Keynote Speeches

Professor Ce Zhu

Topic:

3D-TV System with Depth-Image-Based Rendering: Towards High Quality 3D Video

Abstract:

Three-dimensional Television (3D-TV) is a promising candidate for the next generation broadcasting applications. Among 3D video technologies and viable prototypes, stereoscopic 3D video has gained a strong momentum in consumer market, establishing itself as the first wave of 3D-TV. Conventional multi-view video is a natural representation for stereoscopic displays, while more sophisticated 2D-plus-depth format with depth-image-based rendering (DIBR) has been attracting increasing attention from both industry and academia, as the DIBR technique helps to further advance the interactivity in 3D video systems and to significantly enhance the 3D visual experience relative to conventional stereoscopic systems. This talk will present a technical overview of DIBR-oriented 3D-TV system comprising main functional components of 3D content generation, coding and transmission, and displaying for creating 3D visual sensation, with specific discussions of recent advances on 3D visual distortion detection and reduction.

Biography:

Ce Zhu (M’03–SM’04) received the B.S. degree from Sichuan University, Chengdu, China, and the M.Eng and Ph.D. degrees from Southeast University, Nanjing, China, in 1989, 1992, and 1994, respectively, all in electronic and information engineering. He pursued postdoctoral research at Chinese University of Hong Kong in 1995, City University of Hong Kong, and University of Melbourne, Australia, from 1996 to 1998. Dr. Zhu is now a professor with the school of Electronic Engineering, University of Electronic Science and Technology of China, China. He was with Nanyang Technological University, Singapore, for 14 years, where he had been an Associate Professor since 2005. He has held visiting positions at Queen Mary, University of London (UK), and Nagoya University (Japan). His research interests include image/video coding, streaming and processing, 3D video, joint source-channel coding, multimedia systems and applications. He has authored or co-authored over 100 papers, lead-edited 3 books and contributed 4 book chapters, and filed 7 patents (5 granted and 1 transferred). He received best paper and student paper awards at two international conferences. Dr. Zhu serves on the editorial boards of seven international journals, including as an Associate Editor of IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Broadcasting, IEEE Signal Processing Letters, Editor of IEEE Communications Surveys and Tutorials, Area Editor of Signal Processing: Image Communication (Elsevier), Associate Editor of Multidimensional Systems and Signal Processing (Springer), and Editorial Board Member of Multimedia Tools and Applications (Springer). He has served on technical/program committees, organizing committees and as track/session chairs for over 50 international conferences. He received 2010 Special Service Award from IEEE Broadcast Technology Society, and is an IEEE BTS Distinguished Lecturer (2012-2014).

Professor Daniel S. Yeung

Topic:

Sensitivity based Image Filtering for Multi-Hashing

Abstract:

Hashing is an effective method to retrieve similar images from a large scale database. However, a single hash table requires searching an exponentially increasing number of hash buckets with large hamming distance for a better recall which is time consuming. The union of results from multiple hash tables (multi-hashing) yields a high recall but low precision with exact hash code matching. Methods using image filtering to reduce dissimilar images rely on hamming distance or hash code difference between query and candidate images. However, they treat all hash buckets to be equally important which is generally not true. Different buckets may return different numbers of image and yield different importance to hashing results. We propose two descriptors to score both the hash bucket and the returned image in buckets using a location based sensitivity measure. A neural network is trained to filter dissimilar images based on the hamming distance and the two proposed descriptors. Both the neural network and the two new descriptors could be computed offline when hash tables are available. Hence the proposed Sensitivity based Image Filtering method (SIF) is efficient during retrieval. Experimental results using four large scale databases show that the proposed method improves precision at the expense of a small drop in recall for both data-dependent and data-independent multi-hashing methods as well as multi-hashing combining both types.

