Woo Yong Han
Papers
1
Total Citations
6
H-Index
1
About
Woo Yong Han is a researcher whose work centers on computer vision, particularly object recognition and pose estimation. His most cited paper, "Object recognition and pose estimation using KLT" (2012, 6 citations), introduces a novel approach that leverages Kanade-Lucas-Tomasi (KLT) and Speeded-Up Robust Features (SURF) to achieve rotation and position invariance. The key innovation lies in using a distance-based method and an anchor point—the center of the target—to enhance recognition accuracy and robustness. This contribution addresses fundamental challenges in robotic vision and augmented reality, where precise object localization is critical. While his citation count reflects a focused impact, Han’s work demonstrates a practical, algorithmic solution for real-world applications. His research bridges theoretical feature extraction with applied pose estimation, offering a streamlined method that balances computational efficiency with reliability. For students and researchers exploring feature-based object recognition, Han’s approach provides a clear, implementable framework for achieving invariance under varying orientations and positions.
Research Focus
Key Achievements
Top Papers
- 1Object recognition and pose estimation using KLT6 citations · 2012