SURF

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For the urban renewal body, see Scottish Urban Regeneration Forum. For the university computing organisation in the Netherlands, see SURFnet.
Feature detection
Output of a typical corner detection algorithm
Edge detection
Corner detection
  • Harris operator
  • Shi and Tomasi
  • Level curve curvature
  • SUSAN
  • FAST
Blob detection
Ridge detection
Hough transform
Structure tensor
Affine invariant feature detection
Feature description
Scale space

SURF (Speeded Up Robust Features) is a robust local feature detector, first presented by Herbert Bay et al. in 2006, that can be used in computer vision tasks like object recognition or 3D reconstruction. It is partly inspired by the SIFT descriptor. The standard version of SURF is several times faster than SIFT and claimed by its authors to be more robust against different image transformations than SIFT. SURF is based on sums of 2D Haar wavelet responses and makes an efficient use of integral images.

It uses an integer approximation to the determinant of Hessian blob detector, which can be computed extremely quickly with an integral image (3 integer operations). For features, it uses the sum of the Haar wavelet response around the point of interest. Again, these can be computed with the aid of the integral image.

An application of the algorithm is patented in the US.[1]

See also

References

  1. ↑ US 2009238460, Ryuji Funayama, Hiromichi Yanagihara, Luc Van Gool, Tinne Tuytelaars, Herbert Bay, "ROBUST INTEREST POINT DETECTOR AND DESCRIPTOR", published 2009-09-24 
  • Herbert Bay, Andreas Ess, Tinne Tuytelaars, Luc Van Gool "SURF: Speeded Up Robust Features", Computer Vision and Image Understanding (CVIU), Vol. 110, No. 3, pp. 346–359, 2008

External links

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