Biography:

Daniel S.Yeung (Ph.D., M.Sc., M.B.A., M.S., M.A., B.A.) is a Past President of the IEEE Systems, Man and Cybernetics (SMC) Society and a Fellow of the IEEE. He received the Ph.D. degree in applied mathematics from Case Western Reserve University. In the past, he has worked as an Assistant Professor of Mathematics and Computer Science at Rochester Institute of Technology, as a Research Scientist in the General Electric Corporate Research Center, and as a System Integration Engineer at TRW, all in the United States. He was the chairman of the Department of Computing, The Hong Kong Polytechnic University, Hong Kong, and a Chair Professor from 1999 to 2006. Currently he is a visiting Professor in the School of Computer Science and Engineering, South China University of Technology, Guangzhou, China.

His current research interests include neural-network sensitivity analysis, data mining, and fuzzy rough set. He was the Chairman of IEEE Hong Kong Computer Chapter (91and 92), an associate editor for both IEEE Transactions on Neural Networks and IEEE Transactions on SMC (Part B), and for the International Journal on Wavelet and Multiresolution Processing. He has served as the President (2008 and 2009), President-Elect (2007), a member of the Board of Governors, Vice President for Technical Activities, and Vice President for Long Range Planning and Finance for the IEEE SMC Society. He co-founded and served as a General Co-Chair since 2002 for the International Conference on Machine Learning and Cybernetics held annually in China. He also served as a General Co-Chair (Technical Program) of the 2006 International Conference on Pattern Recognition, and 2012, 2013 and 2015 International Conference on Systems, Man and Cybernetics. He is also the founding Chairman of the IEEE SMC Hong Kong Chapter.

His past teaching and academic administrative positions include a Chair Professor and Head at Department of Computing, The Hong Kong Polytechnic University, the Head of the Management Information Unit at the Hong Kong Polytechnic University, Associate Head/Principal Lecturer at the Department of Computer Science, City Polytechnic of Hong Kong, a tenured Assistant Professor at the School of Computer Science and Technology and an Assistant Professor at the Department of Mathematics, both at  Rochester Institute of Technology, Rochester, New York.

He also held industrial and business positions as a Technical Specialist/Application Software Group Leader at the Computer Consoles, Inc., Rochester, New York, an Information Resource Sub-manager/Staff Engineer at the Military and Avionics Division, TRW Inc., San Diego, California, and an Information Scientist of the Information System Operation Lab, General Electric Corporate Research and Development Centre, Schenectady, New York.

Professor Han-Chieh Chao

Topic:

Terrain Definition for WSN Power Conservation

Abstract:

Coverage is a vital issue that ensures that basic functions are available in wireless sensor networks (WSNs). These functions provide communications during emergency rescue or in war environments. Sensor nodes must be dispersed and survive in place for a long time in order to accurately monitor all events. However, sensor nodes rely on limited battery energy restricted to the lifetime of the entire network. When few sensor nodes are awake during an epoch, the entire network lifetime becomes longer. This implies that the coverage problem must be solved based on the energy efficiency issue. In order to strengthen the coverage ratio with a limited number of sensor nodes, practical coverage algorithms were proposed in the existing researches but investigated in a 2-D plan or use of the traditional Delaunay Triangulation which are not suitable for a realistic environment. Therefore, a more terrain definition of the method is necessary. According to some experiments and analysis, we believe that the Spline function is a best solution. This keynote speech introduces its characteristics and how we use the Spline function to define the terrains. Finally, we will show some results of simulation to prove this method is of great worth.

