568e102216731d44340d5,Sparse Quantization for Patch Description,"Sparse Quantization for Patch Description
Xavier Boix
Michael Gygli
Gemma Roig
Luc Van Gool
Computer Vision Lab, ETH Zurich, Switzerland"
1f47a13548317602ec76eafbea44d7b39926c4cd,Stacking-Based Deep Neural Network: Deep Analytic Network for Pattern Classification,"Stacking-Based Deep Neural Network: Deep
Analytic Network for Pattern Classification
Cheng-Yaw Low, Jaewoo Park, and, Andrew Beng-Jin Teoh, Senior Member, IEEE"
1fbb66a9407470e1da332c4ef69cdc34e169a3d7,A Baseline for General Music Object Detection with Deep Learning,"Article
A Baseline for General Music Object Detection with
Deep Learning
Alexander Pacha 1,*
, Jan Hajiˇc, Jr. 2 and Jorge Calvo-Zaragoza 3
Institute for Visual Computing and Human-Centered Technology, TU Wien, 1040 Wien, Austria
Institute of Formal and Applied Linguistics, Charles University, 116 36 Staré Mˇesto, Czech Republic;
PRHLT Research Center, Universitat Politècnica de València, 46022 València, Spain;
* Correspondence:
Received: 31 July 2018; Accepted: 26 August 2018; Published: 29 August 2018"
1f94734847c15fa1da68d4222973950d6b683c9e,Embedding Label Structures for Fine-Grained Feature Representation,"Embedding Label Structures for Fine-Grained Feature Representation
Xiaofan Zhang
UNC Charlotte
Charlotte, NC 28223
Feng Zhou
NEC Lab America
Cupertino, CA 95014
Yuanqing Lin
NEC Lab America
Cupertino, CA 95014
Shaoting Zhang
UNC Charlotte
Charlotte, NC 28223"
1f8304f4b51033d2671147b33bb4e51b9a1e16fe,Beyond Trees : MAP Inference in MRFs via OuterPlanar Decomposition,"Noname manuscript No.
(will be inserted by the editor)
Beyond Trees:
MAP Inference in MRFs via Outer-Planar Decomposition
Dhruv Batra · Andrew C. Gallagher · Devi Parikh · Tsuhan Chen
Received: date / Accepted: date"
1f4fff64adef5ec6ae21e8647d5a042bf71d64d9,Human detection in surveillance videos and its applications - a review,"Paul et al. EURASIP Journal on Advances in Signal Processing 2013, 2013:176
http://asp.eurasipjournals.com/content/2013/1/176
R EV I E W
Human detection in surveillance videos and its
pplications - a review
Manoranjan Paul*, Shah M E Haque and Subrata Chakraborty
Open Access"
1f09ee546815c7bcd6d8b70a74d2c3fab482c72a,Performance Comparison of Principal Component Analysis-Based Face Recognition in Color Space,"We are IntechOpen,
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1f1d553be51339208c572f927354033e86a5be36,Machine Learning for Image Based Motion Capture,"INSTITUT NATIONAL POLYTECHNIQUE DE GRENOBLE
Num´ero attribu´e par la biblioth`eque
TH`ESE
pour obtenir le grade de
DOCTEUR DE L’INSTITUT NATIONAL POLYTECHNIQUE DE GRENOBLE
Sp´ecialit´e : Imagerie, Vision et Robotique
Ecole Doctorale : Math´ematiques, Sciences et Technologie de l’Information
pr´esent´ee et soutenue publiquement
Ankur Agarwal
le 26 Avril 2006
Machine Learning for Image Based Motion Capture
Directeur de th`ese : M. William Triggs
Pr´esident
M. Roger Mohr
M. Andrew Zisserman Rapporteur
Rapporteur
M. Pascal Fua
Directeur de th`ese
M. William Triggs
M. Philip Torr"
1f2c99bf032868ce520b9c5586a0c20051367b60,A Study of The Illumination Cones Method for Face Recognition Under Variable Illumination T.J. Chin and D. Suter A Study of The Illumination Cones Method for Face Recognition Under Variable Illumination,"Department of Electrical
Computer Systems Engineering
Technical Report
MECSE-7-2004
A Study of The Illumination Cones Method for Face
Recognition Under Variable Illumination
T.J. Chin and D. Suter"
1f8e44593eb335c2253d0f22f7f9dc1025af8c0d,Fine-Tuning Regression Forests Votes for Object Alignment in the Wild,"Fine-tuning regression forests votes for object alignment in the wild.
Yang, H; Patras, I
© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be
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more information contact"
1f6e627efb1ac2b31f2b18dce8359ffdc352b3f2,A Boosting Approach to Multiple Instance Learning,"A Boosting Approach
to Multiple Instance Learning
Peter Auer and Ronald Ortner
University of Leoben, Franz-Josef-Strasse 18,
8700 Leoben, Austria"
1ff89bd94d8a21b7ca4bf844e2d366f854822918,Robust Online Multi-object Tracking by Maximum a Posteriori Estimation with Sequential Trajectory Prior,"Robust Online Multi-object Tracking
y Maximum a Posteriori Estimation
with Sequential Trajectory Prior
Min Yang(B), Mingtao Pei, Jiajun Shen, and Yunde Jia
Beijing Laboratory of Intelligent Information Technology, School of Computer
Science, Beijing Institute of Technology, Beijing 100081, People’s Republic of China"
1fd8c71a8859da611a8fde1cbb2bba1c7cf00b4c,EYEDIAP: a database for the development and evaluation of gaze estimation algorithms from RGB and RGB-D cameras,"This paper was presented at the 2014 Symposium on Eye Tracking Research & Applications 2014
EYEDIAP: A Database for the Development and Evaluation of Gaze Estimation
Algorithms from RGB and RGB-D Cameras
Kenneth Alberto Funes Mora1,2, Florent Monay1 and Jean-Marc Odobez1,2
Idiap Research Institute 2 ´Ecole Polytechnique F´ed´erale de Lausanne (EPFL), Switzerland
{kfunes, monay,"
1ff616ae8b61f8167f2d626b7c1a36e018b23e94,Learning with Parsimony for Large Scale Object Detection and Discovery,"Learning with Parsimony for Large Scale Object
Detection and Discovery
Hyun Oh Song
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2014-148
http://www.eecs.berkeley.edu/Pubs/TechRpts/2014/EECS-2014-148.html
August 12, 2014"
4d20fbd6dcdb4408dd6268951d86b92e8d96f332,Robust Face Recognition of Variations in Blur and Illumination by Using LDA Ms,"International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622
International Conference on Humming Bird ( 01st March 2014)
RESEARCH ARTICLE
OPEN ACCESS
Robust Face Recognition of Variations in Blur and Illumination
y Using LDA
Ms. K. Hema
PG Student
Department of AE
University College of Engineering
Nagercoil-629004.
Mr. J. Arun Prem Santh M. E.,
Teaching Fellow
Department of ECE
University College of Engineering
Nagercoil-629004 ."
4d87784afdb704d9eca14010212afd5cd74c60ec,Cosine Similarity Search with Multi Index Hashing,"Cosine Similarity Search
with Multi-Index Hashing
Sepehr Eghbali and Ladan Tahvildari"
4d0ef449de476631a8d107c8ec225628a67c87f9,Face system evaluation toolkit: Recognition is harder than it seems,"© 2010 IEEE. Personal use of this material is permitted. Permission from IEEE
must  be  obtained  for  all  other  uses,  in  any  current  or  future  media,  including
reprinting/republishing  this  material  for  advertising  or  promotional  purposes,
reating  new  collective  works,  for  resale  or  redistribution  to  servers  or  lists,  or
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Pre-print of article that appeared at BTAS 2010.
The published article can be accessed from:
http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5634517"
4de83b6025526ef7a340ffca30626dac53d7f8cb,SIFT / LBP 3 D face recognition,"SIFT/LBP 3D face recognition
Narimen SAAD1  NourEddine DJEDI
Department of Computer Science
LESIA Laboratory
University of Biskra, Algeria"
4ddd55a9f103001da8dc24d123d9223dbb67f884,Combining Face and Facial Feature Detectors for Face Detection Performance Improvement,"Combining face and facial feature detectors for
face detection performance improvement
M. Castrill´on-Santana, D. Hern´andez-Sosa, and J. Lorenzo-Navarro(cid:63)
SIANI
Universidad de Las Palmas de Gran Canaria, Spain"
4da735d2ed0deeb0cae4a9d4394449275e316df2,"The rhythms of head, eyes and hands at intersections","Gothenburg, Sweden, June 19-22, 2016
978-1-5090-1820-8/16/$31.00 ©2016 IEEE"
4d530a4629671939d9ded1f294b0183b56a513ef,Facial Expression Classification Method Based on Pseudo Zernike Moment and Radial Basis Function Network,"International Journal of Machine Learning and Computing, Vol. 2, No. 4, August 2012
Facial Expression Classification Method Based on Pseudo
Zernike Moment and Radial Basis Function Network
Tran Binh Long, Le Hoang Thai, and Tran Hanh"
4dff517b1578dec9155936f29aa9e8ca174e0804,Integrated Object Segmentation and Tracking for 3D LIDAR Data,
4d5a12fe88e35bb5c4a556a6b2598f7dd5284f46,Sentence transition matrix: An efficient approach that preserves sentence semantics,"Sentence transition matrix: An efficient approach that preserves
sentence semantics
Myeongjun Jang 1 Pilsung Kang 1"
4d5c34fb36cf8c74880a62814750760bce0aef16,Boosting descriptors condensed from video sequences for place recognition,"Boosting Descriptors Condensed from Video Sequences for Place Recognition
Tat-Jun Chin, Hanlin Goh and Joo-Hwee Lim
Institute for Infocomm Research
1 Heng Mui Keng Terrace, Singapore 119613.
{tjchin, hlgoh,"
4db64fbc3dd2486a74dba3350d44c51e561f515f,An Ecological Visual Exploration Tool to Support the Analysis of Visual Processing Pathways in Children with Autism Spectrum Disorders,"Article
An Ecological Visual Exploration Tool to Support the
Analysis of Visual Processing Pathways in Children
with Autism Spectrum Disorders
Dario Cazzato 1, Marco Leo 2,*, Cosimo Distante 2, Giulia Crifaci 3,
Giuseppe Massimo Bernava 4, Liliana Ruta 4, Giovanni Pioggia 4 and Silvia M. Castro 5
Interdisciplinary Centre for Security Reliability and Trust (SnT), University of Luxembourg, 29,
Avenue JF Kennedy, L-1855 Luxembourg, Luxembourg;
Institute of Applied Sciences and Intelligence Systems—CNR, 73100 Lecce, Italy;
Department of Clinical Physiology, CNR Pisa, 56124 Pisa, Italy;
Institute of Applied Sciences and Intelligence Systems—CNR, 98164 Messina, Italy;
(G.M.B.); (L.R.); (G.P.)
5 Universidad Nacional del Sur, 8000 Bahía Blanca, Argentina;
* Correspondence:
Received: 6 November 2017; Accepted: 19 December 2017; Published: 29 December 2017"
4d442ea40635a10fd3e642a7161dfc8f2b15a71e,An Image reranking model based on attributes and visual features eliminating duplication,"© 2016 IJEDR | Volume 4, Issue 2 | ISSN: 2321-9939
An Image reranking model based on attributes and
visual features eliminating duplication
Ms.Madhuri Mhaske,2Prof.Sachin Patil
PG Scholar at G. H. Raisoni College of Engineering and Management, Chas, Ahmednagar
, 2Professor at G. H. Raisoni College of Engineering and Management, Vagholi, Pune
________________________________________________________________________________________________________"
4d6043a25bf48c6fd6aff6a46597fe1902a9c6a7,Long-term tracking of multiple interacting pedestrians using a single camera by Advice,"Long-term tracking of multiple interacting
pedestrians using a single camera
Mogomotsi Keaikitse∗, Willie Brink† and Natasha Govender∗
Modelling and Digital Sciences
Council for Scientific and Industrial Research
Pretoria, South Africa
Department of Mathematical Sciences
Stellenbosch University
Stellenbosch, South Africa"
4dd6d511a8bbc4d9965d22d79ae6714ba48c8e41,Automatic Pixel Boosting for Face Enhancement in Dim Light,"The 23rd International Technical Conference on Circuits/Systems,
Computers and Communications (ITC-CSCC 2008)
Automatic Pixel Boosting for Face Enhancement in Dim Light
Hataikan Poncharoensil1 and Seri Pansang2
School of Science, Maefahluang University
33 Moo1 Thasud, Muang Chiangrai 57100, Thailand
Department of Computer Engineering, Rajabhat Chiangmai University
02 ChangPuek, Muang Chiangmai 50300, Thailand
E-mail:"
4d2975445007405f8cdcd74b7fd1dd547066f9b8,Image and Video Processing for Affective Applications,"Image and Video Processing
for Affective Applications
Maja Pantic and George Caridakis"
4d7e1eb5d1afecb4e238ba05d4f7f487dff96c11,Largest center-specific margin for dimension reduction,"978-1-5090-4117-6/17/$31.00 ©2017 IEEE
ICASSP 2017"
4dd2744a37bd1e666346a41dcd2a271945c74e2f,Human-Robot Teaming : Approaches from Joint Action and Dynamical Systems,"Human Robot Teaming: Approaches from Joint
Action and Dynamical Systems
Tariq Iqbal and Laurel D. Riek"
4d2022e3db712237b95fe381a75dbeb827551924,Running Head : GENDER CATEGORIZATION IN INFANTS AND CHILDREN 1 Gender Categorization in Infants and Children,"Running Head: GENDER CATEGORIZATION IN INFANTS AND CHILDREN
Gender Categorization in Infants and Children
Hong N. T. Bui
Senior Thesis in Psychology
Advisor: Karen Wynn
April 27, 2018"
4dade6faf6d5d6db53d5bcb2e107311da1ad48ac,Facial Expression Biometrics Using Statistical Shape Models,"Hindawi Publishing Corporation
EURASIP Journal on Advances in Signal Processing
Volume 2009, Article ID 261542, 17 pages
doi:10.1155/2009/261542
Research Article
Facial Expression Biometrics Using Statistical Shape Models
Wei Quan, Bogdan J. Matuszewski (EURASIP Member), Lik-Kwan Shark,
nd Djamel Ait-Boudaoud
Applied Digital Signal and Image Processing Research Centre, University of Central Lancashire, Preston PR1 2HE, UK
Correspondence should be addressed to Bogdan J. Matuszewski,
Received 30 September 2008; Revised 2 April 2009; Accepted 18 August 2009
Recommended by Jonathon Phillips
This paper describes a novel method for representing different facial expressions based on the shape space vector (SSV) of the
statistical shape model (SSM) built from 3D facial data. The method relies only on the 3D shape, with texture information not
eing used in any part of the algorithm, that makes it inherently invariant to changes in the background, illumination, and to
some extent viewing angle variations. To evaluate the proposed method, two comprehensive 3D facial data sets have been used
for the testing. The experimental results show that the SSV not only controls the shape variations but also captures the expressive
haracteristic of the faces and can be used as a significant feature for facial expression recognition. Finally the paper suggests
improvements of the SSV discriminatory characteristics by using 3D facial sequences rather than 3D stills.
Copyright © 2009 Wei Quan et al. This is an open access article distributed under the Creative Commons Attribution License,"
4dea287ad9271d4ac73c58c03b8e6e714dd2db6c,Pyramid Center-symmetric Local 1 Binary / Trinary Patterns for Pedestrian 2 Detection,"Pyramid Center-symmetric Local
Binary/Trinary Patterns for Pedestrian
Detection
Yongbin Zheng, Chunhua Shen, Richard Hartley and Xinsheng Huang
Australian National University and NICTA, Canberra"
4d50de8ec7335ab320f9743d199336699307b523,The Bochum / USC Face Recognition Systemand How it Fared in the FERET Phase,"TheBochum/USCFaceRecognitionSystem
ndHowitFaredintheFERETPhaseIII
KazunoriOkada
,JohannesSte(cid:11)ens
;;,ThomasMaurer,HaiHong
,Egor
Elagin
;,HartmutNeven
;,andChristophvonderMalsburg
;

ComputerScienceDepartmentandCenterforNeuralEngineering
UniversityofSouthernCalifornia
LosAngeles,CA		-,USA
Institutf(cid:127)urNeuroinformatik
Ruhr-Universit(cid:127)atBochum
D{Bochum,Germany
NowalsoatEyematicInterfaces,Inc
thStreet,SantaMonica,CA	,USA
Summary.ThispapersummarizestheBochum/USCfacerecognitionsystem,our
preparationsfortheFERETPhaseIIItest,andtestresultsasfarastheyhavebeen
madeknowntous.OurtechnologyisbasedonGaborwaveletsandelasticbunch
graphmatching.Webrie(cid:13)ydiscussourtechnologyinrelationtobiologicaland
PCAbasedsystemsandindicatecurrentactivitiesinthelabandpotentialfuture
pplications.

