Mixture model

Known as: Contaminated Gaussian noise, Mixture coefficient, Contaminated Gaussian 
In statistics, a mixture model is a probabilistic model for representing the presence of subpopulations within an overall population, without… (More)
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Highly Cited
2004
Highly Cited
2004
Background subtraction is a common computer vision task. We analyze the usual pixel-level approach. We develop an efficient… (More)
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Highly Cited
2001
Highly Cited
2001
Real-time segmentation of moving regions in image sequences is a fundamental step in many vision systems including automated… (More)
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Highly Cited
2000
Highly Cited
2000
In this paper we describe the major elements of MIT Lincoln Laboratory’s Gaussian mixture model (GMM)-based speaker verification… (More)
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Highly Cited
2000
Highly Cited
2000
Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org… (More)
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Highly Cited
2000
Highly Cited
2000
In a Bayesian analysis of finite mixture models, parameter estimation and clustering are sometimes less straightforward that… (More)
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Highly Cited
2000
Highly Cited
2000
Normal mixture models are being increasingly used to model the distributions of a wide variety of random phenomena and to cluster… (More)
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Highly Cited
1999
Highly Cited
1999
A common method for real-time segmentation of moving regions in image sequences involves “background subtraction,” or… (More)
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Highly Cited
1999
Highly Cited
1999
In a Bayesian mixture model it is not necessary a priori to limit the number of components to be finite. In this paper an… (More)
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Highly Cited
1995
Highly Cited
1995
This paper introduces and motivates the use of Gaussian mixture models (GMM) for robust text-independent speaker identification… (More)
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Highly Cited
1994
Highly Cited
1994
The algorithm described in this paper discovers one or more motifs in a collection of DNA or protein sequences by using the… (More)
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