Abstract
This work deals with the estimation of mixtures with applications to unsupervised statistical multisensor image segmentation. A mixture is said to be generalized when the exact nature of the noise components is not known; one assumes, however, that each belongs to a finite known set of families of distributions. The authors propose some methods of estimation of such mixtures based on expectation-maximization (EM), and iterative conditional estimation (ICE) algorithms. The set of families of distributions is assumed to lie in Pearson's system.
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