Plenary Talks

Abstract
This talk will introduce recent methods to compute solutions to fundamental problems in image processing. Several meaningful problems in image processing are usually defined as /non-convex/ energy minimization problems, which are sensitive to initial condition and slow to minimize. The ultimate objective of our work is to overcome the bottleneck problem of non-convexity. In other words, our goal is to "convexify" the original problems to produce more robust and faster algorithms for real-world applications. Our approach consists in finding a convex relaxation of the original non-convex optimization problems and thresholding the relaxed solution to reach the solution of the original problem. We will show that this approach is able to convexify important and difficult image processing problems such as multiphase image segmentation based on the level set method and image registration. Our algorithms are not only guaranteed to find global solution to the original problem, they are also at least as fast as graph-cuts combinatorial techniques while being more accurate. Joint work with X. Bresson.

Biography
Prof Tony F Chan assumed the presidency of HKUST on 1 September 2009. Prior to joining HKUST, Prof Chan was Assistant Director of the U.S. National Science Foundation (NSF) in charge of the Mathematical and Physical Sciences Directorate, which is the largest directorate at NSF. In that position, he guided and managed research funding of about HK$10 billion a year in astronomy, physics, chemistry, mathematical science, material science, and multidisciplinary activities.

Prof Chan's scientific background is in Mathematics, Computer Science and Engineering. He received his BS and MS degrees in Engineering from the California Institute of Technology (Caltech) and his PhD in Computer Science from Stanford University. He pursued postdoctoral research at Caltech as Research Fellow, and taught Computer Science at Yale University before joining the University of California at Los Angeles (UCLA) as Professor of Mathematics in 1986. He was appointed Chair of the Department of Mathematics in 1997 and served as Dean of Physical Science from 2001 to 2006. He also holds honorary joint appointments with the University's BioEngineering Department and the Computer Science Department.

Prof Chan was one of the principal investigators who made the successful proposal to the NSF to form the Institute for Pure and Applied Mathematics (IPAM) at UCLA. He served as IPAM's Director from 2000 to 2001.

Prof Chan is an active member of many scientific societies, including the Society of Industrial and Applied Mathematics (SIAM), the American Mathematical Society, the Institute of Electrical and Electronic Engineers (IEEE) and was elected as a SIAM Fellow in March 2010. Prof Chan is also a member of Committee of 100 and has served on the editorial boards of many journals in mathematics and computing, including SIAM Review, SIAM Journal of Scientific Computing, and the Asian Journal of Mathematics, and is one of the three Editors-in-Chief of Numerische Mathematik. He co-wrote the proposal to start a new SIAM Journal of Imaging Sciences and serves on its inaugural editorial board. He formerly served on the NSF Mathematical and Physical Sciences Advisory Committee and the US National Committee on Mathematics, and represented the US to the 2006 General Assembly of the International Mathematics Union in Spain.

His current research interests include mathematical image processing and computer vision, Very Large-Scale Integration (VLSI) physical design and computational brain mapping. He has published over 200 refereed papers and is one of the most cited mathematicians. He has mentored over 25 PhD students and 15 postdoctoral fellows.

Abstract
The fields of visual signal analysis and compression have made significant advances during the last two decades, incorporating sophisticated signal processing techniques and models of human perception. One of the keys to further advances is a better understanding of texture. We examine a number of applications that critically depend on texture analysis, including image and video compression, computer vision, content-based retrieval, visual to tactile image conversion, and multimodal interfaces.

We first look at image and video compression. Traditional compression techniques have relied on point-by-point comparisons -- whether in the original image domain or in a transform domain -- that cannot adequately exploit the stochastic nature of texture. We discuss the idea of "structurally lossless" compression that allows significant differences between the original and decoded images, which may be perceptible when they are viewed side-by-side, but do not affect the overall quality of the image.

In computer vision and content-based retrieval, texture is a key (low-level) perceptual attribute that plays a critical role in material perception, and along with color and shape, in the extraction of semantic information. Texture analysis is also an essential element of a new segmentation-based approach for converting images into tactile patterns. Such a conversion can dramatically increase the amount of information that can be made available to the visually impaired segment of the population. Finally, a better understanding of the joint perception of visual, acoustic, and tactile textures is critical for the development of multimodal interfaces for the next generation of interactive environments for entertainment, commerce, education, and medicine.

A key problem in all of the above applications is the inability of existing metrics to quantify specific texture attributes and to predict perceptual texture similarity. We discuss the development of objective texture similarity metrics that allow substantial point-by-point deviations between textures that according to human judgment are virtually identical. Such metrics are essential for all of the above applications, but we show that different applications impose different requirements on metric performance. We also discuss the development of metrics for texture attributes (perceptual dimensions), such as directionality and roughness.

