Spatial based Expectation Maximizing (EM)

Spatial based Expectation Maximizing (EM)

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...

Expectation-Maximization (EM) algorithm

The Expectation Maximization (EM) algorithm

In the previous class we already mentioned that many of the most powerful probabilistic models contain hidden variables. We will denote these variables with y. It is usually also the case that these models are most easily written in terms of their joint density, p(d,y,θ) = p(d|y,θ) p(y|θ) p(θ) (1) Remember also that the objective function we want to maximize is the log-likelihood (possibly incl...

Maximizing Expectation on Vertex-Disjoint Cycle Packing

This paper proposes a method for computing the expectation for the length of the maximum set of vertex-disjoint cycles in a digraph where vertices and/or arcs are subject to failure with a known probability. This method has an immediate practical application: it can be used for the solution of a kidney exchange program in the common situation where the underlying graph is unreliable. Results fo...

EM Demystified: An Expectation-Maximization Tutorial

After a couple of disastrous experiments trying to teach EM, we carefully wrote this tutorial to give you an intuitive and mathematically rigorous understanding of EM and why it works. We explain the standard applications of EM to learning Gaussian mixture models (GMMs) and hidden Markov models (HMMs), and prepare you to apply EM to new problems. This tutorial assumes you have an advanced under...