.Introduction
Visionisthemostimportantofoursensesbywhichweestablishcontinuitybetween"
4d3a6c2cee0cf06ff6471fad3d65a5835d0552f8,3-D Face Recognition Using Geodesic-Map Representation and Statistical Shape Modelling,"Article
­D Face Recognition Using Geodesic­Map
Representation and Statistical Shape Modelling
Quan, Wei, Matuszewski, Bogdan and Shark, Lik
Available at http://clok.uclan.ac.uk/13240/
Quan, Wei, Matuszewski, Bogdan and Shark, Lik (2016) 3­D Face Recognition Using Geodesic­
Map Representation and Statistical Shape Modelling. Lecture Notes in Computer Science, 9493 .
pp. 199­212. ISSN 0302­9743
It is advisable to refer to the publisher’s version if you intend to cite from the work.
http://dx.doi.org/10.1007/978-3-319-27677-9_13
For more information about UCLan’s research in this area go to
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y the individual authors and/or other copyright owners. Terms and conditions for use
of this material are defined in the http://clok.uclan.ac.uk/policies/
Central Lancashire online Knowledge
www.clok.uclan.ac.uk"
4d1e28368e1121872bcd4ce75bc7ba5e43bd42d0,Attend to You: Personalized Image Captioning with Context Sequence Memory Networks,"Post generationTask1. Hashtag predictionQuery ImageImage featureUser contextWord outputCNNytytUser’s Active VocabularyCSMN modelContextMemorySoftmaxFigure1.ProblemstatementofpersonalizedimagecaptioningwithanInstagramexample.Asmainapplications,weaddresshashtagpredictionandpostgenerationtasks.Givenaqueryim-age,theformerpredictsalistofhashtags,whilethelattergener-atesadescriptivetexttocompleteapost.Weproposeaversatilecontextsequencememorynetwork(CSMN)model.captioning,whichhavenotbeendiscussedinpreviousre-search.Weaimtogenerateadescriptivesentenceforanimage,accountingforpriorknowledgesuchastheuser’sactivevocabulariesorwritingstylesinpreviousdocuments.Potentially,personalizedimagecaptioningisapplicabletoawiderangeofautomationservicesinphoto-sharingsocialnetworks.Forexample,inInstagramorFacebook,usersin-stantlytakeandsharepicturesaspostsusingmobilephones.Onebottlenecktocompleteanimagepostistocrafthash-tagsorassociatedtextdescriptionusingtheirownwords.Indeed,craftingtextismorecumbersomethantakingapic-tureforgeneralusers;photo-takingcanbedonewithonlyasingletabonthescreenofasmartphone,whereastextwrit-ingrequiresmoretimeandmentalenergyforselectingsuit-ablekeywordsandcompletingasentencetodescribetheme,sentiment,andcontextoftheimage.Inthispaper,asexamplesofpersonalizedimagecap-tioning,wefocusontwopostautomationtasks:hashtag"
4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b38461,A 3 D Dynamic Database for Unconstrained Face Recognition,"A 3D Dynamic Database for Unconstrained Face
Recognition
Taleb Alashkar, Boulbaba Ben Amor, Mohamed Daoudi, Stefano Berretti
To cite this version:
Taleb Alashkar, Boulbaba Ben Amor, Mohamed Daoudi, Stefano Berretti. A 3D Dynamic Database
for Unconstrained Face Recognition. 5th International Conference and Exhibition on 3D Body Scan-
ning Technologies, Oct 2014, Lugano, Switzerland. 2014. <hal-01074992>
HAL Id: hal-01074992
https://hal.archives-ouvertes.fr/hal-01074992
Submitted on 17 Oct 2014
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
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destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
4d6ad0c7b3cf74adb0507dc886993e603c863e8c,Human Activity Recognition Based on Wearable Sensor Data : A Standardization of the State-ofthe-Art,"Human Activity Recognition Based on Wearable
Sensor Data: A Standardization of the
State-of-the-Art
Artur Jord˜ao, Antonio C. Nazare Jr., Jessica Sena and William Robson Schwartz
Smart Surveillance Interest Group, Computer Science Department
Universidade Federal de Minas Gerais, Brazil
Email: {arturjordao, antonio.nazare, jessicasena,"
4d6e7d73f5226142ffc42b4e8380882d5071e187,Discretion Within Constraint: Homophily and Structure in a Formal Organization,"This article was downloaded by: [128.32.74.70] On: 03 July 2014, At: 15:15
Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
INFORMS is located in Maryland, USA
Publication details, including instructions for authors and subscription information:
http://pubsonline.informs.org
Discretion Within Constraint: Homophily and Structure in
Formal Organization
Adam M. Kleinbaum, Toby E. Stuart, Michael L. Tushman
To cite this article:
Adam M. Kleinbaum, Toby E. Stuart, Michael L. Tushman (2013) Discretion Within Constraint: Homophily and Structure in a
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Copyright © 2013, INFORMS
Please scroll down for article—it is on subsequent pages"
4da80ad59adbe530838b5685935b488edab07c7d,Effective Proximity Retrieval by Ordering Permutations,"Effective Proximity Retrieval
y Ordering Permutations
EDGAR CHAVEZ, Universidad Michoacana, Mexico
KARINA FIGUEROA, Universidad Michoacana, Mexico,
nd Universidad de Chile, Chile
GONZALO NAVARRO, Universidad de Chile, Chile
Supported by CONACyT (first and second author) and Millennium Nucleus Center for Web Research, Grant P04-067-F,
Mideplan, Chile (second and third author). A preliminary partial version of this paper appeared in Proc. 4th Mexican International
Conference on Artificial Intelligence (MICAI 2005), pp. 405–414, LNAI Series vol. 3789.
September 3, 2007
DRAFT"
4d334cfafd11a93394917adcffef6c1d27aa178b,Refined Clustering technique based on boosting and outlier detection,"International Journal of Scientific & Engineering Research, Volume 6, Issue 11, November-2015                                                                                                 472
ISSN 2229-5518
Refined Clustering technique based on boosting
nd outlier detection
Ms. Reshma Y. Nagpure, Prof. P. P. Rokade"
4d1fc3245b05731a313e61165c1109f42f5b4a0c,Facial expression recognition using local binary patterns and discriminant kernel locally linear embedding,"Zhao and Zhang EURASIP Journal on Advances in Signal Processing 2012, 2012:20
http://asp.eurasipjournals.com/content/2012/1/20
RESEARCH
Facial expression recognition using local binary
patterns and discriminant kernel locally linear
embedding
Xiaoming Zhao1 and Shiqing Zhang2*
Open Access"
4d7bbaa2c7e89d5ba6940ee5804cf10a6b24d6ec,Multi-target Unsupervised Domain Adaptation without Exactly Shared Categories,"Multi-target Unsupervised Domain Adaptation
without Exactly Shared Categories
Huanhuan Yu, Menglei Hu and Songcan Chen"
4d47261b2f52c361c09f7ab96fcb3f5c22cafb9f,Deep multi-frame face super-resolution,"Deep multi-frame face super-resolution
Evgeniya Ustinova, Victor Lempitsky
October 17, 2017"
4df3143922bcdf7db78eb91e6b5359d6ada004d2,The Chicago face database: A free stimulus set of faces and norming data.,"Behav Res (2015) 47:1122–1135
DOI 10.3758/s13428-014-0532-5
The Chicago face database: A free stimulus set of faces
nd norming data
Debbie S. Ma & Joshua Correll & Bernd Wittenbrink
Published online: 13 January 2015
# Psychonomic Society, Inc. 2015"
4df54d4758b1a883902c036b2a10ef6d0f2d4af9,An Automatic Face Recognition System Based On Adaptive Wavelet Transforms,"International Journal of Scientific Research and Engineering Studies (IJSRES)
Volume 2 Issue 4, April 2015
ISSN: 2349-8862
An Automatic Face Recognition System Based On Adaptive
Wavelet Transforms
Prof. Khaladkar
Nilam Chavan
Apurva Kadam"
4dc8b1c193c421f8f570c0a7eac2fc73da06cb51,MODS: Fast and Robust Method for Two-View Matching,"MODS: Fast and Robust Method for Two-View
Matching
Dmytro Mishkin, Jiri Matas, Michal Perdoch
Center for Machine Perception, Faculty of Electrical Engineering,
Czech Technical University in Prague. Karlovo namesti, 13. Prague 2, 12135"
4dba7e19e2958d8ab75261219747aebc675c6f8a,Finding the Topic of a Set of Images,"Finding the Topic of a Set of Images
Gonzalo Vaca-Castano
Univeristy of Central Florida"
4d231311cdfe3aba13766bd0b358d4db0a9af3d3,Processing and Recognising Faces in 3D Images,"We are IntechOpen,
the world’s leading publisher of
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4dc6659b5022ecc2c4e1459e9dff16ddece4147e,Transfer Learning for Illustration Classification,"CEIG - Spanish Computer Graphics Conference (2017)
F. J. Melero and N. Pelechano (Editors)
Transfer Learning for Illustration Classification
Manuel Lagunas1 Elena Garces2
Universidad de Zaragoza, I3A
Technicolor
Figure 1: Comparison of the probabilities of the images that belong to the class pelican using our method and the network VGG19 [SZ14].
image (a) is a photograph and image (b) is an illustration which has similar colours, gradients and edges than the natural image. On the"
4d848b2055e1bba4ce80d5d050879c26686eda50,Face recognition with enhanced privacy protection,"978-1-4244-2354-5/09/$25.00 ©2009 IEEE
ICASSP 2009"
4d45612c41d3e27a30a5ec64e0d8e2362dcb6b73,Brand > Logo: Visual Analysis of Fashion Brands,"Brand > Logo: Visual Analysis of Fashion
Brands
M. Hadi Kiapour and Robinson Piramuthu
eBay, San Francisco CA 94105, USA"
4d8347a69e77cc02c1e1aba3a8b6646eac1a0b3d,Re-ID done right: towards good practices for person re-identification.,"Re-ID done right: towards good practices for person re-identification
Jon Almaz´an1 Bojana Gaji´c2∗ Naila Murray1 Diane Larlus1
Computer Vision Group
NAVER LABS Europe
Computer Vision Center
Dept. de Ci`encies de la Computaci´o, UAB"
12919f98aecdd74c1e0db56cba13d107553e421b,Temporal Model Adaptation for Person Re-Identification : Supplementary Material,"Temporal Model Adaptation for
Person Re-Identification:
Supplementary Material
Niki Martinel1,3, Abir Das2,
Christian Micheloni1, and Amit K. Roy-Chowdhury3
University of Udine, 33100 Udine, Italy
University of Massatchussets Lowell, 01852 Lowell, MA, USA
University of California Riverside, 92507 Riverside, CA, USA"
1222705b626a33974e85985ddabfcea135e9ddce,k-fold Subsampling based Sequential Backward Feature Elimination,
1267eae42798940c105355a9590363fb6560595b,From Coarse to Fine: Robust Hierarchical Localization at Large Scale,"From Coarse to Fine: Robust Hierarchical Localization at Large Scale
Paul-Edouard Sarlin1 Cesar Cadena1 Roland Siegwart1 Marcin Dymczyk1,2
Autonomous Systems Lab, ETH Z¨urich
Sevensense Robotics AG"
127759fc41d62b516298fff2706dfcc754ff1ee8,Fabrik: An Online Collaborative Neural Network Editor,"FABRIK: AN ONLINE COLLABORATIVE NEURAL NETWORK EDITOR
Utsav Garg 1 Viraj Prabhu 2 Deshraj Yadav 2 Ram Ramrakhya 3 Harsh Agrawal 2 Dhruv Batra 2 4
fabrik.cloudcv.org"
12cd96a419b1bd14cc40942b94d9c4dffe5094d2,Leveraging Captions in the Wild to Improve Object Detection,"Proceedings of the 5th Workshop on Vision and Language, pages 29–38,
Berlin, Germany, August 12 2016. c(cid:13)2016 Association for Computational Linguistics"
12b58a712396bc2fd74cd19a4d758d7b9c104c3f,Cross-Domain Recommendation in the Hotel Sector,"Cross-Domain Recommendation in the Hotel Sector
Marie Al-Ghossein
LTCI, T´el´ecom ParisTech
Paris, France
Talel Abdessalem
LTCI, T´el´ecom ParisTech
Paris, France
UMI CNRS IPAL NUS
Anthony Barr´e
AccorHotels
Paris, France"
12417ed7ae81fb4e6c07f501ace9ea463349481b,Pairwise Augmented GANs with Adversarial Reconstruction Loss,"PAIRWISE AUGMENTED GANS WITH
ADVERSARIAL RECONSTRUCTION LOSS
Aibek Alanov1,2,3∗, Max Kochurov1,2∗, Daniil Yashkov5, Dmitry Vetrov1,3,4
Samsung AI Center in Moscow
Skolkovo Institute of Science and Technology
National Research University Higher School of Economics
Joint Samsung-HSE lab
5Federal Research Center ""Informatics and Management"" of the Russian Academy of Sciences"
12e80b3a89bc021a6352840fb4552df842a6fe7d,Fast sparse representation with prototypes,"Fast Sparse Representation with Prototypes
Jia-Bin Huang and Ming-Hsuan Yang
University of California at Merced"
12ebb51d50f704b5d0a8d821e90dd336175ec8aa,TUHOI: Trento Universal Human Object Interaction Dataset,"Proceedings of the 25th International Conference on Computational Linguistics, pages 17–24,
Dublin, Ireland, August 23-29 2014."
1295cbaf3b03de2eb8c79530289f5939d7819e5c,DeepFix: A Fully Convolutional Neural Network for Predicting Human Eye Fixations,"DeepFix: A Fully Convolutional Neural Network
for predicting Human Eye Fixations
Srinivas S S Kruthiventi, Kumar Ayush, and R. Venkatesh Babu, Senior Member, IEEE"
1277b1b8b609a18b94e4907d76a117c9783a5373,VirtualIdentity: Privacy preserving user profiling,"VirtualIdentity: Privacy-Preserving User Profiling
Sisi Wang, Wing-Sea Poon, Golnoosh Farnadi, Caleb Horst, Kebra Thompson,
Michael Nickels, Rafael Dowsley, Anderson C. A. Nascimento and Martine De Cock"
127c229a3306bfc8170b84b12316f4a8024cc7ab,"A derived transformation of emotional functions using self-reports, implicit association tests, and frontal alpha asymmetries.","Learn Behav
DOI 10.3758/s13420-015-0198-6
A derived transformation of emotional functions
using self-reports, implicit association tests, and frontal
lpha asymmetries
Micah Amd 1 & Bryan Roche 1
# Psychonomic Society, Inc. 2015"
120785f9b4952734818245cc305148676563a99b,Diagnostic automatique de l'état dépressif(Classification of depressive moods),"Diagnostic automatique de l’état dépressif
S. Cholet
H. Paugam-Moisy
Laboratoire de Mathématiques Informatique et Applications (LAMIA - EA 4540)
Université des Antilles, Campus de Fouillole - Guadeloupe
Résumé
Les troubles psychosociaux sont un problème de santé pu-
lique majeur, pouvant avoir des conséquences graves sur
le court ou le long terme, tant sur le plan professionnel que
personnel ou familial. Le diagnostic de ces troubles doit
être établi par un professionnel. Toutefois, l’IA (l’Intelli-
gence Artificielle) peut apporter une contribution en four-
nissant au praticien une aide au diagnostic, et au patient
un suivi permanent rapide et peu coûteux. Nous proposons
une approche vers une méthode de diagnostic automatique
de l’état dépressif à partir d’observations du visage en
temps réel, au moyen d’une simple webcam. A partir de
vidéos du challenge AVEC’2014, nous avons entraîné un
lassifieur neuronal à extraire des prototypes de visages
selon différentes valeurs du score de dépression de Beck"
12ff1c48f5776fda9d156c7b324af3f2674420a9,Are Large Scale Training Images or Discriminative Features Important for Codebook Construction?,
129ac698f62ca7f9024423671f4128d83a0c3c66,Image Matching Scheme by using Bhattacharyya Coefficient Algorithm,"ISSN(Online): 2320-9801
ISSN (Print):  2320-9798
International Journal of Innovative Research in Computer
nd Communication Engineering
(An ISO 3297: 2007 Certified Organization)
Vol. 3, Issue 7, July 2015
Image Matching Scheme by using
Bhattacharyya Coefficient Algorithm
Nwe Nwe Soe
Lecturer, Dept. of Software Technology, Computer University (Thaton), Myanmar"
12150d8b51a2158e574e006d4fbdd3f3d01edc93,Deep End2End Voxel2Voxel Prediction,"Deep End2End Voxel2Voxel Prediction
Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo
Torresani, Manohar Paluri
Presented by: Ahmed Osman
Ahmed Osman"
12948518bb457d0d302bbf00160f4165ce089b9b,Adversarial Spatio-Temporal Learning for Video Deblurring,"Adversarial Spatio-Temporal Learning for
Video Deblurring
Kaihao Zhang, Wenhan Luo, Yiran Zhong, Lin Ma, Wei Liu, and Hongdong Li"
120437e10236d175fa5c0a752a88b98617763eef,SWIR images evaluation for pedestrian detection in clear visibility conditions,"Proceedings of the 16th International IEEE Annual Conference on
Intelligent Transportation Systems (ITSC 2013), The Hague, The
Netherlands, October 6-9, 2013
MoC3.5
978-1-4799-2914-613/$31.00 ©2013 IEEE"
123a9768700433c405bd7266f4c57ca8222e7fe1,Expanded Parts Model for Human Attribute and Action Recognition in Still Images,"Expanded Parts Model for Human Attribute and Action
Recognition in Still Images
Gaurav Sharma1,2, Fr´ed´eric Jurie1, Cordelia Schmid2
GREYC, CNRS UMR 6072, University of Caen Basse-Normandie
LEAR, INRIA Grenoble Rhˆone-Alpes
inria}.fr"
122ee00cc25c0137cab2c510494cee98bd504e9f,The Application of Active Appearance Models to Comprehensive Face Analysis Technical Report,"The Application of
Active Appearance Models to
Comprehensive Face Analysis
Technical Report
Simon Kriegel
TU M¨unchen
April 5, 2007"
12149fc431d2b3ec4d1f194e92e74c765e51ee67,Concentration in unbounded metric spaces and algorithmic stability,"Concentration in unbounded metric spaces and algorithmic stability
Aryeh Kontorovich
Department of Computer Science, Ben-Gurion University, Beer Sheva 84105, ISRAEL"
12ebeb2176a5043ad57bc5f3218e48a96254e3e9,Traffic Road Sign Detection and Recognition for Automotive Vehicles,"International Journal of Computer Applications (0975 – 8887)
Volume 120 – No.24, June 2015
Traffic Road Sign Detection and Recognition for
Automotive Vehicles
Md. Safaet Hossain
Zakir Hyder
Department of Electrical Engineering and
Department of Electrical Engineering and
Computer Science North South University, Dhaka
Computer Science North South University, Dhaka
Bangladesh
Bangladesh"
1297e68cbfd314697817fd1eb2901fa391594b5c,The Research of the Real-time Detection and Recognition of Targets in Streetscape Videos,"The Research of the Real-time Detection and Recognition of Targets in
Streetscape Videos
Liu Jian-min"
1234323164b4512c5ffca6b6c53fdc8c2e7fa8bf,Design and Recording of Czech Audio-Visual Database with Impaired Conditions for Continuous Speech Recognition,"Design and recording of czech audio-visual database
with impaired conditions for continuous speech recognition
Jana Trojanov´a, Marek Hr´uz, Pavel Campr, Miloˇs ˇZelezn´y
Department of Cybernetics, Faculty of Applied Sciences, University of West Bohemia
Univerzitni 22, 306 14, Plzen, Czech Republic
{trojana, mhruz, campr, zelezny"
12dfc8d4062b83a0b824b1676533482f14e4978c,Cutting Edge: Soft Correspondences in Multimodal Scene Parsing,"Cutting Edge: Soft Correspondences in Multimodal Scene Parsing
Sarah Taghavi Namin1,2 Mohammad Najafi1,2 Mathieu Salzmann2,3
Australian National University (ANU)
Lars Petersson1,2
CVLab, EPFL, Switzerland
NICTA∗
{sarah.namin, mohammad.najafi,"
12c5cd899d5ed85741197baed191f3b8b7fac495,Altered intrinsic functional connectivity of anterior and posterior insula regions in high-functioning participants with autism spectrum disorder.,"Altered Intrinsic Functional Connectivity of
Anterior and Posterior Insula Regions in
High-Functioning Participants With
Autism Spectrum Disorder
Sjoerd J.H. Ebisch,1,2* Vittorio Gallese,3,4 Roel M. Willems,5
Dante Mantini,1,2,6 Wouter B. Groen,7 Gian Luca Romani,1,2
Jan K. Buitelaar,7 and Harold Bekkering8
Department of Clinical Sciences and Bioimaging, G. d’Annunzio University Chieti-Pescara,
Institute for Advanced Biomedical Technologies (ITAB), G. d’Annunzio Foundation, Chieti, Italy
Department of Neuroscience, Section of Physiology, Parma University, Parma, Italy
Chieti, Italy
Italian Institute of Technology (IIT), Section of Parma, Italy
5Donders Institute for Brain, Cognition and Behavior, Centre for Cognitive Neuroimaging,
6Laboratory for Neuro-Psychophysiology, K.U. Leuven Medical School, Leuven, Belgium
7Department of Psychiatry, Radboud University Medical Centre and Karakter University Centre for
Radboud University, Nijmegen, The Netherlands
Child and Adolescent Psychiatry, Nijmegen, The Netherlands
8Donders Institute for Brain, Cognition and Behavior, Centre for Cognition,
Radboud University, Nijmegen, The Netherlands"
12d62f1360587fdecee728e6c509acc378f38dc9,Feature Affinity based Pseudo Labeling for Semi-supervised Person Re-identification,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
Feature Affinity based Pseudo Labeling for
Semi-supervised Person Re-identification
Guodong Ding, Shanshan Zhang, Salman Khan, Zhenmin Tang, Jian Zhang, Senior Member, IEEE and Fatih
Porikli, Fellow, IEEE"
12fd613bc68101176d8fdf1b28d4d6fb3e3fec6f,Training a Scene-Specific Pedestrian Detector Using Tracklets,"Training a Scene-Specific Pedestrian Detector Using Tracklets
Yunxiang Mao
Zhaozheng Yin
Department of Computer Science
Missouri University of Science and Technology, USA"
1246534c3104da030fdb9e041819257e0d57dcbf,Virtual view networks for object reconstruction,"Virtual View Networks for Object Reconstruction
Jo˜ao Carreira, Abhishek Kar, Shubham Tulsiani and Jitendra Malik
University of California, Berkeley - Berkeley, CA 94720"
124fddbb5cbe4e5e6ea69be1467437aad01eb5d9,A Unified Algorithmic Framework for Multi-Dimensional Scaling,"A Unified Algorithmic Framework for Multi-Dimensional Scaling
Arvind Agarwal
Jeff M. Phillips†
Suresh Venkatasubramanian‡"
126be977c03d732fbef2381565a41b957d41a2cc,Discourse-Level Language Understanding with Deep Learning,
12c262912f4c09e6f34d40de77f68b465d80ac8d,Adaptive Graph Guided Embedding for Multi-label Annotation,"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18)
FurOutdoorMentalHumanmadeBrownFur(cid:1850)(cid:3039)(cid:1850)(cid:3048)WaterOutdoorWaterTreesFigure1:Frameworkofourmodel,whereanadaptiveaffinitygraphSconnectspair-wiserelationsacrosslabeledandunlabeledsamples.Apre-definedgraphfullyprovideslocalstructureinformationandacceleratesoptimizationprocess.AprojectionPprojectsdataintoacommonanddistinctivespacewhichalsoeliminatesinterruptionsfromnoiseandoutliers.P,S,andlabelmatrixFaresimultaneouslyupdatedtoachieveaccurateandrobustperformance.performance.Consequently,semi-supervisedlearning[Zhuetal.,2003]especiallygraph-basedapproach[Zhaetal.,2009]hasattractedgreatattention.However,theystillhavelimitationsthatthesemethodshighlydependonthepre-constructedgraphbutrarelyoptimizeitonline.Inaddi-tion,mostmethodsconstructgraphdirectlyonfeaturespace,whichissensitivetonoiseandoutliers.Severalworkuti-lizeadaptivegraphstohandletheproblem[Nieetal.,2012;2016].However,thesemethodsmainlyfocusonsingle-labelclassificationinsteadofmulti-labelscenario.Tothisend,weproposeanovelAdaptiveGraphGuidedEmbedding(AG2E)formulti-labellearninginsemi-supervisedfashion.Figure1showstheframeworkofAG2E,whosecoreideaislearningasemi-supervisedlabelpropaga-tionandaneffectiveembeddingsimultaneouslytoseekala-tentspace,andthusunlabeledimagescanbewellrecovered.Ourmaincontributionsaresummarizedasfollows:(cid:15)Weseekanadaptivegraphtoautomaticallycapturethelatentstructureofthedata.Moreover,apre-defined"
126b98473cc25e604abd58eb6bcf720354ac7e7a,An experimental illustration of 3D facial shape analysis under facial expressions,"Author manuscript, published in ""Annals of Telecommunications 64, 5-6 (2009) 369-379"""
127316fbe268c78c519ceb23d41100e86639418a,CNN Features Off-the-Shelf: An Astounding Baseline for Recognition,"CNN Features off-the-shelf: an Astounding Baseline for Recognition
Ali Sharif Razavian Hossein Azizpour
Josephine Sullivan Stefan Carlsson
CVAP, KTH (Royal Institute of Technology)
Stockholm, Sweden"
12b29fd079e047f564e115e6106f750713eabe61,Attribute-Guided Network for Cross-Modal Zero-Shot Hashing.,"Attribute-Guided Network for Cross-Modal
Zero-Shot Hashing
Zhong Ji, Yuxin Sun, Yunlong Yu, Yanwei Pang, Jungong Han"
12d8730da5aab242795bdff17b30b6e0bac82998,Persistent Evidence of Local Image Properties in Generic ConvNets,"Persistent Evidence of Local Image Properties in Generic ConvNets
Ali Sharif Razavian, Hossein Azizpour,
Atsuto Maki, Josephine Sullivan, Carl Henrik Ek, and Stefan Carlsson
CVAP, KTH (Royal Institute of Technology), Stockholm, SE-10044"
12ba7c6f559a69fbfaacf61bfb2f8431505b09a0,DocFace+: ID Document to Selfie Matching,"DocFace+: ID Document to Selfie* Matching
Yichun Shi, Student Member, IEEE, and Anil K. Jain, Life Fellow, IEEE"
122c674f264c53d762af841669209e131b49b3f2,Non-Rigid Structure from Motion for Building 3 D Face Model,"Faculty of Informatics
Institute for Anthropomatics
Chair Prof. Dr.-Ing. R. Stiefelhagen
Facial Image Processing and Analysis Group
Non-Rigid Structure from Motion
for Building 3D Face Model
DIPLOMA THESIS OF
Chengchao Qu
ADVISORS
Dipl.-Inform. Hua Gao
Dr.-Ing. Hazım Kemal Ekenel
MARCH 2011
KIT – University of the State of Baden-Württemberg and National Laboratory of the Helmholtz Association
www.kit.edu"
1232b992530bff6a8b209d7d71793c343ede0664,Fusion of Multiple Facial Regions for Expression-Invariant Face Recognition,"(cid:176)2007 IEEE. Personal use of this material is permitted.
However, permission to reprint/republish this material for
dvertising or promotional purposes or for creating new col-
lective works for resale or redistribution to servers or lists,
or to reuse any copyrighted component of this work in other
works must be obtained from the IEEE."
1252b27c42bf095f1eccba2aee3765dab49647e3,Optical Flow with Semantic Segmentation and Localized Layers,"Optical Flow with Semantic Segmentation and Localized Layers
Laura Sevilla-Lara1 Deqing Sun2,3 Varun Jampani1 Michael J. Black1
{laura.sevilla, varun.jampani,
MPI for Intelligent Systems
NVIDIA, 3Harvard University
(a) Initial segmentation [9]
(b) Our segmentation
(c) DiscreteFlow [38]
(d) Semantic Optical Flow
Figure 1: (a) Semantic segmentation breaks the image into regions such as road, bike, person, sky, etc. (c) Existing optical
flow algorithms do not have access to either the segmentations or the semantics of the classes. (d) Our semantic optical flow
lgorithm computes motion differently in different regions, depending on the semantic class label, resulting in more precise
flow, particularly at object boundaries. (b) The flow also helps refine the segmentation of the foreground objects."
1272d526614e40ce859e73de7e39a54baffd28cc,A unified approach to learning task-specific bit vector representations for fast nearest neighbor search,"A Unified Approach to Learning Task-Specific Bit Vector
Representations for Fast Nearest Neighbor Search
Vinod Nair
Yahoo! Labs Bangalore
Dhruv Mahajan
Yahoo! Labs Bangalore
S. Sundararajan
Yahoo! Labs Bangalore"
12c7ecbfd714c160d2a6bb9cf03fa8b88e8da62b,Impaired Recognition of Basic Emotions from Facial Expressions in Young People with Autism Spectrum Disorder: Assessing the Importance of Expression Intensity.,"Griffiths, S. L., Jarrold, C., Penton-Voak, I., Woods, A., Skinner, A., &
Munafo, M. (2017). Impaired Recognition of Basic Emotions from Facial
Expressions in Young People with Autism Spectrum Disorder: Assessing the
Importance of Expression Intensity. Journal of Autism and Developmental
Disorders. DOI: 10.1007/s10803-017-3091-7
Publisher's PDF, also known as Version of record
License (if available):
CC BY
Link to published version (if available):
0.1007/s10803-017-3091-7
Link to publication record in Explore Bristol Research
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This is the final published version of the article (version of record). It first appeared online via Springer at
http://link.springer.com/article/10.1007%2Fs10803-017-3091-7. Please refer to any applicable terms of use of
the publisher.
University of Bristol - Explore Bristol Research
General rights
This document is made available in accordance with publisher policies. Please cite only the published
version using the reference above. Full terms of use are available:
http://www.bristol.ac.uk/pure/about/ebr-terms.html"
1287bfe73e381cc8042ac0cc27868ae086e1ce3b,Computational Mid-Level Vision: From Border Ownership to Categorical Object Recognition,
12831caca9674e0ab3fe2fc02a447ddb5a372994,Deep Aesthetic Quality Assessment With Semantic Information,"Deep Aesthetic Quality Assessment with Semantic
Information
Yueying Kao, Ran He, Kaiqi Huang"
28209a6ef1de7c10ec13717eba8bad7c2f4feba7,Deep Representation of Facial Geometric and Photometric Attributes for Automatic 3D Facial Expression Recognition,"Deep Representation of Facial Geometric and
Photometric Attributes for Automatic 3D Facial
Expression Recognition
Huibin Li, Jian Sun∗, Dong Wang, Zongben Xu, and Liming Chen"
28cd46a078e8fad370b1aba34762a874374513a5,"cvpaper.challenge in 2016: Futuristic Computer Vision through 1, 600 Papers Survey","CVPAPER.CHALLENGE IN 2016, JULY 2017
vpaper.challenge in 2016: Futuristic Computer
Vision through 1,600 Papers Survey
Hirokatsu Kataoka, Soma Shirak-
be, Yun He, Shunya Ueta, Teppei Suzuki, Kaori Abe, Asako Kanezaki, Shin’ichiro
Morita, Toshiyuki Yabe, Yoshihiro Kanehara, Hiroya Yatsuyanagi, Shinya Maruyama, Ryosuke Taka-
sawa, Masataka Fuchida, Yudai Miyashita, Kazushige Okayasu, Yuta Matsuzaki"
28eceb438da0b841bbd3d02684dbfa263838ed60,Photographic Image Synthesis with Cascaded Refinement Networks,"Photographic Image Synthesis with Cascaded Refinement Networks
Qifeng Chen† ‡
Vladlen Koltun†
(a) Input semantic layouts
(b) Synthesized images
Figure 1. Given a pixelwise semantic layout, the presented model synthesizes an image that conforms to this layout. (a) Semantic layouts
from the Cityscapes dataset of urban scenes; semantic classes are coded by color. (b) Images synthesized by our model for these layouts.
The layouts shown here and throughout the paper are from the validation set and depict scenes from new cities that were never seen during
training. Best viewed on the screen."
28aa89b2c827e5dd65969a5930a0520fdd4a3dc7,CHARACTERIZATION AND CLASSIFICATION OF FACES ACROSS AGE PROGRESSION by Narayanan Ramanathan,
2866cbeb25551257683cf28f33d829932be651fe,A Two-Step Learning Method For Detecting Landmarks on Faces From Different Domains,"In Proceedings of the 2018 IEEE International Conference on Image Processing (ICIP)
The final publication is available at: http://dx.doi.org/10.1109/ICIP.2018.8451026
A TWO-STEP LEARNING METHOD FOR DETECTING LANDMARKS
ON FACES FROM DIFFERENT DOMAINS
Bruna Vieira Frade
Erickson R. Nascimento
Universidade Federal de Minas Gerais (UFMG), Brazil
{brunafrade,"
28103f6c09fd64c90a738076b0681400d4d31c9f,Color Invariants for Person Reidentification,"Color Invariants for Person
Re-Identification
Igor Kviatkovsky
Technion - Computer Science Department - M.Sc. Thesis  MSC-2012-03 - 2012"
289f58b43794394cd312944147b80986ed5d7bef,Face Verification using SVM : Influence of illumination,"Face Verification using SVM: Influence of illumination.
Cristina Conde*, Antonio Ruiz**, Francisco Martín*, Susana Mata**, Luis Pastor*
Javier Moguerza* and Enrique Cabello*
* Universidad Rey Juan Carlos (ESCET)
C/ Tulipan s/n
8933 Mostoles Madrid (Spain)
e-mail: {cristina.conde, l.pastor, j.moguerza,
** Universidad Politécnica de Madrid (Facultad de Informatica)
Departamento de Tecnologia Fotonica
Campus de Monteganedos/n
8660 Boadilla del Monte (Spain)
e-mail: {aruiz,"
28d65e4d72638983fbc723b102d78b10587c06aa,Low Resolution Sparse Binary Face Patterns,
286c1e0b34ee6d40706ca6a02604420a192204e7,An overview of NuDetective Forensic Tool and its usage to combat child pornography in Brazil,"An overview of NuDetective Forensic Tool and its usage
to combat child pornography in Brazil
Pedro Monteiro da Silva Eleuterio and Mateus de Castro Polastro
Brazilian Federal Police"
284af686292a6119129b410413831f8d2363fcc6,"Learning Representation for Scene Understanding: Epitomes, CRFs, and CNNs","UCLA Electronic Theses and Dissertations
Title
Learning Representation for Scene Understanding: Epitomes, CRFs, and CNNs
Permalink
https://escholarship.org/uc/item/5zd9x7v8
Author
Chen, Liang-Chieh
Publication Date
Peer reviewed|Thesis/dissertation
eScholarship.org
Powered by the California Digital Library
University of California"
28446fa9d9ac0468cc715594a6dcc0ac5d9288a5,Semantic Instance Segmentation for Autonomous Driving Bert,"Semantic Instance Segmentation for Autonomous Driving
Bert De Brabandere
Davy Neven
Luc Van Gool
ESAT-PSI, KU Leuven"
2814d558b4d7425b5dae6b3dbbf5f4a08650fcb1,A joint estimation of head and body orientation cues in surveillance video,"A Joint Estimation of Head and Body Orientation Cues in Surveillance Video
Cheng Chen
Alexandre Heili
Jean-Marc Odobez
Idiap Research Institute – CH-1920, Martigny, Switzerland∗"
2803a7e8e6057d4e9462b37b258e670df61a742d,The Conference on Empirical Methods in Natural Language Processing Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing,"EMNLP2017TheConferenceonEmpiricalMethodsinNaturalLanguageProcessingProceedingsofthe2ndWorkshoponStructuredPredictionforNaturalLanguageProcessingSeptember9-11,2017Copenhagen,Denmark"
28126d165f73c2a18600a9b0440f5e80191d52d9,Clock-Modeled Ternary Spatial Relations for Visual Scene Analysis,"Clock-Modeled Ternary Spatial Relations
for Visual Scene Analysis
Joanna Isabelle Olszewska
School of Computing and Engineering, University of Huddersfield
Queensgate, Huddersfield, HD1 3DH, United Kingdom"
287c5be2610e1c61798851feb32b88c424acfbf9,Hierarchical Co-Attention for Visual Question Answering,"Hierarchical Co-Attention for Visual Question Answering
Jiasen Lu, Jianwei Yang, Dhruv Batra, Devi Parikh
Virginia Tech
{jiasenlu, jw2yang, dbatra,"
28e77337bcb88e37d36f5660709a53e71377a2a8,5 Discriminative Cluster Analysis,",250+OPEN ACCESS BOOKS106,000+INTERNATIONALAUTHORS AND EDITORS112+ MILLIONDOWNLOADSBOOKSDELIVERED TO151 COUNTRIESAUTHORS AMONGTOP 1%MOST CITED SCIENTIST12.2%AUTHORS AND EDITORSFROM TOP 500 UNIVERSITIESSelection of our books indexed in theBook Citation Index in Web of Science™Core Collection (BKCI)Chapter from the book Theory and Novel Applications of Machine LearningDownloaded from:http://www.intechopen.com/books/theory_and_novel_applications_of_machine_learningPUBLISHED BYWorld's largest Science,Technology & Medicine Open Access book publisherInterested in publishing with InTechOpen?Contact us at"
286ea63b1b5df1b8b67718f25b47357ec3168e97,Human parsing using stochastic and-or grammars and rich appearances,"Human Parsing using Stochastic And-Or
Grammars and Rich Appearances
Brandon Rothrock and Song-Chun Zhu
UCLA Dept. of Computer Science
Thursday, November 17, 11"
28f5f8dc2f2f9f2a4e49024fe6aa7e9a63b23ab0,Vision-based bicycle detection and tracking using a deformable part model and an EKF algorithm,"Vision-based Bicycle Detection and Tracking using a Deformable Part
Model and an EKF Algorithm
Hyunggi Cho, Paul E. Rybski and Wende Zhang"
28bd795c580ca24f40dc82cd01d9d277749d2661,Site-adaptation methods for face recognition,"Site-adaptation methods for face recognition
Jilin Tu and Xiaoming Liu and Peter Tu"
283b3160f02db64759259b4eb39dd54c4969d6f8,ActivityNet: A large-scale video benchmark for human activity understanding,"ActivityNet: A Large-Scale Video Benchmark for Human Activity
Understanding
Fabian Caba Heilbron1,2, Victor Escorcia1,2, Bernard Ghanem2 and Juan Carlos Niebles1
King Abdullah University of Science and Technology (KAUST), Saudi Arabia
Universidad del Norte, Colombia"
2842cebee2793c9b4f503895a32b328b7781b60e,BWIBots: A platform for bridging the gap between AI and human-robot interaction research,"Article
BWIBots: A platform for bridging the
gap between AI and human–robot
interaction research
The International Journal of
Robotics Research
© The Author(s) 2017
Reprints and permissions:
sagepub.co.uk/journalsPermissions.nav
DOI: 10.1177/0278364916688949
journals.sagepub.com/home/ijr
Piyush Khandelwal1, Shiqi Zhang1,2, Jivko Sinapov1, Matteo Leonetti1,3, Jesse Thomason1,
Fangkai Yang4, Ilaria Gori5, Maxwell Svetlik1, Priyanka Khante1, Vladimir Lifschitz1,
J. K. Aggarwal5, Raymond Mooney1 and Peter Stone1"
28525f9bb0797fdf7c0a8a9d1666015c60610077,Development of Mobile Face Verification Based on Locally Normalized Gabor Wavelets,"Development of Mobile Face Verification Based on Locally
Normalized Gabor Wavelets
Fadhlan Hafizhelmi Kamaru Zaman*, Ahmad Asari Sulaiman#, Ihsan Mohd Yassin#, Nooritawati Md
Tahir#, Zairi Ismael Rizman&
*Department of Computer Engineering, Faculty of Electrical Engineering, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia
E-mail:
#Department of Communications Engineering, Faculty of Electrical Engineering, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia
E-mail:
&Faculty of Electrical Engineering, Universiti Teknologi MARA, Dungun, Terengganu, Malaysia
E-mail:"
28daa489dace2d2f040dcdbbd2d4ab919b046254,2D/3D Pose Estimation and Action Recognition using Multitask Deep Learning,"D/3D Pose Estimation and Action Recognition using Multitask Deep Learning
ETIS UMR 8051, Paris Seine University, ENSEA, CNRS, F-95000, Cergy, France
Sorbonne Universit´e, CNRS, Laboratoire d’Informatique de Paris 6, LIP6, F-75005 Paris, France
Diogo C. Luvizon1, David Picard1,2, Hedi Tabia1
{diogo.luvizon, picard,"
28f1f6cbe07b117387e2b07c11e7ac9c4ef8cf95,A Machine Learning Approach to Pedestrian Detection for Autonomous Vehicles Using High-Definition 3D Range Data,"Article
A Machine Learning Approach to Pedestrian
Detection for Autonomous Vehicles Using
High-Definition 3D Range Data
Pedro J. Navarro *,†, Carlos Fernández †, Raúl Borraz † and Diego Alonso †
División de Sistemas en Ingeniería Electrónica (DSIE), Universidad Politécnica de Cartagena,
Campus Muralla del Mar, s/n, Cartagena 30202, Spain; (C.F.);
(R.B.); (D.A.)
* Correspondence: Tel.: +34-968-32-6546
These authors contributed equally to this work.
Academic Editor: Felipe Jimenez
Received: 31 October 2016; Accepted: 15 December 2016; Published: 23 December 2016"
286eb053f55e45ad5d0490c1c18f6d80381dfb4b,Block-Sparse Recovery via Convex Optimization,"Block-Sparse Recovery via Convex Optimization
Ehsan Elhamifar, Student Member, IEEE, and Ren´e Vidal, Senior Member, IEEE"
2891ceceaf586e4ae013d932978074ff0a06801f,Joint statistical analysis of images and keywords with applications in semantic image enhancement,"Joint Statistical Analysis of Images and Keywords with
Applications in Semantic Image Enhancement
Albrecht Lindner
School of Computer and
Communication Sciences
EPFL, Switzerland
Nicolas Bonnier
Océ Print Logic Technologies
Créteil, France
Appu Shaji
School of Computer and
Communication Sciences
EPFL, Switzerland
Sabine Süsstrunk
School of Computer and
Communication Sciences
EPFL, Switzerland"
283550fce0fdc0876db5df533625dffdfcd8d099,Fairness-aware scheduling on single-ISA heterogeneous multi-cores,"Fairness-Aware Scheduling on
Single-ISA Heterogeneous Multi-Cores
Kenzo Van Craeynest†◦
Ghent University, Belgium
Shoaib Akram†
Wim Heirman†◦
◦ExaScience Lab, Belgium
Aamer Jaleel‡
Lieven Eeckhout†
VSSAD, Intel Corporation
(e.g.,"
2868622003cbfd5cfc817cb57896298c0858b166,BIFOCAL STEREO FOR MULTIPATH PERSON RE-IDENTIFICATION,"BIFOCAL STEREO FOR MULTIPATH PERSON RE-IDENTIFICATION
G. Blotta,b∗, C. Heipkeb
Robert Bosch, Computer Vision Lab, Hildesheim, Germany,
Institute of Photogrammetry and GeoInformation, Leibniz Universit¨at Hannover, Germany
Commission II
KEY WORDS: Person Re-Identification, PRID, Bifocal Stereo, Multipath Person Re-Identification"
2805daf3795e4e153d79dbecfe88b830ddc068d3,Articulated human motion tracking with foreground learning,"ARTICULATED HUMAN MOTION TRACKING WITH FOREGROUND LEARNING
Aichun Zhu1, Hichem Snoussi1, Abel Cherouat2
ICD - LM2S - Universit´e de Technologie de Troyes (UTT) - UMR STMR CNRS
ICD - GAMMA3 - Universit´e de Technologie de Troyes (UTT) - UMR STMR CNRS
2 rue Marie Curie - CS 42060 - 10004 Troyes cedex - France
E-mail :{aichun.zhu, hichem.snoussi,"
281be1be2f0ecce173e3678a7e87419f0815e016,Studies of Plain-to-Rolled Fingerprint Matching Using the NIST Algorithmic Test Bed ( ATB ) NISTIR 7112,"Studies of Plain-to-Rolled Fingerprint
Matching Using the NIST
Algorithmic Test Bed (ATB)
NISTIR 7112
Stephen S. Wood
Charles L. Wilson
April 2004"
280d59fa99ead5929ebcde85407bba34b1fcfb59,Online nonnegative matrix factorization with outliers,"978-1-4799-9988-0/16/$31.00 ©2016 IEEE
ICASSP 2016"
2830fb5282de23d7784b4b4bc37065d27839a412,Poselets: Body part detectors trained using 3D human pose annotations,"Poselets: Body Part Detectors Trained Using 3D Human Pose Annotations ∗
Lubomir Bourdev1,2 and Jitendra Malik1
EECS, U.C. Berkeley, Berkeley, CA 94720
Adobe Systems, Inc., 345 Park Ave, San Jose, CA 95110"
28b6adbc5ef790413431cdb2f512432862778b3b,Security and Surveillance,"Security and Surveillance
Shaogang Gong and Chen Change Loy and Tao Xiang"
28c9198d30447ffe9c96176805c1cd81615d98c8,No evidence that a range of artificial monitoring cues influence online donations to charity in an MTurk sample,"rsos.royalsocietypublishing.org
Research
Cite this article: Saunders TJ, Taylor AH,
Atkinson QD. 2016 No evidence that a range of
rtificial monitoring cues influence online
donations to charity in an MTurk sample.
R. Soc. open sci. 3: 150710.
http://dx.doi.org/10.1098/rsos.150710
Received: 22 December 2015
Accepted: 13 September 2016
Subject Category:
Psychology and cognitive neuroscience
Subject Areas:
ehaviour/psychology/evolution
Keywords:
prosociality, eye images, charity donation,
reputation, online behaviour
Author for correspondence:
Quentin D. Atkinson
e-mail:"
283181a2173b485726664edc6fe73f0465387629,Random Temporal Skipping for Multirate Video Analysis,"Random Temporal Skipping for Multirate Video
Analysis
Yi Zhu1 and Shawn Newsam1
University of California at Merced, Merced CA 95343, USA"
284b5dafe6d8d7552794ccd2efb4eabb12dc3512,Efficient and accurate inversion of multiple scattering with deep learning,"Efficient and accurate inversion of multiple scattering with deep learning
Yu Sun1, Zhihao Xia1, and Ulugbek S. Kamilov1,2,∗
Department of Computer Science and Engineering, Washington University in St. Louis, MO 63130, USA.
Department of Electrical and Systems Engineering, Washington University in St. Louis, MO 63130, USA.
email:"
287afb29b5aef6255a5882418b87e6b41cc9b29d,Nude Detection in Video Using Bag-of-Visual-Features,"Nude Detection in Video using Bag-of-Visual-Features
Ana Paula B. Lopes∗†, Sandra E. F. de Avila∗, Anderson N. A. Peixoto∗,
Rodrigo S. Oliveira∗, Marcelo de M. Coelho∗‡ and Arnaldo de A. Ara´ujo∗
Computer Science Department, Federal University of Minas Gerais – UFMG
Exact and Technological Sciences Department, State University of Santa Cruz – UESC
1270–010, Belo Horizonte, MG, Brazil
5662–000, Ilh´eus, BA, Brazil
Preparatory School of Air Cadets – EPCAR
6205–900, Barbacena, MG, Brazil
{paula, sandra, andenap, rsilva, mcoelho,"
28bc378a6b76142df8762cd3f80f737ca2b79208,Understanding Objects in Detail with Fine-Grained Attributes,"Understanding Objects in Detail with Fine-grained Attributes
Andrea Vedaldi1
Siddharth Mahendran2
Stavros Tsogkas3
Subhransu Maji4
Ross Girshick5
Juho Kannala6
Esa Rahtu6
Matthew B. Blaschko3
David Weiss7
Ben Taskar8
Naomi Saphra2
Sammy Mohamed9
Iasonas Kokkinos3
Karen Simonyan1"
28795f32b324eb3601e9a8c1ce93335691e120f3,CliqueCNN: Deep Unsupervised Exemplar Learning,"CliqueCNN: Deep Unsupervised Exemplar Learning
Miguel A. Bautista∗, Artsiom Sanakoyeu∗, Ekaterina Sutter, Björn Ommer
Heidelberg Collaboratory for Image Processing
IWR, Heidelberg University, Germany"
2864c8df356b1b915e16bb285bda64bfd7396f74,3D Face Reconstruction from Stereo: A Model Based Approach,"-4244-1437-7/07/$20.00 ©2007 IEEE
III - 65
ICIP 2007"
282578039c767f3d393529565cae6be56fda6242,Augmented Reality Meets Computer Vision: Efficient Data Generation for Urban Driving Scenes,"Augmented Reality Meets Computer Vision : Efficient Data Generation for
Urban Driving Scenes
Hassan Abu Alhaija1
Siva Karthik Mustikovela1
Lars Mescheder2 Andreas Geiger2,3 Carsten Rother1
Computer Vision Lab, TU Dresden
Autonomous Vision Group, MPI for Intelligent Systems T¨ubingen
Computer Vision and Geometry Group, ETH Z¨urich"
28e1c113b1b57e0731c189d28e404cea3bddf260,Template based Mole Detection for Face Recognition,"is  used
recognition"
28f9cf85ebbff86207e1f6067880bb23daff0878,Prime Object Proposals with Randomized Prim's Algorithm,"Prime Object Proposals with Randomized Prim’s Algorithm
Santiago Manen1
Matthieu Guillaumin1
Luc Van Gool1,2
Computer Vision Laboratory
ESAT - PSI / IBBT
{smanenfr, guillaumin,
ETH Zurich
K.U. Leuven"
28589357a7631581e55ec6db3cde2e24e4789482,Involuntary processing of social dominance cues from bimodal face-voice displays.,"Cognition and Emotion
ISSN: 0269-9931 (Print) 1464-0600 (Online) Journal homepage: http://www.tandfonline.com/loi/pcem20
Involuntary processing of social dominance cues
from bimodal face-voice displays
Virginie Peschard, Pierre Philippot & Eva Gilboa-Schechtman
To cite this article: Virginie Peschard, Pierre Philippot & Eva Gilboa-Schechtman (2016):
Involuntary processing of social dominance cues from bimodal face-voice displays, Cognition and
Emotion, DOI: 10.1080/02699931.2016.1266304
To link to this article:  http://dx.doi.org/10.1080/02699931.2016.1266304
Published online: 21 Dec 2016.
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284be8be0c6bedc36dfe43229bc84345ab0aedc2,Faster Training of Mask R-CNN by Focusing on Instance Boundaries,"Faster Training of Mask R-CNN by Focusing on Instance Boundaries$
Roland S. Zimmermanna,b,1, Julien N. Siemsa,c,2
BMW Car IT GmbH, Lise-Meitner-Straße 14, 89081 Ulm, Germany
Georg-August University of G¨ottingen, Friedrich-Hund-Platz 1, 37077 G¨ottingen, Germany
Albert Ludwig University of Freiburg, Fahnenbergplatz, 79085 Freiburg im Breisgau, Germany"
2848cde23fe32c30980183f33b6a2c2ce7526726,Three-Dimensional Model-Based Human Detection in Crowded Scenes,"Title
Three-dimensional model-based human detection in crowded
scenes
Author(s)
Wang, L; Yung, NHC
Citation
v. 13 n. 2, p. 691-703
Issued Date
http://hdl.handle.net/10722/155766
Rights
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for advertising or promotional purposes or for creating new
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Creative Commons Attribution-NonCommercial-NoDerivatives
.0 International License."
28c24f16e20c83c747f2aca8232f2cb6614905f5,The Role of Face Parts in Gender Recognition,"The Role of Face Parts in Gender Recognition
Yasmina Andreu and Ram´on A. Mollineda
Dept. Llenguatges i Sistemes Inform`atics
Universitat Jaume I. Castell´o de la Plana, Spain"
28e9ae07540e3709e7a3a6242f636f893ba557e6,Learning to Select Pre-Trained Deep Representations with Bayesian Evidence Framework,"Learning to Select Pre-trained Deep Representations with
Bayesian Evidence Framework
Yong-Deok Kim∗1
Taewoong Jang∗2 Bohyung Han3
Seungjin Choi3
Software R&D Center, Device Solutions, Samsung Electronics, Korea
Department of Computer Science and Engineering, POSTECH, Korea
Stradvision Inc., Korea"
28af8e1a3cb3a158f8a642c8493fcfb207743d0a,Better Image Segmentation by Exploiting Dense Semantic Predictions,"Better Image Segmentation by Exploiting Dense
Semantic Predictions
Qiyang Zhao, Lewis D Griffin
Beihang University & UCL"
288c03d30821d5b12754c8f21bcd76a76dd4a6fb,Feature-based Face Detection Against Skin-color Like Backgrounds with Varying Illumination,"Journal of Information Hiding and Multimedia Signal Processing
Ubiquitous International
⃝2011 ISSN 2073-4212
Volume 2, Number 2, April 2011
Feature-based Face Detection Against Skin-color Like
Backgrounds with Varying Illumination
Wu-Chih Hu1, Ching-Yu Yang1, Deng-Yuan Huang2, and Chun-Hsiang Huang1
Department of Computer Science and Information Engineering,
National Penghu University of Science and Technology, Taiwan
00, Liu-Ho Rd., Makung, Penghu 880, Taiwan
Department of Electrical Engineering, Dayeh University, Taiwan
68, University Rd., Dacun, Changhua 515, Taiwan
Received January 2010; revised August 2010"
28b061b5c7f88f48ca5839bc8f1c1bdb1e6adc68,Predicting User Annoyance Using Visual Attributes,"Predicting User Annoyance Using Visual Attributes
Gordon Christie
Virginia Tech
Amar Parkash
Goibibo
Ujwal Krothapalli
Virginia Tech
Devi Parikh
Virginia Tech"
28f53ec7732299fa946ed3fc27bf691a6ab5c60c,Spatial as Deep: Spatial CNN for Traffic Scene Understanding,"Spatial As Deep: Spatial CNN for Traffic Scene Understanding
Xingang Pan1, Jianping Shi2, Ping Luo1, Xiaogang Wang1, and Xiaoou Tang1
{px117, pluo,
The Chinese University of Hong Kong 2SenseTime Group Limited"
165d966940dcccf9c9976ebffcabe72d66996b05,Semi-Supervised Nonlinear Hashing Using Bootstrap Sequential Projection Learning,"This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.
Semi-supervised Nonlinear Hashing Using
Bootstrap Sequential Projection Learning
Chenxia Wu, Jianke Zhu, Deng Cai, Chun Chen, and Jiajun Bu"
1654fadee3e70d744a4eb231932b87c41c1e3ae5,Survey on Emotional Body Gesture Recognition,"JOURNAL OF IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, VOL. XX, NO. X, XXX 201X
Survey on Emotional Body Gesture Recognition
Fatemeh Noroozi, Ciprian Adrian Corneanu, Dorota Kami´nska, Tomasz Sapi´nski, Sergio Escalera,
nd Gholamreza Anbarjafari,"
16395b40e19cbc6d5b82543039ffff2a06363845,Action Recognition in Video Using Sparse Coding and Relative Features,"Action Recognition in Video Using Sparse Coding and Relative Features
Anal´ı Alfaro
Domingo Mery
Alvaro Soto
P. Universidad Catolica de Chile
P. Universidad Catolica de Chile
P. Universidad Catolica de Chile
Santiago, Chile
Santiago, Chile
Santiago, Chile"
16da7c95c218e9e97eea7734d6c243e8b825196d,A stable and accurate multi-reference representation for surfaces of R3: Application to 3D faces description,"A stable and accurate multi-reference representation for surfaces of
R3: Application to 3D faces description
Wieme Gadacha1, Faouzi Ghorbel1
CRISTAL laboratory, GRIFT research group
National School of Computer Sciences (NSCS), La Manouba 2010, Tunisia"
166186e551b75c9b5adcc9218f0727b73f5de899,Automatic Age and Gender Recognition in Human Face Image Dataset using Convolutional Neural Network System,"Volume 4, Issue 2, February 2016
International Journal of Advance Research in
Computer Science and Management Studies
Research Article / Survey Paper / Case Study
Available online at: www.ijarcsms.com
ISSN: 2321-7782 (Online)
Automatic Age and Gender Recognition in Human Face Image
Dataset using Convolutional Neural Network System
Subhani Shaik1
Assoc. Prof & Head of the Department
Department of CSE,
Anto A. Micheal2
Associate Professor
Department of CSE,
St.Mary’s Group of Institutions Guntur
St.Mary’s Group of Institutions Guntur
Chebrolu(V&M),Guntur(Dt),
Andhra Pradesh - India
Chebrolu(V&M),Guntur(Dt),
Andhra Pradesh - India"
161c9ef7114bda7c5a60a29ee4a3161b0a76e676,Low Rank Approximation and Decomposition of Large Matrices Using Error Correcting Codes,"Low rank approximation and decomposition of
large matrices using error correcting codes
Shashanka Ubaru, Arya Mazumdar Senior Member, IEEE, and Yousef Saad"
166b5bdea1f4f850af5b045a953d6de74bc18d1e,Best of both worlds: Human-machine collaboration for object annotation,"Best of both worlds: human-machine collaboration for object annotation
Olga Russakovsky1, Li-Jia Li2, Li Fei-Fei1
Stanford University. 2Snapchat (this work was done while at Yahoo! Labs).
The long-standing goal of localizing every object in an image remains
elusive. Manually annotating objects is quite expensive despite crowd en-
gineering innovations. Current automatic object detectors can accurately
detect at most a few objects per image. This paper brings together the latest
dvancements in object detection and in crowd engineering into a principled
framework for accurately and efficiently localizing objects in images.
The input to the system is an image to annotate and a set of annotation
onstraints: (1) desired utility of labeling, which is a generalization of the
number of labeled objects, (2) desired precision of the labeling and/or (3)
the budget, which is the human cost of the labeling. Our system automati-
ally solicits feedback from human workers (“users”) to annotate the image
subject to these constraints, as illustrated in Figure 1. The output is a set of
object annotations, informed by humans and computer vision.
One important decision is which questions to pose to the human label-
ers. In computer vision with human-in-the-loop approaches, human inter-
vention has ranged from binary question-and-answer [1] to attribute-based
feedback [4] to free-form object annotation [6]. Binary questions are not"
16647dc1bc87ba1e7b8bcd7e1ea8ccebcfe20fa5,Psychometric properties of reaction time based experimental paradigms measuring anxiety-related information-processing biases in children,"PDF hosted at the Radboud Repository of the Radboud University
Nijmegen
The following full text is a publisher's version.
For additional information about this publication click this link.
http://repository.ubn.ru.nl/handle/2066/126858
Please be advised that this information was generated on 2018-10-16 and may be subject to
hange."
1670729d1edc9bc6103ee823f1137d302be41397,Patch-based Object Recognition Using Discriminatively Trained Gaussian Mixtures,"Patch-based Object Recognition Using
Discriminatively Trained Gaussian Mixtures
Andre Hegerath, Thomas Deselaers, and Hermann Ney
Human Language Technology and Pattern Recognition Group,
RWTH Aachen University – D-52056 Aachen, Germany
{hegerath, deselaers,"
16597862a1df1a983c439e82e0462424f538bb48,Personalized Saliency and its Prediction,
16d9b983796ffcd151bdb8e75fc7eb2e31230809,GazeDirector : Fully Articulated Eye Gaze Redirection in Video,"EUROGRAPHICS 2018 / D. Gutierrez and A. Sheffer
(Guest Editors)
Volume 37 (2018), Number 2
GazeDirector: Fully Articulated Eye Gaze Redirection in Video
ID: paper1004"
16bfd904f5a76bb52d5cd8a25721277047a02e89,Blindfold Baselines for Embodied QA,"Blindfold Baselines for Embodied QA
Ankesh Anand1 Eugene Belilovsky1 Kyle Kastner1 Hugo Larochelle2,1 Aaron Courville1,3
Mila
Google Brain 3CIFAR Fellow"
16e8d439fbcf8311efea7b0baeb1a5340272b396,Stereo and LIDAR Fusion based Detection of Humans and Other Obstacles in Farming Scenarios,
16c884be18016cc07aec0ef7e914622a1a9fb59d,Exploiting Multimodal Data for Image Understanding. (Données multimodales pour l'analyse d'image),"UNIVERSITÉ DE GRENOBLE
No attribué par la bibliothèque
THÈSE
pour obtenir le grade de
DOCTEUR DE L’UNIVERSITÉ DE GRENOBLE
Spécialité : Mathématiques et Informatique
préparée au Laboratoire Jean Kuntzmann
dans le cadre de l’École Doctorale Mathématiques,
Sciences et Technologies de l’Information, Informatique
présentée et soutenue publiquement
Matthieu Guillaumin
le 27 septembre 2010
Exploiting Multimodal Data for Image Understanding
Données multimodales pour l’analyse d’image
Directeurs de thèse : Cordelia Schmid et Jakob Verbeek
M. Éric Gaussier
M. Antonio Torralba
Mme Tinne Tuytelaars Katholieke Universiteit Leuven
M. Mark Everingham University of Leeds
Mme Cordelia Schmid"
163738c0f74ec82ab670a868a051edb732543b6e,Image alignment with rotation manifolds built on sparse geometric expansions,"Image alignment with rotation manifolds built
on sparse geometric expansions
Effrosyni Kokiopoulou and Pascal Frossard
Ecole Polytechnique F´ed´erale de Lausanne (EPFL)
Signal Processing Institute - ITS
CH- 1015 Lausanne, Switzerland"
1642358cd9410abe9ee512d34ba68296b308770e,Robustness Analysis of Pedestrian Detectors for Surveillance,"Robustness Analysis of Pedestrian Detectors
for Surveillance
Yuming Fang, Senior Memmber, IEEE, Guanqun Ding, Yuan Yuan, Weisi Lin, Fellow, IEEE,
nd Haiwen Liu, Senior Memmber, IEEE"
1685ac0f9fedd83a178a2f64f25155fb37998d8f,Human tracking using wearable sensors in the pocket,"Human Tracking using Wearable Sensors in the
Pocket
Wenchao Jiang
Department of Computer Science
Zhaozheng Yin
Department of Computer Science
Missouri University of Science and Technology
Missouri University of Science and Technology"
1602475fdbcb700f10d17c3c3e80ea92c9ba2c44,Learning the Nonlinear Geometry of High-Dimensional Data: Models and Algorithms,"Learning the nonlinear geometry of
high-dimensional data: Models and algorithms
Tong Wu, Student Member, IEEE, and Waheed U. Bajwa, Senior Member, IEEE"
167895bdf0f1ef88acc962e7a6f255ab92769485,Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems,"Article
Object Detection and Classification by Decision-Level
Fusion for Intelligent Vehicle Systems
Sang-Il Oh and Hang-Bong Kang *
Department of Media Engineering, Catholic University of Korea, 43-1, Yeoggok 2-dong, Wonmmi-gu,
Bucheon-si, Gyeonggi-do 14662, Korea;
* Correspondence: Tel.: +82-2-2164-4598
Academic Editor: Simon X. Yang
Received: 13 October 2016; Accepted: 16 January 2017; Published: 22 January 2017"
1630e839bc23811e340bdadad3c55b6723db361d,Exploiting relationship between attributes for improved face verification,"SONG, TAN, CHEN: EXPLOITING RELATIONSHIP BETWEEN ATTRIBUTES
Exploiting Relationship between Attributes for
Improved Face Verification
Fengyi Song
Xiaoyang Tan
Songcan Chen
Department of Computer Science and
Technology, Nanjing University of Aero-
nautics and Astronautics, Nanjing 210016,
P.R. China"
161eb9ecc119952c137959e87a796da0f3c62cd1,Eye tracking in early autism research,"Falck-Ytter et al. Journal of Neurodevelopmental Disorders 2013, 5:28
http://www.jneurodevdisorders.com/content/5/1/28
R EV I E W
Eye tracking in early autism research
Terje Falck-Ytter1,2*, Sven Bölte1,3 and Gustaf Gredebäck2
Open Access"
1611ec5db77d20637f2881121a0457234aacde33,Learning Morphological Operators for Depth Completion,"Learning Morphological Operators for Depth
Completion
Martin Dimitrievski1, Peter Veelaert1, Wilfried Philips1
IMEC-IPI-Ghent University, Ghent, Belgium"
160259f98a6ec4ec3e3557de5e6ac5fa7f2e7f2b,Discriminant multi-label manifold embedding for facial Action Unit detection,"Discriminant Multi-Label Manifold Embedding for Facial Action Unit
Detection
Signal Procesing Laboratory (LTS5), ´Ecole Polytechnique F´ed´erale de Lausanne, Switzerland
Anıl Y¨uce, Hua Gao and Jean-Philippe Thiran"
16286fb0f14f6a7a1acc10fcd28b3ac43f12f3eb,"All Smiles are Not Created Equal: Morphology and Timing of Smiles Perceived as Amused, Polite, and Embarrassed/Nervous.","J Nonverbal Behav
DOI 10.1007/s10919-008-0059-5
O R I G I N A L P A P E R
All Smiles are Not Created Equal: Morphology
nd Timing of Smiles Perceived as Amused, Polite,
nd Embarrassed/Nervous
Zara Ambadar Æ Jeffrey F. Cohn Æ Lawrence Ian Reed
Ó Springer Science+Business Media, LLC 2008"
16f48e8b7f1f6c03c888e3f4664ce3fa1261296b,Steganographic Generative Adversarial Networks,"Steganographic Generative Adversarial Networks
Denis Volkhonskiy1,2,3, Ivan Nazarov1,2, Boris Borisenko3 and Evgeny Burnaev1,2,3
Skolkovo Institute of Science and Technology
The Institute for Information Transmission Problems RAS (Kharkevich Institute)
National Research University Higher School of Economics (HSE)"
16022f7f0cf6492fdc3cf768c9f474791ae3d0ca,Detecting Traffic Lights by Single Shot Detection,"Detecting Traffic Lights by Single Shot Detection
Julian M¨uller1 and Klaus Dietmayer1"
16aec3ee9a97162b85b1d51c3c5ce73a472e74b8,Application of Selective Search to Pose estimation,"Application of Selective Search to Pose estimation
Ujwal Krothapalli
Department of Electrical and
Computer Engineering
Virginia Tech
Blacksburg, Virginia 24061"
16d6d9a71e82b3a53f7a577063f5a4e7cfc298b1,Combining ICA Representations for Recognizing Faces,"ICIT 2015 The 7th International Conference on Information Technology
doi:10.15849/icit.2015.0018   © ICIT 2015 (http://icit.zuj.edu.jo/ICIT15)
Combining ICA Representations for Recognizing
Faces
Ashraf Y. A. Maghari
Faculty of Information Technology
Islamic University of Gaza
Gaza, Palestine"
165abb6fdbadae997135feec447fc825edb31c6c,IMENSIONALITY REDUCTION WITH SIMULTANEOUS SPARSE APPROXIMATIONS,"SCHOOL OF ENGINEERING - STI
SIGNAL PROCESSING INSTITUTE
EffrosyniKokiopoulouandPascalFrossard
CH-1015 LAUSANNE
Telephone: +41216932601
Telefax: +41216937600
e-mail:
ÉCOLE   POLYTECHNIQUE(cid:13)
FÉDÉRALE DE LAUSANNE
DIMENSIONALITY REDUCTION WITH
SIMULTANEOUS SPARSE APPROXIMATIONS
Effrosyni Kokiopoulou and Pascal Frossard
Swiss Federal Institute of Technology Lausanne (EPFL)
Signal Processing Institute Technical Report
TR-ITS-2006.010
October 21st, 2006
Part of this work has been submitted to IEEE TMM.
This work has been supported by the Swiss NSF, under grants PP-002-68737, and NCCR IM2."
167c058b008c358ce5a3cd298c5859ffea441e51,The role of context in image annotation and recommendation,"McParlane, Philip James (2016) The role of context in image annotation
nd recommendation. PhD thesis.
http://theses.gla.ac.uk/7676/
Copyright and moral rights for this thesis are retained by the author
A copy can be downloaded for personal non-commercial research or
study, without prior permission or charge
This thesis cannot be reproduced or quoted extensively from without first
obtaining permission in writing from the Author
The content must not be changed in any way or sold commercially in any
format or medium without the formal permission of the Author
When referring to this work, full bibliographic details including the
uthor, title, awarding institution and date of the thesis must be given
Glasgow Theses Service
http://theses.gla.ac.uk/"
16e2e9e4741795c004d15e95532b07943d3a3242,CPS: 3D Compositional Part Segmentation through Grasping,"CPS: 3D Compositional Part Segmentation through Grasping
Safoura Rezapour Lakani
University of Innsbruck
Innsbruck, Austria
Mirela Popa
University of Innsbruck
Innsbruck, Austria
Antonio J. Rodr´ıguez-S´anchez
University of Innsbruck
Innsbruck, Austria
Justus Piater
University of Innsbruck
Innsbruck, Austria"
1696f6861c208b6a7cac95fbeba524867ad3e8d6,Using deep learning to quantify the beauty of outdoor places,"Downloaded from
http://rsos.royalsocietypublishing.org/
on September 4, 2017
rsos.royalsocietypublishing.org
Research
Cite this article: Seresinhe CI, Preis T, Moat
HS. 2017 Using deep learning to quantify the
eauty of outdoor places. R. Soc. open sci.
: 170170.
http://dx.doi.org/10.1098/rsos.170170
Received: 23 February 2017
Accepted: 19 June 2017
Subject Category:
Computer science
Subject Areas:
environmental science/computer modelling
nd simulation
Keywords:
environmental aesthetics, well-being,
onvolutional neural networks, deep learning,"
1697a4188b9f75ff5324eb9957b8317f459bbf59,Dual-tree fast exact max-kernel search,"Dual-Tree Fast Exact Max-Kernel Search
Ryan R. Curtin and Parikshit Ram
December 11, 2013"
16c855aea9789e2b7a77f35dc4181efc93dec69c,Exploiting Sum of Submodular Structure for Inference in Very High Order MRF-MAP Problems,"SUBMITTED TO IEEE TPAMI
Exploiting Sum of Submodular Structure for
Inference in Very High Order MRF-MAP
Problems
Ishant Shanu Surbhi Goel Chetan Arora Parag Singla"
16e577820999e584c787ec611f55746cf9147518,Cross-Domain Person Reidentification Using Domain Adaptation Ranking SVMs,"Cross-Domain Person Re-Identification Using
Domain Adaptation Ranking SVMs
Andy J Ma, Jiawei Li, Pong C Yuen, Senior Member, IEEE, and Ping Li
label"
16e8b0a1e8451d5f697b94c0c2b32a00abee1d52,UMB-DB: A database of partially occluded 3D faces,"UMB-DB
A Database of Partially Occluded 3D Faces
Alessandro Colombo
Claudio Cusano
Raimondo Schettini
Universit`a degli Studi di Milano-Bicocca
3 November 2011"
161eb88031f382e6a1d630cd9a1b9c4bc6b47652,Automatic facial expression recognition using features of salient facial patches,"Automatic Facial Expression Recognition
Using Features of Salient Facial Patches
S L Happy and Aurobinda Routray"
f74dbf3481fc3228ea821da232128b98ad5f7a60,Using low-level motion for high-level vision,"Using Low-Level Motion for
High-Level Vision
Ben Daubney
A dissertation submitted to the University of Bristol in accordance with the
requirements for the degree of Doctor of Philosophy in the Faculty of Engineering,
Department of Computer Science.
July 2009"
f79267b0f4c0110051c93f9faabe436215e4fc28,Selective Feature Connection Mechanism: Concatenating Multi-layer CNN Features with a Feature Selector,"Selective Feature Connection Mechanism:
Concatenating Multi-layer CNN Features with a Feature Selector
Chen Du1,2, Chunheng Wang1, Cunzhao Shi1, Baihua Xiao1
Institute of Automation, Chinese Academy of Sciences(CASIA)
University of Chinese Academy of Sciences(UCAS)
{duchen2016, chunheng.wang, cunzhao.shi,"
f7dcadc5288653ec6764600c7c1e2b49c305dfaa,Interactive Image Search with Attributes by,"Copyright
Adriana Ivanova Kovashka"
f724cbf5035e2df0dbe9a4992a0100465f5c6db5,Scalable Multicore k-NN Search via Subspace Clustering for Filtering,"Parallel Graph Partitioning for Complex Networks
Henning Meyerhenke, Peter Sanders, and Christian Schulz"
f77563386ac293620ce2b90b5d7250ab5d8f9f50,Regression-based Hypergraph Learning for Image Clustering and Classification,"IEEE TRANSACTIONS ON
Regression-based Hypergraph Learning for Image
Clustering and Classification
Sheng Huang Student Member, IEEE, Dan Yang, Bo Liu, Xiaohong Zhang"
f7580def2dd84a6a083188aadd9c66c99925860b,Effective Use of Synthetic Data for Urban Scene Semantic Segmentation,"Effective Use of Synthetic Data for
Urban Scene Semantic Segmentation(cid:63)
Fatemeh Sadat Saleh1,2[0000−0002−3695−9876], Mohammad Sadegh
Aliakbarian1,2,3[0000−0003−3948−6418], Mathieu Salzmann4[0000−0002−8347−8637],
Lars Petersson2[0000−0002−0103−1904], and Jose M. Alvarez5[0000−0002−7535−6322]
ANU, 2 Data61-CSIRO, 3 ACRV, 4 CVLab, EPFL, 5 NVIDIA"
f7ecc9df67e9d33543c68fb150270e9f7af8814a,Joint Epipolar Tracking (JET): Simultaneous Optimization of Epipolar Geometry and Feature Correspondences,"Simultaneous optimization of epipolar geometry and feature correspondences
Joint Epipolar Tracking (JET):
Henry Bradler1, Matthias Ochs1, Nolang Fanani1
Visual Sensorics & Information Processing Lab
Goethe University, Frankfurt, Germany
Rudolf Mester1,2
Computer Vision Laboratory, ISY
Link¨oping University, Sweden"
f7186eb3d717694d4ab1730a8d0f662e90c44d6f,A Computational Study on Word Meanings and Their Distributed Representations via Polymodal Embedding,"Proceedings of the The 8th International Joint Conference on Natural Language Processing, pages 214–223,
Taipei, Taiwan, November 27 – December 1, 2017 c(cid:13)2017 AFNLP"
f795b4ff67d3ecce0b988ecfc0589cc7e54f5dfa,Grounded Human-Object Interaction Hotspots from Video,"Grounded Human-Object Interaction Hotspots from Video
Tushar Nagarajan∗
UT Austin
Christoph Feichtenhofer
Facebook AI Research
Kristen Grauman
Facebook AI Research"
f77c9bf5beec7c975584e8087aae8d679664a1eb,Local Deep Neural Networks for Age and Gender Classification,"Local Deep Neural Networks for Age and Gender Classification
Zukang Liao, Stavros Petridis, Maja Pantic
March 27, 2017"
f774f80fa4b5a8760084921f093730da519c6681,Development of an Efficient Face Recognition System based on Linear and Nonlinear Algorithms,"Development of an Efficient Face Recognition System based on Linear and Nonlinear Algorithms
{tag}                                                 {/tag}
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 134
Number 7
Year of Publication: 2016
Authors:
Filani Araoluwa S., Adetunmbi Adebayo O.
10.5120/ijca2016907932
{bibtex}2016907932.bib{/bibtex}"
f7de943aa75406fe5568fdbb08133ce0f9a765d4,Year 5 Deliverable Technical Report : Research Challenges in Biometrics and Indexed biography of relevant biometric research literature,"Project 1.5: Human Identification at a Distance - Hornak, Adjeroh, Cukic, Gautum, & Ross
Project 1.5
Biometric Identification and Surveillance1
Don Adjeroh, Bojan Cukic, Arun Ross – West Virginia University
Year 5 Deliverable
Technical Report:
Research Challenges in Biometrics
Indexed biography of relevant biometric research literature
Donald Adjeroh, Bojan Cukic, Arun Ross
April, 2014
""This research was supported by the United States Department of Homeland Security through the National Center for Border Security
nd Immigration (BORDERS) under grant number 2008-ST-061-BS0002. However, any opinions, findings, and conclusions or
recommendations in this document are those of the authors and do not necessarily reflect views of the United States Department of
Homeland Security."""
f7514435495cd76552a4de01652a08ff8c2863c7,Recognition of Emotions From Facial Expression and Situational Cues in Children with Autism,"Dissertations
Loyola University Chicago
Loyola eCommons
Theses and Dissertations
Recognition of Emotions From Facial Expression
nd Situational Cues in Children with Autism
Dina Tell
Loyola University Chicago
Recommended Citation
Tell, Dina, ""Recognition of Emotions From Facial Expression and Situational Cues in Children with Autism"" (2009). Dissertations.
Paper 234.
http://ecommons.luc.edu/luc_diss/234
This Dissertation is brought to you for free and open access by the Theses and Dissertations at Loyola eCommons. It has been accepted for inclusion in
Dissertations by an authorized administrator of Loyola eCommons. For more information, please contact
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.
Copyright © 2009 Dina Tell"
f7a37cf724aef23d0e714a35d54352243e5b52ee,Entire Reflective Object Surface Structure Understanding,"Q.LU ET AL.: ENTIRE REFLECTIVE OBJECT SURFACE STRUCTURE UNDERSTANDING 1
Entire Reflective Object Surface Structure
Understanding
Qinglin Lu1
Olivier Laligant1
Eric Fauvet1
Anastasia Zakharova2
University of Burgundy
Le2i UMR 6306 CNRS
2,Rue de la Fonderie,71200,France
INSA Rouen LMI EA3226
Avenue de l’Université,76800,France"
f7db1a670a99fd68dc3c6478eb9aeadc2838a897,Feature Based Pose Invariant Face Recognition,"FEATURE BASED POSE INVARIANT FACE RECOGNITION
Berk G¨okberk
BS. in Computer Engineering, Bo˘gazi¸ci University, 1999
Submitted to the Institute for Graduate Studies in
Science and Engineering in partial fulfillment of
the requirements for the degree of
Master of Science
Computer Engineering
Bo˘gazi¸ci University"
f736b7cf8388f20bfe9619d63d9c4ce070091863,Automated Crowd Detection in Stadium Arenas,"AUTOMATED CROWD DETECTION IN STADIUM ARENAS
Loris Nanni, 1 Sheryl Brahnam, 2 Stefano Ghidoni, 1 Emanuele Menegatti1
DIE, University of Padua, Via Gradenigo, 6 - 35131- Padova – Italy e-mail: {loris.nanni, ghidoni,
CIS, Missouri State University, 901 S. National, Springfield, MO 65804, USA e-mail:"
f727b12c905ac585de60811048c9f9dd4188b498,R 4-A . 2 : Rapid Forensic Search & Retrieval in Video,"R4-A.2: Rapid Forensic Search & Retrieval in Video
Archives"
f740bac1484f2f2c70777db6d2a11cf4280081d6,Soft Locality Preserving Map (SLPM) for Facial Expression Recognition,"Soft Locality Preserving Map (SLPM) for Facial Expression
Recognition
Cigdem Turana,*, Kin-Man Lama, Xiangjian Heb
Centre for Signal Processing, Department of Electronic and Information Engineering, The Hong
Kong Polytechnic University, Kowloon, Hong Kong
Computer Science, School of Electrical and Data Engineering, University of Technology, Sydney,
Australia
E-mail addresses: (C. Turan), (K.-M. Lam),
(X. He)"
f7ba77d23a0eea5a3034a1833b2d2552cb42fb7a,LOTS about attacking deep features,"This is a pre-print of the original paper accepted at the International Joint Conference on Biometrics (IJCB) 2017.
LOTS about Attacking Deep Features
Andras Rozsa, Manuel G¨unther, and Terrance E. Boult
Vision and Security Technology (VAST) Lab
University of Colorado, Colorado Springs, USA"
f77b3e6b6eb4bc6d6bfeed290a1bc533bb97968a,Real Time Violence Detection in Video with ViF and Horn-Schunck,"Real Time Violence Detection in Video with ViF and
Horn-Schunck
Vicente Machaca Arceda Universidad Nacional de San Agustín Arequipa, Perú
Karla Fernández Fabián Universidad Nacional de San Agustín Arequipa, Perú
Juan Carlos Gutíerrez Universidad Nacional de San Agustín Arequipa, Perú"
f79c4bf83371627ba139b61eb427463b93cd687b,Learning from Few Examples for Visual Recognition Problems,"Learning from Few Examples for Visual
Recognition Problems
Erik Rodner
Dissertation
zur Erlangung des akademischen Grades
doctor rerum naturalium (Dr. rer. nat.)
vorgelegt dem Rat der Fakultät für Mathematik und Informatik
der Friedrich-Schiller-Universität Jena"
f7094d4888e9ef039d283267d310b0673ff0e423,FACE RECOGNITION: A NEW FEATURE SELECTION AND CLASSIFICATION TECHNIQUE,"Complex 2004
Proceedings of the 7th Asia-Pacific
Conference on Complex Systems
Cairns Converntion Centre, Cairns, Australia
6-10th December 2004
Face recognition: a new feature selection and
lassification technique
Xiaolong Fan and Brijesh Verma
Faculty of Informatics and Communication
Central Queensland University
Bruce Hwy
North Rockhampton
QLD 4701
Australia"
64b78b6f13c321da77e3770a748772fa837aa8c8,Parallel Architecture for Face Recognition using MPI,"(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 8, No. 1, 2017
Parallel Architecture for Face Recognition using MPI
Dalia Shouman Ibrahim
Computer Systems Department
Computer and Information Sciences
Ain Shams University
Egypt
Salma Hamdy
Computer Science Department
Computer and Information Sciences
Ain shams University
Egypt"
64f6f1cd23bbac1983ad4115475e4ef26ab86ba4,Person re-identification by unsupervised video matching,"Person Re-Identification by Unsupervised Video Matching
Xiaolong Ma1,4, Xiatian Zhu2, Shaogang Gong2, Xudong Xie1, Jianming Hu1, Kin-Man Lam3, Yisheng Zhong1"
643abe6001946ebb7e262465edcf78d600c38f4f,The COST292 experimental framework for TRECVID 2007,"The COST292 experimental framework for TRECVID 2007
Q. Zhang1, K. Chandramouli1, U. Damnjanovic1, T. Piatrik1, E. Izquierdo1,
M. Corvaglia2, N. Adami2, R. Leonardi2, G. Yakın3, S. Aksoy3, U. Naci4,
A. Hanjalic4, S. Vrochidis5, A. Moumtzidou5, S. Nikolopoulos5, V. Mezaris5,
L. Makris5, I. Kompatsiaris5, E. Esen6, A. Alatan6, E. Spyrou7,
P. Kapsalas7, G. Tolias7, P. Mylonas7, Y. Avrithis7, B. Reljin8, G. Zajic8,
R. Jarina9, M. Kuba9, N. Aginamo10, J. Goya10, B. Mansencal11,
J. Benois-Pineau11, A. M. G. Pinheiro12, L. A. Alexandre12, P. Almeida12
October 22, 2007"
64d9c5ab4cab193046ac839b74d182a472fe45ba,"NosePose : a competitive , landmark-free methodology for head pose estimation in the wild","NosePose: a competitive, landmark-free
methodology for head pose estimation in the wild
Fl´avio H. B. Zavan, Antonio C. P. Nascimento, Olga R. P. Bellon and Luciano Silva
IMAGO Research Group - Universidade Federal do Paran´a"
64195b78fd4f5b9aeb5cceeb060bc9821e63136c,Object Recognition,"Object Recognition
Ming-Hsuan Yang
University of California at Merced
http://faculty.ucmerced.edu/mhyang
SYNONYMS
Object Identification, Object Labeling.
DEFINITION
Object recognition is concerned with determining the identity of an object being observed in the image from a set
of known labels. Oftentimes, it is assumed that the object being observed has been detected or there is a single
object in the image.
HISTORICAL BACKGROUND
As the holy grail of computer vision research is to tell a story from a single image or a sequence of images, object
recognition has been studied for more than four decades [9] [22]. Significant efforts have been paid to develop
representation schemes and algorithms aiming at recognizing generic objects in images taken under different
imaging conditions (e.g., viewpoint, illumination, and occlusion). Within a limited scope of distinct objects, such
s handwritten digits, fingerprints, faces, and road signs, substantial success has been achieved. Object recognition
is also related to content-based image retrieval and multimedia indexing as a number of generic objects can be
recognized.
In addition, significant progress towards object categorization from images has been made in the
recent years [17]. Note that object recognition has also been studied extensively in psychology, computational"
6434b95401aea9ece22b2b29950118afc163c2db,Localized anomaly detection via hierarchical integrated activity discovery,"THIS PAPER APPEARED IN IEEE INT. CONF. ON ADVANCED VIDEO AND SIGNAL-BASED PROCESSING (AVSS), KRAKOW, 2013
Localized Anomaly Detection via Hierarchical Integrated Activity Discovery
Thiyagarajan Chockalingam1
R´emi Emonet2
http://home.heeere.com
Jean-Marc Odobez2,3
: Colorado State University – Fort Collins, CO 80523, United States
: Idiap Research Institute – CH-1920, Martigny, Switzerland
: ´Ecole Polytechnique F´ed´eral de Lausanne – CH-1015, Lausanne, Switzerland"
6446089a2a383ad9e4315aea0199084dc61490f9,Computational analysis of human-robot interactions through first-person vision: Personality and interaction experience,"Proceedings of the 24th IEEE International
Symposium on Robot and Human Interactive Communication
Kobe, Japan, Aug 31 - Sept 4, 2015
978-1-4673-6704-2/15/$31.00 ©2015 IEEE"
6402703b62325865d00da1f58dbbcaf9a2bc417d,Twin Networks: Matching the Future for Sequence Generation,"Published as a conference paper at ICLR 2018
TWIN NETWORKS: MATCHING THE FUTURE
FOR SEQUENCE GENERATION
Dmitriy Serdyuk,* ♦ Nan Rosemary Ke,* ♦ ‡ Alessandro Sordoni♥
Adam Trischler,♥ Chris Pal♣♦ & Yoshua Bengio¶ ♦
♦ Montreal Institute for Learning Algorithms (MILA), Canada
♥ Microsoft Research, Canada
♣ Ecole Polytechnique, Canada
¶ CIFAR Senior Fellow
Work done at Microsoft Research
* Authors contributed equally"
64e205799096448a50ddee218bcd3b81ff342cba,PCA-Initialized Deep Neural Networks Applied to Document Image Analysis,"PCA-Initialized Deep Neural Networks Applied To
Document Image Analysis
Mathias Seuret∗, Michele Alberti∗, Rolf Ingold∗, and Marcus Liwicki∗‡,
University of Fribourg, Department of Informatics
Bd. de P´erolles 90, 1700 Fribourg, Switzerland
Email:
University of Kaiserslautern, Germany"
645de797f936cb19c1b8dba3b862543645510544,Deep Temporal Linear Encoding Networks,"Deep Temporal Linear Encoding Networks
Ali Diba1,(cid:63), Vivek Sharma1,(cid:63), and Luc Van Gool1,2
ESAT-PSI, KU Leuven, 2CVL, ETH Z¨urich"
64b88125bf93a26f9e15bf57208f1cfe6e31fa1c,Retrieval-Induced Forgetting and Its Effects on Episodic Memory,"INSTITUTIONEN FÖR PSYKOLOGI
Retrieval-Induced Forgetting and Its Effects on
Episodic Memory
Anatole Nöstl
Master Thesis VT 2010
Supervisor: Mikael Johansson"
647c6ac5e0bfee0241d583650f18c6314f28aaee,Segmentation Driven Object Detection with Fisher Vectors,"Segmentation Driven Object Detection with Fisher Vectors
Ramazan Gokberk Cinbis
Jakob Verbeek Cordelia Schmid
LEAR, INRIA Grenoble - Rhˆone-Alpes, France
Laboratoire Jean Kuntzmann"
6475c1e95da0a3bd36786a32d00a893d85460e9e,Combined image- and world-space tracking in traffic scenes,"Combined Image- and World-Space Tracking in Traffic Scenes
Aljoˇsa Oˇsep, Wolfgang Mehner, Markus Mathias, and Bastian Leibe"
64d1fcc26c2af47c8ed7436fe91546ba5bfc7a1f,Disentangling Multiple Conditional Inputs in GANs,"Disentangling Multiple Conditional Inputs in GANs
Gökhan Yildirim
Urs Bergmann
Zalando Research
Zalando Research
Berlin, Germany
Berlin, Germany
Calvin Seward∗
Zalando Research
Berlin, Germany
process. Researchers have achieved control of image generation by
using GANs that are conditioned on a categorical input [12, 13].
In this paper, we employ conditional GANs to control the visual
ttributes, such as color, texture, and shape, of a generated apparel.
One of the main challenges of the conditional image generation
GANs is to isolate the effects of input attributes on the final image.
For example, we want the color of an article to stay constant, when
we tune its texture and/or shape. One possibility would be to employ
Adversarial Autoencoders [11] or DNA-GAN [17] to disentangle
the inputs. However, this requires an exhaustive dataset, in other"
6472df86bed51909f7b8aa0631f910db5a627c84,Minimax and Adaptive Estimation of Covariance Operator for Random Variables Observed on a Lattice Graph,"Minimax and Adaptive Estimation of Covariance Operator for
Random Variables Observed on a Lattice Graph
T. Tony Cai∗ and Ming Yuan†
University of Pennsylvania and Georgia Institute of Technology
November 3, 2012"
641fd2edcf93fa29181952356e93a83a26012aa2,Following are some examples from CIFAR dataset : Goal : To alter the training criteria to obtain ‘ objectness ’ in the synthesis of images,"Published as a conference paper at ICLR 2017
IMPROVING GENERATIVE ADVERSARIAL NETWORKS
WITH DENOISING FEATURE MATCHING
David Warde-Farley & Yoshua Bengio(cid:63)
Montreal Institute for Learning Algorithms, (cid:63) CIFAR Senior Fellow
Universit´e de Montr´eal
Montreal, Quebec, Canada"
64be271fd50fce1cf8434020145a1b6e16f75c1a,Intrinsic Divergence for Face Recognition,"Centre for Theoretical Neuroscience
Technical Report
UW-CTN-TR-20090204-001
February 4, 2009
Intrinsic Divergence for Face
Recognition
Yichuan Tang and Xuan Choo
Centre for Theoretical Neuroscience, Waterloo, ON. http://compneuro.uwaterloo.ca/cnrglab"
646fa86edc22ccc452a44ac7a5953ba62fc0929b,Recognizing jumbled images: The role of local and global information in image classification,"The Role of Local and Global Information in Image Classification
Recognizing Jumbled Images:
Toyota Technological Institute, Chicago (TTIC)
Devi Parikh"
641f0989b87bf7db67a64900dcc9568767b7b50f,Reconstructing faces from their signatures using RBF regression,"Reconstructing Faces from their Signatures using RBF
Regression
Alexis Mignon, Fr´ed´eric Jurie
To cite this version:
Alexis Mignon, Fr´ed´eric Jurie. Reconstructing Faces from their Signatures using RBF Regres-
sion. British Machine Vision Conference 2013, Sep 2013, Bristol, United Kingdom. pp.103.1–
03.12, 2013, <10.5244/C.27.103>. <hal-00943426>
HAL Id: hal-00943426
https://hal.archives-ouvertes.fr/hal-00943426
Submitted on 13 Feb 2014
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,
´emanant des ´etablissements d’enseignement et de"
6483ebbb9c28024431c8ada03354217453ca1b3b,Statement in Lieu of an Oath,"Universit¨at des Saarlandes
Max-Planck-Institut f¨ur Informatik
Learning to Track Humans in Videos
Master’s Thesis in Computer Science
Mihai Fieraru
supervised by
Prof. Dr. Bernt Schiele
dvised by
MSc Anna Khoreva
MSc Eldar Insafutdinov
reviewers
Prof. Dr. Bernt Schiele
Dr. Mario Fritz
Saarbr¨ucken, December 2017"
64153df77fe137b7c6f820a58f0bdb4b3b1a879b,Shape Invariant Recognition of Segmented Human Faces using Eigenfaces,"Shape Invariant Recognition of Segmented Human
Faces using Eigenfaces
Zahid Riaz, Michael Beetz, Bernd Radig
Department of Informatics
Technical University of Munich, Germany"
64fb6c31033e38eaaa10c0f7c2b7995f8fa84de3,VISUALIZING VIDEO SOUNDS THROUGH SOUND WORD ANIMATION,"VISUALIZING VIDEO SOUNDS THROUGH
SOUND WORD ANIMATION
擬音語アニメーションによる動画音響の可視化手法
Fangzhou Wang
A Master Thesis
Submitted to
the Graduate School of the University of Tokyo
on February 20, 2014
in Partial Ful(cid:12)llment of the Requirements
for the Degree of Master of Information Science and
Technology
in Computer Science
Thesis Supervisor: Takeo Igarashi 五十嵐健夫
Professor of Computer Science"
64c78c8bf779a27e819fd9d5dba91247ab5a902b,Tracking with multi-level features.,"Tracking with multi-level features
Roberto Henschel, Laura Leal-Taix´e, Bodo Rosenhahn, Konrad Schindler"
648d326990ed78d2160139c1ab307a7b3e44b1bb,Random Sampling for Fast Face Sketch Synthesis,"Random Sampling for Fast Face Sketch
Synthesis
Nannan Wang, and Xinbo Gao, and Jie Li"
64407fe6fc65d3370427770544be0c7f2447e0f6,Zero-Shot Visual Recognition Using Semantics-Preserving Adversarial Embedding Networks,"Zero-Shot Visual Recognition using Semantics-Preserving
Adversarial Embedding Networks
Long Chen1 Hanwang Zhang2
Jun Xiao1∗ Wei Liu3
Shih-Fu Chang4
Zhejiang University 2Nanyang Technological University 3Tencent AI Lab 4Columbia University
{longc, {wliu,
Figure 1: (a) Attribute variance heat maps of the 312 attributes in CUB birds [60] and the 102 attributes in SUN scenes [47]
(lighter color indicates lower variance, i.e., lower discriminability) and the t-SNE [35] visualizations of the test images
represented by all attributes (left) and only the high-variance ones (right). Some of the low-variance attributes (the lighter
part to the left of the cut-off line) discarded at training are still needed in discriminating unseen test classes. (b) Comparison
of reconstructed images using SAE [25] and our proposed SP-AEN method, which is shown to retain sufficient semantics for
photo-realistic reconstruction."
64a6c30ca95e85427c56acb4c1c20f62c6ec0709,PersonNet: Person Re-identification with Deep Convolutional Neural Networks,"PersonNet: Person Re-identification with Deep
Convolutional Neural Networks
Lin Wu, Chunhua Shen, Anton van den Hengel"
64e0bd1210f180e0610b2a1faa188051a1de29bf,Combining Detectors for Robust Head Detection,"Combining Detectors for Robust Head Detection
Henrik Brauer, Christos Grecos and Kai von Luck
Living Place - HAW Hamburg
Berliner Tor 11
0099 Hamburg, Germany"
649eb674fc963ce25e4e8ce53ac7ee20500fb0e3,Toward correlating and solving abstract tasks using convolutional neural networks,
64bd5878170bfab423bc3fc38d693202ef4ba6b6,Monocular 3D Human Pose Estimation in the Wild Using Improved CNN Supervision,"Monocular 3D Human Pose Estimation In The Wild
Using Improved CNN Supervision
Dushyant Mehta1, Helge Rhodin2, Dan Casas3, Pascal Fua2,
Oleksandr Sotnychenko1, Weipeng Xu1, and Christian Theobalt1
MPI for Informatics, Germany
EPFL, Switzerland
Universidad Rey Juan Carlos, Spain"
6497eb53fd7d3ff09190566be8099016fb49f801,Biometric Sensor Interoperability: A Case Study in 3D Face Recognition,
64a336f952ec67f85fe00092847d50dd29e4cddc,Fast Screening Algorithm for Template Matching FAST SCREENING ALGORITHM FOR TEMPLATE MATCHING,Fast Screening Algorithm for Template Matching
64cac22210861d4e9afb00b781da90cf99f9d19c,Facial Landmark Detection for Manga Images,"Noname manuscript No.
(will be inserted by the editor)
Facial Landmark Detection for Manga Images
Marco Stricker · Olivier Augereau ·
Koichi Kise · Motoi Iwata
Received: date / Accepted: date"
64c9cc92ea496b9053fa5326567487b5f08bb13f,3D Human Face Recognition Using Summation Invariants,"(cid:176)2006 IEEE. Personal use of this material is permitted.
However, permission to reprint/republish this material for ad-
vertising or promotional purposes or for creating new collec-
tive works for resale or redistribution to servers or lists, or to
reuse any copyrighted component of this work in other works
must be obtained from the IEEE."
0f1392c1180582a45b42e621e1526f03cc6e9ca6,Learning with Hierarchical-Deep Models,"Learning with Hierarchical-Deep Models
Ruslan Salakhutdinov, Joshua B. Tenenbaum, and Antonio Torralba"
0fa42d4478b514b0f961e26bccbaf2b75d42e912,Extending UML for Conceptual Modeling of Annotation of Medical Images,"Extending UML for Conceptual Modeling of Annotation
International Journal of Computer Applications (0975 – 8887)
Volume 72– No.10, June 2013
of Medical Images
Mouhamed Gaith Ayadi
Riadh Bouslimi
Jalel Akaichi
Department of computer
sciences
ISG university of Tunis
Tunisia
Department of computer
sciences
ISG university of Tunis
Tunisia
Department of computer
sciences
ISG university of Tunis
Tunisia"
0f556558853268d86cd05bf8ea42da6d7862a024,Shade Face: Multiple image-based 3D face recognition,"UWA Research Publication
Mian, A. (2009). Shade Face: Multiple Image-based 3D Face Recognition. In R. Cipolla,
M. Hebert, X. Tang, & N. Yokoya (Eds.), Proceedings of the 2009 IEEE International
Workshop on 3-D Digital Imaging and Modeling (3DIM2009). (pp. 1833-1839). USA:
IEEE Computer Society. 10.1109/ICCVW.2009.5457505
© 2009 IEEE
This is pre-copy-editing, author-produced version of an article accepted for publication,
following peer review. The definitive published version is located at
http://dx.doi.org/10.1109/ICCVW.2009.5457505
This version was made available in the UWA Research Repository on 4 March 2015, in
ompliance with the publisher’s policies on archiving in institutional repositories.
Use of the article is subject to copyright law."
0f5bf2a208d262aa0469bd3185f6e2e56acada81,Pose Estimation and Segmentation of People in 3D Movies,"Pose Estimation and Segmentation of People in 3D
Movies
Karteek Alahari, Guillaume Seguin, Josef Sivic, Ivan Laptev
To cite this version:
Karteek Alahari, Guillaume Seguin, Josef Sivic, Ivan Laptev. Pose Estimation and Segmentation of
People in 3D Movies. ICCV - IEEE International Conference on Computer Vision, Dec 2013, Sydney,
Australia. IEEE, pp.2112-2119, 2013, <10.1109/ICCV.2013.263>. <hal-00874884>
HAL Id: hal-00874884
https://hal.inria.fr/hal-00874884
Submitted on 18 Oct 2013
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
0f25aa473e808de72c6975fdb1e3e65180a38c05,Bag of Soft Biometrics for Person Identification New trends and challenges,"Noname manuscript No.
(will be inserted by the editor)
Bag of Soft Biometrics for Person Identi(cid:12)cation
New trends and challenges.
Antitza Dantcheva (cid:1) Carmelo Velardo (cid:1)
Angela D’Angelo (cid:1) Jean{Luc Dugelay
Received: 01.08.2010 / Accepted: 11.10.2010"
0f04a95ec885cf98e7cee43eacff13de0c888d3b,The FERET September 1996 Database and Evaluation Procedure,"ToappearintheproceedingsoftheFirstInternationalConferenceonAudioand
Video-basedBiometricPersonAuthentication,Crans-Montana,Switzerland,
-

March
		.
TheFERETSeptember
		Databaseand
EvaluationProcedure
P.JonathonPhillips,
?HyeonjoonMoon,yPatrickRauss,
and
SyedA.Rizviy

U.S.ArmyResearchLaboratory,AMSRL-SE-SE,
PowderMillRd.,Adelphi,MD-

	
DepartmentofElectricalandComputerEngineering
StateUniversityofNewYorkatBu(cid:11)alo,Amherst,NY

DepartmentofAppliedSciences
CollegeofStatenIslandofCityUniversityofNewYork,StatenIsland,NY

