 | Prof. Yuan Yan TANG (IEEE Life Fellow, IAPR Fellow, AAIA Fellow, AIIA Fellow)University of Macau, China Professor Yuan Yan Tang is currently the Director of the Smart City Research and Development Center at the Institute for Advanced Studies, University of Macau (Hengqin), a Chair Professor Emeritus at the University of Macau, and a Chair Professor Emeritus at Hong Kong Baptist University. Professor Tang is a reviewer, deputy group leader, and group leader of the Information Science Group II of the National Natural Science Foundation of China, a reviewer for the National Science Fund for Distinguished Young Scholars, and a reviewer for the Yangtze River Scholars Program of the Ministry of Education. He is the Founding Vice Chairman of the Hong Kong University and Colleges Information Technology and Management Association and a Founding Director of the Hong Kong Overseas Students Association. He has received one first prize of the Natural Science Award from the Ministry of Education of China, and five second and third prizes of the Natural Science Award from the Macao Special Administrative Region and Chongqing Municipality. He holds 7 Chinese patents, and his research results have been applied to 15 US patents and 2 European patents. He has been invited to deliver academic reports at the Chinese Academy of Sciences, the National Natural Science Foundation of China, the General Office of the CPC Central Committee, over 50 universities, and in more than 10 countries including the United States, the United Kingdom, and Canada. Title: Non-linear Geometric Distortion Abstract: Image geometric distortion is an important research topic in image processing. Image geometric distortion is divided into linear distortion and nonlinear distortion. This report introduces some methods for handling nonlinear distortion. The most basic principle is to approximate various nonlinear changes using the deformation function in the finite element method. This report focuses on the bilinear model, the biquadratic model, and the bicubic model. Several mathematical formulas for approximating nonlinear image distortion are given. The main contents are as follows: Geometric Transformation Models; Linear Transformation Models; Nonlinear Transformation Models; Harmonic Transformation Model; Partial Differential Equation; Wavelet Collocation Method |
 | Prof. Changwen Chen (IEEE Fellow)The Hong Kong Polytechnic University, China Prof. Changwen Chen is currently Chair Professor of Visual Computing at The Hong Kong Polytechnic University. Previously, he has been an Empire Innovation Professor of Computer Science and Engineering at the University at Buffalo, State University of New York from 2008 to 2021. He also served as Dean of the School of Science and Engineering at The Chinese University of Hong Kong, Shenzhen from 2017 to 2020. He was Allen Henry Endow Chair Professor at the Florida Institute of Technology from 2003 to 2007. He was on the faculty of Electrical and Computer Engineering at the University of Missouri-Columbia from 1996 to 2003 and on the faculty of Electrical and Computer Engineering at the University of Rochester from 1992 to 1996. Prof. Chen has been the Editor-in-Chief for IEEE Transactions on Multimedia from January 2014 to December 2016. He has also served as the Editor-in-Chief for IEEE Transactions on Circuits and Systems for Video Technology from January 2006 to December 2009. Currently, he is the Associate Editor-in-Chief of IEEE Transactions on Biometrics, Behavior, and Identity Science. He has been an Editor for several other major IEEE Transactions and Journals, including the Proceedings of IEEE, IEEE Journal of Selected Areas in Communications, and IEEE Journal of Emerging and Selected Topics in Circuits and Systems. He has served as Conference Chair for several major IEEE, ACM and SPIE conferences related to multimedia video communications and signal processing. His research has been supported by NSF, DARPA, Air Force, NASA, Whitaker Foundation, Microsoft, Intel, Kodak, Huawei, and Technicolor. Prof. Chen received his BS degree from University of Science and Technology of China in 1983, MSEE degree from University of Southern California in 1986, and Ph.D. degree from University of Illinois at Urbana-Champaign in 1992. He and his students have received nine Best Paper Awards or Best Student Paper Awards. He has also received several research and professional achievement awards, including Sigma Xi Excellence in Graduate Research Mentoring Award in 2003, Alexander von Humboldt Research Award in 2009, the University at Buffalo Exceptional Scholar - Sustained Achievement Award in 2012, the State University of New York System Chancellor’s Award for Excellence in Scholarship and Creative Activities in 2016, and the Distinguished ECE Alumni Award from University of Illinois at Urbana-Champaign in 2019. He is an IEEE Fellow since 2004, an SPIE Fellow since 2007 and a member of Academia Europaea since 2021. Title: TBD Abstract: TBD |
