Image segmentation by label anisotropic diffusion

  • Raphaëlle Chaine
  • Saïda Bouakaz
Poster Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1451)


Weighing the difficulties of a symbolic description of 3D surface based scattered data, this article propounds a formalisation of the segmentation in discrete labelling terms. Global consistency of the result is expressed as a constraint satisfaction problem. To solve this problem, the method we present is based on an anisotropic diffusion principle along two structures respectively denoted minimal and maximal escarpment trees. These structures are drawn from the graph theory. Novel aspect of our method is its ability to work on non organised points and to detect arbitrary topological types of features, as crease edge or boundaries between two smooth regions. The proposed approach makes possible an hybrid segmentation, involving the duality between regions and boundaries. The method has proven to be effective, as demonstrated below on both synthetical and real data.


Span Tree Minimal Span Tree Constraint Satisfaction Problem Anisotropic Diffusion Global Consistency 
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.


  1. 1.
    ABDELMALEK, N.N.: Algebraic error analysis for surface curvatures and segmentation of 3D range images. Pattern Recognition 23 (1990) 807–817CrossRefGoogle Scholar
  2. 2.
    ALVAREZ, L., LIONS, P.L., MOREL, J.M.: Image selective smoothing and edge detection by non linear diffusion. SIAM J Numer Anal 29 (1992)Google Scholar
  3. 3.
    ARMAN, F.,AGGARWAL, J.K.: Model-based object recognition in dense-range images — A review. ACM Computing surveys 25 (1993)Google Scholar
  4. 4.
    BERTHOD, M.: L'amélioration d'étiquetage: Une approche pour l'utilisation du contexte en reconnaissance des formes. PhD thesis, Université Pierre et Marie Curie Paris VI (1980)Google Scholar
  5. 5.
    BESL, P.J., JAIN, R.C.: Segmentation Through Variable-Order Surface Fitting. IEEE Transactions on pattern analyse and machine intelligence 1 (1988)Google Scholar
  6. 6.
    DO CARMO: Differential Geometry of Curves and Surfaces. Prentice Hall, Englewood, N.J. (1976)Google Scholar
  7. 7.
    FAUGERAS, O., HEBERT, M.: The representation, recognition and locating of 3D objects. Int. J. Robotics Res 5 (1986) 27–52Google Scholar
  8. 8.
    FAUGERAS, O.: Three dimensional computer vision, a geometric view point. MIT Press (1993)Google Scholar
  9. 9.
    HOFFMAN, R., JAIN, A.K.: Segmentation and classification of range images. IEEE Trans PAMI 9 (1987)Google Scholar
  10. 10.
    HOOVER, A., JEAN-BAPTISTE, G., JIANG, X., FLYNN, P., BUNKE, H., GOLDGOF, D., BOWYER, K., EGGERT, D., FITZGIBBON, A., FISHER, R.: An Experimental Comparison of Range Image Segmentation Algorithms. IEEE Trans PAMI July (1996) 1–17Google Scholar
  11. 11.
    HOPPE, H., DEROSE, T., DUCHAMP, T., MCDONALD, J., STUETZLE, W.: Surface Reconstruction from Unorganized Points. Computer Graphics (1992)Google Scholar
  12. 12.
    MOHAND, R., NEVATIA, R.: Using Perceptual Organization to Extract 3-D Structures. IEEE Tr PAMI 11 (1989)Google Scholar
  13. 13.
    PAL, N.,PAL, S.: A review on image segmentation techniques. Pattern Recognition 26 (1993)Google Scholar
  14. 14.
    PERONA, P., MALIK, J.: Scale space and edge detection using anisotropic diffusion. IEEE Comput. Soc. Workshop on Comput. Vision (1987)Google Scholar
  15. 15.
    SANDER, P.T., ZUCKER, S.W.: Inferring Surface Trace and Differential Structure from 3D Images. IEEE Transactions on pattern analyse and machine intelligencence 12 (1990)Google Scholar
  16. 16.
    SANDER, P.T., ZUCKER, S.W.: Singularities of principal direction fields from 3D Images. IEEE Transactions on pattern analysis and machine intelligence 14 (1992)Google Scholar
  17. 17.
    THIRION, J.P.: The extremal mesh and the understanding of 3D surfaces. Technical Report INRIA (1993)Google Scholar
  18. 18.
    ZUCKER, S.W.: Survey region growing; Childhood and adolescence. CVGIP 5 (1976) 382–399Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Raphaëlle Chaine
    • 1
  • Saïda Bouakaz
    • 1
  1. 1.LIGIM, bât 710Université Claude Bernard LYON 1France

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