"
0fe5d8acc77f54d60edc56c012f35517d9c861da,Interactive Stereoscopic Video Conversion,"Interactive Stereoscopic Video Conversion
Zhebin Zhang, Chen Zhou, Yizhou Wang, and Wen Gao, Fellow, IEEE
erial perspective,"
0f29710e54f714eeea5233628afc68c680d881bb,Tracking Indistinguishable Translucent Objects over Time Using Weakly Supervised Structured Learning,"Tracking indistinguishable translucent objects over time
using weakly supervised structured learning
Luca Fiaschi1, Ferran Diego1, Konstantin Gregor1, Martin Schiegg1, Ullrich Koethe1, Marta Zlatic2 and
Fred A. Hamprecht1
HCI University of Heidelberg, Germany, http://hci.iwr.uni-heidelberg.de
HHMI Janelia Farm, USA, http://janelia.org/"
0f08d62e882026ac83ebf26c0bd288c553873814,Multispecies Fruit Flower Detection Using a Refined Semantic Segmentation Network,"Multispecies fruit flower detection using a refined
semantic segmentation network
Philipe A. Dias1, Amy Tabb2, and Henry Medeiros1"
0f5e10cfca126682e1bad1a07848919489df6a65,Facial emotion processing in patients with social anxiety disorder and Williams-Beuren syndrome: an fMRI study.,"Research Paper
Facial emotion processing in patients with social
nxiety disorder and Williams–Beuren syndrome:
n fMRI study
Cynthia Binelli, PhD; Armando Muñiz, MD; Susana Subira, MD, PhD;
Ricard Navines, MD, PhD; Laura Blanco-Hinojo, MSc; Debora Perez-Garcia, BSc;
Jose Crippa, MD, PhD; Magi Farré, MD, PhD; Luis Pérez-Jurado, MD, PhD;
Jesus Pujol, MD, PhD; Rocio Martin-Santos, MD, PhD
Background:  Social  anxiety  disorder  (SAD)  and  Williams–Beuren  syndrome  (WBS)  are  2  conditions  with  major  differences  in  terms  of
genetics, development and cognitive profiles. Both conditions are associated with compromised abilities in overlapping areas, including so-
ial approach, processing of social emotional cues and gaze behaviour, and to some extent they are associated with opposite behaviours in
these domains. We examined common and distinct patterns of brain activation during a facial emotion processing paradigm in patients with
SAD and WBS. Methods: We examined patients with SAD and WBS and healthy controls matched by age and laterality using functional
MRI during the processing of happy, fearful and angry faces.  Results: We included 20 patients with SAD and 20 with WBS as well as
0 matched controls in our study. Patients with SAD and WBS did not differ in the pattern of limbic activation. We observed differences in
early visual areas of the face processing network in patients with WBS and differences in the cortical prefrontal regions involved in the top–
down regulation of anxiety and in the fusiform gyrus for patients with SAD. Compared with those in the SAD and control groups, participants
in the WBS group did not activate the right lateral inferior occipital cortex. In addition, compared with controls, patients with WBS hypoacti-
vated the posterior primary visual cortex and showed significantly less deactivation in the right temporal operculum. Participants in the SAD
group showed decreased prefrontal activation compared with those in the WBS and control groups. In addition, compared with controls,"
0f0fb26d8999fecc362c0bf65a62c809061c5508,Building Location Models for Visual Place Recognition,"Building Location Models for Visual Place Recognition
Elena Stumm, Christopher Mei, Simon Lacroix
To cite this version:
Elena Stumm, Christopher Mei, Simon Lacroix. Building Location Models for Visual Place Recog-
nition.
International Journal of Robotics Research, SAGE Publications, 2016, 35 (4), pp.334-356.
<10.1177/0278364915570140>. <hal-01064007v2>
HAL Id: hal-01064007
https://hal.archives-ouvertes.fr/hal-01064007v2
Submitted on 26 Jan 2015
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
0fac212f96e4a291482342853b38d5293195d1e1,Mobile robot with vision based navigation and pedestrian detection,"Artículo Científico / Scientific Paper
Mobile robot with vision based
navigation and pedestrian detection
Robot móvil con navegación basada en
https://doi.org/10.17163/ings.n17.2017.09
pISSN: 1390-650X / eISSN: 1390-860X
visión y detección de peatones
Marco A. Luna1, Julio F. Moya1, Wilbert G. Aguilar2,3,4,∗, Vanessa Abad5"
0f0cab9235bbf185acdd4f9713fd111ca50effca,Covariance Pooling for Facial Expression Recognition,"Covariance Pooling for Facial Expression Recognition
Computer Vision Lab, ETH Zurich, Switzerland
VISICS, KU Leuven, Belgium
Dinesh Acharya†, Zhiwu Huang†, Danda Pani Paudel†, Luc Van Gool†‡
{acharyad, zhiwu.huang, paudel,"
0f085f389a52e13586fe50f2dae49e105225303f,Distribution-sensitive learning for imbalanced datasets,"Distribution-Sensitive
Learning
for Imbalanced
Datasets
Yale Songl, Louis-Philippe
Morency2, and Randall Davisl
MIT Computer Science and Artificial
Intelligence
Laboratory
USC Institute
for Creative Technology"
0fbf59328d32e1a9950dfa08c3ec87eb94398651,Beyond RGB: Very High Resolution Urban Remote Sensing With Multimodal Deep Networks,"Beyond RGB: Very High Resolution Urban Remote
Sensing With Multimodal Deep Networks
Nicolas Audeberta,b,, Bertrand Le Sauxa, Sébastien Lefèvreb
ONERA, The French Aerospace Lab, F-91761 Palaiseau, France
Univ. Bretagne-Sud, UMR 6074, IRISA, F-56000 Vannes, France"
0f366de3ea595932dad06389f6e61fe0dd8cbe74,DeepAnomaly: Combining Background Subtraction and Deep Learning for Detecting Obstacles and Anomalies in an Agricultural Field,"Article
DeepAnomaly: Combining Background Subtraction
nd Deep Learning for Detecting Obstacles and
Anomalies in an Agricultural Field
Peter Christiansen 1,*, Lars N. Nielsen 2, Kim A. Steen 3, Rasmus N. Jørgensen 1 and
Henrik Karstoft 1
Department of Engineering, Aarhus University, Aarhus 8200, Denmark;
(R.N.J.); (H.K.)
Danske Commodities, Aarhus 8000, Denmark;
AgroIntelli, Aarhus 8200, Denmark;
* Correspondence: Tel.: +45-2759-2953
Academic Editors: Gabriel Oliver-Codina, Nuno Gracias and Antonio M. López
Received: 15 September 2016; Accepted: 7 November 2016; Published: 11 November 2016"
0fa28b3dfb841cf542edf8d9d5933c7205a88d41,LandmarkBoost: Efficient visualContext Classifiers for Robust Localization,"LandmarkBoost: Efficient Visual Context Classifiers
for Robust Localization
Marcin Dymczyk∗, Igor Gilitschenski‡, Juan Nieto∗, Simon Lynen∗†, Bernhard Zeisl†, and Roland Siegwart∗
Autonomous Systems Lab, ETH Z¨urich, †Google Inc., Z¨urich, ‡CSAIL, MIT"
0fa077478abea097a1b74915ccaaeb4952eaaec7,Object Classification With Joint Projection and Low-Rank Dictionary Learning,"Object Classification with Joint Projection and
Low-rank Dictionary Learning
Homa Foroughi, Nilanjan Ray and Hong Zhang"
0fad544edfc2cd2a127436a2126bab7ad31ec333,Decorrelating Semantic Visual Attributes by Resisting the Urge to Share,"Decorrelating Semantic Visual Attributes by Resisting the Urge to Share
Dinesh Jayaraman
UT Austin
Fei Sha
Kristen Grauman
UT Austin"
0fdcfb4197136ced766d538b9f505729a15f0daf,Multiple pattern classification by sparse subspace decomposition,"Multiple Pattern Classification by Sparse Subspace Decomposition
Institute of Media and Information Technology, Chiba University
Tomoya Sakai
-33 Yayoi, Inage, Chiba, Japan"
0f4b902a2e12378e0ac0cb6fff7dd4c5f81e2c0a,Capturing facial videos with Kinect 2.0: A multithreaded open source tool and database,"Capturing Facial Videos with Kinect 2.0:
A Multithreaded Open Source Tool and Database
Daniel Merget
Tobias Eckl
Institute for Human-Machine Communication, TUM, Germany
Philipp Tiefenbacher
Martin Schwoerer
Gerhard Rigoll"
0f82a869a80b6114bd16437dbf703bcae84da7b9,Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks,"Neural Activation Constellations: Unsupervised Part Model Discovery with
Convolutional Networks
Marcel Simon and Erik Rodner
Computer Vision Group, University of Jena, Germany∗
http://www.inf-cv.uni-jena.de/constellation_model_revisited"
0f282d9aacd194842f8747c4ed37fa6200f601a2,Crowdsourcing Photos in Edge-Clouds with Panoptic Tadeu,"Crowdsourcing
Photos
Edge-Clouds
with Panoptic
Tadeu Augusto Leite Freitas
Mestrado Integrado em Engenharia de Redes e Sistemas Informáticos
Departamento de Ciência de Computadores
Orientador
Rolando Martins, Professor Auxiliar Convidado,
Faculdade de Ciências da Universidade do Porto
Co-Orientador
Miguel Coimbra, Professor Auxiliar,
Faculdade de Ciências da Universidade do Porto"
0fb680b5136d80c13e8d15078ef18ca4aac269f6,Optimizing Deep Neural Network Architecture: A Tabu Search Based Approach,"Optimizing Deep Neural Network Architecture: A Tabu
Search Based Approach
Tarun Kumar Gupta and Khalid Raza*
Department of Computer Science, Jamia Millia Islamia, New Delhi-110025"
0ffee18b495830d373dbc65f67a452d94938900b,Registration-based moving object detection from a moving camera,"IROS 2008 2nd Workshop on Planning, Perception and Navigation for Intelligent Vehicles
Registration-based moving object detection
from a moving camera
Angel D. Sappa, Fadi Dornaika, David Ger´onimo and Antonio L´opez"
0f5275b472344dbfc4a26a9ba73dff23844b7e84,Head movements and postures as pain behavior,"RESEARCH ARTICLE
Head movements and postures as pain
ehavior
Philipp Werner1*, Ayoub Al-Hamadi1, Kerstin Limbrecht-Ecklundt2, Steffen Walter3,
Harald C. Traue3
Neuro-Information Technology group, Institute for Information Technology and Communications, Otto-von-
Guericke University Magdeburg, Magdeburg, Germany, 2 Department of Anesthesiology, University Medical
Center Hamburg-Eppendorf, Hamburg, Germany, 3 Medical Psychology, University Clinic for Psychosomatic
Medicine and Psychotherapy, Ulm, Germany
1111111111
1111111111
1111111111
1111111111
1111111111"
0fd2956ef990443f584112fa093f85a90a43c4af,Performance Evaluation of Multi-camera Visual Tracking,"PEOPLE COUNT ESTIMATION IN SMALL CROWDS
Pietro Morerio, Lucio Marcenaro, Carlo S. Regazzoni
Department of Biophysical and Electronic Engineering
University of Genoa, Genoa, Italy"
0f41f1a4bd5141184ee3ed3cf8874eeb396d7862,Deep Forest: Towards An Alternative to Deep Neural Networks,Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
0f92e9121e9c0addc35eedbbd25d0a1faf3ab529,MORPH-II : A Proposed Subsetting Scheme,"MORPH-II: A Proposed Subsetting Scheme
Participants: K. Kempfert, J. Fabish, K. Park, and R. Towner
Mentors: Y. Wang, C. Chen, and T. Kling
NSF-REU Site at UNC Wilmington, Summer 2017"
0fd1bffb171699a968c700f206665b2f8837d953,Weakly Supervised Object Localization with Multi-Fold Multiple Instance Learning,"Weakly Supervised Object Localization with
Multi-fold Multiple Instance Learning
Ramazan Gokberk Cinbis, Jakob Verbeek, and Cordelia Schmid, Fellow, IEEE"
0ff23392e1cb62a600d10bb462d7a1f171f579d0,Toward Sparse Coding on Cosine Distance,"Toward	Sparse	Coding	on	Cosine
Distance
Jonghyun	Choi,	Hyunjong	Cho,	Jungsuk	Kwak#,
Larry	S.	Davis
UMIACS	|	University	of	Maryland,	College	Park
#Stanford	University"
0f65c91d0ed218eaa7137a0f6ad2f2d731cf8dab,Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition,"Multi-Directional Multi-Level Dual-Cross
Patterns for Robust Face Recognition
Changxing Ding, Jonghyun Choi, Dacheng Tao, Senior Member, IEEE, and Larry S. Davis, Fellow, IEEE"
0f2a910f98e9955d2fbd4841d31b4943b91ab382,Creating and Annotating Affect Databases from Face and Body Display: A Contemporary Survey,"Creating and Annotating Affect Databases from Face and Body
Display: A Contemporary Survey
Hatice Gunes and Massimo Piccardi"
0f0499989f3331396af94f92c29f2eda9b58d4dc,Object detection methods for robot grasping: Experimental assessment and tuning,"Object detection methods for robot
grasping: Experimental assessment and
tuning
Ferran RIGUAL a,1, Arnau RAMISA a, Guillem ALENYA a and Carme TORRAS a
Institut de Rob`otica i Inform`atica Industrial, CSIC-UPC, Barcelona"
0f4cfcaca8d61b1f895aa8c508d34ad89456948e,Local appearance based face recognition using discrete cosine transform,"LOCAL APPEARANCE BASED FACE RECOGNITION USING
DISCRETE COSINE TRANSFORM  (WedPmPO4)
Author(s) :"
0f7ca11acc495f1a45fb2add4cee94d445d8d96f,Fully Distributed Multi-Robot Collision Avoidance via Deep Reinforcement Learning for Safe and Efficient Navigation in Complex Scenarios,"Fully Distributed Multi-Robot Collision
Avoidance via Deep Reinforcement
Learning for Safe and Efficient
Navigation in Complex Scenarios
Tingxiang Fan1*, Pinxin Long2*, Wenxi Liu3 and Jia Pan1
Journal Title
XX(X):1–30
(cid:13)The Author(s) 2018
Reprints and permission:
sagepub.co.uk/journalsPermissions.nav
DOI: 10.1177/ToBeAssigned
www.sagepub.com/"
0f9bd0d528603654de2687d3ae2472a522607ee3,Semantics-aware visual localization under challenging perceptual conditions,"Semantics-aware Visual Localization
under Challenging Perceptual Conditions
Tayyab Naseer
Gabriel L. Oliveira
Thomas Brox
Wolfram Burgard"
0ff14ec76e5fe7f17dce102e781ffce2738c8d4b,Real-time pedestrian detection in urban scenarios,"Real-time Pedestrian Detection in Urban Scenarios
VARGA Robert, VESA Andreea Valeria, JEONG Pangyu, NEDEVSCHI Sergiu
{robert.varga, pangyu.jeong,
Technical University of Cluj Napoca
Telephone: (800) 555–1212"
0f3eb3719b6f6f544b766e0bfeb8f962c9bd59f4,Eliminating Exposure Bias and Loss-Evaluation Mismatch in Multiple Object Tracking,"Eliminating Exposure Bias and Loss-Evaluation Mismatch in Multiple Object
Tracking
Andrii Maksai
Pascal Fua
Computer Vision Laboratory, ´Ecole Polytechnique F´ed´erale de Lausanne (EPFL)
{andrii.maksai,"
0fd1715da386d454b3d6571cf6d06477479f54fc,A Survey of Autonomous Human Affect Detection Methods for Social Robots Engaged in Natural HRI,"J Intell Robot Syst (2016) 82:101–133
DOI 10.1007/s10846-015-0259-2
A Survey of Autonomous Human Affect Detection Methods
for Social Robots Engaged in Natural HRI
Derek McColl · Alexander Hong ·
Naoaki Hatakeyama · Goldie Nejat ·
Beno Benhabib
Received: 10 December 2014 / Accepted: 11 August 2015 / Published online: 23 August 2015
© Springer Science+Business Media Dordrecht 2015"
0f708ace6f4829e466a8a549bd23f6fcf719ab9d,Multi-shot person re-identification via relational Stein divergence,"This is the author’s version of a work that was submitted/accepted for pub-
lication in the following source:
Alavi, Azadeh, Yang, Yan, Harandi, Mehrtash, & Sanderson, Conrad
(2013)
Multi-shot person re-identification via relational stein divergence. In
ICIP 2013 Proceedings : 2013 IEEE International Conference on Image
Processing, Institute of Electrical and Electronics Engineers, Inc., Mel-
ourne Convention and Exhibition Centre, Melbourne, pp. 3542-3546.
This file was downloaded from: https://eprints.qut.edu.au/71704/
(cid:13) c(cid:13) 2013 by the Institute of Electrical and Electronics
Engineers, Inc.
Notice: Changes introduced as a result of publishing processes such as
opy-editing and formatting may not be reflected in this document. For a
definitive version of this work, please refer to the published source:
https://doi.org/10.1109/ICIP.2013.6738731"
0f2ffd582674bd856247bc5482d85e6db3b49b8f,A neural signature of the creation of social evaluation.,"doi:10.1093/scan/nst051
SCAN (2014) 9, 731^736
A neural signature of the creation of social evaluation
Roman Osinsky,1 Patrick Mussel,1 Linda O¨ hrlein,1 and Johannes Hewig1,2
Department of Psychology I, Julius-Maximilians-University Wu¨rzburg, 97070 Wu¨rzburg, Germany and 2Department of Psychology,
Friedrich-Schiller-University Jena, 07743 Jena, Germany
Previous research has shown that receiving an unfair monetary offer in economic bargaining elicits also-called feedback negativity (FN). This scalp-
recorded brain potential probably reflects a bad-vs-good evaluation in the medial frontal cortex and has been linked to fundamental processes of
reinforcement learning. In the present study, we investigated whether the evaluative mechanism indexed by the FN is also involved in learning who is an
unfair vs fair bargaining partner. An electroencephalogram was recorded while participants completed a computerized version of the Ultimatum Game,
repeatedly receiving fair or unfair monetary offers from alleged other participants. Some of these proposers were either always fair or always unfair in
their offers. In each trial, participants first saw a portrait picture of the respective proposer before the monetary offer was presented. Therefore, the faces
ould be used as predictive cues for the fairness of the pending offers. We found that not only unfair offers themselves induced a FN, but also (over the
task) faces of unfair proposers. Thus, when interaction partners repeatedly behave in an unfair way, their faces acquire a negative valence, which
manifests in a basal neural mechanism of bad-vs-good evaluation.
Keywords: social evaluation; feedback negativity; ultimatum game; evaluative conditioning
INTRODUCTION
trading
example,
family, work,"
0f257ae1829b3250723a87781373d339308ea996,A Fully Automated System for Sizing Nasal PAP Masks Using Facial Photographs,"A Fully Automated System for Sizing Nasal PAP Masks Using Facial
Photographs
Benjamin Johnston Student Member, IEEE and Philip de Chazal Senior Member, IEEE"
0f146e17dc24a304f9e9b27cd787ff51edbc33cf,Non Uniform Blur and Illumination Variance Face Recognition Using Local Binary Pattern,"International Research Journal of Engineering and Technology (IRJET)      e-ISSN: 2395 -0056
Volume: 03 Issue: 09 | Sep -2016                      www.irjet.net                                                               p-ISSN: 2395-0072
Non Uniform Blur and Illumination Variance Face Recognition Using
Local Binary Pattern
Sravya konakala1, Dr.Suman maloji2
Department of Electronics and communication Engineering, LBRCE, Andhra Pradesh,
Department of Electronics and communication Engineering, LBRCE, Andhra Pradesh,
---------------------------------------------------------------------***--------------------------------------------------------------------
still
lacking.
for  real-world
face  recognition"
0fcca61391e7ee7718f5d2c05adc658f2978a2e8,Spectral Face Recognition Using Orthogonal Subspace Bases,
0f94f4934d0a26dfd243852036468ecc9bf8d22c,Low Resolution Lidar-Based Multi-Object Tracking for Driving Applications,"Low resolution lidar-based multi-object tracking
for driving applications
Iv´an del Pino(cid:63), V´ıctor Vaquero(cid:63), Beatrice Masini,
Joan Sol`a, Francesc Moreno-Noguer,
Alberto Sanfeliu, and Juan Andrade-Cetto
Institut de Rob`otica i Inform`atica Industrial, CSIC-UPC
Llorens Artigas 4-6, 08028 Barcelona, Spain.
http://www.iri.upc.edu"
0fb75f5cb12d1e1a909b9f698b7617bb9603002f,Design of Weight-Learning Efficient Convolutional Modules in Deep Convolutional Neural Networks and its Application to Large-Scale Visual Recognition Tasks,"Data Analysis Project
Design of Weight-Learning Ef‌f‌icient Convolutional Modules in Deep
Convolutional Neural Networks and its Application to
Large-Scale Visual Recognition Tasks
Felix Juefei-Xu
May 3, 2017"
246597d62f5d4af187a01623d7c353e13693da12,3D active appearance model for aligning faces in 2D images,"D Active Appearance Model for Aligning Faces in 2D Images
Chun-Wei Chen and Chieh-Chih Wang"
24d3e695af619e88613aba7dc0e7492c12fa4d0e,Sparsest Matrix based Random Projection for Classification,"Sparse Matrix-based Random Projection for
Classification
Weizhi Lu, Weiyu Li, Kidiyo Kpalma and Joseph Ronsin"
24727e1b401752c5914fba5c935268e284c9f55f,Calibrating Uncertainties in Object Localization Task,"Calibrating Uncertainties in Object Localization Task
Buu Phan∗
Rick Salay∗
Krzysztof Czarnecki∗
Vahdat Abdelzad∗
Taylor Denouden†
Sachin Vernekar†"
24ff832171cb774087a614152c21f54589bf7523,Beat-Event Detection in Action Movie Franchises,"Beat-Event Detection in Action Movie Franchises
Danila Potapov
Matthijs Douze
Jerome Revaud
Zaid Harchaoui
Cordelia Schmid"
243778aefb3c23d6774309c70217cb83f7204915,"The Mutex Watershed: Efficient, Parameter-Free Image Partitioning","The Mutex Watershed:
Ef‌f‌icient, Parameter-Free Image Partitioning
Steffen Wolf1⋆, Constantin Pape1,2⋆, Alberto Bailoni1, Nasim Rahaman1, Anna
Kreshuk1,2, Ullrich K¨othe1, and Fred A. Hamprecht1
HCI/IWR, University of Heidelberg, Germany
EMBL Heidelberg, Germany"
245f8ec4373e0a6c1cae36cd6fed5a2babed1386,Lucas Kanade Optical Flow Computation from Superpixel based Intensity Region for Facial Expression Feature Extraction,"J. Appl. Environ. Biol. Sci., 7(3S)1-10, 2017
© 2017, TextRoad Publication
ISSN: 2090-4274
Journal of Applied Environmental
nd Biological Sciences
www.textroad.com
Lucas Kanade Optical Flow Computation from Superpixel based Intensity
Region for Facial Expression Feature Extraction
Halina Hassan1,2, Abduljalil Radman1, Shahrel Azmin Suandi1, Sazali Yaacob2
Intelligent Biometric Group, School of Electrical and Electronics Engineering, Universiti Sains Malaysia,
Electrical, Electronics and Automation Section, Universiti Kuala Lumpur Malaysian Spanish Institute, 09000
Engineering Campus, 14300 Nibong Tebal, Pulau Pinang, Malaysia
Kulim Hi-Tech Park, Kedah, Malaysia
Received: February 21, 2017
Accepted: May 14, 2017"
24301df85a669c86ae58962b5645b04a66c63cb1,A Jointly Learned Context-Aware Place of Interest Embedding for Trip Recommendations,"A Jointly Learned Context(cid:173)Aware Place of Interest
Embedding for Trip Recommendations
Jiayuan He, Jianzhong Qi, Kotagiri Ramamohanarao
School of Computing and Information Systems, The University of Melbourne, Australia"
24115d209e0733e319e39badc5411bbfd82c5133,Long-Term Recurrent Convolutional Networks for Visual Recognition and Description,"Long-term Recurrent Convolutional Networks for
Visual Recognition and Description
Jeff Donahue, Lisa Anne Hendricks, Marcus Rohrbach, Subhashini Venugopalan, Sergio Guadarrama,
Kate Saenko, Trevor Darrell"
24c7554823bb8c1c0729c4ece5f3e50965aea74e,Robust Computation of Linear Models by Convex Relaxation,"ROBUST COMPUTATION OF LINEAR MODELS,
OR HOW TO FIND A NEEDLE IN A HAYSTACK
GILAD LERMAN∗, MICHAEL MCCOY†, JOEL A. TROPP†, AND TENG ZHANG◦"
246ec873db261257833231d657ec8995d686cc3e,Extraversion and Social Motives View project Grip Strength and Perceptions View project,"See	discussions,	stats,	and	author	profiles	for	this	publication	at:	https://www.researchgate.net/publication/315831650
Facing	the	implications:	Dangerous	world
eliefs	differentially	predict	men	and	Women's
version	to	facially	communicated...
Article		in		Personality	and	Individual	Differences	·	October	2017
READS
DOI:	10.1016/j.paid.2017.04.018
CITATIONS
authors,	including:
Mitch	Brown
University	of	Southern	Mississippi
9	PUBLICATIONS			14	CITATIONS
SEE	PROFILE
Some	of	the	authors	of	this	publication	are	also	working	on	these	related	projects:
Facially	Communicated	Extraversion	and	Social	Motives	View	project
Grip	Strength	and	Perceptions	View	project
All	content	following	this	page	was	uploaded	by	Mitch	Brown	on	09	April	2017.
The	user	has	requested	enhancement	of	the	downloaded	file.	All	in-text	references	underlined	in	blue	are	added	to	the	original	document
nd	are	linked	to	publications	on	ResearchGate,	letting	you	access	and	read	them	immediately."
24ec4cd704d07865ce31fe539d00cd2597b5dfc9,Face Localization in the Neural Abstraction Pyramid,Face Localization
24585f90bdf30583733841f70430d36948f16ae2,An efficient method for human face recognition using nonsubsampled contourlet transform and support vector machine,"Optica Applicata, Vol. XXXIX, No. 3, 2009
An efficient method for human face recognition
using nonsubsampled contourlet transform
nd support vector machine
XUEBIN XU, DEYUN ZHANG, XINMAN ZHANG*
School of Electronics and Information Engineering, Xi’an Jiaotong University,
8 Xianning West Road, Xi’an 710049, P.R. China
*Corresponding author:
To  improve  the  recognition  rate  in  different  conditions,  a  multiscale  face  recognition  method
ased on nonsubsampled contourlet transform and support vector machine is proposed in this
paper.  Firstly, all face images are decomposed  by using  nonsubsampled  contourlet transform.
The contourlet coefficients of low frequency and high frequency in different scales and various
ngles will be obtained. Most significant information of faces is contained in coefficients, which
is  important  for  face  recognition.  Then,  the  combinations  of  coefficients  are  applied  as  study
samples to the support vector machine classifiers. Finally, the decomposed coefficients of testing
face image are used to test classifiers, then face recognition results are obtained. The experiments
re performed on the YaleB database and the Cambridge University ORL database. The results
indicate that the method proposed has performs better than the wavelet-based method. Compared
with the wavelet-based method, the proposed method can make the best recognition rates increase
y 2.85% for YaleB  database and  1.87% for ORL  database, respectively. Our method is  also"
24b6d839662e5d56f17fc26eab4d2901f6835ddf,Real Time Lip Motion Analysis for a Person Authentication System using Near Infrared Illumination,"REAL TIME LIP MOTION ANALYSIS FOR A
PERSON AUTHENTICATION SYSTEM USING NEAR
INFRARED ILLUMINATION
Faisal Shafait, Ralph Kricke, Islam Shdaifat, Rolf-Rainer Grigat
TUHH Vision Systems (4-08/1)
Harburger Schloßstr. 20, 21079 Hamburg, Germany
Tel: +49 40 42878-3125, Fax: +49 40 42878-2911
http://www.ti1.tu-harburg.de
in: 2006 IEEE International Conference on Image Processing. See also BIBTEX entry below.
BIBTEX:
uthor = {Faisal Shafait and Ralph Kricke and Islam Shdaifat and Rolf-Rainer Grigat},
title = {REAL TIME LIP MOTION ANALYSIS FOR A PERSON AUTHENTICATION SYSTEM
USING NEAR INFRARED ILLUMINATION},
ooktitle = {2006 IEEE International Conference on Image Processing},
year = {2006},
pages = {1957-1960},
month = {oct},
url = {http://www.ti1.tu-harburg.de/Publikationen}
scheduled for October 8-11, 2006 in Atlanta, Georgia, USA. Personal use of this material is permitted.
However, permission to reprint/republish this material for advertising or promotional purposes or for cre-"
24baf0d64384ad67feed9920ab04303bea610c13,Face Authentication System Based on FDA and ANN,"International Journal of Electrical & Computer Sciences IJECS-IJENS Vol: 10 No: 06                                  60
Face Authentication System Based on FDA and
M. A. Teh Noranis, and N. Shahrin Azuan"
247df1d4fca00bc68e64af338b84baaecc34690b,Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces,"Review
Procedure
009/6/12
Paper
 “Evaluation of Gender Classification Methods
with Automatically Detected and Aligned
Faces”
 Erno Makinen & Roope Raisamo
 2008
Decision
resizing
lignment
face detection
resizing
lassification
resizing
lignment
lignment
resizing
face detection"
245130ac792531ca9981f9c5907190eac19ebb50,Detecting Objects using Unsupervised Parts-based Attributes,"Detecting Objects using Unsupervised Parts-based Attributes∗
Santosh K. Divvala1, Larry Zitnick2, Ashish Kapoor2 , Simon Baker2
Carnegie Mellon University.
Microsoft Research.
{larryz, ashishk,"
246fa412f26d5bf5b151a7c3f5287141bd08ae0b,Deep Metric Learning for the Target Cost in Unit-Selection Speech Synthesizer,"Interspeech 2018
-6 September 2018, Hyderabad
0.21437/Interspeech.2018-1305"
247a6b0e97b9447850780fe8dbc4f94252251133,Facial action unit detection: 3D versus 2D modality,"Facial Action Unit Detection: 3D versus 2D Modality
Arman Savran
Electrical and Electronics Engineering
Bo˘gazic¸i University, Istanbul, Turkey
B¨ulent Sankur
Electrical and Electronics Engineering
Bo˘gazic¸i University, Istanbul, Turkey
M. Taha Bilge
Department of Psychology
Bo˘gazic¸i University, Istanbul, Turkey"
2472d6e4459dd65cd77b5fce99220d3b30854408,Towards 3D object recognition via classification of arbitrary object tracks,"Towards 3D Object Recognition
via Classification of Arbitrary Object Tracks
Alex Teichman, Jesse Levinson, Sebastian Thrun
Stanford Artificial Intelligence Laboratory
{teichman, jessel,"
2450c618cca4cbd9b8cdbdb05bb57d67e63069b1,A connexionist approach for robust and precise facial feature detection in complex scenes,"A Connexionist Approach for Robust and Precise Facial Feature Detection in
Complex Scenes
Stefan Duffner and Christophe Garcia
France Telecom Research & Development
, rue du Clos Courtel
5512 Cesson-S´evign´e, France
fstefan.duffner,"
24da9c1eb30ed5ef0052f760d5d847bf5cd1d2ba,A Machine-Learning Approach to Keypoint Detection and Landmarking on 3D Meshes,"Int J Comput Vis
DOI 10.1007/s11263-012-0605-9
A Machine-Learning Approach to Keypoint Detection
nd Landmarking on 3D Meshes
Clement Creusot · Nick Pears · Jim Austin
Received: 14 October 2011 / Accepted: 17 December 2012
© Springer Science+Business Media New York 2013"
2491203e3b268235ea0269f41dbebd113d2a1b0a,"Optimal multiplexed sensing: bounds, conditions and a graph theory link.","Optimal multiplexed sensing: bounds,
onditions and a graph theory link
Netanel Ratner,1 Yoav Y. Schechner,1,∗
nd Felix Goldberg2
Dept. Electrical Engineering, Technion - Israel Inst. Technology
Haifa 32000, Israel
Dept. Mathematics, Technion - Israel Inst. Technology
Haifa 32000, Israel
Corresponding author:"
24d0092a3fc2a414b73fd453c5cdd229fc0cb174,Hierarchical Visual Feature Analysis for City Street View Datasets,"To appear in an IEEE VGTC sponsored conference proceedings
Hierarchical Visual Feature Analysis for City Street View Datasets
Lezhi Li, James Tompkin, Panagiotis Michalatos, and Hanspeter Pfister
Fig. 1. Exploring ‘perceptual neighborhood’ with our hierarchical clustering of visual features for geographically-embedded images.
From top to bottom: tree representation of the hierarchy as an interactive dendrogram; geo-location of the images as cloropleth map;
ircle containment representation of the hierarchy; street-level imagery for geographic regions of the selected hierarchy levels."
2409557812a3d26258949ba73a05031591f42bdc,Exact Discovery of Time Series Motifs,"Abdullah Mueen
Exact Discovery of Time Series Motifs
Eamonn Keogh
Qiang Zhu
Sydney Cash1,2       Brandon Westover1,3
Massachusetts General Hospital, 2Harvard Medical School, 3Brigham and Women's Hospital
University of California – Riverside
{mueen, eamonn,"
24ff2797234e26bb2ffd4558eb4412df0625687e,Mind Your Language: Abuse and Offense Detection for Code-Switched Languages,"Mind Your Language: Abuse and Offense Detection for Code-Switched Languages
Raghav Kapoor
MIDAS Lab, NSIT-Delhi
Yaman Kumar
Adobe Systems
Kshitij Rajput
MIDAS Lab, NSIT-Delhi
Rajiv Ratn Shah
IIIT, Delhi
Ponnurangam Kumaraguru
IIIT, Delhi
Roger Zimmermann
NUS, Singapore"
2475ad865b2102cef83a87adfe0d2e71d4791e53,A Supervised Clustering Algorithm for the Initialization of RBF Neural Network Classifiers,"A Supervised Clustering Algorithm for the Initialization
of RBF Neural Network Classifiers
Hakan Cevikalp, Diane Larlus, Frédéric Jurie
To cite this version:
Hakan Cevikalp, Diane Larlus, Frédéric Jurie. A Supervised Clustering Algorithm for the Ini-
SIU ’07 - 15th Signal Processing and Com-
tialization of RBF Neural Network Classifiers.
munications Applications, Jun 2007, Eskisehir, Turkey.
IEEE Computer society, pp.1-4, 2007,
<10.1109/SIU.2007.4298803>. <hal-00203762>
HAL Id: hal-00203762
https://hal.archives-ouvertes.fr/hal-00203762
Submitted on 14 Jan 2008
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est"
247b8e6b0e1014e707117c08fc880bb341b2c248,Advantages of dynamic analysis in HOG-PCA feature space for video moving object classification,"ADVANTAGES OF DYNAMIC ANALYSIS IN HOG-PCA FEATURE SPACE FOR VIDEO
MOVING OBJECT CLASSIFICATION
Miriam M. L´opez
Lucio Marcenaro
Carlo S. Regazzoni
DITEN, University of Genova
Via Opera Pia 11A, 16145 Genoa - Italy
{miriam.lopez, mlucio,"
24e79933d8d71dd9e72e289d9d89a061ccbb01c3,Analysis of Principal Component Analysis ( PCA ) Face Recognition : Effects of Similarity Measure,"Analysis of Principal Component Analysis (PCA)
Face Recognition: Effects of Similarity Measure
Arjun V Mane#1, Ramesh R Manza#2, Karbhari V Kale#3
#Department of Computer Science & Information Technology,
Dr. Babasaheb Ambedkar Marathwada University, Aurangabad (MS) India"
24e98b70dc6982af2dd3a5bb4e501cc1b61f7d2b,LCR-Net++: Multi-person 2D and 3D Pose Detection in Natural Images,"SUBMITTED TO IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2018
LCR-Net++: Multi-person 2D and 3D Pose
Detection in Natural Images
Gr´egory Rogez, Philippe Weinzaepfel, and Cordelia Schmid, Fellow, IEEE"
24d68f3e7ee737463f81e9bf03500afa7b88f913,Comparing apples to apples in the evaluation of binary coding methods,"Comparing apples to apples in the evaluation of
inary coding methods
Mohammad Rastegari, Shobeir Fakhraei,
Jonghyun Choi, David Jacobs, Larry S. Davis
mrastega,shobeir
jhchoi,djacobs,lsd"
242ae7b1b1c3e1aafcbe9cef3cb23918c6f94f2c,Performance Evaluation of Biometric Template Update,"Performance Evaluation
of Biometric Template Update
Romain Giot and Christophe Rosenberger
Université de Caen, UMR 6072 GREYC
ENSICAEN, UMR 6072 GREYC
CNRS, UMR 6072 GREYC
Email:
Email:
Bernadette Dorizzi
Institut Télécom; Télécom SudParis
UMR 5157 SAMOVAR
Email:"
2431eeb2df8877d78901fa37a091a23dc207c2b2,Rotation-Invariant HOG Descriptors Using Fourier Analysis in Polar and Spherical Coordinates,"Int J Comput Vis
DOI 10.1007/s11263-013-0634-z
Rotation-Invariant HOG Descriptors Using Fourier Analysis
in Polar and Spherical Coordinates
Kun Liu · Henrik Skibbe · Thorsten Schmidt ·
Thomas Blein · Klaus Palme · Thomas Brox ·
Olaf Ronneberger
Received: 30 September 2012 / Accepted: 21 May 2013
© Springer Science+Business Media New York 2013"
247232ab9eabb4f2480dd70557a1ee89afed4f20,Dominant men are faster in decision-making situations and exhibit a distinct neural signal for promptness,"Cerebral Cortex, October 2018;28: 3740–3751
doi: 10.1093/cercor/bhy195
Advance Access Publication Date: 15 August 2018
Original Article
O R I G I N A L A R T I C L E
Dominant men are faster in decision-making situations
nd exhibit a distinct neural signal for promptness
Janir da Cruz1,2, João Rodrigues3, John C. Thoresen3, Vitaly Chicherov1,
Patrícia Figueiredo2, Michael H. Herzog1 and Carmen Sandi
Laboratory of Psychophysics, Brain Mind Institute, School of Life Sciences, Swiss Federal Institute of
Technology Lausanne (EPFL), CH-1015 Lausanne, Switzerland, 2Institute for Systems and Robotics – Lisboa,
Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal
nd 3Laboratory of Behavioral Genetics, Brain Mind Institute, School of Life Sciences, Swiss Federal Institute of
Technology Lausanne (EPFL), CH-1015 Lausanne, Switzerland
Address correspondence to Carmen Sandi, Laboratory of Behavioral Genetics, Brain Mind Institute, School of Life Sciences, Swiss Federal Institute of
Technology Lausanne (EPFL), CH-1015 Lausanne, Switzerland. Email:
orcid.org/0000-0001-7713-8321
Janir da Cruz, João Rodrigues, and John C. Thoresen contributed equally to this work
Michael H. Herzog and Carmen Sandi contributed equally to this work"
2452dfb2c5a4578ac9497cc4dc3c6d5d03997210,On designing an unconstrained tri-band pupil detection system for human identification,"DOI 10.1007/s00138-015-0700-3
ORIGINAL PAPER
On designing an unconstrained tri-band pupil detection system
for human identification
Cameron Whitelam1 · Thirimachos Bourlai1
Received: 30 September 2014 / Revised: 11 February 2015 / Accepted: 15 June 2015
© Springer-Verlag Berlin Heidelberg 2015
facial"
244b57cc4a00076efd5f913cc2833138087e1258,Warped Convolutions: Efficient Invariance to Spatial Transformations,"Warped Convolutions: Efficient Invariance to Spatial Transformations
Jo˜ao F. Henriques 1 Andrea Vedaldi 1"
247b14570940601f5c7a2da1db532ecf1c302288,Dual Attention Networks for Multimodal Reasoning and Matching,"Dual Attention Networks for Multimodal Reasoning and Matching
Hyeonseob Nam
Naver Search Solutions
Jung-Woo Ha
Naver Labs
Jeonghee Kim
Naver Labs"
245922e5251c103c2021577cc0f99791d748ac64,Fusion of Intraoperative 3 D B-mode and Contrast-Enhanced Ultrasound Data for Automatic Identification of Residual Brain Tumors,"Article
Fusion of Intraoperative 3D B-mode and
Contrast-Enhanced Ultrasound Data for Automatic
Identification of Residual Brain Tumors
Elisee Ilunga-Mbuyamba 1,3, Dirk Lindner 2, Juan Gabriel Avina-Cervantes 1,∗, Felix Arlt 2,
Horacio Rostro-Gonzalez 1, Ivan Cruz-Aceves 4 and Claire Chalopin 3
Telematics (CA), Engineering Division (DICIS), University of Guanajuato, Campus Irapuato-Salamanca,
Carr. Salamanca-Valle km 3.5 + 1.8, Comunidad de Palo Blanco, Salamanca, Gto. 36885, Mexico;
(E.I.-M.); (H.R.-G.)
Department of Neurosurgery, University Hospital Leipzig, Leipzig 04103, Germany;
(D.L.); (F.A.)
Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, Leipzig 04103, Germany;
Centro de Investigacion en Matematicas (CIMAT), A.C., Jalisco S/N, Col. Valenciana,
Guanajuato, Gto. 36000, Mexico;
* Correspondence: Tel.: +52-46-4647-9940 (ext. 2400)
Academic Editor: Hideyuki Hasegawa
Received: 15 February 2017; Accepted: 17 April 2017; Published: 19 April 2017"
24c442ac3f6802296d71b1a1914b5d44e48b4f29,Pose and Expression-Coherent Face Recovery in the Wild,"Pose and expression-coherent face recovery in the wild
Xavier P. Burgos-Artizzu
Joaquin Zepeda
Technicolor, Cesson-S´evign´e, France
Franc¸ois Le Clerc
Patrick P´erez"
246218fd60d47975990908c48274341b47255292,Marker-less motion capture in general scenes with sparse multi-camera setups,"Marker-less Motion Capture in General
Scenes with Sparse Multi-camera Setups
Ahmed Elhayek
Saarbr¨ucken, Germany
Dissertation
zur Erlangung des Grades des
Doktors der Ingenieurswissenschaften (Dr.-Ing.)
der Naturwissenschaftlich-Technischen Fakult¨aten
der Universit¨at des Saarlandes
March 2015"
24e099e77ae7bae3df2bebdc0ee4e00acca71250,Robust Face Alignment Under Occlusion via Regional Predictive Power Estimation,"Robust face alignment under occlusion via regional predictive power
estimation.
Heng Yang; Xuming He; Xuhui Jia; Patras, I
© 2015 IEEE
For additional information about this publication click this link.
http://qmro.qmul.ac.uk/xmlui/handle/123456789/22467
Information about this research object was correct at the time of download; we occasionally
make corrections to records, please therefore check the published record when citing. For
more information contact"
5ecf564bc9eab26c96c17304744ff1029215a109,Single-Sample Face Recognition Based on Intra-Class Differences in a Variation Model,"Sensors 2015, 15, 1071-1087; doi:10.3390/s150101071
OPEN ACCESS
sensors
ISSN 1424-8220
www.mdpi.com/journal/sensors
Article
Single-Sample Face Recognition Based on Intra-Class
Differences in a Variation Model
Jun Cai, Jing Chen * and Xing Liang
School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China;
E-Mails: (J.C.); (X.L.)
*  Author to whom correspondence should be addressed; E-Mail:
Tel.: +86-136-8151-5195.
External Editor: Valentina Gatteschi
Received: 17 September 2014 / Accepted: 10 December 2014 / Published: 8 January 2015"
5e0832848fab012b7e59580264257e0a3d05c596,The University of Southampton Multi-Biometric Tunnel and introducing a novel 3 D gait dataset Richard,"The University of Southampton Multi-Biometric Tunnel and
introducing a novel 3D gait dataset
Richard D. Seely, Sina Samangooei, Lee Middleton, John N. Carter and Mark S. Nixon"
5ee220b6fb70a3d4d99be9d81d2c0e5de06ab3b9,LoST? Appearance-Invariant Place Recognition for Opposite Viewpoints using Visual Semantics,"Pre-print of article that will appear in Proceedings of Robotics: Science and Systems XIV, 2018.Please cite this paper as:Sourav Garg, Niko Sunderhauf, and Michael Milford. LoST? Appearance-Invariant Place Recognition for Opposite Viewpoints using Visual Semantics. Proceedings of Robotics: Science and Systems XIV,  title={LoST? Appearance-Invariant Place Recognition for Opposite Viewpoints using Visual Semantics},  author={Garg, Sourav and Suenderhauf, Niko and Milford, Michael},  journal={Proceedings of Robotics: Science and Systems XIV},  year={2018}}"
5e9a6357fd7de7271dac77756c3992dce260eb49,ON THE CONVERGENCE OF AFFECTIVE AND PERSUASIVE TECHNOLOGIES IN COMPUTER-MEDIATED HEALTH-CARE SYSTEMS,"Rebeca I. García-Betances
Life Supporting Technologies (LifeSTech)
Superior Technical School of
Telecommunications Engineers
Polytechnic University of Madrid
Superior Technical School of
Telecommunications Engineers
Polytechnic University of Madrid
Spain
Dario Salvi
Spain
Giuseppe Fico
Life Supporting Technologies (LifeSTech)
Superior Technical School of
Telecommunications Engineers
Polytechnic University of Madrid
Spain
Manuel Ottaviano
Superior Technical School of
Telecommunications Engineers"
5e286a45a4780a142e1420728ab99cb92993ab50,Data-driven image captioning with meta-class based retrieval,"META-SINIF TABANLI GETİRME İLE VERİYE DAYALI İMGE ALTYAZILAMA
DATA-DRIVEN IMAGE CAPTIONING WITH META-CLASS BASED RETRIEVAL
Mert Kılıçkaya1, Erkut Erdem1, Aykut Erdem1, Nazlı İkizler Cinbiş1, Ruket Çakıcı2
Bilgisayar Mühendisliği Bölümü
Hacettepe Üniversitesi
ÖZETÇE
Otomatik imge altyazılama, bir imgenin açıklamasını yaratma
işlemi,  bilgisayarlı  görü  ve  doğal  dil  işleme  topluluklarının
ilgisini  daha  yeni  çeken  çok  zorlu  bir  problemdir.  Bu
çalışmada,  verilen  bir  imge  için;  imge-altyazı  ikilileri  içeren
geniş bir veri kümesinden ona görsel olarak en benzer imgeyi
ulan  ve  onun  altyazısını  girdi  imgesinin  açıklaması  olarak
ktaran  veriye  dayalı  özgün  bir  imge  altyazılama  stratejisi
önerilmiştir.  Özgünlüğümüz,  getirme
için  girdi
görüntüsünün  anlamsal  içeriğini  daha  iyi  yakalamak  için
meta-sınıg gösterimi olarak adlandırılan yeni önerilmiş yüksek
düzey bir global imge gösterimi kullanılmasında yatmaktadır.
Deneylerimiz  meta-sınıf  güdümlü  yaklaşımımızın  dayanak
Im2Text  modeline  kıyasla  daha  doğru  açıklamalar  ürettiğini"
5e8a7a2eef68f568c023f37e41576fa811e5c628,Deep Reinforcement Learning For Sequence to Sequence Models,"Deep Reinforcement Learning for
Sequence-to-Sequence Models
Yaser Keneshloo, Tian Shi, Naren Ramakrishnan, Chandan K. Reddy, Senior Member, IEEE"
5e0eb34aeb2b58000726540336771053ecd335fc,Low-Quality Video Face Recognition with Deep Networks and Polygonal Chain Distance,"Low-Quality Video Face Recognition with Deep
Networks and Polygonal Chain Distance
Christian Herrmann∗†, Dieter Willersinn†, J¨urgen Beyerer†∗
Vision and Fusion Lab, Karlsruhe Institute of Technology KIT, Karlsruhe, Germany
Fraunhofer IOSB, Karlsruhe, Germany"
5ea9063b44b56d9c1942b8484572790dff82731e,Multiclass Support Vector Machines and Metric Multidimensional Scaling for Facial Expression Recognition,"MULTICLASS SUPPORT VECTOR MACHINES AND METRIC MULTIDIMENSIONAL
SCALING FOR FACIAL EXPRESSION RECOGNITION
Irene Kotsiay, Stefanos Zafeiriouy, Nikolaos Nikolaidisy and Ioannis Pitasy
yAristotle University of Thessaloniki, Department of Informatics
Thessaloniki, Greece
email: fekotsia, dralbert, nikolaid,"
5e39deb4bff7b887c8f3a44dfe1352fbcde8a0bd,Supervised COSMOS Autoencoder: Learning Beyond the Euclidean Loss!,"Supervised COSMOS Autoencoder: Learning Beyond the
Euclidean Loss!
Maneet Singh, Student Member, IEEE, Shruti Nagpal, Student Member, IEEE, Mayank Vatsa, Senior Member, IEEE,
Richa Singh, Senior Member, IEEE, and Afzel Noore, Senior Member, IEEE"
5e2266d4ca1377bdf38ad2c07d0d9e0200813522,Recognizing and Mask Removal in 3 D Faces Even In Presence of Occlusions,"ISSN(Online): 2320-9801
ISSN (Print):  2320-9798
International Journal of Innovative Research in Computer and Communication Engineering
(An ISO 3297: 2007 Certified Organization)
Vol.2, Special Issue 1, March 2014
Proceedings of International Conference On Global Innovations In Computing Technology (ICGICT’14)
Organized by
Department of CSE, JayShriram Group of Institutions, Tirupur, Tamilnadu, India on 6th & 7th March 2014
Recognizing and Mask Removal in 3D Faces Even
In Presence of Occlusions
M.Dhivya1, P.Purushothaman2
Dept. of Computer Science and Engineering, Muthayammal Engineering College, Rasipuram, Tamilnadu, India1. 2"
5e4ad1f19e88b6dc87000f64b984d8f09abe7baf,Invariant Spectral Hashing of Image Saliency Graph,"Invariant Spectral Hashing of Image Saliency Graph
Maxime Taquet, Laurent Jacques, Christophe De Vleeschouwer and Benoˆıt Macq
Information and Communication Technologies, Electronics and Applied Mathematics
Universit´e catholique de Louvain, Belgium.
September 17, 2010"
5ef49174ca2b54c1bb54df828acc52075cf1634b,DAPs: Deep Action Proposals for Action Understanding,"DAPs: Deep Action Proposals for Action
Understanding
Victor Escorcia1, Fabian Caba Heilbron1,
Juan Carlos Niebles2,3, Bernard Ghanem1
King Abdullah University of Science and Technology (KAUST), Saudi Arabia.
Stanford University. 3 Universidad del Norte, Colombia.
{victor.escorcia, fabian.caba,"
5eee9c417157916ee66689718af65965c423b2b7,"In Press: Carol Armstrong, Ed., Handbook of Medical Neuropsychology. New York: Springer Science. Autism and Asperger’s Syndrome: A Cognitive Neuroscience Perspective","In Press: Carol Armstrong, Ed., Handbook of Medical Neuropsychology. New York:
Springer Science.
Autism and Asperger’s Syndrome: A Cognitive Neuroscience Perspective
Jeanne Townsend, Ph.D., Marissa Westerfield, Ph.D.
Department of Neurosciences, University of California, San Diego
Table of Contents
History and Background
Biological Underpinnings
Postmortem Studies
MRI Studies
White Matter Connectivity
Neuroanatomy
EEG Abnormalities
Seizures
Diagnosis
Neurocognitive Mechanisms
Screening Guidelines
Clinical & Research Criteria
Increased Prevalence of Autism
It’s not the vaccine"
5eac16cf7551d2968bc2d73200883dca0b5f7f13,Combining complementary kernels in complex visual categorization,"Combining complementary kernels in complex visual categorization
Nicolas Thome1, David Picard2 and Matthieu Cord1
LIP6 UPMC Paris 6
place Jussieu, 75005 Paris, France
ETIS, CNRS ENSEA UCP, 95000 Cergy-Pontoise
Introduction
Bag Of Words model [1] and Fisher Vectors [2] coupled with incorporated spatial information, such as the Spatial
Pyramid Matching (SPM) [3] or the spatial Fisher vectors [4], proved to reach state of the art performances in
many Image categorization tasks, e.g. the PASCAL VOC challenge [5]. However, image categorization remains a
very challenging task because most descriptors present strong intra-class variabilities and inter-class correlations.
Therefore, a natural way to improve categorization performances consists in designing ef‌f‌icient feature combination
strategies.
It is an important issue for both computer vision and machine learning communities, that has been
extensively studied in the last decade. Multiple Kernel Learning (MKL) is appealing for that purpose, since it
offers the possibility to jointly learn the weighting of the different channels (features and similarity kernels) and
the classification function [6]. The goal is to find the optimal classification function f defined as follows: f (x) =
Pi αiyi Pm βmkm(x, xi) − b where the variable to be optimized are both the α and the w. Ef‌f‌icient algorithms exist
for solving the related optimization convex problem [7].
Recent works attempting at using MKL on image datasets for combining different channels [8, 9] use MKL
optimization algorithms based on ℓ1 norm to regularize the kernel weights, like SimpleMKL [7]. Since this leads"
5e6f546a50ed97658be9310d5e0a67891fe8a102,Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?,"Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
Kensho Hara, Hirokatsu Kataoka, Yutaka Satoh
National Institute of Advanced Industrial Science and Technology (AIST)
Tsukuba, Ibaraki, Japan
{kensho.hara, hirokatsu.kataoka,"
5e4a451faf2e47486a5dbeca8a5109b53e22d95a,Research Statement Arun Kumar,"Research Statement
Arun Kumar
Large-scale data analytics using machine learning (ML), popularly known as advanced analytics or “Big
Data” analytics, is transforming almost every data-powered application in the enterprise, Web, science,
government, and other domains. However, there are still many barriers to broad and successful adoption
of advanced analytics. Designing new ML algorithms and faster ML implementations are important issues
that have been studied by researchers for a long time, but for most data-powered applications, the real
showstopper is a different issue that is often glossed over in research: the end-to-end process of building
ML models given raw data is often too painful even for professional analysts, while developers skilled in
oth general-purpose programming and the latest ML are rare. The goal of my research is to improve the
productivity of the users and developers of advanced analytics systems to enable data-powered applications
to realize the full potential of advanced analytics. To this end, my work focuses on fundamental research
questions at the intersection of data management and ML that address usability, developability, perfor-
mance, and scalability issues. My approach to solving a problem involves the whole spectrum of algorithm
design, theoretical analysis, empirical analysis, building prototype systems, and deploying them in practice.
Research Summary. My dissertation opens up a new problem that I call “learning over joins”, which
illustrates my goal of improving the productivity of analysts. My observation is simple: most ML toolkits
ssume the input data is a single table, but many real-world datasets are multi-table. Thus, analysts join
ll tables to create a single table that might be much larger, which means that managing and maintaining
it is a usability headache. Creating a single table also causes storage and performance issues. To mitigate"
5e832ea5328cdcc9b4346458672ad8288a56c0a7,Illumination-robust face recognition with Block-based Local Contrast Patterns,"978-1-5090-4117-6/17/$31.00 ©2017 IEEE
ICASSP 2017"
5eae1a3e0dfd0834be6a003b979bf5b3dc923453,"Far-Field , Multi-Camera , Video-to-Video Face Recognition","Far-Field, Multi-Camera, Video-to-Video Face
Recognition
Aristodemos Pnevmatikakis and Lazaros Polymenakos
Athens Information Technology
Greece
. Introduction
Face recognition on still images has been extensively studied. Given sufficient training data
(many  gallery  stills  of  each  person)  and/or  high  resolution  images,  the  90%  recognition
arrier can be exceeded, even for hundreds of different people to be recognized (Phillips et
l.,  2006).  Face  recognition  on  video  streams  has  only  recently  begun  to  receive  attention
(Weng  et  al.,  2000;  Li  et  al.,  2001;  Gorodnichy,  2003;  Lee  et  al.,  2003;  Liu  and  Chen,  2003;
Raytchev  and  Murase,  2003;  Aggarval  et  al.,  2004;  Xie  et  al.,  2004;  Stergiou  et  al.,  2006).
Video-to-video face recognition refers to the problem of training and testing face recognition
systems using video streams. Usually these video streams are near-field, where the person
to be recognized occupies most of the frame. They are also constrained in the sense that the
person  looks mainly  at  the  camera.  Typical such  video streams  originate  from  video-calls
nd news narration, where a person’s head and upper torso is visible.
A  much  more  interesting  application  domain  is  that  of  the  far-field  unconstrained  video
streams. In such streams the people are far from the camera, which is typically mounted on a
room corner near the ceiling. VGA-resolution cameras in such a setup can easily lead to quite"
5ece99e52efbd43ac7fed8a7d0d604218cba0337,Towards Deep Representation Learning with Genetic Programming,"Towards Deep Representation Learning with Genetic
Programming(cid:63)
Lino Rodriguez-Coayahuitl, Alicia Morales-Reyes, and Hugo Jair Escalante
Instituto Nacional de Astrofisica, Optica y Electronica,
Luis Enrique Erro No.1, Tonantzintla, 72840, Puebla, Mexico,"
5eefe98aafffe665b19de515e3ba90c9c0b7219c,Trimmed Event Recognition Submission to ActivityNet Challenge 2018,"Trimmed Event Recognition​ Submission to ActivityNet Challenge 2018
Jiaqing Lin,  Akikazu Takeuchi
STAIR Lab, Chiba Institute of Technology, Japan
{lin,
. Overview
This paper describes STAIR Lab submission to
ActivityNet 2018 Challenge for guest
task C:
Trimmed Event Recognition (Moments in Time) [1].
Our approach is to utilize three networks, Audio
Net, Spatial-temporal Net, and DenseNet to make
individual predictions, then use MLP to fuses the
results to make an overall prediction. The flow chart
of our approach is shown in figure 1.
. Implementation
.1 Audio network
Our audio dataset training is different from other
methods. Usually, auditory raw waveforms are used
s input and are fed into a model like SoundNet [2].
In our case, firstly, we converted auditory raw"
5e9e3afeea446a2ae19e3a8e0678f08b73b0b36b,Commonsense knowledge acquisition and applications,"Commonsense Knowledge
Acquisition and Applications
Niket Tandon
Max-Planck-Institut f¨ur Informatik
Dissertation
zur Erlangung des Grades
des Doktors der Ingenieurwissenschaften (Dr.-Ing.)
der Naturwissenschaftlich-Technischen Fakult¨aten
der Universit¨at des Saarlandes
Saarbr¨ucken
August, 2016"
5e053cd164b02433c4efc0fc675f6273a8a1c46a,Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling,"Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pages 321–331
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pages 321–331
Vancouver, Canada, July 30 - August 4, 2017. c(cid:13)2017 Association for Computational Linguistics
Vancouver, Canada, July 30 - August 4, 2017. c(cid:13)2017 Association for Computational Linguistics
https://doi.org/10.18653/v1/P17-1030
https://doi.org/10.18653/v1/P17-1030"
5e16cc5dc7ef8b4fc1320abbfeb838b4fe041905,A Proposal for Common Dataset in Neural-Symbolic Reasoning Studies,"A Proposal for Common Dataset in
Neural-Symbolic Reasoning Studies
Ozgur Yilmaz, Artur d’Avila Garcez, and Daniel Silver
Turgut Ozal University, Computer Science Department, Ankara Turkey
City University London, Department of Computer Science, London UK
Acadia University, Jodrey School of Computer Science, Nova Scotia Canada,"
5efbb135fd3a49de6ca1b69ef583d1bcfe761043,APPLICATION OF SUPPORT VECTOR MACHINES IN AUTOMATIC HUMAN FACE RECOGNITION,"JOURNAL OF MEDICAL INFORMATICS & TECHNOLOGIES Vol. 9/2005, ISSN 1642-6037
Michał KAWULOK*
utomatic face recognition, support vector machines,
face detection, feature extraction, multi-method fusion
APPLICATION OF SUPPORT VECTOR MACHINES
IN AUTOMATIC HUMAN FACE RECOGNITION
This paper presents the possibilities of applying the Support Vector Machines (SVM) in the process of
utomatic human face recognition. It is described how the existing methods of face recognition can be improved
y the SVM. Moreover, a new approach to the multi-method fusion utilising the SVM is proposed. Usefulness
of all the methods described in the paper improving the face recognition effectiveness by the SVM is confirmed
y the experimental results.
.  INTRODUCTION
At first an idea of automatic face recognition should be described. It can be imagined that
there  is  a  system  which  acquires  an  image  or  a  set  of  images  as  an  input  data,  processes  them,
detects  faces  in  these  images  and  generates  feature  vectors  describing  each  detected  face.
Furthermore, such a system is able to compare two feature vectors and calculate similarity between
them  in  a  form  of  the  normalised  similarity  value.  This  makes  it  possible  to  assess  similarity
etween any two given images containing faces in an automatic way (i.e. without human attendance
or  interaction).  As  a  result,  such  a  system  discriminates  between  different  faces  and  fulfils  four
elongs to one of the defined classes or to none of them), verification, full identification."
5e2be6b7d8e483f8bd49711b93b37e7886c77be0,Cell-Based Deformation Monitoring via 3D Point Clouds,"Cell-Based Deformation Monitoring via 3D Point Clouds
THÈSE NO 5399 (2012)
PRÉSENTÉE LE 6 SEPTEMBRE 2012
À LA  FACULTÉ DE L'ENVIRONNEMENT NATUREL, ARCHITECTURAL ET CONSTRUIT
PROGRAMME DOCTORAL EN INFORMATIQUE, COMMUNICATIONS ET INFORMATION
LABORATOIRE DE TOPOMÉTRIE
ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE
POUR L'OBTENTION DU GRADE DE DOCTEUR ÈS SCIENCES
Jing WU
Prof. B. Merminod, directeur de thèse
cceptée sur proposition du jury:
Prof. P. Fua, président du jury
Prof. M. Jaboyedoff, rapporteur
Prof. K. Schindler, rapporteur
Prof. R. Urbanke, rapporteur
Suisse"
5e1b42d07eb84cddc1ebae607f3041aa2ef8fce8,RAM: Role Representation and Identification from combined Appearance and Activity Maps,"RAM: Role Representation and Identification
from combined Appearance and Activity Maps
Carlos Torres† Archith J. Bency† Je(cid:130)rey C. Fried‡ B. S. Manjunath†
University of California Santa Barbara ‡Santa Barbara Co(cid:138)age Hospital
{carlostorres, archith,"
5ec94adc9e0f282597f943ea9f4502a2a34ecfc2,Leveraging the Power of Gabor Phase for Face Identification: A Block Matching Approach,"Leveraging the Power of Gabor Phase for Face
Identification: A Block Matching Approach
Yang Zhong, Haibo Li
KTH, Royal Institute of Technology"
5e8966e332a8cfc587fc116f71b97d6412a4472d,SoundNet: Learning Sound Representations from Unlabeled Video,"SoundNet: Learning Sound
Representations from Unlabeled Video
Yusuf Aytar∗
Carl Vondrick∗
Antonio Torralba"
5ef2be1aadd2f666756b2ab66bc05d146ba0681b,Normalization in Training Deep Convolutional Neural Networks for 2D Bio-medical Semantic Segmentation,"Normalization in Training Deep Convolutional Neural Networks for 2D
Bio-medical Semantic Segmentation
Xiao-Yun Zhou1 and Guang-Zhong Yang1"
5e0cdf7d0edee84ab0efb9cbcb9487dac154f747,Public Document State of the Art in Multimodal Biometric Systems Document Evolution Executive Summary,"Project N° IST-2002-507634 - BioSecure
D8.1.1 – Revision: b3
4 June 2005
Contract Number :
Project Acronym :
Project Title :
Instrument :
Start Date of Project :
Duration :
Deliverable Number :
Title of Deliverable :
Contractual Due Date :
Actual Date of Completion :
IST-2002-507634
BioSecure
Biometrics for Secure Authentication
Network of Excellence
01 June, 2004
6 months
D8.1.1"
5e4e55ad902d0749d463187dbd453fd235a1379c,Empirical research on human behavior changes and digital intervention by one-way car-sharing,Yutaka Arakawa ∗
5e8e3d2a79537a6cd0c138545bce63ddafaa853c,Intent-aware long-term prediction of pedestrian motion,"Intent-Aware Long-Term Prediction of Pedestrian Motion
Vasiliy Karasev
Alper Ayvaci
Bernd Heisele
Stefano Soatto"
5e6ba16cddd1797853d8898de52c1f1f44a73279,Face Identification with Second-Order Pooling,"Face Identification with Second-Order Pooling
Fumin Shen, Chunhua Shen and Heng Tao Shen"
5e9f01b44ffda34356dc15451098d6311335d7ae,Efficient Large-Scale Multi-Modal Classification,"Efficient Large-Scale Multi-Modal Classification
Douwe Kiela, Edouard Grave, Armand Joulin and Tomas Mikolov
Facebook AI Research"
5e6944abfed38fd30d8be45ee0c24dc1c0525ba1,An Algorithm for Face Recognition based on Isolated Image Points with Neural Network,"International Journal of Computer Applications (0975 – 8887)
Volume 150 – No.2, September 2016
An Algorithm for Face Recognition based on Isolated
Image Points with Neural Network
Hassan Jaleel Hassan, PhD
Computer Engineering Department,
University of Technology
techniques
Pixel-based"
6cd96f2b63c6b6f33f15c0ea366e6003f512a951,A New Approach in Solving Illumination and Facial Expression Problems for Face Recognition,"A New Approach in Solving Illumination and Facial Expression Problems
for Face Recognition
Yee Wan Wong, Kah Phooi Seng, Li-Minn Ang
The University of Nottingham Malaysia Campus
Tel : 03-89248358, Fax : 03-89248017
E-mail :
Jalan Broga
3500 Semenyih, Selangor"
6cb7648465ba7757ecc9c222ac1ab6402933d983,Visual Forecasting by Imitating Dynamics in Natural Sequences,"Visual Forecasting by Imitating Dynamics in Natural Sequences
Kuo-Hao Zeng†‡ William B. Shen† De-An Huang† Min Sun‡ Juan Carlos Niebles†
{khzeng, bshen88, dahuang,
Stanford University ‡National Tsing Hua University"
6c67f082c4141907d60ef914a151568ef59f58d7,Biometric Solutions for Personal Identification,"Biometric Solutions for Personal
Identification
Tormod Emsell Larsen
Master of Science in Communication Technology
Submission date:
Supervisor:
Co-supervisor:
May 2008
Svein Johan Knapskog, ITEM
Danilo Gligoroski, ITEM
Norwegian University of Science and Technology
Department of Telematics"
6c38ab65df4a1bf546f1426e8a7f2f5cb5f765d3,Pathological Tremor Detection From Video,"Pathological Tremor Detection From Video
Xilin Li"
6ceacd889559cfcf0009e914d47f915167231846,The impact of visual attributes on online image diffusion,"The Impact of Visual Attributes on Online Image Diffusion
Luam Totti
Federal University of
Minas Gerais (UFMG)
Belo Horizonte, MG, Brazil
Felipe Costa
Federal University of
Minas Gerais (UFMG)
Belo Horizonte, MG, Brazil
Sandra Avila
RECOD Lab., DCA / FEEC /
UNICAMP
Campinas, SP, Brazil
Eduardo Valle
RECOD Lab., DCA / FEEC /
UNICAMP
Campinas, SP, Brazil
Wagner Meira Jr.
Federal University of
Minas Gerais (UFMG)"
6cd7a47bbba11a994cd8e68ee5eae2fcb0033054,Driving in the Matrix: Can virtual worlds replace human-generated annotations for real world tasks?,"Driving in the Matrix: Can Virtual Worlds Replace Human-Generated
Annotations for Real World Tasks?
Matthew Johnson-Roberson1, Charles Barto2, Rounak Mehta3, Sharath Nittur Sridhar2, and Ram Vasudevan4"
6ce6da7a6b2d55fac604d986595ba6979580393b,Cross Domain Knowledge Transfer for Person Re-identification,"Cross Domain Knowledge Transfer for Person Re-identification
Qiqi Xiao
Kelei Cao
Haonan Chen
Fangyue Peng
Chi Zhang"
6cf222f7c9ac4b48d95db4a62742c8329fcbd8b6,A System for Automatic Lip Reading,"ISCA Archive
http://www.iscaĆspeech.org/archive
AVSP 2003 - International Conference
on AudioĆVisual Speech Processing
St. Jorioz, France
September 4-7, 2003
A System for Automatic Lip Reading
I. Shdaifat and R. Grigat
D. Langmann
TU Hamburg Harburg, Vision Systems
D-21071 Hamburg, Germany"
6c514a85b840c461cf6959927e6a34414e1e0f5e,Texture descriptors to distinguish radiation necrosis from recurrent brain tumors on multi-parametric MRI,"Medical Imaging 2014: Computer-Aided Diagnosis, edited by Stephen Aylward, Lubomir M. Hadjiiski,
Proc. of SPIE Vol. 9035, 90352B · © 2014 SPIE · CCC code: 1605-7422/14/$18 · doi: 10.1117/12.2043969
Proc. of SPIE Vol. 9035  90352B-1
From: http://proceedings.spiedigitallibrary.org/ on 10/02/2014 Terms of Use: http://spiedl.org/terms"
6c2b392b32b2fd0fe364b20c496fcf869eac0a98,Fully automatic face recognition framework based on local and global features,"DOI 10.1007/s00138-012-0423-7
ORIGINAL PAPER
Fully automatic face recognition framework based
on local and global features
Cong Geng · Xudong Jiang
Received: 30 May 2011 / Revised: 21 February 2012 / Accepted: 29 February 2012 / Published online: 22 March 2012
© Springer-Verlag 2012"
6c06f959397e990e5d68dbcc85cec8b96b4c5c2d,Real-time Accurate Pedestrian Detection and Tracking in Challenging Surveillance Videos,"Real-time Accurate Pedestrian Detection and Tracking in Challenging
Surveillance Videos
Kristof Van Beeck1 and Toon Goedem´e1,2
EAVISE, Campus De Nayer - KU Leuven, J. De Nayerlaan 5, 2860 Sint-Katelijne-Waver, Belgium
ESAT-PSI, KU Leuven, Kasteel Arenbergpark 10, 3100 Heverlee, Belgium
Keywords:
Pedestrian Detection, Tracking, Surveillance, Computer Vision, Real-time."
6c5ce8bf382948bb27a9cc33b700a2402877bb64,Hyperspectral image classification via contextual deep learning,"Ma et al. EURASIP Journal on Image and Video Processing  (2015) 2015:20
DOI 10.1186/s13640-015-0071-8
RESEARCH
Open Access
Hyperspectral image classification via
ontextual deep learning
Xiaorui Ma, Jie Geng and Hongyu Wang*"
6c54261f601c8a569149b77d32efe6c58f2e4a2e,Preliminary evidence that the limbal ring influences facial attractiveness.,"Evolutionary Psychology
www.epjournal.net – 2011. 9(2): 137-146
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Original Article
Preliminary Evidence that the Limbal Ring Influences Facial Attractiveness
Darren Peshek, Department of Cognitive Sciences, University of California Irvine, Irvine, CA, USA. Email:
(Corresponding author).
Negar Semmaknejad, Department of Cognitive Sciences, University of California Irvine, Irvine, CA, USA.
Donald Hoffman, Department of Cognitive Sciences, University of California Irvine, Irvine, CA, USA.
Pete Foley, Innovation Science, Procter & Gamble, Cincinnati, OH, USA."
6cd557019b7775d8647ca31260734c786fdb69ec,Visual Classifier Prediction by Distributional Semantic Embedding of Text Descriptions,"Proceedings of the 2015 Workshop on Vision and Language (VL’15), pages 48–50,
Lisbon, Portugal, 18 September 2015. c(cid:13)2015 Association for Computational Linguistics."
6c9f45c76b4f96fe66d8e1d7b31f89b7cc6caa44,DeNet: Scalable Real-Time Object Detection with Directed Sparse Sampling,"DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling
Lachlan Tychsen-Smith, Lars Petersson
CSIRO (Data61)
7 London Circuit, Canberra, ACT, 2601"
6c24fed42d9a1ec283d2aa39a2dd768256a1a066,Swift: reducing the effects of latency in online video scrubbing,"Swift: Reducing the Effects of Latency in Online Video Scrubbing
Justin Matejka, Tovi Grossman, George Fitzmaurice
Autodesk Research, Toronto, Ontario, Canada
Traditional Video Scrubbing
Swift Video Scrubbing
Figure 1. An illustration of the scrubbing behavior of a traditional streaming video player and the Swift player. With the
Swift system a quick-to-download low resolution version of the video is displayed while scrubbing.
tasks  which
the  effects  of"
6c518aabdbba2c073eab6a3bb4120023851e524c,Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras,"Article
Person Recognition System Based on a Combination
of Body Images from Visible Light and
Thermal Cameras
Dat Tien Nguyen, Hyung Gil Hong, Ki Wan Kim and Kang Ryoung Park *
Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu,
Seoul 100-715, Korea; (D.T.N.); (H.G.H.);
(K.W.K.)
* Correspondence: Tel.: +82-10-3111-7022; Fax: +82-2-2277-8735
Academic Editor: Vittorio M. N. Passaro
Received: 5 January 2017; Accepted: 14 March 2017; Published: 16 March 2017"
6c091c3bd625b3c838831d797c66eae6c8f280cc,Apperance-Based Tracking and Face Identification in Video Sequences,"Apperance-based tracking and face identification
in video sequences
Jos´e Miguel Buenaposada1, Juan Bekios2, and Luis Baumela3
Dept. de Ciencias de la Computaci´on, Universidad Rey Juan Carlos
Calle Tulip´an s/n, 28933, M´ostoles, Spain
Dept. de Ingenier´ıa de Sistemas y Computaci´on, Universidad Cat´olica del Norte
Av. Angamos 0610, Antofagasta, Chile
Dept. de Inteligencia Artificial, Universidad Polit´ecnica de Madrid
Campus Montegancedo s/n, 28660 Boadilla del Monte, Spain"
6cfc337069868568148f65732c52cbcef963f79d,Audio-Visual Speaker Localization via Weighted Clustering Israel -,"Audio-Visual Speaker Localization via Weighted
Clustering
Israel-Dejene Gebru, Xavier Alameda-Pineda, Radu Horaud, Florence Forbes
To cite this version:
Israel-Dejene Gebru, Xavier Alameda-Pineda, Radu Horaud, Florence Forbes. Audio-Visual Speaker
Localization via Weighted Clustering. IEEE Workshop on Machine Learning for Signal Processing,
Sep 2014, Reims, France. pp.1-6, 2014, <10.1109/MLSP.2014.6958874>. <hal-01053732>
HAL Id: hal-01053732
https://hal.archives-ouvertes.fr/hal-01053732
Submitted on 11 Aug 2014
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
6cbb3c47010e406de656d13fe289522bb3071bc0,Improved vehicle detection system based on customized HOG,"Improved vehicle detection system based on
ustomized HOG
Haythem AMEUR1, Abdelhamid HELALI1, Hassen MAAREF1, Anis YOUSSEF2
Laboratory of Micro-Optoelectronic and Nanostructure, University of Monastir
Tunisia, Monastir
2 TELNET Innovation Labs Tunisia, Tunis"
6c984bb3243f3b8d0afd8d90cd4ce85eb8f1dd3c,Ear Recognition System Using Neural Network Based Self Organizing Maps,"International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622
International Conference on Humming Bird ( 01st March 2014)
RESEARCH ARTICLE
OPEN ACCESS
D Ear Recognition System Using Neural Network Based Self
Organizing Maps
M.Sathish Babu1, Assistant Professor
Email:
Department of Computer Science and Engineering, Cape Institute of Technology."
6c8c7065d1041146a3604cbe15c6207f486021ba,Attention Modeling for Face Recognition via Deep Learning,"Attention Modeling for Face Recognition via Deep Learning
Sheng-hua Zhong
Department of Computing, Hung Hom, Kowloon
Hong Kong, 999077 CHINA
Yan Liu
Department of Computing, Hung Hom, Kowloon
Hong Kong, 99907 CHINA
Yao Zhang
Department of Computing, Hung Hom, Kowloon
Hong Kong, 99907 CHINA
Fu-lai Chung
Department of Computing, Hung Hom, Kowloon
Hong Kong, 99907 CHINA"
6cddc7e24c0581c50adef92d01bb3c73d8b80b41,Face Verification Using the LARK Representation,"Face Verification Using the LARK
Representation
Hae Jong Seo, Student Member, IEEE, Peyman Milanfar, Fellow, IEEE,"
6c289ce7cd1c8514f71bf7dc25b1b203b98f8129,Semantic-Aware Image Smoothing,"Vision, Modeling, and Visualization (2017)
M. Hullin, R. Klein, T. Schultz, A. Yao (Eds.)
Semantic-Aware Image Smoothing
Weihao Li1,2, Omid Hosseini Jafari1,2, and Carsten Rother1,2
Computer Vision Lab, TU Dresden, Germany
Visual Learning Lab, Heidelberg University, Germany"
6cad008ad80081dc42752e813ee6924e3c174dc7,Does Facial Resemblance Enhance Cooperation?,"Does Facial Resemblance Enhance Cooperation?
Trang Giang*, Raoul Bell*, Axel Buchner
Department of Experimental Psychology, Heinrich Heine University Du¨ sseldorf, Du¨ sseldorf, Germany"
6c9e12c7ac10b6202762b9cd7ffae3822c90c063,Deployment of Customized Deep Learning based Video Analytics On Surveillance Cameras.,"Deployment of Customized Deep Learning based
Video Analytics On Surveillance Cameras
Pratik Dubal(cid:63), Rohan Mahadev(cid:63), Suraj Kothawade(cid:63),
Kunal Dargan, and Rishabh Iyer
AitoeLabs (www.aitoelabs.com)"
6c22b549d854845c5d2f17d75417e4469e6d3f83,A robust face recognition algorithm for real-world applications,"A Robust Face Recognition
Algorithm for
Real-World Applications
zur Erlangung des akademischen Grades eines
Doktors der Ingenieurwissenschaften
der Fakult¨at f¨ur Informatik
der Universit¨at Fridericiana zu Karlsruhe (TH)
genehmigte
Dissertation
Hazım Kemal Ekenel
us Samsun, T¨urkei
Tag der m¨undlichen Pr¨ufung: 02.02.2009
Erster Gutachter:
Prof. Dr. A. Waibel
Zweiter Gutachter:
Prof. Dr. J. Kittler"
6cd762e7cb1301abd0ddbb265dd9c7661ffc0458,On optimal low rank Tucker approximation for tensors: the case for an adjustable core size,"On Optimal Low Rank Tucker Approximation for Tensors:
The Case for an Adjustable Core Size
Bilian CHEN ∗
Zhening LI †
Shuzhong ZHANG ‡
August 7, 2014"
6c4d5ac0eed17513e3ceacd396526b8ad6c8fc09,Learning to Learn by Exploiting Prior Knowledge,"Learning to Learn by Exploiting
Prior Knowledge
Thèse n. 5587
à présenter le 07 November 2012
à la Faculté des Sciences et Techniques de L'ingénieur
laboratoire de L'Idiap
programme doctoral en Génie Électrique
École Polytechnique Fédérale de Lausanne
pour l'obtention du grade de Docteur ès Sciences
Tatiana Tommasi
cceptée sur proposition du jury :
Prof Dario Floreano, président du jury
Prof Hervé Bourlard, directeur de thèse
Dr Barbara Caputo, co-directeur de thèse
Prof Jean-Philippe Thiran, rapporteur
Prof Jim Little, rapporteur
Dr Vittorio Ferrari, rapporteur
Lausanne, EPFL, 2012"
6c0f9acd62ca9f156ca632dad6d666209eae461e,Discriminative vision-based recovery and recognition of human motion,"Discriminative Vision-Based Recovery and
Recognition of Human Motion
9-789036-528108
CTIT Dissertation Series No. 09-136
Center for Telematics and Information Technology (CTIT)
P.O. Box 217, 7500 AE Enschede, The Netherlands
Ronald Poppe"
6c0c368fca391b4456e64d2943d0bcbe6d8e1ecc,A Pipeline for Creative Visual Storytelling,"A Pipeline for Creative Visual Storytelling
Stephanie M. Lukin, Reginald Hobbs, Clare R. Voss
U.S. Army Research Laboratory
Adelphi, MD, USA"
6c3c845fe484bdb2b3549054644c7a06bd9b87b8,ENCARA: real-time detection of frontal faces,"ENCARA: REAL-TIME DETECTION OF FRONTAL FACES
M. Castrillón Santana, M. Hernández Tejera, J. Cabrera Gámez
Instituto Universitario Sistemas Inteligentes y Aplicaciones Numéricas en Ingeniería
Universidad de Las Palmas de Gran Canaria
5017 Gran Canaria - Spain"
6cb68c1f7558e01966ad1e1fa81feeeae3dee666,Photo Filter Recommendation by Category-Aware Aesthetic Learning,"IEEE TRANSACTION ON MULTIMEDIA
Photo Filter Recommendation
y Category-Aware Aesthetic Learning
Wei-Tse Sun, Ting-Hsuan Chao, Yin-Hsi Kuo, Winston H. Hsu"
6cefb70f4668ee6c0bf0c18ea36fd49dd60e8365,Privacy-Preserving Deep Inference for Rich User Data on The Cloud,"Privacy-Preserving Deep Inference for Rich User
Data on The Cloud
Seyed Ali Osia ♯, Ali Shahin Shamsabadi ♯, Ali Taheri ♯, Kleomenis Katevas ⋆,
Hamid R. Rabiee ♯, Nicholas D. Lane †, Hamed Haddadi ⋆
♯ Sharif University of Technology
⋆ Queen Mary University of London
Nokia Bell Labs & University of Oxford"
6c964e59bdac6b8044993ca96b47a9a0addedfb8,First Impressions: A Survey on Computer Vision-Based Apparent Personality Trait Analysis,"First Impressions: A Survey on Vision-based
Apparent Personality Trait Analysis
Julio C. S. Jacques Junior, Ya˘gmur G¨uc¸l¨ut¨urk, Marc P´erez, Umut G¨uc¸l¨u, Carlos Andujar, Xavier Bar´o,
Hugo Jair Escalante, Isabelle Guyon, Marcel A. J. van Gerven, Rob van Lier and Sergio Escalera"
6c304f3b9c3a711a0cca5c62ce221fb098dccff0,Attentive Semantic Video Generation Using Captions,"Attentive Semantic Video Generation using Captions
Tanya Marwah∗
IIT Hyderabad
Gaurav Mittal∗
Vineeth N. Balasubramanian
IIT Hyderabad"
6c0c7b90e0c7badaa95924530cb50d86444010ff,Semantic Attributes for Transfer Learning in Visual Recognition,"Semantic Attributes for Transfer
Learning in Visual Recognition
zur Erlangung des akademischen Grades eines
Doktors der Ingenieurwissenschaften
der KIT-Fakultät für Informatik
des Karlsruher Instituts für Technologie (KIT)
genehmigte
Dissertation
Ziad Al Halah
Tag der mündlichen Prüfung:
5. Februar 2018
Hauptreferent:
Korreferent:
Prof. Dr. Rainer Stiefelhagen
Karlsruher Institut für Technologie
Prof. Dr. Christoph Lampert
Institute of Science and Technology Austria
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
www.kit.edu"
6cadbc0122376be3c249ecfec7de8247ffbc4fb3,Bidirectional Label Propagation over Graphs,"Int J Software Informatics, Volume 7, Issue 3 (2013), pp.419–433
International Journal of Software and Informatics, ISSN 1673-7288
(cid:176)2013 by ISCAS. All rights reserved.
Tel: +86-10-62661040
http://www.ijsi.org
Email:
Bidirectional Label Propagation over Graphs
Wei Liu1 and Tongtao Zhang2
(IBM T. J. Watson Research Center, Yorktown Heights, NY, USA)
(Columbia University, New York, NY, USA)"
6c27eccf8c4b22510395baf9f0d0acc3ee547862,Using CMU PIE Human Face Database to a Convolutional Neural Network - Neocognitron.,"Using CMU PIE Human Face Database to a
Convolutional Neural Network - Neocognitron
José Hiroki Saito1, Tiago Vieira de Carvalho1, Marcelo Hirakuri1, André Saunite1,
Alessandro Noriaki Ide2 and Sandra Abib1
- Federal University of São Carlos - Computer Science Department - GAPIS
Rodovia Washington Luis, Km 235, São Carlos – SP - Brazil
- University of Genoa - Department of Informatics, Systems and Telematics - Neurolab
Via Opera Pia, 13 – I-16145 – Genoa - Italy"
6c653fcc80c83c87f710588bec039da3c7bfd219,Designing a smart-card-based face verification system: empirical investigation,"DOI 10.1007/s00138-007-0119-6
ORIGINAL PAPER
Designing a smart-card-based face verification system:
empirical investigation
Thirimachos Bourlai · Josef Kittler · Kieron Messer
Received: 17 July 2006 / Accepted: 29 October 2007 / Published online: 2 July 2008
© Springer-Verlag 2008"
6c52c12644321d4256306feaf784ccae6ebc4fea,Enhanced vote count circuit based on nor flash memory for fast similarity search,"978-1-4799-9988-0/16/$31.00 ©2016 IEEE
ICASSP 2016"
050e7e32fdc48150f66cb5edf166790c69652b8b,Land Cover Segmentation of Airborne LiDAR Data Using Stochastic Atrous Network,"Article
Land Cover Segmentation of Airborne LiDAR Data
Using Stochastic Atrous Network
Hasan Asy’ari Arief 1,* ID , Geir-Harald Strand 1,2 ID , Håvard Tveite 1 ID and Ulf Geir Indahl 1
Faculty of Science and Technology, Norwegian University of Life Sciences, 1432 Ås, Norway;
(G.H.S.); (H.T.); (U.G.I.)
Division of Survey and Statistics, Norwegian Institute of Bioeconomy Research, 1431 Ås, Norway
* Correspondence: Tel.: +47-453-91-706
Received: 30 April 2018; Accepted: 17 June 2018; Published: 19 June 2018"
05db46c7745c360fa5938ee204c81efdcc84c1da,An Empirical Evaluation of Current Convolutional Architectures’ Ability to Manage Nuisance Location and Scale Variability,"An Empirical Evaluation of Current Convolutional Architectures’ Ability to
Manage Nuisance Location and Scale Variability
Nikolaos Karianakis
Jingming Dong
Stefano Soatto
UCLA Vision Lab, University of California, Los Angeles, CA 90095"
054738ce39920975b8dcc97e01b3b6cc0d0bdf32,Towards the design of an end-to-end automated system for image and video-based recognition,"Towards the Design of an End-to-End Automated
System for Image and Video-based Recognition
Rama Chellappa1, Jun-Cheng Chen3, Rajeev Ranjan1, Swami Sankaranarayanan1, Amit Kumar1,
Vishal M. Patel2 and Carlos D. Castillo4"
05e3167206bc440d5aacf2256fd2e2e421b0808c,PEOPLE DETECTION AND RE-IDENTIFICATION FOR MULTI SURVEILLANCE CAMERAS,"People detection and re-identification for multi surveillance cameras
Etienne Corvee, Slawomir Bak and Francois Bremond
INRIA, Sophia Antipolis, Pulsar Team
{etienne.corvee, slawomir.bak,
Keywords:
people detection, people tracking, people re-identification, local binary pattern, mean Riemannian covariance"
054953d915f65b66485b653cd2ffbf61568b2849,Face Description with Local Invariant Features: Application to Face Recognition,"Face Description with Local Invariant Features: Application to Face Recognition
{tag}                                  {/tag}
International Journal of Computer Applications
© 2010 by IJCA Journal
Number 24 - Article 12
Year of Publication: 2010
Authors:
Sanjay A. Pardeshi
Dr. S.N. Talbar
10.5120/555-726"
0580edbd7865414c62a36da9504d1169dea78d6f,Baseline CNN structure analysis for facial expression recognition,"Baseline CNN structure analysis for facial expression recognition
Minchul Shin1, Munsang Kim2 and Dong-Soo Kwon1"
05ce0e4e9ae2c7b2320decb3bb29e066f1dd96d3,Patch-wise low-dimensional probabilistic linear discriminant analysis for Face Recognition,"PATCH-WISE LOW-DIMENSIONAL PROBABILISTIC LINEAR DISCRIMINANT ANALYSIS
FOR FACE RECOGNITION
Vitomir ˇStruc, Nikola Paveˇsi´c
Jerneja ˇZganec-Gros, Boˇstjan Vesnicer
Faculty of Electrical Engineering UL
Trˇzaˇska cesta 25, 1000 Ljubljana, Slovenia
Alpineon Ltd., Ulica Iga Grudna 15
000 Ljubljana, Slovenia"
051d8bbf12877c46ae9a598a386c5b72d1b103ac,Object Detection using Geometrical Context Feedback,"Int J Comput Vis (2012) 100:154–169
DOI 10.1007/s11263-012-0547-2
Object Detection using Geometrical Context Feedback
Min Sun · Sid Yingze Bao · Silvio Savarese
Received: 17 December 2010 / Accepted: 16 July 2012 / Published online: 2 August 2012
© Springer Science+Business Media, LLC 2012"
056ba488898a1a1b32daec7a45e0d550e0c51ae4,Cascaded Continuous Regression for Real-Time Incremental Face Tracking,"Cascaded Continuous Regression for Real-time
Incremental Face Tracking
Enrique S´anchez-Lozano, Brais Martinez,
Georgios Tzimiropoulos, and Michel Valstar
Computer Vision Laboratory. University of Nottingham"
05bba1f1626f02ef4ca497090b4a04d47f36ebb6,Social projection increases for positive targets: ascertaining the effect and exploring its antecedents.,"545039 PSPXXX10.1177/0146167214545039Personality and Social Psychology BulletinMachunsky et al.
research-article2014
Article
Social Projection Increases for
Positive Targets: Ascertaining the
Effect and Exploring Its Antecedents
Maya Machunsky1, Claudia Toma2, Vincent Yzerbyt3,
nd Olivier Corneille3
Personality and Social
Psychology Bulletin
014, Vol. 40(10) 1373 –1388
© 2014 by the Society for Personality
nd Social Psychology, Inc
Reprints and permissions:
sagepub.com/journalsPermissions.nav
DOI: 10.1177/0146167214545039
pspb.sagepub.com"
05b8673d810fadf888c62b7e6c7185355ffa4121,A Comprehensive Survey to Face Hallucination,"(will be inserted by the editor)
A Comprehensive Survey to Face Hallucination
Nannan Wang · Dacheng Tao · Xinbo Gao · Xuelong Li · Jie Li
Received: date / Accepted: date"
05c1fde513e23e2ec95bb9366665e716a2fcea7d,People detection in surveillance: classification and evaluation,"Repositorio Institucional de la Universidad Autónoma de Madrid
https://repositorio.uam.es
Esta es la versión de autor del artículo publicado en:
This is an author produced version of a paper published in:
DOI:    http://dx.doi.org/10.1049/iet-cvi.2014.0148
Copyright:  © 2015 Institution of Engineering and Technology
El acceso a la versión del editor puede requerir la suscripción del recurso
Access to the published version may require subscription"
052b35b3b3e8f1a8689a2e3e5c67ab08de094cfe,Clustering-weighted SIFT-based classification method via sparse representation,"Downloaded From: https://www.spiedigitallibrary.org/journals/Journal-of-Electronic-Imaging on 11/26/2018
Terms of Use: https://www.spiedigitallibrary.org/terms-of-use
Clustering-weightedSIFT-basedclassificationmethodviasparserepresentationBoSunFengXuJunHe"
05487784c1c94e17c26862e342c1b81acfe11258,Spontaneous facial expression analysis based on temperature changes and head motions,"Spontaneous Facial Expression Analysis
Based on Temperature
Changes and Head Motions
Peng Liu and Lijun Yin
State University of New York-at Binghamton"
05fd17673f1500d46196b0e38857eb3eaf09296e,Fourier Descriptors Based on the Structure of the Human Primary Visual Cortex with Applications to Object Recognition,"(will be inserted by the editor)
Fourier descriptors based on the structure of the human
primary visual cortex with applications to object recognition
Amine Bohi · Dario Prandi · Vincente Guis · Fr´ed´eric Bouchara ·
Jean-Paul Gauthier
Received: date / Accepted: date"
05384ac77be3211fb7d221802bc79eb3c9fa2873,A Novel Image Classification System Based on Evidence Probabilistic Transformation,"International Journal of  Research in Computer and
Communication Technology, Vol 4,Issue 2 ,February -2015
ISSN (Online) 2278- 5841
ISSN (Print)    2320- 5156
A Novel Image Classification System Based on Evidence
Probabilistic Transformation
Department of Computer Science, Mansoura University, Mansoura 35516, Egypt
A.E. Amin
information
different
identity
paper
evidence"
051a84f0e39126c1ebeeb379a405816d5d06604d,Biometric Recognition Performing in a Bioinspired System,"Cogn Comput (2009) 1:257–267
DOI 10.1007/s12559-009-9018-7
Biometric Recognition Performing in a Bioinspired System
Joan Fa`bregas Æ Marcos Faundez-Zanuy
Published online: 20 May 2009
Ó Springer Science+Business Media, LLC 2009"
058f053f0c0d8359b236e8fb17d541226d74a70d,Probabilistic Semi-Supervised Multi-Modal Hashing,"STUDENT, PROF, COLLABORATOR: BMVC AUTHOR GUIDELINES
Probabilistic Semi-Supervised Multi-Modal
Hashing
Behnam Gholami
Abolfazl Hajisami
Computer Science Department
Rutgers, The state university of New
Jersey,
New Brunswick, NJ, USA
Department of Electrical and Computer
Engineering
Rutgers, The state university of New
Jersey,
New Brunswick, NJ, USA"
05bd3ebba35a683e5035a3920352bc7aec85bd78,License Plate Detection on Autonomous Surveillance Systems,"Journal of Computational Information Systems 6:14 (2010) 4941-4949
Available at http://www.Jofcis.com
License Plate Detection on Autonomous Surveillance Systems
YaoHong TSAI†, KungChun KAO
Department of Information Management, Hsuan Chung University, Hsinchu City, Taiwan"
05a7be10fa9af8fb33ae2b5b72d108415519a698,Multilayer and Multimodal Fusion of Deep Neural Networks for Video Classification,"Multilayer and Multimodal Fusion of Deep Neural Networks
for Video Classification
Xiaodong Yang Pavlo Molchanov Jan Kautz
{xiaodongy, pmolchanov,
NVIDIA"
056be8a896f71be4a1dee67b01f4d59e3e982304,Generative Models of Visually Grounded Imagination,"Published as a conference paper at ICLR 2018
GENERATIVE MODELS OF VISUALLY GROUNDED
IMAGINATION
Ramakrishna Vedantam∗
Georgia Tech
Ian Fischer
Google Inc.
Jonathan Huang
Google Inc.
Kevin Murphy
Google Inc."
05ce73c39368aca1d10ab48dbe0dee80ee084bdb,M ULTI-LABEL L EARNING WITH THE RNN S FOR F ASHION S EARCH,"Under review as a conference paper at ICLR 2017
MULTI-LABEL LEARNING WITH THE RNNS
FOR FASHION SEARCH
Se-Yeoung Kim, Sang-Il Na, Ha-Yoon Kim, Moon-Ki Kim, Byoung-Ki Jeon
Machine Intelligence Lab., SK Planet
Seongnam City, South Korea
Taewan Kim ∗
Naver Labs, Naver Corp.
Seongnam City, South Korea"
05a2547d976420f7d1de19907e16280d15199008,Semantic Road Layout Understanding by Generative Adversarial Inpainting,"Road layout understanding by generative
dversarial inpainting
Lorenzo Berlincioni, Federico Becattini, Leonardo Galteri, Lorenzo Seidenari,
Alberto Del Bimbo"
056e2c82db905b93f7762a2ee7778d3aacc5a1f0,Bag of Attributes for Video Event Retrieval,"Bag of Attributes for Video Event Retrieval
Leonardo A. Duarte1, Ot´avio A. B. Penatti2, and Jurandy Almeida1
Institute of Science and Technology
Federal University of S˜ao Paulo – UNIFESP
2247-014, S˜ao Jos´e dos Campos, SP – Brazil
Email: {leonardo.assuane,
Advanced Technologies
SAMSUNG Research Institute
3097-160, Campinas, SP – Brazil
Email:"
05e03c48f32bd89c8a15ba82891f40f1cfdc7562,Scalable Robust Principal Component Analysis Using Grassmann Averages,"Scalable Robust Principal Component
Analysis using Grassmann Averages
Søren Hauberg, Aasa Feragen, Raffi Enficiaud, and Michael J. Black"
05ad478ca69b935c1bba755ac1a2a90be6679129,Attribute Dominance: What Pops Out?,"Attribute Dominance: What Pops Out?
Naman Turakhia
Georgia Tech"
050a149051a5d268fcc5539e8b654c2240070c82,Magisterské a doktorské studijnı́ programy,MAGISTERSKÉ A DOKTORSKÉSTUDIJNÍ PROGRAMY31. 5. 2018SBORNÍKSTUDENTSKÁ VĚDECKÁ KONFERENCE
052880031be0a760a5b606b2ad3d22f237e8af70,Datasets on object manipulation and interaction: a survey,"Datasets on object manipulation and interaction: a survey
Yongqiang Huang and Yu Sun"
05a22ebec697cfa5e8e2883d68e6f4762bbdebd7,Few-Example Object Detection with Model Communication.,"Few-Example Object Detection
with Model Communication
Xuanyi Dong, Liang Zheng, Fan Ma, Yi Yang, Deyu Meng"
056892b7e573608e64c3c9130e8ce33353a94de2,Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform,"Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs
nd a Discriminatively Trained Domain Transform
Liang-Chieh Chen∗
Jonathan T. Barron, George Papandreou, Kevin Murphy
{barron, gpapan,
Alan L. Yuille"
051830b0ea58d1568f19ec3297e301d9789c9a76,Bringing Semantics into Focus Using Visual Abstraction,
05a35959c6822155bee927305e799613ce99cc2f,New valve and bonding designs for microfluidic biochips containing proteins.,"Research - Articles Digest
Research     Volume 5 Issue 9 - September 26, 2008
[ http://research.ncku.edu.tw/re/articles/e/20080926/1.html ]
New Valve and Bonding Designs for Microfluidic
Biochips Containing Proteins
Yi-Je Juang
Assistant Professor of Department of Chemical Engineering, College of Engineering, National Cheng
Kung University
Polymer-based microfluidic biochips have great potential to serve as low-
ost and easy-to-fabricate analytical tools in clinical applications. One of the
ritical components on microfluidic biochips is valving for fluid manipulation.
There are both active and passive valve designs. The examples of the active
valves include thermally actuated gels, magnetically actuated ferrofluid or
monoliths, pneumatically controlled membranes, and controlled surface
wetting.  Most of the active valves require a mobile part or external stimuli,
which limits their applications in microfluidics when the devices become
smaller and need to be low cost.  The passive valves provide a more affordable
lternative to control the liquid flow because no moving parts are required.
They include capillary valves, polymer check valves, elastomer valves, and hydrophobic valves. Among them, the
apillary valve is the most popular due to its simplicity and easy implementation into the microfluidic design and"
0559fb9f5e8627fecc026c8ee6f7ad30e54ee929,4 Facial Expression Recognition,"Facial Expression Recognition
Bogdan J. Matuszewski, Wei Quan and Lik-Kwan Shark
ADSIP Research Centre, University of Central Lancashire
. Introduction
Facial  expressions  are  visible  signs  of  a  person’s  affective  state,  cognitive  activity  and
personality.  Humans  can  perform  expression  recognition  with  a  remarkable  robustness
without  conscious  effort  even  under  a  variety  of  adverse  conditions  such  as  partially
occluded faces, different appearances and poor illumination. Over the last two decades, the
dvances in imaging technology and ever increasing computing power have opened up a
possibility of automatic facial expression recognition and this has led to significant research
efforts from the computer vision and pattern recognition communities. One reason for this
growing interest is due to a wide spectrum of possible applications in diverse areas, such as
more engaging human-computer interaction (HCI) systems, video conferencing, augmented
reality.  Additionally  from  the  biometric  perspective,  automatic  recognition  of  facial
expressions has been investigated in the context of monitoring patients in the intensive care
nd neonatal units for signs of pain and anxiety, behavioural research, identifying level of
oncentration, and improving face recognition.
Automatic  facial  expression  recognition  is  a  difficult  task  due  to  its  inherent  subjective
nature,  which  is  additionally  hampered  by  usual  difficulties  encountered  in  pattern
recognition and computer vision research. The vast majority of the current state-of-the-art"
0534304bc09e92b2cfa0a8da59cfcf0be84d70a4,Towards reliable real-time person detection,"Towards Reliable Real-Time Person Detection
Silviu-Tudor SERBAN1, Srinidhi MUKANAHALLIPATNA SIMHA1, Vasanth
BATHRINARAYANAN1, Etienne CORVEE1 and Francois BREMOND1
INRIA Sophia Antipolis - Mediterranee, 2004 route des Lucioles, Sophia Antipolis, France
{silviu-tudor.serban,srinidhi.mukanahallipatna
Keywords:
Random sampling, Adaboost, Soft cascade, LBP channel features"
05ef5efd9e42f49dbb9e50ec3fe367f275a94931,Biologically Inspired Processing for Lighting Robust Face Recognition,"We are IntechOpen,
the world’s leading publisher of
Open Access books
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in Web of Science™ Core Collection (BKCI)
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051aa14e0b7dd4231636db39398c0c15b2687682,Robust Subspace Clustering via Thresholding,"Robust Subspace Clustering via Thresholding
Reinhard Heckel and Helmut B¨olcskei
Dept. of IT & EE, ETH Zurich, Switzerland
July 2013; last revised August 2015"
050fdbd2e1aa8b1a09ed42b2e5cc24d4fe8c7371,Spatio-Temporal Scale Selection in Video Data,"Contents
Scale Space and PDE Methods
Spatio-Temporal Scale Selection in Video Data . . . . . . . . . . . . . . . . . . . . .
Tony Lindeberg
Dynamic Texture Recognition Using Time-Causal Spatio-Temporal
Scale-Space Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ylva Jansson and Tony Lindeberg
Corner Detection Using the Affine Morphological Scale Space . . . . . . . . . . .
Luis Alvarez
Nonlinear Spectral Image Fusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Martin Benning, Michael Möller, Raz Z. Nossek, Martin Burger,
Daniel Cremers, Guy Gilboa, and Carola-Bibiane Schönlieb
Tubular Structure Segmentation Based on Heat Diffusion. . . . . . . . . . . . . . .
Fang Yang and Laurent D. Cohen
Analytic Existence and Uniqueness Results for PDE-Based Image
Reconstruction with the Laplacian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Laurent Hoeltgen, Isaac Harris, Michael Breuß, and Andreas Kleefeld
Combining Contrast Invariant L1 Data Fidelities with Nonlinear
Spectral Image Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Leonie Zeune, Stephan A. van Gils, Leon W.M.M. Terstappen,"
05e658fed4a1ce877199a4ce1a8f8cf6f449a890,Domain Transfer Learning for Object and Action Recognition,
05ea7930ae26165e7e51ff11b91c7aa8d7722002,Learning And-Or Model to Represent Context and Occlusion for Car Detection and Viewpoint Estimation,"Learning And-Or Model to Represent Context and
Occlusion for Car Detection and Viewpoint Estimation
Tianfu Wu∗, Bo Li∗ and Song-Chun Zhu"
05a2e6a5240b77246e26ba9875213c3157cb2fca,Zero-Shot Event Detection Using Multi-modal Fusion of Weakly Supervised Concepts,"Zero-shot Event Detection using Multi-modal Fusion of Weakly Supervised
Concepts
Shuang Wu†, Sravanthi Bondugula‡, Florian Luisier†, Xiaodan Zhuang† and Pradeep Natarajan†∗
Speech, Language and Multimedia
Raytheon BBN Technologies, Cambridge, MA"
05fcbe4009543ec8943bdc418ee81e9594b899a4,Social perception in autism spectrum disorders: impaired category selectivity for dynamic but not static images in ventral temporal cortex.,"doi:10.1093/cercor/bhs276
Social Perception in Autism Spectrum Disorders: Impaired Category Selectivity
for Dynamic but not Static Images in Ventral Temporal Cortex
Jill Weisberg1, Shawn C. Milleville1, Lauren Kenworthy1,2, Gregory L. Wallace1, Stephen J. Gotts1,
Michael S. Beauchamp3 and Alex Martin1
NIMH, Laboratory of Brain and Cognition, Bethesda, MD 20850, 2Children’s National Medical Center, Center for Autism
Spectrum Disorders, Rockville, MD 20850 and 3Department of Neurobiology and Anatomy, University of Texas Medical School
t Houston, Houston, TX 77030, USA
Address correspondence to Jill Weisberg, San Diego State University Research Foundation, Laboratory for Language and Cognitive Neuroscience,
6495 Alvarado Rd, Suite 200, San Diego, CA 92120, USA. Email:
Studies of autism spectrum disorders (ASDs) reveal dysfunction in
the neural systems mediating object processing (particularly faces)
nd social cognition, but few investigations have systematically as-
sessed the specificity of the dysfunction. We compared cortical
responses in typically developing adolescents and those with ASD
to stimuli from distinct conceptual domains known to elicit cat-
egory-related activity in separate neural systems. In Experiment 1,
subjects made category decisions to photographs, videos, and
point-light displays of people and tools. In Experiment 2, subjects
interpreted displays of simple, geometric shapes in motion depicting"
0582d338a5e5b325c282e2ff13bfd62cf4d08108,Affordance Research in Developmental Robotics: A Survey,"Affordance Research in Developmental
Robotics: A Survey
Huaqing Min, Chang’an Yi, Ronghua Luo, Jinhui Zhu, and Sheng Bi
apture"
edceeaa885f3eb29761580095059f8a34be8408b,Training data ... ... 0 1 1 0 1 0 0 1 Convolution and pooling layers fc layers Hashing layer Regularized center loss Bike Car Bird Dicycle Semantic embedding loss,"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
ModelSimilar?Figure1:Zero-shothashing.Thehashingmodeltrainedwithseenconceptsshouldgeneralizewellontheunseenconcepts.supervisedhashinglikeSupervisedDiscreteHashing[Shenetal.,2015a].Withthesupervisedinformationlikesemanticsimilaritymatrixorclasslabels,thesupervisedapproachesachievesuperiorretrievalperformancebecausetheintrinsicsemanticpropertyinthedataisbetterexplored.Recentlythedeepconvolutionalneuralnetwork(CN-N)hasachievedgreatsuccessinmanycomputervisiontasks,likeimageclassification[Heetal.,2016]andfacerecognition[Wenetal.,2016].InspiredbyCNN’spower-fulfeatureextractionability,someworkshaveattemptedtobuildhashingmodelsbasedonCNN[Laietal.,2015;Liuetal.,2016;Xiaetal.,2014]haveappeared.Theyre-quirethehashcodesproducedbythelastfullyconnectedlay-ertopreservethesimilaritygivenbythesupervisedinfor-mation.ItisdemonstratedthattheimageretrievalaccuracyissignificantlyimprovedbyCNN-basedhashingapproachescomparedwiththenon-CNNones[Liuetal.,2016].Itshouldbenoticedthattheexistinghashingapproachesmainlyfocusontheclose-setretrieval,i.e.,theconceptsofpossibletestingsamples(bothdatabasesamplesandquerysamples)arewithinthetrainingset.However,theexplosivegrowthofWebimagesviolatesthissettingbecausethenewconceptsabouttheimagesmayemergerapidly.Itisexpen-sivetoannotatesufficienttrainingdataforthenewconcept-stimely,andalso,impracticaltoretrainthehashingmodelwhereastheretrievalsystemmeetsanewconcept.Asillus-tratedinFigure1,theexistingapproachesperformwellontheseenconceptsbecausetheyaregivencorrectguidance,buttheymayeasilyfailontheunseenconceptsthattheynev-ermeetbeforesuchasthe“dicycle”whichisakindofvehicle"
ed2420d0fc7087d61633bd9a5b2907d1c2de1810,3 D Facial symmetry evaluation from high – density scanned data,
ed28e8367fcb7df7e51963add9e2d85b46e2d5d6,A NOVEL APPROACH OF FACE RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS WITH AUTO ENCODER,"International J. of Engg. Research & Indu. Appls. (IJERIA).
ISSN 0974-1518, Vol.9, No. III (December 2016), pp.23-42
A NOVEL APPROACH OF FACE RECOGNITION USING
CONVOLUTIONAL NEURAL  NETWORKS WITH AUTO
ENCODER
T. SYED AKHEEL1 AND DR. S. A. K JILANI2
Research Scholar, Dept. of Electronics & Communication Engineering,
Rayalaseema University Kurnool, Andhra Pradesh.
2 Research Supervisor, Professor, Dept. of Electronics & Communication Engineering,
Madanapalle Institute of Technology & Science, Madanapalle, Andhra Pradesh."
edbfbcebb14234b438d90d6dcd9b667e9071952d,Learning Fashion Compatibility with Bidirectional LSTMs,"A.B.C.D.?Task 1: Fill in the blankTask 2: Outfit generation given texts or imagesWhat to dress for a biz meeting?(a)(b)Task 3: Compatibility predictionScore: 0.7Figure1:Wefocusonthreetasksoffashionrecommenda-tion.Task1:recommendingafashionitemthatmatchesthestyleofanexistingset.Task2:generatinganoutfitbasedonusers’text/imageinputs.Task3:predictingthecompatibil-ityofanoutfit.conductedonautomaticfashionanalysisinthemultimediacom-munity.However,mostofthemfocusonclothingparsing[9,26],clothingrecognition[12],orclothingretrieval[10].Although,thereareafewworksthatinvestigatedfashionrecommendation[6,8,10],theyeitherfailtoconsiderthecompositionofitemstoformanout-fit[10]oronlysupportoneofthetworecommendationcategoriesdiscussedabove[6,8].Inaddition,itisdesirablethatrecommenda-tionscantakemultimodalinputsfromusers.Forexample,ausercanprovidekeywordslike“business”,oranimageofabusinessshirt,oracombinationofimagesandtext,togenerateacollec-tionoffashionitemsforabusinessoccasion.However,nopriorapproachsupportsmultimodalinputsforrecommendation.Keytofashionrecommendationismodelingthecompatibilityoffashionitems.Wecontendthatacompatibleoutfit(asshowninFigure3)shouldhavetwokeyproperties:(1)itemsintheout-fitshouldbevisuallycompatibleandsharesimilarstyle;(2)these"
ed6801362ab442097e7f753f163b9e9c0584b257,2 D TO 3 DCONVERSION BASED ON GLOBAL METHOD OF DEPTH LEARNING WITH INPUT IMAGE DENOISING,"International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882
Volume 4, Issue 5, May 2015
Learning Based 2D to 3D Conversion with Input Image Denoising
Divya K.P.1, Sneha K.2, Nafla C.N.3
(Department of CSE, RCET, Akkikkvu, Thrissur)
(Asst. Professor, Department of CSE, RCET, Akkikkvu, Thrissur)
(Department of CSE, RCET, Akkikkvu, Thrissur)"
ed07fa6df6a8fc27015d25717c9f730dc9eede84,of the 19 th Workshop on the Semantics and Pragmatics of Dialogue,"SEMDIAL 2015
goDIAL
Proceedings of the 19th Workshop on
the Semantics and Pragmatics of Dialogue
Christine Howes and Staffan Larsson (eds.)
Gothenburg, 24–26 August 2015"
ed5519a03f52e47047079da2e0c480eb8c4a9805,An Evaluation of Trajectory Prediction Approaches and Notes on the TrajNet Benchmark,"An Evaluation of Trajectory Prediction Approaches and
Notes on the TrajNet Benchmark.
Stefan Becker ∗, Ronny Hug ∗, Wolfgang H¨ubner and Michael Arens
Fraunhofer Institute for Optronics, System Technologies, and Image Exploitation IOSB
Gutleuthausstr. 1, 76275 Ettlingen, Germany"
ed90a9d379f6412a1580e7eda5cb91640000dc42,Highly Efficient 8-bit Low Precision Inference of Convolutional Neural Networks with IntelCaffe,"Highly Efficient 8-bit Low Precision Inference of
Convolutional Neural Networks with IntelCaffe
Jiong Gong, Haihao Shen, Guoming Zhang, Xiaoli Liu, Shane Li, Ge Jin, Niharika Maheshwari,
Evarist Fomenko, Eden Segal
{jiong.gong, haihao.shen, guoming.zhang, xiaoli.liu, li.shane, ge.jin, niharika.maheshwari, evarist.m.fomenko,
Intel Corporation"
ed9967868fcca2ec38402d2bb3e6946b8e554472,Efficient Eye Location for Biomedical Imaging using Two-level Classifier Scheme,"International Journal of Control, Automation, and Systems, vol. 6, no. 6, pp. 828-835, December 2008
Efficient Eye Location for Biomedical Imaging using Two-level Classifier
Scheme
Mi Young Nam, Xi Wang, and Phill Kyu Rhee*"
edcf668846a3aaf55120aef0c806854936208b3d,Human Recognition in RGBD Combining Object Detectors and Conditional Random Fields,
ed732b3a1f8fe733686a35688b090f426d018f9b,Dual-Process Theories in Social Cognitive Neuroscience,"This article was originally published in Brain Mapping: An Encyclopedic
Reference, published by Elsevier, and the attached copy is provided by
Elsevier for the author's benefit and for the benefit of the author's institution,
for non-commercial research and educational use including without limitation
use in instruction at your institution, sending it to specific colleagues who you
know, and providing a copy to your institution’s administrator.
All other uses, reproduction and distribution, including without limitation
ommercial reprints, selling or licensing copies or access, or posting on open
internet sites, your personal or institution’s website or repository, are
prohibited. For exceptions, permission may be sought for such use through
Elsevier's permissions site at:
http://www.elsevier.com/locate/permissionusematerial
Spunt R.P. (2015) Dual-Process Theories in Social Cognitive Neuroscience. In:
Arthur W. Toga, editor. Brain Mapping: An Encyclopedic Reference, vol. 3, pp.
11-215. Academic Press: Elsevier."
edf074a5eb3a1f71cc710ccc42849dceb27e3531,Towards real-time unsupervised monocular depth estimation on CPU,"Towards real-time unsupervised monocular depth estimation on CPU
Matteo Poggi1, Filippo Aleotti2, Fabio Tosi1, Stefano Mattoccia1"
edc5c359ed0fc24a3e85628f57fde59cd9b26dd4,SEARCH SPACE OPTIMIZATION AND FALSE ALARM REJECTION FACE DETECTION FRAMEWORK,"Journal of Theoretical and Applied Information Technology
30th September 2015. Vol.79. No.3
© 2005 - 2015 JATIT & LLS. All rights reserved.
ISSN: 1992-8645                                                       www.jatit.org                                                          E-ISSN: 1817-3195
SEARCH SPACE OPTIMIZATION AND FALSE ALARM
REJECTION FACE DETECTION FRAMEWORK
ALI SHARIFARA, 2MOHD SHAFRY MOHD RAHIM, 3 HAMED SAYYADI,
FARHAD NAVABIFAR
,2, Department of Computer Graphics and Multimedia, Faculty of Computing University Technology
Malaysia (UTM).81310 Skudai Johor, Malaysia.
Department of Computer Systems and Communications, Faculty of Computing University Technology
Malaysia (UTM), 81310 Skudai Johor, Malaysia.
Department of Computer Engineering Mobarakeh Branch-Islamic Azad University, Mobarakeh, Esfahan,
E-mail:
Iran."
ed6a47f0e2e621d8420082ba1d0078189d76352f,3 D FACIAL EXPRESSION INTENSITY MEASUREMENT ANALYSIS,"Proceedings of the 6th International Conference on Computing and Informatics, ICOCI 2017
5-27April, 2017 Kuala Lumpur. Universiti Utara Malaysia (http://www.uum.edu.my )
Paper No.
How to cite this paper:
Alicia Cheong Chiek Ying, Hamimah Ujir, & Irwandi Hipiny. (2017). 3D facial expression intensity measurement
nalysis in Zulikha, J. & N. H. Zakaria (Eds.), Proceedings of the 6th International Conference of Computing &
Informatics (pp 43-48). Sintok: School of Computing.
D FACIAL EXPRESSION INTENSITY MEASUREMENT
ANALYSIS
Alicia Cheong Chiek Ying1, Hamimah Ujir2and Irwandi Hipiny3
Sarawak Information Systems Sdn. Bhd. (SAINS),
Universiti Malaysia Sarawak,
Universiti Malaysia Sarawak,"
ed6003db58b67f1dfac654868b437efcef6e2ccb,Restricted Isometry Property of Gaussian Random Projection for Finite Set of Subspaces,"Restricted Isometry Property of Gaussian Random Projection
for Finite Set of Subspaces
Gen Li and Yuantao Gu∗
submitted April 7, 2017, revised August 11, 2017, accepted November 8, 2017"
ed62a56b81511d7fcf6d247014987163d9668982,"""What happens if..."" Learning to Predict the Effect of Forces in Images","“What happens if...”
Learning to Predict the Effect of Forces in Images
Roozbeh Mottaghi1, Mohammad Rastegari1, Abhinav Gupta1,2, Ali Farhadi1,3
Allen Institute for AI, 2Carnegie Mellon University, 3University of Washington"
ed04e161c953d345bcf5b910991d7566f7c486f7,Mirror my emotions ! Combining facial expression analysis and synthesis on a robot,"Combining facial expression analysis and synthesis on a
Mirror my emotions!
robot
Stefan Sosnowski1 and Christoph Mayer2 and Kolja K¨uhnlenz3 and Bernd Radig4"
ed3c4d2d28faaccbaef876a7daaecc3cccadb48f,3D Human Pose Estimation from a Single Image via Distance Matrix Regression,"D Human Pose Estimation from a Single Image via Distance Matrix Regression
Institut de Rob`otica i Inform`atica Industrial (CSIC-UPC), 08028, Barcelona, Spain
Francesc Moreno-Noguer"
edef98d2b021464576d8d28690d29f5431fd5828,Pixel-Level Alignment of Facial Images for High Accuracy Recognition Using Ensemble of Patches,"Pixel-Level Alignment of Facial Images
for High Accuracy Recognition
Using Ensemble of Patches
Hoda Mohammadzade, Amirhossein Sayyafan, Benyamin Ghojogh"
ed38d22cd5558d1abb40b477027d52ff7b6d09db,SIMULTANEOUS MULTIVIEW FACE TRACKING AND RECOGNITION IN VIDEO USING PARTICLE FILTERING by Naotoshi Seo,
ed510cce08500d8b3df23325ce122c76f3ddc11e,Dual Principal Component Pursuit: Improved Analysis and Efficient Algorithms,"Dual Principal Component Pursuit:
Improved Analysis and Efficient Algorithms
Zhihui Zhu
MINDS
Johns Hopkins University
Yifan Wang
ShanghaiTech University
Daniel Robinson
Johns Hopkins University
Daniel Naiman
Johns Hopkins University
Rene Vidal
MINDS
Johns Hopkins University
ShanghaiTech University
Manolis C. Tsakiris"
ed02b45d05e58803596891d660837c21be70a0af,Entity type modeling for multi-document summarization : generating descriptive summaries of geo-located entities,"Entity Type Modeling for Multi-Document
Summarization: Generating Descriptive Summaries of
Geo-Located Entities
Ahmet Aker
A thesis submitted in fulfilment of requirements for the degree of
Doctor of Philosophy
Department of Computer Science
University of Sheffield
November 2013"
eda20a2f33d0f6db44a2e7d060efad3caa6621e0,"Classification with Global, Local and Shared Features","Classification with Global, Local and Shared
Features
Hakan Bilen1, Vinay P. Namboodiri2, Luc J. Van Gool1,3
ESAT-PSI/IBBT,VISICS/KU Leuven, Belgium
Alcatel-Lucent Bell Labs, Antwerp, Belgium
Computer Vision Laboratory, BIWI/ETH Z¨urich, Switzerland"
eddb1a126eafecad2cead01c6c3bb4b88120d78a,Applications of a Graph Theoretic Based Clustering Framework in Computer Vision and Pattern Recognition,"DEPARTMENT DESIGN AND PLANNING IN COMPLEX ENVIRONMENTS
DOTTORATO DI RICERCA IN NUOVE TECNOLOGIE, INFORMAZIONE TERRITORIO E
UNIVERSIT‘A IUAV DI VENEZIA
AMBIENTE, XXX CICLO
APPLICATIONS OF A GRAPH THEORETIC BASED
CLUSTERING FRAMEWORK IN COMPUTER VISION AND
PATTERN RECOGNITION
Doctoral Dissertation of:
Yonatan Tariku Tesfaye
Supervisor:
Prof. Andrea Prati
The Chair of the Doctoral Program:
Prof. Fabio Peron"
de0eb358b890d92e8f67592c6e23f0e3b2ba3f66,Inference-Based Similarity Search in Randomized Montgomery Domains for Privacy-Preserving Biometric Identification,"ACCEPTED BY IEEE TRANS. PATTERN ANAL. AND MACH. INTELL.
Inference-Based Similarity Search in
Randomized Montgomery Domains for
Privacy-Preserving Biometric Identification
Yi Wang, Jianwu Wan, Jun Guo, Yiu-Ming Cheung, and Pong C Yuen"
de7daa206f1dc3d5f83c5342fc08e3e92ddfa126,Index Codes for Multibiometric Pattern Retrieval,"Index Codes for Multibiometric Pattern Retrieval
Aglika Gyaourova, Student Member, IEEE, and Arun Ross, Senior Member, IEEE"
de7a148970881cbd4e6a12b6a014e3dfeee98cc9,D4h: Final Report on Wp4,"D4h: Final report on WP4
Workpackage 4 Deliverable
Date: 30th January 2008"
de309a1d10f819d69a4ef2c26d968d3b287c3dd5,Preprocessing and Feature Sets for Robust Face Recognition,"Preprocessing and Feature Sets for Robust Face Recognition
Xiaoyang Tan and Bill Triggs
LJK-INRIA, 655 avenue de l’Europe, Montbonnot 38330, France"
de595159fa9f8acbf99e4c39b935cf11cd64fde1,Object Detection with Bootstrapped Learning ∗,"Object Detection with Bootstrapped Learning ∗
Peter M. Roth* , Horst Bischof*, Danijel Skoˇcaj** and Aleˇs Leonardis**
* Graz University of Technology, Institute for Computer Graphics and Vision
Inffeldgasse 16/II, 8010 Graz, Austria
** University of Ljubljana, Faculty of Computer and Information Science
Trˇzaˇska 25, SI-1001 Slovenia
e-mail: {pmroth,
{danijel.skocaj,"
de287480b9ab8dfab1c49e24e5517b0a3c55203b,A Tempt to Unify Heterogeneous Driving Databases using Traffic Primitives,"A Tempt to Unify Heterogeneous Driving Databases
using Traffic Primitives
Jiacheng Zhu1,Wenshuo Wang1, Ding Zhao1,∗"
defa8774d3c6ad46d4db4959d8510b44751361d8,FEBEI-Face Expression Based Emoticon Identification CSB 657 Computer Vision,"FEBEI - Face Expression Based Emoticon Identification
CS - B657 Computer Vision
Nethra Chandrasekaran Sashikar - necsashi
Prashanth Kumar Murali - prmurali
Robert J Henderson - rojahend"
de55bfc96b027b3e2bd44baed656a3f563ff6e71,"Deep Feature Consistent Deep Image Transformations: Downscaling, Decolorization and HDR Tone Mapping","Deep Feature Consistent Deep Image
Transformations: Downscaling, Decolorization and
HDR Tone Mapping
Xianxu Hou, Jiang Duan, and Guoping Qiu"
def3b2254caea169c5cbc4b771c44f1773c004fd,Matching Adversarial Networks,"Matching Adversarial Networks
Gell´ert M´attyus and Raquel Urtasun
Uber Advanced Technologies Group and University of Toronto"
defcfed9c43bdf8a4388daade4899ef9d3345458,Sistema de reconocimiento multimodal de emociones relacionadas al aprendizaje en dispositivos móviles,"Sistema de reconocimiento multimodal de emociones
relacionadas al aprendizaje en dispositivos móviles
María Lucía Barrón-Estrada, Ramón Zatarain-Cabada,
Claudia Guadalupe Aispuro-Gallegos
Instituto Tecnológico de Culiacán, Culiacán, Sinaloa,
México
{lbarron, rzatarain,
Resumen.  Gran  variedad  de  sistemas  reconocedores  de  emociones  han  sido
implementados,  pero  pocos  han  logrado  aplicarse  en  el  mundo  real  debido  al
elevado costo de la tecnología necesaria y al bajo porcentaje de efectividad del
reconocimiento, cuando no se trabaja con emociones espontáneas. Este artículo
presenta  la  implementación  de  un  sistema  de  reconocimiento  multimodal  de
emociones  usando  dispositivos  móviles  y  la  creación  de  una  base  de  datos
fectiva por medio de una aplicación móvil. El reconocedor puede ser integrado
fácilmente a una aplicación educativa móvil para identificar las emociones de un
usuario mientras éste interactúa con el dispositivo. Las emociones que el sistema
reconoce son compromiso y aburrimiento. La base de datos afectiva fue creada
on emociones espontáneas de estudiantes que interactuaron con una aplicación
móvil  educativa  llamada  Duolingo  y  una  aplicación  móvil  recolectora  de
información  llamada  EmoData.  El  sistema  desarrollado  tiene  un  porcentaje  de"
de1505819e145b5c22a6e09002510413019f7228,DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment,"DeepFood: Deep Learning-based Food Image
Recognition for Computer-aided Dietary Assessment
Chang Liu1, Yu Cao1, Yan Luo1, Guanling Chen1, Vinod Vokkarane1, Yunsheng
The University of Massachusetts Lowell, One University Ave, Lowell, MA, 01854, USA
The University of Massachusetts Medical School, 419 Belmont Street, Worcester, MA,
01605, USA"
de0aaf8c6b5dea97327e8ef8060d9a708bf564af,A Benchmark for Iris Location and a Deep Learning Detector Evaluation,"A Benchmark for Iris Location and a Deep
Learning Detector Evaluation
Evair Severo∗, Rayson Laroca∗, Cides S. Bezerra∗, Luiz A. Zanlorensi∗,
Daniel Weingaertner∗, Gladston Moreira† and David Menotti∗
Postgraduate Program in Informatics, Federal University of Paran´a (UFPR), Curitiba, Paran´a, Brazil
Computing Department, Federal University of Ouro Preto (UFOP), Ouro Preto, Minas Gerais, Brazil
Email: {ebsevero, rblsantos, csbezerra, lazjunior, daniel,"
dee406a7aaa0f4c9d64b7550e633d81bc66ff451,Content-Adaptive Sketch Portrait Generation by Decompositional Representation Learning,"Content-Adaptive Sketch Portrait Generation by
Decompositional Representation Learning
Dongyu Zhang, Liang Lin, Tianshui Chen, Xian Wu, Wenwei Tan, and Ebroul Izquierdo"
de2aaabc3fcc21042a64dc266f560dad91028b79,Energy-Aware real-time face recognition system on mobile CPU-GPU platform,"Energy-Aware Real-time Face Recognition
System on Mobile CPU-GPU Platform
Yi-Chu Wang, Bryan Donyanavard, Kwang-Ting (Tim) Cheng
Dept. of Electrical and Computer Engineering
University of California, Santa Barbara, CA 93106, USA"
debbf2f0f9ae128e40e962bef5096adacd1cb264,Who Are You?,"Who Are You?
Modesto Castrill´on-Santana
IUSIANI
Univ. de Las Palmas de G.C.
5017 Spain
Enrico Grosso
DEIR - Univ. of Sassari,
Via Sardegna, 58
07100 Sassari, Italy
Oscar D´eniz-Su´arez
IUSIANI
Univ. de Las Palmas de G. C.
5017 Spain"
de7bbb79384f81d0b3d28aa6531273ce240553c3,Using 3 D Graphics to Train Object Detection Systems,"Using 3D Graphics to Train Object
Detection Systems
Stian Jensen
Andreas Løve Selvik
Master of Science in Computer Science
Submission date: July 2016
Supervisor:
Donn Morrison, IDI
Norwegian University of Science and Technology
Department of Computer and Information Science"
ded968b97bd59465d5ccda4f1e441f24bac7ede5,Large scale 3 D Morphable Models,"Noname manuscript No.
(will be inserted by the editor)
Large scale 3D Morphable Models
James Booth · Anastasios Roussos · Allan Ponniah · David Dunaway · Stefanos
Zafeiriou
Received: date / Accepted: date"
de87a5d5fbae0733806ba965b2d70fd04596f6e9,Optimization-Based Motion Planning and Model Predictive Control for Autonomous Driving,"Predictive control for autonomous driving
with experimental evaluation on a heavy-duty construction truck
PEDRO F. LIMA
Licenciate Thesis
Stockholm, Sweden 2016"
dea749f087a8c9a9baa9167b4eaff50bd3eb9d16,Physically Grounded Spatio-temporal Object Affordances,"Physically Grounded Spatio-Temporal
Object Affordances
Hema S. Koppula and Ashutosh Saxena
Department of Computer Science, Cornell University."
de99971e61613f174c9e5aa41a2c600399f59953,Pixel-wise Attentional Gating for Scene Parsing,"Pixel-wise Attentional Gating for Scene Parsing
Department of Computer Science, University of California, Irvine, CA 92697, USA
Shu Kong, Charless Fowlkes
{skong2,"
de6ab8cd9d402c976082b707b1207c3ad49ae204,End-to-end Image Captioning Exploits Distributional Similarity in Multimodal Space,"MADHYASTHA ET AL.: IMAGE CAPTIONING EXPLOITS DISTRIBUTIONAL SIMILARITY 1
End-to-end Image Captioning Exploits
Multimodal Distributional Similarity
Pranava Madhyastha
Josiah Wang
Lucia Specia
Department of Computer Science
The University of Sheffield
Sheffield, UK"
dedbbb6e588e77969ab87571917d4f84a3b1722d,Multimodal Human-Human-Robot Interactions ( MHHRI ) Dataset for Studying Personality and Engagement,"JOURNAL OF IEEE TRANS. ON AFFECTIVE COMPUTING
Multimodal Human-Human-Robot Interactions
(MHHRI) Dataset for Studying Personality and
Engagement
Oya Celiktutan, Efstratios Skordos and Hatice Gunes"
de724211683bb92931a5d80193e5dee31ca2e045,Sampling Design For Face Recognition,"Sampling Design For Face Recognition
Yanjun Yan and Lisa A. Osadciw
EECS, Syracuse University, Syracuse, NY, USA
{yayan,"
de2faaee4f1b2ecf23149995d0146347a13b9257,Robust Unsupervised Domain Adaptation for Neural Networks via Moment Alignment,"Robust Unsupervised Domain Adaptation for Neural
Networks via Moment Alignment
Werner Zellingera,∗, Bernhard A. Moserb, Thomas Grubingerb, Edwin
Lughofera, Thomas Natschl¨agerb, Susanne Saminger-Platza
Johannes Kepler University, Linz, Austria
Software Competence Center Hagenberg GmbH, Hagenberg, Austria"
de86a9f484addcfee57a6f5a9224aa77bd23345b,Face Recognition Using Elastic Bunch Graph Matching,"International Journal For Technological Research In Engineering
Volume 2, Issue 11, July-2015
ISSN (Online): 2347 - 4718
FACE RECOGNITION USING ELASTIC BUNCH GRAPH
MATCHING
Sandeep R1, D Jayakumar2
Dept. of ECE, Kuppam Engineering College, Chittoor, Andhra Pradesh."
de26c1560db47f63ef2dc8171d7c2c52369ffede,Mathematically inspired approaches to face recognition in uncontrolled conditions : super resolution and compressive sensing,"MATHEMATICALLY INSPIRED
APPROACHES TO
FACE RECOGNITION IN
UNCONTROLLED CONDITIONS -
SUPER RESOLUTION AND
COMPRESSIVE SENSING
NADIA AL-HASSAN
Applied Computing Department
The University of Buckingham / United Kingdom
A Thesis
Submitted for the Degree of Doctor of Philosophy in Mathematical
Science to the school of Science and Medicine in the University of
Buckingham
September 2014"
dedabf9afe2ae4a1ace1279150e5f1d495e565da,Robust Face Recognition With Structurally Incoherent Low-Rank Matrix Decomposition,"Robust Face Recognition With Structurally
Incoherent Low-Rank Matrix Decomposition
Chia-Po Wei, Chih-Fan Chen, and Yu-Chiang Frank Wang"
de741378150a3a841756973d262541018d0acbcd,Learning to Detect Objects with Minimal Supervision,"Learning to Detect Objects with
Minimal Supervision
Karim Ali
Computer Vision Laboratory
´Ecole Polytechnique F´ed´erale de Lausanne
A thesis submitted for the degree of
Ph.D.
December 2011"
6601a0906e503a6221d2e0f2ca8c3f544a4adab7,Detection of Ancient Settlement Mounds : Archaeological Survey Based on the SRTM Terrain Model,"SRTM-2  2/9/06  3:27 PM  Page 321
Detection of Ancient Settlement Mounds:
Archaeological Survey Based on the
SRTM Terrain Model
B.H. Menze, J.A. Ur, and A.G. Sherratt"
66f8115136a11684e3b95c5aaa1476a871d58a66,Face Recognition Using Multiple Image View Line Segments Title Face Recognition Using Multiple Image View Line Segments Primary Author(s) Date,"JAMESCOOKUNIVERSITY
FaceRecognitionusingMultiple
ImageViewLineSegments
StefanAeberhardandOlivierdeVel
TR	/

