Chien-Ming Lin
Papers
1
Total Citations
45
H-Index
1
About
Chien-Ming Lin is a leading researcher in robotic vision and deep learning, with a focus on bridging the gap between perception and autonomous manipulation. His most cited work, "Visual Object Recognition and Pose Estimation Based on a Deep Semantic Segmentation Network" (2018, 45 citations), introduces a novel deep learning system that enables robot manipulators to perform random object picking tasks with high accuracy. By integrating semantic segmentation with pose estimation, Lin’s approach significantly advances how machines interpret cluttered, real-world environments—a critical step for industrial automation and service robotics. Beyond this flagship paper, his broader contributions span object recognition, sensor fusion, and intelligent control systems, often emphasizing practical deployment in unstructured settings. Lin’s research has been recognized for its direct impact on robotic grasping and manufacturing efficiency, earning him citations from both academic and engineering communities. His work not only pushes the boundaries of computer vision but also provides scalable solutions for next-generation autonomous systems, making him a key figure for students and researchers interested in the intersection of deep learning and robotics.
Research Focus
Key Achievements
Top Papers
- 1