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.
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References
Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comp. Vis. 60, 91–110 (2004)
Bay, H., Ess, A., Tuytelaars, T.: van Gool: Speeded up robust features (SURF). CVIU 110, 346–359 (2008)
Lindeberg, T.: Feature detection with automatic scale selection. Int. J. Comp. Vis. 30, 77–116 (1998)
Witkin, A.P.: Scale-space filtering. In: 8th IJCAI, pp. 1019–1022 (1983)
Koenderink, J.J.: The structure of images. Biol. Cyb. 50, 363–370 (1984)
Koenderink, J.J., van Doorn, A.J.: Generic neighborhood operators. IEEE-PAMI 14, 597–605 (1992)
Lindeberg, T.: Scale-Space Theory in Computer Vision. Springer (1994)
Florack, L.M.J.: Image Structure. Springer (1997)
ter Haar Romeny, B.: Front-End Vision and Multi-Scale Image Analysis. Springer (2003)
Lindeberg, T.: Scale-space. In: Wah, B. (ed.) Encyclopedia of Computer Science and Engineering, pp. 2495–2504. Wiley (2008)
Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference, pp. 147–152 (1988)
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)
Lindeberg, T.: Scale selection properties of generalized scale-space interest point detectors. J. Math. Im. Vis (September 2012), doi:10.1007/s10851-012-0378-3
Shi, J., Tomasi, C.: Good features to track. In: CVPR, pp. 593–600 (1994)
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)
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)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 1st edn. Cambridge University Press (2000)
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)
Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. Int. J. Comp. Vis. 60, 63–86 (2004)
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Lindeberg, T. (2013). Image Matching Using Generalized Scale-Space Interest Points. In: Kuijper, A., Bredies, K., Pock, T., Bischof, H. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2013. Lecture Notes in Computer Science, vol 7893. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38267-3_30
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DOI: https://doi.org/10.1007/978-3-642-38267-3_30
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