Yu-Ming Chang
Papers
1
Total Citations
10
H-Index
1
About
Yu-Ming Chang is a leading researcher in mobile robotics and computer vision, with a primary focus on real-time visual localization and deep learning for autonomous navigation. His most-cited work, "Real-Time Visual-Based Localization for Mobile Robot Using Structured-View Deep Learning" (2019, 10 citations), tackles a critical bottleneck in robotics: the labor-intensive process of collecting and annotating training images for supervised deep learning models. Chang’s major contribution lies in devising an efficient visual detection scheme that enables mobile robots to recognize places and localize themselves in real time without exhaustive data preparation. This innovation significantly reduces the overhead of deploying autonomous guidance systems, making them more practical for real-world applications. His structured-view approach demonstrates how deep learning can be adapted to work with limited labeled data, a challenge that has long hindered progress in field robotics. Chang’s work is particularly notable for its emphasis on practicality—bridging the gap between theoretical computer vision and deployable robotic systems. With his research gaining traction among peers, Chang is establishing himself as a key figure in the push toward more intelligent, self-sufficient mobile robots that can navigate complex environments with minimal human intervention.
Research Focus
Key Achievements
Top Papers
- 1