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On the Recognition of Articulated Objects (Generalizing the Generalized Hough Transform)

  • Haim J. Wolfson

Abstract

A new method for model based recognition of articulated objects in cluttered scenes is presented. This method applies for objects consisting of rigid parts connected by either rotary or prismatic joints. It can also handle multiply jointed objects. Our method is based on an extension of the Generalized Hough transform paradigm. It is applicable to various viewing transformations in 2-D from 2-D and 3-D from 3-D recognition situations. A variant of our approach applies also to the recognition of 3-D objects from 2-D images. No significant degradation is expected in performance for recognition of articulated objects compared with the recognition of rigid objects containing similar amount of visual information. The technique is of low polynomial complexity in the number of features representing the objects.

Keywords

Reference Frame Object Recognition Coordinate Frame Interest Point Rigid 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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References

  1. [1]
    D. H. Ballard. Generalizing the Hough Transform to Detect Arbitrary Shapes. Pattern Recognition, 13(2):111–122, 1981.MATHCrossRefGoogle Scholar
  2. [2]
    D. IT. Ballard and Brown C. M. Computer Vision. Prentice-Hall, 1982.Google Scholar
  3. [3]
    A. Beinglass and H. J. Wolfson. Articulated Object Recognition, or, How to Generalize the Generalized Hough Transform. Technical report, Eskenazy Inst. of Computer Sciences, Tel Aviv University, 1990.Google Scholar
  4. [4]
    A. Beinglass and H. J. Wolfson. Articulated Object Recognition, or, How to Generalize the Generalized Hough Transform. In Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, Maui, Hawaii, June 1991. also TR of Eskenazy Inst. of Computer Sciences, Tel Aviv Univ., 1990.Google Scholar
  5. [5]
    R.A. Brooks. Symbolic Reasoning Around 3-D Models and 2-D Images. Artificial Intelligence, 17:285–348, 1981.CrossRefGoogle Scholar
  6. [6]
    J. J. Craig. Introduction to Robotics. Addison-Wesley, Readings, MA., 1986.Google Scholar
  7. [7]
    R. Goldberg and D. Lowe. Verification of 3-D parametric models in 2-D image data. In Proc. of IEEE Workshop on Computer Vision, pages 255-257, Miami-Beach, Florida, 1987.Google Scholar
  8. [8]
    W. E. Grimson and T. Lozano-Pérez. Localizing overlapping parts by searching the interpretation tree. IEEE Trans, on PAMI, 9(4):469–482, 1987.CrossRefGoogle Scholar
  9. [9]
    W.E.L. Grimson. Recognition of Object Families Using Parametrized Models. In Proc. of the IEEE Int. Conf. on Computer Vision, pages 93-101, London, England, 1987.Google Scholar
  10. [10]
    A.J. Heller and J.R. Stenstrom. Verification of Recognition and Alignment Hypothesis by Means of Edge Verification Statistics. In Proc. of the DARPA IU Workshop, pages 957-966, Palo Alto, Ca., 1989.Google Scholar
  11. [11]
    J. Hong and H. J. Wolfson. An Improved Model-Based Matching Method Using Footprints. In Proc. of the Int. Conf. on Pattern Recognition, pages 72-78, Rome, Italy, November 1988.Google Scholar
  12. [12]
    D. P. Huttenlocher and S. Ullman. Recognizing Solid Objects by Alignment with an Image. Int. J. of Computer Vision, 5(2):195–212, 1990.CrossRefGoogle Scholar
  13. [13]
    J. Illingworth and J. Kittler. A Survey of the Hough Transform. J. of Computer Vision, Graphics, and Image Processing, 44:87–116, 1988.CrossRefGoogle Scholar
  14. [14]
    Y. Lamdan, J. T. Schwartz, and H. J. Wolfson. Object Recognition by Affine Invariant Matching. In Proc. of the IEEE Conf on Computer Vision and Pattern Recognition, pages 335-344, Ann Arbor, Michigan, June 1988.Google Scholar
  15. [15]
    Y. Lamdan, J. T. Schwartz, and H. J. Wolfson. Affine Invariant Model-Based Object Recognition. IEEE Trans. on Robotics and Automation, 6(5):578–589, 1990.CrossRefGoogle Scholar
  16. [16]
    S. Linnainmaa, D. Harwood, and L.S. Davis. Pose Determination of a Three-Dimensional Object Using Triangle Pairs. IEEE Trans, on PAMI, 10(5):634–647, 1988.CrossRefGoogle Scholar
  17. [17]
    T.M. Silberberg, L.S. Davis, and D. Harwood. An Iterative Hough Procedure for 3-D Object Recognition. Pattern Recognition, 17:621–629, 1984.CrossRefGoogle Scholar
  18. [18]
    G. Stockman. Object Recognition and Localization via Pose Clustering. J. of Computer Vision, Graphics, and Image Processing, 40(3):361–387, 1987.CrossRefGoogle Scholar
  19. [19]
    D.W. Thompson and J.L. Mundy. Three-Dimensional Model Matching from an Unconstrained Viewpoint. In Proc. of the IEEE Int. Conf. on Robotics and Automation, pages 208-220, Raleigh, N. Carolina, 1987.Google Scholar
  20. [20]
    R.F. Vaz and D. Cyganski. Generation of Affine Invariant Local Contour Feature Data. Pattern Recognition Letters, 11:479–483, 1990.MATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1992

Authors and Affiliations

  • Haim J. Wolfson
    • 1
  1. 1.Computer Science Department, Sackler Faculty of Exact SciencesTel Aviv UniversityIsrael

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