<title>Distance classifier correlation filters for distortion tolerance, discrimination, and clutter rejection</title>

Abstract

A new approach to correlation filters based on quadratic distance calculations is described. The problem of distortion tolerance is addressed in terms of similarity measures. Discrimination is simultaneously addressed by optimizing the filters to maximally separate the classes. Mathematically, filter synthesis requires the inversion of diagonal matrices in the frequency domain and is a generalization of the MACE idea. The approach is shift-invariant, does not require feature extraction or image registration, and is significantly different from traditional pattern recognition techniques such as the Fisher LDF. The proposed approach is also suitable for the rejection of unknown clutter. Since recognition is based on similarity, clutter and false images which exhibit `large' distances from true target classes, are easily rejected. With improved recognition and discrimination performance, and low false alarm rates, the proposed distance classifier is a promising method for multiclass target recognition in cluttered environments.