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Feature distributions and probability of identification

In this paper we present a method of analysis of model based automatic target recognition (ATR) algorithms, as a function of a number of important parameters of the system, including the number and size of the models, the correlations between models, the expected probability of detection of features, the rates of occurrence of unpredicted features, and the spatial resolution of the predicted features, as defined by a local spatial feature density. Analytical results for a two class problem are presented as a function of between-class correlation and feature localization accuracy.