Advertisement

Informative views and sequential recognition

  • Tal Arbel
  • Frank P. Ferrie
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1064)

Abstract

In this paper we introduce a method for distinguishing between informative and uninformative viewpoints as they pertain to an active observer seeking to identify an object in a known environment. The method is based on a generalized inverse theory using a probabilistic framework where assertions are represented by conditional probability density functions. Experimental results are presented showing how the resulting algorithms can be used to distinguish between informative and uninformative viewpoints, rank a sequence of images on the basis of their information (e.g. to generate a set of characteristic views), and sequentially identify an unknown object.

Keywords

Probability Density Function Unknown Object Ambiguous Case Clear Winner Alarm Clock 
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.

References

  1. 1.
    Ed. Aloimonos, Y., “Purposive, qualitative, active vision”, CVGIP: Image Understanding, vol. 56, no. 1, pp. 3–129, 1992, special issue.Google Scholar
  2. 2.
    Albert Tarantola, Inverse Problem Theory: Methods for Data Fitting and Model Parameter Estimation, Elsevier Science Publishing Company Inc., 52, Vanderbuilt Avenue, New York, NY 10017, U.S.A., 1987.Google Scholar
  3. 3.
    Tal Arbel, Peter Whaite, and Frank P. Ferrie, “Recognizing volumetric objects in the presence of uncertainty”, in Proceedings 12th International Conference on Pattern Recognition, Jerusalem, Israel, Oct 9–13 1994, pp. 470–476, IEEE Computer Society Press.Google Scholar
  4. 4.
    Peter Whaite and Frank P. Ferrie, “From uncertainty to visual exploration”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 10, pp. 1038–1049, Oct. 1991.Google Scholar
  5. 5.
    Peter Whaite and Frank P. Ferrie, “Autonomous exploration: Driven by uncertainty”, in Proceedings, Conference on Computer Vision and Pattern Recognition, Seattle, Washington, June 21–23 1994, Computer Society of the IEEE, pp. 339–346, IEEE Computer Society Press.Google Scholar
  6. 6.
    K. Bowyer and C. Dyer, “Aspect graphs: An introduction and survey of recent results”, in Close Range Photogrammetry Meets Machine Vision, Proc. of SPIE, 1990, vol. 1395, pp. 200–208.Google Scholar
  7. 7.
    Farshid Arman and J.K. Aggarwal, “Model-based object recognition in dense-range images — a review”, ACM Computing Surveys, vol. 25, no. 1, pp. 5–43, apr 1993.Google Scholar
  8. 8.
    A. H. Barr, “Superquadrics and angle preserving transformations”, IEEE Computer Graphics and Applications, vol. 1, no. 1, pp. 11–23, Jan. 1981.Google Scholar
  9. 9.
    R. Bajcsy and F. Solina, “Three dimensional object recognition revisited”, in Proceedings, 1ST International Conference on Computer Vision, London,U.K., June 1987, Computer Society of the IEEE, IEEE Computer Society Press.Google Scholar
  10. 10.
    Narayan S. Raja and Anil K. Jain, “Recognizing geons from superquadrics fitted to range data”, Image and Vision Computing, April 1992.Google Scholar
  11. 11.
    Frank P. Ferrie, Jean Lagarde, and Peter Whaite, “Darboux frames, snakes, and super-quadrics: Geometry from the bottom up”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 8, pp. 771–784, Aug. 1993.Google Scholar
  12. 12.
    A. Pentland and S. Sclaroff, “Closed form solutions for physically based shape modelling and recognition”, in IEEE Transactions on Pattern Analysis and Machine Intelligence: Special Issue on Physical Modeling in Computer Vision, T. Kanade and K. Ikeuchi, Eds., July 1991, vol. 13(7), pp. 715–729.Google Scholar
  13. 13.
    Jayashree Subrahmonia, David B. Cooper, and Daniel Keren, “Practical reliable bayesian recognition of 2D and 3D objects using implicit polynomials and algebraic invariants”, LEMS 107, Brown University LEMS, Laboratory fo Engineering Man/Machine systems, Division of Engineering, Brown University, Providence, RI 02912, USA, 1992.Google Scholar
  14. 14.
    A. Lejeune and F.P. Ferrie, “Partitioning range images using curvature and scale”, in PROC. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, New York City, New York, June 15–17 1993, pp. 800–801.Google Scholar
  15. 15.
    Daniel Keren, David Cooper, and Jayashree Subrahmonia, “Describing complicated objects by implicit polynomials”, Tech. Rep. 102, Brown University LEMS, Laboratory for Engineering Man/Macine Systems, Division of Engineering, Brown University, Providence RI 021912 USA, 1992.Google Scholar
  16. 16.
    Roland T. Chin and Charles R. Dyer, “Model-based recognition in robot vision”, Computing Surveys, vol. 18, no. 1, pp. 67–108, mar 1986.Google Scholar
  17. 17.
    Tal Arbel and Frank P. Ferrie, “Parametric shape recognition using a probabilistic inverse theory”, Pattern Recognition Letters, p. TBA, 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Tal Arbel
    • 1
  • Frank P. Ferrie
    • 1
  1. 1.Centre for Intelligent MachinesMcGill UniversityMontréalCanada

Personalised recommendations