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11th Asian Conference on computer vision, ACCV 2012

作者： Komulainen, Jukka Hadid, Abdenour Pietikäinen, Matti Center for Machine Vision Research Department of Computer Science and Engineering University of Oulu P. O. Box 4500 FI-90014 Oulu Finland

ISBN: (纸本)9783642374098

While there is a significant number of works addressing e.g. pose and illumination variation problems in face recognition, the vulnerabilities to spoofing attacks were mostly unexplored until very recently when an increasing attention is started to be paid to this threat. A spoofing attack occurs when a person tries to masquerade as someone else e.g. by wearing a mask to gain illegitimate access and advantages. This work provides the first investigation in research literature on the use of dynamic texture for face spoofing detection. Unlike masks and 3D head models, real faces are indeed non-rigid objects with contractions of facial muscles which result in temporally deformed facial features such as eye lids and lips. Our key idea is to learn the structure and the dynamics of the facial micro-textures that characterise only real faces but not fake ones. Hence, we introduce a novel and appealing approach to face spoofing detection using the spatiotemporal (dynamic texture) extensions of the highly popular local binary pattern approach. We experiment with two publicly available databases consisting of several fake face attacks of different natures under varying conditions and imaging qualities. The experiments show excellent results beyond the state-of-the-art. © 2013 Springer-Verlag.

关键词： Local binary pattern

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Human genome meeting 2016 : Houston, TX, USA. 28 February - 2 March 2016

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Human genomics 2016年第1期10 Suppl 1卷 12页

作者： A. K. Srivastava Y. Wang R. Huang C. Skinner T. Thompson L. Pollard T. Wood F. Luo R. Stevenson R. Polimanti J. Gelernter X. Lin I. Y. Lim Y. Wu A. L. Teh L. Chen I. M. Aris S. E. Soh M. T. Tint J. L. MacIsaac F. Yap K. Kwek S. M. Saw M. S. Kobor M. J. Meaney K. M. Godfrey Y. S. Chong J. D. Holbrook Y. S. Lee P. D. Gluckman N. Karnani A. Kapoor D. Lee A. Chakravarti C. Maercker F. Graf M. Boutros G. Stamoulis F. Santoni P. Makrythanasis A. Letourneau M. Guipponi N. Panousis M. Garieri P. Ribaux E. Falconnet C. Borel S. E. Antonarakis S. Kumar J. Curran J. Blangero S. Chatterjee J. Akiyama D. Auer C. Berrios L. Pennacchio T. R. Donti G. Cappuccio M. Miller P. Atwal A. Kennedy A. Cardon C. Bacino L. Emrick J. Hertecant F. Baumer B. Porter M. Bainbridge P. Bonnen B. Graham R. Sutton Q. Sun S. Elsea Z. Hu P. Wang Y. Zhu J. Zhao M. Xiong David A. Bennett A. Hidalgo-Miranda S. Romero-Cordoba S. Rodriguez-Cuevas R. Rebollar-Vega E. Tagliabue M. Iorio E. D’Ippolito S. Baroni B. Kaczkowski Y. Tanaka H. Kawaji A. Sandelin R. Andersson M. Itoh T. Lassmann Y. Hayashizaki P. Carninci A. R. R. Forrest C. A. Semple E. A. Rosenthal B. Shirts L. Amendola C. Gallego M. Horike-Pyne A. Burt P. Robertson P. Beyers C. Nefcy D. Veenstra F. Hisama R. Bennett M. Dorschner D. Nickerson J. Smith K. Patterson D. Crosslin R. Nassir N. Zubair T. Harrison U. Peters G. Jarvik F. Menghi K. Inaki X. Woo P. Kumar K. Grzeda A. Malhotra H. Kim D. Ucar P. Shreckengast K. Karuturi J. Keck J. Chuang E. T. Liu B. Ji A. Tyler G. Ananda G. Carter H. Nikbakht M. Montagne M. Zeinieh A. Harutyunyan M. Mcconechy N. Jabado P. Lavigne J. Majewski J. B. Goldstein M. Overman G. Varadhachary R. Shroff R. Wolff M. Javle A. Futreal D. Fogelman L. Bravo W. Fajardo H. Gomez C. Castaneda C. Rolfo J. A. Pinto K. C. Akdemir L. Chin S. Patterson C. Statz S. Mockus S. N. Nikolaev X. I. Bonilla L. Parmentier B. King F. Bezrukov G. Kaya V. Zoete V. Seplyarskiy H. Sharpe T. McKee K. Popadin N. Basset-Seguin R. Ben Chaabene M. Andrianova C. Verdan K. Grosdemange O. Sumara M. E JCSRI Greenwood Genetic Center Greenwood USA School of Computing Clemson University Clemson USA Biochemical Genetics Laboratory Greenwood Genetic Center Greenwood USA Department Psychiatry Yale Sch Med and VA CT Healthcare Center West Haven USA Department Genetics Yale Sch Med and VA CT Healthcare Center West Haven USA Department Neurobiology Yale Sch Med and VA CT Healthcare Center West Haven USA Singapore Institute for Clinical Sciences Singapore Singapore National University of Singapore Singapore Singapore University of British Columbia Vancouver Canada KK Women’s and Children’s Hospital Singapore Singapore University of Southampton and University Hospital Southampton NHS Foundation Trust Southampton UK University of Auckland Auckland New Zealand McKusick-Nathans Institute of Genetic Medicine Johns Hopkins University School of Medicine Baltimore USA Esslingen University of Applied Sciences Esslingen Germany German Cancer Research Center Heidelberg Germany Department of Genetic Medicine and Development University of Geneva Medical School Geneva Switzerland Geneva University Hospitals-HUG Service of Genetic Medicine Geneva Switzerland GE3 Institute of Genetics and Genomics of Geneva University of Geneva Medical School Geneva Switzerland South Texas Diabetes and Obesity Institute School of Medicine University of Texas Rio-Grande Valley Edinburg USA South Texas Diabetes and Obesity Institute School of Medicine University of Texas Rio-Grande Valley Brownsville USA Institute of Genetic Medicine Johns Hopkins University Baltimore USA Genomics Division Lawrence Berkeley National Laboratory Berkeley USA Molecular and Human Genetics Baylor College of Medicine Houston USA Department of Translational Medical Sciences Federico II University Naples Italy Metabolon Inc Durham USA Section of Pediatric Neurology and Neuroscience Baylor College of Medicine Houston USA Tawam Hospital Abu Dhabi United Arab Emirates Stanford Medical School Stanford USA Sch