DEPARTMENTOFCOMPUTERSCIENCETOWNSVILLE
QUEENSLAND


AUSTRALIA"
669727b3258bb3edc38709147f348dc67e3fcac4,A Lightweight approach for biometric template protection,"A Lightweight approach for biometric template protection*
Hisham Al-Assam, Harin Sellahewa, & Sabah Jassim
University of Buckingham, Buckingham MK18 1EG, U.K.
{hisham.al-assam , harin.sellahewa,"
66d512342355fb77a4450decc89977efe7e55fa2,LEARNING NON-LINEAR TRANSFORM WITH DISCRIM- INATIVE AND MINIMUM INFORMATION LOSS PRIORS,"Under review as a conference paper at ICLR 2018
LEARNING NON-LINEAR TRANSFORM WITH DISCRIM-
INATIVE AND MINIMUM INFORMATION LOSS PRIORS
Anonymous authors
Paper under double-blind review"
66d087f3dd2e19ffe340c26ef17efe0062a59290,Dog Breed Identification,"Dog Breed Identification
Whitney LaRow
Brian Mittl
Vijay Singh"
66533107f9abdc7d1cb8f8795025fc7e78eb1122,Visual Servoing for a User's Mouth with Effective Intention Reading in a Wheelchair-based Robotic Arm,"Vi	a Sevig f a Ue 	h wih E(cid:11)ecive 
ei Readig
i a Wheechai	baed Rbic A