Biography
Thrasyvoulos (Thrasos) Pappas (www.eecs.northwestern.edu/~pappas) received the S.B., S.M., and Ph.D. degrees in electrical engineering and computer science from MIT in 1979, 1982, and 1987, respectively. From 1987 until 1999, he was a Member of the Technical Staff at Bell Laboratories, Murray Hill, NJ. In 1999, he joined the Department of Electrical and Computer Engineering (now EECS) at Northwestern University as an associate professor. His research interests are in image and video quality and compression, image and video analysis, content-based retrieval, perceptual models for multimedia processing, model-based halftoning, and tactile and multimodal interfaces.

Dr. Pappas is a Fellow of the IEEE and SPIE. He has served as an elected member of the Board of Governors of the Signal Processing Society of IEEE (2004-2007), chair of the IEEE Image and Multidimensional Signal Processing Technical Committee (2002-2003), and technical program co-chair of ICIP-01 and ICIP-09. He has also served as co-chair of the 2005 SPIE/IS&T Electronic Imaging Symposium. Since 1997 he has been co-chair of the SPIE/IS&T Conference on Human Vision and Electronic Imaging. Dr. Pappas has served on the editorial boards of the IEEE Transactions on Image Processing, the IEEE Signal Processing Magazine, the IS&T/SPIE Journal of Electronic Imaging, and the Foundations and Trends in Signal Processing. He is currently editor-in-chief of the IEEE Transactions on Image Processing.

Abstract
Within the past decade, the proliferation of multimedia social network communities, such as Napster, and YouTube where millions of users form a dynamically changing infrastructure to share content, have introduced the new concept of social networking that creates a technological revolution as well as brings new experiences to users. The massive content production poses new challenges to the scalable and reliable sharing of multimedia content over large and heterogeneous networks. It also raises critical issues of intellectual property protection and privacy issues.

In a multimedia social network, users actively interact with each other, and such user dynamics not only influence each individual user but also affect the system performance. To provide a predictable and satisfactory level of service, it is of ample importance to understand the impact of human factors on multimedia social networks. Such an understanding provides fundamental guidelines to the better design of multimedia systems and networking, and offers more secure and personalized services. For example, in a peer-to-peer file-sharing system, users pool together the resources and cooperate with each other to provide an inexpensive, highly scalable, and robust platform for distributed data sharing. However, since the nature of participation in many multimedia social networks is often voluntary and unregulated, there is a need to provide incentives and mechanism to stimulate cooperation among users to improve system performance.

The influence of human behavior and factors has seldom been recognized in signal and image processing research. Therefore, first in this talk the goal is to illustrate why understanding of human factors and behavior plays an important role in designing and improving multimedia communications and security. Such a journey leads us to reconsider many classical signal and image processing problems from the concept/notion of social networking. The second goal of the talk is to demonstrate that the social networking approach can indeed offer a new and unified view to many classical problems and has the potential of becoming a new signal and image processing paradigm.

Biography
Dr. K. J. Ray Liu was named a Distinguished Scholar-Teacher of University of Maryland in 2007. He leads the Maryland Signals and Information Group conducting research encompassing broad aspects of wireless communications and networking, information forensics and security, multimedia signal processing, and biomedical engineering.

Dr. Liu is the recipient of numerous honors and awards including the 1994 National Science Foundation Young Investigator Award; best paper awards from IEEE and EURASIP; IEEE Signal Processing Society 2004 Distinguished Lecturer; EURASIP 2004 Meritorious Service Award; and 2009 IEEE Signal Processing Society Technical Achievement Award. A Fellow of the IEEE and AAAS, he is recognized by Thomson Reuters as an ISI Highly Cited Researcher. Dr. Liu is President-Elect of IEEE Signal Processing Society. He was the Editor-in-Chief of IEEE Signal Processing Magazine and the founding Editor-in-Chief of EURASIP Journal on Advances in Signal Processing.

Dr. Liu also received various research and teaching recognitions from the University of Maryland, including Poole and Kent Senior Faculty Teaching Award and Outstanding Faculty Research Award, both from A. James Clark School of Engineering; and Invention of the Year Award from Office of Technology Commercialization.

His recent books include Behavior Dynamics in Media-Sharing Social Networks, Cambridge University Press (to appear); Cognitive Radio Networking and Security: A Game Theoretical View, Cambridge University Press, 2010; Handbook on Array Processing and Sensor Networks, IEEE-Wiley, 2009; Cooperative Communications and Networking, Cambridge University Press, 2008; Resource Allocation for Wireless Networks: Basics, Techniques, and Applications, Cambridge University Press, 2008; Ultra-Wideband Communication Systems: The Multiband OFDM Approach, IEEE-Wiley, 2007; Network-Aware Security for Group Communications, Springer, 2007; Multimedia Fingerprinting Forensics for Traitor Tracing, Hindawi, 2005.