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Image and Informatics Group, LBNL : Home

A Bayesian Approach for Image Segmentation with Shape Priors

IEEE Computer Vision and Pattern Recognition, 2008

    H. Chang
    Q. Yang
    B. Parvin


    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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