W	y	g Sgy Dae	i i
y g	S	g i
z ad Ze	ga
 Biey
y EECS A
ST 373	1 	g	Dg Y	g	G	 Taej 305	701 REA
z VR Cee ETR
 161 ajg	Dg Y	g	G	 Taej 305	350 REA
Abac
Thee exi he c	eaive aciviy bewee a h

a beig ad ehabiiai b beca	e he h

a eae ehabiiai b i he a
e evi

e ad ha he bee(cid:12) f ehabiiai b
	ch a 
ai	ay  
bie f	ci. 
ei
eadig i e f he eeia f	ci f h	
a
fiedy ehabiiai b i de  
ie he

f ad afey f a wh eed he
. Fi f
 he vea 	c	e f a ew wheechai	baed
bic a
 ye
 ARES 

 ad i h	
a	b
ieaci echgie ae eeed. A
g he
echgie we cceae  vi	a evig ha
w hi bic a
  eae a	
	y via
vi	a feedback. E(cid:11)ecive iei eadig 	ch a"
66c792b7e9946f8cb92fac185267d03371437451,Adaptive Affinity Fields for Semantic Segmentation,"Adaptive Affinity Fields for Semantic Segmentation
Tsung-Wei Ke*, Jyh-Jing Hwang*, Ziwei Liu, and Stella X. Yu
UC Berkeley / ICSI"
66a9935e958a779a3a2267c85ecb69fbbb75b8dc,Fast and Robust Fixed-Rank Matrix Recovery,"FAST AND ROBUST FIXED-RANK MATRIX RECOVERY
Fast and Robust Fixed-Rank Matrix
Recovery
German Ros*, Julio Guerrero, Angel Sappa, Daniel Ponsa and
Antonio Lopez"
66884ce29c44dc1dcf1c4a40180cd3baacceaabe,Face Recognition Algorithm Based on Doubly Truncated Gaussian Mixture Model Using Hierarchical Clustering Algorithm,"Face Recognition Algorithm Based on Doubly Truncated Gaussian
Mixture Model Using Hierarchical Clustering Algorithm
D. Haritha1, K. Srinivasa Rao2 and Ch.Satyanaraya3
1 Department of Computer Science and Engineering, University College of Engineering, JNTU,
Kakinada, Andhra Pradesh, India.
Department of Statistics, Andhra University, Visakhapatnam, Andhra Pradesh, India.
Department of Computer Science and Engineering, University College of Engineering, JNTU,
Kakinada, Andhra Pradesh, India."
6668ca5ab57d68070f90671a4f92a6bc25f80470,Measuring cues for stand-off deception detection based on full-body non-verbal features in body-worn cameras,"Measuring cues for stand-off deception detection based on full-body
non-verbal features in body-worn cameras
Henri Bouma 1, Gertjan Burghouts, Richard den Hollander, Sophie Van Der Zee, Jan Baan,
Johan-Martijn ten Hove, Sjaak van Diepen, Paul van den Haak, Jeroen van Rest
TNO, Oude Waalsdorperweg 63, 2597 AK The Hague, The Netherlands"
66a2c229ac82e38f1b7c77a786d8cf0d7e369598,A Probabilistic Adaptive Search System for Exploring the Face Space,"Proceedings of the 2016 Industrial and Systems Engineering Research Conference
H. Yang, Z. Kong, and MD Sarder, eds.
A Probabilistic Adaptive Search System
for Exploring the Face Space
Andres G. Abad and Luis I. Reyes Castro
Escuela Superior Politecnica del Litoral (ESPOL)
Guayaquil-Ecuador"
66c0fcf637bede76a6ea61b58655c5fc7e890630,IMPROVING THE GENERALIZATION OF NEURAL NETWORKS BY CHANGING THE STRUCTURE OF ARTIFICIAL NEURON,"Improving the Generalization of Neural Networks by Changing the Structure of Artificial Neuron. pp 195-204
IMPROVING THE GENERALIZATION OF NEURAL NETWORKS BY CHANGING THE STRUCTURE OF
ARTIFICIAL NEURON
Mohammad Reza Daliri1, Mehdi Fatan2
Biomedical Engineering Department and Iran Neural Technology Center,
Faculty of Electrical Engineering, Iran University of Science and Technology (IUST),
Narmak, 16846-13114 Tehran, Iran (Email:
Mechatronics Group, Faculty of Electrical Engineering,
Qazvin Islamic Azad University, Qazvin, Iran (Email:
Corresponding author: M.R. Daliri, Email:"
6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c,Ordinal Regression with Multiple Output CNN for Age Estimation,"Ordinal Regression with Multiple Output CNN for Age Estimation
Zhenxing Niu1
Gang Hua3
Xidian University 2Xi’an Jiaotong University 3Microsoft Research Asia
Xinbo Gao1
Mo Zhou1
Le Wang2"
66f9e488496fafe853f49f834b8041740ac82926,Moving towards in object recognition with deep learning for autonomous driving applications,"Moving Towards in Object Recognition with Deep
Learning for Autonomous Driving Applications
Ay(cid:250)egül Uçar, Yakup Demir
Department of Mechatronic Engineering, Elaz(cid:213)(cid:247), Turkey
Department of Electrical Electronics Engineering, Elaz(cid:213)(cid:247), Turkey"
66af2afd4c598c2841dbfd1053bf0c386579234e,Context-assisted face clustering framework with human-in-the-loop,"Noname manuscript No.
(will be inserted by the editor)
Context Assisted Face Clustering Framework with
Human-in-the-Loop
Liyan Zhang · Dmitri V. Kalashnikov ·
Sharad Mehrotra
Received: date / Accepted: date"
66c92c9145c2b6a304eb1b3a58e2a717884fe064,Emotions in Pervasive Computing Environments,"IJCSI International Journal of Computer Science Issues, Vol. 6, No. 1, 2009
ISSN (Online): 1694-0784
ISSN (Print): 1694-0814
Emotions in Pervasive Computing Environments
Nevin VUNKA JUNGUM1 and Éric LAURENT2
1 Computer Science and Engineering Department,
University of Mauritius
Réduit, Mauritius
Laboratoire de Psychologie, ENACT-MCA,
University of Franche-Comté
Besançon, France"
668e93e89835ec662d21cf695b7347339ce74c78,LIKELIHOOD RATIO FUSION WITHIN SCORES OF INDEPENDENT COMPONENT ANALYSIS FEATURES BASED FACE BIOMETRICS VERIFICATION SYSTEMS,"June. 2015. Vol. 6, No.3
ISSN 2305-1493
International Journal of Scientific Knowledge
Computing and Information Technology
© 2012 - 2015 IJSK & K.A.J. All rights reserved
www.ijsk.org/ijsk
LIKELIHOOD RATIO FUSION WITHIN SCORES OF
INDEPENDENT COMPONENT ANALYSIS FEATURES BASED
FACE BIOMETRICS VERIFICATION SYSTEMS
SOLTANE MOHAMED
Electrical Engineering & Computing Department, Faculty of Sciences & Technology,
DOCTOR YAHIA FARES UNIVERSITY OF MEDEA, 26000 MEDEA, ALGERIA
Laboratoire des Systèmes Électroniques Avancées (LSEA)"
661be86559295d3b2cbabcd31cc90848f601f55c,Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks,"Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks
The Chinese University of Hong Kong 2SenseTime Group Limited 3Nanyang Technological University
Yuenan Hou1, Zheng Ma2, Chunxiao Liu2, and Chen Change Loy3
{mazheng,"
666e08b6921a28fed75f35dd70d322f0edc06e41,Rain Removal in Traffic Surveillance: Does it Matter?,"IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Rain Removal in Traffic Surveillance:
Does it Matter?
Chris H. Bahnsen
nd Thomas B. Moeslund"
66e21b99ae3aeae79589d260e3daca3b04981a9c,MULTI-AGENT APPROACH TOWARDS FACE RECOGNITION,"http://www.ijccr.com
VOLUME 1 ISSUE 3 MANUSCRIPT 10 NOVEMBER 2011
MULTI-AGENT APPROACH TOWARDS FACE RECOGNITION"
66ee33bf0064eee159f3563e32b15c5bbd4140a0,Face Recognition Under Varying Viewing Conditions with Subspace Distance,"Face Recognition Under Varying Viewing Conditions with Subspace Distance
Jen-Mei Chang
Department of Mathematics and Statistics
California State University, Long Beach
250 Bellflower Blvd.
Long Beach, California 90840-1001"
66e2c3d23af8ed76b116121827b9bc5e99cf4acc,Video Prediction with Appearance and Motion Conditions,"Video Prediction with Appearance and Motion Conditions
Yunseok Jang 1 2 Gunhee Kim 2 Yale Song 3"
6624e30564908c233bfa45c3503ec8aff741515e,Nearest-Neighbors Based Weighted Method for the BOVW Applied to Image Classification,"J Electr Eng Technol.2015; 10(?): 709-718
http://dx.doi.org/10.5370/JEET.2015.10.?.709
ISSN(Print)    1975-0102
ISSN(Online)  2093-7423
Nearest-Neighbors Based Weighted Method for the BOVW Applied to
Image Classification
Mengxi Xu*,**, Quansen Sun†, Yingshu Lu*** and Chenming Shen**"
660c8a9fa166c1d81e65192e011eacfec208ec00,Discrimination of visual pedestrians data by combining projection and prediction learning,"Discrimination of visual pedestrians data by combining
projection and prediction learning
Mathieu Lefort, Alexander Gepperth
To cite this version:
Mathieu Lefort, Alexander Gepperth. Discrimination of visual pedestrians data by combining
projection and prediction learning. ESANN - European Symposium on Artificial Neural Net-
works, Computational Intelligence and Machine Learning, Apr 2014, Bruges, Belgium. 2014.
<hal-01061654>
HAL Id: hal-01061654
https://hal.inria.fr/hal-01061654
Submitted on 8 Sep 2014
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
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broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,"
6603e7de5b155c86407edc43099b46b974b7f0bb,Local Feature Based Face Recognition,"We are IntechOpen,
the world’s leading publisher of
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6643a7feebd0479916d94fb9186e403a4e5f7cbf,Chapter 8 3 D Face Recognition,"Chapter 8
D Face Recognition
Ajmal Mian and Nick Pears"
66886f5af67b22d14177119520bd9c9f39cdd2e6,Learning Additive Kernel For Feature Transformation and Its Application to CNN Features,"T. KOBAYASHI: LEARNING ADDITIVE KERNEL
Learning Additive Kernel For Feature
Transformation and Its Application to CNN
Features
Takumi Kobayashi
National Institute of Advanced Industrial
Science and Technology
Tsukuba, Japan"
66b37797286952e7735901e152b4cdea171e8567,Recovering 3D Planes from a Single Image via Convolutional Neural Networks,"Recovering 3D Planes from a Single Image via
Convolutional Neural Networks
Fengting Yang and Zihan Zhou
The Pennsylvania State University
{fuy34,"
66f55dc04aaf4eefdecef202211ad7563f7a703b,Synthesizing Programs for Images using Reinforced Adversarial Learning,"Synthesizing Programs for Images using Reinforced Adversarial Learning
Yaroslav Ganin 1 Tejas Kulkarni 2 Igor Babuschkin 2 S. M. Ali Eslami 2 Oriol Vinyals 2"
66660f5e8b2a4a695abe0f9e1df32d230126f773,Applying Deep Learning to Improve Maritime Situational Awareness,"Applying Deep Learning to Improve
Maritime Situational Awareness
Kathy Tang
Stottler Henke Associates, Inc.
650 S. Amphlett Blvd. Ste. 300
San Mateo, CA 94402
Intelligence"
66b955311ab6841c4644414d8ce2faf6ca721602,RoboCupRescue 2009-Robot League Team Darmstadt Rescue Robot Team ( Germany ),"RoboCupRescue 2009 - Robot League Team
Darmstadt Rescue Robot Team (Germany)
Micha Andriluka1, Martin Friedmann1, Stefan Kohlbrecher1, Johannes Meyer2,
Karen Petersen1, Christian Reinl1, Peter Schauß1, Paul Schnitzspan1, Armin
Strobel2, Dirk Thomas1, Anguelina Vatcheva1, Oskar von Stryk1(cid:63)
Department of Computer Science (1) and Department of Mechanical Engineering (2),
Technische Universit¨at Darmstadt,
Karolinenplatz 5, D-64289 Darmstadt, Germany
E-Mail:
Web: www.gkmm.tu-darmstadt.de/rescue"
665e6aa652b99350a08090faaf9d4bcc7800186e,Detection-Free Multiobject Tracking by Reconfigurable Inference With Bundle Representations,"Detection-Free Multiobject Tracking by
Reconfigurable Inference With
Bundle Representations
Liang Lin, Yongyi Lu, Chenglong Li, Hui Cheng, and Wangmeng Zuo, Senior Member, IEEE"
664ccdcc614a8ecfbfedadc7b42b9537fe43d3f1,Probabilistic integration of sparse audio-visual cues for identity tracking,"Probabilistic Integration of Sparse Audio-Visual Cues for
Identity Tracking
Keni Bernardin
Universität Karlsruhe, ITI
Am Fasanengarten 5
76131, Karlsruhe, Germany
Rainer Stiefelhagen
Universität Karlsruhe, ITI
Am Fasanengarten 5
76131, Karlsruhe, Germany
Alex Waibel
Universität Karlsruhe, ITI
Am Fasanengarten 5
76131, Karlsruhe, Germany"
66837b29270f3e03df64941a081d70c687c7955c,ActionXPose: A Novel 2D Multi-view Pose-based Algorithm for Real-time Human Action Recognition,"ActionXPose: A Novel 2D Multi-view Pose-based
Algorithm for Real-time Human Action Recognition
Federico Angelini, Student Member, IEEE, Zeyu Fu, Student Member, IEEE, Yang Long, Senior Member, IEEE,
Ling Shao, Senior Member, IEEE, and Syed Mohsen Naqvi, Senior Member, IEEE"
66e6f08873325d37e0ec20a4769ce881e04e964e,The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding,"Int J Comput Vis (2014) 108:59–81
DOI 10.1007/s11263-013-0695-z
The SUN Attribute Database: Beyond Categories for Deeper Scene
Understanding
Genevieve Patterson · Chen Xu · Hang Su ·
James Hays
Received: 27 February 2013 / Accepted: 28 December 2013 / Published online: 18 January 2014
© Springer Science+Business Media New York 2014"
660c6a47ea29de2b4f40ac942ba682954118722f,SUPER-RESOLUTION : LIMITS AND BEYOND,"Chapter 1
SUPER-RESOLUTION: LIMITS AND BEYOND
Simon Baker
nd Takeo Kanade"
66860100a3355f26ffcb9dcbf27e27e4757d641d,Feature Selection in Supervised Saliency Prediction,"Feature Selection in Supervised Saliency Prediction
Ming Liang, Student Member, IEEE, and Xiaolin Hu, Senior Member, IEEE"
669ae4a3a21b5800829ac9ee7e780fa42f9bc5ad,LATENT ASPECT DISCOVERY WITH DEEP REPRESENTATIONS,"978-1-4799-9988-0/16/$31.00 ©2016 IEEE
ICASSP 2016"
66b9e9d488ef2bad9bf0d2fb98f73f38fec2bff8,Context-aware Cascade Attention-based RNN for Video Emotion Recognition,"Context-aware Cascade Attention-based RNN for
Video Emotion Recognition
Man-Chin Sun
Emotibot Inc.
Taipei, Taiwan
Shih-Huan Hsu
Emotibot Inc.
Taipei, Taiwan
Min-Chun Yang
Emotibot Inc.
Taipei, Taiwan
Jen-Hsien Chien
Emotibot Inc.
Taipei, Taiwan"
1ab7d8da096c418c0bf93de14d128eb008a92db4,Towards three-dimensional face recognition in the real Huibin,"Towards three-dimensional face recognition in the real
Huibin Li
To cite this version:
Huibin Li. Towards three-dimensional face recognition in the real. Other. Ecole Centrale de
Lyon, 2013. English. <NNT : 2013ECDL0037>. <tel-00998798>
HAL Id: tel-00998798
https://tel.archives-ouvertes.fr/tel-00998798
Submitted on 2 Jun 2014
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,
´emanant des ´etablissements d’enseignement et de
recherche fran¸cais ou ´etrangers, des laboratoires
publics ou priv´es."
1aff495a36ceecbe2d49a95fe5779c2062fc5127,A NEW BAG OF PIXELS APPROACH BASED ON EDGE DETECTION TO FACE IMAGE RETRIEVAL,"International Journal of Applied Science and Technology                                               Vol. 2 No. 1; January 2012
A NEW BAG OF PIXELS APPROACH BASED ON EDGE DETECTION TO FACE IMAGE RETRIEVAL
Mir Hossein Dezfoulian
Image Processing Lab, Computer Engineering Department
Bu-Ali Sina University
Hamedan, Iran
Mostafa Parchami
Image Processing Lab, Computer Engineering Department
Bu-Ali Sina University
Hamedan, Iran
Saman Bashbaghi
Image Processing Lab, Computer Engineering Department
Bu-Ali Sina University
Hamedan, Iran"
1a0b09e7e9182a68fc457bb888536b9023f6c9fd,Multi-affinity spectral clustering,"MULTI-AFFINITY SPECTRAL CLUSTERING
Hsin-Chien Huang(cid:63)†
Yung-Yu Chuang(cid:63)
Chu-Song Chen†
(cid:63)National Taiwan University
Academia Sinica"
1a1654456decd116f4ca84c98006dfda0a8a3134,INTEGRATED VISUAL INFORMATION FOR MARITIME SURVEILLANCE,"INTEGRATED VISUAL INFORMATION FOR
MARITIME SURVEILLANCE
Domenico D. Bloisia, Luca Iocchia, Daniele Nardia and Michele Fiorinib
Department of Computer, Control, and Management Engineering, Sapienza
University of Rome, via Ariosto 25, 00185 Rome, Italy.
SELEX-ES S.p.A. - A Finmeccanica Company, Rome, Italy."
1ae3a26a985fe525b23f080a9e1041ecff0509ad,A Comparative Study of Statistical Conversion of Face to Voice Based on Their Subjective Impressions,"Interspeech 2018
-6 September 2018, Hyderabad
0.21437/Interspeech.2018-2005"
1a2431e3b35a4a4794dc38ef16e9eec2996114a1,Automated Face Recognition: Challenges and Solutions,"We are IntechOpen,
the world’s leading publisher of
Open Access books
Built by scientists, for scientists
,800
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Open access books available
International  authors and editors
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Selection of our books indexed in the Book Citation Index
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Numbers displayed above are based on latest data collected."
1a8ccc23ed73db64748e31c61c69fe23c48a2bb1,Extensive Facial Landmark Localization with Coarse-to-Fine Convolutional Network Cascade,"Extensive Facial Landmark Localization
with Coarse-to-fine Convolutional Network Cascade
Erjin Zhou Haoqiang Fan Zhimin Cao Yuning Jiang Qi Yin
Megvii Inc."
1a6300ce4fb5eb593419a92d75ee4a4120d1c58f,Person-specific face representation for recognition,"




 

























