Abstract
We present in this paper a unifying generalization of the Mumford-Shah functional, in the Ambrosio-Totorelli set up, and the Beltrami framework. The generalization of the Ambrosio-Tortorelli is in using a diffusion tensor as an indicator of the edge set instead of a function. The generalization of the Beltrami framework is in adding a penalty term on the metric such that it is defined dynamically from minimization of the functional.
We show that we are able, in this way, to have the benefits of true anisotropic diffusion together with a dynamically tuned metric/diffusion tensor. The functional is naturally defined in terms of the vielbein-the metric’s square root. Preliminary results show improvement on both the Beltrami flow and the Mumford-Shah flow.
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© 2012 Springer-Verlag Berlin Heidelberg
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Sochen, N., Bar, L. (2012). The Beltrami-Mumford-Shah Functional. In: Bruckstein, A.M., ter Haar Romeny, B.M., Bronstein, A.M., Bronstein, M.M. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2011. Lecture Notes in Computer Science, vol 6667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24785-9_16
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DOI: https://doi.org/10.1007/978-3-642-24785-9_16
Publisher Name: Springer, Berlin, Heidelberg
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