O1 The metabolomics approach to autism: identification of biomarkers for early detection of autism spectrum disorder A. K. Srivastava, Y. Wang, R. Huang, C. Skinner, T. Thompson, L. Pollard, T. Wood, F. Luo, R. Stevenson O2 Phenome-wide association study for smoking- and drinking-associated genes in 26,394 American women with African, Asian, European, and Hispanic descents R. Polimanti, J. Gelernter O3 Effects of prenatal environment, genotype and DNA methylation on birth weight and subsequent postnatal outcomes: findings from GUSTO, an Asian birth cohort X. Lin, I. Y. Lim, Y. Wu, A. L. Teh, L. Chen, I. M. Aris, S. E. Soh, M. T. Tint, J. L. MacIsaac, F. Yap, K. Kwek, S. M. Saw, M. S. Kobor, M. J. Meaney, K. M. Godfrey, Y. S. Chong, J. D. Holbrook, Y. S. Lee, P. D. Gluckman, N. Karnani, GUSTO study group O4 High-throughput identification of specific qt interval modulating enhancers at the SCN5A locus A. Kapoor, D. Lee, A. Chakravarti O5 Identification of extracellular matrix components inducing cancer cell migration in the supernatant of cultivated mesenchymal stem cells C. Maercker, F. Graf, M. Boutros O6 Single cell allele specific expression (ASE) IN T21 and common trisomies: a novel approach to understand DOWN syndrome and other aneuploidies G. Stamoulis, F. Santoni, P. Makrythanasis, A. Letourneau, M. Guipponi, N. Panousis, M. Garieri, P. Ribaux, E. Falconnet, C. Borel, S. E. Antonarakis O7 Role of microRNA in LCL to IPSC reprogramming S. Kumar, J. Curran, J. Blangero O8 Multiple enhancer variants disrupt gene regulatory network in Hirschsprung disease S. Chatterjee, A. Kapoor, J. Akiyama, D. Auer, C. Berrios, L. Pennacchio, A. Chakravarti O9 Metabolomic profiling for the diagnosis of neurometabolic disorders T. R. Donti, G. Cappuccio, M. Miller, P. Atwal, A. Kennedy, A. Cardon, C. Bacino, L. Emrick, J. Hertecant, F. Baumer, B. Porter, M. Bainbridge, P. Bonnen, B. Graham, R. Sutton, Q. Sun, S. Elsea O10 A novel causal methylation network approach to Alzheimer’s

