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Prof. Ching Y. Suen
Biography Prof. Ching Y. Suen is the Director of CENPARMI and the Honorary Concordia Chair on AI & Pattern Recognition. He received his Ph.D. degree from UBC (Vancouver) and his Master's degree from the University of Hong Kong. He has served as the Chairman of the Department of Computer Science and as the Associate Dean (Research) of the Faculty of Engineering and Computer Science of Concordia University.
Prof. Suen has served at numerous national and international professional societies as President, Vice-President, Governor, and Director. He has been the Principal Investigator or Consultant of 30 industrial projects. His research projects have been funded by the ENCS Faculty and the Distinguished Chair Programs at Concordia University, FCAR (Quebec), NSERC (Canada), the National Networks of Centres of Excellence (Canada), the Canadian Foundation for Innovation, and the industrial sectors in various countries, including Canada, France, Japan, Italy, and the United States.
Prof. Suen has published 5 conference proceedings, 12 books and more than 500 papers, and many of them have been widely cited while the ideas in others have been applied in practical environments involving handwriting recognition, thinning methodologies, and multiple classifiers. Prof. Suen is the recipient of numerous awards, including the Gold Medal from the University of Bari (Italy 2012), the IAPR ICDAR Award (2005), the ITAC/NSERC national award (1992), and the "Concordia Lifetime Research Achievement" and "Concordia Fellow" awards (2008 and 1998 respectively).
Prof. Suen is a fellow of the IEEE (since 1986), IAPR (1994), and the Academy of Sciences of the Royal Society of Canada (1995).Currently, he is the Editor-in-Chief of the journal of Pattern Recognition and an Adviser or Associate Editor of 5 journals. He is not only the founder of three conferences: ICDAR, IWFHR/ICFHR, and VI, but has also organized numerous international conferences including ICPR, ICDAR, ICFHR, ICCPOL, and others. In 1997, he created the IAPR ICDAR Awards, to honour both young and established outstanding researchers in the field of Document Analysis and Recognition.
Concordia University
| Beauty and the Computer
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AbstractBeauty is one of the foremost ideas that define human personality. In this talk, various approaches to the comprehension and analysis of human beauty are presented and the use of these theories is outlined. Each set of theories is translated into a feature model that is tested for classification. Selecting the best set of features that result in the most accurate model for the representation of the human face is a key challenge. This research combines three main groups of features for classification of female facial beauty, to be used with classification through support vector machines. It concentrates on building an automatic system for the measurement of female facial beauty. Our approach analyzes the central tenets of beauty, the successive application image processing techniques, and finally the usage of relevant machine learning methods to build an effective system for the automated assessment and enhancement of facial beauty. Plenty of examples will be illustrated during the talk.
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| Nov. 10, 2015 4:00 p.m. | WLB210 |
Prof. Anil K. JainBiography Anil K. Jain is a University Distinguished Professor in the Department of Computer Science & Engineering at Michigan State University. He was appointed an Honorary Professor at Tsinghua University and WCU Distinguished Professor at Korea University. He received B.Tech. from Indian Institute of Technology, Kanpur in 1969 and M.S. and Ph.D. from Ohio State University in 1970 and 1973, respectively. His research interests include pattern recognition, computer vision and biometric recognition. His articles on biometrics have appeared in Scientific American, Nature, IEEE Spectrum, Comm. ACM, IEEE Computer, Proc. IEEE, Encarta, Scholarpedia, and MIT Technology Review.
He is a recipient of Guggenheim fellowship, Humboldt Research award, Fulbright fellowship, IEEE Computer Society Technical Achievement award, IEEE W. Wallace McDowell award, IAPR King-Sun Fu Prize, IEEE ICDM Research Contribution Award, IAPR Senior Biometric Investigator Award, and the MSU Withrow Teaching Excellence Award. He also received the best paper awards from the IEEE Trans. Neural Networks (1996) and the Pattern Recognition journal (1987, 1991 and 2005) and served as the Editor-in-Chief of the IEEE Trans. Pattern Analysis and Machine Intelligence. He is a Fellow of the ACM, IEEE, AAAS, IAPR and SPIE and was felicitated with the MSU 2014 Innovator of the Year Award.
