Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/23805
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Type: Journal article
Title: Mathematical analysis of an extended mumford-shah model for image segmentation
Author: Tao, T. C. Y.
Crisp, D. J.
van der Hoek, John
Citation: Journal of Mathematical Imaging and Vision, 2006; 24 (3):327-340
Publisher: Springer
Issue Date: 2006
ISSN: 0924-9907
School/Discipline: School of Mathematical Sciences : Applied Mathematics
Abstract: Morel and Solimini have established proofs of important properties of segmentations which can be seen as locally optimal for the simplest Mumford-Shah model in the continuous domain. A weakness of the latter is that it is not suitable for handling noisy images. We propose a Bayesian model to overcome these problems. We demonstrate that this Bayesian model indeed generalizes the original Mumford-Shah model, and we prove it has the same desirable properties as shown by Morel and Solimini.
Keywords: image segmentation, Mumford-Shah model, Bayesian model, maximum a-posteriori, mathematical analysis
Description: The original publication is available at www.springerlink.com
RMID: 0020060583
DOI: 10.1007/s10851-005-3631-1
Published version: http://www.springerlink.com/content/05256245773n5t6v/
Appears in Collections:Mathematical Sciences publications

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