Jinbuem Jang
Papers
1
Total Citations
5
H-Index
1
About
Jinbuem Jang is a researcher in computer vision, with a primary focus on three-dimensional (3D) reconstruction and stereo matching under challenging real-world conditions. His most-cited work, "Illuminant-invariant stereo matching using cost volume and confidence-based disparity refinement" (2019, 5 citations), addresses a critical problem in 3D vision: the degradation of matching accuracy caused by illumination changes between stereo image pairs. Jang’s major contribution lies in developing a robust framework that leverages cost volume analysis and confidence-based disparity refinement to maintain matching reliability even when large intensity differences occur between stereos. This approach directly tackles the difficulty of finding similarity in the matching process under varying lighting, a common obstacle in outdoor and uncontrolled environments. While his citation count is modest, Jang’s work is notable for its practical focus on improving the robustness of stereo vision systems, which are foundational to applications in autonomous navigation, robotics, and augmented reality. His research demonstrates a clear commitment to solving real-world visual perception challenges, making his contributions valuable for students and engineers seeking to advance 3D vision technology in non-ideal conditions.
Research Focus
Key Achievements
Top Papers
- 1