Prof Chuen-Horng Lin | Computer Vision | Best Researcher Award

Prof Chuen-Horng Lin, National Taichung University of Science and Technology, Taiwan

Chuen-Horng Lin, Ph.D., is a distinguished Professor in the Department of Computer Science and Information Engineering at National Taichung University of Science and Technology, Taiwan. He earned his M.S. and Ph.D. in Applied Mathematics from National Chung-Hsing University, Taiwan. Dr. Lin’s research focuses on computer vision, image processing, machine learning, deep learning, and pattern recognition. His work specifically delves into automated analysis of images and videos through advanced detection, tracking, and segmentation methods. With numerous publications and a robust academic presence, Dr. Lin continues to contribute significantly to the field, bridging theoretical advancements with practical applications in digital imaging technologies.

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Education

Chuen-Horng Lin earned both his Master of Science (M.S.) and Doctor of Philosophy (Ph.D.) degrees in Applied Mathematics from National Chung-Hsing University, Taiwan. His academic journey at one of Taiwan’s leading institutions equipped him with a strong foundation in mathematical principles and their applications. This rigorous training provided him with essential skills and insights that have been pivotal to his subsequent career in computer science and engineering. Dr. Lin’s educational background continues to underpin his research and contributions to fields such as computer vision, image processing, machine learning, deep learning, and pattern recognition, shaping his expertise in automated image and video analysis techniques.

Research focus

Chuen-Horng Lin’s research spans several interdisciplinary areas, with a primary focus on advancing techniques in computer vision, image processing, and machine learning. His work is characterized by pioneering contributions to content-based image retrieval systems, leveraging color, texture, and spatial features for enhanced accuracy and efficiency. Dr. Lin has also delved into the development of algorithms like the fast K-means method tailored for image retrieval, and the application of adaptive features using genetic algorithms for image classification. Additionally, he has made significant contributions to queueing theory and optimization models, particularly in multi-server systems and queueing analysis with fuzzy parameters, showcasing his broad expertise in both theoretical and applied aspects of computational sciences.