Matching Contours in Images using Curvature Information and Optimization based on Dynamic Programming

This paper presents a novel methodology to match contours of objects represented in images. In the matching, we use sets of ordered points extracted from the external contours of the objects. Each of these points defines a vertex of a polygon to be associated to the correspondent contour. To establish the matching, we compute a cost matching matrix by comparing the amplitudes of the angles defined by the vertices of one of the contours with the amplitudes of the angles defined by the vertices of the other contour. Afterwards, the optimal global matching that preserves the contours points orders is determined using an optimization algorithm based on dynamic programming; defining the optimal global matching as the one that presents the minimum sum of the costs of all individual matches established. Based on the matching found, we present a methodology to compute the geometric transformation of similarity that best aligns the contours matched. The obtained matching results were good for contours defined by few points and the computation time was always very low.