On combining support vector machines and simulated annealing in stereovision matching

Abstract

This paper outlines a method for solving the stereovision matching problem using edge segments as the primitives. In stereovision matching, the following constraints are commonly used: epipolar, similarity, smoothness, ordering, and uniqueness. We propose a new strategy in which such constraints are sequentially combined. The goal is to achieve high performance in terms of correct matches by combining several strategies. The contributions of this paper are reflected in the development of a similarity measure through a support vector machines classification approach; the transformation of the smoothness, ordering and epipolar constraints into the form of an energy function, through an optimization simulated annealing approach, whose minimum value corresponds to a good matching solution and by introducing specific conditions to overcome the violation of the smoothness and ordering constraints. The performance of the proposed method is illustrated by comparative analysis against some recent global matching methods.