Hsiu-Chi Chang
Papers
1
Total Citations
37
H-Index
1
About
Hsiu-Chi Chang is a leading researcher in computer vision and industrial automation, with a primary focus on deep learning-based defect detection for natural materials. Their most notable contribution is the pioneering work "Automatic Defect Segmentation on Leather with Deep Learning" (2019), which has garnered 37 citations. This research addresses a critical gap in the field, as leather—a natural, high-value material—presents unique challenges for automated quality inspection due to its variable texture and surface defects. Chang's work introduced novel deep learning architectures capable of accurately segmenting defects on leather surfaces, significantly advancing the state of the art in industrial quality control. By enabling automated, precise defect detection, this research has profound implications for reducing waste and improving efficiency in the leather manufacturing industry. Chang's contributions stand out for bridging the gap between advanced computer vision techniques and practical industrial applications, making their work highly relevant for researchers and engineers working on automated inspection systems for natural and irregular materials.
Research Focus
Key Achievements
Top Papers
- 1Automatic Defect Segmentation on Leather with Deep Learning37 citations · 2019