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...

background: routinely collected data from tuberculosis surveillance system can be used to investigate and monitor the irregularities and abrupt changes of the disease incidence. we aimed at using a hidden markov model in order to detect the abnormal states of pulmonary tuberculosis in iran. methods: data for this study were the weekly number of newly diagnosed cases with sputum smear-positive p...

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

It’s very important for us to understand the data structure before doing the data analysis. However, most of the time, there may exist of a lot of missing values or incomplete information in the data subject to the analysis. For example, survival time data always have some missing values because of death or job transfer. These kinds of data are called censored data. Since these data might obtai...

the air transport industry is seeking to manage risks in air travels. its main objective is to detect abnormal behaviors in various flight conditions. the current methods have some limitations and are based on studying the risks and measuring the effective parameters. these parameters do not remove the dependency of a flight process on the time and human decisions. in this paper, we used an hmm...

The Expectation-Maximization (EM) algorithm is a very general and popular iterative computational algorithm to find maximum likelihood estimates from incomplete data and broadly used to statistical analysis with missing data, because of its stability, flexibility and simplicity. However, it is often criticized that the convergence of the EM algorithm is slow. The various algorithms to accelerat...

Bayesian networks (BN) are used in a big range of applications but they have one issue concerning parameter learning. In real application, training data are always incomplete or some nodes are hidden. To deal with this problem many learning parameter algorithms are suggested foreground EM, Gibbs sampling and RBE algorithms. In order to limit the search space and escape from local maxima produce...

Multi-user detection (MUD) is one standard of 3G, which can effectively reduce the multiple access interference (MAI) and increase the system capacity. The Expectation-Maximization (EM) iterative algorithm is commonly used in recent years for missing data, which could be applied to MUD system. But the EM algorithm has a fatal weakness that its slow convergence speed. The new accelerated EM Algo...

Multiresolution analysis is an established part of the human vision system. It builds different representations of an image with a spatial resolution adapted to the size of objects of interest and to its level of relevance. Multiresolution analysis is an efficient tool for image segmentation. It allows the processing of global features as well as local features in a corresponding proper scale. ...