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Feature-Preserving Medial Axis Noise Removal

  • Roger Tam
  • Wolfgang Heidrich
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2351)

Abstract

This paper presents a novel technique for medial axis noise removal. The method introduced removes the branches generated by noise on an object’s boundary without losing the fine features that are often altered or destroyed by current pruning methods. The algorithm consists of an intuitive threshold-based pruning process, followed by an automatic feature reconstruction phase that effectively recovers lost details without reintroducing noise. The result is a technique that is robust and easy to use. Tests show that the method works well on a variety of objects with significant differences in shape complexity, topology and noise characteristics.

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References

  1. 1.
    Blum, H., Nagel, R.: Shape description using weighted symmetric axis features. Pattern Recognition 10 (1978) 167–180zbMATHCrossRefGoogle Scholar
  2. 2.
    Attali, D., Montanvert, A.: Semicontinuous skeletons of 2D and 3D shapes. In: Proceedings of the International Workshop on Visual Form, Capri, World Scientific (1994) 32–41Google Scholar
  3. 3.
    Brandt, J., Algazi, V.: Continuous skeleton computation by voronoi diagram. CVGIP: Image Understanding 55 (1992) 329–337zbMATHCrossRefGoogle Scholar
  4. 4.
    Ogniewicz, R., Ilg, M.: Voronoi skeletons: Theory and applications. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, Champaign, Illinois (1992) 63–69Google Scholar
  5. 5.
    Pizer, S., Oliver, W., Bloomberg, S.: Hierarchical shape description via the multiresolution symmetric axis transform. IEEE Trans. Pattern Analysis and Machine Intelligence 9 (1987) 505–511CrossRefGoogle Scholar
  6. 6.
    Mokhtarian, F., Mackworth, A.: A theory of multiscale, curvature-based shape representation for planar curves. IEEE Trans. Pattern Analysis and Machine Intelligence 14 (1992) 789–805CrossRefGoogle Scholar
  7. 7.
    Ogniewicz, R.: Automatic medial axis pruning by mapping characteristics of boundaries evolving under the euclidean geometric heat flow onto voronoi skeletons. Technical Report 95-4, Harvard Robotics Laboratory (1995)Google Scholar
  8. 8.
    Shaked, D., Bruckstein, A.: Pruning medial axes. Computer Vision and Image Understanding 69 (1998) 156–169CrossRefGoogle Scholar
  9. 9.
    Attali, D., Sanniti di Baja, G., E., T.: Pruning discrete and semicontinuous skeletons. In De Floriani, C, Braccini, C., Vernazza, G., eds.: Lecture Notes in Computer Science, Image Analysis and Processing. Volume 974. Springer-Verlag (1995) 488–493Google Scholar
  10. 10.
    Attali, D., Montanvert, A.: Modeling noise for a better simplification of skeletons. In: Proc. of the International Conference on Image Processing. Volume III., Lausanne, Switzerland (1996) 13–16Google Scholar
  11. 11.
    Ogniewicz, R.: Skeleton-space: A multiscale shape description combining region and boundary information. In: Proceedings of the Conference on Computer Vision and Pattern Recognition, Seattle, WA (1994) 746–751Google Scholar
  12. 12.
    Ranjan, V., Fournier, A.: Matching and interpolation of shapes using unions of circles. Computer Graphics Forum (Proceedings of Eurographics’ 96) 15 (1996) 35–42CrossRefGoogle Scholar
  13. 13.
    Leyton, M.: Shape and causal-history. In Arcelli, C., Cordella, L., Sanniti di Baja, G., eds.: Visual Form: Analysis and Recognition. Plenum Press (1992) 379–388Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Roger Tam
    • 1
  • Wolfgang Heidrich
    • 1
  1. 1.Department of Computer ScienceUniversity of British ColumbiaUSA

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