Abstract
The GPU programmability opens a new perspective for algorithms that have not been studied and used for real applications on commodity state-of-the-art hardware due to their computational expenses. In this paper, we present three implementations of a partitioning algorithm for multi-channel images, which extends an original algorithm for single-channel images presented in the early 1990’s. The segmentation algorithm is based on the information theory concept of minimum description length, which leads to the formulation of an energy functional. The optimal solution is obtained by minimizing the functional. The minimization approach follows a graduated non-convexity approach, which leads to a fully explicit scheme. As the scheme is applied to all pixels of the image simultaneously, it is naturally parallelizable. Besides the optimized sequential implementation in C++ we developed a GLSL version of the algorithm using vertex and fragment shaders as well as a CUDA version using global memory, shared memory, and texture memory. We compare the performance of the implementations, discuss the implementation details, and show that suitability of this algorithm for GPU allows it to become a comparable alternative to the modern partitioning algorithm (multi-label Graph-Cuts).
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References
Blake A., Zisserman A.: Visual Reconstruction. MIT Press, Cambridge, MA (1987)
Boykov Y., Veksler O., Zabih R.: Efficient restoration of multicolor image with independent noise. Technical report (1998)
Boykov Y., Veksler O., Zabih R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 23(11), 1222–1239 (1999)
CUDA: http://www.nvidia.com/
Figueiredo M.A.T., Leitão J.M.N.: Unsupervised image restoration and edge location using compound gauss-markov random fields and the MDL principle. IEEE Trans. Image Process. 6(8), 1089–1102 (1997)
GCC: http://gcc.gnu.org/
Geman S., Geman D.: Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. Readings in uncertain reasoning (1990)
Goeddeke D.: http://www.mathematik.uni-dortmund.de/goeddeke/gpgpu/tutorial.html
Gruenwald P.D.: The Minimum Description Length principle. The MIT Press, Cambridge, MA (2007)
Ivanovska T.: Efficient multichannel image partitioning: theory and application. Ph.D. thesis, Jacobs University Bremen (2009)
Ivanovska T., Hahn H.K., Linsen L.: On global mdl-based multichannel image restoration and partitioning. In: 20th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG) (2012)
Leclerc Y.G.: Constructing simple stable descriptions for image partitioning. J. Comput. Vis. 3(1), 73–102 (1989)
Lehmann E.L., Casella G.: Theory of Point Estimation (Springer Texts in Statistics). Springer, Berlin (2003)
Li S.Z.: Markov Random Field Modeling in Image Analysis. Springer, New York, Inc. (2001) http://portal.acm.org/citation.cfm?id=381180
Mumford D., Shah J.: Optimal approximation by piecewise smooth functions and associated variational problems. Commun. Pure Appl. Math. 42, 577–684 (1989)
Owens J.D., Houston M., Luebke D., Green S., Stone J.E., Phillips J.C.: Gpu computing. Proc. IEEE 96 (5), 879–899 (2008). doi:10.1109/JPROC.2008.917757. http://dx.doi.org/10.1109/JPROC.2008.917757
Owens J.D. et al.: A survey of general-purpose computation on graphics hardware. Comput. Graph. Forum 26(1), 80–133 (2007)
Rost R.J.: OpenGL Shading Language. Addison-Wesley Professional, Reading, MA (2006)
Szeliski R., Zabih R., Scharstein D., Veksler O., Kolmogorov V., Agarwala A., Tappen M., Rother C.: A comparative study of energy minimization methods for markov random fields with smoothness-based priors. IEEE Trans. Pattern Anal. Mach. Intell. 30(6), 1068–1080 (2008)
Vineet V., Narayanan P.J.: Cuda cuts: Fast graph cuts on the gpu. Vis. Pattern Recogn. Workshop 0, 1–8 (2008). doi:10.1109/CVPRW.2008.4563095
Vineet V., Narayanan P.J.: Solving multilabel mrfs using incremental alpha-expansion on the gpus. In: Ninth Asian Conference on Computer Vision (ACCV 2009), vol. poster (2009)
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Communicated by: Gabrid Wittum.
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Ivanovska, T., Linsen, L., Hahn, H.K. et al. GPU implementations of a relaxation scheme for image partitioning: GLSL versus CUDA. Comput. Visual Sci. 14, 217–226 (2011). https://doi.org/10.1007/s00791-012-0176-x
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DOI: https://doi.org/10.1007/s00791-012-0176-x