Chih-Chung Lo

University of Missouri

Papers

2

Total Citations

14

H-Index

2

About

Chih-Chung Lo has made focused contributions at the intersection of neural networks, computer vision, and automated material handling systems. His research centers on applying artificial neural networks and Fourier descriptors to solve practical industrial challenges, particularly in part positioning and object recognition using bar code features. In his seminal 1997 work, Lo demonstrated how neural networks could interpret bar code graphics for precise part positioning in material handling systems, a foundational approach that has garnered 10 citations. His 2002 paper advanced this concept by developing a simplified template matching method to detect the four corners of bar codes on conveyor belts, enabling accurate object positioning without work carriers. This innovation directly addressed real-world manufacturing needs, reducing system complexity while improving efficiency. Although Lo’s citation counts reflect a specialized niche rather than broad impact, his work represents a practical engineering contribution that bridges pattern recognition and industrial automation, offering a cost-effective solution for automated logistics. His research remains relevant for engineers developing vision-based systems for smart manufacturing and warehouse automation.

Research Focus

Key Achievements

2
H-Index
2
Papers
14
Total Citations
7
Avg Citations/Paper
🏆 Most Cited Paper
Neural networks and fourier descriptors for part positioning using bar code features in material handling systems
10 citations · 1997
📈 Most Prolific Year: 1997 (1 Papers)
🤝 Key Collaborators: 2
🏛 Institutions: University of Missouri

Top Papers

  1. 1
  2. 2

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 15 days ago