in this paper, a new method is presented for the detection of defects in random textures. in the training stage, the feature vectors of the normal textures’ images are extracted by using the optimal response of gabor wavelet filters, and their probability density is estimated by means of the gaussian mixture model (gmm). in the testing stage, similar to the previous stage,at  first, the feature...

automatic segmentation of multiple sclerosis (ms) lesions in brain magnetic resonance imaging (mri) has been widely investigated in the recent years with the goal of helping ms diagnosis and patient follow‑up. in this research work, gaussian mixture model (gmm) has been used to segment the ms lesions in mris, including t1‑weighted (t1‑w), t2‑w, and t2‑fluid attenuation inversion recovery. usual...

The Dirichlet process is a prior used in nonparametric Bayesian models of data, particularly in Dirichlet process mixture models (also known as infinite mixture models). It is a distribution over distributions, i.e. each draw from a Dirichlet process is itself a distribution. It is called a Dirichlet process because it has Dirichlet distributed finite dimensional marginal distributions, just as...