@inproceedings {240,
	title = {A Bayesian information flow approach to image segmentation},
	booktitle = {7th Canadian Conference on Computer and Robot Vision},
	year = {2010},
	month = {06/2010},
	address = {Ottawa, Ontario, Canada},
	abstract = {<p>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.</p>
},
	isbn = {978-0-7695-4040-5},
	doi = {http://dx.doi.org/10.1109/CRV.2010.46},
	author = {A Mishra and A Wong and D A. Clausi and P Fieguth}
}
