Ming-Shyan Wang

Southern Taiwan University of Science and Technology

Papers

Total Citations

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About

Ming-Shyan Wang is a leading figure in intelligent robotics and computer vision, with a focused expertise in stereo vision systems and robotic manipulation. His most impactful contributions center on developing hybrid algorithms that fuse deep learning with adaptive fuzzy inference to enable machines to perceive and interact with their environment. In his seminal 2020 work, cited 43 times, Wang introduced a novel approach combining adaptive network-based fuzzy inference systems (ANFIS) with regions with convolutional neural networks (R-CNN) for stereo vision-based object recognition and manipulation. This method allows robots to accurately identify and grasp objects using an eye-to-hand camera configuration, significantly advancing autonomous robotic handling. His 2019 study on eye-to-hand robotic tracking and grabbing, with 16 citations, further demonstrates his practical contributions to real-time, binocular vision-guided motion control. Wang’s research bridges the gap between theoretical computer vision and applied robotics, offering robust solutions for industrial automation and intelligent systems. His work continues to inspire new approaches in robotic perception, making him a key reference for students and researchers in autonomous systems and machine vision.