Yen-Chung Chang

Industrial Technology Research Institute, National Yang Ming Chiao Tung University

Papers

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About

Yen-Chung Chang is a researcher whose work sits at the intersection of computer vision, embedded systems, and real-time robotics. His primary research areas include FPGA-based hardware acceleration for vision algorithms and visual servo control for robotic systems. Chang’s most significant contribution is the development of a real-time FPGA-based template matching module that dramatically accelerates the computationally intensive normalized cross-correlation (NCC) algorithm, achieving practical speeds for industrial visual inspection applications—a paper that has garnered 17 citations for its engineering impact. He also pioneered an embedded visual servo processor for a robotic ball catcher, demonstrating how parallel computing on FPGAs can enable real-time 3D tracking and triangulation of fast-moving objects using stereo vision. This work, cited 6 times, showcases his ability to bridge hardware acceleration with dynamic robotic control. Chang’s research is notable for its practical, application-driven approach, translating complex vision algorithms into deployable hardware solutions that push the boundaries of real-time performance in automation and robotics.