Guee-Sang Lee

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About

Guee-Sang Lee is a researcher whose work sits at the intersection of computer vision and robotics, with a particular focus on game-playing systems. His primary research areas include image processing, object detection, and robotic vision systems, where he has developed innovative approaches to real-world perception challenges. Lee's major contributions center on the development of vision systems for chess-playing robots, specifically for the Korean game of Janggi. In his most cited work, "Chessboard and Pieces Detection for Janggi Chess Playing Robot" (2013, 4 citations), he established a foundational framework for chessboard detection and piece localization, which are critical for subsequent recognition and move calculation processes. His follow-up work, "Tensor voting, Hough transform and SVM integrated in chess playing robot" (2015, 2 citations), advanced this research by integrating multiple computational techniques—tensor voting for structural inference, Hough transform for line detection, and Support Vector Machines for classification—to create a more robust and accurate piece detection and recognition system. While his citation counts are modest, Lee's work represents an important step in bridging computer vision algorithms with practical robotic applications. His integrated approach to combining classical computer vision techniques with machine learning demonstrates a thoughtful methodology that continues to inform researchers working on vision-based robotic systems.