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Proceedings Paper

Three-dimensional target recognition using mART neural networks
Author(s): Eun-Soo Kim; Jin-Woo Cha; Chang Myung Ryu
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Paper Abstract

To give a real-time adaptive self-organizing capability to the automatic target recognition (ATR) system suppressing the over clustering, the modified adaptive resonance theory (mART) neural networks are proposed which include the vigilance test method of self-organizing map (SOM) and the real-time adaptive clustering algorithm of ART. This neural networks effectively cluster the arbitrary feature maps which are mostly invariant to two dimensional distortion, so as to solve the three dimensional distortion problem. As the extraction of features which are invariant to two dimensional distortion, five alternative methods are tested in this paper. And for the purpose of proving the performance of the proposed neural networks, some experiments with the database composed of 9 fighters and 5 tanks are carried out. Under the condition that the system occupies the same size of memory, the mART produces 19% higher recognition rate than that of the SOM neural networks. Consequently, it is proved that the proposed approaches can give a great attribution in realizing the three dimensional distortion invariant target recognition system.

Paper Details

Date Published: 23 June 1997
PDF: 8 pages
Proc. SPIE 3069, Automatic Target Recognition VII, (23 June 1997); doi: 10.1117/12.277098
Show Author Affiliations
Eun-Soo Kim, Kwangwoon Univ. (South Korea)
Jin-Woo Cha, Kwangwoon Univ. (South Korea)
Chang Myung Ryu, Seoul National Polytechnic Univ. (United States)


Published in SPIE Proceedings Vol. 3069:
Automatic Target Recognition VII
Firooz A. Sadjadi, Editor(s)

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