In this paper a new class of random field, defined on a multiresolution array structure, is described. Some of the fundamental statistical properties of the model are established. Estimation from noisy data is then considered and a new procedure, multiresolution maximum a posteriori estimation, is defined. These ideas are then applied to the problem of segmenting images containing a number of regions. Implementation of the Bayesian approach is based on a multiresolution form of Gibbs sampling. It is shown that the model forms an excellent basis for the segmentation of such images, which works with no a priori information on the number or sizes of the regions.
Hidden multiresolution random fields and their application to image segmentation
1999-01-01
259856 byte
Conference paper
Electronic Resource
English
Hidden Multiresolution Random Fields and Their Application to Image Segmentation
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