Simplifying SURF Feature Descriptor to Achieve Real-Time Performance

  • Marek Kraft
  • Adam Schmidt
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 95)

Abstract

The detection and matching of interest points serves as the base for many computer vision algorithms, such as visual odometry, structure from motion, tracking or simultaneous localization and mapping. The accuracy of matching is therefore of very high importance. This requirement is however often irreconciliable with the requirement of real-time performance, especially on resource constrained architectures. In this paper, we analyze a few possible simplifications to the recently developed SURF feature description and matching scheme, enabling it to shorten the processing time on virtually all computing platforms. The introduced simplifications do not introduce any significant matching performance penalty when compared with the full SURF implementation in the aforementioned applications.

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References

  1. 1.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Computer Vision and Image Understanding 110(3), 346–359 (2008)CrossRefGoogle Scholar
  2. 2.
    Bik, A.J.C., Kreitzer, D.L., Tian, X.: A Case Study on Compiler Optimizations for the Intel Core 2 Duo Processor. International Journal of Parallel Programming 36(6), 571–591 (2008)CrossRefGoogle Scholar
  3. 3.
    Bradski, G., Kaehler, A.: Learning OpenCV. O’Reilly Media Inc., Sebastopol (2008)Google Scholar
  4. 4.
    Brown, D.: Close-range camera calibration. Photogrammetric Engineering 37(8), 855–866 (1971)Google Scholar
  5. 5.
    Deschamps, J.-P., Bioul, G.J.A., Sutter, G.D.: Synthesis of Arithmetic Circuits: FPGA, ASIC and Embedded Systems. Wiley-Interscience, Hoboken (2006)Google Scholar
  6. 6.
    Evans, C.: Notes on the OpenSURF library. Technical Report CSTR-09-001, University of Bristol (2009)Google Scholar
  7. 7.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24(6), 381–395 (1981)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of the 4th Alvey Vision Conference, pp. 147–151 (1988)Google Scholar
  9. 9.
    Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004) ISBN: 0521540518 MATHGoogle Scholar
  10. 10.
    Lienhart, R., Maydt, J.: An extended set of haar-like features for rapid object detection. In: Proc. of International Conference on Image Processing, vol. (1), pp. 900–903 (2002)Google Scholar
  11. 11.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  12. 12.
    Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 430–443. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  13. 13.
    Schmidt, A., Kraft, M., Kasinski, A.J.: An evaluation of image feature detectors and descriptors for robot navigation. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2010. LNCS, vol. 6375, pp. 251–259. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Marek Kraft
    • 1
  • Adam Schmidt
    • 1
  1. 1.Institute of Control and Information EngineeringPoznań University of TechnologyPoznańPoland

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