SURF

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SURF (Speeded Up Robust Features) is a robust image descriptor that can be used in computer vision tasks. It is partly inspired by the SIFT descriptor. The standard version of SURF is 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. As basic image features it uses a Haar wavelet approximation of the determinant of Hessian blob detector.

[edit] See also

• Scale-invariant feature transform
• Gradient Location and Orientation Histogram
• LESH - Local Energy based Shape Histogram
• Blob detection
• Feature detection (computer vision)

[edit] External links

• Website of SURF: Speeded Up Robust Features

[edit] References

• Herbert Bay, Tinne Tuytelaars and Luc Van Gool "SURF: Speeded Up Robust Features", Proceedings of the 9th European Conference on Computer Vision, Springer LNCS volume 3951, part 1, pp 404--417, 2006.