Jeongsook Chae

Dongguk University, Agency for Defense Development

Papers

Total Citations

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About

Jeongsook Chae is a researcher whose work sits at the intersection of robotics, computer vision, and human-machine interaction, with a particular focus on how machines perceive and respond to their environments. Her contributions span three critical areas: 3D point cloud processing, object boundary detection, and wearable sensor-based robot control. In her highly cited 2018 work on range image-based clustering, she advanced autonomous perception by adapting density-based spatial clustering methods for LiDAR data, a fundamental step for robot navigation and object detection. Earlier, in 2010, she tackled the challenge of boundary detection using supervised learning with planar laser scanners, providing a robust method for separating foreground objects from backgrounds—a key capability for tracking and recognition systems. Demonstrating her versatility in human-robot interaction, Chae also developed a genetic algorithm-based method to estimate motion from Myo armband data and EMG signals, enabling intuitive remote robot control. With her top papers collectively garnering over 35 citations, Chae’s research provides practical, algorithmic solutions that bridge raw sensor data and actionable robotic intelligence, making her work essential reading for students and engineers developing autonomous systems and wearable interfaces.