Keynote Speakers

Prof. Chin-Chen Chang, Feng Chia University,Taiwan

Professor Chin-Chen Chang obtained his Ph.D. degree in computer engineering from National Chiao Tung University. His first degree is Bachelor of Science in Applied Mathematics and master degree is Master of Science in computer and decision sciences. Both were awarded in National Tsing Hua University. Dr. Chang served in National Chung Cheng University from 1989 to 2005. His current title is Chair Professor in Department of Information Engineering and Computer Science, Feng Chia University, from Feb. 2005. Prior to joining Feng Chia University, Professor Chang was an associate professor in Chiao Tung University, professor in National Chung Hsing University, chair professor in National Chung Cheng University. He had also been Visiting Researcher and Visiting Scientist to Tokyo University and Kyoto University, Japan. During his service in Chung Cheng, Professor Chang served as Chairman of the Institute of Computer Science and Information Engineering, Dean of College of Engineering, Provost and then Acting President of Chung Cheng University and Director of Advisory Office in Ministry of Education, Taiwan. Professor Chang's specialties include, but not limited to, data engineering, database systems, computer cryptography and information security. A researcher of acclaimed and distinguished services and contributions to his country and advancing human knowledge in the field of information science, Professor Chang has won many research awards and honorary positions by and in prestigious organizations both nationally and internationally. He is currently a Fellow of IEEE and a Fellow of IEE, UK. And since his early years of career development, he consecutively won Institute of Information & Computing Machinery Medal of Honor, Outstanding Youth Award of Taiwan, Outstanding Talent in Information Sciences of Taiwan, AceR Dragon Award of the Ten Most Outstanding Talents, Outstanding Scholar Award of Taiwan, Outstanding Engineering Professor Award of Taiwan, Chung-Shan Academic Publication Awards, Distinguished Research Awards of National Science Council of Taiwan, Outstanding Scholarly Contribution Award of the International Institute for Advanced Studies in Systems Research and Cybernetics, Top Fifteen Scholars in Systems and Software Engineering of the Journal of Systems and Software, Top Cited Paper Award of Pattern Recognition Letters, and so on. On numerous occasions, he was invited to serve as Visiting Professor, Chair Professor, Honorary Professor, Honorary Director, Honorary Chairman, Distinguished Alumnus, Distinguished Researcher, Research Fellow by universities and research institutes. He also published over serval hundred papers in Information Sciences. In the meantime, he participates actively in international academic organizations and performs advisory work to government agencies and academic organizations.

Speech Title: Embedding Important Information in Digital Images Using Magic Turtle Shells
Steganography is the science of secret message delivery using cover media. A digital image is a flexible medium used to carry a secret message because the slight modification of a cover image is hard to distinguish by human eyes. In this talk, I will introduce some novel steganographic methods based on different magic matrices. Among them, one method that uses a turtle shell magic matrix to guide cover pixels' modification in order to imply secret data is the newest and the most interesting one. Experimental results demonstrated that this method, in comparison with previous related works, outperforms in both visual quality of the stego image and embedding capacity. In addition, I will introduce some future research issues that derived from the steganographic method based on the magic matrix.

Prof. Ce ZHU, University of Electronic Science & Technology of China

Ce Zhu is currently a Professor with the School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, China. His research interests include image/video coding and communications, video analysis and processing, 3D video, visual perception and applications. He has served on the editorial boards of a few journals, including as an Associate Editor of IEEE Transactions on Image ProcessingIEEE Transactions on Circuits and Systems for Video TechnologyIEEE Transactions on Broadcasting, and IEEE Signal Processing Letters. He has served on technical committees, organizing committees and as track/area/session chairs for over 60 international conferences, including serving as a Technical Program Co-Chair of IEEE ICME 2017. He is a Fellow of the IEEE and a Fellow of the IET. For more information, please visit his homepage at

Speech Title: Depth Image Based View Synthesis in 3D Video
Several 3D video prototypes have been developed based on distinct techniques in 3D visualization, data format representation, and content production. Among them, depth-based 3D video has attracted research attention from both industry and academia, as it has been shown to be more efficient and flexible than stereoscopic 3D video or the conventional multi-view video, where virtual view rendering is a key technical procedure in the whole processing chain of a 3D video system. The talk will present a technical overview of depth-based 3D video system, with a focused discussion of our recent work on high quality view synthesis with depth-image-based rendering (DIBR) technique.

