David Casasent
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
Top Papers
- 1Intelligent Robots and Computer Vision VI48 citations · 1987
- 2Intelligent Robots and Computer Vision VIII: Algorithms and Techniques46 citations · 1990
- 3Intelligent Robots and Computer Vision X: Algorithms and Techniques20 citations · 1992
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- 6<title>New advances in correlation filters</title>8 citations · 1992
- 7Intelligent Robots and Computer Vision XXVIII: Algorithms and Techniques6 citations · 2011
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