Jae Won Jang
Papers
1
Total Citations
12
H-Index
1
About
Jae Won Jang is a researcher in computer vision and robotics, with a primary focus on 3D sensing, camera calibration, and geometric perception. His most cited work, "Multi-Cue-Based Circle Detection and Its Application to Robust Extrinsic Calibration of RGB-D Cameras" (2019), addresses a critical challenge in multi-camera systems: achieving accurate extrinsic calibration without expensive equipment. By developing a novel circle detection algorithm that leverages multiple visual cues, Jang enabled robust, low-cost calibration for RGB-D camera arrays—a key enabler for applications like 3D modeling and human-computer interaction. This work has garnered 12 citations, reflecting its practical value in both academic and industrial settings. Jang’s contributions are particularly notable for their emphasis on robustness and cost-efficiency, making advanced 3D sensing more accessible. His research continues to impact fields such as autonomous systems, augmented reality, and spatial computing, where precise multi-camera alignment is essential. For students and researchers, Jang’s work exemplifies how clever algorithmic design can solve real-world engineering constraints.
Research Focus
Key Achievements
Top Papers
- 1