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Non-Gaussian mixture image models prediction

In this paper we analyze the problem of prediction using generalized Dirichlet mixtures which have been shown to be effective for approximating a wide varieties of probability distributions. The generalized Dirichlet mixture-based predictor is nonlinear and takes into account the fact that images clutter and texture are generally non-Gaussian. Experimental results involve objects detection in images and image restoration.