Work

A Bayesian approach for image segmentation with shape priors

Year: 2008

Type: conference-paper

Abstract: 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.

Source: IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings

DOI: 10.1109/cvpr.2008.4587430

Authors: Hang Chang, Qing Yang, Bahram Parvin

Institutions: Lawrence Berkeley National Laboratory, Chinese Academy of Sciences, Institute of Automation

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: U.S. Department of Energy

Awards: G1286236842, G3083819904, G42916606, G969889393