Robust Image Segmentation with Mixtures of Student's t-Distributions
Proceedings - International Conference on Image ProcessingPublished 1 September 2007
Giorgos Sfikas, Christophoros Nikou, Nikolaos Galatsanos
Citations67
SJR score0.37
SNIP0.54
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TL;DR
This paper considers 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.
Abstract
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.
Keywords
Computer ScienceMathematics
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