<title>Restoration with equivalence to nonorthogonal image expansion for feature extraction and edge detection</title>

Abstract

This paper discusses two additional applications of our newly developed expansion matching scheme: edge detection and feature extraction. Expansion matching optimizes a novel matching criterion called Discriminative Signal to Noise Ratio (DSNR) and has recently been shown to robustly recognize templates under conditions of noise, severe occlusion and superposition. The DSNR criterion is better suited to practical conditions than the traditional SNR since it considers as 'noise', even the off-center response of the filter to the signal itself. In this paper, we introduce a new optimal DSNR edge detector based on the expansion filter for an edge model. This edge detector is compared with the widely used Canny edge detector (CED). Experimental comparisons show that our edge detector is superior to the CED in terms of DSNR even under very noise signal conditions. Expansion matching is also successfully used for extracting features from images. One application that is described is extraction of corners from images. Another application of expansion matching that is outlines here is that of generic face recognition.