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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cootes, T.F., Taylor, C.J.: Statistical models of appearance for computer vision. Technical report, University of Manchester, UK (2004)
Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analyis and Machine Intelligence 24(24), 509–522 (2002)
Coughlan, J., Ferreira, S.: Finding Deformable Shapes Using Loopy Belief Propagation. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 453–468. Springer, Heidelberg (2002)
Rangarajan, A., Coughlan, J., Yuille, A.L.: A Bayesian network framework for relational shape matching. In: Proceedings of the Ninth IEEE International Conference on Computer Vision (ICCV) (2003)
Caetano, T.S., Caeli, T., Barone, D.A.C.: An optimal probabilistic graphical model for point set matching. Technical Report TR 04-03, University of Alberta, Edmonton, Alberta Canada (February 2004)
Hamze, F., de Freitas, N.: Hot coupling: a particle approach to inference and normalization on pairwise undirected graphs of arbitrary topology. Neural Information Processing Systems (NIPS) (2005)
Ma, B., Ellis, R.E.: Surface-based registration with a particle filter. In: Seventh International Conference on Medical Image Computing and Computer Assisted Intervention, France (2004)
Caelli, T., Kosinov, S.: An eigenspace projection clustering method for inexact graph matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(4), 515–519 (2004)
Jain, V., Zhang, H.: Robust 3D shape correspondence in the spectral domain. In: International Conference on Shape Modeling and Applications (SMI) 2006, Matsushima, Japan (in press)
Zinber, T., Schmidt, J., Niemann, H.: A refined ICP algorithm for robust 3D correspondence estimation. In: Proceedings of International Conference on Image Processing (2003)
Chui, H., Rangarajan, A.: A new algorithm for non-rigid point matching. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 44–51 (2000)
Chui, H., Rangarajan, A.: A feature registration framework using mixture models. In: IEEE Workshop on Mathematical Methods in Biomecical Image Analysis (MMBIA), pp. 190–197 (2000)
Sudderth, E.B., Ihler, A.T., Freeman, W.T., Willsky, A.S.: Nonparametric belief propagation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, p. 605 (2003)
Williams, J.L., Maybeck, P.S.: Cost-function-based Gaussian mixture reduction for target tracking. In: Sixth International Conference on Information Fusion, Cairns, Australia, pp. 1047–1054 (2003)
Ihler, A., Sudderth, E., Freeman, W., Willsky, A.: Efficient multiscale sampling from products of Gaussian mixtures. Neural Information Processing Systems 17 (2003)
Jian, B., Vemuri, B.C.: A robust algorithm for point set registration using mixture of Gaussians. In: Tenth IEEE International Conference on Computer Vision (ICCV 2005), vol. 2 (2005)
Caetano, T.S., Caeli, T., Barone, D.A.C.: A comparison of junction tree and relaxation algorithm for point matching using different distance metrics. Technical Report TR 04-03, University of Alberta, Edmonton, Alberta Canada (February 2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dong, X., Zheng, G. (2006). Finding Deformable Shapes by Point Set Matching Through Nonparametric Belief Propagation. In: Yang, GZ., Jiang, T., Shen, D., Gu, L., Yang, J. (eds) Medical Imaging and Augmented Reality. MIAR 2006. Lecture Notes in Computer Science, vol 4091. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11812715_8
Download citation
DOI: https://doi.org/10.1007/11812715_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37220-2
Online ISBN: 978-3-540-37221-9
eBook Packages: Computer ScienceComputer Science (R0)