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SURF: Speeded Up Robust Features

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNIP,volume 3951)

Abstract

In this paper, we present a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features). It approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.

This is achieved by relying on integral images for image convolutions; by building on the strengths of the leading existing detectors and descriptors (in casu, using a Hessian matrix-based measure for the detector, and a distribution-based descriptor); and by simplifying these methods to the essential. This leads to a combination of novel detection, description, and matching steps. The paper presents experimental results on a standard evaluation set, as well as on imagery obtained in the context of a real-life object recognition application. Both show SURF’s strong performance.

Keywords

  • Hessian Matrix
  • Interest Point
  • Integral Image
  • Robust Feature
  • Viewpoint Change

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Bay, H., Tuytelaars, T., Van Gool, L. (2006). SURF: Speeded Up Robust Features. In: Leonardis, A., Bischof, H., Pinz, A. (eds) Computer Vision – ECCV 2006. ECCV 2006. Lecture Notes in Computer Science, vol 3951. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11744023_32

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  • DOI: https://doi.org/10.1007/11744023_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33832-1

  • Online ISBN: 978-3-540-33833-8

  • eBook Packages: Computer ScienceComputer Science (R0)