Skip to main content
Springer Nature Link
Log in

Pattern recognition of strong graphs based on possibilistic c-means and k-formulae matching

  • Conference paper
  • First Online:
Fuzzy Logic in Artificial Intelligence (FLAI 1997)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1566))

Included in the following conference series:

  • 950 Accesses

Abstract

A new graph matching approach based on 1D information is presented. Each node of the matched graphs represents a fuzzy region (fuzzy segmentation step). Each couple of nodes is linked by a relational histogram which can be assumed to the attraction of two regions following a set of directions. This attraction is computed by a continuous function, depending on the distance of the matched objects. Each case of the histogram corresponds to a particular direction. Then, relational graph computed from strong scenes are matched.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. M. Barni, V. Cappellini and A. Mecocci, Comments on “A Possibilistic Approach to Clustering”, IEEE Transactions on Fuzzy Systems, vol 4:3, 1996, pp 393–396

    Article  Google Scholar 

  2. A.T. Berztiss, A Backtrack Procedure for Isomorphism of Directed Graphs, Journal of the Association for Computing Machinery, vol 20:3, 1973, pp 365–377

    MATH  MathSciNet  Google Scholar 

  3. J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981

    MATH  Google Scholar 

  4. R.N. Dave, Characterization and Detection of Noise in Clustering, Pattern Recognition Letters, vol 12, 1991, pp 657–664

    Article  Google Scholar 

  5. D. Dubois and M.C. Jaulent, A General Approach to Parameter Evaluation in Fuzzy Digital Pictures, Pattern Recognition Letters, vol 6, 1987, pp 251–261

    Article  MATH  Google Scholar 

  6. P. Dhérété, L. Wendling and J. Desachy, Fuzzy Segmentation and Astronomical Images Interpretation, ICIAP’95-IAPR, Springer-Verlag, Lecture Notes in Computer Science 974, 1995, pp 21–27

    Google Scholar 

  7. R. Krishnapuram, J.M. Keller and Y. Ma, Quantitative Analysis of Properties and Spatial Relations of Fuzzy Image Regions, IEEE Transactions on Fuzzy Systems, vol 1:3, 1993, pp 222–233

    Article  Google Scholar 

  8. R. Krishnapuram, Generation of Membership Functions via Possibilistic Clustering, 3rd IEEE Conference on Fuzzy System, vol 3, 1994, pp 902–908

    Google Scholar 

  9. J.M. Keller and C.L. Carpenter, Image Segmentation in the Presence of Uncertainty, International Journal of Intelligent System, vol 5:2, 1990, pp 193–208

    MATH  Google Scholar 

  10. R. Krishnapuram, Fuzzy Clustering Methods in Computer Vision, EUFIT’93, 1993, pp 720–730

    Google Scholar 

  11. R. Krishnapuram and J.M. Keller, A Possibilistic Approach to Clustering, IEEE Transactions on Fuzzy Systems, vol 1:2, 1993, pp 98–110

    Article  Google Scholar 

  12. R. Krishnapuram and J.M. Keller, The Possibilistic C-Means Algorithm: Insights and Recommendations. IEEE Transactions on Fuzzy Systems, vol 4:3, 1996, pp 385–393

    Article  Google Scholar 

  13. S. L. Horowitz and T. Pavlidis, Picture Segmentation by a Directed Split and Merge Procedure, 2 nd International Conference on Pattern Recognition, 1974, pp 424–433

    Google Scholar 

  14. P. Matsakis, L. Wendling and J. Desachy, Représentation de la position relative d’objets 2D au moyen d’un histogramme de forces, revue Traitement du Signal, accepté, 1997.

    Google Scholar 

  15. K. Miyajima and A. Ralescu, Spatial Organization in 2D Segmented Images: Representation and Recognition of Primitive Spatial Relations, Fuzzy Sets and Systems, vol. 65, 1994, pp 225–236

    Article  Google Scholar 

  16. A. Rosenfeld, Fuzzy Geometry: An Overview, First IEEE Conference on Fuzzy Systems, 1992, pp 113–118

    Google Scholar 

  17. A. Rosenfeld, R. Hummel and S. Zucker, Scene labelling by relaxation operations, IEEE Transactions on Systems, Man and Cybernetics, SMC-6, 1976, pp 420–433

    Article  MATH  MathSciNet  Google Scholar 

  18. E.H. Ruspini, A New Approach to Clustering, Inform. and Control, vol 15, 1969, pp 22–32

    Article  MATH  Google Scholar 

  19. L. Wendling, Segmentation floue appliquée à la reconnaissance d’objets dans les images numériques, Ph.D. thesis, Université Paul Sabatier, Toulouse, 1997

    Google Scholar 

  20. L. Wendling, A. Pariès and J. Desachy, Pattern Recognition by Splitting Images into Trees of Fuzzy Regions, International Journal of Intelligent Data Analysis, 1997

    Google Scholar 

  21. H. J. Zimmerman and P. Zysno, Quantifying Vagueness in Decision Models, European J. Operational Res., vol 22, 1985, pp 148–158

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Université Paul Sabatier-IRIT, 118, route de Narbonne, 31062, Toulouse Cedex, France

    L. Wendling & J. Desachy

Authors
  1. L. Wendling
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. Desachy
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Anca L. Ralescu James G. Shanahan

Rights and permissions

Reprints and permissions

Copyright information

© 1999 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Wendling, L., Desachy, J. (1999). Pattern recognition of strong graphs based on possibilistic c-means and k-formulae matching. In: Ralescu, A.L., Shanahan, J.G. (eds) Fuzzy Logic in Artificial Intelligence. FLAI 1997. Lecture Notes in Computer Science, vol 1566. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0095078

Download citation

  • DOI: https://doi.org/10.1007/BFb0095078

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66374-4

  • Online ISBN: 978-3-540-48358-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation