A multimodal model for correlation-plane distributions generated by composite filters is presented. From this model a statistical classifier referred to as a composite Bayesian classifier is developed. By exploiting the Gaussian behavior of correlation-plane data, this classifier concisely represents multimodal distributions as composite algebraic functions. These multimodal distributions, each of which is constructed by superposition of many normal distributions, are used to partition a vector signal space into optimum classification regions derived from Bayes’s likelihood ratio test. For the purpose of validating the multimodal model, expected performance for the training images is derived from calibration data and compared with observed performance.

© 1994 Optical Society of America

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