An Analysis of the SURF Method

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. Source Code The source code and the online demo are accessible at the IPOL web page of this article1. The proposed implementation of the SURF algorithm is written in C++ ISO/ANSI. It performs features extraction from digital images and provides local correspondences for a pair of images. An epipolar geometric consistency checking may additionally be used to discard mismatches when considering two pictures from the same scene. This optional post-processing uses the ORSA algorithm by B. Stival and L. Moisan [International Journal of Computer Vision, 57 (2004), pp. 201–218].