Minku Kang

Papers

Total Citations

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About

Minku Kang’s research focuses on computer vision and embedded systems, particularly for robotic applications. Her work bridges the gap between algorithmic performance and real-world hardware constraints, addressing the critical challenge of deploying vision systems on resource-limited platforms. In her most cited paper, “PCA-based face recognition in an embedded module for robot application” (2009, 2 citations), Kang implemented a Principal Component Analysis (PCA) face recognition algorithm on an ARM CPU embedded module. Using the Yale face database, she demonstrated significant improvements in computation time while maintaining recognition accuracy, showcasing the feasibility of real-time biometric identification on low-power devices. Her second notable work, “Performance evaluation procedure for vision based object feature extraction algorithms” (2010, 2 citations), establishes a standardized framework for assessing object feature extraction methods used in robot localization and recognition. This contribution provides a systematic approach for comparing algorithms, aiding researchers and engineers in selecting optimal solutions. Though her citation counts are modest, Kang’s emphasis on practical, embedded implementations and evaluation methodologies offers valuable insights for students and researchers working at the intersection of computer vision, robotics, and embedded systems.