Skip to main content

Optical Flow Computation from an Asynchronised Multiresolution Image Sequence

  • Conference paper
  • 1577 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5875))

Abstract

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.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  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. 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. Guilherme, N.D., Avinash, C.K.: Vision for mobile robot navigation: A survey. IEEE Trans. on PAMI 24, 237–267 (2002)

    Google Scholar 

  4. Ruhnau, P., Knhlberger, T., Schnoerr, C., Nobach, H.: Variational optical flow estimation for particle image velocimetry. Experiments in Fluids 38, 21–32 (2005)

    Article  Google Scholar 

  5. Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artificial Intelligence 17, 185–204 (1981)

    Article  Google Scholar 

  6. Nir, T., Bruckstein, A.M., Kimmel, R.: Over-parameterized variational optical flow. IJCV 76, 205–216 (2008)

    Article  Google Scholar 

  7. Suter, D.: Motion estimation and vector spline. In: Proceedings of CVPR 1994, pp. 939–942 (1994)

    Google Scholar 

  8. Grenander, U., Miller, M.: Computational anatomy: An emerging discipline. Quarterly of applied mathematics 4, 617–694 (1998)

    MathSciNet  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. Bouguet, J.-Y.: Pyramidal implementation of the Lucas Kanade feature tracker: Description of the algorithm, Microsoft Research Labs, Tech. Rep. (1999)

    Google Scholar 

  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)

    Article  Google Scholar 

  15. Weber, J., Malik, J.: Robust computation of optical flow in a multi-scale differential framework. Int. J. Comput. Vision 14, 67–81 (1995)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  17. Condell, J., Scotney, B., Marrow, P.: Adaptive grid refinement procedures for efficient optical flow computation. Int. J. Comput. Vision 61, 31–54 (2005)

    Article  Google Scholar 

  18. Amiz, T., Lubetzky, E., Kiryati, N.: Coarse to over-fine optical flow estimation. Pattern recognition 40, 2496–2503 (2007)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kameda, Y., Ohnishi, N., Imiya, A., Sakai, T. (2009). Optical Flow Computation from an Asynchronised Multiresolution Image Sequence. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2009. Lecture Notes in Computer Science, vol 5875. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10331-5_38

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-10331-5_38

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10330-8

  • Online ISBN: 978-3-642-10331-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics