The modulus constraint: a new constraint self-calibration
- 1 January 1996
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 1 (10514651) , 349-353 vol.1
- https://doi.org/10.1109/icpr.1996.546047
Abstract
To obtain a Euclidean reconstruction from images the cameras have to be calibrated. In recent years different approaches have been proposed to avoid explicit calibration. The problem with these methods is that several parameters have to be retrieved at once. Because of the non-linearity of the equations this is not an easy task and the methods often fail to converge. In the's paper a stratified approach is proposed which allows to first retrieve the affine calibration of the camera using the modulus constraint. Having the affine calibration it is easy to upgrade to Euclidean. The important advantage of this method is that only three parameters have to be evaluated at first. From a practical point of view, the major gain is that an affine reconstruction is obtained from arbitrary sequences of views, whereas so far affine reconstruction has been based on pairs of views with a pure translation in between. A short illustration of another application is also given. Once the affine calibration is known, the constraint can be used to retrieve the Euclidean calibration in the presence of a variable focal length.Keywords
This publication has 6 references indexed in Scilit:
- A comparison of projective reconstruction methods for pairs of viewsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Euclidean reconstruction from constant intrinsic parametersPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1996
- Euclidean Structure from Uncalibrated ImagesPublished by British Machine Vision Association and Society for Pattern Recognition ,1994
- Estimation of relative camera positions for uncalibrated camerasPublished by Springer Nature ,1992
- Camera self-calibration: Theory and experimentsPublished by Springer Nature ,1992
- A multi-frame approach to visual motion perceptionInternational Journal of Computer Vision, 1991