Anil Jain has been assigned six U.S. patents on fingerprint recognition and two Korean patents on surveillance. His research has resulted in technologies for fingerprint recognition, tattoo image matching, facial sketch to photo matching, unconstrained face recognition and fingerprint obsfucation that have been licensed to IBM, Morpho and NEC .He served as an advisor to India's Aadhaar program that provides a 12-digit unique ID number to Indian residents based on their ten fingerprints and both iris images.
He currently serves as a member of the Forensic Science Standards Board (FSSB), co-organizer of program on Forensics (2015-2016) at the NSF Statistical and Mathematical Sciences Institute (SAMSI) and a member of the Latent Fingerprint Working Group of the American Association for the Advancement of Science (AAAS).
Refer to his homepage: http://www.cse.msu.edu/~jain/.
Michigan State University
| Who Goes There? Applications & Challenges of
Face Recognition
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AbstractThere are two main drivers of face recognition technology: (i) security, namely, access to restricted areas and personal devices, de-duplication of passports and driver licenses and identifying suspects in surveillance videos, and (ii) social media, where face recognition is useful for automatic tagging of photos. These applications and their requirements have generated tremendous interest in face recognition research and development. While the origins of machine face recognition date back 50 years, the general problem of face recognition is incredibly difficult due to large intra-person face variability: pose, illumination, expression, occlusion, and aging. In other words, different face images of the same person acquired at different times and under different imaging conditions can have quite different appearances that are difficult to match by state of the art systems. Hence, the challenge is to design salient feature extractors and robust matchers for face images. For this reason, convolution neural networks, also known as, deep networks, have played a major role in the new generation of face recognition systems. This talk will address a number of ongoing research projects in my laboratory that include (i) face identification at scale (gallery of 80 million faces), (ii) large-scale face clustering (120 million faces), (iii) longitudinal study of face recognition, and (iv) detection of spoof faces.
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| Dec. 14, 2015 4:30 p.m. | RRS905 |
Prof. Markus GrossBiography Markus Gross is a Professor of Computer Science at the Swiss Federal Institute of Technology Zürich (ETH), Head of the Computer Graphics Laboratory, and the Director of Disney Research, Zürich. He joined the ETH Computer Science faculty in 1994. His research interests include physically based modeling, computer animation, immersive displays, and video technology. Before joining Disney, Gross was director of the Institute of Computational Sciences at ETH. He received a master of science in electrical and computer engineering and a PhD in computer graphics and image analysis, both from Saarland University in Germany in 1986 and 1989. Gross serves on the boards of numerous international research institutes, societies, and governmental organizations. He received the Technical Achievement Award from EUROGRAPHICS in 2010, the Swiss ICT Champions Award in 2011 and the IEEE Visualization Technical Achievement Award in 2015. He is a fellow of the ACM and of the EUROGRAPHICS Association and a member of the German Academy of Sciences Leopoldina as well as the Berlin-Brandenburg Academy of Sciences and Humanities. In 2013 he received a Technical Achievement Award from the Academy of Motion Picture Arts and Sciences, the Konrad Zuse Medal of GI and the Karl Heinz Beckurts prize. Swiss Federal Institute of Technology Zürich
| Physically Based Simulation for Film and Entertainment
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AbstractPhysically based simulations have become an essential tool for a variety of special effects in movie production and computer animation. Unlike conventional computational science, where simulation is conceived as a tool to replace experiments, the algorithms we develop for computer animation focus onto visual plausibility, robustness and art direct ability. The underlying governing equations are well understood in continuum mechanics and include linear or non-linear elasticity, plasticity and the Navier-Stokes equations. The numerical methods we employ encompass state of the art Eulerian or SPH type solvers for fluids, or discontinuous Galerkin FEM for elasticity. I will present various algorithms to achieve high visual fidelity at low computational cost by either synthesizing details into the computation or by combining high resolution geometry with low resolution simulations. I will also demonstrate how we compromise the laws of physics to achieve controllability of the simulation by the artist.
