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Proceedings Paper

Neural-network-based target detection system for FLIR imagery
Author(s): Chris M. Dwan; Sandor Z. Der
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Paper Abstract

This paper describes a Neural Network based target detection system for Forward-Looking Infrared (FLIR) imagery. We apply a series of four algorithms (detection, two layers of clutter rejection and one of centering) to successively reduce the False Alarm Rate while maintaining a high probability of detection (Pd). The detection stage scans the entire image to find regions approximately the size of a target with pixel statistics that differ from their local background. The clutter rejection stages eliminate portions of these detections, while the centering algorithm moves each detection to the point near it which is most like prior examples of perfectly centered targets. The system was trained and tested on a large set of second generation FLIR data.

Paper Details

Date Published: 1 April 1998
PDF: 8 pages
Proc. SPIE 3307, Applications of Artificial Neural Networks in Image Processing III, (1 April 1998); doi: 10.1117/12.304655
Show Author Affiliations
Chris M. Dwan, Environmental Research Institute of Michigan International (United States)
Sandor Z. Der, U.S. Army Research Lab. (United States)

Published in SPIE Proceedings Vol. 3307:
Applications of Artificial Neural Networks in Image Processing III
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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