Bayesian region merging probability for parametric image models

Steven M Lavalle, Seth Andrew Hutchinson

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

Abstract

We describe a novel Bayesian approach to region merging, which directly uses statistical image models to determine the probability that the union of two regions is homogeneous, and does not require parameter estimation. This approach is particularly beneficial for cases in which the merging decision is most likely to be incorrect: when little information is contained in one or both of the regions and parameter estimates are unreliable. We apply the formulation to the implicit polynomial surface model for range data and texture models for intensity images.

Original languageEnglish (US)
Title of host publicationIEEE Computer Vision and Pattern Recognition
Editors Anon
PublisherPubl by IEEE
Pages778-779
Number of pages2
ISBN (Print)0818638826
StatePublished - 1993
EventProceedings of the 1993 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - New York, NY, USA
Duration: Jun 15 1993Jun 18 1993

Publication series

NameIEEE Computer Vision and Pattern Recognition

Other

OtherProceedings of the 1993 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
CityNew York, NY, USA
Period6/15/936/18/93

ASJC Scopus subject areas

  • Engineering(all)

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