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 highperformance methods to date. In this paper we propose BRISK1 , 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....

• Type: Journal
• Year: 2011
• Where: ICCV
• Authors: Stefan Leutenegger, Margarita Chli, Roland Y. Siegwart
• Keywords: Art Algorithms, Computer Vision, Computer Vision Applications, ICCV 2011, Image Transformations