关键词： Pulmonary Arterial Hypertension Oral Squamous Cell Carcinoma Deep Neural Network Autosomal Recessive Polycystic Kidney Disease Whole Genome Bisulfite Sequencing

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ChaLearn multi-modal gesture recognition 2013: Grand challenge and workshop summary 13

ChaLearn multi-modal gesture recognition 2013: Grand challen...

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2013 15th ACM International Conference on Multimodal Interaction, ICMI 2013

作者： Escalera, Sergio Gonzàlez, Jordi Baró, Xavier Reyes, Miguel Guyon, Isabelle Athitsos, Vassilis Escalante, Hugo Sigal, Leonid Argyros, Antonis Sminchisescu, Cristian Bowden, Richard Sclaroff, Stan Dept. Applied Mathematics Universitat de Barcelona Computer Vision Center Barcelona Spain Dept. Computer Science Univ. Autònoma de Barcelona Computer Vision Center Barcelona Spain EIMT Open University of Catalonia Barcelona Spain ChaLearn Berkeley CA United States Texas University United States INAOE Puebla Mexico Disney Research Pittsburgh PA United States Institute of Computer Science FORTH Creete Greece Lund University Lund Sweden University of Surrey Surrey United Kingdom Department of Computer Science Boston University Boston United States Computer Vision Center UAB Spain

ISBN: (纸本)9781450321297

We organized a Grand Challenge and Workshop on Multi-Modal Gesture Recognition. The MMGR Grand Challenge focused on the recognition of continuous natural gestures from multi-modal data (including RGB, Depth, user mask, Skeletal model, and audio). We made available a large labeled video database of 13,858 gestures from a lexicon of 20 Italian gesture categories recorded with a Kinect™ camera. More than 54 teams participated in the challenge and a final error rate of 12% was achieved by the winner of the competition. Winners of the competition published their work in the workshop of the Challenge. The MMGR Workshop was held at ICMI conference 2013, Sidney. A total of 9 relevant papers with basis on multi-modal gesture recognition were accepted for presentation. This includes multi-modal descriptors, multi-class learning strategies for segmentation and classification in temporal data, as well as relevant applications in the field, including multi-modal Social Signal Processing and multi-modal Human computer Interfaces. Five relevant invited speakers participated in the workshop: Profs. Leonid Signal from Disney research, Antonis Argyros from FORTH, Institute of computer science, Cristian Sminchisescu from Lund University, Richard Bowden from University of Surrey, and Stan Sclaroff from Boston University. They summarized their research in the field and discussed past, current, and future challenges in Multi-Modal Gesture Recognition. © 2013 ACM.

关键词： Gesture recognition

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Robust object tracking in crowd dynamic scenes using explicit stereo depth

Robust object tracking in crowd dynamic scenes using explici...

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11th Asian Conference on computer vision, ACCV 2012

作者： Li, Chi Lu, Le Hager, Gregory D. Tang, Jianyu Wang, Hanzi Cognitive Science Department Xiamen University Xiamen China Imaging and Computer Vision Siemens Corporate Research Princeton NJ United States Computer Science Department Johns Hopkins University Baltimore MD United States Center for Pattern Analysis and Machine Intelligence Xiamen University China

