A Bayesian Framework for Image Segmentation With Spatially Varying Mixtures

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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 compound multinomial (DCM) probability density to model the mixing proportions (i.e., the probabilities of class labels) and a Gauss

Christophoros Nikou, Aristidis Likas, Nikolas P. G

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Education | Image Segmentation | Mixing Proportions | Model | TIP 2010 |

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Added	22 May 2011
Updated	22 May 2011
Type	Journal
Year	2010
Where	TIP
Authors	Christophoros Nikou, Aristidis Likas, Nikolas P. Galatsanos

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A Bayesian Framework for Image Segmentation With Spatially Varying Mixtures

Education | Image Segmentation | Mixing Proportions | Model | TIP 2010 |

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