In the Bayesian mixture modeling framework it is possible to infer the necessary number of components to model the data and therefore it is unnecessary to explicitly restrict the n...
Abstract--A new Bayesian model is proposed for image segmentation based upon Gaussian mixture models (GMM) with spatial smoothness constraints. This model exploits the Dirichlet co...
Christophoros Nikou, Aristidis Likas, Nikolas P. G...
In this paper, we propose a novel boosted mixture learning (BML) framework for Gaussian mixture HMMs in speech recognition. BML is an incremental method to learn mixture models fo...
We present a method for the removal of noise including nonGaussian impulses from a signal. Impulse noise is removed jointly a homogenous Gaussian noise floor using a Gabor regres...
This paper proposes an efficient region-based background subtraction technique using static camera for motion detection in various monitoring applications. Color histograms, textu...
Parisa Darvish Zadeh Varcheie, Michael Sills-Lavoi...