 INSTITUTO DE COMPUTAÇÃO
UNIVERSIDADE ESTADUAL DE CAMPINAS
Person-Specic Face Representation
for Recognition
Giovani Chiachia
Alexandre X. Falcão
Anderson Rocha
Technical Report
IC-11-16
- Relatório Técnico
- 2011
Julho
The contents of this report are the sole responsibility of the authors.
O conteúdo do presente relatório é de única responsabilidade dos autores."
1a7243913d9b8c6855b1eb3bb6566f2f1041d50a,Articulated clinician detection using 3D pictorial structures on RGB-D data,"Articulated Clinician Detection Using 3D Pictorial
Structures on RGB-D Data
Abdolrahim Kadkhodamohammadi, Afshin Gangi, Michel de Mathelin and Nicolas Padoy"
1a0912bb76777469295bb2c059faee907e7f3258,Mask R-CNN,"Mask R-CNN
Kaiming He Georgia Gkioxari
Piotr Doll´ar Ross Girshick
Facebook AI Research (FAIR)"
1a9e0bf9f7a9495bcdf1aeb214ccc9df9f2a9030,Challenges and Opportunities The Main Memory System : Challenges and Opportunities,"특집원고Ⅰ
The Main Memory System: Challenges and Opportunities
Carnegie Mellon University  Onur Mutlu･Justin Meza･Lavanya Subramanian
The  memory  system  is  a  fundamental  performance  and
energy  bottleneck  in  almost  all  computing  systems.  Recent
system  design,  application,  and  technology  trends  that
require more capacity, bandwidth, efficiency, and predictability
out  of  the  memory  system  make  it  an  even  more  important
system  bottleneck.  At  the  same  time,  DRAM  technology
is  experiencing  difficult    technology  scaling  challenges
that  make  the  maintenance  and  enhancement  of  its  capacity,
energy-efficiency,  and  reliability  significantly  more  costly
with  conventional  techniques.
In  this  article,  after  describing  the  demands  and  challenges
faced  by  the  memory  system,  we  examine  some  promising
research  and  design  directions  to  overcome  challenges  posed
y  memory  scaling.  Specifically,  we  describe  three  major
new  research  challenges  and  solution  directions:  1)  enabling
new  DRAM  architectures,  functions,  interfaces,  and  better
integration  of  the  DRAM  and  the  rest  of  the  system  (an"
1a6b2972506d7d85100552bee99ce2b267e30d41,Learning Optimal Embedded Cascades,"Learning Optimal Embedded Cascades
Mohammad Javad Saberian and Nuno Vasconcelos, Senior Member, IEEE"
1a9997d8421d577a728f6ac119d4b14a3f46402c,Using Tectogrammatical Annotation for Studying Actors and Actions in Sallust ’ s Bellum Catilinae,"The Prague Bulletin of Mathematical Linguistics
NUMBER 111 OCTOBER 2018 5–28
Using Tectogrammatical Annotation for Studying
Actors and Actions in Sallust’s Bellum Catilinae
Berta González Saavedra,a Marco Passarottib
Dep. de Filología Clásica, Universidad Autónoma de Madrid, Spain
CIRCSE Research Centre. Università Cattolica del Sacro Cuore, Milan, Italy"
1a878e4667fe55170252e3f41d38ddf85c87fcaf,Discriminative Machine Learning with Structure,"Discriminative Machine Learning with Structure
Simon Lacoste-Julien
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2010-4
http://www.eecs.berkeley.edu/Pubs/TechRpts/2010/EECS-2010-4.html
January 12, 2010"
1a382d4e436e3e4f3d735f6e34ba2bc61e30838e,Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection,
1ad88221f308bf9f36775650f880f32d91ce929a,Learning a Recurrent Residual Fusion Network for Multimodal Matching,"Learning a Recurrent Residual Fusion Network for Multimodal Matching
Yu Liu
Yanming Guo
Erwin M. Bakker
Michael S. Lew
LIACS Media Lab, Leiden University, Leiden, The Netherlands
{y.liu, y.guo, e.m.bakker,"
1a9839698e29c9f80db0a2ea8c4568be4e1e84d8,An Information Theoretic Approach to the Study of Auditory Coding,"An Information Theoretic
Approach to the Study
of Auditory Coding
Thesis submitted for the degree
“Doctor of Philosophy”
Gal Chechik
Submitted to the Senate of the Hebrew University
July 2003"
1a86620ea59816564db30fe0ae94cc422c5266e3,Can 3D Pose be Learned from 2D Projections Alone?,"Can 3D Pose be Learned from
D Projections Alone?
Dylan Drover, Rohith MV, Ching-Hang Chen,
Amit Agrawal, Ambrish Tyagi, and Cong Phuoc Huynh
Amazon Lab126 Inc., Sunnyvale, CA, USA
{droverd, kurohith, chinghc, aaagrawa,
mbrisht,"
1a8a2539cffba25ed9a7f2b869ebb737276ccee1,Pros and Cons of GAN Evaluation Measures,"Pros and Cons of GAN Evaluation Measures
Ali Borji"
1a9a192b700c080c7887e5862c1ec578012f9ed1,Discriminant Subspace Analysis for Face Recognition with Small Number of Training Samples,"IEEE TRANSACTIONS ON SYSTEM, MAN AND CYBERNETICS, PART B
Discriminant Subspace Analysis for Face
Recognition with Small Number of Training
Samples
Hui Kong, Xuchun Li, Matthew Turk, and Chandra Kambhamettu"
1a765cccff5e14c8f72d3cce13683e506b35c5c6,Real-time multi-platform pedestrian detection in a heavy duty driver assistance system,"CVT 2016 – March 8–10, 2016, Kaiserslautern, Germany
Real-time multi-platform pedestrian detection in a
heavy duty driver assistance system
Songlin Piao1, Lisa Kiekbusch1, Daniel Schmidt1, Karsten Berns1, Daniel Hering2,
Stefan Wirtz2, Nils Hering2 and J¨urgen Weiland2
Technische Universit¨at Kaiserslautern, Gottlieb-Daimler-Straße, 67663 Kaiserslautern
Motec Entwicklungszentrum f¨ur Nutzfahrzeugassistenzsysteme, Universit¨atsstr. 3, 56070
Koblenz"
1a849b694f2d68c3536ed849ed78c82e979d64d5,This is a repository copy of Symmetric Shape Morphing for 3 D Face and Head Modelling,"This is a repository copy of Symmetric Shape Morphing for 3D Face and Head Modelling.
White Rose Research Online URL for this paper:
http://eprints.whiterose.ac.uk/131760/
Version: Accepted Version
Proceedings Paper:
Dai, Hang, Pears, Nicholas Edwin orcid.org/0000-0001-9513-5634, Smith, William Alfred
Peter orcid.org/0000-0002-6047-0413 et al. (1 more author) (2018) Symmetric Shape
Morphing for 3D Face and Head Modelling. In: The 13th IEEE Conference on Automatic
Face and Gesture Recognition. IEEE .
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If you consider content in White Rose Research Online to be in breach of UK law, please notify us by
emailing including the URL of the record and the reason for the withdrawal request.
https://eprints.whiterose.ac.uk/"
1a618ff6fd0f0f94ab070555a4746e120eb9fbab,Why Is My Classifier Discriminatory?,"Why Is My Classifier Discriminatory?
Irene Y. Chen
Fredrik D. Johansson
David Sontag"
1a3f7b9fc451b54110aaebae56c65413c620f6e2,Multilevel Linear Dimensionality Reduction for Data Analysis using Nearest-Neighbor Graphs ∗,"Multilevel Linear Dimensionality Reduction for Data
Analysis using Nearest-Neighbor Graphs∗
Sophia Sakellaridi
Department of Computer
Science and Engineering
University of Minnesota;
Minneapolis, MN 55455
Haw-ren Fang
Department of Computer
Science and Engineering
University of Minnesota;
Minneapolis, MN 55455
Yousef Saad
Department of Computer
Science and Engineering
University of Minnesota;
Minneapolis, MN 55455"
1a5151b4205ab27b1c76f98964debbfc11b124d5,Self Paced Deep Learning for Weakly Supervised Object Detection,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
Self Paced Deep Learning for Weakly
Supervised Object Detection
Enver Sangineto†, Moin Nabi†, Dubravko Culibrk and Nicu Sebe,"
1a1ed320882c00c94d9f738b7b14eadd941376ed,Extracting Human Face Similarity Judgments: Pairs or Triplets?,"Extracting Human Face Similarity Judgments: Pairs or Triplets?
Linjie Li1, Vicente Malave2, Amanda Song2, and Angela J. Yu2
Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, USA
Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA"
1abdf07ce2fca11a26222dedd581b68b141af3f2,Face Recognition Aiding Historical Photographs Indexing Using a Two-Stage Training Scheme and an Enhanced Distance Measure,"Face Recognition Aiding Historical Photographs Indexing
Using a Two-stage Training Scheme and an Enhanced Distance Measure
Ana Paula Brand˜ao Lopes1,2, Camillo Jorge Santos Oliveira1,3, Arnaldo de Albuquerque Ara´ujo1
Computer Science Department – Federal University of Minas Gerais
Av. Antˆonio Carlos, 6627, Pampulha, CEP 31270–901, Belo Horizonte, MG, Brazil
Exact and Technological Sciences Department – State University of Santa Cruz
Rodovia Ilh´eus-Itabuna, km 16 – Pavilh˜ao Jorge Amado, CEP 45600-000, Ilh´eus, BA, Brazil
Informatics Department – Pontifical Catholic University of Minas Gerais
Rua Rio Comprido, 4.580 - CEP 32.010-025, Contagem, MG, Brazil,
{paula, camillo,"
1a1955920ee36d58265fe17100ca451d899e8372,A Local Feature based on Lagrangian Measures for Violent Video Classification,"Best Paper Award, IET 6th International Conference on Imaging for Crime Prevention and Detection, 2015
A Local Feature based on Lagrangian Measures for Violent Video
Classification
Tobias Senst, Volker Eiselein, Thomas Sikora
Communication Systems Group, Technische Universität Berlin, Germany
Keywords: violent video detection,
recognition, lagrangian measures, lagrangian framework
local feature, action"
1a5b39a4b29afc5d2a3cd49087ae23c6838eca2b,Competitive Game Designs for Improving the Cost Effectiveness of Crowdsourcing,"Competitive Game Designs for Improving the Cost
Effectiveness of Crowdsourcing
Markus Rokicki, Sergiu Chelaru, Sergej Zerr, Stefan Siersdorfer
L3S Research Center, Hannover, Germany"
1ad823bf77c691f1d2b572799f8a8c572d941118,Towards The Deep Model: Understanding Visual Recognition Through Computational Models,"implement
the system.
Précis of “Towards ​The Deep Model
: Understanding Visual
Recognition Through Computational Models”
Panqu Wang
Introduction
Vision, due to its significance in surviving and socializing, is one of the most important and
extensively studied sensory functions in the human brain. In order to fully understand visual
information processing, or more specifically, visual recognition, David Marr proposed the
Tri-level Hypothesis [29], in that three levels of the system should be studied: the computational
goal of the system, the internal representation or the algorithm the system uses to achieve the
goal, and the neural substrates that
is well-known that visual
recognition in the human brain is implemented by the ventral visual pathway [32], which
receives visual information from the retina and goes through a layered structure including V1
(also known as the primary visual cortex), V2, V4, before reaching the inferior temporal cortex
(IT). The topographic mapping between the retina and the human visual cortex follows a
log-polar transformation, in which the Cartesian coordinates of the retina are transformed to
polar coordinates (polar angle and eccentricity) in the human visual cortex. From V1 to V4, each"
1aa52a25c2967b8bc228268c9ab5a96a32d2189b,Visual Fashion-Product Search at SK Planet,"Visual Fashion-Product Search at SK Planet
Taewan Kim, Seyeoung Kim, Sangil Na, Hayoon Kim, Moonki Kim, Byoung-Ki Jeon
Machine Intelligence Lab.
SK Planet, SeongNam City, South Korea"
1afe9919ddb2b245e21b610fa96037724bcdf648,2 SceneNet : An Online Database for Scene Understanding,"SceneNet: A Perceptual Ontology for Scene
Understanding
Ilan Kadar and Ohad Ben-Shahar
Ben-Gurion University of the Negev"
1abf6491d1b0f6e8af137869a01843931996a562,ParseNet: Looking Wider to See Better.,"ParseNet: Looking Wider to See Better
Wei Liu
UNC Chapel Hill
Andrew Rabinovich
MagicLeap Inc.
Alexander C. Berg
UNC Chapel Hill"
1a54a8b0c7b3fc5a21c6d33656690585c46ca08b,Fast Feature Pyramids for Object Detection,"Fast Feature Pyramids for Object Detection
Piotr Doll´ar, Ron Appel, Serge Belongie, and Pietro Perona"
1afbca9f7011ec659db9cb3692cbab5b5e38499b,"A mixed bag of emotions: Model, predict, and transfer emotion distributions","Motivation
From our observation:
. Different people have different emotional reactions to the same image.
. The same person may have multiple emotional reactions to one image.
Contributions
. Propose an image database, Emotion6, that models emotion distributions.
. Outperform the state-of-the-art affective image classification with our
method using convolutional neural networks (CNN).
. Introduce a method for emotion transfer between images.
Emotion6 Image Database
Emotion6 properties
Image source
Image size
Total # of images
# of categories
# of images per category
Ground truth provided
Description
Flickr.
∼VGA (keep aspect ratio)."
1a51bc5f9f12f6794297a426739350ae57c87731,Image classification with CNN-based Fisher vector coding,"Kent Academic Repository
Full text document (pdf)
Citation for published version
Song, Yan and Hong, Xinhai and McLoughlin, Ian Vince and Dai, Li-Rong  (2017) Image Classification
with CNN-based Fisher Vector Coding.    In: IEEE International Conference on Visual Communications
nd Image Processing 2016, 27-30 Nov 2016, Chengdu, Sichuan, China.
https://doi.org/10.1109/VCIP.2016.7805494
Link to record in KAR
http://kar.kent.ac.uk/57115/
Document Version
Author's Accepted Manuscript
Copyright & reuse
Content in the Kent Academic Repository is made available for research purposes. Unless otherwise stated all
ontent is protected by copyright and in the absence of an open licence (eg Creative Commons), permissions
for further reuse of content should be sought from the publisher, author or other copyright holder.
Versions of research
The version in the Kent Academic Repository may differ from the final published version.
Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the
published version of record.
Enquiries"
1a219e7bcd8f30f886a1f24a8c05bc26bef83ff9,Crowd Counting with Density Adaption Networks,"Crowd Counting with Density Adaption Networks
Li Wang, Weiyuan Shao, Yao Lu, Hao Ye, Jian Pu, Yingbin Zheng"
1ad97cce5fa8e9c2e001f53f6f3202bddcefba22,Grassmann Averages for Scalable Robust PCA,"Grassmann Averages for Scalable Robust PCA
Aasa Feragen
DIKU and MPIs T¨ubingen∗
Denmark and Germany
Søren Hauberg
DTU Compute∗
Lyngby, Denmark"
1afd481036d57320bf52d784a22dcb07b1ca95e2,Automated Content Metadata Extraction Services Based on MPEG Standards,"The Computer Journal Advance Access published December 6, 2012
© The Author 2012. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved.
For Permissions, please email:
doi:10.1093/comjnl/bxs146
Automated Content Metadata Extraction
Services Based on MPEG Standards
D.C. Gibbon∗, Z. Liu, A. Basso and B. Shahraray
AT&T Labs Research, Middletown, NJ, USA
Corresponding author:
This paper is concerned with the generation, acquisition, standardized representation and transport
of video metadata. The use of MPEG standards in the design and development of interoperable
media architectures and web services is discussed. A high-level discussion of several algorithms
for metadata extraction is presented. Some architectural and algorithmic issues encountered when
designing services for real-time processing of video streams, as opposed to traditional offline media
processing, are addressed. A prototype real-time video analysis system for generating MPEG-7
Audiovisual Description Profile from MPEG-2 transport stream encapsulated video is presented.
Such a capability can enable a range of new services such as content-based personalization of live
roadcasts given that the MPEG-7 based data models fit in well with specifications for advanced
television services such as TV-Anytime andAlliance for Telecommunications Industry Solutions IPTV
Interoperability Forum."
1afe5d933b58b4dd982a559cc6ec1d17959239de,Enhanced canonical correlation analysis with local density for cross-domain visual classification,"978-1-5090-4117-6/17/$31.00 ©2017 IEEE
ICASSP 2017"
1a7e385d2aa041ca8931784fb7664e9905194565,Chapter 2 Sentiment Analysis Using Social Multimedia,"Chapter 2
Sentiment Analysis Using Social
Multimedia
Jianbo Yuan, Quanzeng You and Jiebo Luo"
1a7a2221fed183b6431e29a014539e45d95f0804,Person Identification Using Text and Image Data,"Person Identification Using Text and Image Data
David S. Bolme, J. Ross Beveridge and Adele E. Howe
Computer Science Department
Colorado State Univeristy
Fort Collins, Colorado 80523"
1acf1000d47fd8273ac0bca6f9345b55709f6357,Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning,"Pedestrian Detection with Spatially Pooled
Features and Structured Ensemble Learning
Sakrapee Paisitkriangkrai, Chunhua Shen, Anton van den Hengel"
1af5f188abbfc38ac2f5985f636e0f53a4cdea10,On-line visual vocabularies for robot navigation and mapping,"The 2009 IEEE/RSJ International Conference on
Intelligent Robots and Systems
October 11-15, 2009 St. Louis, USA
978-1-4244-3804-4/09/$25.00 ©2009 IEEE"
1af68c5b5ff61d95de7c585275e119796eb81b1f,Leader tracking for a walking logistics robot,"Congress Center Hamburg
Sept 28 - Oct 2, 2015. Hamburg, Germany
978-1-4799-9993-4/15/$31.00 ©2015 IEEE"
1a20ddce2349bc995dceea66cd2378f8888c8027,SAN: Learning Relationship Between Convolutional Features for Multi-scale Object Detection,"SAN: Learning Relationship between
Convolutional Features
for Multi-Scale Object Detection
Yonghyun Kim1[0000−0003−0038−7850], Bong-Nam Kang2[0000−0002−6818−7532],
nd Daijin Kim1[0000−0002−8046−8521]
Department of Computer Science and Engineering, POSTECH, Korea
Department of Creative IT Engineering, POSTECH, Korea"
1a41831a3d7b0e0df688fb6d4f861176cef97136,A Biological Model of Object Recognition with Feature Learning,"massachusetts institute of technology — artificial intelligence laboratory
A Biological Model of Object
Recognition with Feature Learning
Jennifer Louie
AI Technical Report 2003-009
CBCL Memo 227
June 2003
© 2 0 0 3   m a s s a c h u s e t t s   i n s t i t u t e   o f
t e c h n o l o g y, c a m b r i d g e , m a   0 2 1 3 9   u s a   —   w w w. a i . m i t . e d u"
1a03716411e72722f853b904a83d9c15a0d737a3,Using color texture sparsity for facial expression recognition,"Using Color Texture Sparsity for Facial Expression
Recognition
Seung Ho Lee, Hyungil Kim,
Korea Advanced
Department
Institute
of Electrical
of Science
of Korea
Republic
Daejeon,
nd Y ong Man Ro
Engineering
nd Technology
Department
Engineering
Konstantinos
of Electrical
University
N. Plataniotis"
1a515f0b852c2e93272677dbf6ecb05c7be0ea2e,Reduced serotonin receptor subtypes in a limbic and a neocortical region in autism.,"RESEARCH ARTICLE
Reduced Serotonin Receptor Subtypes in a Limbic and a Neocortical
Region in Autism
Adrian Oblak, Terrell T. Gibbs, and Gene J. Blatt
Autism is a behaviorally defined, neurological disorder with symptom onset before the age of 3. Abnormalities in
social-emotional behaviors are a core deficit in autism, and are characterized by impaired reciprocal–social interaction,
lack of facial expressions, and the inability to recognize familiar faces. The posterior cingulate cortex (PCC) and fusiform
gyrus (FG) are two regions within an extensive limbic-cortical network that contribute to social-emotional behaviors.
Evidence indicates that changes in brains of individuals with autism begin prenatally. Serotonin (5-HT) is one of the
earliest expressed neurotransmitters, and plays an important role in synaptogenesis, neurite outgrowth, and neuronal
migration. Abnormalities in 5-HT systems have been implicated in several psychiatric disorders, including autism, as
evidenced by immunology, imaging, genetics, pharmacotherapy, and neuropathology. Although information is known
regarding peripheral 5-HT in autism, there is emerging evidence that 5-HT systems in the central nervous system,
including various 5-HT receptor subtypes and transporters, are affected in autism. The present study demonstrated
significant reductions in 5-HT1A receptor-binding density in superficial and deep layers of the PCC and FG, and in the
density of 5-HT2A receptors in superficial layers of the PCC and FG. A significant reduction in the density of serotonin
transporters (5-HTT) was also found in the deep layers of the FG, but normal levels were demonstrated in both layers of
the PCC and superficial layers of the FG. This study provides potential substrates for decreased 5-HT modulation/
innervation in the autism brain, and implicate two 5-HT receptor subtypes as potential neuromarkers for novel or
existing pharmacotherapies. Autism Res 2013, 6: 571–583. © 2013 International Society for Autism Research, Wiley"
1a66f37f37e4fbf9a8e657853933539256bbda88,Spectral Clustering by Joint Spectral Embedding and Spectral Rotation.,"This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.
Spectral Clustering by Joint Spectral Embedding
nd Spectral Rotation
Yanwei Pang , Senior Member, IEEE, Jin Xie, Feiping Nie , and Xuelong Li
, Fellow, IEEE"
00dfd58bbaff871603e4a8aa81e67915b0675aeb,Human Sensing Using Computer Vision for Personalized Smart Spaces,"013 IEEE 10th International Conference on Ubiquitous Intelligence & Computing and 2013 IEEE 10th International Conference
on Autonomic & Trusted Computing
Human Sensing using Computer Vision for
Personalized Smart Spaces
Dipak Surie, Saeed Partonia, Helena Lindgren
User Interaction and Knowledge Modeling Group
Dept. of Computing Science
Umeå University, Sweden
{dipak, mcs10spa,
spaces
everyday"
0066dc9131c25e93111fc092098f6c9db6255f1a,RoI-based Robotic Grasp Detection in Object Overlapping Scenes Using Convolutional Neural Network,"RoI-based Robotic Grasp Detection for Object Overlapping Scenes
Using Convolutional Neural Network
Hanbo Zhang, Xuguang Lan, Xinwen Zhou, Zhiqiang Tian and Nanning Zheng"
0063b44da282eec78045ab59d2debbf61959a4a4,Improving person re-identification by viewpoint cues,"Improving Person Re-identification by Viewpoint Cues
Sławomir B ˛ak
Sofia Zaidenberg Bernard Boulay
Francois Brémond
INRIA Sophia Antipolis, STARS/Neosensys
004, route des Lucioles, BP93
06902 Sophia Antipolis Cedex - France"
004e3292885463f97a70e1f511dc476289451ed5,Quadruplet-Wise Image Similarity Learning,"Quadruplet-wise Image Similarity Learning
Marc T. Law
Nicolas Thome
Matthieu Cord
LIP6, UPMC - Sorbonne University, Paris, France
{Marc.Law, Nicolas.Thome,"
0033e0ce8720f913761f9edb9a6c378eed8366a8,Interactive Object Retrieval using Interpretable Visual Models,"UNIVERSIT´EPARIS-SUD11Facult´edessciencesd’OrsayN◦Ordre:2011PA112054PHDTHESISInteractiveObjectRetrievalusingInterpretableVisualModelsSubmittedforthedegreeof“docteurensciences”oftheUniversityParis-Sud11Speciality:ComputerScienceByAhmedRebaiMay2011INRIAParis-Rocquencourt,ImediaTeamThesiscommittee:Reviewers:FredStentiford-Prof.atUniversityCollegeLondon(UK)SylviePhilipp-Foliguet-Prof.atUniversit´eCergy/Pontoise(FR)Director:NozhaBoujemaa-DirectoroftheINRIA-SaclayCenter(FR)Advisor:AlexisJoly-ResearcheratINRIA-Rocquencourt(FR)Examinator:MichelCrucianu-Prof.atCNAM(FR)President:Fran¸coisYvon-Prof.atUniversit´eParis-Sud11(FR)Copyrightc(cid:13)2011AhmedRebaiAllrightsreserved."
00d8f67ac0ea0bb2c9827b60e1f47c300346cd7a,Face recognition using color local binary pattern from mutually independent color channels,"Anbarjafari EURASIP Journal on Image and Video Processing 2013, 2013:6
http://jivp.eurasipjournals.com/content/2013/1/6
R ES EAR CH
Open Access
Face recognition using color local binary pattern
from mutually independent color channels
Gholamreza Anbarjafari"
0041afaf2b17f1a33bd514db27b17ce34670fdb8,Deep Reinforcement Learning-Based Image Captioning with Embedding Reward,"Deep Reinforcement Learning-based Image Captioning with Embedding Reward
Zhou Ren1
Xiaoyu Wang1
Ning Zhang1
Xutao Lv1
Li-Jia Li2∗
{zhou.ren, xiaoyu.wang, ning.zhang,
Snap Inc.
Google Inc."
00d9d88bb1bdca35663946a76d807fff3dc1c15f,Subjects and Their Objects: Localizing Interactees for a Person-Centric View of Importance,"Subjects and Their Objects: Localizing Interactees for a
Person-Centric View of Importance
Chao-Yeh Chen · Kristen Grauman"
0079d56c8e183ef36f876b84327b97ee9454825b,Scene Parsing by Weakly Supervised Learning with Image Descriptions,"Hierarchical Scene Parsing by Weakly
Supervised Learning with Image Descriptions
Ruimao Zhang, Liang Lin, Guangrun Wang, Meng Wang, and Wangmeng Zuo"
0083a395fded81d562317d83e194dfbc47b5c04a,AT&T Research at TRECVID 2010,"AT&T Research at TRECVID 2010
Zhu Liu, Eric Zavesky, Neela Sawant∗, Behzad Shahraray
AT&T Labs Research, 200 Laurel Avenue, Middeltown, NJ 07748
College of Information Sciences and Technology, Penn State University, University Park, PA 16802"
004a1bb1a2c93b4f379468cca6b6cfc6d8746cc4,Balanced k-Means and Min-Cut Clustering,"Balanced k-Means and Min-Cut Clustering
Xiaojun Chang, Feiping Nie, Zhigang Ma, and Yi Yang"
0077cd8f97cafd2b389783858a6e4ab7887b0b6b,Face Image Reconstruction from Deep Templates,"MAI et al.: ON THE RECONSTRUCTION OF DEEP FACE TEMPLATES
On the Reconstruction of Deep Face Templates
Guangcan Mai, Kai Cao, Pong C. Yuen, Senior Member, IEEE, and Anil K. Jain, Life Fellow, IEEE"
0089a590154694e0de340f357a022f6a38d60946,Speeding-up Object Detection Training for Robotics with FALKON,"Speeding-up Object Detection Training for Robotics with FALKON
Elisa Maiettini1,2,3, Giulia Pasquale1,2, Lorenzo Rosasco2,3 and Lorenzo Natale1"
0052de4885916cf6949a6904d02336e59d98544c,Generalized low rank approximations of matrices,"Machine Learning, 61, 167–191, 2005
005 Springer Science + Business Media, Inc. Manufactured in The Netherlands.
DOI: 10.1007/s10994-005-3561-6
Generalized Low Rank Approximations of Matrices
JIEPING YE
Department of Computer Science & Engineering,University of Minnesota-Twin Cities, Minneapolis,
MN 55455, USA
Editor:
Peter Flach
Published online: 12 August 2005"
00b03ee4a7e31a999715d7a0c31d283d646106fa,Multi-level Semantic Feature Augmentation for One-shot Learning,"Multi-level Semantic Feature Augmentation for
One-shot Learning
Zitian Chen, Yanwei Fu*, Yinda Zhang, Leonid Sigal"
00a3cfe3ce35a7ffb8214f6db15366f4e79761e3,Using Kinect for real-time emotion recognition via facial expressions,"Qi-rong Mao, Xin-yu Pan, Yong-zhao Zhan, Xiang-jun Shen, 2015. Using
Kinect for real-time emotion recognition via facial expressions. Frontiers of
Information Technology & Electronic Engineering, 16(4):272-282.
[doi:10.1631/FITEE.1400209]
Using Kinect for real-time emotion
recognition via facial expressions
Key words: Kinect, Emotion recognition, Facial expression, Real-time
lassification, Fusion algorithm, Support vector machine (SVM)
Contact: Qi-rong Mao
E-mail:
ORCID: http://orcid.org/0000-0002-5021-9057
Front Inform Technol & Electron Eng"
00433d2ad90b40bc5ad22a591aac0da68037003e,K-means Based Automatic Pests Detection and Classification for Pesticides Spraying,"(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 8 No. 11, 2017
K-means Based Automatic Pests Detection and
Classification for Pesticides Spraying
Muhammad Hafeez Javed
Foundation University Islamabad
M Humair Noor
Babar Yaqoob Khan
Foundation University Islamabad
Foundation University Islamabad
Nazish Noor
Foundation University Islamabad
Tayyaba Arshad
Foundation University Islamabad"
001719ac5585722d14bf4f2d807383a368504a4a,Pedestrian detection in low resolution videos,"Pedestrian Detection in Low Resolution Videos
Hisham Sager
Colorado School of Mines
Golden, CO  80401
William Hoff
Colorado School of Mines
Golden, CO  80401"
00fb2836068042c19b5197d0999e8e93b920eb9c,Genetic Algorithm for Weight Optimization in Descriptor based Face Recognition Methods,
00d931eccab929be33caea207547989ae7c1ef39,The natural input memory model,"The Natural Input Memory Model
Joyca P.W. Lacroix
Department of Computer Science, IKAT, Universiteit Maastricht, St. Jacobsstraat 6, 6211 LB Maastricht, The Netherlands
Department of Psychology, Universiteit van Amsterdam, Roeterstraat 15, 1018 WB Amsterdam, The Netherlands
Jaap M.J. Murre
Department of Computer Science, IKAT, Universiteit Maastricht, St. Jacobsstraat 6, 6211 LB Maastricht, The Netherlands
Eric O. Postma
H. Jaap van den Herik"
005503ccf270890ea2582370feed4506f3785004,Characterizing the temporal dynamics of object recognition by deep neural networks : role of depth,"ioRxiv preprint first posted online Sep. 10, 2017;
peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.
http://dx.doi.org/10.1101/178541
The copyright holder for this preprint (which was not
Characterizing the temporal dynamics of object
recognition by deep neural networks : role of depth
Kandan Ramakrishnan1, Iris I.A. Groen2, Arnold W.M. Smeulders1,
H. Steven Scholte*3, Sennay Ghebreab*1
Institute of Informatics, University of Amsterdam.
Laboratory of Brain and Cognition, National Institute of Health.
Department of Psychology, University of Amsterdam.
Keywords: deep neural network, ERP, architecture, number of layers"
001dc49f7f3348841b4086f966bfe4e9dfadf03e,Automatic image captioning using multitask learning,"Automatic image captioning using multi-task learning
Anna Fariha"
00f0ed04defec19b4843b5b16557d8d0ccc5bb42,Modeling Spatial and Temporal Cues for Multi-label Facial Action Unit Detection,
004dc8de3a6832c8d4764144570dc122b5265ec5,Hyper-dimensional computing for a visual question-answering system that is trainable end-to-end,"Hyper-dimensional computing for a visual
question-answering system that is trainable
end-to-end
Guglielmo Montone
J.Kevin O’Regan
Laboratoire Psychologie de la Perception
Laboratoire Psychologie de la Perception
Université Paris Descartes
75006 Paris, France
Université Paris Descartes
75006 Paris, France
Alexander V. Terekhov
Laboratoire Psychologie de la Perception
Université Paris Descartes
75006 Paris, France"
00319cd17cebae5e1095a248260bd7be15781362,A Dataset for Improved RGBD-Based Object Detection and Pose Estimation for Warehouse Pick-and-Place,"A Dataset for Improved RGBD-based Object
Detection and Pose Estimation for Warehouse
Pick-and-Place
Colin Rennie1, Rahul Shome1, Kostas E. Bekris1, and Alberto F. De Souza2"
008dafebbb27eb64a1af8ded8bfe2e7a04c1d703,CANDLE/Supervisor: A Workflow Framework for Machine Learning Applied to Cancer Research,"CANDLE/Supervisor: A Workflow Framework for
Machine Learning Applied to Cancer Research
Justin M. Wozniak, Rajeev Jain,
Prasanna Balaprakash
Mathematics & Computer Science
Argonne National Laboratory
Argonne, IL, USA
Jamaludin Mohd-Yusof,
Cristina Garcia Cardona
Computer, Computational &
Statistical Sciences
Los Alamos National Laboratory
Los Alamos, NM, USA
Jonathan Ozik,
Nicholson Collier
Global Security Sciences
Argonne National Laboratory
Argonne, IL, USA
Brian Van Essen
Lawrence Livermore National"
00b370765678c44acd5313f3946b2431890721a9,Dynamic Scene Classification: Learning Motion Descriptors with Slow Features Analysis,"Dynamic Scene Classification: Learning Motion Descriptors with Slow Features
Analysis
Christian Th´eriault, Nicolas Thome, Matthieu Cord
UPMC-Sorbonne Universities, Paris, France"
0098f42c6f24d5e6d2471c1d7d1e864ff8e83226,Review on Face Recognition across Age Progression,"Review on Face Recognition across Age Progression
Rashmi P1, Shifana Begaum2, B R Kishore3
,2,3Srinivas School of Engineering, Mukka, Mangalore"
000a83a533f9c945addce83e466e308df1ae79c5,Efficient max-margin multi-label classification with applications to zero-shot learning,"Mach Learn manuscript No.
(will be inserted by the editor)
Efficient Max-Margin Multi-Label Classification with
Applications to Zero-Shot Learning
Bharath Hariharan · S. V. N. Vishwanathan ·
Manik Varma
Received: 30 September 2010 / Accepted: date"
006a9f68bcf6edca62d8750af55168971cf0890c,Dynamic Programming Bipartite Belief Propagation For Hyper Graph Matching,Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
00d63b30e7e8383ea3dd2993499df70a51295d13,Exploiting structure in man-made environments,"Exploiting structure in man-made environments
ALPER AYDEMIR
Doctoral Thesis
Stockholm, Sweden, 2012"
006350ae14784bb929b6a749d4e5c265a10168b7,Abstract Eye Detection Using Discriminatory Features and an Efficient Support Vector Machine Eye Detection Using Discriminatory Features and an Efficient Support Vector Machine Eye Detection Using Discriminatory Features and an Efficient Support Vector Machine,"Copyright Warning & Restrictions
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00b605f19ae4502362831e33be0f9c03228fc640,Securing Mass Biometric Templates Using Blockwise Multi-Resolution Clustering,"Securing Mass Biometric Templates Using
Blockwise Multi-Resolution Clustering Watermarking
Bassem S. Rabil, Robert Sabourin, and Eric Granger
Laboratoire d’ Imagerie, de Vision, et d’Intelligence Artificielle
´Ecole de Technologie Superi´eure (´ETS), University of Quebec
100, rue Notre-Dame Ouest, Montreal, QC H3C 1K3, Canada.
http://www.etsmtl.ca"
006f283a50d325840433f4cf6d15876d475bba77,Preserving Structure in Model-Free Tracking,"Preserving Structure in Model-Free Tracking
Lu Zhang and Laurens van der Maaten"
003b141fb02078a4b5d02f4f803001ce22d73ba7,Real-time 3D Multiple Human Tracking with Robustness Enhancement through Machine Learning,"REAL-TIME 3D MULTIPLE HUMAN TRACKING WITH
ROBUSTNESS ENHANCEMENT THROUGH MACHINE LEARNING
Keywords:
Visual Tracking"
00e39fad9846084eb435b6cddd675ee11f2dfb90,Person Re-identification Using Haar-based and DCD-based Signature,"Person Re-identification Using Haar-based and
DCD-based Signature
Slawomir Bak, Etienne Corvee, François Bremond, Monique Thonnat
To cite this version:
Slawomir Bak, Etienne Corvee, François Bremond, Monique Thonnat. Person Re-identification Us-
ing Haar-based and DCD-based Signature. 2nd Workshop on Activity Monitoring by Multi-Camera
Surveillance Systems, AMMCSS 2010, in conjunction with 7th IEEE International Conference on Ad-
vanced Video and Signal-Based Surveillance, AVSS - 2010, Aug 2010, Boston, United States. 2010.
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00edd45d8f4fd75fc329d6a6fcc7d87108baa3a9,Distance Measures for Gabor Jets-Based Face Authentication: A Comparative Evaluation,"Distance Measures for Gabor Jets-based Face
Authentication: A Comparative Evaluation
Daniel Gonz´alez-Jim´enez1, Manuele Bicego2, J.W.H. Tangelder3, B.A.M
Schouten3, Onkar Ambekar3, Jos´e Luis Alba-Castro1, Enrico Grosso2, Massimo
Tistarelli4
TSC Department, University of Vigo, Vigo (Spain)
DEIR - University of Sassari, Sassari (Italy)
CWI, Amsterdam (The Netherlands)
DAP - University of Sassari, Alghero (Italy)"
003846e4559fa32699f08ecd09de13ed5a4e92d2,Analysis of Brain Waves in Violent Images - Are Differences in Gender?,
0014a057ebdeca672b1cdee8104cca4dc928ef3e,Training Deformable Part Models with Decorrelated Features,"Training deformable part models with decorrelated features
Ross Girshick and Jitendra Malik
UC Berkeley
{rbg,"
00d14af37bc75b6477b4846f6ab561cdc89c96a2,"UvA-DARE ( Digital Academic Repository ) Infants ’ Temperament and Mothers ’ , and Fathers ’ Depression Predict Infants ’ Attention to Objects Paired with Emotional","UvA-DARE (Digital Academic Repository)
Infants’ Temperament and Mothers’, and Fathers’ Depression Predict Infants’ Attention
to Objects Paired with Emotional Faces
Aktar, E.; Mandell, D.J.; de Vente, W.; Majdandzic, M.; Raijmakers, M.E.J.; Bögels, S.M.
Published in:
Journal of Abnormal Child Psychology
0.1007/s10802-015-0085-9
Link to publication
Citation for published version (APA):
Aktar, E., Mandell, D. J., de Vente, W., Majdandži, M., Raijmakers, M. E. J., & Bögels, S. M. (2016). Infants’
Temperament and Mothers’, and Fathers’ Depression Predict Infants’ Attention to Objects Paired with Emotional
Faces. Journal of Abnormal Child Psychology, 44(5), 975-990. DOI: 10.1007/s10802-015-0085-9
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0029418d56d8fe71d1d45bdaad88e5cc75dc58e7,Pushing the “Speed Limit”: High-Accuracy US Traffic Sign Recognition With Convolutional Neural Networks,"Pushing the “Speed Limit”: High-Accuracy U.S.
Traffic Sign Recognition with Convolutional Neural
Networks
Yuan Li, Andreas Møgelmose, and Mohan M. Trivedi"
002d1619748a99aa683b5c30b7eafebdfe6adfc4,Nearest feature line embedding for face hallucination,"Nearest feature line embedding for face
hallucination
Junjun Jiang, Ruimin Hu, Zhen Han and Tao Lu
A new manifold learning method, called nearest feature line (NFL)
embedding, for face hallucination is proposed. While many manifold
learning based face hallucination algorithms have been proposed in
recent years, most of them apply the conventional nearest neighbour
metric to derive the subspace and may not effectively characterise
the geometrical
information of the samples, especially when the
number of training samples is limited. This reported work proposes
using the NFL metric to define the neighbourhood relations between
face samples to improve the expressing power of the given training
samples for reconstruction. The algorithm preserves the linear relation-
ship in a smaller local space than traditional manifold learning based
methods, which better reflects the nature of manifold learning theory.
Experimental results demonstrate that
the method is effective at
preserving detailed visual information.
Introduction: Face super-resolution (SR), or face hallucination, refers to"
0037bff7be6d463785d4e5b2671da664cd7ef746,Multiple Instance Metric Learning from Automatically Labeled Bags of Faces,"Author manuscript, published in ""European Conference on Computer Vision (ECCV '10) 6311 (2010) 634--647""
DOI : 10.1007/978-3-642-15549-9_46"
00796052277d41e2bb3a1284d445c1747aed295f,Performance and Energy Consumption Characterization and Modeling of Video Decoding on Multi-core Heterogenous SoC and their Applications,"Performance and Energy Consumption Characterization
nd Modeling of Video Decoding on Multi-core
Heterogenous SoC and their Applications
Yahia Benmoussa
To cite this version:
Yahia Benmoussa. Performance and Energy Consumption Characterization and Modeling of
Video Decoding on Multi-core Heterogenous SoC and their Applications. Multimedia [cs.MM].
Universit´e de Bretagne Occidentale, 2015. English. <tel-01313326>
HAL Id: tel-01313326
https://hal.archives-ouvertes.fr/tel-01313326
Submitted on 9 May 2016
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,"
00cb08dcef72bfaa1aab0664d34168615ac6a5cc,Amygdala Surface Modeling with Weighted Spherical Harmonics,"Amygdala Surface Modeling with
Weighted Spherical Harmonics
Moo K. Chung1,2, Brendon M. Nacewicz2, Shubing Wang1,
Kim M. Dalton2, Seth Pollak3, and Richard J. Davidson2,3
Department of Statistics, Biostatistics and Medical Informatics
Waisman Laboratory for Brain Imaging and Behavior
Department of Psychology and Psychiatry
University of Wisconsin, Madison, WI 53706, USA"
00f17fca3cf3ab4262edde3626e6230a89ff1a1f,Human Pose Estimation with Iterative Error Feedback,"Human Pose Estimation with Iterative Error
Feedback
Jo˜ao Carreira
UC Berkeley
Pulkit Agrawal
UC Berkeley
Katerina Fragkiadaki
UC Berkeley
Jitendra Malik
UC Berkeley"
aff672a7e2c5dca5921ddfa3cf3223e2f94d5838,Towards Secure and Usable User Authentication on Mobile Devices,"Towards
Secure and Usable
User Authentication
on Mobile Devices
Dissertation
zur Erlangung des Grades eines Doktor-Ingenieurs
der Fakult¨at f¨ur Elektrotechnik und Informationstechnik
n der Ruhr-Universit¨at Bochum
vorgelegt von
Sebastian Uellenbeck
us Herdecke
Bochum, 07. April 2014
Fakultät fürElektrotechnik undInformationstechnik"
afa004a8daaa7fc093a798bf97babdb00273e1a0,EXPERIMENTAL STUDY ON FAST 2 D HOMOGRAPHY ESTIMATION FROM A FEW POINT CORRESPONDENCES,"Tutkimusraportti 111
Research Report 111
EXPERIMENTAL STUDY ON FAST 2D
HOMOGRAPHY ESTIMATION FROM A FEW
POINT CORRESPONDENCES
Joni-Kristian Kämäräinen and Pekka Paalanen
Lappeenranta University of Technology
Faculty of Technology Management
Department of Information Technology
Box 20
FIN-53851 Lappeenranta
ISBN 978-952-214-772-1 (paperback)
ISBN 978-952-214-773-8 (PDF)
ISSN 0783-8069
Lappeenranta 2009"
af64854f653f2c1724d04c9657adfcdabe0f8440,Structure propagation for zero-shot learning,"Structure propagation for zero-shot learning
Guangfeng Lina,∗, Yajun Chena, Fan Zhaoa
Information science department, Xian University of Technology,
5 South Jinhua Road, Xi’an, Shaanxi Province 710048, PR China"
afd3a6dde8fa3b330cb146678731b150213ddd95,Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning,"Discovery of Latent 3D Keypoints via
End-to-end Geometric Reasoning
Supasorn Suwajanakorn(cid:46)∗ Noah Snavely(cid:47)
Jonathan Tompson(cid:47) Mohammad Norouzi(cid:47)
{snavely, tompson,
(cid:46)Vidyasirimedhi Institute of Science and Technology
(cid:47)Google AI"
af4e6fa65a692c29b9d475de360f7994808a8ff8,Boosted Linear Projections for Discriminant Analysis,"Boosted Linear Projections for Discriminant
Analysis
David Masip
nd Jordi Vitri`a
Centre de Visi´o per Computador - Dept. Inform`atica, UAB
Edifici O - Campus UAB, 08193 Bellaterra, Barcelona, Catalonia, Spain"
afc7092987f0d05f5685e9332d83c4b27612f964,Person-independent facial expression detection using Constrained Local Models,"Person-Independent Facial Expression Detection using Constrained
Local Models
Sien. W. Chew, Patrick Lucey, Simon Lucey, Jason Saragih, Jeffrey F. Cohn and Sridha Sridharan"
af8261ca1fc8e111cc344c0f3e26c09e39246983,Casualty Detection from 3D Point Cloud Data for Autonomous Ground Mobile Rescue Robots,"Casualty Detection from 3D Point Cloud Data
for Autonomous Ground Mobile Rescue Robots
Roni Permana Saputra1,2 and Petar Kormushev1"
afe3a0d463e2f099305c745ddbf943844583795d,Learning Visual Question Answering by Bootstrapping Hard Attention,"Learning Visual Question Answering by
Bootstrapping Hard Attention
Mateusz Malinowski, Carl Doersch, Adam Santoro, and Peter Battaglia
DeepMind, London, United Kingdom"
af9cc1767f50f63291d7ca9ab709f6849cd1e46c,Graph-Driven Diffusion and Random Walk Schemes for Image Segmentation.,"IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 26, NO. 1, JANUARY 2017
Graph-Driven Diffusion and Random Walk
Schemes for Image Segmentation
Christos G. Bampis, Student Member, IEEE, Petros Maragos, Fellow, IEEE, and Alan C. Bovik, Fellow, IEEE"
af6b14c0bf427b22baffc06a0bd515b30649d8c3,Face recognition using kernel scatter-difference-based discriminant analysis,"[23] Y. He, M. Wu, and J. H. She, “An improved global asymptotic stability
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afb51f0e173cd9ab1d41075862945ae6bc593cde,Large databases of real and synthetic images for feature evaluation and prediction,"Large databases of real and synthetic images for
feature evaluation and prediction
Biliana K. Kaneva
B.A., Computer Science and Mathematics, Smith College (2000)
M.S., Computer Science, University of Washington (2005)
Submitted to the Department of Electrical Engineering and Computer Science
in partial fulfillment of the requirements for the degree of
Doctor of Philosophy
in Electrical Engineering and Computer Science
t the Massachusetts Institute of Technology
February 2012
(cid:13) 2012 Massachusetts Institute of Technology
All Rights Reserved.
Author:
Certified by:
Certified by:
Accepted by:
Department of Electrical Engineering and Computer Science
December 22, 2011
William T. Freeman, Professor of Computer Science"
afaa607aa9ad0e9dad0ce2fe5b031eb4e525cbd8,Towards an automatic face indexing system for actor-based video services in an IPTV environment,"J. Y. Choi et al.: Towards an Automatic Face Indexing System for Actor-based Video Services in an IPTV Environment
Towards an Automatic Face Indexing System for Actor-based
Video Services in an IPTV Environment
Jae Young Choi, Wesley De Neve, and Yong Man Ro, Senior Member, IEEE"
af34388e69800a168876f7446a621f68ca2215c0,Low-cost scene modeling using a density function improves segmentation performance,"Low-Cost Scene Modeling using a Density Function Improves Segmentation
Performance
Vivek Sharma(cid:5)(cid:63), S¸ule Yildirim-Yayilgan(cid:63), and Luc Van Gool(cid:5)∓"
af80dfe0dacf992c49543a4bac53d1974cb70e2e,When Holistic Processing is Not Enough : Local Features Save the Day Lingyun Zhang,"When Holistic Processing is Not Enough: Local Features Save the Day
Lingyun Zhang and Garrison W. Cottrell
UCSD Computer Science and Engineering
9500 Gilman Dr., La Jolla, CA 92093-0114 USA"
af24595c0c8f1b317b6fe2f2b49417cc40094b5c,LSH Softmax : Sub-Linear Learning and Inference of the Softmax Layer in Deep Architectures,"LSH Softmax: Sub-Linear Learning and
Inference of the Softmax Layer in Deep
Daniel Levy∗
Architectures
Danlu Chen†
January 31, 2018"
af267b44c3ae6c2a0587310021a6180962e835d6,Shape and Symmetry Induction for 3D Objects,"Shape and Symmetry Induction for 3D Objects
Shubham Tulsiani1, Abhishek Kar1, Qixing Huang2, Jo˜ao Carreira1 and Jitendra Malik1
University of California, Berkeley 2Toyota Technological Institute at Chicago
{shubhtuls, akar, carreira,"
af8cd04bbe4902123d7042985159a6a5da9d9fb9,Représenter pour suivre : Exploitation de représentations parcimonieuses pour le suivi multi-objets. (Representing to follow: Exploitation of parsimonious representations for multi-object tracking),"Représenter pour suivre : exploitation de représentations
parcimonieuses pour le suivi multi-objets
Loïc Pierre Fagot-Bouquet
To cite this version:
Loïc Pierre Fagot-Bouquet. Représenter pour suivre : exploitation de représentations parcimonieuses
pour le suivi multi-objets. Automatique. Université Paul Sabatier - Toulouse III, 2017. Français.
<NNT : 2017TOU30030>. <tel-01516921v2>
HAL Id: tel-01516921
https://tel.archives-ouvertes.fr/tel-01516921v2
Submitted on 4 May 2018
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
afad16c9fee11d8f78785af6b1856beb86b5ccf4,Explain to Fix: A Framework to Interpret and Correct DNN Object Detector Predictions,"Explain to Fix: A Framework to Interpret and
Denis Gudovskiy, Alec Hodgkinson
Panasonic Beta Research Lab, Mountain View, CA, 94043, USA
{denis.gudovskiy,
Takuya Yamaguchi, Yasunori Ishii, Sotaro Tsukizawa
Panasonic AI Solutions Center, Osaka, Japan
{yamaguchi.takuya2015, ishii.yasunori,"
af053b8cf39612cec0148e14a9c4b7a789d7db11,Paris-Lille-3D: a large and high-quality ground truth urban point cloud dataset for automatic segmentation and classification,"Paris-Lille-3D: a large and high-quality ground truth urban point cloud
dataset for automatic segmentation and classification
Xavier Roynard, Jean-Emmanuel Deschaud and François Goulette
{xavier.roynard ; jean-emmanuel.deschaud ;
Mines ParisTech, PSL Research University, Centre for Robotics"
af8f59ceed0392159c3475c58af5b7ca8e4f6412,Facial Expression Recognition,"We are IntechOpen,
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afb6d1e72d5b5506867a74beeb1e661599b8fff3,Dynamic Feature Learning for Partial Face Recognition,"Dynamic Feature Learning for Partial Face Recognition
Lingxiao He1
, Haiqing Li1
, Qi Zhang1
, and Zhenan Sun1
CRIPAC & NLPR, CASIA 2 University of Chinese Academy of Sciences, Beijing, P.R. China
Center for Excellence in Brain Science and Intelligence Technology, CAS
{lingxiao.he, hqli, qi.zhang,"
af97b793a61ba6e2b02d0d29503b73b5bdc2150d,Wavelet-Local binary pattern based face recognition Azad,"I S S N   2 2 7 7 - 3 0 6 1
V o l u m e   1 6   N u m b e r   1
I N T E R N A T I O N A L   J O U R N A L   O F   C O M P U T E R S   &   T E C H N O L O G Y
Wavelet-Local binary pattern based face recognition
Azad Abdullah Ameen(1), Hardi M. M-Saleh(2) ,Zrar Kh. Abdul(3)
(1) Charmo University, College of Basic Education, Computer Department,Chamchamal, Raperin, Iraq
(2) Charmo University, College of Basic Education, Computer Department, Chamchamal, Raperin, Iraq
(3)Charmo University, College of Basic Education, Computer Department, Chamchamal, Raperin, Iraq"
af62621816fbbe7582a7d237ebae1a4d68fcf97d,Active Shape Model Based Recognition Of Facial Expression,"International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622
International Conference on Humming Bird ( 01st March 2014)
RESEARCH ARTICLE
OPEN ACCESS
Active Shape Model Based Recognition Of Facial Expression
AncyRija V  , Gayathri. S2
AncyRijaV,Author is currently pursuing M.E (Software Engineering) in Vins Christian College of
Engineering,
e-mail:
Gayathri.S, M.E., Asst.Prof.,Department of Information Technology , Vins Christian college of Engineering."
afb1bc830febdb9893fd938fbdb20856b4ff3922,Defoiling Foiled Image Captions,"Defoiling Foiled Image Captions
Pranava Madhyastha, Josiah Wang and Lucia Specia
Department of Computer Science
University of Sheffield, UK
{p.madhyastha, j.k.wang,"
af1fa9d29512fc8f4c07efdf75d3f640567a5262,Sparse Representation for Face Recognition Based on Constraint Sampling and Face Alignment,"TSINGHUA SCIENCE AND TECHNOLOGY
ISSNll1007-0214ll08/12llpp62-67
Volume 18, Number 1, February 2013
Sparse Representation for Face Recognition Based on Constraint
Sampling and Face Alignment
Jing Wang, Guangda Su(cid:3), Ying Xiong, Jiansheng Chen, Yan Shang, Jiongxin Liu, and Xiaolong Ren"
af740db182b541eef80bb0a2dfebd1f07bb0e316,Deformable Kernel Networks for Joint Image Filtering,"Deformable Kernel Networks for Joint Image Filtering
Beomjun Kim, Jean Ponce, Bumsub Ham
To cite this version:
Beomjun Kim, Jean Ponce, Bumsub Ham. Deformable Kernel Networks for Joint Image Filtering.
018. <hal-01857016v2>
HAL Id: hal-01857016
https://hal.archives-ouvertes.fr/hal-01857016v2
Submitted on 10 Oct 2018
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés."
39df6ca15f41e5a674ed8cd1654e699dbc8b8c11,Human tracking over camera networks: a review,"Hou et al. EURASIP Journal on Advances in Signal Processing  (2017) 2017:43
DOI 10.1186/s13634-017-0482-z
EURASIP Journal on Advances
in Signal Processing
R EV I E W
Human tracking over camera networks: a
review
Li Hou1,2,3*, Wanggen Wan1,3, Jenq-Neng Hwang4, Rizwan Muhammad1,3, Mingyang Yang1,3 and Kang Han1,3
Open Access"
39d406df1823aad167a429f60ae8f1d3dc4250fa,Scaling for Multimodal 3D Object Detection,"Scaling for Multimodal 3D Object Detection
Andrej Karpathy
Stanford"
39803a9c075d543e19384d79fb4c36b207892179,Regression Techniques versus Discriminative Methods for Face Recognition,"Regression Techniques versus Discriminative Methods for Face
Recognition
Vitomir ˇStruc, France Miheliˇc, Rok Gajˇsek and Nikola Paveˇsi´c"
3903cbd56446436a4a3b8443c26c90fc1b69f5e0,Event driven software architecture for multi-camera and distributed surveillance research systems,"Event Driven Software Architecture for Multi-camera and Distributed
Surveillance Research Systems
Roberto Vezzani, Rita Cucchiara
University of Modena and Reggio Emilia - Italy"
39ad6ce5f67c76fc1488ce6a31ebdf229b3f34df,Detection of Humans Using Color Information,"Detection of Humans Using Color
Information
V·(cid:17)t Nov·ak
May 23, 2001"
39dc2ce4cce737e78010642048b6ed1b71e8ac2f,Recognition of six basic facial expressions by feature-points tracking using RBF neural network and fuzzy inference system,"Recognition of Six Basic Facial Expressions by Feature-Points Tracking using
RBF Neural Network and Fuzzy Inference System
Hadi Seyedarabi*, Ali Aghagolzadeh **, Sohrab Khanmohammadi **
*Islamic Azad University of AHAR
**Elect. Eng. Faculty, Tabriz University, Tabriz, Iran"
39a71ceb241f34b30ffd248669346c059dd1ec97,Gait Recognition Based on Human Body Components,"-4244-1437-7/07/$20.00 ©2007 IEEE
I - 353
ICIP 2007"
398ad0036b899aec04502c243dd129c1f3e4c21e,Object detection using voting spaces trained by few samples,"Downloaded From: https://www.spiedigitallibrary.org/journals/Optical-Engineering on 12/17/2017 Terms of Use: https://www.spiedigitallibrary.org/terms-of-use
ObjectdetectionusingvotingspacestrainedbyfewsamplesPeiXuMaoYeXueLiLishenPeiPengweiJiao"
399a5f7500648462fd8cf1704dfaeaea9d560e7e,Spoof Detection for Finger-Vein Recognition System Using NIR Camera,"Article
Spoof Detection for Finger-Vein Recognition System
Using NIR Camera
Dat Tien Nguyen, Hyo Sik Yoon, Tuyen Danh Pham and Kang Ryoung Park *
Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu,
Seoul 100-715, Korea; (D.T.N.); (H.S.Y.);
(T.D.P.)
* Correspondence: Tel.: +82-10-3111-7022; Fax: +82-2-2277-8735
Received: 15 August 2017; Accepted: 27 September 2017; Published: 1 October 2017"
390e212d4a874d8d2256e55fe0dee9193e4c376a,Just in Time: Controlling Temporal Performance in Crowdsourcing Competitions,"Just in Time: Controlling Temporal Performance in
Crowdsourcing Competitions
Markus Rokicki
L3S Research Center,
Hannover, Germany
Electronics and Computer
Science, University of
Southampton, Southampton,
Sergej Zerr"
39149c51c5ab3442b43b8d19eca704efde450f51,Unsupervised object discovery and localization in the wild: Part-based matching with bottom-up region proposals,"Unsupervised Object Discovery and Localization in the Wild:
Part-based Matching with Bottom-up Region Proposals
Suha Kwak1,∗
´Ecole Normale Sup´erieure / PSL Research University
Cordelia Schmid1,†
Jean Ponce2,∗
Minsu Cho1,∗
Inria"
39a6b9c8d45aa3bf29aede1ba0d4060a4afb834a,Inferring The Latent Structure of Human Decision-Making from Raw Visual Inputs,"Inferring The Latent Structure of Human Decision-Making
from Raw Visual Inputs
Yunzhu Li 1 Jiaming Song 2 Stefano Ermon 2"
39c8b34c1b678235b60b648d0b11d241a34c8e32,Learning to Deblur Images with Exemplars,"Learning to Deblur Images with Exemplars
Jinshan Pan∗, Wenqi Ren∗, Zhe Hu∗, and Ming-Hsuan Yang"
39df4f8ad7add3863208a5f7b71e22ed1970ca58,Bayesian supervised dictionary learning,"Bayesian Supervised Dictionary learning
B. Babagholami-Mohamadabadi
A. Jourabloo
M. Zolfaghari
M.T. Manzuri-Shalmani
CE Dept.
Sharif University
Tehran, Iran
CE Dept.
Sharif University
Tehran, Iran
CE Dept.
Sharif University
Tehran, Iran
CE Dept.
Sharif University
Tehran, Iran"
3910b1cc849f999dc8a2c02a0313be32dd5d2b43,A Systematic Comparison of Deep Learning Architectures in an Autonomous Vehicle,"A Systematic Comparison of Deep Learning Architectures in an
Autonomous Vehicle
Michael Teti1†, William Edward Hahn1, Shawn Martin2, Christopher Teti3, and Elan Barenholtz1
such tasks, or an attempt
largely due to recent developments"
397085122a5cade71ef6c19f657c609f0a4f7473,Using Segmentation to Predict the Absence of Occluded Parts,"GHIASI, FOWLKES: USING SEGMENTATION TO DETECT OCCLUSION
Using Segmentation to Predict the Absence
of Occluded Parts
Golnaz Ghiasi
Charless C. Fowlkes
Dept. of Computer Science
University of California
Irvine, CA"
39ed31ced75e6151dde41944a47b4bdf324f922b,Pose-Guided Photorealistic Face Rotation,"Pose-Guided Photorealistic Face Rotation
Yibo Hu1,2, Xiang Wu1, Bing Yu3, Ran He1,2 ∗, Zhenan Sun1,2
CRIPAC & NLPR & CEBSIT, CASIA 2University of Chinese Academy of Sciences
Noah’s Ark Laboratory, Huawei Technologies Co., Ltd.
{yibo.hu, {rhe,"
39b080aea9b342947058884ca25fb5bb1b8f6d66,Fully Automated and Highly Accurate Dense Correspondence for Facial Surfaces,"Fully Automated and Highly Accurate Dense
Correspondence for Facial Surfaces
C. Martin Grewe and Stefan Zachow
Mathematics for Life and Materials Sciences,
Zuse Institute Berlin, Germany
Fig. 1: Two facial expressions (a,b) from our database set into dense correspon-
dence using the proposed framework. High geometric and photometric details are
ccurately morphed between both expressions via a dense corresponding mesh."
3964caa0a1d788eb30365972880f83b71df1ab21,Multi-Modal Obstacle Detection in Unstructured Environments with Conditional Random Fields,"Multi-Modal Obstacle Detection in Unstructured
Environments with Conditional Random Fields
Mikkel Kragh1 and James Underwood2"
39a76fdc4b2d4b9e8ef8f69a87d17ae930520acc,Occlusion-Aware Human Pose Estimation with Mixtures of Sub-Trees,"Occlusion-Aware Human Pose Estimation with
Mixtures of Sub-Trees
Ibrahim Radwan∗, Abhinav Dhall and Roland Goecke"
392c3cabe516c0108b478152902a9eee94f4c81e,Tiny images,"Computer Science and Artificial Intelligence Laboratory
Technical Report
MIT-CSAIL-TR-2007-024
April 23, 2007
Tiny images
Antonio Torralba, Rob Fergus, and William T. Freeman
m a s s a c h u s e t t s   i n s t i t u t e   o f   t e c h n o l o g y,   c a m b r i d g e ,   m a   0 213 9   u s a   —   w w w. c s a i l . m i t . e d u"
3918dcfddf2da218a615dd8f008f6fce436e06f7,Learning Sight from Sound: Ambient Sound Provides Supervision for Visual Learning,"Int J Comput Vis manuscript No.
(will be inserted by the editor)
Learning Sight from Sound:
Ambient Sound Provides Supervision for Visual Learning
Andrew Owens · Jiajun Wu · Josh H. McDermott · William T. Freeman ·
Antonio Torralba
Received: date / Accepted: date"
3902f38412c6dd8915bb408dea500650864b8d6e,Predicting Actions from Static Scenes,"Predicting Actions from Static Scenes
Tuan-Hung Vu1, Catherine Olsson2, Ivan Laptev1,
Aude Oliva2, and Josef Sivic1
WILLOW, ENS/INRIA/CNRS UMR 8548, Paris, France
CSAIL, MIT, Cambridge, Massachusetts, USA"
39a49d7d84305d334b5fb00c25b812bffdd772a6,Sélection de modèles en classification supervisée,"INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET EN AUTOMATIQUE
Model selection in supervised classification
Guillaume Bouchard — Gilles Celeux
N° 5391
November 2004
Thème COG
p p o r t  (cid:13)
(cid:13) d e  r e c h e r c h e (cid:13)"
39fc0fe46ddf43f13073cbab077d981547889dc1,USING GRADIENT FEATURES FROM SCALE-INVARIANT KEYPOINTS ON FACE RECOGNITION,"International Journal of Innovative
Computing, Information and Control
Volume 7, Number 4, April 2011
ICIC International c⃝2011 ISSN 1349-4198
pp. 1639{1649
USING GRADIENT FEATURES FROM SCALE-INVARIANT
KEYPOINTS ON FACE RECOGNITION
Shinfeng D. Lin, Jia-Hong Lin and Cheng-Chin Chiang
Department of Computer Science and Information Engineering
National Dong Hwa University
No. 1, Sec. 2, Da Hsueh Rd., Shoufeng, Hualien 97401, Taiwan
f david; bbmac;
Received November 2009; revised March 2010"
3917bf2cc075ef075d9c879fc9ec3349ea116735,Discriminant Analysis by Locally Linear Transformations,"Discriminant Analysis by Locally Linear
Transformations
Tae-Kyun Kim1,2, Josef Kittler2, Hyun-Chul Kim3, and Seok Cheol Kee1
: Samsung Advanced Institute of Technology, KOREA
: Center for Vision, Speech and Signal Processing, University of
Surrey,U.K.
: Pohang University of Science and Technology, KOREA"
397fffa6f785762acb3cd3c96c4c6b65058b816f,Modeling mutual context of object and human pose in human-object interaction activities,"Modeling Mutual Context of Object and
Human Pose in Human-object Interaction
Activities
•  Bangpeng Yao
•  Li Fei-Fei
Presented by Sahil Shah"
397c395aed9d96aef064b9ceb9f0eae9421eb00a,An Evaluation of the Pedestrian Classification in a Multi-Domain Multi-Modality Setup,"Sensors 2015, 15, 13851-13873; doi:10.3390/s150613851
OPEN ACCESS
sensors
ISSN 1424-8220
www.mdpi.com/journal/sensors
Article
An Evaluation of the Pedestrian Classification in
Multi-Domain Multi-Modality Setup
Alina Miron 1,*, Alexandrina Rogozan 2, Samia Ainouz 2, Abdelaziz Bensrhair 2
nd Alberto Broggi 3
ISR Laboratory, University of Reading, Reading RG6 6AY, UK
INSA Rouen/LITIS laboratory - EA4108, Saint-Etienne du Rouvray 76801, France;
E-Mails: (A.R.); (S.A.);
(A.B.)
VisLab, University of Parma, Parco Area delle Scienze 181A, 43100 Parma, Italy;
E-Mail:
* Author to whom correspondence should be addressed; E-Mail:
Tel.: +44-118-378-7631.
Academic Editor: Vittorio M.N. Passaro
Received: 2 April 2015 / Accepted: 8 June 2015 / Published: 12 June 2015"
39d08fa8b028217384daeb3e622848451809a422,Variational Approaches for Auto-Encoding Generative Adversarial Networks,"Variational Approaches for Auto-Encoding
Generative Adversarial Networks
Mihaela Rosca∗ Balaji Lakshminarayanan∗ David Warde-Farley
Shakir Mohamed
DeepMind"
390dc36d547dbf9bc9774ec8de454e6317a2d170,InclusiveFaceNet: Improving Face Attribute Detection with Race and Gender Diversity,"Improving Face Attribute Detection with Race and Gender Diversity
InclusiveFaceNet:
Hee Jung Ryu 1 Hartwig Adam * 1 Margaret Mitchell * 1"
39340257d9a478b3c3b736ad31df1c0a6a78c851,Parts-based object recognition seeded by frequency-tuned saliency for child detection in active safety,"Parts-based object recognition seeded by frequency-tuned saliency for
Child Detection in Active Safety
Shinko Y. Cheng, Jose Molineros, Yuri Owechko
HRL Laboratories, LLC
011 Malibu Canyon Road
Malibu CA 90265"
3965d73c9d7c97cdb391bfd86a15bfd3534cbd32,Deep Learning for Visual Question Answering,"Deep Learning for Visual Question Answering
Avi Singh"
39b0bce87eec467adfe5bebcfe628ff5bd397fc7,"R 4-A . 2 : Rapid Similarity Prediction , Forensic Search & Retrieval in Video","R4-A.2: Rapid Similarity Prediction, Forensic
Search & Retrieval in Video
PARTICIPANTS
Venkatesh Saligrama
David Castañón
Ziming Zhang
Gregory Castañón
Yuting Chen
Marc Eder
Faculty/Staff
Institution
Title
Co-PI
Co-PI
Post-Doc
Graduate, Undergraduate and REU Students
Degree Pursued
Institution
Email
Month/Year of Graduation"
395978c1dee9fd75bbcb249e74ad6fb4d3c2b9fc,A Reliable Hybrid Technique for Human Face Detection,"Hakim A., Marsland S. and W. Guesgen H. (2010).
A RELIABLE HYBRID TECHNIQUE FOR HUMAN FACE DETECTION.
In Proceedings of the International Conference on Computer Vision Theory and Applications, pages 241-244
Copyright c(cid:13) SciTePress"
39bce1d5e4b31a555f12f0a44e92abcad73aab4f,"Explorer "" Here ' s looking at you , kid ""","""Here's looking at you, kid""
Citation for published version:
Marin-Jimenez, M, Zisserman, A & Ferrari, V 2011, ""Here's looking at you, kid"": Detecting people looking at
each other in videos. in Proceedings of the British Machine Vision Conference (BMVC): Dundee, September
011. BMVA Press, pp. 22.1-22.12. DOI: 10.5244/C.25.22
Digital Object Identifier (DOI):
0.5244/C.25.22
Link:
Link to publication record in Edinburgh Research Explorer
Document Version:
Peer reviewed version
Published In:
Proceedings of the British Machine Vision Conference (BMVC)
General rights
Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s)
nd / or other copyright owners and it is a condition of accessing these publications that users recognise and
bide by the legal requirements associated with these rights.
Take down policy
The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer
ontent complies with UK legislation. If you believe that the public display of this file breaches copyright please"
39af7f265dc9d647d97d94a1d0c12fa36f9f79e9,Deep Anticipation: Light Weight Intelligent Mobile Sensing in IoT by Recurrent Architecture,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
Deep Anticipation: Lightweight Intelligent
Mobile Sensing in IoT by Recurrent Architecture
Guang Chen*1,4, Shu Liu*2, Kejia Ren1, Zhongnan Qu2, Changhong Fu5, Gereon Hinz3, and Alois Knoll4"
3979e8ddcf95fedf7a220b7d39a72fa120d436f8,Deep Learning Applied to Image and Text Matching,"Deep Learning applied to Image and Text
matching
MASTER THESIS IN COMPUTER SCIENCE
Author:
Afroze Ibrahim Baqapuri
Supervisors:
Dr. François Fleuret
Dr. Eric Cosatto
January 15, 2016"
39e7ac344b17d97267ec80681aeded17e3e6d786,Joint Parsing of Cross-view Scenes with Spatio-temporal Semantic Parse Graphs,"Joint Parsing of Cross-view Scenes with Spatio-temporal Semantic Parse Graphs∗
Hang Qi1∗, Yuanlu Xu1∗, Tao Yuan1∗, Tianfu Wu2, Song-Chun Zhu1
Dept. Computer Science and Statistics, University of California, Los Angeles (UCLA)
{hangqi, tianfu
Dept. Electrical and Computer Engineering, NC State University"
39db2ff704cc30a7e94989de33ff4290ea4a6df1,Low-Cost Visual Feature Representations For Image Retrieval,"Low-Cost Visual Feature Representations For Image
Retrieval
Ramon F. Pessoa, William R. Schwartz, Jefersson A. dos Santos
Department of Computer Science
Universidade Federal de Minas Gerais (UFMG)
Belo Horizonte - Minas Gerais, Brazil, 31270-901
Email: {ramon.pessoa, william,"
391e52ac04408d3e6496614ffafd6ac89c1b6c45,Seeing 3D Chairs: Exemplar Part-Based 2D-3D Alignment Using a Large Dataset of CAD Models,"Seeing 3D chairs: exemplar part-based 2D-3D alignment
using a large dataset of CAD models
Mathieu Aubry1,∗ Daniel Maturana2 Alexei A. Efros3,∗ Bryan C. Russell4
Josef Sivic1,∗
INRIA 2Carnegie Mellon University
UC Berkeley
Intel Labs"
3988ed2b900af26c07432d0f9f3c2679f3c532ac,Vision Meets Drones: A Challenge,"Vision Meets Drones: A Challenge
Pengfei Zhu, Longyin Wen, Xiao Bian, Haibin Ling and Qinghua Hu"
399f321089a3a35ddf0e92435dfc374cbcc3dbbd,A Novel Approach for Automatic Standing Upper Body Extraction Based on Skin Detection and Anthropometric Constraint from Single Image,"International Journal of Engineering, Management & Sciences (IJEMS)
ISSN-2348 –3733,  Volume-3, Issue-6, JUNE- 2016
A Novel Approach for Automatic Standing Upper
Body Extraction Based on Skin Detection and
Anthropometric Constraint from Single Image
Rashmi sinha, Vijay Kumar Sharma"
3933e323653ff27e68c3458d245b47e3e37f52fd,Evaluation of a 3 D-aided Pose Invariant 2 D Face Recognition System,"Evaluation of a 3D-aided Pose Invariant 2D Face Recognition System
Xiang Xu, Ha A. Le, Pengfei Dou, Yuhang Wu, Ioannis A. Kakadiaris
{xxu18, hale4, pdou, ywu35,
Computational Biomedicine Lab
800 Calhoun Rd. Houston, TX, USA"
395dadff1eab9c8177f843326ec864567342eba5,Vision-Based People Detection System for Heavy Machine Applications,"Article
Vision-Based People Detection System for Heavy
Machine Applications
Vincent Fremont 1,*, Manh Tuan Bui 1, Djamal Boukerroui 1 and Pierrick Letort 2
Received: 12 October 2015; Accepted: 13 January 2016; Published: 20 January 2016
Academic Editor: Vittorio M. N. Passaro
Sorbonne Universités, Université de Technologie de Compiègne, CNRS, UMR 7253,
Heudiasyc-CS 60 319, 60 203 Compiègne Cedex, France; (M.T.B.);
(D.B.)
Technical Center for the Mechanical Industry (CETIM), 60300 Senlis, France;
* Correspondence: Tel.: +33-344-237-917; Fax: +33-344-234-477"
3921afded8bc8471d784df86f64432fb14b8ef58,Egocentric Gesture Recognition for Head-Mounted AR devices,"Egocentric Gesture Recognition for Head-Mounted AR devices
Tejo Chalasani*
Jan Ondrej†
Aljosa Smolic‡
V-SENSE, School of Computer Science and Statistics
Trinity College Dublin"
3998c5aa6be58cce8cb65a64cb168864093a9a3e,Understanding head and hand activities and coordination in naturalistic driving videos,Intelligent Vehicles Symposium 2014
3907d83f14ba9e2b8a93c3f02b04ca0b81901c4b,Semantic segmentation-using Convolutional Neural Networks and Sparse Dictionaries,"Master of Science Thesis in Electrical Engineering
Department of Electrical Engineering, Linköping University, 2017
Semantic segmentation
- using Convolutional Neural Networks
nd Sparse Dictionaries
Viktor Andersson"
39d900da87fa2f8987567d22a924fb7674f9be67,Generating Notifications for Missing Actions: Don't Forget to Turn the Lights Off!,"Generating Notifications for Missing Actions:
Don’t forget to turn the lights off!
Bilge Soran*, Ali Farhadi*†, Linda Shapiro*
*University of Washington
Allen Institute for Artificial Intelligence
{bilge, ali,
Figure 1: Our purpose is to issue notifications about missing actions given an unsegmented input stream of egocentric video.
For the latte making sequence above, our system recognizes the actions that happened so far, predicts the ongoing action,
reasons about missing actions and the associated cost, and generates notifications for the costly missing actions. In this figure,
the brackets refer to segmented action boundaries, the blue arrows show the prediction points and the graphs below show the
