Abstract
In this paper we investigate several dynamics to optimize a posterior distribution defined to solve segmentation problems. We first consider the Metropolis and the Kawasaki dynamics. We also compare the associated Bayesian cost functions. The Kawasaki dynamic appears to provide better results but requires the exact values of the class ratios. Therefore, we define alternative dynamics which conserve the properties of the Kawasaki dynamic and require only an estimation of the class ratios. We show on synthetic data that these new dynamics can improve the segmentation results by incorporating some information on the class ratios. Results are compared using a Potts model as prior distribution.
This work was partially supported by the French-Russian Institute A.M. Liapunov (Grant 98-02)
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
R. Chellappa, A. Jain. Markov random fields: theory and application. Academic Press, Inc., 1993. Collective work.
S. Geman, D. Geman. Stochastic relaxation, Gibbs distribution, and the Bayesian restoration of images. IEEE trans. on Pattern Analysis and Machine Intelligence, 6(6):721–741, 1984.
H. O. Georgii. Gibbs Measures and Phase Transitions. De Gruyter-Studies in Mathematics, 1988. Vol. 9.
A. Maruani, E. Pechersky, M. Sigelle. On Gibbs fields in image processing. Markov Processes and Related Fields, 1:419–442, 1995.
B. Prum. Processus sur un réseau et mesures de Gibbs (in French), chapter 9, Dynamic of Ising systems, pages 150–162. Masson, 1986.
J. Besag. Spatial interaction and statistical analysis of lattice systems. Journal of the Royal Statistical Society Series B, 36:721–741, 1974.
H. Derin, H. Elliott. Modelling and segmentation of noisy and textured images using Gibbs random fields. IEEE trans. on Pattern Analysis and Machine Intelligence, 9(1):39–55, January 1987.
S. Lakshmanan, H. Derin. Simultaneous parameter estimation and segmentation of Gibbs random fields using simulated annealing. IEEE trans. on Pattern Analysis and Machine Intelligence, 11(8):799–813, August 1989.
X. Descombes, J.F. Mangin, E. Pechersky, M. Sigelle. Fine structures preserving model for image processing. In Proc. 9th SCIA 95 Uppsala, Sweden, pages 349–356, 1995.
R. D. Morris, X. Descombes, J. Zerubia. The Ising/Potts model is not well suited to segmentation tasks. In proc. IEEE Digital Signal Processing Worshop, 1996. Sept. 1–4 1996, Norway.
W. Press, S. Teukolski, W. Vetterling, B. Flannery. Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, 2nd ed., 1992.
G. Wolberg, T. Pavlidis. Restoration of binary images using stochastic relaxation with annealing. Pattern Recognition Letters, 3:375–388, 1985.
H. Tjelmeland, J. Besag. Markov Random Fields with higher order interactions. submitted to JASA. Preprint.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Descombes, X., Pechersky, E. (1999). Metropolis vs Kawasaki Dynamic for Image Segmentation Based on Gibbs Models. In: Hancock, E.R., Pelillo, M. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 1999. Lecture Notes in Computer Science, vol 1654. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48432-9_8
Download citation
DOI: https://doi.org/10.1007/3-540-48432-9_8
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66294-5
Online ISBN: 978-3-540-48432-5
eBook Packages: Springer Book Archive