Papers
2
Total Citations
17
H-Index
2
About
Ouk Choi is a researcher whose work lies at the intersection of computer vision and robotics, with a primary focus on 3D sensing, camera calibration, and visual navigation. His most cited paper, "Multi-Cue-Based Circle Detection and Its Application to Robust Extrinsic Calibration of RGB-D Cameras" (2019, 12 citations), addresses a critical challenge in multi-camera setups: achieving precise extrinsic calibration for RGB-D cameras used in 3D modeling and human-computer interaction. By introducing a multi-cue circle detection method, Choi significantly improved the robustness and accuracy of calibration, enabling more reliable 3D data capture from multiple viewpoints. Earlier, his work "Efficient feature tracking for scene recognition using angular and scale constraints" (2008, 5 citations) contributed to visual SLAM and autonomous navigation by enhancing feature tracking stability. This research provided practical constraints that improved scene recognition in dynamic environments, supporting real-time robotic applications. While his citation counts reflect focused, technical contributions, Choi’s work is notable for its direct applicability to real-world systems, particularly in reducing costs and increasing reliability for multi-camera configurations. His research continues to influence advancements in 3D vision and robotics, offering foundational techniques for students and engineers working on perception systems.
Research Focus
Key Achievements
Top Papers
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