Media type: E-Article Title: An Estimation Theoretical View on Ambrosio‐Tortorelli Image Segmentation Contributor: Krajsek, Kai; Dedovic, Ines; Scharr, Hanno Published: Wiley, 2012 Published in: PAMM, 12 (2012) 1, Seite 741-742 Language: English DOI: 10.1002/pamm.201210359 ISSN: 1617-7061 Keywords: General Medicine Origination: Footnote: Description: <jats:title>Abstract</jats:title><jats:p>In this paper, we examine the Ambrosio‐Tortorelli (AT) functional [1] for image segmentation from an estimation theoretical point of view. Instead of considering a single point estimate, i.e. the maximum‐a‐posteriori (MAP) estimate, we adopt a wider estimation theoretical view‐point, meaning we consider images to be random variables and investigate their distribution. We derive an effective block‐Gibbs‐sampler for this posterior probability density function (PDF) based on the theory of Gaussian Markov random fields (GMRF) [2]. (© 2012 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)</jats:p> Access State: Open Access