ML Anthology

Calibration of Image Sequences for Model Visualization

Adrian Broadhurst, Roberto Cipolla
CVPR 1999 pp. 1100-1105
doi:10.1109/CVPR.1999.786924 /cvpr/1999/broadhurst1999cvpr-calibration/

Abstract

The object of this paper is to find a quick and accurate method for computing the projection matrices of an image sequence, so that the error is distributed evenly along the sequence. It assumes that a set of correspondences between points in the images is known, and that these points represent rigid points in the world. This paper extends the algebraic minimisation approach developed by Hartley so that it can be used for long image sequences. This is achieved by initially computing a trifocal tensor using the three most extreme views. The intermediate views are then computed linearly using the trifocal tensor. An iterative algorithm as presented which perturbs the twelve entries of one camera matrix so that the algebraic error along the whole sequence is minimised.

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Cite

Text

Broadhurst and Cipolla. "Calibration of Image Sequences for Model Visualization." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1999. doi:10.1109/CVPR.1999.786924

Markdown

[Broadhurst and Cipolla. "Calibration of Image Sequences for Model Visualization." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1999.](https://mlanthology.org/cvpr/1999/broadhurst1999cvpr-calibration/) doi:10.1109/CVPR.1999.786924

BibTeX

@inproceedings{broadhurst1999cvpr-calibration,
  title     = {{Calibration of Image Sequences for Model Visualization}},
  author    = {Broadhurst, Adrian and Cipolla, Roberto},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1999},
  pages     = {1100-1105},
  doi       = {10.1109/CVPR.1999.786924},
  url       = {https://mlanthology.org/cvpr/1999/broadhurst1999cvpr-calibration/}
}