Keynote Speakers of 2017 (More will be updated）
Keynote Speaker I
Prof. Xudong Jiang, Nanyang Technological University, Singapore
Prof. Xudong Jiang received the B.Sc. and M.Sc.
degree from the University of Electronic Science and
Technology of China, and received the Ph.D. degree
from Helmut Schmidt University Hamburg, Germany.
From 1986 to 1993, he worked as Lecturer at UESTC
where he received two Science and Technology Awards
from the Ministry for Electronic Industry of China.
He was a recipient of the German Konrad-Adenauer
Foundation young scientist scholarship. From 1993 to
1997, he was with Helmut Schmidt University
Hamburg, Germany as scientific assistant. From 1998
to 2004, He worked with the Institute for Infocomm
Research, A*Star, Singapore, as Senior Research
Fellow, Lead Scientist and appointed as the Head of
Biometrics Laboratory where he developed an software
that achieved the fastest and the second most
accurate fingerprint verification in the
International Fingerprint Verification Competition
(FVC2000). He joined Nanyang Technological
University, Singapore as a faculty member in 2003
and served as the Director of the Centre for
Information Security from 2005 to 2011. Currently,
Dr Jiang is a tenured Association Professor in
Nanyang Technological University. Dr Jiang has
published over 120 research papers, including 20
papers in top IEEE journals: TPAMI, TIP, TSP and
SPM, which are well-cited on Web of Science. He is
also an inventor of 7 patents (3 US patents). Dr
Jiang is a senior member of IEEE, elected voting
member of IFS technical committee of IEEE Signal
Processing Society, Associate editor of IEEE Signal
Processing Letters and IET Biometrics. He has been
serving as General Chair, Technical Program
Committee Chair, Keynote Speaker and Session Chair
of multiple international conferences. His research
interest includes pattern recognition, computer
vision, machine learning, image analysis, signal
processing, machine learning and biometrics.
Keynote Speaker II
Prof. Reinhard Klette, Auckland University of Technology, New Zealand
Towards Autonomous Driving: Vision-based Driver Assistant Systems
Abstract: The talk will discuss computer vision challenges in the context of vision-based driver assistant systems, certainly one of the most difficult, but also most dynamically developing areas of current 3D image analysis. Car companies started in about 1995 to add vision solutions into their top-end models, and have now autonomously driving models on the road (e.g. Daimler in their S-class since March 2013, or the current E-Class). — Questions to be answered are as follows: What may happen next in front of the car, at the location where the ego-vehicle is expected to be in the next few seconds? How far can we understand a complex environment, as defined by traffic scenarios, by stereo vision or by using just a single camera? — Various computer-vision modules have reached the state of being robust under various conditions (e.g. for lane analysis, driver monitoring, or distance calculation). A new quality of tasks is now defined by creating combined solutions for the better understanding of traffic related events, such as combining lane analysis or distance calculations with driver monitoring. The talk informs about current work in the .enpeda.. project at Auckland University of Technology (AUT) directed on adaptive and intelligent solutions for vision-based driver assistance.
Bio: Dr. Reinhard Klette is a professor at Auckland University of Technology (AUT) in New Zealand. He has several degrees from Jena University in Mathematics and Computer Sciences, including an M.Sc., a Ph.D. and an D.Sc. degree. His previous appointments include positions as a full professor at Auckland University, the Technical University of Berlin, and the Academy of Sciences Berlin. His research interests include computer vision, pattern recognition, and algorithm design. He represents New Zealand in the International Association for Pattern Recognition (IAPR) and in the Asian Federation for Computer Vision (AFCV). He has been elected as a fellow of the Royal Society of New Zealand (RSNZ. He is also a member of the Institute of Mathematics and its Applications (IMA) in the United States and a member of the Korea Institute of Information and Communication Engineering (KIICE) in South Korea. From 2003 to 2008, he was an associate editor of IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI); from 2011 to 2013 he was the founding editor-in-chief of the Journal of Control Engineering and Technology; and from 2005 to 2014, he was a member of the editorial board for the International Journal on Computer Vision. Currently, he is an editor of “Computational Imaging and Vision” published by Springer and a member on the editorial boards of several scientific journals, including the Journal of Information and Communication Convergence Engineering. He is a member of the steering committees of the European biennial conferences on Computer Analysis of Image and Patterns (CAIP) and the Pacific-Rim Symposia on Image and Video Technology (PSIVT).
