1 April 1992 Locally connected network for real-time target detection
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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.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Gregory M. Budzban, Gregory M. Budzban, Arthur V. Forman, Arthur V. Forman, Richard Skoblick, 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); doi: 10.1117/12.58072; https://doi.org/10.1117/12.58072

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