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Discretization in Hausdorff Space

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Abstract

In this paper, a new approach to the discretization of n-dimensional Euclidean figures is studied: the discretization of a compact Euclidean set K is a discrete set S whose Hausdorff distance to K is minimal; in particular such a discretization depends on the choice of a metric in the Euclidean space, for example the Euclidean or a chamfer distance. We call such a set S a Hausdorff discretizing set of K. The set of Hausdorff discretizing sets of K is nonvoid, finite, and closed under union; we consider thus in particular the greatest one among such sets, which we call the maximal Hausdorff discretization of K. We give a mathematical description of Hausdorff discretizing sets: it is related to the discretization by dilation considered by Heijmans and Toet and the cover discretization studied by Andrès. We have a bound on the Hausdorff distance between a compact set and its maximal Hausdorff discretization, and the latter converges (for the Hausdorff metric) to the compact set when the spacing of the discrete grid tends to zero. Such a convergence result holds also for the discretization by dilation when the structuring element satisfies the covering assumption. Our approach is here the most general possible. In a next paper we will consider the case where the underlying metric on points satisfies some general constraints in relation to the cells associated to the discrete points, and we will then see that these constraints guarantee that the usual supercover and cover discretizations give indeed Hausdorff discretizing sets.

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References

  1. E. Andrès, “Cercles discrets et rotations discrètes,” Thèse de Doctorat, Université Louis Pasteur, Strasbourg, France, Dec. 1992.

    Google Scholar 

  2. E. Andrès, “Standard cover: A new class of discrete primitives,” Internal Report, IRCOM, Université de Poitiers, 1998.

  3. E. Andrès, P. Nehlig, and J. Fran¸con, “Tunnel-free supercover 3D polygons and polyhedra,” in Proc. Eurographics'97, Budapest, Sept. 1997, D. Fellner and L. Szirmay-Kalos (Eds.), Vol. 16, No. 3, pp. C3-C13.

  4. E. Andrès, P. Nehlig, and J. Françon, “Supercover of straight lines, planes and triangles,” in Proc. 7th Discrete Geometry and Computer Imagery (DGCI) Conference, Montpellier, France. LNCS, Vol. 1347, Springer-Verlag, 1997, pp. 243-254.

    Google Scholar 

  5. M.F. Barnsley, Fractals Everywhere, 2nd ed., Academic Press, 1993.

  6. J.E. Bresenham, “Algorithm for computer control of a digital plotter,” IBM Systems Journal, Vol. 4, No. 1, pp. 25-30, 1965.

    Google Scholar 

  7. J.M. Chassery and A. Montanvert, Géométrie Discrète en Analyse d'Images, Hermès: Paris, 1991.

    Google Scholar 

  8. D. Cohen-Or and A. Kaufman, “Fundamentals of surface voxelization,” Graphical Models & Image Processing, Vol. 57, No. 6, pp. 453-461, 1995.

    Google Scholar 

  9. S. Duval and M. Tajine, “Digital geometry and fractal geometry,” in Proc. 5th Digital Geometry and Computer Imagery (DGCI) Conference, Clermont-Ferrand, France, 25-27 Sept., 1995, D. Richard (Ed.), pp. 93-104.

  10. S. Duval, M. Tajine, and D. Ghazanfarpour, “Fractal modeling with infinite trees,” Theoretical Computer Science, submitted.

  11. A. Giraldo, A. Gross, and L.J. Latecki, “Digitizations preserving shape,” Pattern Recognition, Vol. 32, No. 3, pp. 365-376, 1999.

    Google Scholar 

  12. A. Gross and L.J. Latecki, “Digitizations preserving topological and differential geometric properties,” Computer Vision & Image Processing, Vol. 62, No. 3, pp. 370-381, 1995.

    Google Scholar 

  13. A. Gross and L.J. Latecki, “A realistic digitization model of straight-lines,” Computer Vision & Image Processing, Vol. 67, No. 2, pp. 131-142, 1997.

    Google Scholar 

  14. A. Gross and L.J. Latecki, “Digital geometric methods in document image analysis,” Pattern Recognition, Vol. 32, No. 3, pp. 407-424, 1999.

    Google Scholar 

  15. R.C. Gonzalez and R.E. Woods, Digital Image Processing, Addison-Wesley, 1992.

  16. H.J.A.M. Heijmans, “Morphological discretization,” in Geometrical Problems in Image Processing, U. Eckhardt et al. (Eds.), Akademie Verlag: Berlin, 1991, pp. 99-106.

    Google Scholar 

  17. H.J.A.M. Heijmans, “Discretization of morphological operators,” J. Visual Communication & Image Representation, Vol. 3, No. 2, pp. 182-193, 1992.

