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A Spatially Correlated Mixture Model for Image Segmentation
IEICE transactions on information and systems
In image segmentation, finite mixture modeling has been widely used. In its simplest form, the spatial correlation among neighboring pixels is not taken into account, and its segmentation results can be largely deteriorated by noise in images. We propose a spatially correlated mixture model in which the mixing proportions of finite mixture models are governed by a set of underlying functions defined on the image space. The spatial correlation among pixels is introduced by putting a Gaussiandoi:10.1587/transinf.2014edp7307 fatcat:codxscjxjvftzogby4dks4egby