ML Anthology

Using Geometry Invariants for Camera Response Function Estimation

Tian-Tsong Ng, Shih-Fu Chang, Mao-Pei Tsui
CVPR 2007
doi:10.1109/CVPR.2007.383000 /cvpr/2007/ng2007cvpr-using/

Abstract

In this paper, we present a new single-image camera response function (CRF) estimation method using geometry invariants (GI). We derive mathematical properties and geometric interpretation for GI, which lend insight to addressing various algorithm implementation issues in a principled way. In contrast to the previous single-image CRF estimation methods, our method provides a constraint equation for selecting the potential target data points. Comparing to the prior work, our experiment is conducted over more extensive data and our method is flexible in that its estimation accuracy and stability can be improved whenever more than one image is available. The geometry invariance theory is novel and may be of wide interest.

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Cite

Text

Ng et al. "Using Geometry Invariants for Camera Response Function Estimation." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007. doi:10.1109/CVPR.2007.383000

Markdown

[Ng et al. "Using Geometry Invariants for Camera Response Function Estimation." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007.](https://mlanthology.org/cvpr/2007/ng2007cvpr-using/) doi:10.1109/CVPR.2007.383000

BibTeX

@inproceedings{ng2007cvpr-using,
  title     = {{Using Geometry Invariants for Camera Response Function Estimation}},
  author    = {Ng, Tian-Tsong and Chang, Shih-Fu and Tsui, Mao-Pei},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2007},
  doi       = {10.1109/CVPR.2007.383000},
  url       = {https://mlanthology.org/cvpr/2007/ng2007cvpr-using/}
}