Multiscale Markov random fields and constrained relaxation in low level image analysis
- 1 January 1992
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 3 (15206149) , 61-64 vol.3
- https://doi.org/10.1109/icassp.1992.226276
Abstract
The authors investigate a new approach to multigrid image analysis based on Markov random field (MRF) models. The multigrid algorithms under consideration are based on constrained optimization schemes. The global optimization problem associated with MRF modeling is solved sequentially over particular subsets of the original configuration space. Those subsets consist of constrained configurations describing the desired resulting field at different scales. The constrained optimization can be implemented via a coarse-to-fine multigrid algorithm defined on a sequence of consistent multiscale MRF models. The proposed multiscale paradigm yields fast convergence toward high-quality estimates when compared to standard monoresolution or multigrid relaxation schemes.Keywords
This publication has 5 references indexed in Scilit:
- Multigrid Bayesian Estimation Of Image Motion Using Stochastic RelaxationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Parallel visual motion analysis using multiscale Markov random fieldsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Multiple resolution segmentation of textured imagesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1991
- A renormalization group approach to image processing problemsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1989
- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of ImagesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1984