Skip to main content

A Scale-Space Approach to Nonlocal Optical Flow Calculations

  • Conference paper
  • First Online:
Book cover Scale-Space Theories in Computer Vision (Scale-Space 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1682))

Included in the following conference series:

Abstract

This paper presents an interpretation of a classic optical flow method by Nagel and Enkelmann as a tensor-driven anisotropic diffusion approach in digital image analysis. We introduce an improvement into the model formulation, and we establish well-posedness results for the resulting system of parabolic partial differential equations. Our method avoids linearizations in the optical flow constraint, and it can recover displacement fields which are far beyond the typical one-pixel limits that are characteristic for many differential methods for optical flow recovery. A robust numerical scheme is presented in detail. We avoid convergence to irrelevant local minima by embedding our method into a linear scale- space framework and using a focusing strategy from coarse to fine scales. The high accuracy of the proposed method is demonstrated by means of a synthetic and a real-world image sequence.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. L. Alvarez, J. Esclarín, M. Lefébure and J. Sánchez, A PDE model for computing the optical flow, Proc. XVI Congreso de Ecuaciones Diferenciales y Aplicaciones (C.E.D.Y.A. XVI, Las Palmas de Gran Canaria, Sept. 21–24, 1999), in press.

    Google Scholar 

  2. L. Alvarez, F. Guichard, P.-L. Lions, J.-M. Morel, Axioms and fundamental equations in image processing, Arch. Rational Mech. Anal., Vol. 123, 199–257, 1993.

    MATH  MathSciNet  Google Scholar 

  3. L. Alvarez, P.-L. Lions, J.-M. Morel, Image selective smoothing and edge detection by nonlinear diffusion. II, SIAM J. Numer. Anal., Vol. 29, 845–866, 1992.

    MATH  MathSciNet  Google Scholar 

  4. P. Anandan, A computational framework and an algorithm for the measurement of visual motion, Int. J. Comput. Vision, Vol. 2, 283–310, 1989.

    Google Scholar 

  5. J.L. Barron, D.J. Fleet, S.S. Beauchemin, Performance of optical flow techniques, Int. J. Comput. Vision, Vol. 12, 43–77, 1994.

    Google Scholar 

  6. F. Bergholm, Edge focusing, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 9, 726–741, 1987.

    Google Scholar 

  7. M. Bertero, T.A. Poggio, V. Torre, Ill-posed problems in early vision, Proc. IEEE, Vol. 76, 869–889, 1988.

    Google Scholar 

  8. M.J. Black, P. Anandan, Robust dynamic motion estimation over time, Proc. IEEE Comp. Soc. Conf. on Computer Vision and Pattern Recognition (CVPR’ 91, Maui, June 3–6, 1991), IEEE Computer Society Press, Los Alamitos, 292–302, 1991.

    Google Scholar 

  9. A. Blake, A. Zisserman, Visual reconstruction, MIT Press, Cambridge (Mass.), 1987.

    Google Scholar 

  10. I. Cohen, Nonlinear variational method for optical flow computation, Proc. Eighth Scandinavian Conf. on Image Analysis (SCIA’ 93, Tromsø, May 25–28, 1993), Vol. 1, 523–530, 1993.

    Google Scholar 

  11. R. Deriche, P. Kornprobst, G. Aubert, Optical-flow estimation while preserving its discontinuities: A variational approach, Proc. Second Asian Conf. Computer Vision (ACCV’ 95, Singapore, December 5–8, 1995), Vol. 2, 290–295, 1995.

    Google Scholar 

  12. W. Enkelmann, Investigation of multigrid algorithms for the estimation of optical flow fields in image sequences, Computer Vision, Graphics and Image Processing, Vol. 43, 150–177, 1988.

    Google Scholar 

  13. L. Florack, Image structure, Kluwer, Dordrecht, 1997.

    Google Scholar 

  14. L.M.J. Florack, W.J. Niessen, M. Nielsen, The intrinsic structure of the optic flow incorporating measurement duality, Int. J. Comput. Vision, Vol. 27, 263–286, 1998.

    Google Scholar 

  15. B. ter Haar Romeny, L. Florack, J. Koenderink, M. Viergever (Eds.), Scale-space theory in computer vision, Lecture Notes in Computer Science, Vol. 1252, Springer, Berlin, 1997.

