In this paper, we try to use graphical model based probabilistic inference methods to solve the problem of contour matching, which is a fundamental problem in computer vision. Specifically, belief propagation is used to develop the contour matching framework. First, an undirected loopy graph is constructed by treating each point of source contour as a graphical node. Then, the distances between the source contour points and the target contour points are used as the observation data, and supplied to this graphical model. During message transmission, we explicitly penalize two kinds of incorrect correspondences: many-to-one correspondence and cross correspondence. A final geometrical mapping is obtained by minimizing the energy function and maximizing a posterior for each node. Comparable experimental results show that better correspondences can be achieved. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Xiang, S., Nie, F., & Zhang, C. (2006). Contour matching based on belief propagation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3852 LNCS, pp. 489–498). Springer Verlag. https://doi.org/10.1007/11612704_49
Mendeley helps you to discover research relevant for your work.