In this paper, we address the problem of classifying clutter returns into statistically homogeneous subsets. The classification procedures are devised assuming latent variables, which represent classes to each range bin belongs, and three different models for structure covariance matrix. Then, expectation-maximization algorithm is exploited in conjunction with cyclic estimation come up suitable...

Positron emission tomography (PET) is a popular research topic. People are becoming more interested in PET images as they become widely available. However, the partial volume effect (PVE) remains one of most influential factors causing resolution to degrade. It possible reduce this PVE and achieve better image quality by measuring modeling point spread function (PSF) then accounting for it insi...

This notes reviews the basics about the Expectation-Maximization (EM) algorithm, a popular approach to perform model estimation of the generative model with latent variables. We first describe the E-steps and M-steps, and then use finite mixture model as an example to illustrate this procedure in practice. Finally, we discuss its intrinsic relations with an optimization problem, which reveals t...

We consider the problem of autonomous mobile robot exploration in an unknown environment for the purpose of building an accurate feature-based map efficiently. Most literature on this subject is focused on the combination of a variety of utility functions, such as curbing robot pose uncertainty and the entropy of occupancy grid maps. However, the effect of uncertain poses is typically not well ...

A problem of increasing importance in computer graphics is to generate data with the style of some previous training data, but satisfying new constraints. If we use a probabilistic latent variable model, then learning the model will normally be performed using Expectation-Maximization (EM), or one of its generalizations. We show that data synthesis for such problems can also be performed using ...