ML Anthology

Conformal Transplantation of Lightness to Varying Resolution Sensors

Brian V. Funt, Michael Brockington, Frank Tong
CVPR 1993 pp. 563-569
doi:10.1109/CVPR.1993.341074 /cvpr/1993/funt1993cvpr-conformal/

Abstract

Many standard computer vision algorithms for shape from shading, optical flow, color constancy or surface reconstruction depend upon the solution of Poisson equations. Using retinex lightness computation as an example, it is shown how these algorithms can be dramatically sped up using a varying resolution sensor, so long as the sensor architecture is based on a conformal mapping. The varying resolution lightness algorithm is developed by the technique of using a conformal transplant. In the final analysis, very little of the algorithm needs to be changed, but a speed up in running time of roughly 50 times is obtained, even though the total amount of data is reduced by only a factor of 28. The speed increases at the expense of peripheral resolution.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

CVPR Semantic Scholar

Cite

Text

Funt et al. "Conformal Transplantation of Lightness to Varying Resolution Sensors." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1993. doi:10.1109/CVPR.1993.341074

Markdown

[Funt et al. "Conformal Transplantation of Lightness to Varying Resolution Sensors." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1993.](https://mlanthology.org/cvpr/1993/funt1993cvpr-conformal/) doi:10.1109/CVPR.1993.341074

BibTeX

@inproceedings{funt1993cvpr-conformal,
  title     = {{Conformal Transplantation of Lightness to Varying Resolution Sensors}},
  author    = {Funt, Brian V. and Brockington, Michael and Tong, Frank},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1993},
  pages     = {563-569},
  doi       = {10.1109/CVPR.1993.341074},
  url       = {https://mlanthology.org/cvpr/1993/funt1993cvpr-conformal/}
}