Image restoration keeping sharp edges is achieved by bilateral filter. In this paper, an approach to improve edges for the filter is presented. The proposed algorithm relies on clustering by Expectation Maximization that produced clusters of intensive values. A stage is followed where standard deviation of Gaussian filters for scales of the spatial and intensity are adjusted by features of the ...

Medical image reconstruction from projections is computationally intensive task that demands solutions for reducing the processing delay in clinical diagnosis applications. This paper analyzes reconstruction methods combined with preand post-filtering for Single Photon Emission Computed Tomography (SPECT) in terms of convergence speed and image quality. The evaluation is performed by means of a...

This paper considers parameter estimations in Lomax distribution under progressive type-II censoring with random removals, assuming that the number of units removed at each failure time has a binomial distribution. The maximum likelihood estimators (MLEs) are derived using the expectation-maximization (EM) algorithm. The Bayes estimates of the parameters are obtained using both the squared erro...

Abstract—An expectation-maximization (EM) algorithm for independent component analysis in the presence of gaussian noise is presented. The estimation of the conditional moments of the source posterior can be accomplished by maximum a posteriori estimation. The approximate conditional moments enable the development of an EM algorithm for inferring the most probable sources and learning the param...

We propose a new method for the Maximum Likelihood Estimator (MLE) of nonlinear mixed effects models when the variance matrix of Gaussian random effects has a prescribed pattern of zeros (PPZ). The method consists of coupling the recently developed Iterative Conditional Fitting (ICF) algorithm with the Expectation Maximization (EM) algorithm. It provides positive definite estimates for any samp...

The purpose of this paper is to give an overview on the use of the Expectation-Maximization (EM) algorithm in software reliability modeling. This algorithm is related to Maximum Likelihood Estimates (MLE) of parameters in a context of missing data. Different ways to implement this algorithm are highlighted for hidden Markov models in software reliability.

A (Stochastic) EM in General Expectation-Maximization (EM) is an iterative method for finding the maximum likelihood or maximum a posteriori (MAP) estimates of the parameters in statistical models when data is only partially, or when model depends on unobserved latent variables. This section is inspired from lecture of Dr Namrata Vaswani available at http://www.ece.iastate.edu/∼namrata/EE527 Sp...

In this paper the authors derive a new class of finite-dimensional recursive filters for linear dynamical systems. The Kalman filter is a special case of their general filter. Apart from being of mathematical interest, these new finite-dimensional filters can be used with the expectation maximization (EM) algorithm to yield maximum likelihood estimates of the parameters of a linear dynamical sy...

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...

Expectation-maximization (EM) is an iterative algorithm for finding the maximum likelihood or maximum a posteriori estimate of the parameters of a statistical model with latent variables or when we have missing data. In this work, we view EM in a generalized surrogate optimization framework and analyze its convergence rate under commonly-used assumptions. We show a lower bound on the decrease i...