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Proceedings Paper

Optoelectronic neural network utilizing a joint transform correlator
Author(s): Marc J. Paquin; Jonathan S. Kane
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Paper Abstract

Adaptive Resonance Theory provides a neural network architecture for self-organizing arbitrary input patterns into stable categories. In our work we utilize a model of ART known as ART2-A, which is capable of processing both analog and binary patterns. Our model is adapted to handle 2-D images for input patterns, and to allow for translation invariance. The computation of image patterns is assisted by a joint transform correlator (JTC), providing a fast, real-time, translation-invariant method of initial comparison. The JTC has an advantage over other optical correlator architectures in that a separate matched filter for each input need not be constructed. In this paper, we present a brief overview of the ART2-A algorithm and our optoelectronic implementation of this neural network model. This paper is based on work by Kane and Paquin submitted to IEEE Transactions on Neural Networks entitled 'POPART: Partial Optical ImPlementation of Adaptive Resonance Theory 2'.

Paper Details

Date Published: 12 January 1993
PDF: 10 pages
Proc. SPIE 1772, Optical Information Processing Systems and Architectures IV, (12 January 1993); doi: 10.1117/12.140933
Show Author Affiliations
Marc J. Paquin, Rome Lab. (United States)
Jonathan S. Kane, Rome Lab. (United States)


Published in SPIE Proceedings Vol. 1772:
Optical Information Processing Systems and Architectures IV
Bahram Javidi, Editor(s)

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