Visual Form pp 109-118 | Cite as

Multidimensional Indexing for Recognizing Visual Shapes

  • Andrea Califano
  • Rakesh Mohan


We present an efficient and uniform paradigm for automatic model acquisition and recognition of contour shapes. Model acquisition time is linear to cubic in the number of object features. Object recognition time is constant to linear in the number of models in the database and linear to cubic in the number of features in the image.


Shape Memory Lookup Table Local Shape Model Acquisition Shape Instance 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [Ba81a]
    D.H. Ballard, Parameter nets: A theory of low level vision, in Proc. 7th Int. Joint Conf. On Artificial Intell., Aug. 1981, pp. 1068-1078.Google Scholar
  2. [Ba81b]
    D.H. Ballard, Generalizing the Hough transform to detect arbitrary shapes, Pattern Recognition, Vol. 13, No. 2, 1981, pp. 111–122.zbMATHCrossRefGoogle Scholar
  3. [Ca88]
    A. Califano, Feature recognition using correlated information contained in multiple neighborhoods, in Proc. 7thNat Conf. on Artificial Intell., July 1988, pp. 831-836.Google Scholar
  4. [CBT89]
    A. Califano, R.M. Bolle, and R.W. Taylor, Generalized neighborhoods: A new approach to complex feature extraction, in Proc. of IEEE Conf on Comp. Vision and Pattern Recognition, June 1989.Google Scholar
  5. [CM90]
    A. Califano and R. Mohan, Generalized shape autocorrelation, in Proc. 8th Natnl. Conf. on Artificial Intelligence, AAAI-90, July 1990.Google Scholar
  6. [Et88]
    G.J Ettinger, Large hierarchical object recognition using libraries of parameterized model sub-parts, in Proc. IEEE Conf on Comp. Vision and Pattern Recognition, June 1988, pp 32-41.Google Scholar
  7. [Gr89]
    W.E.L. Grimson, “The combinatorics of heuristic search termination for object recognition in cluttered environments,” Tech. Report AI Memo 1111, MIT, May 1989.Google Scholar
  8. [Gr90]
    W.E.L. Grimson and D.P. Huttenlocher, “On the Sensitivity of Geometric Hashing,” MIT AI Memo, to appear in 1990 Intnl. Conf. on Comp. Vision, Osaka Japan.Google Scholar
  9. [Ho62]
    P.V.C. Hough, Methods and Means for Recognizing Complex Patterns, U.S. Patent 3069654, 1962.Google Scholar
  10. [KS86]
    A. Kalvin, E. Schonberg, J.T. Schwartz, M. Sharir, Two-dimensional, model-based, boundary matching using footprints, The International Journal of Robotics Research, Vol. 6, No. 4, Winter 1986.Google Scholar
  11. [KD79]
    J. Koenderink and A. van Doorn, The internal representation of solid shape with respect to vision, Biological Cybernetics, Vol. 32, 1979, pp. 211–216.zbMATHCrossRefGoogle Scholar
  12. [LSW88a]
    Y. Lamdan, J.T Schwartz and H.J. Wolfson, On recognition of 3D object from 2D images, in Proc. IEEE Conf. Rob. Aut., pp. 1407-1413.Google Scholar
  13. [LSW88b]
    Y. Lamdan, J.T. Schwartz and H.J. Wolfson, Object recognition by affine invariant matching, in Proc. IEEE Conf on Comp. Vision and Patt. Recog., pp. 335-344.Google Scholar
  14. [LW88]
    Y. Lamdan and H.J. Wolfson, Geometric hashing: a general and efficient model-based recognition scheme, in Proc. 2nd Intnl. Conf on Comp. Vision, Dec 1988.Google Scholar
  15. [Mo90]
    R. Mohan, Constraints satisfaction networks for computer vision, in Progress in Neural Networks, O. Omidvar Ed., Ablex, 1990.Google Scholar

Copyright information

© Springer Science+Business Media New York 1992

Authors and Affiliations

  • Andrea Califano
    • 1
  • Rakesh Mohan
    • 1
  1. 1.Exploratory Computer Vision GroupIBM, Thomas J. Watson Research CenterYorktown HeightsUSA

Personalised recommendations