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Proceedings Paper

Application of a vision neural network in an automatic target recognition system
Author(s): James G. Landowski; Baldamar Gil
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Paper Abstract

This paper describes the application of a visual pattern recognition neural network in a hybrid model based automatic target recognition (ATR) system. This neural network forms the feature extraction front end of the ATR and is derived from the Neocognitron network first proposed by K. Fukushima. For complex target recognition, modifications to the basic Neocognitron network paradigm were required to enhance robustness against image distortions due to undersampling (aliasing) and poor feature selection during training. The focus of the paper is on the enhancements, their rationale, and on the use of the network as a self- organizing feature extraction element of an ATR. Results of experiments with the overall ATR system against target imagery are shown and discussed.

Paper Details

Date Published: 16 September 1992
PDF: 10 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.140026
Show Author Affiliations
James G. Landowski, Lockheed Palo Alto Research Lab. (United States)
Baldamar Gil, Lockheed Palo Alto Research Lab. (United States)


Published in SPIE Proceedings Vol. 1709:
Applications of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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