Contour matching based on belief propagation

Shiming Xiang, Feiping Nie, Changshui Zhang

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)489-498
Number of pages10
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3852 LNCS
DOIs
StatePublished - 2006
Externally publishedYes
Event7th Asian Conference on Computer Vision, ACCV 2006 - Hyderabad, India
Duration: 13 Jan 200616 Jan 2006

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