Biography:

Dr. Han-Chieh Chao is a joint appointed Full Professor of the Department of Computer Science & Information Engineering and Electronic Engineering of National Ilan University, I-Lan, Taiwan (NIU). He is serving as the President since August 2010 for NIU as well. He was the Director of the Computer Center for Ministry of Education Taiwan from September 2008 to July 2010. His research interests include High Speed Networks, Wireless Networks, IPv6 based Networks, Digital Creative Arts, e-Government and Digital Divide. He received his MS and Ph.D. degrees in Electrical Engineering from Purdue University in 1989 and 1993 respectively. He has authored or co-authored 4 books and has published about 400 refereed professional research papers. He has completed more than 100 MSEE thesis students and 4 PhD students. Dr. Chao has been invited frequently to give talks at national and international conferences and research organizations. Dr. Chao is the Editor-in-Chief for IET Networks, Journal of Internet Technology, International Journal of Internet Protocol Technology, and International Journal of Ad Hoc and Ubiquitous Computing. Dr. Chao has served as the guest editors for Mobile Networking and Applications (ACM MONET), IEEE JSAC, IEEE Communications Magazine, IEEE Systems Journal, Computer Communications, IEE Proceedings Communications, the Computer Journal, Telecommunication Systems, Wireless Personal Communications, and Wireless Communications & Mobile Computing. Dr. Chao is an IEEE senior member and a Fellow of IET (IEE).

Professor Tzung-Pei Hong

Topic:

Applying Computational Intelligence Techniques to Knowledge Engineering

Abstract:

Knowledge engineering is an important research field for intelligent systems. It includes any kind of data or knowledge preprocessing, learning, mining, and integration. In this speech, I will introduce how computational intelligence can help improve the performance and quality of knowledge engineering. Three parts will be covered. In the first part, I will introduce several GA-based fuzzy data-mining methods for automatically extracting membership functions for fuzzy association rules. All the genetic-fuzzy mining methods first use evolutional computation to find membership functions suitable for mining problems and then use the final best set of membership functions to mine fuzzy association rules. Through appropriately designed fitness functions, these approaches can avoid the formation of bad kinds of membership functions and can provide important mining results to users. In addition, feature selection is an important pre-processing step in mining and learning. A good set of features can not only improve the accuracy of classification, but also reduce the time to derive rules. Thus in the second part, I will state some GA-based clustering methods for attribute clustering and feature selection. The proposed approaches for attribute clustering can also easily handle the problem of missing values in classification. In the last part, I will describe suitable integration techniques for various kinds of mined knowledge based on evolutionary computation. Some integration algorithms respectively derived from the Michigan approach and the Pittsburgh approach will be explained. The Michigan approach encodes each rule as an individual; on the contrary, the Pittsburgh approach encodes a rule set as an individual. Both of them have their own advantages and disadvantages.

Biography:

Tzung-Pei Hong received his B.S. degree in chemical engineering from National Taiwan University in 1985, and his Ph.D. degree in computer science and information engineering from National Chiao-Tung University in 1992. From 1987 to 1994, he was with the Laboratory of Knowledge Engineering, National Chiao-Tung University, where he was involved in applying techniques of parallel processing to artificial intelligence. He was an associate professor at the Department of Computer Science in Chung-Hua Polytechnic Institute from 1992 to 1994, and at the Department of Information Management in I-Shou University (originally Kaohsiung Polytechnic Institute) from 1994 to 1999. He was a professor in I-Shou University from 1999 to 2001. He was in charge of the whole computerization and library planning for National University of Kaohsiung in Preparation from 1997 to 2000 and served as the first director of the library and computer center in National University of Kaohsiung from 2000 to 2001, as the Dean of Academic Affairs from 2003 to 2006, as the Administrative Vice President from 2007 to 2008, and as the Academic Vice President from 2010 to 2011. He is currently a distinguished professor at the Department of Computer Science and Information Engineering and at the Department of Electrical Engineering. He has published more than 400 research papers in international/national journals and conferences and has planned more than fifty information systems. He is also the board member of more than forty journals and the program committee member of more than three hundred conferences. His current research interests include knowledge engineering, data mining, soft computing, management information systems, and www applications. Dr. Hong is a member of the Association for Computing Machinery, the IEEE, the Chinese Fuzzy Systems Association, the Taiwanese Association for Artificial Intelligence, and the Institute of Information and Computing Machinery.