Map estimation of epipolar geometry by em algorithm and local diffusion

Wenfeng Li, Baoxin Li

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

1 Scopus citations

Abstract

Finding epipolar geometry for two images is a fundamental problem in computer vision. While this typically relies on feature point correspondence, the epipolar constraint can also be used for improving the accuracy of correspondence. We propose a probabilistic framework for estimating the epiploar geometry, in which the geometry and the feature correspondence are estimated iteratively at the same time. Using the EM algorithm to maximize a posteriori, our approach updates feature correspondence with estimated epipolar geometry. The correspondence is further improved with local diffusion on a prior Markov Random Field model. In turn, more accurate epipolar geometry is recovered. Experiments show this approach produces more accurate fundamental matrix compared with typical methods and can handle some challenging situations such as view rotation and scale changes.

Original languageEnglish (US)
Title of host publication2007 IEEE International Conference on Image Processing, ICIP 2007 Proceedings
PublisherIEEE Computer Society
PagesV201-V204
ISBN (Print)1424414377, 9781424414376
DOIs
StatePublished - Jan 1 2007
Event14th IEEE International Conference on Image Processing, ICIP 2007 - San Antonio, TX, United States
Duration: Sep 16 2007Sep 19 2007

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume5
ISSN (Print)1522-4880

Other

Other14th IEEE International Conference on Image Processing, ICIP 2007
CountryUnited States
CitySan Antonio, TX
Period9/16/079/19/07

Keywords

  • EM algorithm
  • Epipolar geometry
  • Local diffusion
  • MAP

ASJC Scopus subject areas

  • Engineering(all)

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