Sungyoung Kim
Papers
1
Total Citations
5
H-Index
1
About
Sungyoung Kim is a researcher focused on computer vision and human-computer interaction, with a particular emphasis on hand gesture recognition. His key contributions lie in developing robust methods for interpreting hand shapes using moment invariants, a technique that extracts distinctive features from hand regions to enable accurate gesture classification. In his most cited work, "A Robust Hand Gesture Recognition Using Combined Moment Invariants in Hand Shape" (2016), Kim proposed a system that segments hand regions from video streams via skin color detection, then applies combined moment invariants to recognize gestures—even when using standard web cameras rather than specialized depth sensors like Kinect. While this paper has garnered 5 citations, it represents a foundational approach to accessible, low-cost gesture recognition systems. Kim’s work addresses the challenge of making gesture-based interfaces practical for everyday applications, from smart device control to assistive technologies. His research contributes to the broader goal of enabling natural, intuitive human-machine interaction without requiring expensive hardware, making his methods particularly valuable for researchers and developers working on inclusive, real-world computer vision systems.
Research Focus
Key Achievements
Top Papers
- 1