    Google Scholar 

  18. H.J.A.M. Heijmans, Morphological Image Operators, Academic Press: Boston, 1994.

    Google Scholar 

  19. H.J.A.M. Heijmans and C. Ronse, “The algebraic basis of mathematical morphology I: dilations and erosions,” Computer Vision, Graphics&Image Processing,Vol. 50, No. 3, pp. 245-295, 1990.

    Google Scholar 

  20. H.J.A.M. Heijmans and J. Serra, “Convergence, continuity and iteration in mathematical morphology,” J. Visual Communication & Image Representation, Vol. 3, No. 1, pp. 84-102, 1992.

    Google Scholar 

  21. H.J.A.M. Heijmans and A. Toet, “Morphological sampling,” Computer Vision, Graphics & Image Processing: Image Understanding, Vol. 54, No. 3, pp. 384-400, 1991.

    Google Scholar 

  22. J.G. Hocking and G.S. Young, Topology, Dover Publications Inc.: New York, 1988.

    Google Scholar 

  23. R. Hummel and D. Lowe, “Computational considerations in convolution and feature-extraction in images,” in From Pixels to Features, J.C. Simon (Ed.), Elsevier Science Publishers (North-Holland): Amsterdam, 1989, pp. 91-102.

    Google Scholar 

  24. R. Klette, “The m-dimensional grid point space,” Computer Vision, Graphics, & Image Processing, Vol. 30, No. 1, pp. 1-12, 1985.

    Google Scholar 

  25. L.J. Latecki, C. Conrad, and A. Gross, “Preserving topology by a digitization process,” J. Mathematical Imaging & Vision, Vol. 8, pp. 131-159, 1998.

    Google Scholar 

  26. G. Matheron, Random Sets and Integral Geometry, J. Wiley & Sons: New York, 1975.

    Google Scholar 

  27. T. Pavlidis, Algorithms for Graphics and Image Processing, Springer-Verlag: Berlin, 1982.

    Google Scholar 

  28. J.-P. R´eveillès, “Géométrie discrète, calcul en nombres entiers et algorithmique,” Thèse de Doctorat d'Etat, Universit´e Louis Pasteur, Strasbourg, France, Dec. 1991.

    Google Scholar 

  29. C. Ronse, “A bibliography on digital and computational convexity,” IEEE Trans. Pattern Analysis & Machine Intelligence, Vol. 11, No. 2, pp. 181-190, 1989.

    Google Scholar 

  30. C. Ronse and M. Tajine, “Hausdorff discretization for cellular distances, and its relation to cover and supercover discretizations,” submitted.

  31. P.V. Sankar, “Grid intersect quantization schemes for solid object digitization,” Computer Graphics & Image Processing, Vol. 8, pp. 25-42, 1978.

    Google Scholar 

  32. M. Schmitt, “Digitization and connectivity,” in Mathematical Morphology and its Applications to Image and Signal Processing IV, H. Heijmans and J. Roerdink (Eds.), Kluwer Academic Publishers, June 1998, pp. 91-98. International Symposium on Mathematical Morphology 1998.

  33. J. Serra, Image Analysis and Mathematical Morphology, Academic Press: London, 1982.

    Google Scholar 

  34. J. Serra, Image Analysis and Mathematical Morphology, Vol. 2: Theoretical Advances, Academic Press: London, 1988.

    Google Scholar 

  35. K. Sivakumar and J. Goutsias, “On the discretization of morphological operators,” J. Visual Communication & Image Representation, Vol. 8, No. 1, pp. 39-49, 1997.

    Google Scholar 

  36. M. Tajine and C. Ronse, “Preservation of topology by Hausdorff discretization, and comparison to other discretization schemes,” submitted.

  37. M. Tajine and C. Ronse, “Hausdorff sampling of closed sets into a boundedly compact space,” in preparation.

  38. M. Tajine, D. Wagner, and C. Ronse, “Hausdorff discretization and its comparison to other discretization schemes,” in Proc. 9th Digital Geometry and Computer Imagery (DGCI) Conference, Paris. LNCS, Vol. 1568, Springer-Verlag, 1999, pp. 399-410.

    Google Scholar 

  39. D. Wagner, “Distance de Hausdorff et problème discretcontinu,” Mémoire de D.E.A. (M.Sc. Dissertation), Université Louis Pasteur, Strasbourg (France), June 1997. URL: http:// orange.u-strasbg.fr/FIPA/DEA 97 Wagner.ps.gz

    Google Scholar 

  40. D.Wagner,M. Tajine, and C. Ronse, “An approach to discretization based on the Hausdorff metric,” in Mathematical Morphology and its Applications to Image and Signal Processing IV, H. Heijmans and J. Roerdink (Eds.), Kluwer Academic Publishers, June 1998, pp. 67-74. International Symposium on Mathematical Morphology 1998.

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Ronse, C., Tajine, M. Discretization in Hausdorff Space. Journal of Mathematical Imaging and Vision 12, 219–242 (2000). https://doi.org/10.1023/A:1008366032284

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