Kuo Feng Hung
Papers
1
Total Citations
17
H-Index
1
About
Kuo Feng Hung is a researcher specializing in real-time computer vision and FPGA-based hardware acceleration for industrial inspection systems. His most notable contribution is the development of a high-speed, FPGA-based template matching module that dramatically accelerates normalized cross-correlation (NCC) computations—a critical but computationally intensive task for object localization in automated visual inspection. His 2012 paper on this topic has garnered 17 citations, demonstrating its influence in bridging the gap between algorithmic complexity and real-time hardware implementation. Hung’s work addresses a key bottleneck in manufacturing quality control, enabling faster and more reliable defect detection without sacrificing accuracy. By optimizing hardware-software co-design, he has advanced practical solutions for embedded vision systems, making his research highly relevant for engineers and scientists working on real-time image processing, FPGA architectures, and industrial automation. His achievements highlight the transformative potential of reconfigurable computing in overcoming the performance limitations of traditional software-based inspection methods.
Research Focus
Key Achievements
Top Papers
- 1