Papers
23
Total Citations
173
H-Index
8
About
Min Young Kim is a robotics and computer vision researcher whose work spans over two decades, encompassing intelligent sensing systems, autonomous navigation, industrial automation, and more recently, deep learning-based perception. Kim's early career made significant contributions to shipyard automation, developing visual sensing and neural network-based recognition systems that enabled robotic welding in hazardous closed-block assembly environments — work that garnered nearly 30 citations and addressed critical worker safety challenges. A central thread throughout Kim's research is the development of sophisticated 3D sensing architectures, most notably active trinocular vision systems for mobile robot navigation, which accumulated over 35 citations and established foundational approaches to dense range map reconstruction in robotic perception. Kim further extended this expertise to self-organizing neural networks for environmental map building and wearable assistive technologies integrating eye-tracking with scene understanding. In more recent years, the research portfolio has evolved to embrace deep learning applications, including robotic prosthetic hand control, smart fitness systems, and multi-object detection and tracking for autonomous vehicles. With a citation record reflecting sustained relevance across multiple domains, Kim represents a researcher who has continuously adapted classical robotics expertise to meet emerging technological frontiers.
Research Focus
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