BRISK: Binary Robust invariant scalable keypoints

S. Leutenegger, M. Chli, R.Y. Siegwart

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publicationComputer Vision (ICCV), 2011 IEEE International Conference on
Number of pages8
Publication statusPublished - 2011

Keywords / Materials (for Non-textual outputs)

  • computer vision
  • feature extraction
  • image matching
  • transforms
  • BRISK method
  • SIFT algorithm
  • SURF algorithm
  • binary robust invariant scalable keypoints
  • bit-string descriptor
  • computer vision application
  • image transformation
  • keypoint description
  • keypoint detection
  • keypoint generation
  • keypoint matching
  • scale-space FAST-based detector
  • Boats
  • Brightness
  • Complexity theory
  • Detectors
  • Feature extraction
  • Kernel
  • Robustness


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