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Toward Self-calibration of a Stereo Rig from Noisy Stereoscopic Images

  • Slimane Larabi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1998)

Abstract

This paper deals with the analysis of uncertainty of epipole localizations in case of noisy stereo images. Initial uncertainty in point locations can be propagated through to an uncertainty in epipole localization, resulting in a region in the image called epipolar zone

Keywords

Image Point Convergence Zone Projective Geometry Stereoscopic Image Projective Basis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    H.S.M. Coxeter: Projective Geometry, Springer, New York (1987). 61Google Scholar
  2. 2.
    G. Csurka, C. Zeller, Z. Zhang, O. Faugeras: Characterizing the uncertainty of the fundamental matrix. Computer Vision and Image Understanding 68 (1997) 18–35. 60CrossRefGoogle Scholar
  3. 3.
    R. Deriche, Z. Zhang, Q. T. Luong, O. D. Faugeras: Robust recovery of the epipolar geometry for an uncalibrated stereo rig. In: Proceed. ECCV’94 Stockholm (1994). 60Google Scholar
  4. 4.
    R. Deriche, R. Vaillant, O. D. Faugeras: From noisy edge points to 3D reconstruction of scenes:a robust approachand its uncertainty analysis. In: Proceed. 7th Scandinavian Conf. on Image Analysis Alborg, Denmark, (August 1991) 225–232. 63Google Scholar
  5. 5.
    R. Deriche, J.P. Cocquerez: Extraction de composants connexes basé sur une détection optimale des contours. In:CESTA Paris (1987).Google Scholar
  6. 6.
    O. D. Faugeras: What can be seen in three dimensions with a uncalibrated stereo rig. In: Proceed. ECCV’92 Santa Margherita Ligure,taly, (1992).Google Scholar
  7. 7.
    O. D. Faugeras, Q. T. Luong, S. J. Maybank: Camera self-calibration:theory and experiments. In: Proceed. ECCV’92 Santa Margherita Ligure, Italy, (1992). 60Google Scholar
  8. 8.
    W. E. L. Grimson, D. P. Huttenlocher, D. W. Jacobs: A study of affine matching withbounded sensor error. In: Proceed. ECCV’92 Santa Margherita Ligure, taly, (1992). 64Google Scholar
  9. 9.
    S. J. Maybank, O. D. Faugeras: A theory of self calibration of a moving camera. Internat. J. of Computer Vision 8 (1992) 123–151. 60CrossRefGoogle Scholar
  10. 10.
    R. Mohr, B. Triggs: Projective geometry for image analysis. Tutorial given at ISPRS, Vienna,(July 1996). 61Google Scholar
  11. 11.
    R. Mohr, E. Arbogast: It can be done without camera calibration. Pattern Recog-nition Letters 12 (1991) 39–43. 60, 61CrossRefGoogle Scholar
  12. 12.
    P. R. S. Mendonca, R. Cipolla: Estimation of epipolar geometry from apparent contours: affine and circular motion cases. In: Proceed. CVPR’ 99 (1999). 60Google Scholar
  13. 13.
    P. Remagnino, P. Brand, R. Mohr: Correlation techniques in adaptive template matching with uncalibrated cameras. In: SPIE Proceed. Vision Geometry III Boston Mass., (November 1994) 252–263. 61Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Slimane Larabi
    • 1
  1. 1.Computer Science nstitute of U.S.T.H.B UniversityAlgiersAlgeria

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