In this paper we formulate pose estimation statistically and show that pose can be estimated from a low dimensional feature space obtained by maximizing the mutual information between the aspect angle and the output of a nonlinear mapper. We use the Havrda-Charvat definition of entropy to implement a nonparametric estimator based on the Parzen window method. Results in the MSTAR data set are presented and show the performance of the methodology.
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