YoungChul Kim

Papers

Total Citations

H-Index

About

YoungChul Kim’s research centers on robotics, computer vision, and intelligent game-playing systems, with a particular focus on integrating machine learning and sensor-based perception into autonomous platforms. His most notable work, “Tensor voting, Hough transform and SVM integrated in chess playing robot” (2015), presents a novel approach to detecting and recognizing chess pieces in the Korean game of Janggi. By combining tensor voting for robust feature extraction, Hough transforms for geometric analysis, and support vector machines for classification, Kim’s system enables a robot to perceive and interact with a complex, unconstrained board environment—pushing forward the frontier of human-robot interaction in strategic games. Although his citation count is modest, the work exemplifies a practical fusion of classical computer vision with modern machine learning, offering a blueprint for real-time, adaptive robotic gameplay. Kim’s contributions highlight the growing importance of integrating multiple algorithmic paradigms to solve real-world perception challenges, making his research a valuable reference for students and engineers interested in autonomous systems, game AI, and applied computer vision.