In this work we propose a clustering algorithm that learns on-line a finite gaussian mixture model from multivariate data based on the expectation maximization approach. The convergence of the right number of components as well as their means and covariances is achieved without requiring any careful initialization. Our methodology starts from a single mixture component covering the whole data s...

texture image analysis is one of the most important working realms of image processing in medical sciences and industry. up to present, different approaches have been proposed for segmentation of texture images. in this paper, we offered unsupervised texture image segmentation based on markov random field (mrf) model. first, we used gabor filter with different parameters’ (frequency, orientatio...

In this paper, maximum likelihood estimator is proposed for passive localization of narrowband sources in the spherical coordinates (azimuth, elevation, and range). We adapt Expectation/Maximization iterative method to solve the complicated multi-parameter optimization problem appearing on the 3-D localization problem. The proposed algorithm is based on maximum likelihood criterion which employ...

In this paper a novel algorithm for estimating the parametric form of the camera motion is proposed. A novel stochastic vector field model is presented which can handle smooth motion patterns derived from long periods of stable camera movement and also can cope with rapid motion changes and periods where camera remains still. A set of rules for robust and online updating of the model parameters...

Texture image analysis is one of the most important working realms of image processing in medical sciences and industry. Up to present, different approaches have been proposed for segmentation of texture images. In this paper, we offered unsupervised texture image segmentation based on Markov Random Field (MRF) model. First, we used Gabor filter with different parameters’ (frequency, orientatio...

Performance of Language Identification (LID) System using Gaussian Mixture Models (GMM) is limited by the convergence of Expectation Maximization (EM) algorithm to local maxima. In this paper an LID system is described using Gaussian Mixture Models for the extracted features which are then trained using Split and Merge Expectation Maximization Algorithm that improves the global convergence of E...

In this paper a new method for segmenting medical images is presented, the multiresolution diffused expectation-maximization (MDEM) algorithm. The algorithm operates within a multiscale framework, thus taking advantage of the fact that objects/regions to be segmented usually reside at different scales. At each scale segmentation is carried out via the expectation-maximization algorithm, coupled...