• DocumentCode
    2698668
  • Title

    Robust self-calibration and Euclidean reconstruction via affine approximation

  • Author

    Kahl, Fredrik ; Heyden, Anders

  • Author_Institution
    Dept. of Math., Lund Univ., Sweden
  • Volume
    1
  • fYear
    1998
  • fDate
    16-20 Aug 1998
  • Firstpage
    56
  • Abstract
    A new approach to self-calibration and Euclidean reconstruction from image sequences is presented. The key idea is to start with the affine camera model as a first approximation to obtain the affine 3D structure. It is then upgraded to an Euclidean structure and finally, refined by applying the full perspective camera model and bundle adjustment. The proposed scheme makes no assumption about the scene nor the camera motion. The only assumption required is that the camera has zero skew, which is a minimal condition in order to self-calibrate the camera. However, if other information is available about the camera, it can and should be incorporated. The method is robust and it also provides an estimate of the accuracy of the estimated parameters. Experiments are presented to illustrate the performance of the approach
  • Keywords
    approximation theory; calibration; computer vision; image reconstruction; image sequences; self-adjusting systems; Euclidean reconstruction; affine 3D structure; affine approximation; affine camera model; computer vision; image reconstruction; image sequences; self-calibration; Cameras; Image reconstruction; Image sequences; Layout; Matrix decomposition; Parameter estimation; Robustness;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on
  • Conference_Location
    Brisbane, Qld.
  • ISSN
    1051-4651
  • Print_ISBN
    0-8186-8512-3
  • Type

    conf

  • DOI
    10.1109/ICPR.1998.711078
  • Filename
    711078