Keynote Speakers & Invited Speaker
Yulin Wang, Wuhan University, China
Prof. Yulin Wang is a full professor and PhD supervisor in International School of Software, Wuhan University, China. He got PhD degree in 2005 in Queen Mary, University of London, UK. Before that, he has worked in high-tech industry for more than ten years. He has involved many key projects, and hold 8 patents. He got his master and bachelor degree in 1990 and 1987 respectively from Xi-Dian University, and Huazhong University of Science and Technology（HUST）, both in China. His research interests include digital rights management, digital watermarking, multimedia and network security, and signal processing. In recently 10 years, Prof. Wang has published as first author 3 books, 40 conference papers and 45 journal papers, including in IEEE Transactions and IEE proceedings and Elsevier Journals. Prof. Wang served as editor-in-chief for International Journal of Advances in Multimedia in 2010. He served as reviewer for many journals, including IEEE Transactions on Image Processing, IEEE Signal Processing Letters, Elsevier Journal of Information Sciences. He served as reviewer for many research funds, including National High Technology Research and Development Program of China ( ‘863’ project). Prof. Wang was the external PhD adviser of Dublin City University, Ireland during 2008-2010. He was the keynote speakers in many international conferences. He bas been listed in Marcus ‘who’s who in the world’ since 2008.
Title: Image Authentication and Tamper Localization based on Semi-Fragile Hash Value
Abstract: Image authentication can be used in many fields, including e-government, e-commerce, national security, news pictures, court evidence, medical image, engineering design, and so on. Since some content-preserving manipulations, such as JPEG compression, contrast enhancement, and brightness adjustment, are often acceptable—or even desired—in practical application, an authentication method needs to be able to distinguish them from malicious tampering, such as removal, addition, and modification of objects. Therefore, the traditional hash-based authentication is not suitable for the application. As for the semi-fragile watermarking technique, it meets the requirements of the above application at the expense of severely damaging image fidelity. In this talk, we propose a hybrid authentication technique based on what we call fragile hash value. The technique can blindly detect and localize malicious tampering, while maintaining reasonable tolerance to conventional content-preserving manipulations. The hash value is derived from the relative difference between each pair of the selected DCT AC coefficient in a central block and its counterpart which is estimated by the DC values of the center block and its adjacent blocks. In order to maintain the relative difference relationship when the image undergoes legitimate processing, we make a pre-compensation for the AC coefficients. Experimental results show that our technique is superior to semi-fragile techniques, especially in image fidelity, tolerance range of legitimate processing, and/or the ability to detect and localize the tampered area. Due to its low computational cost, our algorithm can be used in real-time image or video frame authentication. In addition, this kind of proposed techniques can be extended to use other characteristic data, such as high-level moment, statistical data of image, and so on.
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, in 1983 and 1986, respectively, and received the Ph.D. degree from 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 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 a fingerprint verification algorithm 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 2004 and served as the Director of the Centre for Information Security from 2005 to 2011. Currently, Dr Jiang is a tenured Associate Professor in School of Electrical and Electronic Engineering, Nanyang Technological University. Dr Jiang has published over hundred research papers in international refereed journals and conferences, some of which are well cited on Web of Science. He is also an inventor of 7 patents (3 US 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 Chair, Keynote Speaker and Session Chair of multiple international conferences. His research interest includes pattern recognition, computer vision, machine learning, image analysis, signal/image processing, machine learning and biometrics.
Title: Feature Extraction and Dimensionality Reduction for Visual Recognition
Abstract: Finding/extracting low-dimensional structures in high-dimensional data is of increasing importance, where images/signals lie in observational spaces of thousands, millions or billions of dimensions. The curse of dimensionality is in full play here: We have to conduct inference with a limited or no human knowledge. Machine learning is a solution that becomes hotter and hotter to boiling. This is evidenced by numerous techniques published in the past decade, many of which are in prestige journals. Nevertheless, there are some fundamental concepts and issues still unclear or in paradox. For example, we often need many processing steps in a complex information discovery/recognition system. As the information amount cannot be increased and must be reduced by any processing, why do we need it before the main processing? This seemly simple question easily answerable if each step uses different prior knowledge is nontrivial in machine learning. People proposed numerous machine learning approaches but seem either unaware of or avoiding this fundamental issue. Although extracting the most discriminative information is indisputably the ultimate objective for pattern recognition, this talk will challenge it as a proper or effective criterion for the machine learning-based dimension reduction or information/feature extraction, despite the fact that it has been employed by almost all researchers.