 | Prof. Junzhi Yu (IEEE Fellow)Peking University, China Junzhi Yu is a Boya Distinguished Professor at Peking University, an IEEE Fellow, a CAA Fellow, a recipient of the National Science Fund for Distinguished Young Scholars, a national-level leading talent, selected into the National Hundred-Thousand-Ten Thousand Talent Program, and enjoys the Special Government Allowance of the State Council. His main research interests include bio-inspired robotics, embodied intelligence, computational intelligence, and automatic control. He has led over 30 major research projects, including key projects of the National Natural Science Foundation of China, the National High-Tech R&D Program (863 Program), and the National Key R&D Program of China. He has published more than 300 papers in internationally renowned journals and conferences in robotics and automation, including over 200 papers in IEEE Transactions. He has authored 5 Chinese monographs and 4 English monographs, and holds 75 authorized Chinese invention patents and 6 U.S. patents, with some of his achievements having been industrialized. His related work has been recognized with awards such as the Second Prize of the National Natural Science Award, the First Prize of the Beijing Science and Technology Award, the First Prize of the Jilin Science and Technology Progress Award, and the First Prize of the Natural Science Award of the Chinese Association of Automation. Title: Underwater Fisheye Vision Based Motion Perception and Control Abstract: Underwater vision systems are crucial for enhancing the perception capabilities of underwater vehicles in complex environments. Fisheye lenses, with their ultra-wide field of view, are widely used in such systems to provide near-panoramic visual perception, enabling robots to better understand and interact with their surroundings. Research on underwater fisheye visual perception encompasses various aspects, including fisheye vision system design, image distortion correction, underwater image enhancement, object detection and recognition, visual stabilization control, and visual navigation and localization. This report focuses on the latest research achievements in underwater fisheye visual perception and control. Specifically, we have developed a movable gimbal-based vision system and a bio-inspired fisheye binocular vision system, and proposed a series of methods for image enhancement, object detection, and visual stabilization control, significantly improving underwater visual perception capabilities. Building on this, we have conducted studies on fisheye-based visual localization methods and navigation based on a disparity attention mechanism, further demonstrating their unique application advantages and broad prospects. |
| Prof. Shuyin Xia (IEEE Senior Member)Chongqing University of Posts and Telecommunications, China Dr. Xia Shuyin is a Professor and Doctoral Supervisor. He serves as the Vice Chairman of the Chongqing Association for Artificial Intelligence and has been recognized as a National-Level Young Talent, Chongqing Outstanding Young Scholar, and Chongqing Talent. He is a Senior Member of IEEE and ranks among the top 2% of the world's top scientists. He holds the positions of Vice Dean of the School of Artificial Intelligence at Chongqing University of Posts and Telecommunications, Director of the Chongqing Key Laboratory of Computational Intelligence, and Deputy Director of the Ministry of Education’s Key Laboratory of Big Data Intelligent Security for Cyberspace. He has led several national key projects, including the National Key R&D Program, NSFC Original Exploration Project, and the National Excellent Young Scientists Fund. Collaborating with Professors Wang Guoyin and Gao Xinbo, he proposed and developed the Granular-Ball Computing Theory. His research outcomes, published as first or corresponding author in top-tier artificial intelligence journals and conferences such as IEEE TPAMI, TKDE, TIP, ICML, AAAI, IJCAI, and ICDE, have attracted follow-up studies by scholars from over 40 world-renowned research institutions. His work has earned awards including the Second Prize of the National Teaching Achievement Award, First Prize of the Chongqing Natural Science Award, First Prize of the CCF Natural Science Award, and the Wu Wenjun Artificial Intelligence Science and Technology Progress Award. Title: Granular-Ball Computing: An Efficient, Robust, and Interpretable Novel Artificial Intelligence Theory Abstract: Current artificial intelligence methods predominantly rely on the finest-grained pixel-level or single-granularity representations, lacking the innate efficiency, robustness, and interpretability of human cognitive mechanisms, which prioritize large-scale perception first. To address this, Granular-Ball Computing has been proposed and developed based on multi-granularity cognitive computation theory. This approach mimics the human brain’s "large-scale-first" cognitive process by adopting a coarse-to-fine generation strategy. It uses granular balls of varying sizes to cover data samples, enabling adaptive and efficient multi-granularity representations. By constructing a novel computational paradigm centered on granular balls, it achieves superior efficiency, robustness, and interpretability compared to traditional AI methods. This theory has been tracked and studied by renowned scholars from over 100 research institutions in more than 10 countries/regions worldwide, including the National University of Singapore, the University of Michigan, Seoul National University, the Indian Institute of Technology, and the University of Alberta. This lecture will present key research outcomes and recent advancements in Granular-Ball Computing, including: Granular-Ball Classifier, Granular-Ball Fuzzy Sets, Granular-Ball Clustering, Granular-Ball Graph Learning, Granular-Ball Reinforcement Learning, Granular-Ball Large Models, Granular-Ball Evolutionary Computation, Granular-Ball Rough Sets, Granular-Ball Computer Vision, Granular-Ball Natural Language Processing and so on. |
