A Comparison Study of Inferences on Graphical Model for Registering Surface Model to 3D Image

  • Yoshihide Sawada
  • Hidekata Hontani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7009)


In this article, we report on a performance comparison study of inferences on graphical models for model-to-image registration. Both Markov chain Monte Carlo (MCMC) and nonparametric belief propagation (NBP) are widely used for inferring marginal posterior distributions of random variables on graphical models. It is known that the accuracy of the inferred distributions changes according to the methods used for the inference and to the structures of graphical models. In this article, we focus on a model-to-image registration method, which registers a surface model to given 3D images based on the inference on a graphical model. We applied MCMC and NBP for the inference and compared the accuracy of the registration on different structures of graphical models. Then, MCMC outperformed NBP significantly in the accuracy.


registration Markov chain Monte Carlo nonparametric belief propagation graphical model 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  2. 2.
    Hontani, H., Watanabe, W.: Point-Based Non-Rigid Surface Registration with Accuracy Estimation. In: Computer Vision and Pattern Recognition, pp. 446–452 (2010)Google Scholar
  3. 3.
    Sudderth, E.B., Ihler, A.T., Isard, M., Freeman, W.T., Willsky, A.S.: Nonparametric belief propagation. Communication of the ACM 53, 95–103 (2010)CrossRefGoogle Scholar
  4. 4.
    Simonson, K.M., Drescher, S.M., Tanner, F.R.: A statistics-based approach to binary image registration with uncertainty analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 112–125 (2007)Google Scholar
  5. 5.
    Murphy, K., Weiss, Y., Jordan, M.I.: Loopy Belief Propagation for Approximate Inference: An Empirical Stydy. In: Proceedings of Uncertainty in AI, pp. 467–475 (1999)Google Scholar
  6. 6.
    Han, T.X., Ning, H., Huang, T.S.: Efficient Nonparametric Belief Propagation with Application to Articulated Body Tracking. In: Computer Vision and Pattern Recognition, pp. 214–221 (2006)Google Scholar
  7. 7.
    Cates, J.E., Fletcher, P.T., Styner, M.A., Shenton, M.E., Whitaker, R.T.: Shape Modeling and Analysis with Entropy-Based Particle Systems. Information Processing in Medical Imaging, 333–345 (2007)Google Scholar
  8. 8.
    Bickel, P.J., Levina, E.: Covariance regularization by thresholding. Ann. Statist. 36, 2577–2604 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Meinshausen, N.: A Note on the Lasso for Gaussian Graphical Model Selection. Statistics and Probability Letters 78, 880–884 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Donner, R., Reiter, M., Langs, G., Peloschek, P., Bischof, H.: Fast active appearance model search using canonical correlation analysis. IEEE Transaction on Pattern Analysis and Machine Intelligence 28, 1690–1694 (2006)CrossRefGoogle Scholar
  11. 11.
    Book, S., Gelman, A.: Inference and Monitoring Convergence (chapter for Gilks, Richardson, and Spiegelhalter book), vol.10 (2007)Google Scholar
  12. 12.
    Weiss, Y., Freeman, W.T.: Correctness of belief propagation in graphical models with arbitrary topology. Neural Computation 13, 2173–2200 (2001)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yoshihide Sawada
    • 1
  • Hidekata Hontani
    • 1
  1. 1.Nagoya Institute of TechnologyJapan

Personalised recommendations