inter-action dependencies. The most recently completed action is marked in red, the predicted action is marked in blue, and
the missing action is marked in orange. In this example, the actor is about to miss an important action: steam milk, and a
reminder for that is given."
399ab5652908d99a5be1a664425f6463f67df2aa,Mechanisms of Diminished Attention to Eyes in Autism.,"Mechanisms of diminished attention to eyes in
utism
Jennifer M. Moriuchi, Emory University
Ami Klin, Emory University
Warren R Jones, Emory University
Journal Title: American Journal of Psychiatry
Volume: Volume 174, Number 1
Publisher: American Psychiatric Publishing | 2017-01-01, Pages 26-35
Type of Work: Article | Post-print: After Peer Review
Publisher DOI: 10.1176/appi.ajp.2016.15091222
Permanent URL: https://pid.emory.edu/ark:/25593/s8mpz
Final published version: http://dx.doi.org/10.1176/appi.ajp.2016.15091222
Copyright information:
018 American Psychiatric Association
Accessed June 11, 2018 8:03 PM EDT"
39a19a687b3182054b30f36f627bc6875b09dbd3,A new boostrapping strategy for the AdaBoost-based face detector T.-J. Chin and D. Suter A new boostrapping strategy for the AdaBoost-based face detector,"Department of Electrical
Computer Systems Engineering
Technical Report
MECSE-13-2005
A new boostrapping strategy for the AdaBoost-based face
detector
T.-J. Chin and D. Suter"
39c6897ed1a7157cd8370f2b9269f9cfc477d64b,Intent-Aware Diverse Social Image Retrieval,Intent-AwareDiverseSocialImageRetrievalWangBo
399e1b0b84d6b5199646892e8bddb9e4d7f45362,Memorable Maps: A Framework for Re-defining Places in Visual Place Recognition,"Memorable Maps: A Framework for
Re-defining Places in Visual Place
Recognition
Mubariz Zaffar1, Shoaib Ehsan1, Michael Milford2 and Klaus
Mcdonald Maier1"
a2505774d5654685c6d899760759520b339e6c1e,Ranking Eigenfaces Through Adaboost and Perceptron Ensembles,"Ranking Eigenfaces Through Adaboost and
Perceptron Ensembles
Tiene A. Filisbino, Gilson A. Giraldi
Laborat´orio Nacional de Computac¸˜ao Cient´ıfica - LNCC
Petr´opolis, Brasil
Email:
Carlos Eduardo Thomaz
Departamento de Engenharia El´etrica
Centro Universit´ario da FEI
S˜ao Bernardo do Campo - Brasil
Email:"
a271f83cb1f72e0f9ca077499f51adb086fb449d,Unsupervised and Semi-supervised Methods for Human Action Analysis,"Unsupervised and
Semi-supervised Methods
for Human Action Analysis
Simon Jones
September 22, 2014
A thesis submitted in partial fulfillment of the
requirements for the degree of
Doctor of Philosophy
Department of Electronic and Electrical Engineering
The University of Shef‌f‌ield"
a2268e3ec394bd8e7dfb131b931fdc00547d3f39,Neural learning methods for visual object detection; Neuronale Lernverfahren zur visuellen Objekterkennung,"Neural learning methods for
visual object detection
(Neuronale Lernverfahren zur
visuellen Objekterkennung)
Dissertation
zur Erlangung des Grades
”Doktor der Naturwissenschaften”
n der Fakult¨at f¨ur Physik und Astronomie
der Ruhr-Universit¨at Bochum
vorgelegt von
Alexander Rainer Tassilo Gepperth
m 19.April 2006"
a237e3d89c460e1b2e3f12c5d4275bd0c6eb47a8,Domain Adaptation on Graphs by Learning Aligned Graph Bases,"Domain Adaptation on Graphs by Learning
Aligned Graph Bases
Mehmet Pilancı and Elif Vural"
a2ad9ae7c5adbbce9ded16ac3ebdfa96505c0f46,Déjà Image-Captions: A Corpus of Expressive Descriptions in Repetition,"Human Language Technologies: The 2015 Annual Conference of the North American Chapter of the ACL, pages 504–514,
Denver, Colorado, May 31 – June 5, 2015. c(cid:13)2015 Association for Computational Linguistics"
a24a0126f76ba1423ac3548ef95aa24ac4e670dd,How Would You Say It ? Eliciting Lexically Diverse Data for Supervised Semantic Parsing,"Saarbr¨ucken, Germany, 15-17 August 2017. c(cid:13)2017 Association for Computational Linguistics
Proceedings of the SIGDIAL 2017 Conference, pages 374–383,"
a22b6aa079c07229d813c53663db689f34290d4d,Dropout Distillation for Efficiently Estimating Model Confidence,"Dropout Distillation for Efficiently Estimating Model Confidence
Corina Gurau, Alex Bewley and Ingmar Posner"
a2b78155f74ba697b76f83aa3cf2d4935434628d,An Evaluation of Trajectory Prediction Approaches and Notes on the TrajNet Benchmark,"An Evaluation of Trajectory Prediction Approaches and
Notes on the TrajNet Benchmark.
Stefan Becker ∗, Ronny Hug ∗, Wolfgang H¨ubner and Michael Arens
Fraunhofer Institute for Optronics, System Technologies, and Image Exploitation IOSB
Gutleuthausstr. 1, 76275 Ettlingen, Germany"
a248805c5d9a5626dbd4f58906b35d991e2aec4b,Multi-Hypothesis Visual-Inertial Flow,"Multi-Hypothesis Visual-Inertial Flow
E. Jared Shamwell1, William D. Nothwang2, Donald Perlis3"
a2c08d63682b348a9848a77f9b97378a37a6b447,Curriculum Adversarial Training,
a23e7e71fb92a56c2e7717f6356e8b69fc2f4bfc,"Multimodal fusion of audio, scene, and face features for first impression estimation","Multimodal Fusion of Audio, Scene, and Face
Features for First Impression Estimation
Furkan G¨urpınar
Program of Computational
Science and Engineering
Bo˘gazic¸i University
Bebek, Istanbul, Turkey
Email:
Heysem Kaya
Albert Ali Salah
Department of Computer Engineering
Department of Computer Engineering
Namık Kemal University
C¸ orlu, Tekirda˘g, Turkey
Email:
Bo˘gazic¸i University
Bebek, Istanbul, Turkey
Email:"
a212be7ec1ff75ecfee52c7c49c73d7244a87eb7,Video Scene-Aware Dialog Track in DSTC 7,"Video Scene-Aware Dialog Track in DSTC7
Chiori Hori∗, Tim K. Marks∗, Devi Parikh∗∗, and Dhruv Batra∗∗
Mitsubishi Electric Research Laboratories
Cambridge, MA, USA
{chori,
School of Interactive Computing
Georgia Tech
{parikh,"
a2bfab80a4b48717aa647cb38069632c5962c6a6,Countering Bias in Tracking Evaluations,
a21b8aadb27cd10d8a228fe1aad27c0c88d67f15,Design and Implementation of PC Operated Flying Robot for Rescue Operation in Coalmines,"ISSN: 2278 – 7798
International Journal of Science, Engineering and Technology Research (IJSETR)
Volume 2, Issue 1, January 2013
Design and Implementation of PC Operated
Flying Robot for Rescue Operation in
Coalmines
Aditya Kumar T , Pravin A, M S Madhan mohan, T V Janardhanarao"
a27735e4cbb108db4a52ef9033e3a19f4dc0e5fa,Intention from Motion,"Intention from Motion
Andrea Zunino, Jacopo Cavazza, Atesh Koul, Andrea Cavallo, Cristina Becchio and Vittorio Murino"
a2afaa782be91f5baf9e9f1794d57dd29143cbf4,IGCV$2$: Interleaved Structured Sparse Convolutional Neural Networks,"IGCV2: Interleaved Structured Sparse Convolutional Neural Networks
Guotian Xie1,2,∗ Jingdong Wang3 Ting Zhang3
Jianhuang Lai1,2 Richang Hong4 Guo-Jun Qi5
Sun Yat-Sen University 2Guangdong Key Laboratory of Information Security Technology
Microsoft Research 4Hefei University of Technology 5University of Central Florida"
a2db611b6179f3bc4cfe0e891df7b9d4ab58d642,On the usability of deep networks for object-based image analysis,"ON THE USABILITY OF DEEP NETWORKS FOR OBJECT-BASED IMAGE ANALYSIS
Nicolas Audeberta, b, Bertrand Le Sauxa, Sébastien Lefèvreb
ONERA, The French Aerospace Lab, F-91761 Palaiseau, France
Univ. Bretagne-Sud, UMR 6074, IRISA, F-56000 Vannes, France -
KEY WORDS: deep learning, vehicle detection, semantic segmentation, object classification"
a2359c0f81a7eb032cff1fe45e3b80007facaa2a,Towards Structured Analysis of Broadcast Badminton Videos,"Towards Structured Analysis of Broadcast Badminton Videos
Anurag Ghosh
Suriya Singh
C.V.Jawahar
{anurag.ghosh,
CVIT, KCIS, IIIT Hyderabad"
a27c7afac5a34141ec5415defed6d4d85325230a,Utrecht Multi-Person Motion ( UMPM ) benchmark,"Utrecht Multi-Person Motion (UMPM)
enchmark
N.P. van der Aa, X. Luo, G.-J. Giezeman
R.T. Tan, R.C. Veltkamp
Technical Report UU-CS-2011-027
September 2011
Department of Information and Computing Sciences
Utrecht University, Utrecht, The Netherlands
www.cs.uu.nl"
a2fce1c551a3c3b1cac16a96f86a59cd7fbd4c80,Attachment and Children’s Biased Attentional Processing: Evidence for the Exclusion of Attachment-Related Information,"Attachment and Children’s Biased Attentional
Processing: Evidence for the Exclusion of Attachment-
Related Information
Eva Vandevivere1*, Caroline Braet1, Guy Bosmans2, Sven C. Mueller3, Rudi De Raedt3
Department of Developmental, Personality and Social Psychology, Ghent University, Gent, Belgium, 2 Parenting and Special Education Research Unit, Leuven, Belgium,
Department of Experimental Clinical and Health Psychology, Ghent University, Ghent, Belgium"
a2b9c998264ab1920ea8f2e07c3590ebb3dc6f35,Shopper Analytics: A Customer Activity Recognition System Using a Distributed RGB-D Camera Network,"Shopper Analytics: a customer activity
recognition system using a distributed RGB-D
amera network
Daniele Liciotti, Marco Contigiani, Emanuele Frontoni, Adriano Mancini,
Primo Zingaretti1, and Valerio Placidi2
Dipartimento di Ingegneria dell’Informazione, Universit`a Politecnica delle Marche,
{d.liciotti, m.contigiani,e.frontoni, a.mancini,
Via Brecce Bianche, 60131 Ancona, Italy,
Grottini Lab srl,
Via S.Maria in Potenza, 62017, Porto Recanati, Italy,"
a2f2996145d3d670608af1cbbda59c1ac28d4f7c,Real-Time Hand Posture Recognition for Human-Robot Interaction Tasks,"Article
Real-Time Hand Posture Recognition for
Human-Robot Interaction Tasks
Uriel Haile Hernandez-Belmonte and Victor Ayala-Ramirez *
Received: 30 October 2015; Accepted: 18 December 2015; Published: 4 January 2016
Academic Editor: Lianqing Liu
Universidad de Guanajuato DICIS, Carr. Salamanca-Valle Km. 3.5 + 1.8, Palo Blanco, Salamanca, C.P. 36885,
Mexico;
* Correspondence: Tel.: +52-464-647-9940 (ext. 2413); Fax: +52-464-647-9940 (ext. 2311)"
a24f84b156bbb1edeb1d0761f5940de318b7ed9d,Copula Eigenfaces - Semiparametric Principal Component Analysis for Facial Appearance Modeling,
a290019f7125f6ebdc0dcec3b03b771de6905dd0,Heterogeneous AdaBoost with Real-time Constraints - Application to the Detection of Pedestrians by Stereovision,"HETEROGENEOUS ADABOOST WITH REAL-TIME
Application to the Detection of Pedestrians by stereovision
CONSTRAINTS
Lo¨ıc Jourdheuil1, Nicolas Allezard1, Thierry Chateau2 and Thierry Chesnais1
CEA, LIST, Laboratoire Vision et Ing´enierie des Contenus, Gif-sur-Yvette, France
LASMEA, UMR UBP-CNRS 6602, 24 Avenue des Landais, AUBIERE, France
{loic.jourdheuil, nicolas.allezard,
Keywords:
Adaboost. stereovision. real time."
a2a42aa37641490213b2de9eb8e83f3dab75f5ed,Multilinear Supervised Neighborhood Preserving Embedding Analysis of Local Descriptor Tensor,"We are IntechOpen,
the world’s leading publisher of
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a28f831b4014fa75a69f3c56e39d9c40fc0af48f,AAD: Adaptive Anomaly Detection through traffic surveillance videos,"AAD: Adaptive Anomaly Detection through traffic
surveillance videos
Mohammad Farhadi Bajestani
Seyed Soroush Heidari Rahmat Abadi
Seyed Mostafa Derakhshandeh Fard
Roozbeh Khodadadeh"
a2aa272b32c356ec9933b32ca5809c09f2d21b9f,Clockwork Convnets for Video Semantic Segmentation,"Clockwork Convnets for Video Semantic Segmentation
Evan Shelhamer(cid:63)
Kate Rakelly(cid:63)
Judy Hoffman(cid:63)
Trevor Darrell
UC Berkeley"
a27740f8a3834d6bc605a6b383c4d802ced373c9,"Exploiting feature representations through similarity learning, post-ranking and ranking aggregation for person re-identification","Exploiting feature representations through similarity learning, post-ranking and
ranking aggregation for person re-identification
Julio C. S. Jacques Juniora,b,∗, Xavier Bar´oa,b, Sergio Escalerac,b
Faculty of Computer Science, Multimedia and Telecommunication - Universitat Oberta de Catalunya, Spain
Computer Vision Center - Universitat Aut`onoma de Barcelona, Spain
Department of Mathematics and Informatics - University of Barcelona, Spain"
f3a9651405695d03bb76e141a7999f2addc4225d,Constellation Models for Recognition of Generic Objects,
f3b56b873c48929361c1cada7b18177e3f4d2727,"Development of a N-type GM-PHD Filter for Multiple Target, Multiple Type Visual Tracking","Development of a N-type GM-PHD Filter for
Multiple Target, Multiple Type Visual Tracking
Nathanael L. Baisa , Student Member, IEEE, and Andrew Wallace, Fellow, IET
faced challenges not only in the uncertainty caused by data
ssociation but also in algorithmic complexity that increases
exponentially with the number of targets and measurements.
For instance, the MHT has an exponential complexity with
time and cubic with the number of targets.
To address the problems of increasing complexity, a unified
framework which directly extends single to multiple target
tracking by representing multi-target states and observations
s random finite sets (RFS) was developed by Mahler [7].
This estimates the states and cardinality of an unknown and
time varying number of targets in the scene, and allows for
target birth, death, handling clutter (false alarms), and missing
detections. Mahler [7] proposed to propagate the first-order
moment of the multi-target posterior, called the Probability
Hypothesis Density (PHD), rather than the full multi-target
posterior."
f3b3d2c0d1d84a7f7bbaaaecb58457c15a947544,Understanding Grounded Language Learning Agents,"UNDERSTANDING GROUNDED LANGUAGE LEARNING
AGENTS
Felix Hill, Karl Moritz Hermann, Phil Blunsom & Stephen Clark
Deepmind
London
{felixhill, kmh, pblunsom,"
f3dc67bb4cd3601ae9bdb7df4ed5036f525ff21d,Multimodal 2 DCNN action recognition from RGB-D Data with Video Summarization,"Master’s Thesis
Multimodal 2DCNN action recognition from
RGB-D Data with Video Summarization
Vicent Roig Ripoll
Master
Artificial Intelligence
Advisor: Sergio Escalera Guerrero
Co-advisor: Maryam Asadi-Aghbolaghi
October, 2017"
f308e00f4fa0f0a995a82e9b825ebcfa3ba0d9f1,Embedded System for Biometric Identiication,"We are IntechOpen,
the world’s leading publisher of
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f3a34525fa7021322f132c80c9517f240cf1e742,Pose and Pathosformel in Aby Warburg's Bilderatlas,"Pose and Pathosformel in Aby Warburg’s
Bilderatlas
Leonardo Impett, Sabine S¨usstrunk
School of Computer and Communication Sciences,
´Ecole F´ed´erale Polytechnique de Lausanne, Switzerland"
f3161b75de1e37b0591f250068b676ea72d1ba22,"Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning","Motion Planning Among Dynamic, Decision-Making Agents
with Deep Reinforcement Learning
Michael Everett‡, Yu Fan Chen†, and Jonathan P. How‡"
f3412c087282fcd60f08083515714f97179bedcb,"A study on different experimental configurations for age, race, and gender estimation problems","Carcagnì et al. EURASIP Journal on Image and Video Processing  (2015) 2015:37
DOI 10.1186/s13640-015-0089-y
RESEARCH
Open Access
A study on different experimental
onfigurations for age, race, and gender
estimation problems
Pierluigi Carcagnì*, Marco Del Coco, Dario Cazzato, Marco Leo and Cosimo Distante"
f34c85c24661ba9990146737fd557f7508677263,A New Pedestrian Detection Descriptor Based on the Use of Spatial Recurrences,"A New Pedestrian Detection Descriptor
Based on the Use of Spatial Recurrences
Carlos Serra-Toro and V. Javier Traver
Departamento de Lenguajes y Sistemas Inform´aticos &
Institute of New Imaging Technologies,
Universitat Jaume I, 12071 Castell´on, Spain"
f3d9e347eadcf0d21cb0e92710bc906b22f2b3e7,"NosePose : a competitive , landmark-free methodology for head pose estimation in the wild","NosePose: a competitive, landmark-free
methodology for head pose estimation in the wild
Fl´avio H. B. Zavan, Antonio C. P. Nascimento, Olga R. P. Bellon and Luciano Silva
IMAGO Research Group - Universidade Federal do Paran´a"
f33c427dc152c20537d2857bee1dda2287e85860,Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks,
f3c60536cf7a397c9df6bca549824841a6d7598c,Automatic architecture selection for probability density function estimation in computer vision,"7308011
UNIVERSITY  OF  SURREY  LIBRARY"
f36c3ddd43ea7c2e803694aad89e5fd903715c81,"Biometric quality: a review of fingerprint, iris, and face","Bharadwaj et al. EURASIP Journal on Image and Video Processing 2014, 2014:34
http://jivp.eurasipjournals.com/content/2014/1/34
REVIEW
Open Access
Biometric quality: a review of fingerprint, iris,
nd face
Samarth Bharadwaj, Mayank Vatsa* and Richa Singh"
f31c9328b5b4678388c19a39064a8056313f7cf4,Two-Stream Multi-Rate Recurrent Neural Network for Video-Based Pedestrian Re-Identification,"IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. XX, NO. XX, AUGUST 201X
Two-Stream Multi-Rate Recurrent Neural
Network for Video-Based Pedestrian
Re-Identification
Zhiqiang Zeng, Zhihui Li*, De Cheng, Huaxiang Zhang, Kun Zhan and Yi Yang"
f3002f126f7da75f838637cc314b5f8adc09da53,Robust Face Sketch Synthesis via Generative Adversarial Fusion of Priors and Parametric Sigmoid,"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18)
CUHK(b) XM2VTS(c) AR(d) Lighting(e) Pose(f) CelebrityPhotoMWFcGANpGANFigure1:Examplesoffacesketchesgeneratedfromfacephotostakeninthewild,i.e.,underuncontrolledconditions.Weonlyse-lect88subjectsintheCUHKstudentdatasetfortraining.Thesketchesgeneratedbydata-drivenmethod(MWF),model-basedmethod(cGAN)andourmethod(pGAN)arelistedinthesecond,thirdandfourthrows,respectively.(a)-(f)aretheresultsofdifferenttestingphotosfrom(a)theCUHKstudentdataset,(b)theXM2VTSdataset,(c)theARdataset,(d)thelightingvariationset,(e)theposevariationsetand(f)celebrityphotosobtainedfromtheWeb.Ourmethodachievessatisfactoryresultsundersuchuncontrolledcondi-tions.stillfailtosynthesizefacesketchesinthewild.Insuchcir-cumstances,thetestingphotosaretakenunderdifferentcon-ditionsfromthetrainingphotos,containingvariationsofil-lumination,pose,backgroundandethnicorigin,asshowninFigure1.Althoughtherearesomeworks[Zhangetal.,2010;Zhangetal.,2015b;Pengetal.,2016;Zhangetal.,2017;Songetal.,2017;Zhuetal.,2017b]payingattentiontothischallengingproblem,thesemethodsonlydealwithcertainaspects,suchasthelightingandposevariations[Zhangetal.,2010],thebackgroundandnon-facialfactors[Zhangetal.,2015b]orthelightingvariationsandclutterbackground[Pengetal.,2016].Mostexistingmethodssimulatethepro-cessofsketchsynthesisbyusinglowlevelfeatures,whichishardlytobegeneralizedtouncontrolledconditions.Incon-"
f3cf10c84c4665a0b28734f5233d423a65ef1f23,Title Temporal Exemplar-based Bayesian Networks for facialexpression recognition,"Title
Temporal Exemplar-based Bayesian Networks for facial
expression recognition
Author(s)
Shang, L; Chan, KP
Citation
Proceedings - 7Th International Conference On Machine
Learning And Applications, Icmla 2008, 2008, p. 16-22
Issued Date
http://hdl.handle.net/10722/61208
Rights
This work is licensed under a Creative Commons Attribution-
NonCommercial-NoDerivatives 4.0 International License.;
International Conference on Machine Learning and Applications
Proceedings. Copyright © IEEE.; ©2008 IEEE. Personal use of
this material is permitted. However, permission to
reprint/republish this material for advertising or promotional
purposes or for creating new collective works for resale or
redistribution to servers or lists, or to reuse any copyrighted
omponent of this work in other works must be obtained from"
f32db58cbb8319eb8f2cfa2720c810f8410eb569,A software suite for large-scale video- and image-based analytics,"The 8th International Conference on Bioinspired Information and Communications Technologies (BICT2014), pp. 384-385, Boston, December 1-3, 2014
A software suite for large-scale video- and image-based
nalytics
Jasmin Léveillé
Isao Hayashi
Kansai University"
f3062992cb10107b9d1e3699c8a61d5281886c4b,Foreground Consistent Human Pose Estimation Using Branch and Bound,"Foreground Consistent Human Pose Estimation
Using Branch and Bound(cid:2)
Jens Puwein1, Luca Ballan1, Remo Ziegler2, and Marc Pollefeys1
Department of Computer Science, ETH Zurich, Switzerland
Vizrt"
f39b88ac61264e9a33dcdf47722f0d048a8e490f,Interactive Data Integration and Entity Resolution for Exploratory Visual Data Analytics,"(cid:13)Copyright 2015
Kristi Morton"
f3ea181507db292b762aa798da30bc307be95344,Covariance Pooling For Facial Expression Recognition,"Covariance Pooling for Facial Expression Recognition
Computer Vision Lab, ETH Zurich, Switzerland
VISICS, KU Leuven, Belgium
Dinesh Acharya†, Zhiwu Huang†, Danda Pani Paudel†, Luc Van Gool†‡
{acharyad, zhiwu.huang, paudel,"
f3ca251ac3b05397ea6d72f2a9a6f0cf619a2a32,Leveraging Weakly Annotated Data for Fashion Image Retrieval and Label Prediction,"Leveraging Weakly Annotated Data for Fashion Image Retrieval and Label
Prediction
Charles Corbi`ere1, Hedi Ben-Younes1,2, Alexandre Ram´e1, and Charles Ollion1
Heuritech, Paris, France
UPMC-LIP6, Paris, France"
f375bc91a5f7b1f2d36e41841ccc22f202be2dcf,Unsupervised Learning of Depth and Ego-Motion from Video,"Unsupervised Learning of Depth and Ego-Motion from Video
Tinghui Zhou∗
UC Berkeley
Matthew Brown
Google
Noah Snavely
Google
David G. Lowe
Google"
934a77d099a38374ef1babe02d95952c089cce5f,Set of texture descriptors for music genre classification,"Set of texture descriptors for music genre classification
Loris Nanni
Yandre Costa
Department of
Information Engineering
University of Padua
viale Gradenigo 6
5131, Padua, Italy
State University of
Maringa (UEM)
Av. Colombo, 5790
87020-900, Maringa,
Parana, Brazil"
93dce341666b6a57f8888dddb25a3fd37df69b02,Deep Layer Aggregation,"Deep Layer Aggregation
Fisher Yu Dequan Wang
Evan Shelhamer
Trevor Darrell
UC Berkeley"
9391618c09a51f72a1c30b2e890f4fac1f595ebd,Globally Tuned Cascade Pose Regression via Back Propagation with Application in 2D Face Pose Estimation and Heart Segmentation in 3D CT Images,"Globally Tuned Cascade Pose Regression via
Back Propagation with Application in 2D Face
Pose Estimation and Heart Segmentation in 3D
CT Images
Peng Sun
James K Min
Guanglei Xiong
Dalio Institute of Cardiovascular Imaging, Weill Cornell Medical College
April 1, 2015
This work was submitted to ICML 2015 but got rejected. We put the initial
submission ”as is” in Page 2 - 11 and add updated contents at the tail. The
ode of this work is available at https://github.com/pengsun/bpcpr5."
93cbb3b3e40321c4990c36f89a63534b506b6daf,Learning from examples in the small sample case: face expression recognition,"IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 35, NO. 3, JUNE 2005
Learning From Examples in the Small Sample Case:
Face Expression Recognition
Guodong Guo and Charles R. Dyer, Fellow, IEEE"
93cfc6fd29d50fe6589f9506b503f32f6d0372f4,A Face-to-Face Neural Conversation Model,"A Face-to-Face Neural Conversation Model
Hang Chu1,2 Daiqing Li1 Sanja Fidler1,2
University of Toronto 2Vector Institute
{chuhang1122, daiqing,"
939cd1da477fb04ffbe95b20d0ebe0853cb86025,Äääöòòòò Äó Blockin Blockinððþþøøóò Ù××òò Àààööö Blockin Blockin Blockinð Êê Blockinùöööòø Aeaeøûóöö× Ëúò Òò,"eaigFa
izai
igiea
eewk
SveBehke
FeieUivei(cid:127)aBei
i	efC
	eS
Tak	.914195BeiGe
ay
ehkeif.f		bei.dewww.if.f		bei.de/(cid:24)behke
Aba
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izaifafa
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age.
eewee	ehiea
e	aewkwih"
936c7406de1dfdd22493785fc5d1e5614c6c2882,Detecting Visual Text,"012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 762–772,
Montr´eal, Canada, June 3-8, 2012. c(cid:13)2012 Association for Computational Linguistics"
93d3f2e546314305e8102538c4714e30e9146858,Image categorization combining neighborhood methods and boosting,"Image Categorization Combining Neighborhood Methods
nd Boosting
Matthew Cooper
FX Palo Alto Laboratory
Palo Alto, CA 94304 USA"
93e962f8886eae13b02ad2aa98bdedfbd7e68709,Dual Conditional GANs for Face Aging and Rejuvenation,"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18)
Source： datasets(b)Target: our outputs are a series of images belonging to the same person010Input FaceOutputs2  78x/Age groupy/Personality  2178Non-sequential facial imagesSequential facial imagesFigure1:Anillustrationofourfaceagingandrejuvenationpro-cess.As(a)shows,ourtrainingexamplesarenon-sequentialandun-paired,andweaimtosimultaneouslyrenderaseriesofage-changedfacialimagesofapersonandpreservepersonality,asshownin(b).specificallydescribethechangesoffacesindifferentages.Thesemethodsparametricallymodelshapeandtexturepa-rametersfordifferentfeaturesofeachagegroup,e.g.,mus-cles[Suoetal.,2012],wrinkles[RamanathanandChellappa,2008;Suoetal.,2010]andfacialstructure[RamanathanandChellappa,2006;Lanitisetal.,2002].Ontheotherhand,prototype-basedmethods[KemelmacherShlizermanetal.,2014;Tiddemanetal.,2001]dividefacesintogroupsbyage,andthenconstructanaveragefaceasitsprototypeforeachagegroup.Afterthat,thesemethodscantransferthetexturedifferencebetweentheprototypestotheinputfacialimage.Morerecently,thedeeplearning-basedmethod[Wangetal.,2016;Liuetal.,2017]achievedthestate-of-the-artper-formance.In[Wangetal.,2016],RNNisappliedonthecoefficientsofeigenfacesforagepatterntransition.Itper-formsthegroup-basedlearningwhichrequiresthetrueageoftestingfacestolocalizethetransitionstatewhichmightnotbeconvenient.Inaddition,theseapproachesonlypro-videageprogressionfromyoungerfacetoolderones.Toachieveflexiblebidirectionalagechanges,itmayneedtoretrainthemodelinversely.GenerativeAdversarialNet-"
93a4c7ac0b09671db8cd3adbe62851d7befc4658,Machine Analysis of Facial Expressions,"We are IntechOpen,
the world’s leading publisher of
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936627bb98bc9dfe8feef0621af2865356a49e4b,The Future of Video Analytics for Surveillance and Its Ethical Implications,"The Future of Video Analytics for Surveillance
nd Its Ethical Implications
Andrew A. Adams*
James M. Ferryman**
Keywords: Surveillance, CCTV, Video Analytics, Ethics, Regulation, Facial Recognition,
Computer Vision, Cognitive Vision"
9339123da4b56f695b952ca08a052446e6303ed6,Pose2Seg: Detection Free Human Instance Segmentation,"Pose2Seg: Detection Free Human Instance Segmentation
Song-Hai Zhang1, Ruilong Li1, Xin Dong1, Paul Rosin2, Zixi Cai1, Xi Han1, Dingcheng Yang1,
Hao-Zhi Huang3 and Shi-Min Hu1
{li-rl16, dong-x16, caizx15, hanx15,
Tsinghua University 2 Cardiff University 3 Tencent AI Lab"
938566dc8ee83a12d07e4d26bbb75e65ca7963cd,Multi-Scale Singularity Trees (MSSTs),"Multi-Scale Singularity Trees
(MSSTs)
Kerawit Somchaipeng"
93ed1c9274906f1916d58cd618a9a82858448a3f,Deep Learning for Accurate Population Counting in Aerial Imagery,"Deep Learning for Accurate Population Counting in
Aerial Imagery
Matt Epperson, James Rotenberg, Eric Lo, Sebastian Afshari & Brian Kim"
93798ead90afe86636ca582a92cadd846905a95d,Title of dissertation : Learning Visual Classifiers From Limited Labeled Images,
93498110032a458fddebfae80d7a93991e11673d,Brownian descriptor: A rich meta-feature for appearance matching,"Brownian descriptor: a Rich Meta-Feature for Appearance Matching
Sławomir B ˛ak
Ratnesh Kumar
François Brémond
INRIA Sophia Antipolis, STARS group
004, route des Lucioles, BP93
06902 Sophia Antipolis Cedex - France"
931a70ec0bfc1d86894ff37a6f702a033e0129e3,ParlAI: A Dialog Research Software Platform,"ParlAI: A Dialog Research Software Platform
Alexander H. Miller, Will Feng, Adam Fisch, Jiasen Lu,
Dhruv Batra, Antoine Bordes, Devi Parikh and Jason Weston
Facebook AI Research"
930a6ea926d1f39dc6a0d90799d18d7995110862,Privacy-preserving photo sharing based on a secure JPEG,"Privacy-Preserving Photo Sharing
ased on a Secure JPEG
Lin Yuan, Pavel Korshunov, and Touradj Ebrahimi
Multimedia Signal Processing Group, EPFL, Lausanne, Switzerland
Email: {lin.yuan, pavel.korshunov,"
93610676003ef1dcda3864b236bca3852cb05388,RECOGNIZING ACTIVITIES WITH CLUSTER-TREES OF TRACKLETS 1 Recognizing activities with cluster-trees of tracklets,"Recognizing activities with cluster-trees of tracklets
Adrien Gaidon, Zaid Harchaoui, Cordelia Schmid
To cite this version:
Adrien Gaidon, Zaid Harchaoui, Cordelia Schmid. Recognizing activities with cluster-trees of
tracklets. Richard Bowden and John P. Collomosse and Krystian Mikolajczyk. BMVC 2012
- British Machine Vision Conference, Sep 2012, Guildford, United Kingdom. BMVA Press,
pp.30.1-30.13, 2012, <10.5244/C.26.30>. <hal-00722955v2>
HAL Id: hal-00722955
https://hal.inria.fr/hal-00722955v2
Submitted on 7 Aug 2012
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,
´emanant des ´etablissements d’enseignement et de"
930663a0812a7a53963563b647c5957807d3d97d,A unified view of non-monotonic core selection and application steering in heterogeneous chip multiprocessors,"A Unified View of Non-monotonic Core Selection
nd Application Steering in Heterogeneous
Chip Multiprocessors
Sandeep Navada*, Niket K. Choudhary*,
Salil V. Wadhavkar*
CPU Design Center
Qualcomm
Raleigh, NC, USA
{snavada, niketc,"
dcc414adda5432011bfc53359075dd341b924910,LF-Net: Learning Local Features from Images,"LF-Net: Learning Local Features from Images
Yuki Ono
Sony Imaging Products & Solutions Inc.
Eduard Trulls
École Polytechnique Fédérale de Lausanne
Pascal Fua
École Polytechnique Fédérale de Lausanne
Visual Computing Group, University of Victoria
Kwang Moo Yi"
dc452f3e531c4057c930f0538d5652ad9034d1aa,Quality metrics for practical face recognition,"1st International Conference on Pattern Recognition (ICPR 2012)
November 11-15, 2012. Tsukuba, Japan
978-4-9906441-0-9 ©2012 ICPR"
dcf17cc3b4f8519a6789c1ea086689bcbc1d6f11,Unsupervised Learning of Deep Feature Representation for Clustering Egocentric Actions,Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
dcc064b8bf7744801ae7dfe4cbfd11b7e5a5b673,Men's physical strength moderates conceptualizations of prospective foes in two disparate societies.,"Hum Nat
DOI 10.1007/s12110-014-9205-4
Men’s Physical Strength Moderates Conceptualizations
of Prospective Foes in Two Disparate Societies
Daniel M. T. Fessler & Colin Holbrook &
Matthew M. Gervais
# Springer Science+Business Media New York 2014"
dce44d81e06f6703b0e85e778e7afbf3c8d0a401,Multispectral Pedestrian Detection using Deep Fusion Convolutional Neural Networks,"Multispectral Pedestrian Detection using Deep
Fusion Convolutional Neural Networks
J¨org Wagner1,2, Volker Fischer1, Michael Herman1 and Sven Behnke2
- Robert Bosch GmbH - 70442 Stuttgart - Germany
- University Bonn - Computer Science VI, Autonomous Intelligent Systems
Friedrich-Ebert-Allee 144, 53113 Bonn - Germany"
dc6c47d15ffc0fd59e51ed03556c3566afe5710b,Robust Object Recognition Through Symbiotic Deep Learning In Mobile Robots *,"CONFIDENTIAL. Limited circulation. For review only.
Preprint submitted to 2018 IEEE/RSJ International Conference
on Intelligent Robots and Systems. Received March 1, 2018."
dc2c353fa43a75ec92327e323ba5715c400bf92b,Face Recognition in Hyperspectral Images,"IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 25, NO. 12, DECEMBER 2003
Face Recognition in Hyperspectral Images
Zhihong Pan, Student Member, IEEE, Glenn Healey, Senior Member, IEEE,
Manish Prasad, and Bruce Tromberg"
dc2e805d0038f9d1b3d1bc79192f1d90f6091ecb,Face Recognition and Facial Attribute Analysis from Unconstrained Visual Data,
dc090aea412cef17c7a68ec84c34797806feab24,A mixture of gated experts optimized using simulated annealing for 3D face recognition,"978-1-4577-1302-6/11/$26.00 ©2011 IEEE
D FACE RECOGNITION
. INTRODUCTION"
dcace6f0611b77177f4aff4bb650afab0a819575,3D Face Recognition,BMVC 2006 doi:10.5244/C.20.89
dc92aee7d44223cdfa85a8f196d24594703d6ff5,Real-time Joint Object Detection and Semantic Segmentation Network for Automated Driving,"Real-time Joint Object Detection and Semantic
Segmentation Network for Automated Driving
Ganesh Sistu1, Isabelle Leang2 and Senthil Yogamani1
: Valeo Vision Systems, Ireland
: Valeo Bobigny, France"
dc771cd7780538953811a5b6ae0e901ca68cce3d,Multiple People Tracking Using Hierarchical Deep Tracklet Re-identification,"Multiple People Tracking Using Hierarchical Deep Tracklet Re-identification
Maryam Babaee∗
Ali Athar∗
Gerhard Rigoll
Institute for Human-Machine Communication, Technical University of Munich
Arcisstrasse 21, Munich, Germany"
dc23beb1e5c7402b1a9d5a7c854e62a253d0815e,Microscopic crowd simulation : evaluation and development of algorithms. (Simulation microscopique de foules : évaluation et développement d'algorithmes),"Microscopic crowd simulation : evaluation and
development of algorithms
David Wolinski
To cite this version:
David Wolinski. Microscopic crowd simulation : evaluation and development of algorithms. Data
Structures and Algorithms [cs.DS]. Université Rennes 1, 2016. English. <NNT : 2016REN1S036>.
<tel-01420105>
HAL Id: tel-01420105
https://tel.archives-ouvertes.fr/tel-01420105
Submitted on 20 Dec 2016
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
dceaef5e7cbfc4d0150c2d765cc3df4349b8b2bd,Sentiment Analysis Using Social Multimedia,"Chapter 2
Sentiment Analysis Using Social
Multimedia
Jianbo Yuan, Quanzeng You and Jiebo Luo"
dc0c7d66d46919fe5f9fb2b5dde8bf3ab7972d1d,Construction of a Multi-Modal Database for the Evaluation of Person Authentication Systems operating under Near Infrared Illumination,"Construction of a Multi-Modal Database for the
Evaluation of Person Authentication Systems operating
under Near Infrared Illumination
Shuyan Zhao and Ralph Kricke and Rolf-Rainer Grigat
TUHH Vision Systems (E-2)
Harburger Schloßstr. 20, 21079 Hamburg, Germany
Tel: +49 40 42878-3125, Fax: +49 40 42878-2911
http://www.ti1.tu-harburg.de
in: WSEAS Transactions on Signal Processing. See also BIBTEX entry below.
BIBTEX:
uthor = {Shuyan Zhao and Ralph Kricke and Rolf-Rainer Grigat},
title = {Construction of a Multi-Modal Database for the Evaluation of Person
Authentication Systems operating under Near Infrared Illumination},
journal = {WSEAS Transactions on Signal Processing},
year = {2007},
volume = {3},
pages = {249-254},
number = {2},
month = {Feb.},
© copyright by the author(s)"
dc041f307d467918ba684d3c425fb23016f3b28e,A survey of 3d face recognition methods,"A Survey of 3D Face Recognition Methods
Alize Scheenstra1, Arnout Ruifrok2, and Remco C. Veltkamp1
Utrecht University, Institute of Information and Computing Sciences,
Padualaan 14, 3584 CH Utrecht, The Netherlands
Netherlands Forensic Institute,
Laan van Ypenburg 6, 2497 GB Den Haag, The Netherlands,"
dc6d518585c18504b2e69223c062cdd691c79bbd,Domain Adaptation Through Synthesis for Unsupervised Person Re-identification,
dc7a4d5ba20ca07d29c360b26e1e72afae9a77be,The ApolloScape Open Dataset for Autonomous Driving and its Application,"The ApolloScape Open Dataset for Autonomous
Driving and its Application
Xinyu Huang*, Peng Wang*, Xinjing Cheng, Dingfu Zhou, Qichuan Geng, Ruigang Yang"
dcb44fc19c1949b1eda9abe998935d567498467d,Ordinal Zero-Shot Learning,"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
labelunseen labelFigure1:Supervisionintensityfordifferentlabels.Greenrepre-sentsseenlabelsandredrepresentsunseenlabels.Thegroundtruthlabelofthisinstanceis“Good”,soithasthestrongestsupervisionintensity.Although“Common”isanunseenlabel,itstillhascertainsupervisioninformationbecauseitiscloselyrelatedto“Good”.classifier;[ZhangandSaligrama,2016]learnsajointlatentspaceusingstructuredlearning.Thedifficultyinobtainingthesideinformationorusingothertechniquestoprocessthesideinformationarethemostseriousissuesformanyexistingzero-shotlearningmethods.Fortheattribute-basedmethods,humanexpertsareneededtolabelattributesandthisisverytime-consumingandnoteasytoobtainthediscriminativecategory-levelattributes.Somemethodsdiscoverattributesinteractively[ParikhandGrau-man,2011][Bransonetal.,2010],butthisalsorequiresla-borioushumanparticipation.Althoughmanyalgorithmscandiscoverattribute-relatedconceptsontheWeb[Rohrbachetal.,2010][Bergetal.,2010],theycanalsobebiasedorlackinformationthatiscriticaltoaparticulartask[ParikhandGrauman,2011].Forthetextcorpora-basedmethods,theyfirstrequirealargelanguagecorpora,suchasWikipedia,andthenneedtolearnwordrepresentation[Socheretal.,2013]orusestandardNaturalLanguageProcessing(NLP)techniquestoproduceclassdescriptions[Elhoseinyetal.,2013].Itishardtoguaranteethecorrectnessofsuchclassdescriptionsforzero-shotlearning.Conclusively,althoughsideinforma-tionishelpfulforzero-shotlearning,ithasmanydisadvan-tages.Generatingthesesideinformationisverytediousandsometimeswecannotknowwhichsideinformationistrulywanted.IfwedependonhumanlabororNLPtechniques,noisysideinformationwillbecomealmostinevitableandin-fluencethefinalperformance.Toavoidtheseproblems,itisimportanttosolvezero-shotlearninginwhateverpossiblecasesthathavesomepropertieswecanutilizetoavoidusingsideinformation."
dc22de0ed56958013234cf7128952390fb47345a,Towards dense object tracking in a 2D honeybee hive,"Towards dense object tracking in a 2D honeybee hive
Katarzyna Bozek a, Laetitia Hebert a, Alexander S Mikheyev a & Greg J Stephens a,b∗
Okinawa Institute of Science and Technology, 1919-1 Tancha Onna-son, Kunigami-gun, Okinawa 904-0495, Japan
Department of Physics and Astronomy, VU University Amsterdam, 1081 HV Amsterdam, The Netherlands
From human crowds to cells in tissue, the detection and ef‌f‌icient tracking of multiple objects
in dense configurations is an important and unsolved problem. In the past, limitations of image
nalysis have restricted studies of dense groups to tracking a single or subset of marked individ-
uals, or to coarse-grained group-level dynamics, all of which yield incomplete information. Here,
we combine convolutional neural networks (CNNs) with the model environment of a honeybee hive
to automatically recognize all individuals in a dense group from raw image data. We create new,
dapted individual labeling and use the segmentation architecture U-Net with a loss function depen-
dent on both object identity and orientation. We additionally exploit temporal regularities of the
video recording in a recurrent manner and achieve near human-level performance while reducing
the network size by 94% compared to the original U-Net architecture. Given our novel applica-
tion of CNNs, we generate extensive problem-specific image data in which labeled examples are
produced through a custom interface with Amazon Mechanical Turk. This dataset contains over
75,000 labeled bee instances across 720 video frames at 2 FPS, representing an extensive resource
location error of ∼ 7% of a typical body dimension, and orientation error of 12◦, approximating the
variability of human raters. Our results provide an important step towards ef‌f‌icient image-based
dense object tracking by allowing for the accurate determination of object location and orientation"
dc9a6a821689de877bd07e970e52d4cdb1dd2714,Transfer of gender aftereffects in face silhouettes reveals face-specific mechanisms,"Transfer of gender aftereffects in face silhouettes reveals face-specific mechanisms
Nicolas Davidenko
Nathan Witthoft
Jonathan Winawer
Department of Psychology, 450 Serra Street, Building 420
Stanford, CA 94305 USA"
dc9f29118e38602c03bb2866f8b12ce478aad52c,Large scale evolution of convolutional neural networks using volunteer computing,"Large Scale Evolution of Convolutional Neural
Networks Using Volunteer Computing
Travis Desell∗
March 17, 2017"
dcce157aa2e5db081b36fd16544a038becb408ab,Fast and Accurate Pedestrian Detection in a Truck's Blind Spot Camera,"Fast and Accurate Pedestrian Detection
in a Truck’s Blind Spot Camera
Kristof Van Beeck1,2(B) and Toon Goedem´e1,2
EAVISE, KU Leuven - Campus De Nayer, J. De Nayerlaan 5, 2860
ESAT/PSI - VISICS, KU Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium
Sint-katelijne-waver, Belgium"
dc53c4bb04e787a0d45dd761ba2101cc51c17b82,Multiple-Person Tracking by Detection,"http://excel.fit.vutbr.cz
Multiple-Person Tracking by Detection
Jakub Vojvoda*"
dc94db59fddedb3fc6ae5317ad719808bb5482f2,Humanitarian Logistics and Cultural Diversity within Crowd Simulation,"Humanitarian logistics and cultural diversity within
rowd simulation
Alberto Ochoa, Isaac Rudomin, Genoveva Vargas-Solar, Javier
Espinosa-Oviedo, Hugo Pérez, José-Luis Zechinelli-Martini
To cite this version:
Alberto Ochoa, Isaac Rudomin, Genoveva Vargas-Solar, Javier Espinosa-Oviedo, Hugo Pérez, et al..
Humanitarian logistics and cultural diversity within crowd simulation. computacion y sistemas, Centro
de Investigación en computación, 2017, 21 (1), pp.7-21. <hal-01517161>
HAL Id: hal-01517161
https://hal.archives-ouvertes.fr/hal-01517161
Submitted on 2 May 2017
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,"
dcd2ac544a8336d73e4d3d80b158477c783e1e50,Improving Landmark Localization with Semi-Supervised Learning,"Improving Landmark Localization with Semi-Supervised Learning
Sina Honari1∗, Pavlo Molchanov2, Stephen Tyree2, Pascal Vincent1,4,5, Christopher Pal1,3, Jan Kautz2
MILA-University of Montreal, 2NVIDIA, 3Ecole Polytechnique of Montreal, 4CIFAR, 5Facebook AI Research.
{honaris,
{pmolchanov, styree,"
dc3cd4e110b526cb59bd7527d540120c5fae77ce,Adversarially Tuned Scene Generation,"Adversarially Tuned Scene Generation
VSR Veeravasarapu1, Constantin Rothkopf2, Ramesh Visvanathan1
Center for Cognition and Computation, Dept. of Computer Science, Goethe University, Frankfurt
Center for Cognitive Science & Dept. of Psychology, Technical University Darmstadt."
dca246cd06666a331b0203cb09a6ef51727bfdcc,The micro-foundations of email communication networks,"The Micro-Foundations of Email
Communication Networks
Ofer Engel
London School of Economics and Political Science
Department of Management
Information Systems and Innovation Group
Thesis submitted for the degree of
PhilosophiæDoctor (PhD)
013 June"
dc6263270cd23a51d8fffdfd7e408250442b40f3,"SimpleElastix: A User-Friendly, Multi-lingual Library for Medical Image Registration","SimpleElastix: A user-friendly, multi-lingual library for medical image
registration
Kasper Marstal1, Floris Berendsen2, Marius Staring2 and Stefan Klein1
Biomedical Imaging Group Rotterdam (BIGR), Department of Radiology & Medical Informatics,
Erasmus Medical Center, PO Box 2040, Rotterdam, 3000 CA, the Netherlands,
Division of Image Processing (LKEB), Department of Radiology, Leiden University Medical Center,
PO Box 9600, 2300 RC Leiden, the Netherlands,"
8768b8a972debe697ee5f8c3bd67a6df6ff44977,A Comparative Study on Image Hashing for Document Authentication,"A Comparative Study on Image Hashing for Document
Authentication ∗
Dominik Klein†
Bundesamt f¨ur Sicherheit in der Informationstechnik, 53133 Bonn
Jan Kruse‡
Hochschule Emden/Leer, 26723 Emden Stadt"
87a937dff0ecffefd8a7e4ca4ce068aca9731a0a,Embracing Error to Enable Rapid Crowdsourcing,"Embracing Error to Enable Rapid Crowdsourcing
Ranjay Krishna1, Kenji Hata1, Stephanie Chen1, Joshua Kravitz1,
David A. Shamma2, Li Fei-Fei1, Michael S. Bernstein1
{ranjaykrishna, kenjihata, stephchen, kravitzj, feifeili,
Stanford University1, Yahoo! Labs2"
87ea22438e33fd98a5bac577598f26a6a02cf8a3,Deteksi Pemain Basket Terklasifikasi Berbasis Histogram of Oriented Gradients,"INFORM : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi
Vol.3 No.1, Januari 2018, P-ISSN : 2502-3470, EISSN : 2581-0367
Deteksi Pemain Basket Terklasifikasi Berbasis Histogram of
Oriented Gradients
Nisa’ul Hafidhoh1, Septian Enggar Sukmana2
1, 2 Program Studi Teknik Informatika, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro
,2{nisa,"
8796f2d54afb0e5c924101f54d469a1d54d5775d,Illumination Invariant Face Recognition Using Fuzzy LDA and FFNN,"Journal of Signal and Information Processing, 2012, 3, 45-50
http://dx.doi.org/10.4236/jsip.2012.31007 Published Online February 2012 (http://www.SciRP.org/journal/jsip)
Illumination Invariant Face Recognition Using Fuzzy LDA
nd FFNN
Behzad Bozorgtabar, Hamed Azami, Farzad Noorian
School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran.
Email:
Received October 20th, 2011; revised November 24th, 2011; accepted December 10th, 2011"
87bee0e68dfc86b714f0107860d600fffdaf7996,Automated 3D Face Reconstruction from Multiple Images Using Quality Measures,"Automated 3D Face Reconstruction from Multiple Images
using Quality Measures
Marcel Piotraschke and Volker Blanz
Institute for Vision and Graphics, University of Siegen, Germany"
877aff9bd05de7e9d82587b0e6f1cda28fd33171,Long-Term Visual Localization Using Semantically Segmented Images,"Long-term Visual Localization using Semantically Segmented Images
Erik Stenborg1,2 Carl Toft1 and Lars Hammarstrand1"
87ad56e06d48fa9b30e2915473c488c1b4b7e6ae,Learn from experience: Probabilistic prediction of perception performance to avoid failure,"Article
Learn from experience: probabilistic
prediction of perception performance to
void failure
The International Journal of
Robotics Research
© The Author(s) 2017
Reprints and permissions:
sagepub.co.uk/journalsPermissions.nav
DOI: 10.1177/0278364917730603
journals.sagepub.com/home/ijr
Corina Gur˘au1, Dushyant Rao1, Chi Hay Tong2, and Ingmar Posner1"
87f0a779ce4e060e3e076df3cc651e0f3f01b2ae,Bimodal Biometric Person Identification System Under Perturbations,"Bimodal Biometric Person Identification System
Under Perturbations
Miguel Carrasco1, Luis Pizarro2, and Domingo Mery1
Pontificia Universidad Cat´olica de Chile
Av. Vicu˜na Mackenna 4860(143), Santiago, Chile
Mathematical Image Analysis Group
Faculty of Mathematics and Computer Science
Saarland University, Bldg. E11, 66041 Saarbr¨ucken, Germany"
87f285782d755eb85d8922840e67ed9602cfd6b9,INCORPORATING BOLTZMANN MACHINE PRIORS FOR SEMANTIC LABELING IN IMAGES AND VIDEOS,"INCORPORATING BOLTZMANN MACHINE PRIORS
FOR SEMANTIC LABELING IN IMAGES AND VIDEOS
A Dissertation Presented
ANDREW KAE
Submitted to the Graduate School of the
University of Massachusetts Amherst in partial fulfillment
of the requirements for the degree of
DOCTOR OF PHILOSOPHY
May 2014
Computer Science"
87da8bd9eb2fff2d77809c8bee3bed8c93cb5b4b,A Generative Model For Zero Shot Learning Using Conditional Variational Autoencoders,"A Generative Model For Zero Shot Learning
Using Conditional Variational Autoencoders
Ashish Mishra1 , Shiva Krishna Reddy1, Anurag Mittal, and Hema A Murthy
Indian Institute of Technology Madras"
8765f22fbcdcf610a08b01db01edc4b8cc67d082,Probability Models for Open Set Recognition,"for  all  other  uses,
© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be
obtained
including
reprinting/republishing  this  material  for  advertising  or  promotional  purposes,  creating
new  collective  works,  for  resale  or  redistribution  to  servers  or  lists,  or  reuse  of  any
opyrighted component of this work in other works.
in  any  current  or
future  media,
Pre-print of article that will appear in T-PAMI."
87c2806f1fd20287f00b43dab07822ab13035169,Verfahren zur Analyse von Ähnlichkeit im Ortsbereich,"Matthias Fiedler
Verfahren zur Analyse von Ähnlichkeit im Ortsbereich"
87363751b8e3d51a002dea6d32df553ee5315cb7,Conventional SBIR Fine-grained SBIR Conventional Methods Our Method,"Fine-Grained Sketch-Based Image Retrieval: The Role of Part-Aware Attributes
Ke Li1&2
Kaiyue Pang1&2
Yi-Zhe Song2
Timothy Hospedales2
Honggang Zhang1
School of Electronic Engineering and Computer Science Queen Mary University of London.
Beijing University of Posts and Telecommunications.
Yichuan Hu1"
878169be6e2c87df2d8a1266e9e37de63b524ae7,Image interpretation above and below the object level.,"CBMM Memo No. 089
May 10, 2018
Image interpretation above and below the object level
Guy Ben-Yosef, Shimon Ullman"
87bb183d8be0c2b4cfceb9ee158fee4bbf3e19fd,Craniofacial Image Analysis,"Craniofacial Image Analysis
Ezgi Mercan, Indriyati Atmosukarto, Jia Wu, Shu Liang and Linda G. Shapiro"
878301453e3d5cb1a1f7828002ea00f59cbeab06,Faceness-Net: Face Detection through Deep Facial Part Responses,"Faceness-Net: Face Detection through
Deep Facial Part Responses
Shuo Yang, Ping Luo, Chen Change Loy, Senior Member, IEEE and Xiaoou Tang, Fellow, IEEE"
87ba4cce558c2defde90f4b42853262fd572ca3e,Silhouette estimation.,"J. Opt. Soc. Am. A / Vol. 31, No. 7 / July 2014
Paxman et al.
Silhouette estimation
Richard G. Paxman,1,* David A. Carrara,1 Paul D. Walker,1 and Nicolas Davidenko2
General Dynamics Advanced Information Systems, P.O. Box 990, Ypsilanti, Michigan 48197, USA
Psychology Department, UC Santa Cruz, 1156 High Street, Santa Cruz, California 95064, USA
*Corresponding author:
Received February 13, 2014; revised April 8, 2014; accepted April 9, 2014;
posted May 23, 2014 (Doc. ID 206333); published June 30, 2014
Silhouettes arise in a variety of imaging scenarios. Pristine silhouettes are often degraded via blurring, detector
sampling, and detector noise. We present a maximum a posteriori estimator for the restoration of parameterized
facial silhouettes. Extreme dealiasing and dramatic superresolution, well beyond the diffraction limit, are
demonstrated through the use of strong prior knowledge. © 2014 Optical Society of America
OCIS codes:
(100.3010) Image reconstruction techniques; (100.3008) Image recognition, algorithms and
filters; (100.1830) Deconvolution.
http://dx.doi.org/10.1364/JOSAA.31.001636
. INTRODUCTION
A. Silhouettes in Imaging
A silhouette is the image of an object represented as a solid"
871001c87c2c1b4059576ca5dcb95ab61afd3c9a,Improving Generalization via Scalable Neighborhood Component Analysis,"Improving Generalization via
Scalable Neighborhood Component Analysis
Zhirong Wu1,2, Alexei A. Efros1, and Stella X. Yu1
UC Berkeley / ICSI
Microsoft Research Asia"
871f5f1114949e3ddb1bca0982086cc806ce84a8,Discriminative learning of apparel features,"Discriminative Learning of Apparel Features
Rasmus Rothe1, Marko Ristin1, Matthias Dantone1, and Luc Van Gool1,2
Computer Vision Laboratory, D-ITET, ETH Z¨urich, Switzerland
ESAT - PSI / IBBT, K.U. Leuven, Belgium"
87dd3fd36bccbe1d5f1484ac05f1848b51c6eab5,SPATIO-TEMPORAL MAXIMUM AVERAGE CORRELATION HEIGHT TEMPLATES IN ACTION RECOGNITION AND VIDEO SUMMARIZATION by MIKEL RODRIGUEZ,"SPATIO-TEMPORAL MAXIMUM AVERAGE CORRELATION
HEIGHT TEMPLATES IN ACTION RECOGNITION AND VIDEO
SUMMARIZATION
MIKEL RODRIGUEZ
B.A. Earlham College, Richmond Indiana
M.S. University of Central Florida
A dissertation submitted in partial fulfillment of the requirements
for the degree of Doctor of Philosophy
in the School of Electrical Engineering and Computer Science
in the College of Engineering and Computer Science
t the University of Central Florida
Orlando, Florida
Summer Term
Major Professor: Mubarak Shah"
8765f312e35bba0650aa769b59da7e8fac9e98aa,A Cognitively-Motivated Framework for Partial Face Recognition in Unconstrained Scenarios,"Sensors 2015, 15, 1903-1924; doi:10.3390/s150101903
OPEN ACCESS
sensors
ISSN 1424-8220
www.mdpi.com/journal/sensors
Article
A Cognitively-Motivated Framework for Partial Face
Recognition in Unconstrained Scenarios
João C. Monteiro * and Jaime S. Cardoso
INESC TEC and Faculdade de Engenharia, Universidade do Porto, Campus da FEUP,
Rua Dr. Roberto Frias, n 378, 4200-465 Porto, Portugal; E-Mail:
* Author to whom correspondence should be addressed; E-Mail:
Tel.: +351-22-209-4299.
Academic Editor: Vittorio M.N. Passaro
Received: 24 November 2014 / Accepted: 7 January 2015 / Published: 16 January 2015"
878f70f6abb83f5158ca0bacfc2bacd49b1886b1,Aligning Artificial Neural Networks to the Brain Yields Shallow Recurrent Architec- Tures,"Under review as a conference paper at ICLR 2019
ALIGNING ARTIFICIAL NEURAL NETWORKS TO THE
BRAIN YIELDS SHALLOW RECURRENT ARCHITEC-
TURES
Anonymous authors
Paper under double-blind review"
874082164d9ab9fced08b9890c009b91a2e846f1,Understanding Convolution for Semantic Segmentation,"Understanding Convolution for Semantic Segmentation
Panqu Wang1, Pengfei Chen1, Ye Yuan2, Ding Liu3, Zehua Huang1, Xiaodi Hou1, Garrison Cottrell4
TuSimple, 2Carnegie Mellon University, 3University of Illinois Urbana-Champaign, 4UC San Diego"
87bdafbcf3569c06eef4a397beffc451f5101f94,Facial expression: An under-utilised tool for the assessment of welfare in mammals.,"published February 8, 2017
Review article
Facial expression: An under-utilised tool for
the assessment of welfare in mammals1
Kris A. Descovich1,2,3, Jennifer Wathan4, Matthew C. Leach5, Hannah M. Buchanan-Smith1,
Paul Flecknell6, David Farningham7 and Sarah-Jane Vick1
Psychology, Faculty of Natural Sciences, University of Stirling; 2Environmental and Animal Sciences, Unitec Institute of
Technology; 3Centre for Animal Welfare and Ethics, University of Queensland; 4School of Psychology, University of Sussex,
United Kingdom; 5School of Agriculture, Food & Rural Development, University of Newcastle; 6Comparative Biology
Centre, University of Newcastle; 7Centre for Macaques, Medical Research Council
Summary
Animal welfare is a key issue for industries that use or impact upon animals. The accurate identification of welfare
states is particularly relevant to the field of bioscience, where the 3Rs framework encourages refinement of
experimental procedures involving animal models. The assessment and improvement of welfare states in animals
is reliant on reliable and valid measurement tools. Behavioural measures (activity, attention, posture and
vocalisation) are frequently used because they are immediate and non-invasive, however no single indicator can
yield a complete picture of the internal state of an animal. Facial expressions are extensively studied in humans
s a measure of psychological and emotional experiences but are infrequently used in animal studies, with the
exception of emerging research on pain behaviour. In this review, we discuss current evidence for facial
representations of underlying affective states, and how communicative or functional expressions can be useful"
8722ab37a03336f832e4098224cb63cd02cdfe0a,Face recognition with 3 D face asymmetry,"Face recognition with 3D face asymmetry
Janusz Bobulski
Czestochowa University of Technology
Institute of Computer and Information Sciences
Dabrowskiego 73, 42-200, Czestochowa, Poland
Summary. Using of 3D images for the identification was in a field of the interest
of many researchers which developed a few methods offering good results. However,
there are few techniques exploiting the 3D asymmetry amongst these methods. We
propose fast algorithm for rough extraction face asymmetry that is used to 3D
face recognition with hidden Markov models. This paper presents conception of fast
method for determine 3D face asymmetry. The research results indicate that face
recognition with 3D face asymmetry may be used in biometrics systems.
Introduction
Biometrics systems use individual and unique biological features of person
for user identification. The most popular features are: fingerprint, iris, voice,
palm print, face image et al. Most of them are not accepted by users, because
they feel under surveillance or as criminals. Others, in turn, are characterized
y problems with the acquisition of biometric pattern and require closeness
to the reader. Among the biometric methods popular technique is to identify
people on the basis of the face image, the advantage is the ease of obtaining"
87c6ba55b0f817de4504e39dbb201842ae102c9f,Three Dimensional Face Recognition Using Iso-Geodesic and Iso-Depth Curves,"Three Dimensional Face Recognition Using Iso-Geodesic and Iso-Depth
Curves
Sina Jahanbin, Hyohoon Choi, Yang Liu, Alan C. Bovik"
877d083b2a3a75cc1bb25f770a9c5684bf5f6f44,Learning to Hash with Binary Reconstructive Embeddings,"Learning to Hash with Binary Reconstructive
Embeddings
Brian Kulis and Trevor Darrell
UC Berkeley EECS and ICSI
Berkeley, CA"
87d5b53580ca5f77ccc3ff157337ef3456308943,Augmented Autoencoders for object orientation estimation trained on synthetic RGB images,MasterarbeitAugmented Autoencoders for object orientation estimation trained on synthetic RGB imagesMartin Sundermeyer
0b4d3e59a0107f0dad22e74054bab1cf1ad9c32e,Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations,"Int J Comput Vis
DOI 10.1007/s11263-016-0981-7
Visual Genome: Connecting Language and Vision Using
Crowdsourced Dense Image Annotations
· Yuke Zhu1 · Oliver Groth2 · Justin Johnson1 · Kenji Hata1 ·
Ranjay Krishna1
Joshua Kravitz1 · Stephanie Chen1 · Yannis Kalantidis3 · Li-Jia Li4 ·
David A. Shamma5 · Michael S. Bernstein1 · Li Fei-Fei1
Received: 23 February 2016 / Accepted: 12 September 2016
© The Author(s) 2017. This article is published with open access at Springerlink.com"
0b61cad6ae6e7ab99f2e3c187bd8530da71f10ae,Gameplay Genre Video Classification by Using Mid-Level Video Representation,"Gameplay genre video classification by using
mid-level video representation
Renato Augusto de Souza‡, Raquel Pereira de Almeida‡, Arghir-Nicolae Moldovan∗,
Zenilton Kleber G. do Patrocínio Jr.‡, Silvio Jamil F. Guimarães‡
Audio-Visual Information Proc. Lab. (VIPLAB)
Computer Science Department – ICEI – PUC Minas
School of Computing, National College of Ireland, Dublin, Ireland
named GameGenre, consists of 700 videos (more than 116
hours), classified into 7 game genres."
0b5c3cf7c8c643cb09d55a08b15de22e134081be,Online Tracking and Offline Recognition Using Scale Invariant Feature Transform,"IJMTES | International Journal of Modern Trends in Engineering and Science                                         ISSN: 2348-3121
Online Tracking and Offline Recognition Using Scale
Invariant Feature Transform
A. Bahmidha Banu1; Dr. V. Venkatesa kumar2
PG Scholar, Department of CSE, Anna University Regional Centre, Tamilnadu,
Assistant Professor, Department of CSE, Anna University Regional Centre, , Tamilnadu,
________________________________________________________________________________________________________"
0bce54bfbd8119c73eb431559fc6ffbba741e6aa,Recurrent Neural Networks,"Published as a conference paper at ICLR 2018
SKIP RNN: LEARNING TO SKIP STATE UPDATES IN
RECURRENT NEURAL NETWORKS
V´ıctor Campos∗†, Brendan Jou‡, Xavier Gir´o-i-Nieto§, Jordi Torres†, Shih-Fu ChangΓ
Barcelona Supercomputing Center, ‡Google Inc,
§Universitat Polit`ecnica de Catalunya, ΓColumbia University
{victor.campos,"
0b0eb562d7341231c3f82a65cf51943194add0bb,Facial Image Analysis Based on Local Binary Patterns : A Survey,"> REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) <
Facial Image Analysis Based on Local Binary
Patterns: A Survey
Di Huang, Caifeng Shan, Mohsen Ardebilian, Liming Chen"
0b0e29ff990877127963eb064ea12926f7aeb52a,A Cost-Effective Framework for Automated Vehicle-Pedestrian Near-Miss Detection Through Onboard Monocular Vision,"A Cost-effective Framework for Automated Vehicle-pedestrian Near-miss
Detection through Onboard Monocular Vision
Ruimin Ke1               Jerome Lutin2               Jerry Spears3               Yinhai Wang1,*
University of Washingtion    2New Jersey Transit    3Washington State Transit Insurance Pool
{ker27,"
0b19177107a102ee81e5ef1bb9fb2f2881441503,Comparing Robustness of Pairwise and Multiclass Neural-Network Systems for Face Recognition,"Hindawi Publishing Corporation
EURASIP Journal on Advances in Signal Processing
Volume 2008, Article ID 468693, 7 pages
doi:10.1155/2008/468693
Research Article
Comparing Robustness of Pairwise and Multiclass
Neural-Network Systems for Face Recognition
J. Uglov, L. Jakaite, V. Schetinin, and C. Maple
Computing and Information System Department, University of Bedfordshire, Luton LU1 3JU, UK
Correspondence should be addressed to V. Schetinin,
Received 16 June 2007; Revised 28 August 2007; Accepted 19 November 2007
Recommended by Konstantinos N. Plataniotis
Noise, corruptions, and variations in face images can seriously hurt the performance of face-recognition systems. To make these
systems robust to noise and corruptions in image data, multiclass neural networks capable of learning from noisy data have been
suggested. However on large face datasets such systems cannot provide the robustness at a high level. In this paper, we explore a
pairwise neural-network system as an alternative approach to improve the robustness of face recognition. In our experiments, the
pairwise recognition system is shown to outperform the multiclass-recognition system in terms of the predictive accuracy on the
test face images.
Copyright © 2008 J. Uglov et al. This is an open access article distributed under the Creative Commons Attribution License, which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited."
0b174d4a67805b8796bfe86cd69a967d357ba9b6,A Survey on Face Detection and Recognition Approaches,"Research Journal of Recent Sciences _________________________________________________ ISSN 2277-2502
Vol. 3(4), 56-62, April (2014)
Res.J.Recent Sci."
0b8c92463f8f5087696681fb62dad003c308ebe2,On matching sketches with digital face images,"On Matching Sketches with Digital Face Images
Himanshu S. Bhatt, Samarth Bharadwaj, Richa Singh, and Mayank Vatsa
in local"
0b0535fbdc468d1fd6ff32545a717a8af14f634f,The Discriminative Generalized Hough Transform as a Proposal Generator for a Deep Network in Automatic Pedestrian Localization,
0b9db62b26b811e8c24eb9edc37901a4b79a897f,Structured Face Hallucination,"Structured Face Hallucination
Chih-Yuan Yang Sifei Liu Ming-Hsuan Yang
Electrical Engineering and Computer Science
University of California at Merced
{cyang35, sliu32,"
0b70facac4d10c7c73e7fdf3a85848ce429d98ab,"Segmentation features, visibility modeling and shared parts for object detection","Segmentation Features, Visibility Modeling and
Shared Parts for Object Detection
Patrick Ott
Submitted in accordance with the requirements
for the degree of Doctor of Philosophy.
The University of Leeds
School of Computing
February 2012
The candidate confirms that the work submitted is his own and that the appropriate
redit has been given where reference has been made to the work of others.
This copy has been supplied on the understanding that it is copyright material
nd that no quotation from the thesis may be published without proper
cknowledgment."
0b9ce839b3c77762fff947e60a0eb7ebbf261e84,LOGARITHMIC FOURIER PCA : A NEW APPROACH TO FACE RECOGNITION 1,"Proceedings of the IASTED International Conference
Computer Vision (CV 2011)
June 1 - 3, 2011  Vancouver, BC, Canada
LOGARITHMIC FOURIER PCA: A NEW APPROACH TO FACE
RECOGNITION
Lakshmiprabha  Nattamai  Sekar,
Jhilik Bhattacharya,
omjyoti
Majumder
Surface Robotics Lab
Central Mechanical Engineering Research Institute
Mahatma Gandhi Avenue,
Durgapur - 713209, West Bengal, India.
email: 1 n prabha 2 3"
0bc7d8e269a8c8018a7cb120ff25adf02d45c7ed,Exploiting dissimilarity representations for person re-identification,"Exploiting Dissimilarity Representations for
Person Re-Identification
Riccardo Satta, Giorgio Fumera, and Fabio Roli
Dept. of Electrical and Electronic Engineering, University of Cagliari
Piazza d’Armi, 09123 Cagliari, Italy"
0b0958493e43ca9c131315bcfb9a171d52ecbb8a,A Unified Neural Based Model for Structured Output Problems,"A Unified Neural Based Model for Structured Output Problems
Soufiane Belharbi∗1, Cl´ement Chatelain∗1, Romain H´erault∗1, and S´ebastien Adam∗2
LITIS EA 4108, INSA de Rouen, Saint ´Etienne du Rouvray 76800, France
LITIS EA 4108, UFR des Sciences, Universit´e de Rouen, France.
April 13, 2015"
0b1cf351a4a6758606bea32d29c7d529e79ab7ce,Fake Face Detection System Using Pupil Reflection 양재준,"한국지능시스템학회  논문지  2010,  Vol.  20,  No.  5,  pp.  645-651
동공의  반사특징을  이용한  얼굴위조판별  시스템
Fake  Face  Detection  System  Using  Pupil  Reflection
양재준*․조성원*․정선태**
JaeJun  Yang,  Seongwon  Cho  and  Sun-Tae  Chung
*  홍익대학교  전기정보제어공학과
**숭실대학교  정보통신전자공학부
최근  지능형  범죄가  늘면서  첨단  보안  기술에  대한  요구가  점차  늘어나고  있다.  현재까지  보고된  위조영상검출방법은  실용
화를  위하여  정확도  개선이  요구된다.  본  논문에서는  사람의  얼굴에  대하여  동공의  반사광을  이용한  얼굴위조판별  시스템
을  제안한다.  제안된  시스템은  먼저  다중  스케일  가버특징  벡터를  기반으로  눈의  위치를  찾은  후  2단계의  템플릿  매칭을
통해서  설정된  적용범위를  벗어나는  눈에  대하여  위조판별을  고려하지  않음으로써  정확도를  높이는  방법을  사용한다.  신뢰
도가  확보된  눈의  위치를  기반으로  적외선  조명에  반사되는  동공의  특징을  이용하여  눈위치  근처에서의
화소값을  계산하
여  위조  여부를  판단한다.  실험을  통하여  본  논문에서  제안한  방법이  더욱  신뢰성  높은  위조판별시스템임을  확인하였다.
키워드  :  변조영상  검출,  얼굴  검출,  EBGM,  템플릿  매칭,  얼굴  식별"
0be43cf4299ce2067a0435798ef4ca2fbd255901,Title A temporal latent topic model for facial expression recognition,"Title
A temporal latent topic model for facial expression recognition
Author(s)
Shang, L; Chan, KP
Citation
The 10th Asian Conference on Computer Vision (ACCV 2010),
Queenstown, New Zealand, 8-12 November 2010. In Lecture
Notes in Computer Science, 2010, v. 6495, p. 51-63
Issued Date
http://hdl.handle.net/10722/142604
Rights
Creative Commons: Attribution 3.0 Hong Kong License"
0bb574ad77f55f395450b4a9f863ecfdd4880bcd,Learning the Base Distribution in Implicit Generative Models,"Learning the Base Distribution in Implicit Generative Models
Y. Cem Subakan(cid:91), Oluwasanmi Koyejo(cid:91), Paris Smaragdis(cid:91),(cid:93)
(cid:91)UIUC, (cid:93)Adobe Inc."
0b4189d874ee67f259a1a366ac93740d500064a5,Single-Shot Multi-person 3D Pose Estimation from Monocular RGB,"Single-Shot Multi-Person 3D Pose Estimation From Monocular RGB
Dushyant Mehta[1,2], Oleksandr Sotnychenko[1,2], Franziska Mueller[1,2],
Weipeng Xu[1,2], Srinath Sridhar[3], Gerard Pons-Moll[1,2], Christian Theobalt[1,2]
[1] MPI For Informatics
[2] Saarland Informatics Campus
[3] Stanford University"
0b888196dda951287dddb60bd44798aab16d6fca,Learning Common Sense through Visual Abstraction,
0b937abb3b356a2932d804f9fc4b463485f63d0e,Visual word disambiguation by semantic contexts,"Visual word disambiguation by semantic contexts
Yu Su, Frédéric Jurie
To cite this version:
Yu Su, Frédéric Jurie. Visual word disambiguation by semantic contexts. IEEE Intenational Confer-
ence on Computer Vision (ICCV), 2011, Spain. pp.311-318, 2011, <10.1109/ICCV.2011.6126257>.
<hal-00808655>
HAL Id: hal-00808655
https://hal.archives-ouvertes.fr/hal-00808655
Submitted on 5 Apr 2013
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
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0bc0f9178999e5c2f23a45325fa50300961e0226,Recognizing facial expressions from videos using Deep Belief Networks,"Recognizing facial expressions from videos using Deep
Belief Networks
CS 229 Project
Advisor: Prof. Andrew Ng
Adithya Rao Narendran Thiagarajan"
0bc9f1749e23b37ea5b5588c5bfe23879174d343,Pythia v0.1: the Winning Entry to the VQA Challenge 2018,"Pythia v0.1: the Winning Entry to the VQA Challenge 2018
Yu Jiang∗, Vivek Natarajan∗, Xinlei Chen∗, Marcus Rohrbach, Dhruv Batra, Devi Parikh
Facebook AI Research"
0bdd8f824fa4d4e770e34268a78dca12fb6a135b,Compact Hash Codes for Efficient Visual Descriptors Retrieval in Large Scale Databases,"Compact Hash Codes for Efficient Visual Descriptors
Retrieval in Large Scale Databases
Simone Ercoli, Marco Bertini and Alberto Del Bimbo
Media Integration and Communication Center, Università degli Studi di Firenze
Viale Morgagni 65 - 50134 Firenze, Italy"
0bbd0060e79c5370c6d07bef7b5830e8732e7c1b,Automatic tracker selection w.r.t object detection performance,"Automatic Tracker Selection w.r.t Object Detection Performance
Duc Phu Chau
Monique Thonnat
Franc¸ois Br´emond
Slawomir Bak
STARS team, INRIA, France
{Duc-Phu.Chau, Francois.Bremond, Monique.Thonnat, Slawomir.Bak}
004 route des Lucioles, 06560 Valbonne, France"
0ba544ff0d837ba5279b03eb91246d00f2c78817,Direct Prediction of 3D Body Poses from Motion Compensated Sequences,"Direct Prediction of 3D Body Poses from Motion Compensated Sequences
