Robust Identification of Locally Planar Objects Represented by 2D Point Clouds under Affine Distortions

  • Dominic Mai
  • Thorsten Schmidt
  • Hans Burkhardt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6376)


The matching of point sets that are characterized only by their geometric configuration is a challenging problem. In this paper, we present a novel point registration algorithm for robustly identifying objects represented by two dimensional point clouds under affine distortions. We make no assumptions about the initial orientation of the point clouds and only incorporate the geometric configuration of the points to recover the affine transformation that aligns the parts that originate from the same locally planar surface of the three dimensional object. Our algorithm can deal well with noise and outliers and is inherently robust against partial occlusions. It is in essence a GOODSAC approach based on geometric hashing to guess a good initial affine transformation that is iteratively refined in order to retrieve a characteristic common point set with minimal squared error. We successfully apply it for the biometric identification of the bluespotted ribbontail ray Taeniura lymma.


Point Cloud Object Recognition Invariant Mapping Iterative Close Point Planar Object 
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.


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  1. 1.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)CrossRefGoogle Scholar
  2. 2.
    Burghardt, T., Campbell, N.: Individual animal identification using visual biometrics on deformable coat patterns. In: ICVS 2007 (March 2007), doi:10.2390Google Scholar
  3. 3.
    Costa, M., Haralick, R., Shapiro, L.: Object recognition using optimal affine-invariant matching (1990)Google Scholar
  4. 4.
    Burkhardt, H., Reisert, M., Li, H.: Invariants for discrete structures - an extension of haar integrals over trf. groups to dirac delta functions. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds.) DAGM 2004. LNCS, vol. 3175, pp. 137–144. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Speed, C.W., Meekan, M.G., Bradshaw, C.J.a.: Spot the match - wildlife photo-identification using information theory. Frontiers in zoology 4, 2 (2007)CrossRefGoogle Scholar
  6. 6.
    Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 509–522 (2002)CrossRefGoogle Scholar
  7. 7.
    Lazebnik, S., Schmid, C., Ponce, J.: Semi-local affine parts for object recognition. In: BMVC, pp. 959–968 (2004)Google Scholar
  8. 8.
    Coetzee, L., Botha, E.C.: Fingerprint recognition in low quality images, vol. 26, pp. 1441–1460 (1993)Google Scholar
  9. 9.
    Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: IEEE Computer Society Conference on CVPR 1991, pp. 586–591 (1991)Google Scholar
  10. 10.
    Rusinkiewicz, S., Levoy, M.: Efficient variants of the icp algorithm. In: International Conference on 3D Digital Imaging and Modeling (2001)Google Scholar
  11. 11.
    Winkelbach, S.: Efficient methods for solving 3d-puzzle-problems. IT - Information Technology 50(3), 199–201 (2008)CrossRefGoogle Scholar
  12. 12.
    Lamdan, Y., Schwartz, J., Wolfson, H.: Object recognition by affine invariant matching. In: CVPR 1988, pp. 335–344 (1988)Google Scholar
  13. 13.
    Wolfson, H., Rigoutsos, I.: Geometric hashing: An overview. CalSE 4(4), 10–21 (1997)Google Scholar
  14. 14.
    Aiger, D., Mitra, N.J., Cohen-Or, D.: 4-points congruent sets for robust surface registration. ACM Transactions on Graphics 27(3), #85, 1–10 (2008)CrossRefGoogle Scholar
  15. 15.
    Burkhardt, H.: Transformationen zur lageinvarianten Merkmalgewinnung. In: Habilitation, Fortschrittbericht(Reihe 10, Nr.7). VDI-Verlag (1979)Google Scholar
  16. 16.
    Weiss, I.: Geometric invariants and object recognition. Int. J. Comput. Vision 10(3), 207–231 (1993)CrossRefGoogle Scholar
  17. 17.
    Michaelsen, E., von Hansen, W., Kirchhof, M., Meidow, J., Stilla, U.: Estimating the essential matrix: Goodsac versus ransac. In: PCV 2006 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Dominic Mai
    • 1
  • Thorsten Schmidt
    • 1
  • Hans Burkhardt
    • 1
  1. 1.Computer Science DepartmentUniversity of FreiburgFreiburg i. Br.Germany

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