We describe the maximum-likelihood parameter estimation problem and how the Expectation- Maximization (EM) algorithm can be used for its solution. We first describe the abstract form of the EM algorithm as it is often given in the literature. We the
Real-time segmentation of moving regions in image sequences is a fundamental step in many vision systems including automated visual surveillance, human-machine interface, and very low-bandwidth telecommunications. A typical method is background subt
A common method for real-time segmentation of moving regions in image sequences involves “back- ground subtraction,” or thresholding the error between an estimate of the image without moving objects and the current image. The numerous approaches to
This chapter aims to introduce the reader to the construction, prior mod- elling, estimation and evaluation of mixture distributions in a Bayesian paradigm. We will show that mixture distributions provide a flexible, para- metric framework for stati
EM算法的高斯混合模型参数估计：Descr iption This is a function performs maximum likelihood estimation of Gaussian mixture model by using expectation maximization algorithm. It can work on data of arbitrary dimensions. Several techniques are applied in order to avo
