Jiun-Yan Chen
Papers
1
Total Citations
17
H-Index
1
About
Jiun-Yan Chen is a leading researcher in real-time embedded vision systems and industrial visual inspection. His work focuses on overcoming the computational bottlenecks of image processing algorithms through hardware acceleration, particularly using Field-Programmable Gate Arrays (FPGAs). Chen’s most-cited contribution, “Real-time FPGA-based template matching module for visual inspection application” (2012, 17 citations), addresses the high computational complexity of normalized cross-correlation (NCC) template matching—a critical technique for object localization in automated inspection. By designing a dedicated FPGA-based module, Chen achieved significant speedups, enabling real-time performance that was previously unattainable with software-only approaches. This work has been instrumental in advancing high-speed, low-latency inspection systems for manufacturing and quality control. Chen’s research bridges the gap between algorithmic efficiency and practical deployment, making him a key figure in embedded computer vision. His achievements highlight the transformative potential of hardware-software co-design in industrial automation, inspiring further innovation in real-time visual inspection technologies.
Research Focus
Key Achievements
Top Papers
- 1