[IPOL announce] new article: An Analysis of the SURF Method

[announcements about the IPOL journal]

A new article is available in IPOL: http://www.ipol.im/pub/art/2015/69/

Edouard Oyallon, and Julien Rabin,
An Analysis of the SURF Method,
Image Processing On Line, 5 (2015), pp. 176–218.
http://dx.doi.org/10.5201/ipol.2015.69

Abstract
The SURF method (Speeded Up Robust Features) is a fast and robust 
algorithm for local, similarity invariant representation and comparison 
of images. Similarly to many other local descriptor-based approaches, 
interest points of a given image are defined as salient features from a 
scale-invariant representation. Such a multiple-scale analysis is 
provided by the convolution of the initial image with discrete kernels 
at several scales (box filters). The second step consists in building 
orientation invariant descriptors, by using local gradient statistics 
(intensity and orientation). The main interest of the SURF approach lies 
in its fast computation of operators using box filters, thus enabling 
real-time applications such as tracking and object recognition. The SURF 
framework described in this paper is based on the PhD thesis of H. Bay 
[ETH Zurich, 2009], and more specifically on the paper co-written by H. 
Bay, A. Ess, T. Tuytelaars and L. Van Gool [Computer Vision and Image 
Understanding, 110 (2008), pp. 346–359]. An implementation is proposed 
and used to illustrate the approach for image matching. A short 
comparison with a state-of-the-art approach is also presented, the SIFT 
algorithm of D. Lowe [International Journal of Computer Vision, 60 
(2004), pp. 91–110], with which SURF shares a lot in common.




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