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Parameterized Image Varieties and Estimation with Bilinear Constraints

Yakup Genc, Jean Ponce, Yoram Leedan, Peter Meer
CVPR 1999 pp. 2067-2072
doi:10.1109/CVPR.1999.784610 /cvpr/1999/genc1999cvpr-parameterized/

Abstract

This paper addresses the problem of reliably estimating the coefficients of the parameterized image variety (PIV) associated with the set of weak perspective images of a rigid scene, with applications in image-based rendering. Exploiting the fact that the constraints defining the PIV are linear in its coefficients and bilinear in the image data, the estimation procedure is cast in the errors-in-variables framework and solved using the method proposed by Y. Leedan and P. Meer (1998) for this type of problems. The proposed approach has been implemented, and experiments with real data are shown to yield much better prediction power than the original method based on singular value decomposition. Extensions to the more difficult case of paraperspective projection are briefly discussed.

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Cite

Text

Genc et al. "Parameterized Image Varieties and Estimation with Bilinear Constraints." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1999. doi:10.1109/CVPR.1999.784610

Markdown

[Genc et al. "Parameterized Image Varieties and Estimation with Bilinear Constraints." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1999.](https://mlanthology.org/cvpr/1999/genc1999cvpr-parameterized/) doi:10.1109/CVPR.1999.784610

BibTeX

@inproceedings{genc1999cvpr-parameterized,
  title     = {{Parameterized Image Varieties and Estimation with Bilinear Constraints}},
  author    = {Genc, Yakup and Ponce, Jean and Leedan, Yoram and Meer, Peter},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1999},
  pages     = {2067-2072},
  doi       = {10.1109/CVPR.1999.784610},
  url       = {https://mlanthology.org/cvpr/1999/genc1999cvpr-parameterized/}
}