Parallel image classification using multiscale Markov random fields
- 1 January 1993
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 5, 137-140 vol.5
- https://doi.org/10.1109/icassp.1993.319766
Abstract
The application of massively parallel multiscale relaxation algorithms to image classification is considered. First, a classical multiscale model applied to supervised image classification is presented. The model consists of a label pyramid and a whole observation field. The potential functions of the coarse grid are derived by simple computations. Then, a scheme which introduces a local interaction between two neighbor grids in the label pyramid is proposed. This is a way to incorporate cliques, with far-apart sites for a reasonable price. Finally, results on noisy synthetic data and on a SPOT image obtained by different relaxation methods using these models are presented.Keywords
This publication has 4 references indexed in Scilit:
- Multiscale Markov random fields and constrained relaxation in low level image analysisPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1992
- Satellite image classification using a modified Metropolis dynamicsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1992
- Simulated Annealing: Theory and ApplicationsPublished by Springer Nature ,1987
- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of ImagesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1984