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Image Matching Using Generalized Scale-Space Interest Points

  • Tony Lindeberg
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7893)

Abstract

The performance of matching and object recognition methods based on interest points depends on both the properties of the underlying interest points and the associated image descriptors. This paper demonstrates the advantages of using generalized scale-space interest point detectors when computing image descriptors for image-based matching. These generalized scale-space interest points are based on linking of image features over scale and scale selection by weighted averaging along feature trajectories over scale and allow for a higher ratio of correct matches and a lower ratio of false matches compared to previously known interest point detectors within the same class. Specifically, it is shown how a significant increase in matching performance can be obtained in relation to the underlying interest point detectors in the SIFT and the SURF operators. We propose that these generalized scale-space interest points when accompanied by associated scale-invariant image descriptors should allow for better performance of interest point based methods for image-based matching, object recognition and related vision tasks.

Keywords

interest points scale selection scale linking matching object recognition feature detection scale invariance scale space 

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References

  1. 1.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comp. Vis. 60, 91–110 (2004)CrossRefGoogle Scholar
  2. 2.
    Bay, H., Ess, A., Tuytelaars, T.: van Gool: Speeded up robust features (SURF). CVIU 110, 346–359 (2008)Google Scholar
  3. 3.
    Lindeberg, T.: Feature detection with automatic scale selection. Int. J. Comp. Vis. 30, 77–116 (1998)Google Scholar
  4. 4.
    Witkin, A.P.: Scale-space filtering. In: 8th IJCAI, pp. 1019–1022 (1983)Google Scholar
  5. 5.
    Koenderink, J.J.: The structure of images. Biol. Cyb. 50, 363–370 (1984)MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Koenderink, J.J., van Doorn, A.J.: Generic neighborhood operators. IEEE-PAMI 14, 597–605 (1992)CrossRefGoogle Scholar
  7. 7.
    Lindeberg, T.: Scale-Space Theory in Computer Vision. Springer (1994)Google Scholar
  8. 8.
    Florack, L.M.J.: Image Structure. Springer (1997)Google Scholar
  9. 9.
    ter Haar Romeny, B.: Front-End Vision and Multi-Scale Image Analysis. Springer (2003)Google Scholar
  10. 10.
    Lindeberg, T.: Scale-space. In: Wah, B. (ed.) Encyclopedia of Computer Science and Engineering, pp. 2495–2504. Wiley (2008)Google Scholar
  11. 11.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference, pp. 147–152 (1988)Google Scholar
  12. 12.
    Lindeberg, T.: Generalized scale-space interest points: Scale-space primal sketch for differential descriptors (2010) (under revision for International Journal of Computer Vision, original version submitted in June 2010)Google Scholar
  13. 13.
    Lindeberg, T.: Scale selection properties of generalized scale-space interest point detectors. J. Math. Im. Vis (September 2012), doi:10.1007/s10851-012-0378-3Google Scholar
  14. 14.
    Shi, J., Tomasi, C.: Good features to track. In: CVPR, pp. 593–600 (1994)Google Scholar
  15. 15.
    Lindeberg, T.: On automatic selection of temporal scales in time-casual scale-space. In: Sommer, G. (ed.) AFPAC 1997. LNCS, vol. 1315, pp. 94–113. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  16. 16.
    Benhimane, S., Malis, E.: Real-time image-based tracking of planes using efficient second-order minimization. In: Intelligent Robots and Systems, pp. 943–948 (2004)Google Scholar
  17. 17.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 1st edn. Cambridge University Press (2000)Google Scholar
  18. 18.
    Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., van Gool, L.: A comparison of affine region detectors. Int. J. Comp. Vis. 65, 43–72 (2005)CrossRefGoogle Scholar
  19. 19.
    Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. Int. J. Comp. Vis. 60, 63–86 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Tony Lindeberg
    • 1
  1. 1.School of Computer Science and CommunicationKTH Royal Institute of TechnologyStockholmSweden

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