Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Object recognition by computer: the role of geometric constraints
Object recognition by computer: the role of geometric constraints
Structural Graph Matching Using the EM Algorithm and Singular Value Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding Deformable Shapes Using Loopy Belief Propagation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
A Bayesian Network Framework for Relational Shape Matching
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognition of Shapes by Editing Their Shock Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape context and chamfer matching in cluttered scenes
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs
IEEE Transactions on Information Theory
Shape retrieval based on dynamic programming
IEEE Transactions on Image Processing
Contour graph based human tracking and action sequence recognition
Pattern Recognition
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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.