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Proceedings Paper

Target recognition for FLIR imagery using learning vector quantization and multilayer perceptrons
Author(s): Vincent Mirelli; Duc Minh Nguyen; Nasser M. Nasrabadi
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Paper Abstract

In this paper a neural-network-based automatic target recognition (ATR) classifier is developed. The ATR classifier consists of a learning vector quantization (LVQ) algorithm followed by a multilayer perceptron (MLP). The LVQ is used as the feature extractor and the MLP as the classifier. The LVQ algorithm adaptively extracts a set of target templates (centroids) that are assumed to represent the target signatures. The Euclidean distances between the centroids and the input target are passed to an MLP. The MLP uses these distances as input and performs a classification. Experimental results are presented for two different test sets. The first test set has similar characteristics to those of the training set, and the ATR classifier does very well. However, the second test set has a different characteristics and the ATR classifier performance is poor.

Paper Details

Date Published: 5 July 1995
PDF: 13 pages
Proc. SPIE 2485, Automatic Object Recognition V, (5 July 1995); doi: 10.1117/12.213075
Show Author Affiliations
Vincent Mirelli, U.S. Army Research Lab. (United States)
Duc Minh Nguyen, U.S. Army Research Lab. (United States)
Nasser M. Nasrabadi, SUNY/Buffalo (United States)

Published in SPIE Proceedings Vol. 2485:
Automatic Object Recognition V
Firooz A. Sadjadi, Editor(s)

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