BRISK: Binary Robust Invariant Scalable Keypoints
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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.
We propose BRISK, 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.
Watch BRISK in action.
ICCV 2011 Paper
Stefan Leutenegger, Margarita Chli and Roland Siegwart, BRISK: Binary Robust Invariant Scalable Keypoints, to appear in Proceedings of the IEEE International Conference on Computer Vision (ICCV) 2011.
Open-source package containing windows and linux libraries, a demo application, and a Matlab mex interface:
download brisk.zip (16 MB)
(v0.1 December 10th 2011)
Provided under the terms and conditions of the BSD license.
Please contact email@example.com, if you have related questions or suggestions.