Keynote Speaker III
Prof. Julian FIERREZ, Biometric Recognition Group, Escuela Politecnica Superior, Universidad Autonoma de Madrid, Spain
Bio: Julian Fierrez received the M.Sc. and the Ph.D. degrees in telecommunications engineering from Universidad Politecnica de Madrid, Spain, in 2001 and 2006, respectively. Since 2002 he has been affiliated with the Biometric Recognition Group (ATVS), first at Universidad Politecnica de Madrid, and since 2004 at Universidad Autonoma de Madrid, where he is currently an Associate Professor. From 2007 to 2009 he was a visiting researcher at Michigan State University in USA under a Marie Curie fellowship. His research interests include general signal and image processing, pattern recognition, and biometrics, with emphasis on signature and fingerprint verification, multi-biometrics, biometric databases, system security, and forensic applications of biometrics. Dr. Fierrez is actively involved in multiple EU projects focused on biometrics (e.g. TABULA RASA and BEAT), has attracted notable impact for his research (more than 6,000 citations with h-index = 42 in Google Scholar), and is the recipient of a number of distinctions, including: Best Paper Awards at AVBPA 2003, ICB 2006, ICPR 2008, and ICB 2015; Best PhD Thesis Award in Computer Vision and Pattern Recognition in 2005-2007 by the IAPR Spanish liaison (AERFAI), EBF European Biometric Industry Award 2006, EURASIP Best PhD Award 2012, Medal in the Young Researcher Awards 2015 by the Spanish Royal Academy of Engineering, and the Miguel Catalan Award to the Best Researcher under 40 in the Community in Madrid in the general area of Science and Technology.
Plenary Speaker I
Prof. Masayuki Arai, Teikyo University, Japan
General Introduction of Our Recent Research
Bio: Professor in the Graduate School of Sciences and Engineering at Teikyo University. He received his B.E. degree from Tokyo University of Science in 1981 and Dr. Eng. degree from Utsunomiya University in 1995. His research interests include pattern recognition, natural language processing, and information visualization. He is a member of the Information Processing Society of Japan and IEEE.
Plenary Speaker II
Assoc. Prof. Kin Hong Wong, The Chinese University of Hong Kong, Hong Kong
Professor Wong Kin Hong is an Associate Professor of the Department of Computer Science and Engineering of the Chinese University of Hong Kong. He received a BSc in Electronics and Computer Engineering from the University of Birmingham, and a PhD from the Department of Engineering of the University of Cambridge. He was a Croucher Research Fellow at the University of Cambridge. Professor Wong joined the Department of Computer Science at the Chinese University of Hong Kong in 1988. His research interests are 3D computer vision, virtual reality system development, pattern recognition, microcomputer applications, and music signal processing.
Invited Speaker I
Assoc. Prof. Andrew B.J. Teoh, Yonsei University, South Korea
Advances in Analytic Manifold for Structured Pattern Recognition Problems
Abstract: Statistical learning on analytic manifolds (Lie Group, Riemannian, Stiefel and Grassmann Manifolds) is a new emerging and powerful means for solving structured pattern recognition problems. Analytic manifold learning is particular useful for many applications whereby input is framed by structured patterns such covariance matrices, linear dynamic models and linear subspaces. Analytic manifold learning can be reliable and accurate for inference, clustering, classification as well as prediction problems. This talk gives an overview of common analytic manifolds employed in various pattern recognition and computer vision problems. In particular, we will showcase a solution based on Grassmann manifold for multi-view gait recognition.
