swMATH ID: 29764
Software Authors: Stefan Leutenegger, Margarita Chli, Roland Siegwart
Description: BRISK: Binary robust invariant scalable keypoints. 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, 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.
Homepage: https://ieeexplore.ieee.org/abstract/document/6126542
Source Code:  https://github.com/kornerc/brisk
Related Software: SURF; SIFT; FREAK; PCA-SIFT; ASIFT; HPatches; KAZE Features; L2-Net; R2D2; D2-Net; SuperPoint; SIFER; PointNet; SOSNet; Theia; SuperGlue; ImageNet; ORB-SLAM2; KinectFusion; ORB-SLAM
Cited in: 19 Documents

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