Finding deformable shapes by point set matching through nonparametric belief propagation

Authors:
Xiao Dong;Guoyan Zheng
Affiliations:
MEM Research Center, University of Bern, Bern, Switzerland;MEM Research Center, University of Bern, Bern, Switzerland
Venue:
Miar'06 Proceedings of the Third international conference on Medical Imaging and Augmented Reality
Year:
2006

Citing 8
Cited 0

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 Feature Registration Framework Using Mixture Models

MMBIA '00 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis
A Bayesian Network Framework for Relational Shape Matching

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
An Eigenspace Projection Clustering Method for Inexact Graph Matching

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Robust Algorithm for Point Set Registration Using Mixture of Gaussians

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Robust 3D Shape Correspondence in the Spectral Domain

SMI '06 Proceedings of the IEEE International Conference on Shape Modeling and Applications 2006
Nonparametric belief propagation

CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper addresses the problem of finding a deformable shape by matching a point distribution model to the observation. A probabilistic graphical model is built for the point distribution model. The point correspondence and optimal model parameters are found by carrying out nonparametric belief propagation on the graphical model. Experiments on a point distribution model of the proximal model verified the idea.