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Machine Vision and Applications

, Volume 16, Issue 1, pp 59–63 | Cite as

Efficient pose estimation using view-based object representations

  • Gabriele PetersEmail author
Special issue on ICVS 2003

Abstract.

We present an efficient method for estimating the pose of a three-dimensional object. Its implementation is embedded in a computer vision system which is motivated by and based on cognitive principles concerning the visual perception of three-dimensional objects. Viewpoint-invariant object recognition has been subject to controversial discussions for a long time. An important point of discussion is the nature of internal object representations. Behavioral studies with primates, which are summarized in this article, support the model of view-based object representations. We designed our computer vision system according to these findings and demonstrate that very precise estimations of the poses of real-world objects are possible even if only a small number of sample views of an object is available. The system can be used for a variety of applications.

Keywords:

Pose estimation 3d object recognition tracking cognitive modeling 

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References

  1. 1.
    Burr DC, Morrone MC, Spinelli D (1989) Evidence for edge and bar detectors in human vision. Vision Res 29(4):419-431CrossRefGoogle Scholar
  2. 2.
    Cutzu F, Edelman S (1994) Canonical views in object representation and recognition. Vision Res 34:3037-3056CrossRefGoogle Scholar
  3. 3.
    Chvatal V (1979) A greedy heuristic for the set-covering problem. Math Oper Res 4(3):233-235MathSciNetzbMATHGoogle Scholar
  4. 4.
    Dhome M, Richetin M, Lapreste J, Rives G (1989) Determination of the attitude of 3-D objects from a single perspective view. IEEE Trans Patt Anal Mach Intell 11(12):1265-1278CrossRefGoogle Scholar
  5. 5.
    Edelman S, Bülthoff HH (1992) Orientation dependence in the recognition of familiar and novel views of three-dimensional objects. Vision Res 32(12):2385-2400CrossRefGoogle Scholar
  6. 6.
    Eckes C, Vorbrüggen JC (1996) Combining data-driven and model-based cues for segmentation of video sequences. In: Proc. WCNN96, pp 868-875Google Scholar
  7. 7.
    Horaud R, Conio B, Leboulleux O, Lacolle B (1989) An analytic solution for the perspective 4-point problem. Comput Vision Graph Image Process 47:33-44Google Scholar
  8. 8.
    Haralick RM, Lee C, Ottenberg K, Nölle M (1991) Analysis and solutions of the three point perspective pose estimation problem. In: Proc. IEEE conference on computer vision and pattern recognition, pp 592-598Google Scholar
  9. 9.
    Jones JP, Palmer LA (1987) An evaluation of the two-dimensional gabor filter model of simple receptive fields in cat striate cortex. J Neurophysiol 58(6):1233-1258Google Scholar
  10. 10.
    Koenderink JJ, van Doorn AJ (1976) The singularities of the visual mapping. Biol Cybern 24:51-59zbMATHGoogle Scholar
  11. 11.
    Krüger V, Sommer G (2002) Gabor wavelet networks for efficient head pose estimation. Image Vision Comput 20(9-10):665-672Google Scholar
  12. 12.
    Lades M, Vorbrüggen JC, Buhmann J, Lange J, von der Malsburg C, Würtz RP, Konen W (1993) Distortion invariant object recognition in the dynamic link architecture. IEEE Trans Comput 42:300-311CrossRefGoogle Scholar
  13. 13.
    Logothetis NK, Pauls J, Bülthoff HH, Poggio T (1994) View-dependent object recognition by monkeys. Curr Biol 4:401-414CrossRefGoogle Scholar
  14. 14.
    Logothetis NK, Pauls J, Poggio T (1995) Shape representation in the inferior temporal cortex of monkeys. Curr Biol 5(5):552-563CrossRefGoogle Scholar
  15. 15.
    Lowe DG (1987) Three-dimensional object recognition from single two-dimensional images. Artif Intell 31:355-395CrossRefGoogle Scholar
  16. 16.
    Maurer T, von der Malsburg C (1996) Tracking and learning graphs and pose on image sequences of faces. In: Proc. international conference on automatic face- and gesture- recognition, pp 176-181Google Scholar
  17. 17.
    Peters G (2002) A view-based approach to three-dimensional object perception. Ph.D. Thesis, Shaker Verlag, Aachen, GermanyGoogle Scholar
  18. 18.
    Peters G, von der Malsburg C (2001) View reconstruction by linear combination of sample views. In: Proc. BMVC 2001, pp 223-232Google Scholar
  19. 19.
    Pötzsch M (1994) Die Behandlung der Wavelet-Transformation von Bildern in der Nähe von Objektkanten. Technical Report IRINI 94-04, Institut für Neuroinformatik, Ruhr-Universität Bochum, GermanyGoogle Scholar
  20. 20.
    Tarr MJ (1993) Orientation dependence in three-dimensional object recognition. Ph.D. Thesis, MIT, Cambridge, MAGoogle Scholar
  21. 21.
    Ullman S, Basri R (1990) Recognition by linear combinations of models. IEEE Trans Patt Anal Mach Intell 13(10):992-1006CrossRefGoogle Scholar
  22. 22.
    Wexler M, Kosslyn SM, Berthoz A (1998) Motor processes in mental rotation. Cognition 68:77-94CrossRefGoogle Scholar
  23. 23.
    Wiskott L, Fellous J-M, Krüger N, von der Malsburg C (1997) Face recognition by elastic bunch graph matching. IEEE Trans Patt Anal Mach Intell 19(7):775-779CrossRefGoogle Scholar
  24. 24.
    Yuan J (1989) A general photogrammetric method for determining object position and orientation. IEEE J Robot Automat 5(2):129-142CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin/Heidelberg 2004

Authors and Affiliations

  1. 1.Informatik VIIUniversität DortmundDortmundGermany

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