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| Feb. 16, 2016 4:30 p.m. | RRS905 |
| Technological Innovation for Entertainment
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AbstractDisney Research was launched in 2008 as a network of research laboratories that collaborate closely with academic institutions such as the Swiss Federal Institute of Technology in Zurich and Carnegie Mellon University. Its mission is to push the frontiers of technology in areas relevant to Disney's creative entertainment businesses. Disney Research develops innovations for Parks, Film, Animation, Television, Games, and Consumer Products. Research areas include video and animation technologies, postproduction and special effects, digital fabrication, robotics, and much more. This talk gives an overview of Disney Research spiced with some examples of our latest and greatest inventions. The focus is on the collaboration between ETH Zurich and the Walt Disney Company displaying the synergies arising from this program. This talk will highlight a company perspective as well as a view from the academic angle.
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| Feb. 17, 2016 4:30 p.m. | AAB201 |
Prof. Tieniu TanBiography Tieniu Tan received his B.Sc. degree in electronic engineering from Xi'an Jiaotong University, China, in 1984, and his MSc and PhD degrees in electronic engineering from Imperial College London, U.K., in 1986 and 1989, respectively.
In October 1989, he joined the Department of Computer Science, The University of Reading, U.K., where he worked as a Research Fellow, Senior Research Fellow and Lecturer. In January 1998, he returned to China to join the National Laboratory of Pattern Recognition (NLPR), Institute of Automation of the Chinese Academy of Sciences (CAS) as a full professor. He was the Director General of the CAS Institute of Automation from 2000-2007, and the Director of the NLPR from 1998-2013. He is currently Director of the Center for Research on Intelligent Perception and Computing at the Institute of Automation and also serves as Deputy Secretary-General of the CAS and the Director General of the CAS Bureau of International Cooperation. He has published more than 450 research papers in refereed international journals and conferences in the areas of image processing, computer vision and pattern recognition, and has authored or edited 11 books. He holds more than 70 patents. His current research interests include biometrics, image and video understanding, and information forensics and security.
Dr. Tan is a Member (Academician) of the Chinese Academy of Sciences, Fellow of The World Academy of Sciences for the advancement of sciences in developing countries (TWAS), an International Fellow of the UK Royal Academy of Engineering, and a Fellow of the IEEE and the IAPR (the International Association of Pattern Recognition). He is Editor-in-Chief of the International Journal of Automation and Computing. He has given invited talks and keynotes at many universities and international conferences, and has received numerous national and international awards and recognitions.
Institute of Automation Chinese Academy of Sciences
| Artificial Intelligence: Key to Chinese Manufacturing 2025
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AbstractFor many years, China has been seen as the ‘world factory’ because of cheap labor and large market, and the manufacturing industry remains to be the backbone of the Chinese economy. However Chinese manufacturing industry faces grand challenges due to increasing labor costs, environmental constraints, etc. The Chinese Manufacturing 2025 strategy is a timely response to these challenges. This talk starts with a brief overview of the status quo of the Chinese manufacturing industry and outlines the Chinese Manufacturing 2025 strategy. It focuses on discussing why artificial intelligence is the key to the success of the strategy. Some promising AI progress, innovations and directions relevant to the strategy are described in this context such as brain-inspired AI, big data driven AI, AI chips and sensors, hybrid intelligence, intelligent-DIY, AI as a service, etc.