Bugra Tekin1
Artem Rozantsev1
Vincent Lepetit1,2
Pascal Fua1
CVLab, EPFL, Lausanne, Switzerland,
TU Graz, Graz, Austria,"
0b32cd6dff1561889652450cac5c1abb9833557d,Face Verification in Polar Frequency Domain: A Biologically Motivated Approach,"Face Verification in Polar Frequency Domain: a
Biologically Motivated Approach
Yossi Zana1, Roberto M. Cesar-Jr1, Rogerio S. Feris2, and Matthew Turk2 ⋆
Dept. of Computer Science, IME-USP, Brazil,
University of California, Santa Barbara,"
0b24cca96ca61248a3fa3973525a967f94292835,Two Novel Face Recognition Approaches,"We are IntechOpen,
the world’s leading publisher of
Open Access books
Built by scientists, for scientists
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0b6f810f287561ff694a9406c7b319fd8549ca68,Face Recognition Based on Texture Features using Local Ternary Patterns,"I.J. Image, Graphics and Signal Processing, 2015, 10, 37-46
Published Online September 2015 in MECS (http://www.mecs-press.org/)
DOI: 10.5815/ijigsp.2015.10.05
Face Recognition Based on Texture Features
using Local Ternary Patterns
Associate Professor, Dept. of CSE, BVRIT Hyderabad College of Engineering for Women, Hyderabad, T.S., India.
K. Srinivasa Reddy
Director-CACR, Dean-Computer Sciences (CSE & IT), Anurag Group of Institutions, Hyderabad, T.S., India.
Email:
V. Vijaya Kumar
Email:
B. Eswara Reddy
Professor, Dept. of CSE, JNTUA, Ananthapuram, A.P., India.
Email:"
0b57eb772ad9129ea4011c7fcb16c57967409018,Identifying Paintings from Text Descriptions,"Proceedings of 2016 NAACL Human-Computer Question Answering Workshop, pages 43–47,
San Diego, California, June 12-17, 2016. c(cid:13)2016 Association for Computational Linguistics"
0bf26d2fd1b375f50c0a6bef086f09f7698c3156,Predicting Entry-Level Categories,"Noname manuscript No.
(will be inserted by the editor)
Predicting Entry-Level Categories
Vicente Ordonez · Wei Liu · Jia Deng · Yejin Choi ·
Alexander C. Berg · Tamara L. Berg
Received: date / Accepted: date"
0b2c543e0c47454c4512569175094e6cb6ae02a9,The VizWiz Grand Challenge : A Large Visual Question Answering Dataset from Blind People Anonymous CVPR submission,"#1687
CVPR 2016 Submission #1687. CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE.
The VizWiz Grand Challenge:
A Large Visual Question Answering Dataset from Blind People
Anonymous CVPR submission
Paper ID 1687"
0bbaa29d1203d3a241074d4c6c7d01171b15afdb,AN EFFICIENT FRAMEWORK FOR IMAGE DATA RECOGNITION & RETRIEVAL,"ISSN: 2277-9655
[Singh* et al., 5.(7): July, 2016]
IC™ Value: 3.00                                                                                                         Impact Factor: 4.116
IJESRT
INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH
TECHNOLOGY
AN EFFICIENT FRAMEWORK FOR IMAGE DATA RECOGNITION & RETRIEVAL
Diljot Singh*, Tejpreet Singh, Sanjeev Kumar
* M.Tech Research Scholar, KCET, Amritsar
Assistant Professor, Department of CSE, KCET, Amritsar
Assistant Professor, Department of ECE, KCET, Amritsar
DOI: 10.5281/zenodo.57938"
0b02bfa5f3a238716a83aebceb0e75d22c549975,Learning Probabilistic Models for Recognizing Faces under Pose Variations,"Learning Probabilistic Models for Recognizing Faces
under Pose Variations
M. Saquib Sarfraz and Olaf Hellwich
Computer vision and Remote Sensing, Berlin university of Technology
Sekr. FR-3-1, Franklinstr. 28/29, Berlin, Germany"
0b635b58cd4d739bed415f77de8d1ec3d79e26d4,Chapter 8 . Face Tracking and Recognition from Video ?,"Stan Z. Li
Anil K. Jain (Eds.)
Handbook of Face Recognition
Springer"
0ba6f4fb548d8289fb42d68ac64d55f9e3a274ca,Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation,"Auto-context and Its Application to High-level Vision Tasks
nd 3D Brain Image Segmentation
Lab of Neuro Imaging, University of California, Los Angeles
Zhuowen Tu and Xiang Bai
July 9, 2009"
0bcd89b356dc78aaf3573086f13e94b8e7b5bee6,Comparative Testing of Face Detection Algorithms,"Comparative Testing of Face Detection
Algorithms⋆
Nikolay Degtyarev and Oleg Seredin
Tula State University
http://lda.tsu.tula.ru"
0babc4af06d210cf38bdf8324c339b6cf3f424fa,A Predictive Model of Patient Readmission Using Combined ICD-9 Codes as Engineered Features,"A Predictive Model of Patient Readmission Using Combined ICD-9
Codes as Engineered Features"
0b4c4ea4a133b9eab46b217e22bda4d9d13559e6,MORF: Multi-Objective Random Forests for face characteristic estimation,"MORF: Multi-Objective Random Forests for Face Characteristic Estimation
Dario Di Fina1
MICC - University of Florence
Svebor Karaman1,3
Andrew D. Bagdanov2
{dario.difina,
CVC - Universitat Autonoma de Barcelona
Alberto Del Bimbo1
DVMM Lab - Columbia University"
0beaf17d42b1171dd245131825d2de67000f45ac,Expert Gate: Lifelong Learning with a Network of Experts,"Expert Gate: Lifelong Learning with a Network of Experts
Rahaf Aljundi
Punarjay Chakravarty
Tinne Tuytelaars
KU Leuven, ESAT-PSI, iMinds, Belgium
{rahaf.aljundi, Punarjay.Chakravarty,"
0b4453df81091bcdafedc07b64bea946bf3441b2,Fast and Accurate 3 D Face Recognition Using Registration to an Intrinsic Coordinate System and Fusion of Multiple Region Classifiers,"Int J Comput Vis
DOI 10.1007/s11263-011-0426-2
Fast and Accurate 3D Face Recognition
Using Registration to an Intrinsic Coordinate System and Fusion of Multiple Region
Classifiers
Luuk Spreeuwers
Received: 20 September 2010 / Accepted: 7 February 2011
© The Author(s) 2011. This article is published with open access at Springerlink.com"
0b02365863b5037cb64a7de0aee805d0371f6b21,"Face Recognition Committee Machine : Methodology , Experiments and A System Application","Face Recognition Committee
Machine:
Methodology, Experiments and
A System Application
Tang Ho-Man
A Thesis Submitted in Partial Ful(cid:12)lment
of the Requirements for the Degree of
Master of Philosophy
Computer Science and Engineering
Supervised by
Prof. Michael R. Lyu
(cid:13)The Chinese University of Hong Kong
July 2003
The Chinese University of Hong Kong holds the copyright of this thesis. Any
person(s) intending to use a part or whole of the materials in the thesis in
proposed publication must seek copyright release from the Dean of the
Graduate School."
0b8c9acb478c856eb157c648b25b3d60117392fd,Learning to Divide and Conquer for Online Multi-target Tracking,"Learning to Divide and Conquer for Online Multi-Target Tracking
Francesco Solera Simone Calderara Rita Cucchiara
Department of Engineering
University of Modena and Reggio Emilia"
0b605b40d4fef23baa5d21ead11f522d7af1df06,Label-Embedding for Attribute-Based Classification,"Label-Embedding for Attribute-Based Classification
Zeynep Akataa,b, Florent Perronnina, Zaid Harchaouib and Cordelia Schmidb
Computer Vision Group∗, XRCE, France
LEAR†, INRIA, France"
0b6bd0a6f396e1479dc30318102bf49c12959783,griffith . edu . au Face recognition using local binary decisions,"Face recognition using local binary decisions
Author
James, Alex, Dimitrijev, Sima
Published
Journal Title
IEEE Signal Processing Letters
https://doi.org/10.1109/LSP.2008.2006339
Copyright Statement
© 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be
obtained for all other uses, in any current or future media, including reprinting/republishing this
material for advertising or promotional purposes, creating new collective works, for resale or
redistribution to servers or lists, or reuse of any copyrighted component of this work in other
works.
Downloaded from
http://hdl.handle.net/10072/23556
Griffith Research Online
https://research-repository.griffith.edu.au"
0bc82ec532228427a497ac47391d524e3b4537ae,Fluid Annotation: A Human-Machine Collaboration Interface for Full Image Annotation,"Fluid Annotation: A Human-Machine Collaboration Interface
for Full Image Annotation
Mykhaylo Andriluka∗
Jasper R. R. Uijlings∗
Google Research
Z¨urich, Switzerland
Vi(cid:138)orio Ferrari"
0bbde476713458acbdbe86157bc5067b36cad2ad,Automated Pain Detection from Facial Expressions using FACS: A Review,"Automated Pain Detection from Facial
Expressions using FACS: A Review
Zhanli Chen, Student Member, IEEE, Rashid Ansari, Fellow, IEEE, and Diana J. Wilkie, Fellow, AAN,"
0b8ef6f5ec5dfc3eded5241fd3d636a596b94d26,Stereological analysis of amygdala neuron number in autism.,"7674 • The Journal of Neuroscience, July 19, 2006 • 26(29):7674 –7679
Neurobiology of Disease
Stereological Analysis of Amygdala Neuron Number
in Autism
Cynthia Mills Schumann and David G. Amaral
Department of Psychiatry and Behavioral Sciences and The M.I.N.D. Institute, University of California, Davis, Sacramento, California 95817
The amygdala is one of several brain regions suspected to be pathological in autism. Previously, we found that young children with autism
have a larger amygdala than typically developing children. Past qualitative observations of the autistic brain suggest increased cell density
in some nuclei of the postmortem autistic amygdala. In this first, quantitative stereological study of the autistic brain, we counted and
measured neurons in several amygdala subdivisions of 9 autism male brains and 10 age-matched male control brains. Cases with
omorbid seizure disorder were excluded from the study. The amygdaloid complex was outlined on coronal sections then partitioned into
five reliably defined subdivisions: (1) lateral nucleus, (2) basal nucleus, (3) accessory basal nucleus, (4) central nucleus, and (5) remaining
nuclei. There is no difference in overall volume of the amygdala or in individual subdivisions. There are also no changes in cell size.
However, there are significantly fewer neurons in the autistic amygdala overall and in its lateral nucleus. In conjunction with the findings
from previous magnetic resonance imaging studies, the autistic amygdala appears to undergo an abnormal pattern of postnatal devel-
opment that includes early enlargement and ultimately a reduced number of neurons. It will be important to determine in future studies
whether neuron loss in the amygdala is a consistent characteristic of autism and whether cell loss occurs in other brain regions as well.
Key words: autism; neuropathology; stereology; neuronal density; medial temporal lobe; neuroanatomy; amygdaloid complex
Introduction
Autism is a lifelong neurodevelopmental disorder characterized"
0b4b6932d5df74b366d9235b40334bc40d719c72,Temporal Ensembling for Semi-Supervised Learning,"Temporal Ensembling for Semi-Supervised Learning
Samuli Laine
NVIDIA
Timo Aila
NVIDIA"
0bdfc21178347ed4f137d4c7d0ba14c996c66b6e,Automated X-Ray Object Recognition Using an Efficient Search Algorithm in Multiple Views,"Automated X-ray object recognition using
n efficient search algorithm in multiple views
Domingo Mery, Vladimir Riffo, Irene Zuccar, Christian Pieringer
Department of Computer Science – Pontificia Universidad Cat´olica de Chile
Av. Vicu˜na Mackenna 4860(143) – Santiago de Chile
http://dmery.ing.puc.cl"
0b3a146c474166bba71e645452b3a8276ac05998,Who ’ s in the Picture ?,"Who’s in the Picture?
Tamara L. Berg, Alexander C. Berg, Jaety Edwards and D.A. Forsyth
Berkeley, CA 94720
Computer Science Division
U.C. Berkeley"
b47ea4d5b0040d85181925bda74da4ab5303768f,LIFEisGAME: A Facial Character Animation System to Help Recognize Facial Expressions,"LIFEisGAME:A Facial Character Animation System to
Help Recognize Facial Expressions
Tiago Fernandes1,5, Samanta Alves2, José Miranda3,5, Cristina Queirós2,
Verónica Orvalho1,4
Instituto de Telecomunicações, Lisboa, Portugal,
Faculdade de Psicologia da Universidade do Porto, Porto, Portugal,
Instituto Politécnico da Guarda, Porto, Portugal,
Faculdade de Ciências da Universidade do Porto, Porto, Portugal,
5 Faculdade de Engenharia da Universidade do Porto, Porto, Portugal,"
b4a16fe1469f2048d031db626b7cbc5bec7f4055,ONLINE FACIAL CARICATURE GENERATOR,"ONLINE FACIAL CARICATURE GENERATOR
Rudy Adipranata
Informatics Department
Petra Chistian University
Siwalankerto 121-131
Surabaya, Indonesia
62-31-8439040
Stephanus Surya Jaya
Informatics Department
Petra Chistian University
Siwalankerto 121-131
Surabaya, Indonesia
62-31-8439040
Kartika Gunadi
Informatics Department
Petra Chistian University
Siwalankerto 121-131
Surabaya, Indonesia
62-31-8439040"
b4fe9594e1de682e7270645ba95ab64727b6632e,Generative Adversarial Positive-Unlabelled Learning,"Generative Adversarial Positive-Unlabelled Learning
Ming Hou1, Brahim Chaib-draa2, Chao Li1, Qibin Zhao1,
Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan
Department of Computer Science and Software Engineering, Laval University, Quebec, Canada"
b4f6962068c27d10df9016090a0ca14f65f26b70,A Statisitical Shape Model for Deformable Surface Registration.,"A STATISITICAL SHAPE MODEL FOR DEFORMABLE
SURFACE REGISTRATION
Wei Quan, Bogdan J. Matuszewski and Lik-Kwan Shark
Applied Digital Signal and Image Processing (ADSIP) Research Centre
University of Central Lancashire, Preston PR1 2HE, United Kingdom
{wquan, bmatuszewski1,
Keywords:
Deformable Registration, Surface Matching, Shape Modelling and Face Articulation."
b40290a694075868e0daef77303f2c4ca1c43269,Combining Local and Global Information for Hair Shape Modeling,"第 40 卷 第 4 期
014 年 4 月
自 动 化 学 报
ACTA AUTOMATICA SINICA
Vol. 40, No. 4
April, 2014
融合局部与全局信息的头发形状模型
王 楠 1 艾海舟 1
摘 要 头发在人体表观中具有重要作用, 然而, 因为缺少有效的形状模型, 头发分割仍然是一个非常具有挑战性的问题. 本
文提出了一种基于部件的模型, 它对头发形状以及环境变化更加鲁棒. 该模型将局部与全局信息相结合以描述头发的形状. 局
部模型通过一系列算法构建, 包括全局形状词表生成, 词表分类器学习以及参数优化; 而全局模型刻画不同的发型, 采用支持
向量机 (Support vector machine, SVM) 来学习, 它为所有潜在的发型配置部件并确定势函数. 在消费者图片上的实验证明
了本文算法在头发形状多变和复杂环境等条件下的准确性与有效性.
关键词 头发形状建模, 部件模型, 部件配置算法, 支持向量机
引用格式 王楠, 艾海舟. 融合局部与全局信息的头发形状模型. 自动化学报, 2014, 40(4): 615−623
DOI 10.3724/SP.J.1004.2014.00615
Combining Local and Global Information for Hair Shape Modeling
WANG Nan1
AI Hai-Zhou1"
b4d117e109b3a6762d1b675defd9f2b228613ac1,Financialized methods for market-based multi-sensor fusion,"Congress Center Hamburg
Sept 28 - Oct 2, 2015. Hamburg, Germany
978-1-4799-9993-4/15/$31.00 ©2015 IEEE"
b4a3f480e2004bdc8106de2f772283101bb290d0,Multi-stage ranking approach for fast person re-identification,"IET Research Journals
A Multi-Stage Ranking Approach for Fast Person Re-Identification
A Multi-Stage Ranking Approach for Fast
Person Re-Identification
Bahram Lavi, Giorgio Fumera , Fabio Roli
Department of Electrical and Electronic Engineering, University of Cagliari
Piazza d’Armi, 09123, Cagliari, Italy
E-mail:
ISSN 1751-8644
doi: 0000000000
www.ietdl.org"
b408b939c0f3be9cce0f84871a78a71d1684cd77,Identifying spatial relations in images using convolutional neural networks,"Identifying Spatial Relations in Images using
Convolutional Neural Networks
Mandar Haldekar, Ashwinkumar Ganesan
Dept. Of Computer Science & Engineering,
Tim Oates
Dept. Of Computer Science & Engineering,
UMBC,
Baltimore, MD
mandarh1,
UMBC,
Baltimore, MD"
b44d8ecac21867c540d9122a150c8d8c0875cbe6,Mixture Density Generative Adversarial Networks,"Mixture Density Generative Adversarial Networks
Hamid Eghbal-zadeh1 ∗
Werner Zellinger2
Gerhard Widmer1
LIT AI Lab & Institute of Computational Perception
Department of Knowledge-Based Mathematical Systems
{hamid.eghbal-zadeh, werner.zellinger,
Johannes Kepler University of Linz, Austria"
b498640d8f0ac5a628563ff84dbef8d35d12a7ec,Overcoming catastrophic forgetting with hard attention to the task,"Overcoming Catastrophic Forgetting with Hard Attention to the Task
Joan Serr`a 1 D´ıdac Sur´ıs 1 2 Marius Miron 1 3 Alexandros Karatzoglou 1"
b47386e10125462d60d66f8d6d239a69c5966853,ROBUST MULTI GRADIENT ENTROPY METHOD FOR FACE RECOGNITION SYSTEM FOR LOW CONTRAST NOISY IMAGES,"International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)
Web Site: www.ijettcs.org Email:
ISSN 2278-6856
Volume 2, Issue 3, May – June 2013
ROBUST MULTI GRADIENT ENTROPY
METHOD FOR FACE RECOGNITION
SYSTEM FOR LOW CONTRAST NOISY
IMAGES
C. Naga Raju1, P.Prathap Naidu2, R. Pradeep Kumar Reddy3, G. Sravana Kumari4
Associate Professor, CSE Dept, YSR Engg College of YVU
Asst. Professor, CSE Dept, RGM Engg College
Asst. Professor, CSE Dept, YSR Engg College.
M.Tech In CSE RGM Engg College
the  most
recognition  under  difficult"
b4616157d8f602bedf7b4f69db73f6b28a08bf9e,Multiclass semantic segmentation of faces using CRFs,"Turk J Elec Eng & Comp Sci
(2017) 25: 3164 { 3174
⃝ T (cid:127)UB_ITAK
doi:10.3906/elk-1607-332
Multiclass semantic segmentation of faces using CRFs
Khalil KHAN1;(cid:3)
, Nasir AHMAD2, Khalil ULLAH3, Irfanud DIN4
Department of Electrical Engineering, University of Poonch, Rawlakot, Pakistan
Department of Computer Engineering, University of Engineering & Technology, Peshawar, Pakistan
Department of Electrical Engineering, National University of Computer & Emerging Sciences, Peshawar, Pakistan
Department of Information Engineering, Inha University, Tashkent, Uzbekistan
Received: 30.07.2016
(cid:15)
Accepted/Published Online: 23.12.2016
(cid:15)
Final Version: 30.07.2017"
b41374f4f31906cf1a73c7adda6c50a78b4eb498,Iterative Gaussianization: From ICA to Random Rotations,"This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.
Iterative Gaussianization: From ICA to
Random Rotations
Valero Laparra, Gustavo Camps-Valls, Senior Member, IEEE, and Jesús Malo"
b4223cc72543656c28b55af1ffdabb1e47a0f2dd,Stacking with Auxiliary Features for Visual Question Answering,"New Orleans, Louisiana, June 1 - 6, 2018. c(cid:13)2018 Association for Computational Linguistics
Proceedings of NAACL-HLT 2018, pages 2217–2226"
b4c48aa7a93f38d2eb60209120a1a8daa61c4545,Diversity in Object Proposals,"DIVERSITY IN OBJECT PROPOSALS
Anton Winschel
Rainer Lienhart
Christian Eggert
Multimedia Computing and Computer Vision Lab
{anton.winschel, rainer.lienhart,
University of Augsburg, Germany"
b49aa569ff63d045b7c0ce66d77e1345d4f9745c,Convolutional Neural Networks for Crop Yield Prediction using Satellite Images,"Convolutional Neural Networks for Crop Yield Prediction using Satellite Images
H. Russello"
b4270de7380d305b4417f662686093c40d842da4,UNIVERSITY OF CALIFORNIA RIVERSIDE Graphical Models for Wide-Area Activity Analysis in Continuous Videos A Dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy,"UNIVERSITY OF CALIFORNIA
RIVERSIDE
Graphical Models for Wide-Area Activity Analysis in Continuous Videos
A Dissertation submitted in partial satisfaction
of the requirements for the degree of
Doctor of Philosophy
Computer Science
Nandita M. Nayak
May 2014
Dissertation Committee:
Professor Amit K. Roy-Chowdhury, Chairperson
Professor Christian Shelton
Professor Eamonn Keogh
Professor Victor Zordan"
b4ee2a6b5fdf66f57e94a998cff2acef4af7d256,Monocular Visual Scene Understanding: Understanding Multi-Object Traffic Scenes,"Monocular Visual Scene Understanding:
Understanding Multi-Object Traffic Scenes
Christian Wojek, Stefan Walk, Stefan Roth, Konrad Schindler, Bernt Schiele"
b49425f78907fcc447d181eb713abffc74dd85e4,Sampling Matters in Deep Embedding Learning,"Sampling Matters in Deep Embedding Learning
Chao-Yuan Wu∗
UT Austin
R. Manmatha
A9/Amazon
Alexander J. Smola
Amazon
Philipp Kr¨ahenb¨uhl
UT Austin"
b4a5136a5080f62a4123b6218e7a3570da6d5d3b,Robust human detection using multiple scale of cell based histogram of oriented gradients and adaboost learning,"Robust Human Detection Using Multiple  Scale
of Cell Based Histogram of Oriented Gradients
nd AdaBoost Learning
Van-Dung Hoang, My-Ha Le, and Kang-Hyun Jo
School of Electrical Engineering, University of Ulsan, Korea"
b411850a3614fbb06bc77e6f776b2f23af563a90,Size Does Matter: Improving Object Recognition and 3D Reconstruction with Cross-Media Analysis of Image Clusters,"Size does matter: improving object recognition
nd 3D reconstruction with cross-media analysis
of image clusters
Stephan Gammeter1, Till Quack1, David Tingdahl2, and Luc van Gool1,2
BIWI, ETH Z¨urich1 http://www.vision.ee.ethz.ch
VISICS, K.U. Leuven2 http://www.esat.kuleuven.be/psi/visics"
b4b1b39f8902208bbd37febfb68e08809098036d,TRECVid Semantic Indexing of Video : A 6-year Retrospective,"UvA-DARE (Digital Academic Repository)
TRECVid Semantic Indexing of Video: A 6-year Retrospective
Awad, G.; Snoek, C.G.M.; Smeaton, A.F.; Quénot, G.
Published in:
ITE Transactions on Media Technology and Applications
0.3169/mta.4.187
Link to publication
Citation for published version (APA):
Awad, G., Snoek, C. G. M., Smeaton, A. F., & Quénot, G. (2016). TRECVid Semantic Indexing of Video: A 6-
year Retrospective. ITE Transactions on Media Technology and Applications, 4(3), 187-208. DOI:
0.3169/mta.4.187
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b46f96ee1ef0c7b31e5cec9abc60aa5f77fe4245,Literature Review on Real Time People Tracking in a Camera Network,"________________________________________________________________________________________________
International Journal of Electrical, Electronics and Computer Systems (IJEECS)
Literature Review on Real Time People Tracking in a Camera
Network
Khan Farheen Wahab, 2Aniruddha Kailuke
,2Department of Electronics (Communication), Priyadarshini Institute of Engineering, The University of Nagpur
(RTMNU) Nagpur"
b4b6a0129bf6a716fca80a4cfc322687a72fa927,Automatic Generation of Planar Marionettes from Frontal Images,"Automatic Generation of Planar Marionettes from Frontal Images
Elad Richardson and Gil Ben-Shachar
Supervised by Anastasia Dubrovina and Aaron Weltzer"
b4c02e071432a9a986501b7317b524f216e87ec8,Visual Saliency Prediction using Deep learning Techniques,"Visual Saliency Prediction
using Deep learning Techniques
A Degree Thesis
Submitted to the Faculty of the
Escola Tècnica d'Enginyeria de Telecomunicació de
Barcelona
Universitat Politècnica de Catalunya
Junting Pan
In partial fulfilment
of the requirements for the degree in
TELECOMUNICATION ENGINEERING
Advisor: Xavier Giró i Nieto
Barcelona, July 2015"
b49affdff167f5d170da18de3efa6fd6a50262a2,Linking Names and Faces : Seeing the Problem in Different Ways,"Author manuscript, published in ""Workshop on Faces in 'Real-Life' Images: Detection, Alignment, and Recognition, Marseille : France
(2008)"""
b4ee64022cc3ccd14c7f9d4935c59b16456067d3,Unsupervised Cross-Domain Image Generation,"Unsupervised Cross-Domain Image Generation
Xinru Hua, Davis Rempe, and Haotian Zhang"
b40881a905cf6c4963658df4f64b860f9b1755fe,Unrestricted Facial Geometry Reconstruction Using Image-to-Image Translation,"Unrestricted Facial Geometry Reconstruction Using Image-to-Image Translation
Matan Sela
Elad Richardson
Ron Kimmel
Department of Computer Science, Technion - Israel Institute of Technology
Figure 1: Results of the proposed method. Reconstructed geometries are shown next to the corresponding input images."
b4e0e4e8b92067ac4d31961832ce30543cffff0a,Using lip features for multimodal speaker verification,"ISCA Archive
http://www.isca-speech.org/archive
001: A Speaker Odyssey
The Speaker Recognition Workshop
Crete, Greece
June 18-22, 2001
Using Lip Features for Multimodal Speaker Verification
Xiaozheng Zhang† and Charles C. Broun‡
The Georgia Institute of Technology, Atlanta, Georgia , USA
Motorola Human Interface Lab, Tempe, Arizona, USA"
a0e03c5b647438299c79c71458e6b1776082a37b,Areas of Attention for Image Captioning,"transformerFigure1.Weproposeanattentionmechanismthatjointlypredictsthenextcaptionwordandthecorrespondingregionateachtime-stepgiventheRNNstate(top).BesidesimplementingourmodelusingattentionareasdefinedoverCNNactivationgridsorobjectproposals,asusedinpreviouswork,wealsopresentaend-to-endtrainableconvolutionalspatialtransformerapproachtocomputeimagespecificattentionareas(bottom).typeorlocation,objectproperties,andtheirinteractions.Neuralencoder-decoderbasedapproaches,similartothoseusedinmachinetranslation[30],havebeenfoundveryeffectiveforthistask,seee.g.[19,23,32].Thesemethodsuseaconvolutionalneuralnetwork(CNN)toen-codetheinputimageintoacompactrepresentation.Are-currentneuralnetwork(RNN)isusedtodecodethisrepre-sentationword-by-wordintoanaturallanguagedescriptionoftheimage.Whileeffective,thesemodelsarelimitedinthattheimageanalysisis(i)static,i.e.doesnotchangeovertimeasthedescriptionisproduced,and(ii)notspatiallylo-calized,i.e.describesthesceneasawholeinsteadoffo-cousingonlocalaspectsrelevanttopartsofthedescription.Attentionmechanismscanaddresstheselimitationsbydy-namicallyfocusingondifferentpartsoftheinputastheout-putsequenceisgenerated.Suchmechanismsareeffectiveforavarietyofsequentialpredictiontasks,includingma-1"
a0e3775fd5d5df951ac7f65d3a9165bf4b96fbd8,Towards Automatic Image Editing: Learning to See another You,"Towards Automatic Image Editing: Learning to See another You
Amir Ghodrati1∗, Xu Jia1∗, Marco Pedersoli2†, Tinne Tuytelaars1
KU Leuven, ESAT-PSI, iMinds
INRIA"
a0dfb8aae58bd757b801e2dcb717a094013bc178,Reconocimiento de expresiones faciales con base en la dinámica de puntos de referencia faciales,"Reconocimiento de expresiones faciales con base
en la din´amica de puntos de referencia faciales
E. Morales-Vargas, C.A. Reyes-Garcia, Hayde Peregrina-Barreto
Instituto Nacional de Astrof´ısica ´Optica y Electr´onica,
Divisi´on de Ciencias Computacionales, Tonantzintla, Puebla,
M´exico
Resumen. Las expresiones faciales permiten a las personas comunicar
emociones, y es pr´acticamente lo primero que observamos al interactuar
on alguien. En el ´area de computaci´on, el reconocimiento de expresiones
faciales es importante debido a que su an´alisis tiene aplicaci´on directa en
´areas como psicolog´ıa, medicina, educaci´on, entre otras. En este articulo
se presenta el proceso de dise˜no de un sistema para el reconocimiento de
expresiones faciales utilizando la din´amica de puntos de referencia ubi-
ados en el rostro, su implementaci´on, experimentos realizados y algunos
de los resultados obtenidos hasta el momento.
Palabras clave: Expresiones faciales, clasificaci´on, m´aquinas de soporte
vectorial,modelos activos de apariencia.
Facial Expressions Recognition Based on Facial
Landmarks Dynamics"
a0541d4a28d90a17cd3eaa9d1797882eacc8ccf0,Improving Person Re-identification via Pose-Aware Multi-shot Matching,"Improving Person Re-identification via Pose-aware Multi-shot Matching
Yeong-Jun Cho and Kuk-Jin Yoon
Computer Vision Laboratory, GIST, South Korea
{yjcho,"
a06ef8ef4838c048b814563f7cca479c7d4513f2,Multi-module Singular Value Decomposition for Face Recognition,"ORIENTAL JOURNAL OF
COMPUTER SCIENCE & TECHNOLOGY
An International Open Free Access, Peer Reviewed Research Journal
Published By: Oriental Scientific Publishing Co., India.
www.computerscijournal.org
ISSN: 0974-6471
April  2014,
Vol. 7, No. (1):
Pgs. 09-14
Multi-module  Singular Value  Decomposition
for  Face  Recognition
A. NAMACHIVAYAM and KALIYAPERUMAL KARTHIKEYAN
Eritrea Institute of Technology, Asmara, Eritrea, North East Africa.
(Received: March 20, 2014; Accepted: March 30, 2014)"
a0303a4d37e8264028808661050c4443b6b880de,Robust Video Object Tracking in Distributed Camera Networks,"(cid:13)Copyright 2017
Younggun Lee"
a06761b3181a003c2297d8e86c7afc20e17fd2c6,Convolutional Neural Network-Based Human Detection in Nighttime Images Using Visible Light Camera Sensors,"Article
Convolutional Neural Network-Based Human
Detection in Nighttime Images Using Visible Light
Camera Sensors
Jong Hyun Kim, Hyung Gil Hong and Kang Ryoung Park *
Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu,
Seoul 100-715, Korea; (J.H.K.); (H.G.H.)
* Correspondence: Tel.: +82-10-3111-7022; Fax: +82-2-2277-8735
Academic Editor: Vittorio M. N. Passaro
Received: 31 March 2017; Accepted: 4 May 2017; Published: 8 May 2017"
a022eff5470c3446aca683eae9c18319fd2406d5,Deep learning for semantic description of visual human traits. (Apprentissage profond pour la description sémantique des traits visuels humains),"017-ENST-0071
EDITE - ED 130
Doctorat ParisTech
T H È S E
pour obtenir le grade de docteur délivré par
TÉLÉCOM ParisTech
Spécialité « SIGNAL et IMAGES »
présentée et soutenue publiquement par
Grigory ANTIPOV
le 15 décembre 2017
Apprentissage Profond pour la Description Sémantique des Traits
Visuels Humains
Directeur de thèse : Jean-Luc DUGELAY
Co-encadrement de la thèse : Moez BACCOUCHE
Mme Bernadette DORIZZI, PRU, Télécom SudParis
Mme Jenny BENOIS-PINEAU, PRU, Université de Bordeaux
M. Christian WOLF, MC/HDR, INSA de Lyon
M. Patrick PEREZ, Chercheur/HDR, Technicolor Rennes
M. Moez BACCOUCHE, Chercheur/Docteur, Orange Labs Rennes
M. Jean-Luc DUGELAY, PRU, Eurecom Sophia Antipolis"
a044dc0ff3fa2a8f0a80afff9eb2cbc762996e3b,An Efficient Face Recognition System Based On the Hybridization of Pose Invariant and Illumination Process,"An An An An Efficient Face Recognition System Based On
Efficient Face Recognition System Based On thethethethe
Efficient Face Recognition System Based On
Efficient Face Recognition System Based On
Pose Invariant andandandand    Illumination Process
Hybridization ofofofof    Pose Invariant
Hybridization
Illumination Process
Pose Invariant
Hybridization
Hybridization
Pose Invariant
Illumination Process
Illumination Process
S. Muruganantham† and  Dr. T. Jebarajan††,
Assistant Professor, S.T.Hindu College, Nagercoil.
Principal, Kings College of Engineering, Chennai.
that  compensates"
a01f9461bc8cf8fe40c26d223ab1abea5d8e2812,Facial Age Estimation Through the Fusion of Texture and Local Appearance Descriptors,"Facial Age Estimation Through the Fusion of Texture
nd local appearance Descriptors
Ivan Huerta1, Carles Fern´andez2, and Andrea Prati1
DPDCE, University IUAV, Santa Croce 1957, 30135 Venice, Italy
Herta Security, Pau Claris 165 4-B, 08037 Barcelona, Spain"
a0e5afb1237d47f7a8ac66e7b5ada24cec5222cb,Semantic pooling for image categorization using multiple kernel learning,"SEMANTIC POOLING FOR IMAGE CATEGORIZATION USING MULTIPLE KERNEL
LEARNING
Thibaut Durand(1,2), David Picard(1), Nicolas Thome(2), Matthieu Cord(2)
(1) ETIS, UMR 8051 / ENSEA, Universit´e Cergy-Pontoise, CNRS, F-95000, Cergy,
(2) Sorbonne Universit´es, UPMC Univ Paris 06, UMR 7606, LIP6, F-75005, Paris, France"
a080154a6668cc9d37944a9ae4650a14b9146aa7,Enhanced maximum likelihood face recognition,"Enhanced maximum likelihood face
recognition
X.D. Jiang, B. Mandal and A. Kot
A method to enhance maximum likelihood face recognition is
presented. It selects a more robust weighting parameter and discards
unreliable dimensions to circumvent problems of the unreliable small
nd zero eigenvalues. This alleviates the over-fitting problem in face
recognition, where the high dimensionality and limited number of
training samples are critical
issues. The proposed method gives
superior experimental results.
Introduction: The maximum likelihood (ML) method [1] is one of the
est performing face recognition approaches. It decomposes the face
image space into a principal subspace F and a residual subspace F¯ . A
main contribution of ML is the replacement of the unreliable eigen-
values in F¯ by a constant. This solves the singularity problem of the
ovariance matrix. However, ML does not well solve the over-fitting
problem. The constant used to replace the eigenvalues in F¯
estimated as the average eigenvalue over F¯. The high-dimensional
face image and the limited number of training samples result in over-"
a00c0928a03d58a365d086a8df76ae93caf7cc63,Improving condition- and environment-invariant place recognition with semantic place categorization,"© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtainedfor all other uses, in any current or future media, including reprinting/republishing this materialfor advertising or promotional purposes, creating new collective works, for resale or redistributionto servers or lists, or reuse of any copyrighted component of this work in other works.Pre-print of article that will appear at the 2017 IEEE International Conference on IntelligentRobots and Systems.Please cite this paper as:S. Garg, A. Jacobson, S. Kumar, and M. Milford, “Improving Condition- and Environment-Invariant Place Recognition with Semantic Place Categorization,” in Intelligent Robots and Systems (IROS), 2017 IEEE/RSJ International Conference on. IEEE, author = {Garg, Sourav and Jacobson, Adam and Kumar, Swagat and Milford, Michael}, booktitle = {Intelligent Robots and Systems (IROS), 2017 IEEE/RSJ International Conference on}, organization = {IEEE}, title = {{Improving Condition- and Environment-Invariant Place Recognition with Semantic Place Categorization}}, year = {2017} }"
a01ba008252d2ce32f326f50c208c9ad9d5c78a6,Detecting Sudden Pedestrian Crossings and Avoiding Accidents Using Arm 11 K,"K. Sri Krishna Aditya et al Int. Journal of Engineering Research and Applications       www.ijera.com
ISSN : 2248-9622, Vol. 3, Issue 5, Sep-Oct 2013, pp.1213-1216
RESEARCH ARTICLE                                                                               OPEN ACCESS
Detecting Sudden Pedestrian Crossings and Avoiding Accidents
Using Arm 11
K. Sri Krishna Aditya1, T. Surya Kavita2, U. Yedukondalu3
Assistant Professor, 2Associate Professor, 3Head of the Department E.C.E.
Aditya Engineering College, 2Aditya Engineering College, 3Aditya Engineering College"
a079309d28b6f8753ca26a789bd0bc43de9bd9f8,Interpretable Counting for Visual Question Answering,"Published as a conference paper at ICLR 2018
INTERPRETABLE COUNTING FOR VISUAL QUESTION
ANSWERING
Alexander Trott, Caiming Xiong∗, & Richard Socher
Salesforce Research
Palo Alto, CA"
a000149e83b09d17e18ed9184155be140ae1266e,Chapter 9 Action Recognition in Realistic Sports Videos,"Chapter 9
Action Recognition in Realistic
Sports Videos
Khurram Soomro and Amir R. Zamir"
a016fbe8d09402316c7b38946ccd502d76aa8c74,Using a Single RGB Frame for Real Time 3D Hand Pose Estimation in the Wild,"Using a single RGB frame for real time 3D hand pose estimation in the wild
Paschalis Panteleris1
Iason Oikonomidis1
Institute of Computer Science, FORTH
Computer Science Department, UOC
Antonis Argyros1,2"
a0c37f07710184597befaa7e6cf2f0893ff440e9,Fast Retinomorphic Event Stream for Video Recognition and Reinforcement Learning,
a098fab95db313d8bd2a5175b519d7de5a3a675a,Performance evaluation and comparison of PCA Based human face recognition methods for distorted images,"Int. J. Mach. Learn. & Cyber. (2011) 2:245–259
DOI 10.1007/s13042-011-0023-2
O R I G I N A L A R T I C L E
Performance evaluation and comparison of PCA Based human
face recognition methods for distorted images
Bruce Poon • M. Ashraful Amin • Hong Yan
Received: 7 April 2011 / Accepted: 27 May 2011 / Published online: 5 July 2011
Ó Springer-Verlag 2011"
a0798a0a422520241cc02282946882dd1ef853cd,Full Quantification of Left Ventricle via Deep Multitask Learning Network Respecting Intra- and Inter-Task Relatedness,"Full Quantification of Left Ventricle via Deep
Multitask Learning Network Respecting
Intra- and Inter-Task Relatedness
Wufeng Xue, Andrea Lum, Ashley Mercado, Mark Landis, James Warrington,
nd Shuo Li*
Department of Medical Imaging, Western University, ON, Canada
Digital Imaging Group of London, ON, Canada"
a090d61bfb2c3f380c01c0774ea17929998e0c96,On the dimensionality of video bricks under varying illumination,"On the Dimensionality of Video Bricks under Varying Illumination
Beijing Lab of Intelligent Information Technology, School of Computer Science,
Youdong Zhao, Xi Song, Yunde Jia
Beijing Institute of Technology, Beijing 100081, PR China
{zyd458, songxi,"
a06805d4de16df54395e1700ec51797e5a65eb64,Fusion of Stereo Vision for Pedestrian Recognition using Convolutional Neural Networks,"Fusion of Stereo Vision for Pedestrian
Recognition using Convolutional Neural
Networks
D˘anut¸ Ovidiu Pop1,2,3, Alexandrina Rogozan2, Fawzi Nashashibi1,
Abdelaziz Bensrhair2
- INRIA Paris - RITS Team
Paris - France
- INSA Rouen - LITIS Laboratory
Rouen - France
- Babe¸s-Bolyai University - Department of Computer Science
Cluj Napoca- Romania
Pedestrian detection is a highly debated issue in the sci-"
a0a950f513b4fd58cee54bccc49b852943ffd02c,Image Inpainting using Block-wise Procedural Training with Annealed Adversarial Counterpart,"Image Inpainting using Block-wise Procedural Training with Annealed
Adversarial Counterpart
Chao Yang1, Yuhang Song1, Xiaofeng Liu2, Qingming Tang3, and C.-C. Jay Kuo1
USC, 2Carnegie Mellon University, 3Toyota Technological Institute at Chicago,"
a010835842ac0e49eade395f056e1e33d45b6ea5,Four Way Local Binary Pattern for Gender Classification Using Periocular Images,"Four Way Local Binary Pattern for
Gender Classification Using Periocular
Images
Md. Siyam Sajeeb Khan
(2014-1-60-024)
Rifat Mehreen Amin
(2014-1-60-003)
Department of Computer Science and Engineering
East West University
Aftabnagar, Dhaka-1212, Bangladesh
August, 2017"
a0e286f3c6a72c857ffd03bd8ab9a9f9b98c4432,AI Learns to Recognize Bengali Handwritten Digits: Bengali.AI Computer Vision Challenge 2018,"AI Learns to Recognize Bengali Handwritten Digits:
Bengali.AI Computer Vision Challenge 2018
Sharif Amit Kamran
Ahmed Imtiaz Humayun
Bengali.AI
Dhaka, Bangladesh
Bengali.AI
Dhaka, Bangladesh
Samiul Alam
Bengali.AI
Dhaka, Bangladesh
Rashed Mohammad Doha
Bengali.AI
Dhaka, Bangladesh
Manash Kumar Mandal
Bengali.AI
Dhaka, Bangladesh
Tahsin Reasat
Bengali.AI
Dhaka, Bangladesh"
a0b2df8f72ff672cb0760c5221657a5f48f0ec5d,Searching Image Databases Using Appearance Models,"Searching Image Databases
Using Appearance Models
A thesis submitted to the University of Manchester for the degree of
Doctor of Philosophy in the Faculty of Medicine, Dentistry, Nursing
nd Pharmacy
Ian M. Scott
Division of Imaging Science and Biomedical Engineering"
a021b2bc1ec07ca751f6ecec5527a4205f843e1b,Fast LIDAR-based road detection using fully convolutional neural networks,"Fast LIDAR-based Road Detection
Using Fully Convolutional Neural Networks
Luca Caltagirone, Samuel Scheidegger, Lennart Svensson, Mattias Wahde
{luca.caltagirone, samsch, lennart.svensson,"
43e11904ca961006be79f650025b5d8fbac9913f,Unsupervised Deep Video Hashing with Balanced Rotation,"Unsupervised Deep Video Hashing with Balanced Rotation
IJCAI Anonymous Submission 2367"
434fe2cca3321c08ef30a0076864298cf608e0d5,Multiple Human Tracking in High-Density Crowds,"Multiple Human Tracking in High-Density Crowds
Irshad Ali1, Matthew N. Dailey 2
Computer Science and Information Management Program, Asian Institute of Technology
(AIT), Pathumthani, Thailand"
433a7ee7c30a4d0b7035a1bace3b8ffc9229791b,A Novel Biometric Personal Verification System Based on the Combination of Palmprints and Faces,"INFORMATICA, 2008, Vol. 19, No. 1, 81–100
© 2008 Institute of Mathematics and Informatics, Vilnius
A Novel Biometric Personal Verification System
Based on the Combination of Palmprints and Faces
Slobodan RIBARI ´C, Ivan FRATRI ´C, Kristina KIŠ
Faculty of Electrical Engineering and Computing, University of Zagreb
Unska 3, 10000 Zagreb, Croatia
e-mail:
Received: February 2007"
434ad689f9f8bc034fa8489f80f851686b8b449e,Regularized Multi-Concept MIL for weakly-supervised facial behavior categorization.,"A.RUIZ, X.BINEFA, J.VAN DE WEIJER: RMC-MIL FACIAL BEHAVIOR CATEGORIZATION 1
Regularized Multi-Concept MIL for
weakly-supervised facial behavior
ategorization
Adria Ruiz1
Joost Van de Weijer2
Xavier Binefa1
Universitat Pompeu Fabra (DTIC)
Barcelona, Spain
Centre de Visió per Computador
Barcelona, Spain"
43cb50f669a0d492256d11c6cc4128ba0ce79a3e,Per-Pixel Feedback for improving Semantic Segmentation,"Indian Institute of Technology Roorkee
Department of Mathematics
Per-Pixel Feedback for improving Semantic
Segmentation
Aditya Ganeshan
Submitted in part fulfilment of the requirements for the degree of
Integrated Masters of Science in Applied Mathematics, May 2017"
43461e78874aff6b33b8231daf6068ec7ff15098,Automated tracing of myelinated axons and detection of the nodes of Ranvier in serial images of peripheral nerves.,"Received 19 September 2014; accepted 18 April 2015
doi: 10.1111/jmi.12266
Automated tracing of myelinated axons and detection of the nodes
of Ranvier in serial images of peripheral nerves
A . K R E S H U K∗, R . W A L E C K I∗, U . K O E T H E∗, M . G I E R T H M U E H L E N†, D . P L A C H T A‡, C . G E N O U D §,
K . H A A S T E R T - T A L I N I(cid:3) & F . A . H A M P R E C H T∗
Heidelberg Collaboratory for Image Processing (HCI), Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg, Heidelberg, Germany
Department of Neurosurgery, University Medical Center, Freiburg, Germany
Department of Microsystems, Engineering, University of Freiburg, Freiburg, Germany
§Facility for Advanced Imaging and Microscopy, Friedrich Miescher Institute for Biomedical Research (FMI), Basel, Switzerland
(cid:3)Institute of Neuroanatomy, Hannover Medical School, Hannover, Germany and Center for Systems Neurosciences (ZSN), Hannover, Germany
Key words. Axon, detection, nerve, Ranvier, segmentation, tracing.
Summary
The development of realistic neuroanatomical models of pe-
ripheral nerves for simulation purposes requires the recon-
struction of the morphology of the myelinated fibres in the
nerve, including their nodes of Ranvier. Currently, this infor-
mation has to be extracted by semimanual procedures, which
severely limit the scalability of the experiments.
In this contribution, we propose a supervised machine learn-"
430482d92007a3eec7009a2603aa5c1f2e63f661,Synaesthesia : mechanisms and broader traits,"Synaesthesia: mechanisms and broader traits.
Agnieszka Barbara Janik
Department of Psychology
Goldsmiths University of London
PhD in Psychology
I, Agnieszka Barbara Janik, confirm that the work presented in this thesis is my own.
Where information has been derived from other sources, I confirm that this has been
indicated in the thesis."
43c1bf9bd7b18c9603324c328f0f2696278c5327,Tracking Multiple Players using a Single Camera,"Noname manuscript No.
(will be inserted by the editor)
Tracking Multiple Players using a Single Camera
Horesh BenShitrit · Mirko Raca · Fran¸cois
Fleuret · Pascal Fua
Received: date / Accepted: date"
43f6953804964037ff91a4f45d5b5d2f8edfe4d5,Multi-feature fusion in advanced robotics applications,"Multi-Feature Fusion in Advanced Robotics Applications
Zahid Riaz, Christoph Mayer, Michael Beetz,
Bernd Radig
Institut für Informatik
Technische Universität München
D-85748 Garching, Germany"
43e3cd896d4dada4114a8961b98ae9f6d6ff9401,Image 2 speech : Automatically generating audio descriptions of images,"Image2speech: Automatically generating audio descriptions of images
Mark Hasegawa-Johnson1, Alan Black2, Lucas Ondel3, Odette Scharenborg4, Francesco Ciannella2
. University of Illinois, Urbana, IL USA 2. Carnegie-Mellon University, Pittsburgh, PA USA
. Brno University of Technology, Brno, Czech Republic
. Centre for Language Studies, Radboud University, Nijmegen, Netherlands"
4353d0dcaf450743e9eddd2aeedee4d01a1be78b,Learning Discriminative LBP-Histogram Bins for Facial Expression Recognition,"Learning Discriminative LBP-Histogram Bins
for Facial Expression Recognition
Caifeng Shan and Tommaso Gritti
Philips Research, High Tech Campus 36, Eindhoven 5656 AE, The Netherlands
{caifeng.shan,"
4344413b7814b2ba99cc79ead2903f259e98ed4b,Modelling Uncertainty in Representation of Facial Features for Face Recognition,"We are IntechOpen,
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43bb20ccfda7b111850743a80a5929792cb031f0,Discrimination of Computer Generated versus Natural Human Faces,"PhD Dissertation
International Doctorate School in Information and
Communication Technologies
DISI - University of Trento
Discrimination of Computer Generated
versus Natural Human Faces
Duc-Tien Dang-Nguyen
Advisor:
Prof. Giulia Boato
Universit`a degli Studi di Trento
Co-Advisor:
Prof. Francesco G. B. De Natale
Universit`a degli Studi di Trento
February 2014"
43d8a8737d763f2aba1d1aabbf1d9c74c25308ec,Visual Interaction Including Biometrics Information for a Socially Assistive Robotic Platform,"Visual interaction including biometrics information for
socially assistive robotics platform
Pierluigi Carcagn`ı, Dario Cazzato, Marco Del Coco, Cosimo Distante, and Marco Leo
National Research Council of Italy - Institute of Optics, Arnesano (LE), Italy"
43476cbf2a109f8381b398e7a1ddd794b29a9a16,A Practical Transfer Learning Algorithm for Face Verification,"A Practical Transfer Learning Algorithm for Face Verification
Xudong Cao
David Wipf
Fang Wen
Genquan Duan
Jian Sun"
43de246e9cc197623e27ab41a69530a8d121c77e,Developmental disruption of amygdala transcriptome and socioemotional behavior in rats exposed to valproic acid prenatally,"Barrett et al. Molecular Autism  (2017) 8:42
DOI 10.1186/s13229-017-0160-x
R ES EAR CH
Developmental disruption of amygdala
transcriptome and socioemotional behavior
in rats exposed to valproic acid prenatally
Catherine E. Barrett 1,2*, Thomas M. Hennessey1,2, Katelyn M. Gordon1,2, Steve J. Ryan1,2, Morgan L. McNair1,2,
Kerry J. Ressler3 and Donald G. Rainnie1,2
Open Access"
439da29cf857151f386e6af488b2d60c098c4fd8,Person Authentication Using Color Face Recognition,"Kiran Davakhar et al. Int. Journal of Engineering Research and Applications               www.ijera.com
Vol. 3, Issue 5, Sep-Oct 2013, pp.178-182
RESEARCH ARTICLE                                                                               OPEN ACCESS
Person Authentication Using Color Face Recognition
Kiran Davakhar1, S. B. Mule2, Achala Deshmukh3
(Department of E&TC, Sinhgad COE, Vadgaon, Pune, Pune University, India)
(Department of E&TC, Sinhgad COE, Vadgaon, Pune, Pune University, India)
(Department of E&TC, Sinhgad COE, Vadgaon, Pune, Pune University, India)"
43d4927f5113c5e376ab05d41e33063a6d06d727,Pedestrian Detection : Exploring Virtual Worlds,"Pedestrian Detection: Exploring Virtual Worlds
Javier Mar´ın
Computer Vision Center,
Universitat Aut`onoma de Barcelona, Spain
David Ger´onimo, David V´azquez, Antonio M. L´opez
Computer Vision Center and Computer Science Department,
Universitat Aut`onoma de Barcelona, Spain
Introduction
The objective of advanced driver assistance systems (ADAS) is to improve traffic safety by assisting the driver
through warnings and by even automatically taking active countermeasures. Two examples of successfully com-
mercialised ADAS are lane departure warnings and adaptive cruise control, which make use of either active
(e.g., radar) or passive (e.g., cameras) sensors to keep the vehicle on the lane and maintain a safe distance from
the preceding vehicle, respectively. One of the most complex safety systems are pedestrian protection systems
(PPSs) (Bishop, 2005; Gandhi & Trivedi, 2007; Enzweiler & Gavrila, 2009; Ger´onimo et al., 2010), which are
specialised in avoiding vehicle-to-pedestrian collisions. In fact, this kind of accidents results in approximately
50000 injuries and 7000 killed pedestrians every year just in the European Union (UN-ECE, 2007). Similar
statistics apply to the United States, while underdeveloped countries are increasing theirs year after year. In the
ase of PPSs, the most promising approaches make use of images as main source of information, as can be seen
in the large amount of proposals exploiting them (Ger´onimo et al., 2010). Hence, the core of a PPS is a forward
facing camera that acquires images and processes them using Computer Vision techniques. In fact, the Computer"
43836d69f00275ba2f3d135f0ca9cf88d1209a87,Effective hyperparameter optimization using Nelder-Mead method in deep learning,"Ozaki et al. IPSJ Transactions on Computer Vision and
Applications  (2017) 9:20
DOI 10.1186/s41074-017-0030-7
IPSJ Transactions on Computer
Vision and Applications
RESEARCH PAPER
Open Access
Effective hyperparameter optimization
using Nelder-Mead method in deep learning
Yoshihiko Ozaki1,2, Masaki Yano1,2 and Masaki Onishi1,2*"
438b88fe40a6f9b5dcf08e64e27b2719940995e0,Building a classification cascade for visual identification from one example,"Building a Classi(cid:2)cation Cascade for Visual Identi(cid:2)cation from One Example
Andras Ferencz
Erik G. Learned-Miller
Computer Science, U.C. Berkeley
Computer Science, UMass Amherst
Jitendra Malik
Computer Science, U.C. Berkeley"
43ed518e466ff13118385f4e5d039ae4d1c000fb,Classification of Occluded Objects Using Fast Recurrent Processing,"Classification of Occluded Objects using Fast Recurrent
Processing
Ozgur Yilmaza,∗
Turgut Ozal University, Department of Computer Engineering, Ankara Turkey"
43f7aeea3ff5bbcbff6cce387f8e9b6c588e4f66,Systematic Review and Classification on Video Surveillance Systems,"I.J. Information Technology and Computer Science, 2013, 07, 87-102
Published Online June 2013 in MECS (http://www.mecs-press.org/)
DOI: 10.5815/ijitcs.2013.07.11
Systematic Review and Classification on Video
Surveillance Systems
Fereshteh Falah Chamasemani, Lilly Suriani Affendey
Faculty of Computer Science and Information Technology, University Putra Malaysia, Malaysia
E-mail:
systems
surveillance"
43167658228d59b81d48fce14bba4ae616b866ec,Face Recognition Using Multi-Classifier,"Applied Mathematical Sciences, Vol. 6, 2012, no. 45, 2235 - 2244
Face Recognition Using Multi-Classifier
Mohammad Said El-Bashir
Faculty of Computer Science and Information Technology
Al-al Bayt University - Jordan"
43fbe350681185ec9a18991dbcb19d694ce4f245,The Perspective Face Shape Ambiguity,"The Perspective Face Shape Ambiguity
William A. P. Smith"
43c76cf17767a43a345cd1a8d7c08d18578b53ec,Boosting Color Feature Selection for Color Face Recognition,"Accepted Manuscript for Publication in IEEE Transaction on Image Processing
Boosting Color Feature Selection for Color Face Recognition
Jae Young Choi, Student Member, IEEE, Yong Man Ro, Senior Member, IEEE, and
Konstantinos N. Plataniotis, Senior Member, IEEE"
43fe9006b90137d6ce85a539685ce66c13f0e38e,A review of image-based automatic facial landmark identification techniques,"Johnston and Chazal EURASIP Journal on Image and Video
Processing  (2018) 2018:86
https://doi.org/10.1186/s13640-018-0324-4
EURASIP Journal on Image
nd Video Processing
REVIEW
Open Access
A review of image-based automatic facial
landmark identification techniques
Benjamin Johnston1,2*
nd Philip de Chazal1"
43cff54bb47d0e96cda86671b2fdd9a0d09f4060,Unsupervised Learning of Head Pose through Spike-Timing Dependent Plasticity,"Unsupervised Learning of Head Pose through
Spike-Timing Dependent Plasticity
Ulrich Weidenbacher and Heiko Neumann
University of Ulm, Institute of Neural Information Processing, 89069 Ulm"
430e0b4577b7b2a82461241bb509b6830139b924,Decoding hazardous Events in driving Videos,"DECODING HAZARDOUS EVENTS IN DRIVING VIDEOS
H. Kolkhorst1, W. Burgard1, M. Tangermann1
Cluster of Excellence BrainLinks-BrainTools
Department of Computer Science
University of Freiburg, Freiburg, Germany
E-mail:"
4350bb360797a4ade4faf616ed2ac8e27315968e,What Is the Range of Surface Reconstructions from a Gradient Field?,"MITSUBISHI ELECTRIC RESEARCH LABORATORIES
http://www.merl.com
Edge Suppression by Gradient Field
Transformation using Cross-Projection
Tensors
Amit Agrawal, Ramesh Raskar, Rama Chellappa
TR2006-058
June 2006"
43b8b5eeb4869372ef896ca2d1e6010552cdc4d4,Large-scale Supervised Hierarchical Feature Learning for Face Recognition,"Large-scale Supervised Hierarchical Feature Learning for Face Recognition
Jianguo Li, Yurong Chen
Intel Labs China"
43e63603a5794c4e289d1f41d7079ed9620a355e,Invisible Mask: Practical Attacks on Face Recognition with Infrared,"Invisible Mask: Practical Attacks on Face
Recognition with Infrared
Zhe Zhou1, Di Tang2, Xiaofeng Wang3, Weili Han1, Xiangyu Liu4, Kehuan Zhang2
Fudan Univiersity, 2CUHK, 3IUB, 4Alibaba Inc."
43a2c871450ba4d8888e8692aa98cb10e861ea71,Learning Generative ConvNet with Continuous Latent Factors by Alternating Back-Propagation,"Alternating Back-Propagation for Generator Network
Tian Han †, Yang Lu †, Song-Chun Zhu, Ying Nian Wu
Department of Statistics, University of California, Los Angeles, USA"
431fc5903ab4853820eac6614073c5b7aec0ac31,Semantic-visual concept relatedness and co-occurrences for image retrieval,"978-1-4673-2533-2/12/$26.00 ©2012 IEEE
ICIP 2012"
439ec47725ae4a3660e509d32828599a495559bf,Facial Expressions Tracking and Recognition : Database Protocols for Systems Validation and Evaluation,"Facial Expressions Tracking and Recognition: Database Protocols for Systems Validation
nd Evaluation"
432a78fce47e2cc38fd742ab10cf80dec82ad49a,Boost K-Means,"Boost K-Means
Wan-Lei Zhao, Cheng-Hao Deng, and Chong-Wah Ngo, Senior Memeber, IEEE"
4332314ac4ab56153f68a9e55e92b3659e93a5b4,Learning Collective Crowd Behaviors with Dynamic Pedestrian-Agents,"Int J Comput Vis
DOI 10.1007/s11263-014-0735-3
Learning Collective Crowd Behaviors with Dynamic
Pedestrian-Agents
Bolei Zhou · Xiaoou Tang · Xiaogang Wang
Received: 9 September 2013 / Accepted: 24 May 2014
© Springer Science+Business Media New York 2014"
434bf475addfb580707208618f99c8be0c55cf95,DeXpression: Deep Convolutional Neural Network for Expression Recognition,"UNDER CONSIDERATION FOR PUBLICATION IN PATTERN RECOGNITION LETTERS
DeXpression: Deep Convolutional Neural
Network for Expression Recognition
Peter Burkert∗‡, Felix Trier∗‡, Muhammad Zeshan Afzal†‡,
Andreas Dengel†‡ and Marcus Liwicki‡
German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany
University of Kaiserslautern, Gottlieb-Daimler-Str., Kaiserslautern 67663, Germany"
439ac8edfa1e7cbc65474cab544a5b8c4c65d5db,Face authentication with undercontrolled pose and illumination,"SIViP (2011) 5:401–413
DOI 10.1007/s11760-011-0244-6
ORIGINAL PAPER
Face authentication with undercontrolled pose and illumination
Maria De Marsico · Michele Nappi · Daniel Riccio
Received: 15 September 2010 / Revised: 14 December 2010 / Accepted: 17 February 2011 / Published online: 7 August 2011
© Springer-Verlag London Limited 2011"
434627a03d4433b0df03058724524c3ac1c07478,Online Multi-Target Tracking With Unified Handling of Complex Scenarios,"IEEE TRANSANCTIONS ON IMAGE PROCESSING, VOL. XX, NO. XX, NOVEMBER 2014
Online Multi-Target Tracking
with Unified Handling of Complex Scenarios
Huaizu Jiang, Jinjun Wang, Yihong Gong, Senior Member, IEEE
Na Rong, Zhenhua Chai, and Nanning Zheng, Fellow, IEEE"
434c2c53af7b3cb3c0a47583a372fa3dc8143951,DeeSIL: Deep-Shallow Incremental Learning,"DeeSIL: Deep-Shallow Incremental Learning
Eden Belouadah, Adrian Popescu
CEA, LIST, Vision and Content Engineering Lab, F-91191 Gif-sur-Yvette, France"
432be99dde7d93001044048501c72c70e4ea2927,People and Mobile Robot Classification Through Spatio-Temporal Analysis of Optical Flow,"June 3, 2015
3:29 WSPC/INSTRUCTION FILE
People and mobile robot classification through spatio-temporal analysis
of optical flow
Plinio Moreno and Dario Figueira and Alexandre Bernardino and Jos´e Santos-Victor
Institute for Systems and Robotics (ISR/IST)
LARSyS, Instituto Superior T´ecnico
Universidade de Lisboa
{plinio, dfigueira, alex,
Lisboa, Portugal
The goal of this work is to distinguish between humans and robots in a mixed human-
robot environment. We analyze the spatio-temporal patterns of optical flow-based fea-
tures along several frames. We consider the Histogram of Optical Flow (HOF) and the
Motion Boundary Histogram (MBH) features, which have shown good results on people
detection. The spatio-temporal patterns are composed by groups of feature components
that have similar values on previous frames. The groups of features are fed into the
FuzzyBoost algorithm, which at each round selects the spatio-temporal pattern (i.e.
feature set) having the lowest classification error. The search for patterns is guided by
grouping feature dimensions, considering three algorithms: (a) similarity of weights from
dimensionality reduction matrices, (b) Boost Feature Subset Selection (BFSS) and (c)"
b008d973ee93fd3b13d1148fb7533dbdbc8374d6,New Representations for Analyzing Motion and Applications,"New Representations for Analyzing Motion and Applications
Ce Liu
Submitted to the Department of Electrical Engineering and Computer Science in partial
fulfillment of the requirements for the degree of
Doctor of Philosophy
Electrical Engineering and Computer Science
t the Massachusetts Institute of Technology
June 2009
(cid:13) 2009 Massachusetts Institute of Technology
All Rights Reserved.
Signature of Author:
Certified by:
Accepted by:
Department of Electrical Engineering and Computer Science
May 1, 2009
William T. Freeman, Professor of EECS
Thesis Supervisor
Terry P. Orlando, Professor of Electrical Engineering
Chair, Department Committee on Graduate Students"
b08203fca1af7b95fda8aa3d29dcacd182375385,Object and Text-guided Semantics for CNN-based Activity Recognition,"OBJECT AND TEXT-GUIDED SEMANTICS FOR CNN-BASED ACTIVITY RECOGNITION
(cid:63)Sungmin Eum †§, (cid:63)Christopher Reale †, Heesung Kwon†, Claire Bonial †, Clare Voss†
U.S. Army Research Laboratory, Adelphi, MD, USA
§Booz Allen Hamilton Inc., McLean, VA, USA"
b0c3bc3e3ca143444f5193735f2aad89d1776276,Training Generative Reversible Networks,"Training Generative Reversible Networks
Robin Tibor Schirrmeister 1 2 Patryk Chrab ˛aszcz 2 Frank Hutter 2 Tonio Ball 1"
b0554290e1c1d19ee5378485fadd1ff99c31bf2d,VANETs Meet Autonomous Vehicles: A Multimodal 3D Environment Learning Approach,"VANETs Meet Autonomous Vehicles:
A Multimodal 3D Environment Learning Approach
Yassine Maalej, Sameh Sorour, Ahmed Abdel-Rahim and Mohsen Guizani
University of Idaho, Moscow, ID, USA"
b05ac3b2286c30fcab385f682b3519a823857112,UvA-DARE ( Digital Academic Repository ) Spatial frequency information modulates response inhibition and decision-making processes,"UvA-DARE (Digital Academic Repository)
Spatial frequency information modulates response inhibition and decision-making
processes
Jahfari, S.; Ridderinkhof, K.R.; Scholte, H.S.
Published in:
PLoS One
0.1371/journal.pone.0076467
Link to publication
Citation for published version (APA):
Jahfari, S., Ridderinkhof, K. R., & Scholte, H. S. (2013). Spatial frequency information modulates response
inhibition and decision-making processes. PLoS One, 8(10), e76467. [e76467]. DOI:
0.1371/journal.pone.0076467
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The Netherlands. You will be contacted as soon as possible."
b069e125442995787119db3bfa71dff5d965f3aa,MCYT baseline corpus : a bimodal biometric database,"BIOMETRICS ON THE INTERNET
MCYT baseline corpus: a bimodal biometric database
J. Ortega-Garcia, J. Fierrez-Aguilar, D. Simon, J. Gonzalez, M. Faundez-Zanuy, V. Espinosa, A. Satue,
I. Hernaez, J.-J. Igarza, C. Vivaracho, D. Escudero and Q.-I. Moro"
b0d6e204c36f029300787f6334cb727325f8983a,Neural networks related to dysfunctional face processing in autism spectrum disorder,"Brain Struct Funct
DOI 10.1007/s00429-014-0791-z
O R I G I N A L A R T I C L E
Neural networks related to dysfunctional face processing
in autism spectrum disorder
Thomas Nickl-Jockschat • Claudia Rottschy •
Johanna Thommes • Frank Schneider •
Angela R. Laird • Peter T. Fox • Simon B. Eickhoff
Received: 6 September 2013 / Accepted: 28 April 2014
Ó Springer-Verlag Berlin Heidelberg 2014"
b028e74cbbba184e2ae01f10290961ddd1d09b9b,Audio-Visual Speech Recognition for Slavonic Languages ( Czech and Russian ),"Audio-Visual Speech Recognition for Slavonic Languages
(Czech and Russian)
Petr Císař1, Jan Zelinka1, Miloš Železný1, Alexey Karpov2, Andrey Ronzhin2
Department of Cybernetics, University of West Bohemia in Pilsen (UWB), Czech Republic
Speech Informatics Group, St. Petersburg Institute for Informatics and Automation of the Russian
Academy of Sciences (SPIIRAS), Russian Federation"
b00796447d670f9413e831ffb4ed548a380816a2,Servoing across object instances: Visual servoing for object category,"Servoing Across Object Instances: Visual Servoing for Object Category
Harit Pandya1, K Madhava Krishna1 and C. V. Jawahar1"
b00d87ca1398fe8df674292740f87dfa1fdd5802,Neuro-memristive Circuits for Edge Computing: A review,"Neuro-memristive Circuits for Edge Computing: A
Review
Olga Krestinskaya, Student Member, IEEE, Alex James, Senior Member, IEEE and Leon O. Chua, Fellow, IEEE"
b0c1615ebcad516b5a26d45be58068673e2ff217,How Image Degradations Affect Deep CNN-Based Face Recognition?,"How Image Degradations Affect Deep CNN-based Face
Recognition?
S¸amil Karahan1 Merve Kılınc¸ Yıldırım1 Kadir Kırtac¸1 Ferhat S¸ ¨ukr¨u Rende1
G¨ultekin B¨ut¨un1Hazım Kemal Ekenel2"
b0e7c177084be76fb73df3c4bcf1846676a2d615,Joint action recognition and pose estimation from video,"Joint Action Recognition and Pose Estimation From Video
Bruce Xiaohan Nie, Caiming Xiong and Song-Chun Zhu
Center for Vision, Cognition, Learning and Art
University of California, Los Angeles, USA"
b0c379f740292ad2cad2c990a445f69167e18894,Knowledge distillation using unlabeled mismatched images,"Workshop track - ICLR 2017
KNOWLEDGE DISTILLATION USING UNLABELED MIS-
MATCHED IMAGES
Mandar Kulkarni(*), Kalpesh Patil(**), Shirish Karande(*)
TCS Innovation Labs, Pune, India (*), IIT Bombay, Mumbai, India(**)"
b007ba011ba7876ca8fdb10a44bb6b2ea4dadbc6,"A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation","A Large Dataset to Train Convolutional Networks
for Disparity, Optical Flow, and Scene Flow Estimation
Nikolaus Mayer∗1, Eddy Ilg∗1, Philip H¨ausser∗2, Philipp Fischer∗1†
University of Freiburg
Technical University of Munich
Alexey Dosovitskiy, Thomas Brox
University of Freiburg
Daniel Cremers
Technical University of Munich"
b0d607d5e9e79540c9f2673f2224b2d51be3393c,Kernel Truncated Regression Representation for Robust Subspace Clustering,"Kernel Truncated Regression Representation for
Robust Subspace Clustering
Liangli Zhen, Dezhong Peng, Xin Yao"
b07546f26a99b61c5045e313bc024b0fe7de590a,Bilinear CNNs for Fine-grained Visual Recognition,"Bilinear CNNs for Fine-grained Visual
Recognition
Tsung-Yu Lin
Aruni RoyChowdhury
Subhransu Maji"
b03d5ed5b3f253703fa37d6445fab0e7cdf38ba1,Separate-Group Covariance Estimation With Insufficient Data for Object Recognition,"Separate-Group Covariance Estimation With Insufficient Data for
Object Recognition
Carlos Eduardo Thomaz1, Raul Queiroz Feitosa2, Álvaro Veiga3
,2,3Catholic University of Rio de Janeiro
Department of Electrical Engineering
Department of Computer Engineering
University of Rio de Janeiro
r. Marquês de São Vicente 225,22453-900, Rio de
r. São Francisco Xavier, 524, 20559-900, Rio de
Janeiro, Brazil
Janeiro, Brazil"
b0c651f23516055583060e2197756e1390455de5,Multimodal verification of identity for a realistic access control application,"Multimodal Verification of Identity for a
Realistic Access Control Application
Thesis submitted in partial fulfilment of the requirements for the degree
Doctor Ingeneriae
Mechanical Engineering
Rand Afrikaans University
Supervisor: Professor A.L. Nel
Nele Denys
t the
May 2004"
b0070d14db2f0e9cbd48b96af3dde66b0461c555,Humans Differ: So Should Models - Systematic Differences Call for Per-subject Modeling,"HUMANS DIFFER: SO SHOULD MODELS
Systematic Differences Call for Per-subject Modeling
Wolfgang Heidl, Stefan Thumfart and Christian Eitzinger
Profactor GmbH, Im Stadtgut A2, 4407 Steyr-Gleink, Austria
Keywords:
Machine learning, Human diversity."
b04d4b1e8b510180726f49a66dbaaf23c9ef64a0,Introspective Generative Modeling: Decide Discriminatively.,"Introspective Generative Modeling: Decide Discriminatively
Justin Lazarow ∗
Dept. of CSE
Long Jin∗
Dept. of CSE
Zhuowen Tu
Dept. of CogSci"
b0158b26f01d5fa18aac51ece055cad9a12f6d87,Memory-based Gait Recognition,"Pages 82.1-82.12
DOI: https://dx.doi.org/10.5244/C.30.82"
b09b693708f412823053508578df289b8403100a,Two-Stream SR-CNNs for Action Recognition in Videos,"WANG et al.: TWO-STREAM SR-CNNS FOR ACTION RECOGNITION IN VIDEOS
Two-Stream SR-CNNs for Action
Recognition in Videos
Yifan Wang1
Jie Song1
Limin Wang2
Luc Van Gool2
Otmar Hilliges1
Advanced Interactive Technologies Lab
ETH Zurich
Zurich, Switzerland
Computer Vision Lab
ETH Zurich
Zurich, Switzerland"
b0623c1d8493d273d704ba1d0413db0de579ae77,Attributes-Based Re-identification,"Attributes-based Re-Identification
Ryan Layne, Timothy M. Hospedales and Shaogang Gong"
b0a376888a33defd6fcfe396a11e6ea6d4f99f0e,Soft Measure of Visual Token Occurrences for Object Categorization,"Soft Measure of Visual Token Occurrences for
Object Categorization
Yanjie Wang, Xiabi Liu(cid:2), and Yunde Jia
Beijing Laboratory of Intelligent Information Technology, School of Computer
Science, Beijing Institute of Technology
Tel.: +86-10-68913447, Fax: +86-10-86343158"
b0de0892d2092c8c70aa22500fed31aa7eb4dd3f,A Robust and Efficient Video Representation for Action Recognition,"(will be inserted by the editor)
A robust and efficient video representation for action recognition
Heng Wang · Dan Oneata · Jakob Verbeek · Cordelia Schmid
Received: date / Accepted: date"
b07582d1a59a9c6f029d0d8328414c7bef64dca0,Employing Fusion of Learned and Handcrafted Features for Unconstrained Ear Recognition,"Employing Fusion of Learned and Handcrafted
Features for Unconstrained Ear Recognition
Maur´ıcio Pamplona Segundo∗†
Earnest E. Hansley∗
Sudeep Sarkar∗‡
October 24, 2017"
b0fafe26b03243a22e12b021266872afdb96572c,Factors of Transferability for a Generic ConvNet Representation,"Factors of Transferability for a Generic ConvNet Representation
Hossein Azizpour, Ali Sharif Razavian, Josephine Sullivan, Atsuto Maki, Stefan Carlsson
{azizpour, razavian, sullivan, atsuto,
Computer Vision and Active Perception (CVAP), Royal Institute of Technology (KTH), Stockholm, SE-10044 Sweden
Evidence is mounting that Convolutional Networks (ConvNets) are the most effective representation learning method for visual
recognition tasks. In the common scenario, a ConvNet is trained on a large labeled dataset (source) and the feed-forward units
ctivation of the trained network, at a certain layer of the network, is used as a generic representation of an input image for a
task with relatively smaller training set (target). Recent studies have shown this form of representation transfer to be suitable for a
wide range of target visual recognition tasks. This paper introduces and investigates several factors affecting the transferability of
such representations. It includes parameters for training of the source ConvNet such as its architecture, distribution of the training
data, etc. and also the parameters of feature extraction such as layer of the trained ConvNet, dimensionality reduction, etc. Then,
y optimizing these factors, we show that significant improvements can be achieved on various (17) visual recognition tasks. We
further show that these visual recognition tasks can be categorically ordered based on their distance from the source task such that
correlation between the performance of tasks and their distance from the source task w.r.t. the proposed factors is observed.
Index Terms—Convolutional Neural Networks, Transfer Learning, Representation Learning, Deep Learning, Visual Recognition
I. INTRODUCTION
C ONVOLUTIONAL NETWORKS (ConvNets) trace back
to the early works on digit and character recognition
[11], [23]. Prior to 2012, though, in computer vision field,
neural networks were more renowned for their propensity to"