Abstract
SURF (Speeded Up Robust Features) is a detector and descriptor of local scale- and rotation-invariant image features. By using integral images for image convolutions it is faster to compute than other state-of-the-art algorithms, yet produces comparable or even better results by means of repeatability, distinctiveness and robustness. A library implementing SURF is provided by the authors. However, it is closed-source and thus not suited as a basis for further research.
Several open source implementations of the algorithm exist, yet it is unclear how well they realize the original algorithm. We have evaluated different SURF implementations written in C++ and compared the results to the original implementation.
We have found that some implementations produce up to 33% lower repeatability and up to 44% lower maximum recall than the original implementation, while the implementation provided with the software Pan-o-matic produced almost identical results.
We have extended the Pan-o-matic implementation to use multi-threading, resulting in an up to 5.1 times faster computation on an 8-core machine. We describe our comparison criteria and our ideas that lead to the speed-up. Our software is put into the public domain.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Agrawal, M., Konolige, K., Blas, M.R.: CenSurE: Center surround extremas for realtime feature detection and matching. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 102–115. Springer, Heidelberg (2008)
Bay, H., Ess, A., Tuytelaars, T., van Gool, L.: Speeded-up robust features (surf). Journal of Computer Vision 110(3), 346–359 (2008)
Burghouts, G.J., Geusebroek, J.-M.: Performance evaluation of local colour invariants. Comput. Vis. Image Underst. 113(1), 48–62 (2009)
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)
Förstner, W.: 10 pros and cons against performance characterization of vision algorithms Technical report, Institut für Photogrammetrie, Universität Bonn (1996)
Haralick, R.M.: Performance characterization in computer vision. Computer Vision Graphics and Image Processing 60(2), 245–249 (1994)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Moreels, P., Perona, P.: Evaluation of features detectors and descriptors based on 3d objects. International Journal of Computer Vision 73(3), 263–284 (2007)
Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. International Journal of Computer Vision 60(1), 63–86 (2004)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(10), 1615–1630 (2005)
Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Van Gool, L.: A comparison of affine region detectors. International Journal of Computer Vision 65(1-2), 43–72 (2005)
van de Sande, K.E.A., Gevers, T., Snoek, C.G.M.: Evaluation of color descriptors for object and scene recognition. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 0, pp. 1–8 (2008)
Zhang, N.: Computing optimised parallel speeded-up robust features (p-surf) on multi-core processors. International Journal of Parallel Programming (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gossow, D., Decker, P., Paulus, D. (2011). An Evaluation of Open Source SURF Implementations. In: Ruiz-del-Solar, J., Chown, E., Plöger, P.G. (eds) RoboCup 2010: Robot Soccer World Cup XIV. RoboCup 2010. Lecture Notes in Computer Science(), vol 6556. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20217-9_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-20217-9_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20216-2
Online ISBN: 978-3-642-20217-9
eBook Packages: Computer ScienceComputer Science (R0)