Statistically Robust Approach to Lens Distortion Calibration with Model Selection
Abstract
This paper addresses the problem of calibrating camera lens distortion, which can be significant in medium to wide angle lenses. While almost all existing nonmetric distortion calibration methods need user involvement in one form or another, we present an approach to distortion calibration based on the robust the-least-median-of-squares (LMedS) estimator. Our approach is thus able to proceed in a ful ly-automatic manner while being less sensitive to erroneous input data such as image curves that are mistakenly considered as projections of 3D linear segments. Our approach uniquely uses fast, closed-form solutions to the distortion coefficients, which serve as an initial point for a non-linear optimization algorithm to straighten imaged lines. Moreover we propose a method for distortion model selection based on geometrical inference. Successful experiments to evaluate the performance of this approach on synthetic and real data are reported.
Cite
Text
El-Melegy and Farag. "Statistically Robust Approach to Lens Distortion Calibration with Model Selection." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2003. doi:10.1109/CVPRW.2003.10096Markdown
[El-Melegy and Farag. "Statistically Robust Approach to Lens Distortion Calibration with Model Selection." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2003.](https://mlanthology.org/cvprw/2003/elmelegy2003cvprw-statistically/) doi:10.1109/CVPRW.2003.10096BibTeX
@inproceedings{elmelegy2003cvprw-statistically,
title = {{Statistically Robust Approach to Lens Distortion Calibration with Model Selection}},
author = {El-Melegy, Moumen Taha and Farag, Aly A.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2003},
pages = {91},
doi = {10.1109/CVPRW.2003.10096},
url = {https://mlanthology.org/cvprw/2003/elmelegy2003cvprw-statistically/}
}