Fundamental matrix estimation using generalized least squares

Huiyu Zhou, Patrick R. Green, Andrew M. Wallace, Shida Xu

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

2 Citations (Scopus)

Abstract

Classical approaches for estimating the fundamental matrix assume that Gaussian noise is contained in the estimates in view of mathematical tractability. However, this assumption will not be justified when the distribution computed is not normally distributed. We propose a new approach that does not make the Gaussian assumption, and so can attain robustness and accuracy in different conditions. The proposed framework, generalized least squares (GLS), is the extension of linear mixed-effects models considering the correlation between different data subsamples. We test the new model by using synthetic and real images, comparing it to the least median of squares technique.

Original languageEnglish
Title of host publicationProceedings of the Fourth IASTED International Conference on Visualization, Imaging, and Image Processing
Pages263-268
Number of pages6
Publication statusPublished - 2004
EventProceedings of the Fourth IASTED International Conference on Visualization, Imaging, and Image Processing - Marbella, Spain
Duration: 6 Sep 20048 Sep 2004

Conference

ConferenceProceedings of the Fourth IASTED International Conference on Visualization, Imaging, and Image Processing
CountrySpain
CityMarbella
Period6/09/048/09/04

Keywords

  • Epipolar geometry
  • Fundamental matrix
  • Least-square
  • Outlier

Cite this

Zhou, H., Green, P. R., Wallace, A. M., & Xu, S. (2004). Fundamental matrix estimation using generalized least squares. In Proceedings of the Fourth IASTED International Conference on Visualization, Imaging, and Image Processing (pp. 263-268)