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Proceedings Paper

Hybrid ANN-ES architecture for automatic target recognition
Author(s): Chungte Teng; Panos A. Ligomenides
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Paper Abstract

Automatic target recognition can benefit from cooperation of artificial neural networks (ANNs) and expert systems (ESs). Bottom-up training and generalization properties of artificial neural networks, and top-down utilization of accumulated knowledge by expert system processors, can be combined to offer robust performance of the automatic target recognition models. In this paper, we propose a modular, flexible and expandable, hybrid architecture which provides cooperative, functional and operational interfaces between expert system and artificial neural networks facilities. In order to make the problem more specific, we apply this architecture to the Multline Optical Character Reader (MLOCR) system, which is being developed to sort the postal mail pieces automatically.

Paper Details

Date Published: 1 March 1992
PDF: 12 pages
Proc. SPIE 1608, Intelligent Robots and Computer Vision X: Neural, Biological, and 3-D Methods, (1 March 1992); doi: 10.1117/12.135108
Show Author Affiliations
Chungte Teng, Univ. of Maryland (United States)
Panos A. Ligomenides, Univ. of Maryland (United States)

Published in SPIE Proceedings Vol. 1608:
Intelligent Robots and Computer Vision X: Neural, Biological, and 3-D Methods
David P. Casasent, Editor(s)

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