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| Mar. 22, 2016 4:30 p.m. | WLB210 |
Prof. Vijayakumar BhagavatulaBiography Prof. Vijayakumar (“Kumar”) Bhagavatula received his Ph.D. in Electrical Engineering from Carnegie Mellon University (CMU), Pittsburgh and since 1982, he has been a faculty member in the Electrical and Computer Engineering (ECE) Department at CMU where he is now the U.A. & Helen Whitaker Professor of ECE and the Associate Dean for the College of Engineering. He served as the Associate Head of the ECE Department and also as its Acting Department Head. Professor Kumar's research interests include Pattern Recognition and Coding and Signal Processing for Data Storage Systems and for Digital Communications. He has authored or co-authored over 600 technical papers, twenty book chapters and one book entitled Correlation Pattern Recognition. He served as a Topical Editor for Applied Optics and as an Associate Editor of IEEE Trans. Information Forensics and Security. Professor Kumar is a Fellow of IEEE, a Fellow of SPIE, a Fellow of Optical Society of America (OSA) and a Fellow of the International Association of Pattern Recognition (IAPR). Carnegie Mellon University
| New Correlation Filter Designs and Applications
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AbstractIn many pattern recognition problems, the main task is to match two images of an object (e.g., face, iris, vehicle, etc.) that may exhibit appearance differences due to factors such as translation, rotation, scale change, occlusion, illumination variations and others. One class of methods to achieve accurate object recognition in the presence of such appearance variations is one where features computed in a sliding window in the target image are compared to features computed in a stationary window of the reference image. Correlation filters are an efficient frequency-domain method to implement such sliding window matching. They also offer benefits such as shift-invariance (i.e., the object of interest can be off-center), no need for segmentation, graceful degradation and closed-form solutions. While the origins of correlation filters go back more than thirty years, there have been some very interesting and useful advances in correlation filter designs and their applications. For example, the new maximum margin correlation filters (MMCFs) show how the superior localization capabilities of correlation filters can be combined with the generalization capabilities of support vector machines (SVMs). Another major research advance is the development of vector correlation filters that use features (e.g., HOG) extracted from the input image rather than just input image pixel values. While past application of correlation filters focused mainly on automatic target recognition, more recent applications include face recognition, iris recognition, palmprint recognition and visual tracking. This talk will provide an overview of correlation filter designs and applications, with particular emphasis on these more recent advances. - • Poster
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| Jun. 22, 2016 4:00 p.m. | LT1 |
Prof. Xiaofang ZhouBiography Professor Xiaofang Zhou is a Professor of Computer Science at The University of Queensland, Australia, leading the Data and Knowledge Engineering (DKE) Group at UQ. His research focus is to find effective and efficient solutions for managing, integrating and analyzing very large amount of complex data for business, scientific and personal applications. He has been working in the area of spatial and multimedia databases, data quality, high performance database systems and data mining. He is a Program Committee Chair for IEEE ICDE 2013, CIKM 2016 and a General Chair of ACM Multimedia 2015. He has been an Associate Editor of The VLDB Journal, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Cloud Computing, World Wide Web Journal, Distributed and Parallel Databases, and IEEE Data Engineering Bulletin. He is the current Chair of IEEE Technical Committee on Data Engineering (TCDE). The University of Queensland
| Making Sense of Spatial Trajectories
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AbstractSpatial trajectory data is widely available today. Over a sustained period of time, trajectory data has been collected from numerous GPS devices, smartphones, sensors and social media applications. How do we manage them? What values can a business derive from them, and how? Due to their very large volumes, the nature of streaming itself, highly variable levels of data quality, as well as many possible links with other types of data, making sense of spatial trajectory data is one of the crucial areas for big data analytics. In this talk, we will introduce this increasingly important research area, with new applications, new problems and new opportunities. We will discuss recent advances in trajectory data management and trajectory mining in the context of machine-to-machine data processing, from their foundations to high performance processing with modern computing infrastructures. - • Poster
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| Jun. 29, 2016 4:00 p.m. | LT1 |