Keynote speaker: Fatih Porikli

Title: Riemannian Geometry in Computer Vision

Abstract: Computer vision is concerned with understanding the world through visual data. To accomplish this, it aims to find suitable mathematical models and versatile inference techniques in order to derive meaningful information for specific applications. One of the biggest, yet unjustified, assumptions in modeling visual data is the notion of flat spaces: most of the time, we attempt to solve fundamental detection, classification, and tracking tasks using the traditional Euclidean space. However, a large number of computer vision problems are naturally formulated on differentiable manifolds. In the past few years, computer vision has made significant advancement in the analytical and geometric understanding of the non-flat spaces. This makes an important development in computer vision by moving away from purely data-driven approaches to incorporate more prior information via geometry-based approaches. This talk will address key points in developing learning methods for image and video content using Riemannian manifolds.

Bio: Fatih Porikli is an IEEE Fellow and a Professor in the Research School of Engineering, Australian National University (ANU). He is also managing the Computer Vision Research Group at NICTA. He has received his PhD from New York University in 2002. Previously he served Distinguished Research Scientist at Mitsubishi Electric Research Laboratories. His research interests include computer vision, pattern recognition, manifold learning, robust and sparse optimization, online learning, grid computing, and image enhancement with commercial applications in video surveillance, car navigation, intelligent transportation, satellite, and medical systems. Prof Porikli is the recipient of the R&D 100 Scientist of the Year Award in 2006. He won 4 best paper awards at premier IEEE conferences and received 5 other professional prizes. Prof Porikli authored more than 130 publications and invented 66 patents. He is the coeditor of 3 books. He is serving as the Associate Editor of 5 journals for the past 8 years including IEEE Signal Processing Magazine, SIAM Imaging Sciences, EURASIP Journal of Image & Video Processing, Springer Journal on Machine Vision Applications, and Springer Journal on Real-time Image & Video Processing. He was the General Chair of AVSS 2010 and WACV 2014, and the Program Chair of WACV 2015 and AVSS 2012. He served at the organizing committees of several conferences including CVPR, ICCV, ECCV, ICIP, ICME, ISVC, and ICASSP. He organized more than a twenty IEEE Computer Society cosponsored workshops.

Keynote speaker: Maja Pantic

Title: Automatic Analysis of Facial Expressions

Abstract: Facial behaviour is our preeminent means to communicating affective and social signals. This talk discusses a number of components of human facial behavior, how they can be automatically sensed and analysed by computers, what is the past research in the field conducted by the iBUG group at Imperial College London, and how far we are from enabling computers to sense and recognise human facial expressions and behaviour.

Bio: Maja Pantic obtained her PhD degree in computer science in 2001 from Delft University of Technology, the Netherlands. Until 2005, she was an Assistant/ Associate Professor at Delft University of Technology. In 2006, she joined the Imperial College London, Department of Computing, UK, where she is Professor of Affective & Behavioural Computing and the Head of the iBUG group, working on machine analysis of human non-verbal behaviour. From November 2006, she also holds an appointment as the Professor of Affective & Behavioural Computing at the University of Twente, the Netherlands. Prof. Pantic is one of the world's leading experts in the research on machine understanding of human behavior including vision-based detection, tracking, and analysis of human behavioral cues like facial expressions and body gestures, and multimodal analysis of human behaviors like laughter, social signals, and affective states. She has published more than 200 technical papers on her research in the field. Her work is widely cited and was covered by popular press many times (including by New Scientist, BBC Radio, and NL TV 1 and 3). In 2011, Prof. Pantic received BCS Roger Needham Award, awarded annually to a UK based researcher for a distinguished research contribution in computer science within ten years of their PhD. Prof. Pantic serves as the Editor in Chief of the Image and Vision Computing Journal (IVCJ/ IMAVIS), an Associate Editor of the IEEE Transactions on Pattern Analysis and Machine Intelligenve (IEEE TPAMI), and an Associate Editor of the IEEE Transactions on on Affective Computing (IEEE TAC). She is an IEEE Fellow.

Keynote speaker: Xiaoou Tang

Title: Computer Vision in Daily Life

Bio: Xiaoou Tang (S’93-M’96-SM’02-F’09) received the B.S. degree from the University of Science and Technology of China, Hefei, in 1990, and the M.S. degree from the University of Rochester, Rochester, NY, in 1991. He received the Ph.D. degree from the Massachusetts Institute of Technology, Cambridge, in 1996. He is a Professor in the Department of Information Engineering and Associate Dean (Research) of the Faculty of Engineering of the Chinese University of Hong Kong. He is the Associate Director of Shenzhen Institutes of Advanced Technology of CAS. He worked as the group manager of the Visual Computing Group at the Microsoft Research Asia from 2005 to 2008. His research interests include computer vision, pattern recognition, and video processing. Dr. Tang received the Best Paper Award at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2009 and Outstanding Student Paper Award at the AAAI 2015. He is a program chair of the IEEE International Conference on Computer Vision (ICCV) 2009 and has served as an Associate Editor of IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) and International Journal of Computer Vision (IJCV). He is a Fellow of IEEE.

Keynote speaker: Demetri Terzopoulos

Title:Virtual Vision: Computer Vision in Virtual Reality

Abstract: Realistic virtual worlds can serve as software laboratories within which vision researchers may efficiently develop and evaluate sophisticated, active machine perception systems. This unorthodox philosophy is known as "Virtual Vision". Posited at the intersection of the fields of computer vision and computer graphics, it enables virtual reality to sub-serve computer vision research. In the context of this new paradigm, this talk will focus on the rapid development and evaluation of distributed smart-camera sensor networks and intelligent surveillance systems that can persistently monitor humans in large-scale urban environments. The visually realistic virtual environments exploited in this work are populated by autonomous virtual humans, which are the product of a comprehensive, artificial life approach to multi-human simulation.

Bio: Demetri Terzopoulos is Chancellor's Professor of Computer Science at UCLA, where he holds the rank of Distinguished Professor and directs the UCLA Computer Graphics & Vision Laboratory. He is or was a Guggenheim Fellow, a Fellow of the ACM, IEEE, Royal Society of London and Royal Society of Canada, and a Member of the European Academy of Sciences and Sigma Xi. Among his many awards are an Academy Award for Technical Achievement from the Academy of Motion Picture Arts and Sciences for his pioneering work on physics-based computer animation, and the inaugural Computer Vision Distinguished Researcher Award from the IEEE for his pioneering and sustained research on deformable models and their applications. One of the most highly cited authors in engineering and computer science according to ISI and other indexes, his publications include more than 300 research papers and several volumes, primarily in computer graphics, computer vision, medical imaging, computer-aided design, and artificial intelligence/life. Prior to joining UCLA in 2005, Dr.Terzopoulos held the Lucy and Henry Moses Endowed Professorship in Science at New York University and was Professor of Computer Science and Mathematics at NYU's Courant Institute of Mathematical Sciences. Previously, he was Professor of Computer Science and Professor of Electrical and Computer Engineering at the University of Toronto. He received his PhD degree in EECS from the Massachusetts Institute of Technology (MIT) in 1984.