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A Bayesian approach for image segmentation with shape priors

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3 Author(s)
Hang Chang ; Inst. of Autom., Chinese Acad. of Sci., beijing ; Qing Yang ; Parvin, B.

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.

Published in:

Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on

Date of Conference:

23-28 June 2008

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