Stefan Leutenegger, Margarita Chli and Roland Siegwart, "BRISK: Binary Robust Invariant Scalable Keypoints", Proceedings of the IEEE International Conference on Computer Vision (ICCV) 2011.
Effective and efficient generation of keypoints from animage 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, 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
Video Presentation: http://margaritachli.com/videos/ICCV2011video1.avi
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