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Waterfall Segmentation of Complex Scenes

  • Allan Hanbury
  • Beatriz Marcotegui
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3851)

Abstract

We present an image segmentation technique using the morphological Waterfall algorithm. Improvements in the segmentation are brought about by using improved gradients. These are based on the detection of object boundaries learnt from human segmentations introduced by Martin et al. (2004). We avoid the usual pitfall found when applying Watershed algorithms to these boundaries, namely that the boundary lines usually contain gaps, by making use of distance functions on the boundary image. Two types of distance function are used: the classic distance function and a distance function for numerical images recently introduced by Beucher (2005). Resulting segmentations are compared to human segmentations using the Berkeley segmentation benchmark. The benchmark results show that the proposed segmentation algorithm produces segmentations comparable to those produced by the Normalised Cuts algorithm.

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References

  1. 1.
    Barnard, K., Duygulu, P., de Freitas, N., Forsyth, D., Blei, D., Jordan, M.I.: Matching words and pictures. Journal of Machine Learning Research 3, 1107–1135 (2003)zbMATHCrossRefGoogle Scholar
  2. 2.
    Chen, Y., Wang, J.Z.: Image categorization by learning and reasoning with regions. Journal of Machine Learning Research 5, 913–939 (2004)Google Scholar
  3. 3.
    Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proc. 8th Int’l Conf. Computer Vision, vol. 2, pp. 416–423 (2001)Google Scholar
  4. 4.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. on Pattern Analysis and Machine Intelligence 22, 888–905 (2000)CrossRefGoogle Scholar
  5. 5.
    Beucher, S., Meyer, F.: The morphological approach to segmentation: the watershed transformation. In: Dougherty, E. (ed.) Mathematical Morphology in Image Processing, pp. 433–481. Marcel Dekker, New York (1993)Google Scholar
  6. 6.
    Martin, D., Fowlkes, C., Malik, J.: Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 530–549 (2004)CrossRefGoogle Scholar
  7. 7.
    Lantuejoul, C., Beucher, S.: On the use of geodesic metric in image analysis. Journal of Microscopy 121, 39–49 (1981)Google Scholar
  8. 8.
    Soille, P.: Morphological Image Analysis, 2nd edn. Springer, Heidelberg (2002)Google Scholar
  9. 9.
    Beucher, S.: Numerical residues. In: Mathematical Morphology and its Applications to Image Processing, Proc. ISMM 2005, pp. 23–32 (2005)Google Scholar
  10. 10.
    Beucher, S.: Watershed, hierarchical segmentation and waterfall algorithm. In: Mathematical Morphology and its Applications to Image Processing, Proc. ISMM 1994, pp. 69–76 (1994)Google Scholar
  11. 11.
    Marcotegui, B., Beucher, S.: Fast implementation of waterfall based on graphs. In: Mathematical Morphology and its Applications to Image Processing, Proc. ISMM 2005, pp. 177–186 (2005)Google Scholar
  12. 12.
    Malik, J., Belongie, S., Leung, T., Shi, J.: Contour and texture analysis for image segmentation. International Journal of Computer Vision 43, 7–27 (2001)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Allan Hanbury
    • 1
  • Beatriz Marcotegui
    • 2
  1. 1.Pattern Recognition and Image Processing Group (PRIP), Institute of Computer-Aided AutomationVienna University of TechnologyViennaAustria
  2. 2.Centre de Morphologie MathématiqueFontainebleauFrance

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