Chun‐Ming Chang

Papers

Total Citations

H-Index

About

Chun-Ming Chang’s research career has centered on intelligent automation and computer vision, with a particular focus on material handling systems. His most notable contributions involve the innovative use of neural networks and bar code features to solve positioning challenges in industrial environments. In his 1997 paper, which has accumulated 10 citations, Chang pioneered the combination of neural networks with Fourier descriptors to enable part positioning using bar code features—a foundational approach for automated material handling. His 2002 work, cited 4 times, advanced this concept by developing a practical method that leverages the graphic design of bar codes to position objects on conveyor belts without the need for work carriers. By employing a simplified template matching technique to detect the four corners of a bar code, Chang’s method offered an efficient and cost-effective solution for real-world industrial applications. His research demonstrates a keen ability to bridge theoretical machine learning with tangible manufacturing needs, making his work a valuable reference for engineers and researchers exploring vision-based automation in logistics and production systems.