Abstract
This paper deals with hierarchical Markov Random Field models. We propose to introduce newhi erarchical models based on a hybrid structure which combines a spatial grid of a reduced size at the coarsest level with sub-trees appended below it, down to the finest level. These models circumvent the algorithmic drawbacks of grid-based models (computational load and/or great dependance on the initialization) and the modeling drawbacks of tree-based approaches (cumbersome and somehowa rtificial structure). The hybrid structure leads to algorithms that mix a non-iterative inference on sub-trees with an iterative deterministic inference at the top of the structure. Experiments on synthetic images demonstrate the gains provided in terms of both computational efficiency and quality of results. Then experiments on real satellite spot images illustrate the ability of hybrid models to perform efficiently the multispectral image analysis.
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
L. E. Baum, T. Petrie, G. Soules, and N. Weiss. A maximization technique occuring in the statistical analysis of probabilistic functions of Markov chains. Ann. Math. Stat., Vol 41: pp. 164–171, 1970.
J. Besag. Spatial interaction and the statistical analysis of lattice systems. J. Royal Statist. Soc., 36, Série B:192–236, 1974.
C. Bouman and B. Liu. Multiple resolution segmentation of textured images. IEEE Trans. Pattern Anal. Machine Intell., Vol. 13, No. 2: pages 99–113, Février 1991.
C. Bouman and M. Shapiro. A multiscale random field model for Bayesian image segmentation. IEEE Trans. Image Processing, 3, No. 2:162–177, March 1994.
B. Chalmond. An iterative Gibbsian technique for reconstruction of m-ary images. Pattern Recognition, Vol. 22, No 6: pages 747–761, 1989.
A. Chardin and P. Pérez. Semi-iterative inference with hierarchical models. In Int. Conf. on Image Processing, pages 630–634, Chicago, USA, october 1998.
G. D. Forney. The Viterbi algorithm. Proc. IEEE, Vol. 13: pages 268–278, March 1973.
F. Heitz, P. Pérez, and P. Bouthemy. Multiscale minimization of global energy functions in some visual recovery problems. CVGIP: Image Understanding, Vol. 59, No 1: pages 125–134, Jan. 1994.
J-M Laferté, P. Pérez, and F. Heitz. Discrete markov image modeling and inference on the quad-tree. IEEE Trans. Image Processing, Accepted for publication, 1999.
M. Luettgen, W. Karl, and A. Willsky. Efficient multiscale regularization with applications to the computation of optical flow. IEEE Trans. Image Processing, 3(1):41–64, 1994.
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
Chardin, A., PĂ©rez, P. (1999). Semi-iterative Inferences with Hierarchical Energy-Based Models for Image Analysis. 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_7
Download citation
DOI: https://doi.org/10.1007/3-540-48432-9_7
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