Bio: Andrew Beng Jin Teoh obtained his BEng (Electronic) in 1999
and Ph.D degree in 2003 from National University of Malaysia. He is
currently an associate professor in Electrical and Electronic
Engineering Department, College Engineering of Yonsei University,
His research, for which he has received funding, focuses on biometric applications and biometric security. His current research interests are Machine Learning and Information Security. He has published more than 250 international refereed journal papers, conference articles, edited several book chapters and edited book volume. He served and is serving as a guest editor of IEEE Signal Processing Magazine, associate editor of IEEE Biometrics Compendium and editor-in- chief of IEEE Biometrics Council Newsletter. He was a program co-chair of ICONIP 2014, area chair of ICPR 2016, track chair and TPC for several conferences related to computer vision, pattern recognition and biometrics.
Invited Speaker II
Assoc. Prof. Jiande Sun, Shandong University, China
Jiande Sun received the Ph.D. degree in communication and information system from Shandong University, Jinan, China, in 2005. He did the Postdoc work in both Peking University and Hisense Ltd from 2010 to 2013. He has been a visiting researcher in Technical University of Berlin, University of Konstanz, and Carnegie Mellon University. He is a full professor with the School of Information Science and Engineering, Shandong Normal University. He has published more than 60 journal and conference papers. He is the co-author of two books. He was authorized 17 patents. His research interests include multimedia content analysis, video hashing, gaze tracking, image/video watermarking, 2D to 3D conversion, and so on.
Invited Speaker III
Dr. Bo Jiang, Nanjing University of Posts and Telecommunications, China
Bio: He joined the Department of Digital Media Technology, School of Education Science and Technology, Nanjing University of Posts and Telecommunications as lecturer since Jul. 2014. Before that, he finished my Ph.D. study at State Key Lab of CAD & CG, Zhejiang University under the supervision of Prof. Xinguo Liu in Mar. 2014. He received my Bachelor's degree in School of Computer Science and Technology from Nanjing University of Posts and Telecommunications in 2006. In Sep. 2007, he became a Master student in State Key Lab of CAD & CG. Starting from Sep. 2008, he transfered to the Ph.D. program. During Nov. 2010 - Nov. 2011, he visited the Manufacturing System Research Lab, General Motors Research & Development at Warren, MI, USA under the supervision of Dr. Wuhua Yang (Sponsored by China Scholarship Concil and General Motors).
His research interests include digital geometry processing, shape analysis, computer vision based applications and virtual/augmented reality.
Invited Speaker IV
Dr. Juno Kim, University of New South Wales, Australia
Image properties for material appearance
Abstract: Surfaces reflect light that provides valuable information about their physical properties of 3D shape, colour, gloss and transparency. A major challenge for computational vision science is to understand how we perceive the material composition of objects from single images. Some researchers have proposed that image statistics can account for this experience, but evidence suggests that material perception can only be explained by theories that consider the structure of luminance variations in images. The presentation takes a revealing look at some of the geometric constraints that appear to account for our visual experience of objects and their material properties. The understanding to be gained has direct practical applications to the design of psychophysically-based artificial systems that can model human visual performance in a variety of real-world tasks (e.g., medical diagnosis and coordinating industrial processes using robotics).
Bio: Dr Kim is a Senior Research Fellow based in the School of Optometry and Vision Science at the University of New South Wales. Since completing his PhD in Psychology in 2005 (University of Sydney), he undertook postdoctoral studies on the perception of object form and motion at the University of Wollongong, the University of New South Wales, and the University of Sydney. In 2015, Dr Kim commenced an Australian Research Council (ARC) Future Fellowship awarded for his ongoing research on material appearance, which has collectively generated outputs featuring in Current Biology, i-Perception, Attention Perception & Psychophysics, Journal of Vision, and on the cover of Nature Neuroscience.
Invited Speaker V
Asst. Prof. Bhupendra Gupta, Indian Institute of Information Technology Disgn and Manufactruing Jabalpur, India