Locally connected network for real-time target detection

Gregory M. Budzban; Arthur V. Forman Jr.; Richard Skoblick

doi:10.1117/12.58072

1 April 1992 Locally connected network for real-time target detection

Gregory M. Budzban, Arthur V. Forman Jr., Richard Skoblick

Proceedings Volume 1623, The 20th AIPR Workshop: Computer Vision Applications: Meeting the Challenges; (1992) https://doi.org/10.1117/12.58072
Event: The 20th AIPR Workshop, 1991, McLean, VA, United States

Abstract

The detection of human-made objects with low false alarm rates in lit imagery remains a technically challenging problem. In addition, many currently proposed systems for autonomous air vehicles require the algorithms to process images at the rate of 30 frames/second (real-time). Parallel distributed processes, such as neural networks, offer potential solutions to problems of this complexity. The current algorithm takes advantage of the presence of both long straight lines and curvature points in human-made objects. These features are among those recognized pre-attentively by the human visual system. It is a generalization of work done by Sha'Ashua and Ullman at MIT on the extraction of so-called salient features. The addition of curvature detection, however, is what allows the algorithm to achieve acceptable false alarm rates. On simulated FUR imagery taken from the U.S. Army C2NVEO terrain board, low false alarm rates have been achieved while maintaining 100% target detection.

Citation Download Citation

Gregory M. Budzban, Arthur V. Forman Jr., and Richard Skoblick "Locally connected network for real-time target detection", Proc. SPIE 1623, The 20th AIPR Workshop: Computer Vision Applications: Meeting the Challenges, (1 April 1992); https://doi.org/10.1117/12.58072

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