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Comparision of DdQm models in image optical flow analysis based on LBM

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Journal of Shanghai University (English Edition)

Abstract

The optical flow analysis of the image sequence based on the formal lattice Boltzmann equation, with different DdQm models, is discussed in this paper. The algorithm is based on the lattice Boltzmann method (LBM), which is used in computational fluid dynamics theory for the simulation of fluid dynamics. At first, a generalized approximation to the formal lattice Boltzmann equation is discussed. Then the effects of different DdQm models on the results of the optical flow estimation are compared with each other, while calculating the movement vectors of pixels in the image sequence. The experimental results show that the higher dimension DdQm models, e.g., D3Q15 are more effective than those lower dimension ones.

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References

  1. Aubert G, Kornprobst P. Mathematical problems in image processing-partial differential equations and the calculus of variations [M]. New York: Springer-Verlag, 2002: 181–227.

    Google Scholar 

  2. Beauchemin S S, Barron J L. The computation of optical flow [J]. ACM Computing Surveys, 1995, 27(3):433–466.

    Article  Google Scholar 

  3. Horn B K P, Schunck B G. Determining optical flow [J]. Artificial Intelligence, 1981, 17(1): 185–203.

    Article  Google Scholar 

  4. Baker S, Matthews I. Lucas-Kanade 20 years on: A unifying framework [J]. International Journal of Computer Vision, 2004, 56(3): 221–255.

    Article  Google Scholar 

  5. Fleet D J, Weiss Y. Optical flow estimation [M]. New York: Springer-Verlag, 2005: 239–258.

    Google Scholar 

  6. Barron J L, Fleet D J, Beauchemin S S. Performance of optical flow techniques [J]. International Journal of Computer Vision, 1994, 12(1): 43–77.

    Article  Google Scholar 

  7. Mesbah M. Gradient-based optical flow: A critical review [C]// Fifth International Symposium on Signal Processing and Its Applications, ISSPA’99, Brisbane, Australia. 1999: 467–470.

  8. Cofaru C, Philips W, van Paepegem W. Gradientbased optical flow for sub-pixel registration of speckle image sequences using a spatial/temporal postprocessing technique [C]// IEEE International Conference on Image Processing, San Diego, USA. 2008: 841–844.

  9. Li L, Yang Y. Optical flow estimation for a periodic image sequence [C]// IEEE International Conference on Image Processing, San Diego, USA. 2008: 833–836.

  10. Qian Y H, Humières D D, Lallemand P. Lattice BGK models for Navier-Stokes equation [J]. Europhysics Letters, 1992, 17(6): 479–484.

    Article  MATH  Google Scholar 

  11. He X Y, Luo L S. Theory of the lattice Boltzmann method: From the Boltzmann equation to the lattice Boltzmann equation [J]. Physical Review E, 1997, 56(6): 6811–6817.

    Article  Google Scholar 

  12. Jawerth B, Lin P, Sinzinger E. Lattice Boltzmann models for anisotropic diffusion of images [J]. Journal of Mathematical Imaging and Vision, 1999, 11(3): 231–237.

    Article  MathSciNet  MATH  Google Scholar 

  13. Chen Y, Yan Z Z, Shi J. Application of lattice Boltzmann method to image segmentation [C]// Proceedings of the 29th Annual International Conference of the IEEE EMBS Cité Internationale, Lyon, France. 2007:6561–6564.

  14. Chang Q S, Yang T. A lattice Boltzmann method for image denoising [J]. IEEE Transactions on Image Processing, 2009, 18(12): 2797–2802.

    Article  MathSciNet  Google Scholar 

  15. Ding GT, Li S Q, Luo D X. Optical flow analysis based on lattice Boltzmann method and lower order approximation with relaxation factors [C]// 2010 International Conference on Multimedia Technology (ICMT2010), Ningbo, China. 2010: 419–422.

  16. Ding G T, Luo D X, Li S Q. Image sequence segmentation based on the formal lattice Boltzmann equation and its lower order approximation [C]// 2010 International Conference on Audio, Language and Image Processing, Shanghai, China. 2010.

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Authors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, Shanghai, 200072, P. R. China

    Guang-tai Ding  (丁广太), Jia-yin Xu  (徐加银) & Chao Li  (李 超)

Authors
  1. Guang-tai Ding  (丁广太)
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  2. Jia-yin Xu  (徐加银)
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  3. Chao Li  (李 超)
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Corresponding author

Correspondence to Guang-tai Ding  (丁广太).

Additional information

Project supported by the National Natural Science Foundation of China (Grant No.40976108), the Shanghai Leading Academic Discipline Project (Grant No.J50103), and the Innovation Program of Municipal Education Commission of Shanghai Municipality (Grant No.11YZ03)

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Ding, Gt., Xu, Jy. & Li, C. Comparision of DdQm models in image optical flow analysis based on LBM. J. Shanghai Univ.(Engl. Ed.) 15, 363–368 (2011). https://doi.org/10.1007/s11741-011-0752-1

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  • DOI: https://doi.org/10.1007/s11741-011-0752-1

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