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Proceedings Paper

Parallel rule inferencing for automatic target recognition
Author(s): Jean L. Pacelli; Steve L. Geyer; Timothy S. Ramsey
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Paper Abstract

Current research in the area of automatic target recognition (ATR) has produced system concepts shown to be capable of accurately recognizing targets in imagery. However, the ATR task is typically limited in scope and requires long runtimes. The authors have developed an innovative ATR system using a parallel rule based production system. The predicted signature of a three- dimensional target in the two-dimensional scene is coupled with feature extraction information to create a set of rules that will guide the image search and match the predicted signature to extracted features. As various predicted features are found, confidence is accumulated for the object and its orientation. Since there can be tens of thousands of features extracted from an image, data level parallel processing is an ideally suited architecture. The system architecture and performance are described in this paper. Work is ongoing; however, results to date are exceptionally encouraging. The ATR application used during development has generated a few hundred facts and has successfully recognized the target of interest in the presence of other targets and a cluttered background. Performance indicates that rule execution runtimes increase slowly as the number of facts increase, and are orders of magnitude faster than comparable serial implementation benchmarks.

Paper Details

Date Published: 1 August 1991
PDF: 9 pages
Proc. SPIE 1472, Image Understanding and the Man-Machine Interface III, (1 August 1991); doi: 10.1117/12.46473
Show Author Affiliations
Jean L. Pacelli, MRJ, Inc. (United States)
Steve L. Geyer, MRJ, Inc. (United States)
Timothy S. Ramsey, MRJ, Inc. (United States)


Published in SPIE Proceedings Vol. 1472:
Image Understanding and the Man-Machine Interface III
Eamon B. Barrett; James J. Pearson, Editor(s)

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