Advertisement

Optical flow and phase portrait methods for environmental satellite image sequences

  • Isaac Cohen
  • Isabelle Herlin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1065)

Abstract

We present in this paper a motion computation and interpretation framework for oceanographic satellite images. This framework is based on the use of a non quadratic regularization technique in optical flow computation that preserves flow discontinuities. We also show that using an appropriate tessellation of the image according to an estimate of the motion field can improve optical flow accuracy and yields more reliable flows. This method defines a non uniform multiresolution scheme that refines mesh resolution only in the neighborhood of moving structures. The second part of the paper deals with the interpretation of the obtained displacement field. We use a phase portrait model with a new formulation of the approximation of an oriented flow field. This allows us to consider arbitrary polynomial phase portrait models for characterizing salient flow features. This new framework is used for processing oceanographic and atmospheric image sequences and presents an alternative to the very complex physical modelling techniques.

Keywords

Optical flow Non quadratic regularization Finite element method Adaptive mesh Phase portrait Flow pattern classification Ocean circulation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    J.L. Barron, D.J. Fleet, and S.S. Beauchemin. Performance of optical flow techniques. International Journal of Computer Vision, 12(1):43–77, February 1994.Google Scholar
  2. 2.
    M. J. Black. Recursive non-linear estimation of discontinuous flow fields. In Third European Conference on Computer Vision, pages 138–145, Sweden, May 1994. Springer-Verlag.Google Scholar
  3. 3.
    M.J. Black and P. Anandan. Robust dynamic motion estimation over time. In IEEE Proceedings of Computer Vision and Pattern Recognition, pages 296–302, June 1991.Google Scholar
  4. 4.
    P. G. Ciarlet. The finite element methods for elliptic problems. North-Holland, Amsterdam, 1987.Google Scholar
  5. 5.
    I. Cohen and I. Herlin. A motion computation and interpretation framework for oceanographic satellite images. In IEEE, Computer Vision Symposium, pages 13–18, Florida, November 1995.Google Scholar
  6. 6.
    R. M. Ford and R. N. Strickland. Nonlinear phase portrait models for oriented textures. In IEEE Proceedings of Computer Vision and Pattern Recognition, pages 644–645, New-York, June 1993.Google Scholar
  7. 7.
    R. M. Ford, R. N. Strickland, and B. A. Thomas. Image models for 2-D flow visualization and compression. CVGIP: Graphical models and Image Processing, 56(1):75–93, January 1994.Google Scholar
  8. 8.
    R. Glowinski. Numerical Methods for Nonlinear Variational Problems. Springer-Verlag, New-York, 1984. Springer Series in Computational Physics.Google Scholar
  9. 9.
    G. H. Golub and C. F. Van Loan. Matrix Computations. The Johns Hopkins University Press, London, second edition, 1989.Google Scholar
  10. 10.
    B.K.P. Horn and G. Schunck. Determining optical flow. Artificial Intelligence, 17:185–203, 1981.Google Scholar
  11. 11.
    M. Irani, B. Rousso, and S. Peleg. Detecting and tracking multiple moving objects using temporal integration. In Proceedings of the Second European Conference on Computer Vision 1992, pages 282–287, May 1992.Google Scholar
  12. 12.
    J.R. Muller, P. Anandan, and J.R. Bergen. Adaptive-complexity registration of images. In IEEE Proceedings of Computer Vision and Pattern Recognition, pages 953–957, 1994.Google Scholar
  13. 13.
    V. V. Nemytskii and V. V. Stepanov. Qualitative Theory of Differential Equations. Dover Publications, New York, 1989.Google Scholar
  14. 14.
    A. R. Rao and R. C. Jain. Computerized flow field analysis: Oriented textures fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(7):693–709, July 1992.Google Scholar
  15. 15.
    J.B. Rosen. The gradient projection method for nonlinear programming. Part I: Linear constraints. J. Soc. Indust. Appl. Math., 8(1):181–217, March 1960.Google Scholar
  16. 16.
    C.-F. Shu and R.C. Jain. Vector field analysis for oriented patterns. In IEEE Proceedings of Computer Vision and Pattern Recognition, pages 673–676, Urbana Champaign, Illinois, June 1992.Google Scholar
  17. 17.
    R. Szeliski and H.Y. Shum. Motion estimation with quadtree splines. Technical report, DEC Cambridge Research Lab, March 1995.Google Scholar
  18. 18.
    A. Verri and T. Poggio. Motion field and optical flow: Qualitative properties. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(5):490–498, May 1989.Google Scholar
  19. 19.
    J. Zhong, T.S. Huang, and R.J. Adrian. Salient structure analysis of fluid flow. In IEEE Proceedings of Computer Vision and Pattern Recognition, pages 310–315, Seattle, Washington, June 1994.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Isaac Cohen
    • 1
  • Isabelle Herlin
    • 1
  1. 1.AIR ProjectINRIALe Chesnay CedexFrance

Personalised recommendations