Advertisement

Flexible syntactic matching of curves

  • Yoram Gdalyahu
  • Daphna Weinshall
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1407)

Abstract

We present a flexible curve matching algorithm which performs qualitative matching between curves that are only weakly similar. While for model based recognition it is sufficient to determine if two curves are identical or not, for image database organization a continuous similarity measure, which indicates the amount of similarity between the curves, is needed. We demonstrate how flexible matching can serve to define a suitable measure. Extensive experiments are described, using real images of 3D objects. Occluding contours are matched under partial occlusion and change of viewpoint, and even when the two objects are different (such as the two side views of a horse and a cow). Using the resulting similarity measure between images, automatic hierarchical clustering of an image database is also shown, which faithfully capture the real structure in the data.

Keywords

Invariant Attribute Partial Match Edit Operation Syntactical Representation Chinese Character Recognition 
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.
    Ansari N., Delp E., “Partial shape recognition: a landmark based approach”, PAMI 12, 470–489, 1990.Google Scholar
  2. 2.
    Arkin E., Paul Chew L., Huttenlocher D., Kedem K. and Mitchel J., “An efficiently computable metric for comparing polygonal shapes”, PAMI 13, 209–216, 1991.Google Scholar
  3. 3.
    Ayach N. and Faugeras O., “HYPER: a new approach for the recognition and positioning of two dimensional objects”, PAMI 8, 44–54, 1986.Google Scholar
  4. 4.
    Basri R., Costa L., Geiger D. and Jacobs D., “Determining the similarity of deformable shapes” IEEE Workshop on Physics Based Modeling in Computer Vision, 135–143, 1995.Google Scholar
  5. 5.
    Bhanu B. and Faugeras O., “Shape matching of two dimensional objects”, PAMI 6, 137–155, 1984.Google Scholar
  6. 6.
    Blatt M., Wiseman S. and Domany E., “Data Clustering Using a Model Granular Magnet”, Neural Computation 9, 1805–1842, 1997.CrossRefGoogle Scholar
  7. 7.
    Brint A. and Brady M., “Stereo matching of curves”, Image and vision computing 8, 50–56, 1990.CrossRefGoogle Scholar
  8. 8.
    Christmas W., Kittler J. and Petrou M., “Structural matching in computer vision using probabilistic relaxation”, PAMI 17, 749–764, 1995.Google Scholar
  9. 9.
    Del Bimbo A. and Pala P., “Visual image retrieval by elastic matching of user sketches”, PAMI 19, 121–132, 1997.Google Scholar
  10. 10.
    Geiger D., Gupta A., Costa L. and Vlontzos J., “Dynamic programming for detecting, tracking and matching deformable contours”, PAMI 17, 294–302, 1995.Google Scholar
  11. 11.
    Gorman J., Mitchell O. and Kuhl F., “Partial shape recognition using Dynamic programming”, PAMI 10, 257–266, 1988.Google Scholar
  12. 12.
    Gregor J. and Thomason M., “Dynamic programming alignment of sequences representing cyclic patterns”, PAMI 15, 129–135, 1993.Google Scholar
  13. 13.
    Huttenlocher D. and Ullman S., “Object recognition using alignment”, Proc. ICCV (London), 102–111, 1987.Google Scholar
  14. 14.
    Koch M. and Kashyap R., “Using polygons to recognize and locate partially occluded objects”, PAMI 9, 483–494, 1987.Google Scholar
  15. 15.
    Li S., “Matching: invariant to translations, rotations and scale changes”, Pattern Recognition 25, 583–594, 1992.MathSciNetCrossRefGoogle Scholar
  16. 16.
    Liu H. and Srinath M., “Partial shape classification using contour matching in distance transformation”, PAMI 12, 1072–1079, 1990.Google Scholar
  17. 17.
    Lu C. and Dunham J., “Shape matching using polygon approximation and dynamic alignment”, PRL 14, 945–949, 1993.Google Scholar
  18. 18.
    Marzal A. and Vidal E., “Computation of normalized edit distance and applications”, PAMI 15, 926–932, 1993.Google Scholar
  19. 19.
    Rocha J. and Pavlidis T. “A shape analysis model with applications to a character recognition system”, PAMI 16, 393–404, 1994.Google Scholar
  20. 20.
    Sclaroff S. and Pentland A., “Modal matching for correspondence and recognition”, PAMI 17, 545–561, 1995.Google Scholar
  21. 21.
    Shapiro L. and Brady M., “Feature based correspondence: an eigenvector approach”, Image and vision computing 10, 283–288, 1992.CrossRefGoogle Scholar
  22. 22.
    Tsai W. and Yu S., “Attributed string matching with merging for shape recognition”, PAMI 7, 453–462, 1985.Google Scholar
  23. 23.
    Tsay Y. and Tsai W., “Attributed string matching by split and merge for on-line Chinese character recognition”, PAMI 15, 180–185, 1993.Google Scholar
  24. 24.
    Ueda N. and Suzuki S., “Learning visual models from shape contours using multiscale covex/concave structure matching”, PAMI 15, 337–352, 1993.Google Scholar
  25. 25.
    Umeyama S., “Parameterized point pattern matching and its application to recognition of object families”, PAMI 15, 136–144, 1993.Google Scholar
  26. 26.
    Wang Y. and Pavlidis T., “Optimal correspondence of string subsequences”, PAMI 12, 1080–1086, 1990.Google Scholar
  27. 27.
    Weinshall D. and Werman M., “On View Likelihood and Stability”, PAMI 19, 97–108, 1997.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Yoram Gdalyahu
    • 1
  • Daphna Weinshall
    • 1
  1. 1.Institute of Computer ScienceThe Hebrew UniversityJerusalemIsrael

Personalised recommendations