In this paper, we propose a new tracking method that uses Gaussian Mixture Model (GMM) and Optical Flow approach for object tracking. The GMM approach consists of three different Gaussian distributions, the average, standard deviation and weight respectively. There are two important steps to establish the background for model, and background updates which separate the foreground and background....

In this article we present an efficient approach to modeling the acoustic features for the tasks of recognizing various paralinguistic henomena. Instead of the standard scheme of adapting the Universal Background Model (UBM), represented by the Gaussian ixture Model (GMM), normally used to model the frame-level acoustic features, we propose to represent the UBM by building monophone-based Hidde...

In this paper, we investigate the Hidden Markov Model (HMM) and the temporal Gaussian Mixture Model (GMM) systems based on the Universal Background Model (UBM) concept to capture temporal information of speech for Text Dependent (TD) Speaker Verification (SV). In TD-SV, target speakers are constrained to use only predefined fixed sentence/s during both the enrollment and the test process. The t...

A comparative study of the application of Gaussian Mixture Model (GMM) and Radial Basis Function (RBF) in biometric recognition of voice has been carried out and presented. The application of machine learning techniques to biometric authentication and recognition problems has gained a widespread acceptance. In this research, a GMM model was trained, using Expectation Maximization (EM) algorithm...

The general Markov model (GMM) of nucleotide substitution does not assume the evolutionary process to be stationary, reversible, or homogeneous. The GMM can be simplified by assuming the evolutionary process to be stationary. A stationary GMM is appropriate for analyses of phylogenetic data sets that are compositionally homogeneous; a data set is considered to be compositionally homogeneous if ...

In this paper, we present a new approach towards user-customized password speaker verification combining the advantages of hybrid HMM/ANN systems, usingArtificial Neural Networks (ANN) to estimate emission probabilities of Hidden Markov Models , and Gaussian Mixture Models. In the approach presented here, we indeed exploit the properties of hybrid HMM/ANN systems, usually resulting in high phon...

A maximum likelihood parameter trajectory estimation based on a Gaussian mixture model (GMM) has been successfully implemented for acoustic-to-articulatory inversion mapping. In the conventional method, GMM parameters are optimized by maximizing a likelihood function for joint static and dynamic features of acoustic-articulatory data, and then, the articulatory parameter trajectories are estima...

To solve the task of segmenting clusters of nearly identical objects we here present the template rotation expectation maximization (TREM) approach which is based on a generative model. We explore both a non-linear optimization approach for maximizing the loglikelihood and a modification of the standard expectation maximization (EM) algorithm. The non-linear approach is strict template matching...

Volume representations of blood vessels acquired by 3D rotational angiography are very suitable for diagnosing a stenosis or an aneurysm. For optimal treatment, physicians need to know the shape of the diseased vessel parts. Binary segmentation by thresholding is the first step in our shape extraction procedure. Assuming a twofold Gaussian mixture model (GMM), the model parameters (and thus the...