Towards a Parallel Topological Watershed: First Results

  • Joël van Neerbos
  • Laurent Najman
  • Michael H. F. Wilkinson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6671)

Abstract

In this paper we present a parallel algorithm for the topological watershed, suitable for a shared memory parallel architecture. On a 24-core machine an average speed-up of about 11 was obtained. The method opens up possibilities for segmentation of gigapixel images such as found in remote sensing routinely.

References

  1. 1.
    Bertrand, G.: On topological watersheds. Journal of Mathematical Imaging and Vision 22(2), 217–230 (2005)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Beucher, S., Lantuéjoul, C.: Use of watersheds in contour detection. In: International Workshop on Image Processing, Real-Time Edge and Motion Detection/Estimation, pp. 17–21 (1979)Google Scholar
  3. 3.
    Couprie, M., Bertrand, G.: Topological grayscale watershed transformation. In: SPIE Vision Geometry V Proceedings, vol. 3168, pp. 136–146. Citeseer (1997)Google Scholar
  4. 4.
    Couprie, M., Najman, L., Bertrand, G.: Quasi-linear algorithms for the topological watershed. J. Math. Imag. Vis. 22, 231–249 (2005)MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Matas, P., Dokládalová, E., Akil, M., Grandpierre, T., Najman, L., Poupa, M., Georgiev, V.: Parallel Algorithm for Concurrent Computation of Connected Component Tree. In: Blanc-Talon, J., Bourennane, S., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2008. LNCS, vol. 5259, pp. 230–241. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Najman, L., Couprie, M.: Building the component tree in quasi-linear time. IEEE Trans. Image Proc. 15, 3531–3539 (2006)CrossRefGoogle Scholar
  7. 7.
    Najman, L.: On the equivalence between hierarchical segmentations and ultrametric watersheds. J. Math. Imag. Vis. (2010) (to appear), http://www.laurentnajman.org
  8. 8.
    Najman, L., Couprie, M., Bertrand, G.: Watersheds, mosaics and the emergence paradigm. Discrete Applied Mathematics 147(2-3), 301–324 (2005)MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Roerdink, J.B.T.M., Meijster, A.: The Watershed Transform: Definitions, Algorithms and Parallelization Strategies. Fundamenta Informaticae 41, 187–228 (2001)MathSciNetMATHGoogle Scholar
  10. 10.
    Schieber, B., Vishkin, U.: On finding lowest common ancestors: simplification and parallelization. SIAM Journal on Computing 17(6), 1253–1262 (1988)MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    Wilkinson, M.H.F., Gao, H., Hesselink, W.H., Jonker, J.E., Meijster, A.: Concurrent computation of attribute filters using shared memory parallel machines. IEEE Trans. Pattern Anal. Mach. Intell. 30(10), 1800–1813 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Joël van Neerbos
    • 1
  • Laurent Najman
    • 2
  • Michael H. F. Wilkinson
    • 1
  1. 1.Johann Bernoulli InstituteUniversity of GroningenThe Netherlands
  2. 2.Laboratoire d’Informatique Gaspard-MongeUniversité Paris-Est, A3SI, ESIEEFrance

Personalised recommendations