Apr 8, 2010 · A new Bayesian model is proposed for image segmentation based upon Gaussian mixture models (GMM) with spatial smoothness constraints.
A new Bayesian model is proposed for image segmentation based upon Gaussian mixture models (GMM) with spatial smoothness constraints.
Abstract—A new Bayesian model is proposed for image seg- mentation based upon Gaussian mixture models (GMM) with spatial smoothness constraints.
A new Bayesian model is proposed for image segmentation based upon Gaussian mixture models (GMM) with spatial smoothness constraints.
Finite mixture models (FMMs) [1] are a widely applied and flexible statistical modelling tool. Their applications include image analysis, psychiatry, computer ...
Title: A Bayesian Framework for Image Segmentation With Spatially Varying Mixtures. Institution and School/Department of submitter: Πανεπιστήμιο Ιωαννίνων.
A Bayesian framework for image segmentation with spatially varying mixtures. scientific article published on 8 April 2010. In more languages.
In this paper, we present a Bayesian framework for image segmentation based upon spatial nonparametric clustering. To estimate the density function on a ...
We use a Markov random field as the prior model of the spacial relationship between image pixels, and approximate an observed image by a Gaussian mixture model.
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In this work, we propose a new Bayesian model for unsupervised image segmentation based on a combination of the spatially varying finite mixture models ...