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Viewpoint Selection – Planning Optimal Sequences of Views for Object Recognition

  • Frank Deinzer
  • Joachim Denzler
  • Heinrich Niemann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2756)

Abstract

In the past decades most object recognition systems were based on passive approaches. But in the last few years a lot of research was done in the field of active object recognition. In this context there are several unique problems to be solved, like the fusion of several views and the selection of the best next viewpoint.

In this paper we present an approach to solve the problem of choosing optimal views (viewpoint selection) and the fusion of these for an optimal 3D object recognition (viewpoint fusion). We formally define the selection of additional views as an optimization problem and we show how to use reinforcement learning for viewpoint training and selection in continuous state spaces without user interaction. We also present an approach for the fusion of multiple views based on recursive density propagation.

The experimental results show that our viewpoint selection is able to select a minimal number of views and perform an optimal object recognition with respect to the classification.

Keywords

Bayesian Network Object Recognition Recognition Rate Camera Movement Multiple View 
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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References

  1. 1.
    Bertsekas, D.P., Tstsiklis, J.N.: Neuro–Dynamic Programming. Athena Scientific, Belmont (1996)zbMATHGoogle Scholar
  2. 2.
    Borotschnig, H., Paletta, L., Prantl, M., Pinz, A.: A Comparison of Probabilistic, Possibilistic and Evidence Theoretic Fusion Schemes for Active Object Recognition. Computing 62, 293–319 (1999)zbMATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Deinzer, F., Denzler, J., Niemann, H.: On Fusion of Multiple Views for Active Object Recognition. In: Radig, B., Florczyk, S. (eds.) DAGM 2001. LNCS, vol. 2191, pp. 239–245. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  4. 4.
    Deinzer, F., Denzler, J., Niemann, H.: Improving Object Recognition By Fusion Of Multiple Views. In: 3rd Indian Conference on Computer Vision Graphics and Image Processing, Ahmedabad, Indien, pp. 161–166. Allied Publishers Pvt. Ltd (2002)Google Scholar
  5. 5.
    Denzler, J., Brown, C.M.: Information theoretic sensor data selection for active object recognition and state estimation. PAMI 24(2) (2002)Google Scholar
  6. 6.
    Grässl, C., Deinzer, F., Niemann, H.: Continuous Parametrization of Normal Distributions for Improving the Discrete Statistical Eigenspace Approach for Object Recognition. In: PRIP 2003 (May 2003) (submitted)Google Scholar
  7. 7.
    Isard, M., Andrew, B.: CONDENSATION – Conditional Density Propagation for Visual Tracking. In: IJCV 1998, vol. 29(1), pp. 5–28 (1998)Google Scholar
  8. 8.
    Kalman, R.E.: A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering, 35–44 (1960)Google Scholar
  9. 9.
    Krebs, B., Burkhardt, M., Korn, B.: Handling Uncertainty in 3D Object Recognition using Bayesian Networks. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 782–795. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  10. 10.
    Madsen, C.B., Christensen, H.I.: A Viewpoint Planning Strategy for Determining True Angles on Polyhedral Objects by Camera Alignment. PAMI 19(2) (1997)Google Scholar
  11. 11.
    Lehel, P., Hemayed, E.E., Farag, A.A.: Sensor Planning for a Trinocular Active Vision System. In: CVPR, vol. II, pp. 306–312 (1999)Google Scholar
  12. 12.
    Roy, S.D., Chaudhury, S., Banerjee, S.: Recognizing Large 3-D Objects through Next View Planning using an Uncalibrated Camera. In: ICCV 2001, Vancouver, Canada, vol. II, pp. 276–281. IEEE Computer Press, Los Alamitos (2001)Google Scholar
  13. 13.
    Schiele, B., Crowley, J.L.: Transinformation for Active Object Recognition. In: ICCV 1998, Bombay, India, pp. 249–254 (1998)Google Scholar
  14. 14.
    Sutton, R.S., Barto, A.G.: Reinforcement Learning. A Bradford Book. Cambridge, London (1998)Google Scholar
  15. 15.
    Viola, P., Wells III, W.M.: Alignment by Maximization of Mutual Information. International Journal of Computer Vision 24(2), 137–154 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Frank Deinzer
    • 1
  • Joachim Denzler
    • 1
  • Heinrich Niemann
    • 1
  1. 1.Chair for Pattern Recognition, Department of Computer ScienceUniversity of Erlangen-NürnbergErlangen

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