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)
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