New image segmentation models

Tien D. Bui, S. Gao, Q. H. Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper the Mumford-Shah (MS) model and its variations are studied for image segmentation. It is found that many previous models cannot detect edges with low contrast. We have studied a variety of different models of energy minimization a la Mumford-Shah approach for image segmentation. All these new models are better than the piecewise constant approximation since they can detect the low contrast edges of objects. This paper will present and compare these models for segmentations of medical and natural images.

Original languageEnglish (US)
Title of host publicationProceedings of the Seventh IASTED International Conference on Signal and Image Processing, SIP 2005
EditorsM.W. Marcellin
Pages400-403
Number of pages4
StatePublished - 2005
Externally publishedYes
EventSeventh IASTED International Conference on Signal and Image Processing, SIP 2005 - Honolulu, HI, United States
Duration: Aug 15 2005Aug 17 2005

Publication series

NameProceedings of the Seventh IASTED International Conference on Signal and Image Processing, SIP 2005

Other

OtherSeventh IASTED International Conference on Signal and Image Processing, SIP 2005
Country/TerritoryUnited States
CityHonolulu, HI
Period8/15/058/17/05

Keywords

  • Constant and linear approximations
  • Image segmentation
  • Level set method
  • Modified Mumford-Shah model
  • Mumford-shah model

ASJC Scopus subject areas

  • General Engineering

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