Convex non-convex image segmentation

Convex non-convex image segmentation A convex non-convex variational model is proposed for multiphase image segmentation. We consider a specially designed non-convex regularization term which adapts spatially to the image structures for a better control of the segmentation boundary and an easy handling of the intensity inhomogeneities. The nonlinear optimization problem is efficiently solved by an alternating directions methods of multipliers procedure. We provide a convergence analysis and perform numerical experiments on several images, showing the effectiveness of this procedure. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Numerische Mathematik Springer Journals

Convex non-convex image segmentation

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2017 by Springer-Verlag GmbH Deutschland
Subject
Mathematics; Numerical Analysis; Mathematics, general; Theoretical, Mathematical and Computational Physics; Mathematical Methods in Physics; Numerical and Computational Physics, Simulation; Mathematical and Computational Engineering
ISSN
0029-599X
eISSN
0945-3245
D.O.I.
10.1007/s00211-017-0916-4
Publisher site
See Article on Publisher Site

Abstract

A convex non-convex variational model is proposed for multiphase image segmentation. We consider a specially designed non-convex regularization term which adapts spatially to the image structures for a better control of the segmentation boundary and an easy handling of the intensity inhomogeneities. The nonlinear optimization problem is efficiently solved by an alternating directions methods of multipliers procedure. We provide a convergence analysis and perform numerical experiments on several images, showing the effectiveness of this procedure.

Journal

Numerische MathematikSpringer Journals

Published: Sep 6, 2017

References

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