ML Anthology

Efficient Subpixel Refinement with Symbolic Linear Predictors

Vincent Lui, Jonathon Geeves, Winston Yii, Tom Drummond
CVPR 2018
doi:10.1109/CVPR.2018.00852 /cvpr/2018/lui2018cvpr-efficient/

Abstract

We present an efficient subpixel refinement method using a learning-based approach called Linear Predictors. Firstly, we present a novel technique, called Symbolic Linear Predictors, which makes the learning step efficient for subpixel refinement. This makes our approach feasible for online applications without compromising accuracy, while taking advantage of the run-time efficiency of learning based approaches. Secondly, we show how Linear Predictors can be used to predict the expected alignment error, allowing us to use only the best keypoints in resource constrained applications. We show the efficiency and accuracy of our method through extensive experiments.

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Cite

Text

Lui et al. "Efficient Subpixel Refinement with Symbolic Linear Predictors." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. doi:10.1109/CVPR.2018.00852

Markdown

[Lui et al. "Efficient Subpixel Refinement with Symbolic Linear Predictors." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.](https://mlanthology.org/cvpr/2018/lui2018cvpr-efficient/) doi:10.1109/CVPR.2018.00852

BibTeX

@inproceedings{lui2018cvpr-efficient,
  title     = {{Efficient Subpixel Refinement with Symbolic Linear Predictors}},
  author    = {Lui, Vincent and Geeves, Jonathon and Yii, Winston and Drummond, Tom},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2018},
  doi       = {10.1109/CVPR.2018.00852},
  url       = {https://mlanthology.org/cvpr/2018/lui2018cvpr-efficient/}
}