Optical Flow Computation from an Asynchronised Multiresolution Image Sequence

  • Yusuke Kameda
  • Naoya Ohnishi
  • Atsushi Imiya
  • Tomoya Sakai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5875)


We develop a method for the optical flow computation from a zooming image sequence. The synchronisation of image resolution for a pair of successive images in an image sequence is a fundamental requirement for optical flow computation. In a real application, we are, however, required to deal with a zooming and dezooming image sequences, that is, we are required to compute optical flow from a multiresolution image sequence whose resolution dynamically increases and decreases. As an extension of the multiresolution optical flow computation which computes the optical flow vectors using coarse-to-fine propagation of the computation results across the layers, we develop an algorithm for the computation of optical flow from a zooming image sequence.


Particle Image Velocimetry Image Sequence Optical Flow Active Vision Successive Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Zhou, Z., Synolakis, C.E., Leahy, R.M., Song, S.M.: Calculation of 3D internal displacement fields from 3D X-ray computer tomographic images. In: Proceedings of Royal Society: Mathematical and Physical Sciences, vol. 449, pp. 537–554 (1995)Google Scholar
  2. 2.
    Kalmoun, E.M., Köstler, H., Rüde, U.: 3D optical flow computation using parallel vaiational multigrid scheme with application to cardiac C-arem CT motion. Image and Vision Computing  25, 1482–1494 (2007)Google Scholar
  3. 3.
    Guilherme, N.D., Avinash, C.K.: Vision for mobile robot navigation: A survey. IEEE Trans. on PAMI 24, 237–267 (2002)Google Scholar
  4. 4.
    Ruhnau, P., Knhlberger, T., Schnoerr, C., Nobach, H.: Variational optical flow estimation for particle image velocimetry. Experiments in Fluids 38, 21–32 (2005)CrossRefGoogle Scholar
  5. 5.
    Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artificial Intelligence 17, 185–204 (1981)CrossRefGoogle Scholar
  6. 6.
    Nir, T., Bruckstein, A.M., Kimmel, R.: Over-parameterized variational optical flow. IJCV 76, 205–216 (2008)CrossRefGoogle Scholar
  7. 7.
    Suter, D.: Motion estimation and vector spline. In: Proceedings of CVPR 1994, pp. 939–942 (1994)Google Scholar
  8. 8.
    Grenander, U., Miller, M.: Computational anatomy: An emerging discipline. Quarterly of applied mathematics 4, 617–694 (1998)MathSciNetGoogle Scholar
  9. 9.
    Weickert, J., Schnörr, C.: Variational optic flow computation with a spatio-temporal smoothness constraint. Journal of Mathematical Imaging and Vision 14, 245–255 (2001)zbMATHCrossRefGoogle Scholar
  10. 10.
    Weickert, J., Bruhn, A., Papenberg, N., Brox, T.: Variational optic flow computation: From continuous models to algorithms. In: Proceedings of International Workshop on Computer Vision and Image Analysis, IWCVIA 2003 (2003)Google Scholar
  11. 11.
    Papenberg, N., Bruhn, A., Brox, T., Didas, S., Weickert, J.: Highly accurate optic flow computation with theoretically justified warping. International Journal of Computer Vision 67, 141–158 (2006)CrossRefGoogle Scholar
  12. 12.
    Werner, T., Pock, T., Cremers, D., Bischof, H.: An unbiased second-order prior for high-accuracy motion estimation. In: Rigoll, G. (ed.) DAGM 2008. LNCS, vol. 5096, pp. 396–405. Springer, Heidelberg (2008)Google Scholar
  13. 13.
    Bouguet, J.-Y.: Pyramidal implementation of the Lucas Kanade feature tracker: Description of the algorithm, Microsoft Research Labs, Tech. Rep. (1999)Google Scholar
  14. 14.
    Hwan, S., Hwang, S.-H., Lee, U.K.: A hierarchical optical flow estimation algorithm based on the interlevel motion smoothness constraint. Pattern Recognition 26, 939–952 (1993)CrossRefGoogle Scholar
  15. 15.
    Weber, J., Malik, J.: Robust computation of optical flow in a multi-scale differential framework. Int. J. Comput. Vision 14, 67–81 (1995)CrossRefGoogle Scholar
  16. 16.
    Battiti, R., Amaldi, E., Koch, C.: Computing optical flow across multiple scales: An adaptive coarse-to-fine strategy. Int. J. Comput. Vision 2, 133–145 (1991)CrossRefGoogle Scholar
  17. 17.
    Condell, J., Scotney, B., Marrow, P.: Adaptive grid refinement procedures for efficient optical flow computation. Int. J. Comput. Vision 61, 31–54 (2005)CrossRefGoogle Scholar
  18. 18.
    Amiz, T., Lubetzky, E., Kiryati, N.: Coarse to over-fine optical flow estimation. Pattern recognition 40, 2496–2503 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yusuke Kameda
    • 1
  • Naoya Ohnishi
    • 2
  • Atsushi Imiya
    • 3
  • Tomoya Sakai
    • 3
  1. 1.Graduate School of Advanced Integration ScienceChiba University 
  2. 2.School of Science and TechnologyChiba University 
  3. 3.Institute of Media and Information TechnologyChiba UniversityChibaJapan

Personalised recommendations