Rahman Farnoosh

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Recently stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. Also image segmen-tation means to divide one picture into different types of classes or regions, for example a picture of geometric shapes has some classes with different colors such as 'circle',(More)
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SUMMARY We investigate a Bayesian method for the segmentation of muscle fibre images. The images are reasonably well approximated by a Dirichlet tessellation, and so we use a deformable template model based on Voronoi polygons to represent the segmented image. We consider various prior distributions for the parameters and suggest an appropriate likelihood.(More)
SUMMARY A model is used to describe a digitized image and as a basis for segmentation. Following the Bayesian paradigm the mathematical form for the likelihood and the posterior distribution are obtained, where the prior distribution is based on a tessellation derived from an inhibition point process. We introduce two algorithms for estimating the posterior(More)