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Proceedings Paper

Knowledge- and model-based ATR algorithms adaptation
Author(s): Hatem N. Nasr
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Paper Abstract

One of the most critical problems in Automatic Target Recognition systems (ATR) is multi-scenario adaptation. Today's ATR systems perform unpredictably i.e perform well in certain scenarios, and they perform poorly in others. Unless ATR systems can be made adaptable, their utility in battlefield missions remain questionable. We have developed (under internal research and development) a novel concept called Knowledge and Model Based Algorithm Adaptation (KMBAA). KMBAA automatically adapts the ATR parameters as the scenario changes so that ATR can maintain optimum performance. The KMBAA approach has been tested with a non-real-time ATR simulation system and has demonstrated a significant improvement in detection, false alarm rate reduction and segmentation accuracy performance.

Paper Details

Date Published: 1 November 1991
PDF: 8 pages
Proc. SPIE 10307, Automatic Object Recognition, 103070C (1 November 1991); doi: 10.1117/12.2283653
Show Author Affiliations
Hatem N. Nasr, Honeywell, Inc. (United States)

Published in SPIE Proceedings Vol. 10307:
Automatic Object Recognition
Hatem N. Nasr, Editor(s)

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