Yen-Han Wang
Papers
1
Total Citations
2
H-Index
1
About
Yen-Han Wang is a researcher whose work centers on computer vision and human-robot interaction, with a particular focus on leveraging RGB-D sensing technologies for dynamic object recognition. His most notable contribution, "Dynamic human object recognition by combining color and depth information with a clothing image histogram" (2019), addresses a critical challenge in developing assistance robots and intelligent monitoring systems. By integrating color and depth data from Kinect v2 cameras with clothing-based image histograms, Wang proposed a novel framework that enhances the robustness of human detection and tracking in real-world environments. This approach simplifies the traditionally complex process of distinguishing individuals in dynamic scenes, offering practical solutions for applications ranging from service robotics to surveillance. While his citation count remains modest, his work represents an important step toward more adaptive and context-aware visual systems. Wang's research bridges the gap between sensor technology and practical deployment, demonstrating how multimodal data fusion can improve machine perception. His contributions are particularly relevant for students and engineers seeking to understand the intersection of computer vision, robotics, and human-centered design.
Research Focus
Key Achievements
Top Papers
- 1