ML Anthology

On Finding Gray Pixels

Yanlin Qian, Joni-Kristian Kamarainen, Jarno Nikkanen, Jiri Matas
CVPR 2019
doi:10.1109/CVPR.2019.00825 /cvpr/2019/qian2019cvpr-finding/

Abstract

We propose a novel grayness index for finding gray pixels and demonstrate its effectiveness and efficiency in illumination estimation. The grayness index, GI in short, is derived using the Dichromatic Reflection Model and is learning-free. GI allows to estimate one or multiple illumination sources in color-biased images. On standard single-illumination and multiple-illumination estimation benchmarks, GI outperforms state-of-the-art statistical methods and many recent deep methods. GI is simple and fast, written in a few dozen lines of code, processing a 1080p image in 0.4 seconds with a non-optimized Matlab code.

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Cite

Text

Qian et al. "On Finding Gray Pixels." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. doi:10.1109/CVPR.2019.00825

Markdown

[Qian et al. "On Finding Gray Pixels." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.](https://mlanthology.org/cvpr/2019/qian2019cvpr-finding/) doi:10.1109/CVPR.2019.00825

BibTeX

@inproceedings{qian2019cvpr-finding,
  title     = {{On Finding Gray Pixels}},
  author    = {Qian, Yanlin and Kamarainen, Joni-Kristian and Nikkanen, Jarno and Matas, Jiri},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2019},
  doi       = {10.1109/CVPR.2019.00825},
  url       = {https://mlanthology.org/cvpr/2019/qian2019cvpr-finding/}
}