Advances on Watershed Processing on GPU Architecture

  • André Körbes
  • Giovani Bernardes Vitor
  • Roberto de Alencar Lotufo
  • Janito Vaqueiro Ferreira
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6671)


This paper presents an overview on the advances of watershed processing algorithms executed on GPU architecture. The programming model, memory hierarchy and restrictions are discussed, and its influence on image processing algorithms detailed. The recently proposed algorithms of watershed transform for GPU computation are examined and briefly described. Its implementations are analyzed in depth and evaluations are made to compare them both on the GPU, against a CPU version and on two different GPU cards.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Galilée, B., Mamalet, F., Renaudin, M., Coulon, P.-Y.: Parallel asynchronous watershed algorithm-architecture. IEEE Transactions on Parallel and Distributed Systems 18(1), 44–56 (2007)CrossRefGoogle Scholar
  2. 2.
    Hawick, K.A., Leist, A., Playne, D.P.: Parallel graph component labelling with GPUs and CUDA. Parallel Computing 36(12), 655–678 (2010)CrossRefMATHGoogle Scholar
  3. 3.
    Kauffmann, C., Piche, N.: A cellular automaton for ultra-fast watershed transform on GPU. In: 19th International Conference on Pattern Recognition, pp. 1–4. IEEE Computer Society, Tampa (2008)Google Scholar
  4. 4.
    Körbes, A., Lotufo, R.: Analysis of the watershed algorithms based on the breadth-first and depth-first exploring methods. In: SIBGRAPI 2009, pp. 133–140. IEEE Computer Society, Rio de Janeiro (2009)Google Scholar
  5. 5.
    Körbes, A., Lotufo, R.: On watershed transform: Plateau treatment and influence of the different definitions in real applications. In: Proceedings of the 17th International Conference on Systems, Signals and Image Processing, Rio de Janeiro, Brazil, pp. 376–379 (2010)Google Scholar
  6. 6.
    Meyer, F.: Topographic distance and watershed lines. Signal Processing 38(1), 113–125 (1994)CrossRefMATHGoogle Scholar
  7. 7.
  8. 8.
    Roerdink, J.B.T.M., Meijster, A.: The watershed transform: definitions, algorithms and parallelization strategies. Fundam. Inf. 41(1-2), 187–228 (2000)MathSciNetMATHGoogle Scholar
  9. 9.
    Trieu, D.B.K., Maruyama, T.: Real-time image segmentation based on a parallel and pipelined watershed algorithm. Journal of Real-Time Image Processing 2(4), 319–329 (2007)CrossRefGoogle Scholar
  10. 10.
    Vincent, L., Soille, P.: Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(6), 583–598 (1991), doi:10.1109/34.87344CrossRefGoogle Scholar
  11. 11.
    Vitor, G.: Rastreamento de alvo móvel em mono–visão aplicado no sistema de navegação autônoma utilizando GPU. Master’s thesis, Universidade Estadual de Campinas, Campinas, São Paulo, Brazil (March 2010)Google Scholar
  12. 12.
    Vitor, G., Ferreira, J., Körbes, A.: Fast image segmentation by watershed transform on graphical hardware. In: Proceedings of the 30ºCILAMCE. Armação dos Búzios, Brazil (November 2009)Google Scholar
  13. 13.
    Wagner, B., Godehardt, M.: Cell reconstruction on stream computing architectures. In: ISMM 2009 Abstract Book, pp. 45–48. University of Groningen (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • André Körbes
    • 1
  • Giovani Bernardes Vitor
    • 2
  • Roberto de Alencar Lotufo
    • 1
  • Janito Vaqueiro Ferreira
    • 2
  1. 1.School of Electrical and Computer Engineering (Department of Computer Engineering and Industrial Automation)University of CampinasCampinasBrazil
  2. 2.School of Mechanical EngineeringUniversity of CampinasCampinasBrazil

Personalised recommendations