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Proceedings Paper

Automatic target recognition using a feature-based optical neural network
Author(s): Tien-Hsin Chao
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Paper Abstract

An optical neural network based upon the Neocognitron paradigm is introduced. A novel aspect of the architectural design is shift-invariant multichannel Fourier optical correlation within each processing layer. Multilayer processing is achieved by iteratively feeding back the output of the feature correlator to the input spatial light modulator and updating the Fourier filters. By training the neural net with characteristic features extracted from the target images, successful pattern recognition with intra-class fault tolerance and inter-class discrimination is achieved. A detailed system description is provided. Experimental demonstrations of a two- layer neural network for space objects discrimination is also presented.

Paper Details

Date Published: 1 July 1992
PDF: 7 pages
Proc. SPIE 1701, Optical Pattern Recognition III, (1 July 1992); doi: 10.1117/12.138335
Show Author Affiliations
Tien-Hsin Chao, Jet Propulsion Lab. (United States)

Published in SPIE Proceedings Vol. 1701:
Optical Pattern Recognition III
David P. Casasent; Tien-Hsin Chao, Editor(s)

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