David Casasent

Carnegie Mellon University

Papers

27

Total Citations

220

H-Index

6

About

David Casasent is a pioneering figure in optical pattern recognition, intelligent robotics, and computer vision. For over three decades, his research has centered on developing advanced algorithms and architectures for distortion-invariant object identification and scene analysis. He is best known for his foundational work on correlation filters and hierarchical feature-space recognition systems, which enable robust object detection and classification despite variations in scale, rotation, and illumination. His contributions include novel morphological processing techniques to mitigate shading and illumination effects in robotics and automatic target recognition. Casasent has also been instrumental in advancing active vision and materials handling through his leadership of the influential *Intelligent Robots and Computer Vision* conference series, editing numerous proceedings volumes that have collectively garnered hundreds of citations. His work on unified multifunctional correlator architectures has provided a theoretical and practical framework for integrating clutter reduction, detection, and recognition tasks. With over 170 citations across his most-cited papers alone, Casasent’s legacy lies in bridging optical computing and machine vision, laying groundwork for modern real-time robotic perception systems.

Research Focus

Key Achievements

6
H-Index
27
Papers
220
Total Citations
8
Avg Citations/Paper
🏆 Most Cited Paper
Intelligent Robots and Computer Vision VI
48 citations · 1987
📈 Most Prolific Year: 1992 (4 Papers)
🤝 Key Collaborators: 12
🏛 Institutions: Carnegie Mellon University

Top Papers

  1. 1
  2. 2
  3. 3
  4. 4
  5. 5
  6. 6
  7. 7
  8. 8
  9. 9
  10. 10

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 15 days ago