@inbook {1243,
	title = {Parallel image classification using multiscale Markov random fields},
	booktitle = {ICASSP-93},
	year = {1993},
	note = {ScopusID: 0027266514doi: 10.1109/ICASSP.1993.319766},
	month = {1993///},
	pages = {137 - 140},
	publisher = {IEEE},
	organization = {IEEE},
	address = {New York},
	abstract = {In this paper, we are interested in massively parallel multiscale relaxation algorithms applied to image classification. First, we present a classical multiscale model applied to supervised image classification. 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, we propose another scheme introducing a local interaction between two neighbor grids in the label pyramid. This is a way to incorporate cliques with far apart sites for a reasonable price. Finally we present the results on noisy synthetic data and on a SPOT image obtained by different relaxation methods using these models.}
}