Jimmy Liu, Chinese Academy of Science, China
Prof. Jimmy Liu Jiang joined Chinese Academy of Sciences in March 2016 through the China“Thousand Talent Program”, and became the founding executive director of Cixi Institute of Biomedical Engineering of Ningbo Institute of Industrial Technology, Chinese Academic of Science. He graduated from the University of Science and Technology of China with a computer engineering bachelor degree, and obtained his Master and Ph.D degrees from the National University of Singapore majoring in Computer Science.Jimmy is currently holding the position of an Honorary Professor in Dundee University and is an adjunct principle research scientist in the Singapore National Eye Research Institute. Jimmy has served many years in IEEE EMBS (Engineering in Medicine and Biology Society) society, and was the 2014 chairman of the IEEE EMBS society of Singapore.
Jimmy has spent 27 years in Singapore before 2016. Jimmy established the Intelligent Medical Imaging Program (iMED), which was once the largest ocular imaging research team in the world, in A*STAR (Agency for Science, Technology and Research) Singapore. Ever since joining the Chinese Academy of Sciences. In June 2016, he established an international joint lab “Sino-US Eye-Brain joint research lab” with North Carolina University United States to conduct eye and brain diseases diagnosis research; in Feb 2017, he signed a MOU with Singapore Eye Research Institute to jointly conduct ocular imaging research, and in April 2017, he signed an agreement with Singapore National Health Group in Ningbo to jointly conduct medical technology research as well as explore translational and clinical research in China and Singapore. In May 2017, Jimmy established a new joint laboratory with world leading ophthalmological equipment manufacture TOPCON Inc. in China focusing on new areas such as advanced ocular medical equipment manufacturing and Artificial Intelligence based Chinese “big” medical image and data research. In Feb 2018, he further set up a new joint laboratory with another world leading ophthalmological equipment manufacture Tomey Inc. focusing on OCT image processing and Cataract Automatic Diagnosis Research.
Title: Artificial Intelligence and Ocular Medical Image Processing
Abstract: In the talk, Jimmy will update the ocular imaging research work in the past years. He will share his AI-based image processing work on various ocular imaging modalities on the following 4 areas: ocular disease screening, robot assisted eye micro-surgery, ocular biometrics, as well as ocular medical informatics using genome study. He will introduce the current issues, technologies and approaches in this inter-disciplinary research area.
Chi-Man Pun, University of Macau, China
Prof. Pun received his B.Sc. and M.Sc. degrees in Software Engineering from the University of Macau in 1995 and 1998 respectively, and Ph.D. degree in Computer Science and Engineering from the Chinese University of Hong Kong in 2002. He is currently an Associate Professor and Head of the Department of Computer and Information Science of the University of Macau. He has investigated many funded research projects and published more than 100 refereed scientific papers in international journals, books and conference proceedings. He has also served as the editorial member / referee for many international journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing, IEEE Transactions on Information Forensics and Security, Pattern Recognition, etc. His research interests include Digital Image Processing; Multimedia Security and Digital Watermarking; Pattern Recognition and Computer Vision. He is also a senior member of the IEEE and a professional member of the ACM.
Title: Reversible Watermarking Using Prediction Value Computation with Gradient analysis
Abstract: This paper proposes a reversible watermarking method that embeds binary bits into a digital image. The embedded information could be inserted into the host image without much image quality degradation and both of the watermark as well as the original image could be restored at the decoding end. By using the gradient analysis method, the prediction value computation process could be more accurate which reduces the prediction error correspondingly. The watermark embedding procedure is implemented based on the difference expansion of image pixels in each stage of two-step embedding process. The gradient analysis is introduced to detect whether a horizontal or vertical edge exists in the pixel context which would improve the accuracy of the prediction value. The two-step embedding process also aims at accurate prediction value computation. Since the prediction error is the key factor in the embedding process, the lower of the prediction error, the better the watermarked image quality. Several standard grayscal images are tested to show the performances of the proposed reversible watermarking method. Both of the watermarked images and image quality related line charts are exhibited in this paper to show the property of the proposed method that reflects decent image quality in different embedding payload situations. Experimental results illustrate a higher percentage of zeros in the prediction error distribution histogram. Compared with other state-of-the-art reversible watermarking methods, better image quality can be realized by proposed method.