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https://hdl.handle.net/2440/23805
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DC Field | Value | Language |
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dc.contributor.author | Tao, T. C. Y. | en |
dc.contributor.author | Crisp, D. J. | en |
dc.contributor.author | van der Hoek, John | en |
dc.date.issued | 2006 | en |
dc.identifier.citation | Journal of Mathematical Imaging and Vision, 2006; 24 (3):327-340 | en |
dc.identifier.issn | 0924-9907 | en |
dc.identifier.uri | http://hdl.handle.net/2440/23805 | - |
dc.description | The original publication is available at www.springerlink.com | en |
dc.description.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. | en |
dc.language.iso | en | en |
dc.publisher | Springer | en |
dc.source.uri | http://www.springerlink.com/content/05256245773n5t6v/ | en |
dc.subject | image segmentation, Mumford-Shah model, Bayesian model, maximum a-posteriori, mathematical analysis | en |
dc.title | Mathematical analysis of an extended mumford-shah model for image segmentation | en |
dc.type | Journal article | en |
dc.contributor.school | School of Mathematical Sciences : Applied Mathematics | en |
dc.identifier.doi | 10.1007/s10851-005-3631-1 | en |
Appears in Collections: | Mathematical Sciences publications |
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