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Proceedings Paper

Implementation of a statistically based pattern-recognition system
Author(s): Scott C. Newton; Sunanda Mitra
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Paper Abstract

A generalized quadratic (Bayesian-like) classification system has been developed for evaluating the performance of other classifiers such as neural networks in automatic target recognition (ATR). The system was tested using multispectral real data as well as computer generated data sets. The classifier employs the covariance matrix and centroid of the feature set to describe each region. The system then calculates the likelihood associated with an unknown object belonging to a defined region. A multivariate normal distribution is assumed in calculating this likelihood. The system utilizes a learning algorithm to continuously upgrade performance and has shown near 100 percent accuracy even after very short training periods.

Paper Details

Date Published: 1 November 1990
PDF: 12 pages
Proc. SPIE 1349, Applications of Digital Image Processing XIII, (1 November 1990); doi: 10.1117/12.23567
Show Author Affiliations
Scott C. Newton, Texas Tech Univ. (United States)
Sunanda Mitra, Texas Tech Univ. (United States)


Published in SPIE Proceedings Vol. 1349:
Applications of Digital Image Processing XIII
Andrew G. Tescher, Editor(s)

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