Advertisement

The Redundancy Pyramid and Its Application to Segmentation on an Image Sequence

  • Jocelyn Marchadier
  • Walter G. Kropatsch
  • Allan Hanbury
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3175)

Abstract

Irregular pyramids organize a sequence of partitions of images in such a way that each partition is deduced from the preceding one by union of some of its regions. In this paper, we show how a single pyramid can be used to encode redundant subparts of different partitions. We obtain a pyramid that accounts for the redundancy of the partitions. This structure, naturally called the redundancy pyramid, can be used for many purposes. We also demonstrate and discuss some applications for studying image sequences.

Keywords

Great Common Divisor Regional Cover Common Multiple Contour Point Good Segmentation 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Brun, L., Kropatsch, W.G.: Construction of combinatorial pyramids. In: Proceedings of the 4th IAPR-TC15 Workshop on Graph-based Representations in Pattern Recognition, pp. 1–12 (2003)Google Scholar
  2. 2.
    Cho, K., Meer, P.: Image segmentation from consensus information. Computer Vision and Image Understanding 68(1), 72–89 (1997)CrossRefGoogle Scholar
  3. 3.
    Förstner, W.: Generic estimation procedures for orientation with minimum redundant information. 2nd Course on Digital Photogrammetry (1999)Google Scholar
  4. 4.
    Keselman, Y., Dickinson, S.: Generic model abstraction from examples. In: Proc. IEEE Conference CVPR, December 2001, vol. 1, pp. 856–863 (2001)Google Scholar
  5. 5.
    Kropatsch, W.G.: Abstraction Pyramids on Discrete Representations. In: Braquelaire, A., Lachaud, J.-O., Vialard, A. (eds.) DGCI 2002. LNCS, vol. 2301, pp. 1–21. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  6. 6.
    Marchadier, J., Michelin, S., Arquès, D.: Thinning GrayscaleWell-Composed Images. Pattern Recognition Letters 25, 581–590 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Jocelyn Marchadier
    • 1
  • Walter G. Kropatsch
    • 1
  • Allan Hanbury
    • 1
  1. 1.Pattern Recognition and Image Processing Group (PRIP)Vienna University of TechnologyViennaAustria

Personalised recommendations