Prof. Dr. Q. M. Jonathan Wu, Chair, Computer Vision and Sensing Systems Laboratory, University of Windsor, Canada

Q. M. Jonathan Wu (M’92–SM’09) received the Ph.D. degree in electrical engineering from the University of Wales, Swansea, U.K., in 1990. He was with the National Research Council of Canada for ten years from 1995, where he became a Senior Research Officer and a Group Leader. He is currently a Professor with the Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON, Canada. He has published more than 300 peer-reviewed papers in computer vision, image processing, intelligent systems, robotics, and integrated microsystems. His current research interests include machine learning, 3-D computer vision, video content analysis, interactive multimedia, sensor analysis and fusion, and visual sensor networks.
Dr. Wu holds the Tier 1 Canada Research Chair in Automotive Sensors and Information Systems. He was Associate Editor for IEEE Transactions on Systems, Man, and Cybernetics Part A, and the International Journal of Robotics and Automation. Currently, he is an Associate Editor for the IEEE Transaction on Neural Networks and Learning Systems and the journal of Cognitive Computation. He has served on technical program committees and international advisory committees for many prestigious conferences.

Speech Title: Generalized ELM-Deep Network Framework for Representation Learning
Most of actual images such as human face images, industrial images and MRI images are high-dimensional data. The feature representation is mainly for the purpose of extracting useful information and of using this information to build non-supervised classifier/supervised classifier or other types of predictor because the image processing performance is often closely related to the feature data extracted and used. In this talk, we propose a generalized ELM-Deep learning framework which is intended to extract the optimized features. Then, we extend and apply this method for such application fields as dimension reduction, image identification, and image reconstruction, etc. Compared with other feature representation methods, the experimental results show that the generalization performance of the proposed generalized learning framework is very advantageous. A brief overview of other related research activities in the presenter's laboratory related to computer vision and machine learning is also provided. Applications have been extended towards intelligent transportation systems, surveillance and security, face and gesture recognition, vision-guided robotics, and bio-medical imaging, among others.

Prof. Xudong Jiang, Nanyang Technological University, Singapore

Xudong Jiang received the B.Eng. and M.Eng. degree from the University of Electronic Science and Technology of China, Chengdu, China in 1983 and 1986, respectively, and received the Ph.D. degree from the Helmut Schmidt University Hamburg, Germany in 1997, all in electrical and electronic engineering.
From 1986 to 1993, he worked as Lecturer at the University of Electronic Science and Technology of China 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 the Helmut Schmidt University Hamburg, Germany as scientific assistant. From 1998 to 2002, He worked with the Centre for Signal Processing (CSP),Nanyang Technological University, Singapore, first as Research Fellow and then as Senior Research Fellow, where he developed a fingerprint verification algorithm that achieved the fastest and the second most accurate fingerprint verification in theInternational Fingerprint Verification Competition (FVC2000). From 2002 to 2004 he worked as Lead Scientist and appointed as the Head of Biometrics Laboratory at theInstitute for Infocomm Research, A*Star, Singapore. From 2002 to 2004 he was an Adjunct Assistant Professor. and joined NTU as a full time faculty member in 2004. Currently, Dr Jiang is an Associate Professor (tenured) of School of Electrical and Electronic Engineering, Nanyang Technological University and is appointed as Director of Centre for Information Security (CIS).
Dr Jiang has published over seventy research papers in international refereed journals and conferences. He is also an inventor of one PCT patent application, three Singapore patents and three United States patents, some of which were commercialized. Dr Jiang is a senior member of IEEE and has been serving as Editorial Board Member, Guest Editor and Reviewer of multiple international journals, and serving as Program Committee member, Keynote Speaker and Session Chair of multiple international conferences. His research interest includes pattern recognition, computer vision, image and signal processing, biometrics, face recognition and fingerprint recognition.

