Aiming at the shortcomings of Gaussian mixture model background method, a moving object detection method mixed with adaptive iterative block and interval frame difference method in the Gaussian mixture model is proposed. In this method, the video sequences are divided into different size pieces in order to reduce the amount of calculation of the algorithm. It not only effectively solves the pro...

In this paper we present a sequential expectation maximization algorithm to adapt in an unsupervised manner a Gaussian mixture model for a classification problem. The goal is to adapt the Gaussian mixture model to cope with the non-stationarity in the data to classify and hence preserve the classification accuracy. Experimental results on synthetic data show that this method is able to learn th...

In this paper Gaussian mixtures are used to model the distribution of position error in tracking algorithms. An expectation maximization algorithm is constructed to estimate parameters of a k-component Gaussian mixture based on a sample set obtained from a tracking simulator. The modeling and parameter estimation approach is applied to position error data generated by several tracking algorithm...

This paper is concerned with estimating a probability density function of human skin color using a nite Gaussian mixture model whose parameters are estimated through the EM algorithm Hawkins statistical test on the normality and homoscedasticity common covariance matrix of the estimated Gaussian mixture models is performed and McLachlan s bootstrap method is used to test the number of component...

This paper is concerned with estimating a probability density function of human skin color using a finite Gaussian mixture model whose parameters are estimated through the EM algorithm. There are no limitations regarding if person is black or white. Two important sections of Gaussian mixture are parameter estimation and determining the number of mixture components. Experimental results show tha...