ISBN: (纸本)9783642374302

In this paper, we exploit robust depth information with simple color-shape appearance model on single object tracking in crowd dynamic scenes. Since binocular video streams are captured from a moving camera rig, background subtraction cannot provide a reliable enhancement of region of interest. Our main contribution is a novel tracking strategy to employ explicit stereo depth to track and segment object in crowd dynamic scenes with occlusion handling. Appearance cues including color and shape play a secondary role to further extract the foreground acquired by previous depth-based segmentation. The proposed depth-driven tracking approach can largely alleviate the drifting issue, especially when the object frequently interacts with similar background in long sequence tracking. The problems caused by rapid object appearance change can also be avoided due to the stability of the depth cue. Furthermore, we propose a new, yet simple and effective depth-based scheme to cope with complete occlusion in tracking. From experiments on a large collection of challenging outdoor and indoor sequences, our algorithm demonstrates accurate and reliable tracking performance which outperforms other state-of-the-art competing algorithms. © 2013 Springer-Verlag.

关键词： Image segmentation

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Towards Practical Visual Servoing in robotics

Towards Practical Visual Servoing in Robotics

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Canadian Conference on computer and Robot vision, CRV

作者： Romeo Tatsambon Fomena Camilo Perez Quintero Mona Gridseth Martin Jagersand Computer Vision and Robotics Research Group Department of Computing Science University of Alberta Edmonton AB Canada

ISBN: (纸本)9781467364096

Visual servoing was introduced in robotics nearly 4 decades ago. However until now, there are still only a handful of known examples of application of this technique in addressing real word robotics problems such as disaster response, assistance for elderly or handicapped people, etc. As the world is moving towards the use of robotics to improve quality of life, it is time to assess the challenges involved in applying visual servoing to solve real world problems. This paper presents an overview of these challenges, by asking the question what are the missing components for practical visual servoing? and by providing practical possible solutions for these components. Illustration of these challenges and our current practical solutions are given using our 7-DoFs Barrett WAM Arm.

关键词： Visual servoing Visualization Cameras Target tracking Robot kinematics

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Encoding Local Binary Patterns using the re-parametrization of the second order Gaussian jet

Encoding Local Binary Patterns using the re-parametrization ...

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International Conference on Automatic Face and Gesture Recognition

作者： John A. Ruiz-Hernandez Matti Pietikäinen Center for Machine Vision Research Department of Computer Science FI University of Oulu Finland

In object recognition a robust feature set is considered as an important component in almost all the approaches proposed in the literature. In facial analysis, one of the best known feature set is based in Local Binary Patterns (LBP) which extracts the information contained in the image using comparisons between pixels in a region, finally such comparisons are encoded in form of histogram. We argue that this kind of encoding is statistically non-stable and can lead to errors during the recognition process, specially in noisy and low-resolution images, where the information contained in the image is not enough to generate a statistically robust histogram. In this paper, we propose a new method to encode the Local Binary Patterns using an re-parametrization of the second local order Gaussian Jet which generates more robust and reliable histograms suitable for different facial analysis tasks. We show that our method can be used for recognizing micro-expressions with competitive performances on the Spontaneous Micro-expression Corpus (SMIC) and the YORK Deception Detection Test.

关键词： Encoding Kernel Histograms Vectors Robustness Image coding Feature extraction

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PROGRAMMABLE LOW POWER IMPLEMENTATION OF THE HEVC ADAPTIVE LOOP FILTER

PROGRAMMABLE LOW POWER IMPLEMENTATION OF THE HEVC ADAPTIVE L...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Ilkka Hautala Jani Boutellier Jari Hannuksela Center for Machine Vision Research Department of Computer Science and Engineering FI-90014 University of Oulu

ISBN: (纸本)9781479903573

The Adaptive Loop Filter (ALF) is a subjective and objective image quality improving filter in the High Efficiency Video Coding standard (HEVC). The ALF has shown to be computationally complex and its complexity has been reduced during the HEVC development process. In the HEVC Test Model HM-7.0 ALF is a 9×7 cross + 3×3 square shaped filter. This paper presents a programmable application specific instruction processor for the ALF. The proposed processor processes 1920×1080p luminance frames at 30 frames per second, when operated at a clock frequency of 311 MHz. Low power consumption and a low gate count make the proposed processor suitable for embedded devices. The processor program code is written in pure C-language, which allows versatile use of the circuit and updates to the filter functionality without modifying the processor design. To the authors' best knowledge this is the first programmable solution for ALF on embedded devices.

