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Real-Time Image Segmentation on a GPU

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Facing the Multicore-Challenge

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6310))

Abstract

Efficient segmentation of color images is important for many applications in computer vision. Non-parametric solutions are required in situations where little or no prior knowledge about the data is available. In this paper, we present a novel parallel image segmentation algorithm which segments images in real-time in a non-parametric way. The algorithm finds the equilibrium states of a Potts model in the superparamagnetic phase of the system. Our method maps perfectly onto the Graphics Processing Unit (GPU) architecture and has been implemented using the framework NVIDIA Compute Unified Device Architecture (CUDA). For images of 256 ×320 pixels we obtained a frame rate of 30 Hz that demonstrates the applicability of the algorithm to video-processing tasks in real-time.

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References

  1. Shapiro, L.G., Stockman, G.C.: Computer Vision. Prentice-Hall, Englewood Cliffs (2001)

    Google Scholar 

  2. Sahoo, P.K., Soltani, S., Wong, A.K.C., Chen, Y.: A survey of thresholding techniques. Computer Vision, Graphics and Image Processing 41(2), 233–260 (1988)

    Article  Google Scholar 

  3. Swendsen, R.H., Wang, S.: Nonuniversal critical dynamics in Monte Carlo simulations. Physical Review Letters 76(18), 86–88 (1987)

    Article  Google Scholar 

  4. Wolff, U.: Collective Monte Carlo updating for spin systems. Physical Review Letters 62(4), 361–364 (1989)

    Article  Google Scholar 

  5. von Ferber, C., Wörgötter, F.: Cluster update algorithm and recognition. Physical Review E 62(2), 1461–1464 (2000)

    Article  Google Scholar 

  6. Boykov, Y., Funka-Lea, G.: Graph cuts and efficient N-D image segmentation. International Journal of Computer Vision 70(2), 109–131 (2006)

    Article  Google Scholar 

  7. Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. International Journal of Computer Vision 59(2), 167–181 (2004)

    Article  Google Scholar 

  8. Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. Pattern Analysis and Machine Intelligence 24(5), 603–619 (2002)

    Article  Google Scholar 

  9. Potts, R.B.: Some generalized order-disorder transformations. Proc. Cambridge Philos. Soc. 48, 106–109 (1952)

    Article  MathSciNet  MATH  Google Scholar 

  10. Eckes, C., Vorbrüggen, J.C.: Combining data-driven and model-based cues for segmentation of video sequences. In: Proc. of World Congress on Neural Networks, pp. 868–875 (1996)

    Google Scholar 

  11. Blatt, M., Wiseman, S., Domany, E.: Superparamagnetic clustering of data. Physical Review Letters 76(18), 3251–3254 (1996)

    Article  Google Scholar 

  12. Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minmization in vision. Pattern Analysis and Machine Intelligence 9, 1124–1137 (2004)

    Article  Google Scholar 

  13. Ising, E.: Beitrag zur Theorie des Ferromagnetismus. Z. Phys. 31, 253–258 (1925)

    Article  Google Scholar 

  14. Geman, D., Geman, S.: Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. Pattern Analysis and Machine Intelligence 6, 721–741 (1984)

    Article  MATH  Google Scholar 

  15. Meribout, M., Nakanishi, M.: A new real time object segmentation and tracking algorithm and its parallel architecture. Journal of VLSI Signal Processing 39(3), 249–266 (2005)

    Article  Google Scholar 

  16. Carnevali, P., Coletti, L., Patarnello, S.: Image processing by simulated annealing. IBM Journal of Research and Development 29(6), 569–579 (1985)

    Article  Google Scholar 

  17. Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., Teller, E.: Equation of state calculations by fast computing machines. J. of Chem. Phys. 21(11), 1087–1091 (1953)

    Article  Google Scholar 

  18. Barkema, G.T., MacFarland, T.: Parallel simulation of the ising model. Physical Review E 50(2), 1623–1628 (1994)

    Article  Google Scholar 

  19. He, L., Chao, Y., Suzuki, K., Wu, K.: Fast connected-component labeling. Pattern Recognition 42, 1977–1987 (2009)

    Article  MATH  Google Scholar 

  20. Kim, E., Wang, W., Li, H., Huang, X.: A parallel annealing method for automatic color cervigram image segmentation. In: Medical Image Computing and Computer Assisted Intervention, MICCAI-GRID 2009 HPC Workshop (2009)

    Google Scholar 

  21. Vineet, V., Narayanan, P.J.: CUDA cuts: fast graph cuts on the GPU. In: Proc. CVPRW 2008, pp. 1–8 (2008)

    Google Scholar 

  22. Dellen, B., Aksoy, E.E., Wörgötter, F.: Segment tracking via a spatiotemporal linking process including feedback stabilization in an n-d lattice model. Sensors 9(11), 9355–9379 (2009)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Bernstein Center for Computational Neuroscience, Department for Computational Neuroscience, III Physikalisches Institut, Georg-August University, Göttingen, Germany

    Alexey Abramov, Tomas Kulvicius & Florentin Wörgötter

  2. Department of Informatics, Vytautas Magnus University, Kaunas, Lithuania

    Tomas Kulvicius

  3. Bernstein Center for Computational Neuroscience, Max-Planck-Institute for Dynamics and Self-Organization, Göttingen, Germany

    Babette Dellen

  4. Institut de Robòtica i Informàtica Industrial (CSIC-UPC), Barcelona, Spain

    Babette Dellen

Authors
  1. Alexey Abramov
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  2. Tomas Kulvicius
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  3. Florentin Wörgötter
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  4. Babette Dellen
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Editor information

Editors and Affiliations

  1. High Performance Computing Center Stuttgart (HLRS), Universität Stuttgart, Nobelstr. 19, 70569, Stuttgart, Germany

    Rainer Keller

  2. Institute of Computer Science and Engineering, Karlsruhe Institute of Technology, Haid-und-Neu-Str. 7, 76131, Karlsruhe, Germany

    David Kramer

  3. Engineering Mathematics and Computing Lab (EMCL) & Institute for Applied and Numerical Mathematics 4, Karlsruhe Institute of Technology, Fritz-Erler-Str. 23, 76133, Karlsruhe, Germany

    Jan-Philipp Weiss

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Abramov, A., Kulvicius, T., Wörgötter, F., Dellen, B. (2010). Real-Time Image Segmentation on a GPU. In: Keller, R., Kramer, D., Weiss, JP. (eds) Facing the Multicore-Challenge. Lecture Notes in Computer Science, vol 6310. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16233-6_14

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  • DOI: https://doi.org/10.1007/978-3-642-16233-6_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16232-9

  • Online ISBN: 978-3-642-16233-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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