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Robust Image Segmentation with Mixtures of Student's t-Distributions

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Robust Image Segmentation with Mixtures of Student's t-Distributions
Gaussian mixture models have been widely used in image segmentation. However, such models are sensitive to outliers. In this paper, we consider a robust model for image segmentation based on mixtures of Student's t-distributions which have heavier tails than Gaussian and thus are not sensitive to outliers. The t-distribution is one of the few heavy tailed probability density functions (pdf) closely related to the Gaussian, that gives tractable maximum likelihood inference via the Expectation-Maximization (EM) algorithm. Numerical experiments that demonstrate the properties of the proposed model for image segmentation are presented.
Giorgos Sfikas, Christophoros Nikou, Nikolas P. Ga
Added 21 Oct 2009
Updated 27 Oct 2009
Type Conference
Year 2007
Where ICIP
Authors Giorgos Sfikas, Christophoros Nikou, Nikolas P. Galatsanos
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