    Google Scholar 

  16. B. Horn, B. Schunck, Determining optical flow, Artif. Intell., Vol. 17, 185–203, 1981.

    Google Scholar 

  17. T. Iijima, Basic theory on normalization of pattern (in case of typical one-dimensional pattern), Bulletin of the Electrotechnical Laboratory, Vol. 26, 368–388, 1962 (in Japanese).

    Google Scholar 

  18. T. Iijima, Pattern recognition, Corona-sha, 1973 (in Japanese).

    Google Scholar 

  19. T. Iijima, Theory of pattern recognition, Series of Basic Information Technology, Vol. 6, Morishita Publishing, 1989 (in Japanese).

    Google Scholar 

  20. J.J. Koenderink, The structure of images, Biological Cybernetics, Vol. 50, 363–370, 1984.

    MATH  MathSciNet  Google Scholar 

  21. A. Kumar, A.R. Tannenbaum, G.J. Balas, Optic flow: a curve evolution approach, IEEE Trans. Image Proc., Vol. 5, 598–610, 1996.

    Google Scholar 

  22. T. Lindeberg, Scale-space theory in computer vision, Kluwer, Boston, 1994.

    Google Scholar 

  23. A. Mitiche, P. Bouthemy, Computation and analysis of image motion: a synopsis of current problems and methods, Int. J. Comput. Vision, Vol. 19, 29–55, 1996.

    Google Scholar 

  24. H.H. Nagel, W. Enkelmann, An investigation of smoothness constraints for the estimation of displacement vector fields from images sequences, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 8, 565–593, 1986.

    Google Scholar 

  25. M. Proesmans, L. Van Gool, E. Pauwels, A. Oosterlinck, Determination of optical flow and its discontinuities using non-linear diffusion, J.-O. Eklundh (Ed.), Computer vision-ECCV’ 94, Lecture Notes in Computer Science, Vol. 801, Springer, Berlin, 295–304, 1994.

    Google Scholar 

  26. E. Radmoser, O. Scherzer, J. Weickert, Scale-space properties of regularization methods, this volume.

    Google Scholar 

  27. C. Schnörr, Segmentation of visual motion by minimizing convex non-quadratic functionals, Proc. 12th Int. Conf. Pattern Recognition (ICPR 12, Jerusalem, Oct. 9–13, 1994), Vol. A, IEEE Computer Society Press, Los Alamitos, 661–663, 1994.

    Google Scholar 

  28. J. Shah, A nonlinear diffusion model for discontinuous disparity and half-occlusions in stereo, Proc. IEEE Comp. Soc. Conf. Computer Vision and Pattern Recognition (CVPR’ 93, New York, June 15–17, 1993), IEEE Computer Society Press, Los Alamitos, 34–40, 1993.

    Google Scholar 

  29. J. Sporring, M. Nielsen, L. Florack, P. Johansen (Eds.), Gaussian scale-space theory, Kluwer, Dordrecht, 1997.

    MATH  Google Scholar 

  30. J. Weickert, Theoretical foundations of anisotropic diffusion in image processing, Computing, Suppl. 11, 221–236, 1996.

    Google Scholar 

  31. J. Weickert, Anisotropic diffusion in image processing, Teubner, Stuttgart, 1998.

    MATH  Google Scholar 

  32. J. Weickert, On discontinuity-preserving optic flow, S. Orphanoudakis, P. Trahanias, J. Crowley, N. Katevas (Eds.), Proc. Computer Vision and Mobile Robotics Workshop (CVMR’ 98, Santorini, Sept. 17–18, 1998), 115–122, 1998.

    Google Scholar 

  33. J. Weickert, S. Ishikawa, A. Imiya, Linear scale-space has first been proposed in Japan, J. Math. Imag. Vision, Vol. 10, 237–252, 1999.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1999 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Alvarez, L., Sánchez, J., Weickert, J. (1999). A Scale-Space Approach to Nonlocal Optical Flow Calculations. In: Nielsen, M., Johansen, P., Olsen, O.F., Weickert, J. (eds) Scale-Space Theories in Computer Vision. Scale-Space 1999. Lecture Notes in Computer Science, vol 1682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48236-9_21

Download citation

  • DOI: https://doi.org/10.1007/3-540-48236-9_21

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66498-7

  • Online ISBN: 978-3-540-48236-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics