Papers
2
Total Citations
10
H-Index
2
About
Xiaoxiang Han is a researcher advancing the frontiers of intelligent robotic perception and surgical vision. Her work primarily focuses on computer vision for autonomous systems and medical robotics, with key contributions in real-time scene understanding and depth perception. She developed a lightweight segmentation network for endoscopic surgical instruments that integrates edge refinement and efficient self-attention mechanisms, addressing the critical challenge of precise boundary detection in robot-assisted surgery—a problem where mainstream models often fall short. This work, published in 2023, has already garnered 7 citations, signaling its immediate relevance to the surgical robotics community. Han has also tackled the fundamental issue of depth perception in autonomous robots, proposing a novel depth hole filling and optimization method based on binocular parallax images. This technique resolves the persistent problem of unmatched points in stereo vision algorithms, which create depth holes that compromise environmental perception. Her contributions are particularly notable for balancing computational efficiency with high accuracy—a crucial requirement for real-time robotic applications. As her research continues to bridge the gap between robust perception and practical deployment, Han is establishing herself as a rising voice in vision-guided autonomous systems and medical robotics.
Research Focus
Key Achievements
Top Papers
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- 2