Computer Vision – ECCV 2006

Volume 3951 of the series Lecture Notes in Computer Science pp 404-417

SURF: Speeded Up Robust Features

  • Herbert BayAffiliated withETH Zurich
  • , Tinne TuytelaarsAffiliated withKatholieke Universiteit Leuven
  • , Luc Van GoolAffiliated withETH ZurichKatholieke Universiteit Leuven

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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.