关键词： Coprocessors Adaptive filters Video signal processing Coprocessors Video signal processing adaptive filters GTF2A1L gene PROGRAMMABLE Embedded device clock frequency PROCESSOR Filters LOW POWER

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Analysis of Scores, Datasets, and Models in Visual Saliency Prediction

Analysis of Scores, Datasets, and Models in Visual Saliency ...

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International Conference on computer vision (ICCV)

作者： Ali Borji Hamed R. Tavakoli Dicky N. Sihite Laurent Itti Department of Computer Science University of Southern California Los Angeles Center for Machine Vision Research University of Oulu Finland

ISBN: (纸本)9781479928415

Significant recent progress has been made in developing high-quality saliency models. However, less effort has been undertaken on fair assessment of these models, over large standardized datasets and correctly addressing confounding factors. In this study, we pursue a critical and quantitative look at challenges (e.g., center-bias, map smoothing) in saliency modeling and the way they affect model accuracy. We quantitatively compare 32 state-of-the-art models (using the shuffled AUC score to discount center-bias) on 4 benchmark eye movement datasets, for prediction of human fixation locations and scan path sequence. We also account for the role of map smoothing. We find that, although model rankings vary, some (e.g., AWS, LG, AIM, and HouNIPS) consistently outperform other models over all datasets. Some models work well for prediction of both fixation locations and scan path sequence (e.g., Judd, GBVS). Our results show low prediction accuracy for models over emotional stimuli from the NUSEF dataset. Our last benchmark, for the first time, gauges the ability of models to decode the stimulus category from statistics of fixations, saccades, and model saliency values at fixated locations. In this test, ITTI and AIM models win over other models. Our benchmark provides a comprehensive high-level picture of the strengths and weaknesses of many popular models, and suggests future research directions in saliency modeling.

关键词： Predictive models Visualization Benchmark testing Smoothing methods Analytical models Face Accuracy

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Automated segmentation of blood cells in Giemsa stained digitized thin blood films

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Diagn Pathol 2013年第SUPPL 1期8卷 S37-S37页

作者： Margarita Walliander Riku Turkki Nina Linder Mikael Lundin Juho Konsti Ville Ojansivu Taru Meri Ville Holmberg Johan Lundin Institute for Molecular Medicine Finland (FiMM) Finland Center for Machine Vision Research Department of Computer Science and Engineering University of Oulu Finland Haartman Institute Finland

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Human-robotic collaborative intelligent control for reaching performance

Human-robotic collaborative intelligent control for reaching...

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7th International Conference on Foundations of Augmented Cognition, AC 2013, Held as Part of 15th International Conference on Human-computer Interaction, HCI International 2013

作者： Gentili, Rodolphe J. Oh, Hyuk Shuggi, Isabelle M. Goodman, Ronald N. Rietschel, Jeremy C. Hatfield, Bradley D. Reggia, James A. Department of Kinesiology Cognitive Motor Neuroscience Laboratory University of Maryland College Park MD 20742 United States Neuroscience and Cognitive Science Program University of Maryland College Park MD 20742 United States Maryland Robotics Center University of Maryland College Park MD 20742 United States Institute for Systems Research University of Maryland College Park MD 20742 United States Veterans Health Administration Maryland Exercise and Robotics Center of Excellence Baltimore MD 21201 United States Department of Computer Science University of Maryland College Park MD 20742 United States Maryland Institute for Advanced Computer Studies University of Maryland College Park MD 20742 United States

ISBN: (纸本)9783642394539

In most human-robot interfaces, the user completely controls the robot that operates as a passive tool without adaptation capabilities. However, a synergetic human-robot interface where both agents collaborate could improve the user's performance while reducing the cognitive and physical workload. Specifically, when considering this framework applied to rehabilitation, we examined a shared collaborative control between a human user and an adaptive biologically inspired neurocontroller in order to perform reaching movements with a simulated prosthetic arm. When this neurocontroller was enabled, it progressively learned from the user to control the prosthetic arm, increasing its role in the shared performance and facilitating the user's reaching movements. This resulted in the user's performance enhancement and in a reduction of his/her cognitive workload. The long term goal of this work is to contribute to the development of the next generation of intelligent human-robotic interfaces for rehabilitation. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Adaptive control systems

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