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. ...

In this paper, we firstly give a brief introduction of expectation maximization (EM) algorithm, and then discuss the initial value sensitivity of expectation maximization algorithm. Subsequently, we give a short proof of EM's convergence. Then, we implement experiments with the expectation maximization algorithm (We implement all the experiments on Gaussion mixture model (GMM) ). Our experiment...

II. Data Modeling Image segmentation is an essential step in many advanced imaging applications. Accurate segmentation is required for volume measurements, medical diagnosis, image guided procedures, and 3D rendering. In this paper, we have developed a new multiresolution algorithm that extends the well-known expectation maximization (EM) algorithm. The proposed algorithm is based on generating...

A sampling based EM algorithm is proposed. The algorithm tries to add some randomness to the likelihood function thereby increasing the chances of the convergence of the EM algorithm to the true maxima as against getting stuck in the local maximum of likelihood manifold. The experiments for the problem of parameter estimation for a mixture of two gaussians are presented. The improved accuracy i...

The EM algorithm heavily relies on the interpretation of observations as incomplete data but it does not have any control on the uncertainty of missing data. To effectively reduce the uncertainty of missing data, we present a regularized EM algorithm that penalizes the likelihood with the mutual information between the missing data and the incomplete data (or the conditional entropy of the miss...

The present paper extends the idea of job shop scheduling problem with resting constraints to the train scheduling problem with the Muslim praying considerations. For this purpose, after proposing the new mathematical model, a heuristic algorithm based on the Electromagnetism-Like algorithm (EM) which is well adjusted to scheduling problems is employed to solve the large-size practical cases. T...

The EM algorithm has been widely used in many learning or statistical tasks. However, since it requires multiple database scans, applying the EM algorithm to data streams is not straight forward. In this paper we propose an online EM algorithm which can deal with data streams. The algorithm utilizes a component reduction technique which reduces the number of components in a mixture model. A not...

The more sophisticated fuzzy clustering algorithms, like the Gustafson–Kessel algorithm [11] and the fuzzy maximum likelihood estimation (FMLE) algorithm [10] offer the possibility of inducing clusters of ellipsoidal shape and different sizes. The same holds for the EM algorithm for a mixture of Gaussians. However, these additional degrees of freedom often reduce the robustness of the algorithm...

More sophisticated fuzzy clustering algorithms, like the Gustafson–Kessel algorithm [11] and the fuzzy maximum likelihood estimation (FMLE) algorithm [10] offer the possibility of inducing clusters of ellipsoidal shape and different sizes. The same holds for the expectation maximization (EM) algorithm for a mixture of Gaussians. However, these additional degrees of freedom can reduce the robust...