Sujin Jang
Papers
1
Total Citations
7
H-Index
1
About
Sujin Jang’s research centers on robotics, computer vision, and control systems, with a particular focus on estimating the structure and motion of moving objects from dynamic camera feeds. In her most-cited work, “Experimental Results for Moving Object Structure Estimation Using an Unknown Input Observer Approach” (2012, 7 citations), Jang pioneered an online structure-from-motion (SFM) method that leverages an unknown input observer to estimate the position of a moving object observed by a moving camera. This approach was experimentally validated using a two-link robot, demonstrating robust real-time performance in challenging, uncalibrated environments—a critical step for autonomous systems and robotic manipulation. While her citation count reflects a focused, early-career impact, Jang’s contributions lie in bridging theoretical observer design with practical experimental verification, offering a foundation for future work in dynamic scene understanding. Her research is particularly valuable for students and engineers developing vision-based control for drones, mobile robots, and human-robot interaction, where accurate motion estimation from moving platforms remains a key challenge.
Research Focus
Key Achievements
Top Papers
- 1