Continuous Graph Cut Segmentation with Shape Priors

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Continuous Graph Cut Segmentation with Shape Priors

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Publication Article, peer reviewed scientific
Title Continuous Graph Cut Segmentation with Shape Priors
Author(s) Fundana, Ketut ; Heyden, Anders ; Ghosh, Christian ; Schnörr, Christoph
Date 2008-12
English abstract
In this paper we propose a novel prior-based variational object segmentation method in a global minimization framework which unifies image segmentation and image denoising. The idea of the proposed method is to convexify the energy functional of the Chan-Vese method in order to find a global minimizer, so called continuous graph cuts. The method is extended by adding an additional shape constraint into the convex energy functional in order to segment an object using prior information. We show that the energy functional including a shape prior term can be relaxed from optimization over characteristic functions to optimization over arbitrary functions followed by a thresholding at an arbitrarily chosen level between 0 and 1. Experimental results demonstrate the performance and robustness of the method to segment objects in real images.
Publisher International Association of Pattern Recognition
Host/Issue Proceedings International Conference on Pattern Recognition
Language eng (iso)
Subject(s) image segmentation
Variational methods
Technology
Research Subject Categories::TECHNOLOGY::Information technology::Computer science
Research Subject Categories::MATHEMATICS::Applied mathematics
Handle http://hdl.handle.net/2043/7399 (link to this page)

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