Muxin Sun
Papers
1
Total Citations
4
H-Index
1
About
Muxin Sun is a researcher in robotics and computer vision, with a focus on human-robot interaction and multi-modal perception. Their most-cited work, "Feature fusion using Extended Jaccard Graph and word embedding for robot" (2017, 4 citations), introduces a novel feature graph fusion (FGF) method that integrates RGB and depth data from Kinect sensors to enhance robot recognition capabilities. This contribution addresses a critical challenge in enabling robots to interpret complex visual environments by combining spatial and semantic information through Extended Jaccard Graph similarity and word embeddings. Sun’s approach improves the robustness of object and gesture recognition, directly supporting more intuitive human-robot collaboration. While their citation count is modest, the work represents a meaningful step toward practical, sensor-fusion-driven robotic systems. Sun’s research sits at the intersection of machine learning, sensor integration, and interactive robotics, offering valuable insights for students and engineers developing vision-based autonomous systems. Their emphasis on fusing heterogeneous data streams continues to inform ongoing efforts in real-time robotic perception and adaptive human-robot interfaces.
Research Focus
Key Achievements
Top Papers
- 1Feature fusion using Extended Jaccard Graph and word embedding for robot4 citations · 2017