Cheng‐Kok Koh
Papers
2
Total Citations
16
H-Index
2
About
Cheng-Kok Koh is a researcher whose work lies at the intersection of robotics, computer vision, and human-robot interaction, with a primary focus on advancing human-pose estimation. His key contributions center on developing methods that enable robots to better perceive and understand human motion, a critical capability for creating more intuitive and responsive robotic systems. In his highly cited 2013 paper, "A 3D-point-cloud feature for human-pose estimation" (12 citations), Koh introduced a novel geometric feature designed to extract human poses directly from 3D point cloud data, offering a robust alternative to traditional 2D image-based methods. This work directly addresses the challenge of enabling robots to interpret highly articulated human movements. Building on this foundation, his 2014 paper, "Selecting best viewpoint for human-pose estimation" (4 citations), tackled the practical problem of sensor placement, proposing a two-phase approach that allows robots to dynamically select optimal viewpoints to improve pose estimation accuracy. While his citation counts reflect a focused, early-stage impact, Koh’s research is notable for its direct application to enhancing robotic cognitive capabilities, laying essential groundwork for more natural and effective human-robot collaboration.
Research Focus
Key Achievements
Top Papers
- 1A 3D-point-cloud feature for human-pose estimation12 citations · 2013
- 2Selecting best viewpoint for human-pose estimation4 citations · 2014