This paper describes interpolation method of motion field in the Wyner-Ziv video coding (WZVC) based on Expectation-Maximization (EM) algorithm. In the EM algorithm, the estimated motion field distribution is calculated on a block-by-block basis. Each pixel in the block shares similar probability distribution, producing an undesired blocking artefact on the pixel-based motion field. The propose...

An elliptical basis function (EBF) network is proposed in this study for the classification of remotely sensed images. Though similar in structure, the EBF network differs from the well-known radial basis function (RBF) network by incorporating full covariance matrices and uses the expectation-maximization (EM) algorithm to estimate the basis functions. Since remotely sensed data often take on ...

We present sequential parameter estimation in the framework of the Hidden Markov Models. The sequential algorithm is a sequential Kullback proximal algorithm, which chooses the KullbackLiebler divergence as a penalty function for the maximum likelihood estimation. The scheme is implemented as £lters. In contrast to algorithms based on the sequential EM algorithm, the algorithm has faster conver...

The expectation-maximization (EM) algorithm is well established as a computationally efficient method for separable signal parameter estimation. Here, a new geometric derivation and interpretation of the EM algorithm is given that facilitates the understanding of EM convergence properties. Geometric considerations lead to an alternative separable signal parameter estimator based on successive c...

BACKGROUND Expectation maximizing (EM) is one of the common approaches for image segmentation. METHODS an improvement of the EM algorithm is proposed and its effectiveness for MRI brain image segmentation is investigated. In order to improve EM performance, the proposed algorithms incorporates neighbourhood information into the clustering process. At first, average image is obtained as neighb...

We introduce a maximum Lq-likelihood estimation (MLqE) of mixture models using our proposed expectation maximization (EM) algorithm, namely the EM algorithm with Lq-likelihood (EM-Lq). Properties of the MLqE obtained from the proposed EMLq are studied through simulated mixture model data. Compared with the maximum likelihood estimation (MLE) which is obtained from the EM algorithm, the MLqE pro...

The Expectation-Maximization (EM) iterative algorithm is a broadly applicable statistical technique for maximizing complex likelihoods and handling the incomplete data problem. At each iteration step of the algorithm, two steps are performed: (i) E-Step consisting of projecting an appropriate functional containing the augmented data on the space of the original, incomplete data, and (ii) M-Step...

Electromagnetism-like algorithm (EM) is a population-based metaheuristic which has been proposed to solve continuous problems effectively. In this paper, we present a new meta-heuristic that uses the EM methodology to solve the single machine scheduling problem. Single machine scheduling is a combinatorial optimization problem. Schedule representation for our problem is based on random keys. Be...

The EM (Expectation-Maximization) algorithm is becoming more and more popular as a solution to the image reconstruction problem in Positron Emission Tomography (PET). However, as an iterative method, it shows high computational cost in terms of the time required to complete the reconstruction procedure and the computer memory needed (main memory and disk space) for the storage of the weight coe...

The expectation–maximization (EM) algorithm is a popular tool for maximizing likelihood functions in the presence of missing data. Unfortunately, EM often requires the evaluation of analytically intractable and high dimensional integrals. The Monte Carlo EM (MCEM) algorithm is the natural extension of EM that employs Monte Carlo methods to estimate the relevant integrals.Typically, a very large...