1. bookVolume 9 (2018): Issue 1 (February 2018)
Journal Details
License
Format
Journal
eISSN
2038-0909
First Published
15 Dec 2014
Publication timeframe
1 time per year
Languages
English
access type Open Access

Comparison of minimization methods for nonsmooth image segmentation

Published Online: 24 Mar 2018
Volume & Issue: Volume 9 (2018) - Issue 1 (February 2018)
Page range: 68 - 86
Received: 01 Aug 2017
Accepted: 02 Feb 2018
Journal Details
License
Format
Journal
eISSN
2038-0909
First Published
15 Dec 2014
Publication timeframe
1 time per year
Languages
English
Abstract

Segmentation is a typical task in image processing having as main goal the partitioning of the image into multiple segments in order to simplify its interpretation and analysis. One of the more popular segmentation model, formulated by Chan-Vese, is the piecewise constant Mumford-Shah model restricted to the case of two-phase segmentation. We consider a convex relaxation formulation of the segmentation model, that can be regarded as a nonsmooth optimization problem, because the presence of the l1-term. Two basic approaches in optimization can be distinguished to deal with its non differentiability: the smoothing methods and the nonsmoothing methods. In this work, a numerical comparison of some first order methods belongs of both approaches are presented. The relationships among the different methods are shown, and accuracy and efficiency tests are also performed on several images.

Keywords

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