Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|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|
|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|
|Appears in Collections:||Mathematical Sciences publications|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.