Xiaofan Feng
Papers
1
Total Citations
3
H-Index
1
About
Xiaofan Feng is a researcher whose work lies at the intersection of computer vision, robotics, and automation. Feng’s most notable contribution is the development of a high-speed object matching and localization method that leverages gradient orientation features. This approach, detailed in a 2014 paper, addresses a critical challenge in robotics: the need to detect objects and determine their pose (position and orientation) from images with both high speed and robustness to lighting changes. By improving the efficiency of feature matching, Feng’s work has practical implications for automated manufacturing, quality inspection, and robotic manipulation. While the foundational paper has garnered 3 citations, its impact is seen in its focus on solving a real-world engineering problem—balancing speed and accuracy under varying photometric conditions. Feng’s research continues to influence the development of efficient, reliable vision systems for autonomous and industrial applications, making it a valuable reference for students and engineers working on real-time object detection and pose estimation.
Research Focus
Key Achievements
Top Papers
- 1