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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)
In this paper a new cost function is introduced by using the fuzzy entropy to choose a threshold value in image denoising problem. The results are explained with pilot this cost function on the some images.
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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)
We propose modeling a nearly regular point pattern by a generalized Neyman-Scott process in which the offspring are Gaussian perturbations from a regular mean configuration. The mean configuration of interest is an equilateral grid, but our results can be used for any stationary regular grid. The case of uniformly distributed points is first studied as a… (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)
In this paper, we deal with the ridge-type estimator for fuzzy nonlinear regression models using fuzzy numbers and Gaussian basis functions. Shrinkage regularization methods are used in linear and nonlinear regression models to yield consistent estimators. Here, we propose a weighted ridge penalty on a fuzzy nonlinear regression model, then select the… (More)