Fluid Motion Estimation Based on Energy Constraint

  • Han Zhuang
  • Hongyan Quan
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 324)


This paper presents a method for motion estimation of fluid flow in natural scene. Due to drastic brightness changes in images sequence, previous methods based on continuity equation or brightness consistency constraint cannot be applied in this context well. We define Brightness Distribution Matrix (BDM) to present regional brightness. In the initialization of motion field, the BDM consistency between original point and corresponding point is used as a constraint. Towards the incorrect motion vector caused by drastic brightness change, we denoise to the initial motion field by statistical method, and then a novel smoothness constraint is applied to optimization for denoised motion field. The results of natural fluid flow in images show the validity of our method and the obtained motion field can be used to process 3D recovery for fluid flow.


Motion field energy function fluid 


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  1. 1.
    Yu, M.-S., Li, S.-F.: Dynamic Facial Expression Recognition Based on Optical Flow. Journal of Microelectronics and Computer 22, 113–119 (2005)Google Scholar
  2. 2.
    Yang, G.-L., Wang, Z.-L., Wang, G.-J., Chen, F.-J.: Facial Expression Recognition Based on Optical Flow for Non-rigid Motion Analysis. Journal of Computer Science 34, 213–229 (2007)Google Scholar
  3. 3.
    Das Peddada, S., McDevitt, R.: Least Average Residual Algorithm(LARA) for Tracking the Motion of Arctic Sea Ice. IEEE Trans. Geoscience and Remote Sensing 34(4), 915–926 (1996)CrossRefGoogle Scholar
  4. 4.
    Ottenbacher, A., Tomasini, M., Holmund, K., Schmetz, J.: Low-Level Cloud Motion Winds from Metrosat High-Resolution Visible Imagery. Weather and Forecasting 12(1), 175–184 (1997)CrossRefGoogle Scholar
  5. 5.
    Cohen, I., Herlin, I.: Non Uniform Multiresolution Method for Optical Flow and Phase Portrait Models: Environmental Applications. International Journal of Computer Vision 33(1), 29–49 (1999)CrossRefGoogle Scholar
  6. 6.
    Lu, Z., Liao, Q., Pei, J.: A PIV Approach Based on Nonlinear Filter. Journal of Electronics & Information Technology 32(2) (2010)Google Scholar
  7. 7.
    Shu, X., Kang, S., Long, Y.: Method of medical image registration based on optical flow field. Computer Engineering and Applications 44, 191–198 (2008)Google Scholar
  8. 8.
    Nogawa, H., Nakajima, Y., Sato, Y.: Acquisition of Symbolic Description from Flow Fields: A New Approach Based on a Fluid Model. IEEE Trans. Pattern Analysis Machine Intelligence 19, 58–63 (1997)CrossRefGoogle Scholar
  9. 9.
    Horn, B., Schunck, B.: Determining optical flow. Artificial Intelligence 17, 185–203 (1981)CrossRefGoogle Scholar
  10. 10.
    Lucas, B.D., Kanade, T.: An Iterative Image Registration Technique with an Application to Stereo Vision. In: Proceedings of Imaging Understanding Workshop, pp. 121–130 (1981)Google Scholar
  11. 11.
    Tistarelli, M.: Multiple Constraints for Optical Flow. In: Eklundh, J.-O. (ed.) ECCV 1994. LNCS, vol. 800, pp. 61–70. Springer, Heidelberg (1994)CrossRefGoogle Scholar
  12. 12.
    Uras, S., Girosi, F., Verri, A., Torre, V.: A computational approach to motion perception. Biological Cybernetics 60, 79–87 (1988)CrossRefGoogle Scholar
  13. 13.
    Corpetti, T., Memin, E., Perez, P.: Dense Estimation of fluid flows. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(3) (2002)Google Scholar
  14. 14.
    Zhou, L., Kambhamettu, C., Goldof, D.B.: Fluid structure and motion analysis from multi-spectrum 2D cloud image sequence. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 744–751 (2000)Google Scholar
  15. 15.
    Nakajima, Y., Inomata, H., Nogawa, H., Sato, Y., Tamura, S., Okazaki, K., Torii, S.: Physics-based flow estimation of fluids. Pattern Recognition 36, 1203–1212 (2003)CrossRefGoogle Scholar
  16. 16.
    Arnaud, E., Mémin, É., Sosa, R., Artana, G.: A fluid motion estimator for schlieren image velocimetry. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 198–210. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  17. 17.
    Sakaino, H.: Fluid Motion Estimation Method based on Physical Properties of Waves. In: IEEE Conference on Computer Vision and Pattern Recognition (2008)Google Scholar
  18. 18.
    Fitzpatrick, J.M., Pederson, C.A.: A Method for Calculating Fluid Flow in Time Dependent Density Images. Electronic Imaging 1, 347–352 (1988)Google Scholar
  19. 19.
    Wang, Y., Zhai, H., Mu, G.: Shape description matrix and its application to color-image retrieval and recognition. Science in China E 47, 159–165 (2004)CrossRefGoogle Scholar
  20. 20.
    Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: High Accuracy Optical Flow Estimation Based on a Theory for Warping. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 25–36. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  21. 21.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision, 321–331 (1987)Google Scholar
  22. 22.
    DynTex dynamic texture library,
  23. 23.
    Deqing Sun, S., Stefan, R., Michael, J.B.: Secrets of optical flow estimation and their principles. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, California (2010)Google Scholar
  24. 24.
    Nilanjan, R.: Computation of fluid and particle motion from a time-sequenced image pair: A Global Outlier Identification Approach. IEEE Transactions on Image Processing 10(20), 2925–2936 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Han Zhuang
    • 1
  • Hongyan Quan
    • 1
  1. 1.East China Normal University ScienceShanghaiChina

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