BRISK: Binary Robust Invariant Scalable Keypoints
Abstract
Effective and efficient generation of keypoints from an image is a well-studied problem in the literature and forms the basis of numerous Computer Vision applications. Established leaders in the field are the SIFT and SURF algorithms which exhibit great performance under a variety of image transformations, with SURF in particular considered as the most computationally efficient amongst the high-performance methods to date. In this paper we propose BRISK <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , a novel method for keypoint detection, description and matching. A comprehensive evaluation on benchmark datasets reveals BRISK's adaptive, high quality performance as in state-of-the-art algorithms, albeit at a dramatically lower computational cost (an order of magnitude faster than SURF in cases). The key to speed lies in the application of a novel scale-space FAST-based detector in combination with the assembly of a bit-string descriptor from intensity comparisons retrieved by dedicated sampling of each keypoint neighborhood.
Cite
Text
Leutenegger et al. "BRISK: Binary Robust Invariant Scalable Keypoints." IEEE/CVF International Conference on Computer Vision, 2011. doi:10.1109/ICCV.2011.6126542Markdown
[Leutenegger et al. "BRISK: Binary Robust Invariant Scalable Keypoints." IEEE/CVF International Conference on Computer Vision, 2011.](https://mlanthology.org/iccv/2011/leutenegger2011iccv-brisk/) doi:10.1109/ICCV.2011.6126542BibTeX
@inproceedings{leutenegger2011iccv-brisk,
title = {{BRISK: Binary Robust Invariant Scalable Keypoints}},
author = {Leutenegger, Stefan and Chli, Margarita and Siegwart, Roland},
booktitle = {IEEE/CVF International Conference on Computer Vision},
year = {2011},
pages = {2548-2555},
doi = {10.1109/ICCV.2011.6126542},
url = {https://mlanthology.org/iccv/2011/leutenegger2011iccv-brisk/}
}