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Brisk: Binary robust invariant scalable keypoints (2011)
| Venue: | In Computer Vision (ICCV), 2011 IEEE International Conference on |
| Citations: | 125 - 1 self |
BibTeX
@INPROCEEDINGS{Leutenegger11brisk:binary,
author = {Stefan Leutenegger and Margarita Chli and Y. Siegwart},
title = {Brisk: Binary robust invariant scalable keypoints},
booktitle = {In Computer Vision (ICCV), 2011 IEEE International Conference on},
year = {2011},
pages = {2548--2555}
}
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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 highperformance methods to date. In this paper we propose BRISK 1, 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. 1.
Keyphrases
binary robust invariant scalable keypoints great performance state-of-the-art algorithm efficient generation keypoint detection bit-string descriptor high quality performance well-studied problem highperformance method dedicated sampling comprehensive evaluation keypoint neighborhood image transformation intensity comparison brisk adaptive computational cost benchmark datasets novel method novel scale-space fast-based detector numerous computer vision application
