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ICASSP
2009
IEEE
15 years 4 months ago
Comparing maximum a posteriori vector quantization and Gaussian mixture models in speaker verification
Gaussian mixture model - universal background model (GMMUBM) is a standard reference classifier in speaker verification. We have recently proposed a simplified model using vect...
Tomi Kinnunen, Juhani Saastamoinen, Ville Hautam&a...
ICIP
2003
IEEE
15 years 11 months ago
A Bayesian framework for Gaussian mixture background modeling
Background subtraction is an essential processing component for many video applications. However, its development has largely been application driven and done in ad hoc manners. I...
Dar-Shyang Lee, Jonathan J. Hull, Berna Erol
MICCAI
2008
Springer
15 years 10 months ago
MR Brain Tissue Classification Using an Edge-Preserving Spatially Variant Bayesian Mixture Model
In this paper, a spatially constrained mixture model for the segmentation of MR brain images is presented. The novelty of this work is a new, edge preserving, smoothness prior whic...
Giorgos Sfikas, Christophoros Nikou, Nikolas P. ...
ACCV
2009
Springer
15 years 4 months ago
Levels of Details for Gaussian Mixture Models
Mixtures of Gaussians are a crucial statistical modeling tool at the heart of many challenging applications in computer vision and machine learning. In this paper, we first descri...
Vincent Garcia, Frank Nielsen, Richard Nock
CAIP
2007
Springer
217views Image Analysis» more  CAIP 2007»
15 years 3 months ago
Mixture Models Based Background Subtraction for Video Surveillance Applications
— Background subtraction is a method commonly used to segment objects of interest in image sequences. By comparing new frames to a background model, regions of interest can be fo...
Chris Poppe, Gaëtan Martens, Peter Lambert, R...