Level set based shape prior segmentation

Tony Chan*, Wei Zhu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

228 Scopus citations

Abstract

We propose a level set based variational approach that incorporates shape priors into Chan-Vese's model [3] for the shape prior segmentation problem. In our model, besides the level set function for segmentation, as in Cremers' work [5], we introduce another labelling level set function to indicate the regions on which the prior shape should be compared. Our model can segment an object, whose shape is similar to the given prior shape, from a background where there are several objects. Moreover, we provide a proof for a fast solution principle, which was mentioned [7] and similar to the one proposed in [19], for minimizing Chan-Vese's segmentation model without length term. We extend the principle to the minimization of our prescribed functionals.

Original languageEnglish (US)
Title of host publicationProceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005
PublisherIEEE Computer Society
Pages1164-1170
Number of pages7
ISBN (Print)0769523722, 9780769523729
DOIs
StatePublished - Jan 1 2005
Event2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005 - San Diego, CA, United States
Duration: Jun 20 2005Jun 25 2005

Publication series

NameProceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005
VolumeII

Conference

Conference2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005
CountryUnited States
CitySan Diego, CA
Period06/20/0506/25/05

ASJC Scopus subject areas

  • Engineering(all)

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