Three-dimensional target recognition using mART neural networks

Eun-Soo Kim; Jin-Woo Cha; Chang Myung Ryu

doi:10.1117/12.277098

23 June 1997 Three-dimensional target recognition using mART neural networks

Eun-Soo Kim, Jin-Woo Cha, Chang Myung Ryu

Proceedings Volume 3069, Automatic Target Recognition VII; (1997) https://doi.org/10.1117/12.277098
Event: AeroSense '97, 1997, Orlando, FL, United States

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.

Citation Download Citation

Eun-Soo Kim, Jin-Woo Cha, and Chang Myung Ryu "Three-dimensional target recognition using mART neural networks", Proc. SPIE 3069, Automatic Target Recognition VII, (23 June 1997); https://doi.org/10.1117/12.277098

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