Young Chan Kwon
Papers
1
Total Citations
12
H-Index
1
About
Young Chan Kwon is a researcher whose work lies at the intersection of computer vision and robotics, with a particular focus on multi-camera calibration and 3D perception. His most-cited paper, "Multi-Cue-Based Circle Detection and Its Application to Robust Extrinsic Calibration of RGB-D Cameras" (2019), addresses a critical challenge in deploying multiple RGB-D cameras for applications like 3D modeling and human-computer interaction. Kwon’s key contribution is a novel calibration method that leverages multi-cue circle detection to achieve robust extrinsic calibration—aligning the coordinate systems of multiple cameras—without requiring expensive or complex setups. This work has garnered 12 citations, reflecting its practical value in reducing costs and improving accuracy for multi-camera systems. By enabling more reliable 3D data capture from different viewpoints simultaneously, Kwon’s research supports advancements in robotics, augmented reality, and automated 3D reconstruction. His approach exemplifies a focus on real-world applicability, making sophisticated calibration accessible for researchers and engineers working with consumer-grade depth sensors.
Research Focus
Key Achievements
Top Papers
- 1