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 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. As basic image features it uses a Haar wavelet approximation of the determinant of Hessian blob detector.
SURF has been shown to have almost the same performance as SIFT. [http://www.tu-chemnitz.de/etit/proaut/rsrc/iav07-surf.pdf]
Scale-invariant feature transform
* Gradient Location and Orientation Histogram
* LESH - Local Energy based Shape Histogram
Feature detection (computer vision)
* [http://www.vision.ee.ethz.ch/~surf Website of SURF: Speeded Up Robust Features]
* [http://users.student.lth.se/p04pst/surfmex.html MATLAB interface with examples]
* [http://www.vision.ee.ethz.ch/~surf/eccv06.pdf Publication of Speeded Up Robust Features]
* [http://www.vision.ee.ethz.ch/~surf/papers.html 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.]
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