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Gibbs Random Fields (GRF) is a popular prior model widely used in Bayesian segmentation due to its excellent property in describing the spatial information of image. But until now, the classical approaches, describing the Markovian property of single-scale instead that of multi-scale, may come across some difficulties such as expensive computation and unsupervised parameter estimation of GRF. Thus,...
A novel illumination invariant unsupervised multispectral texture segmentation method with unknown number of classes is presented. Multispectral texture mosaics are locally represented by illumination invariants derived from four directional causal multispectral Markovian models recursively evaluated for each pixel. Resulted parametric space is segmented using a Gaussian mixture model based unsupervised...
In this paper we consider the problem of unsupervised separation of mixed text patterns based on blind source separation models. We propose a hierarchical Markov random field model for the source patterns, which enforces piece-wise regularity on both labels and intensities of image pixels. We also presented a hierarchical Bayesian BSS framework, in which the unknown sources and labels is estimated...
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