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Proceedings Paper

A PDE-free variational model for multiphase image segmentation
Author(s): Julia Dobrosotskaya; Weihong Guo
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Paper Abstract

We introduce a PDE-free variational model for multiphase image segmentation that uses a sparse representation basis (wavelets or other) instead of a Fourier basis in a modified diffuse interface context. The segmentation model we present differs from other state-of-the-art models in several ways. The diffusive nature of the method originates from the sparse representations and thus propagates information in a different manner comparing to any existing PDE models, even though it still has such classical features as coarsening and phase separation. The model has a non-local nature, yet with much reduced diffuse interface blur, thus allowing to connect important features and preserve sharp edges in the output. Numerical experiments show that the method is robust to noise and is highly tunable.

Paper Details

Date Published: 24 August 2015
PDF: 10 pages
Proc. SPIE 9597, Wavelets and Sparsity XVI, 959706 (24 August 2015); doi: 10.1117/12.2189147
Show Author Affiliations
Julia Dobrosotskaya, Case Western Reserve Univ. (United States)
Weihong Guo, Case Western Reserve Univ. (United States)

Published in SPIE Proceedings Vol. 9597:
Wavelets and Sparsity XVI
Manos Papadakis; Vivek K. Goyal; Dimitri Van De Ville, Editor(s)

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