Leilei Niu
Papers
1
Total Citations
15
H-Index
1
About
Leilei Niu is a researcher whose work lies at the intersection of computer vision, agricultural automation, and shape analysis. Her most cited paper, "Extracting the symmetry axes of partially occluded single apples in natural scene using convex hull theory and shape context algorithm" (2016, 15 citations), introduces a novel method for identifying fruit symmetry under challenging real-world conditions—a critical step for robotic harvesting and yield estimation. By combining convex hull theory with shape context algorithms, Niu addresses the problem of occlusion, a common obstacle in natural agricultural scenes. This contribution demonstrates her ability to adapt geometric and computational techniques to practical, field-based challenges. While her citation count reflects a focused, early-stage impact, her work is notable for its direct application to precision agriculture, where accurate object detection can improve efficiency and reduce waste. Niu’s research bridges theoretical shape analysis and applied robotics, offering a foundation for further advances in automated fruit recognition and handling systems.
Research Focus
Key Achievements
Top Papers
- 1