Affine invariant matching of broken boundaries in noisy images based on the quality migrant injection genetic algorithm and a successive erosion and distance accumulation scheme

Abstract

Viewpoint invariant identification of fragmented scene contours can be realized by matching them against a collection of known reference models. For near planar objects, the matching of a pair of contours can be encapsulated as the search for the existence of an affine transform between them. Past research has demonstrated that the search process can be effectively accomplished with the integration of a simple genetic algorithm (SGA) and quality migrant injection (QMI), a method referred to as the quality migrant genetic algorithm (QMGA). Despite the favorable outcome, this method is extremely vulnerable to noise contamination on the image scene. In this paper we provide an explanation on the causes of this problem, and propose a solution known as successive erosion and distance accumulation (SEDA). Experimental evaluation shows that by supplementing the QMGA method with the proposed scheme, higher success rates can be attained in identifying matched contours under moderate amount of noise contamination.