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...

An optimal Bayesian classifier using mixture distribution class models with joint learning of loss and prior probability functions is proposed for automatic land cover classification. The probability distribution for each land cover class is more realistically modeled as a population of Gaussian mixture densities. A novel two-stage learning algorithm is proposed to learn the Gaussian mixture mo...

In transform image coding, the histograms of transform coeecients can be approximately modeled by generalized Gaussian (GG) random variables. However, the GG models may not t the DC distribution. One approach uses DPCM for the DC data, which greatly complicates bit allocation; another assumes a single Gaussian (SG) model, which may be a poor model. As an alternative, this paper proposes a nite ...

There are many unavoidable noise interferences in image acquisition and transmission. To make it better for subsequent processing, the noise in the image should be removed in advance. There are many kinds of image noises, mainly including salt and pepper noise and Gaussian noise. This paper focuses on the research of the Gaussian noise removal. It introduces many wavelet threshold denoising alg...

Figure 1: Left: input-output points (red markers) generated by evaluating a non-linear function at uniformly distributed random inputs (x, y) and adding with Gaussian noise to the output z component (blue surface is underlying functional form). Right: three-dimensional points (red markers) drawn from Gaussian mixture model with two mixture components (green ellipsoids are contours of the two Ga...

This paper proposes the use of Gaussian Mixture Models to estimate conditional probability density functions in an environmental risk mapping context. A conditional Gaussian Mixture Model has been compared to the geostatistical method of Sequential Gaussian Simulations and shows good performances in reconstructing local PDF. The data sets used for this comparison are parts of the digital elevat...

This paper presents a review of various speaker verification approaches in realistic world, and explore a combinational approach between Gaussian Mixture Model (GMM) and Support Vector Machine (SVM) as well as Gaussian Mixture Model (GMM) and Universal Background Model (UBM).

Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristics of the data. Recently, the Gaussian Process Latent Variable Model (GPLVM) has successfully been used to find low dimensional manifolds in a variety of complex data. The GPLVM consists of a set of points in a low di...