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A Bayesian information flow approach to image segmentation

TitleA Bayesian information flow approach to image segmentation
Publication TypeConference Paper
Year of Publication2010
AuthorsMishra, A., A. Wong, D. A. Clausi, and P. Fieguth
Conference Name7th Canadian Conference on Computer and Robot Vision
Date Published06/2010
Conference LocationOttawa, Ontario, Canada
ISBN Number978-0-7695-4040-5
Abstract

A novel Bayesian information flow approach is presented for accurate image segmentation, formulated as a maximum a posteriori (MAP) problem as per the popular Mumford-Shah (MS) model. The model is solved using an iterative Bayesian estimation approach conditioned on the flow of information within the image, where the flow is based on inter-pixel interactions and intra-region smoothness constraints. In this way, a localized and accurate Bayesian estimate of the underlying piece-wise constant regions within an image can be found, even under high noise and low contrast situations. Experimental results using 2-D images show that the proposed Bayesian information flow approach is capable of producing more accurate segmentations when compared to state-of-the-art segmentation methods, especially under scenarios with high noise levels and poor contrast.

DOI10.1109/CRV.2010.46
Refereed DesignationRefereed