A common framework for image segmentation

Davi Geiger, Alan Yuille

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

Abstract

An attempt is made to unify several approaches to image segmentation in early vision under a common framework. The energy function, or Markov random field, formalism is very attractive since it enables the assumptions used to be explicitly stated in the energy functions, and it can be extended to deal with many other problems in vision. It is shown that specified discrete formulations for the energy function are closely related to the continuous formulation. When the mean field (MF) theory approach is used, several previous attempts to solve these energy functions are effectively equivalent. By varying the parameters of the energy functions, one can obtain a class of solutions and several nonlinear diffusion approaches to image segmentation. The theory is developed for image segmentation, but it can be applied equally well to image or surface reconstruction (where the data are sparse).

Original languageEnglish (US)
Title of host publicationProceedings - International Conference on Pattern Recognition
PublisherPubl by IEEE
Pages502-507
Number of pages6
ISBN (Print)0818620625
StatePublished - 1990
EventProceedings of the 10th International Conference on Pattern Recognition - Atlantic City, NJ, USA
Duration: Jun 16 1990Jun 21 1990

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume1

Other

OtherProceedings of the 10th International Conference on Pattern Recognition
CityAtlantic City, NJ, USA
Period6/16/906/21/90

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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