Implementation of a statistically based pattern-recognition system

Scott C. Newton; Sunanda Mitra

doi:10.1117/12.23567

1 November 1990 Implementation of a statistically based pattern-recognition system

Scott C. Newton, Sunanda Mitra

Proceedings Volume 1349, Applications of Digital Image Processing XIII; (1990) https://doi.org/10.1117/12.23567
Event: 34th Annual International Technical Symposium on Optical and Optoelectronic Applied Science and Engineering, 1990, San Diego, CA, United States

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.

Citation Download Citation

Scott C. Newton and Sunanda Mitra "Implementation of a statistically based pattern-recognition system", Proc. SPIE 1349, Applications of Digital Image Processing XIII, (1 November 1990); https://doi.org/10.1117/12.23567

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