Speech Title: Vision and Image Recognition: from Subspace Approach and Sparse Coding to Deep Learning
Vision and image recognition handles high-dimensional data that contains rich information. Fully utilizing the rich information in image undoubtedly increases the possibilities of solving difficult real world problems such as identifying people, object and understanding the behavior of people, objects and crowds. This, however, brings the difficulty for us to design a robust recognition system due to the complex characteristics of image and large variations of images taken under different conditions. Machine learning from the training database is a solution to extract effective features from the high dimensional image for classification. It is thus not a surprise that approaches of the learning-based methods emerge in various research journals and conferences. This speech reviews various research efforts and technologies developed in solving difficult real world vision and image recognition problems. The first attempt is subspace approaches that extract features or reduce the data dimensionality. The speech will be far more than just PCA and LDA. The sparse representation-based classifier (SRC) significantly differentiates itself from the other classifiers in three aspects. One is the utilization of training samples of all classes collaboratively to represent the query images and another is the sparse representation code that coincides with the general classification target. The last is the L1-norm minimization of the representation error that enables SRC to recognize query images heavily corrupted by outlier pixels and occlusions. The analysis of these three merits of SRC pave the way for us to investigate how the recent developments solve these problems and overcome the limitations of SRC, which bring the sparse representation-based image classification to a significantly higher level. Finally, deep learning in vision and image recognition, CNN, is explored and its merits and limitations are investigated.

Prof. Guo-Neng LU, Claude Bernard Lyon I Univeristy, France

Guo-Neng Lu received the B.S. degree from South-China University of Technology in 1981, the DEA (French equivalent B.S.) degree from Central Engineering School of Lyon in France in 1984, and the PhD degree from Paris-Sud University (Paris 11) in 1986. He obtained HDR (Habilitation à Diriger des Recherches – French research supervision certification) from Paris Diderot University (Paris 7) in 1998.
From 1988 to 1999, he was an associate professor at Paris Diderot University (Paris 7). Since 1999, he has been a full professor at Electrical Engineering Department of Claude Bernard Lyon 1 University.
For his research activities, he has been working at Lyon Institute of Nanotechnology, on integrated sensors and associated electronics. His current researches mainly focus on CMOS image sensors, photodetectors and dosimetric devices and systems, with industrial and academic collaborations. He has supervised 23 PhD students and has authored and co-authored more than 150 papers in international journals and conference proceedings.
Seech Title: Development of small-sized pixel structures for high-resolution CMOS image sensors
We present our studies on small-sized pixel structures for high-resolution CMOS image sensors. To minimize the number of pixel components, single-transistor pixel and 2T pixel architecture were proposed. To deal with crosstalk between pixels, MOS capacitor deep trench isolation (CDTI) was integrated. CDTI-integrated pixel allows better achievements in dark current and full-well capacity in comparison with the configuration integrating oxide-filled deep trench isolation (DTI). To improve quantum efficiency (QE) and minimize optical crosstalk, back-side illumination (BSI) was developed. Also, vertical photodiode was proposed to maximize its charge-collection region. To take advantages of these structures/technologies, we developed two pixel options (P-type and N-type) combining CDTI or DTI, BSI and vertical photodiode. All the presented pixel structures were designed in 1.4µm-pitch sensor arrays, fabricated and tested.

Prof. Young-Chang Hou, visiting scholar at the Tsinghua University, Beijing, China

Young-Chang Hou was born in Guangdong, China in 1949. He received his BS degree in Atmospheric Physics from National Central University, Taiwan, R.O.C. in 1972, his MS degree in Computer Applications from Asian Institute of Technology, Bangkok, Thailand, in 1983, and his PhD degree in Computer Science and Information Engineering from National Chiao-Tung University, Taiwan, R.O.C. in 1990. From 1976 to 1987, he was a senior engineer of Air Navigation and Weather Services, Civil Aeronautical Administration, Taiwan, R.O.C. where his works focused on the automation of weather services. From 1987 to 2004, he was with the faculty at the Department of Information Management, National Central University. From 2004 to 2016, he was a professor with the Department of Information Management, Tamkang University. Currently he works for R. Elamparo enterprises, Philippines. He is a visiting scholar at the Tsinghua University, Beijing, China during the period of May and June 2017. He has published more than 100 referred papers in technical journals and conferences. His research interests include digital watermarking and information hiding, fuzzy logic, genetic algorithms, and visual cryptography.

Seech Title: Researches in Visual Cryptography
Visual cryptography is developed based on the need of information sharing. A secret is partitioned into n shadow images (shares), and each participant receives only one share. Once any k or more shares of a secret are stacked together, the secret image will be visually retrieved without the help of the computer. The secret image will be invisible if the number of stacked shares is less than k. This speech introduces our studies in extending the capabilities of visual cryptography for handling gray-level and color images. Besides of information hiding, applications of visual cryptography have been extended towards steganography, watermarking and progressive revealing, among others.