ML Anthology

A Robust Method for Strong Rolling Shutter Effects Correction Using Lines with Automatic Feature Selection

Yizhen Lao, Omar Ait-Aider
CVPR 2018
doi:10.1109/CVPR.2018.00504 /cvpr/2018/lao2018cvpr-robust/

Abstract

We present a robust method which compensates RS distortions in a single image using a set of image curves, basing on the knowledge that they correspond to 3D straight lines. Unlike in existing work, no a priori knowledge about the line directions (e.g. Manhattan World assumption) is required. We first formulate a parametric equation for the projection of a 3D straight line viewed by a moving rolling shutter camera under a uniform motion model. Then we propose a method which efficiently estimates ego angular velocity separately from pose parameters, using at least 4 image curves. Moreover, we propose for the first time a RANSAC-like strategy to select image curves which really correspond to 3D straight lines and reject those corresponding to actual curves in 3D world. A comparative experimental study with both synthetic and real data from famous benchmarks shows that the proposed method outperforms all the existing techniques from the state-of-the-art.

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Cite

Text

Lao and Ait-Aider. "A Robust Method for Strong Rolling Shutter Effects Correction Using Lines with Automatic Feature Selection." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. doi:10.1109/CVPR.2018.00504

Markdown

[Lao and Ait-Aider. "A Robust Method for Strong Rolling Shutter Effects Correction Using Lines with Automatic Feature Selection." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.](https://mlanthology.org/cvpr/2018/lao2018cvpr-robust/) doi:10.1109/CVPR.2018.00504

BibTeX

@inproceedings{lao2018cvpr-robust,
  title     = {{A Robust Method for Strong Rolling Shutter Effects Correction Using Lines with Automatic Feature Selection}},
  author    = {Lao, Yizhen and Ait-Aider, Omar},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2018},
  doi       = {10.1109/CVPR.2018.00504},
  url       = {https://mlanthology.org/cvpr/2018/lao2018cvpr-robust/}
}