Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
Hawick, K.A., Leist, A., Playne, D.P.: Parallel graph component labelling with GPUs and CUDA. Parallel Computing 36(12), 655–678 (2010)
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)
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)
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)
Meyer, F.: Topographic distance and watershed lines. Signal Processing 38(1), 113–125 (1994)
Podlozhnyuk, V.: Image Convolution with CUDA. NVIDIA (June 2007), http://developer.download.nvidia.com/compute/cuda/sdk/website/projects/convolutionSeparable/doc/convolutionSeparable.pdf
Roerdink, J.B.T.M., Meijster, A.: The watershed transform: definitions, algorithms and parallelization strategies. Fundam. Inf. 41(1-2), 187–228 (2000)
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)
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:10.1109/34.87344
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)
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)
Wagner, B., Godehardt, M.: Cell reconstruction on stream computing architectures. In: ISMM 2009 Abstract Book, pp. 45–48. University of Groningen (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Körbes, A., Vitor, G.B., de Alencar Lotufo, R., Ferreira, J.V. (2011). Advances on Watershed Processing on GPU Architecture. In: Soille, P., Pesaresi, M., Ouzounis, G.K. (eds) Mathematical Morphology and Its Applications to Image and Signal Processing. ISMM 2011. Lecture Notes in Computer Science, vol 6671. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21569-8_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-21569-8_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21568-1
Online ISBN: 978-3-642-21569-8
eBook Packages: Computer ScienceComputer Science (R0)