A Bayesian Approach for Image Segmentation with Shape Priors
IEEE Computer Vision and Pattern Recognition, 2008
Color and texture have been widely used in image segmentation;
however, their performance is often hindered by
scene ambiguities, overlapping objects, or missing parts. In this
paper, we propose an interactive image segmentation approach with shape prior models within a Bayesian framework.
Interactive features, through mouse strokes, reduce ambiguities,
and the incorporation of shape priors enhances quality of the segmentation
where color and/or texture are not solely adequate.
The novelties of our approach are in (i)
formulating the segmentation problem in a well-defined Bayesian
framework with multiple shape priors, (ii) efficiently
estimating parameters of the Bayesian model, and (iii)
multi-object segmentation through user-specified priors. We
demonstrate the effectiveness of our method on a set of natural
and synthetic images.
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