Skip to main content

A Contrast Invariant Approach to Motion Estimation

  • Conference paper
Scale Space and PDE Methods in Computer Vision (Scale-Space 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3459))

Included in the following conference series:

  • 1591 Accesses

  • 5 Citations

Abstract

Motion estimation is one of the key tools in many video processing applications. Most of the existing motion estimation approaches use the brightness constancy assumption in order to model the movements of the objects present in the scene. In this paper the motion of objects is modeled from a geometrical-based point of view, leading thus to a contrast invariant formulation. The present approach is region-based and assumes affine motion model for each region.

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

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Alvarez, L., Weickert, J., Sánchez, J.: A scale-space approach to nonlocal optical flow calculations. In: Nielsen, M., Johansen, P., Fogh Olsen, O., Weickert, J. (eds.) Scale-Space 1999. LNCS, vol. 1682, pp. 235–246. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  2. Ballester, C., Caselles, V., Igual, L.: Minimal morphological shape selection for segmentation and encoding. Preprint (2004)

    Google Scholar 

  3. Barron, J., Fleet, D., Beauchemin, S.: Performance of optic flow techniques. IJCV 12, 43–77 (1994)

    Article  Google Scholar 

  4. Bertero, M., Poggio, T.A., Torre, V.: Ill-posed problems in early vision. Procedings of the IEEE 76(8), 869–889 (1988)

    Article  Google Scholar 

  5. Black, M.J., Anandan, P.: The robust estimation of multiple motions: parametric and piecewise-smooth flow fields. CVIU 63(1), 75–104 (1996)

    Google Scholar 

  6. Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: High accuracy optical flow estimation based on a theory for warping. In: European Conf. on Computer Vision, pp. 25–36. Springer, Heidelberg (2004)

    Google Scholar 

  7. Bruhn, A., Weickert, J., Feddern, C., Kohlberger, T., Schnoerr, C.: Real-time optic flow computation with variational methods. In: Petkov, N., Westenberg, M.A. (eds.) CAIP 2003. LNCS, vol. 2756, pp. 222–229. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  8. Burgi, P.Y.: Motion estimation based on the direction of intensity gradient. Image and Vision Computing 22, 637–653 (2004)

    Article  Google Scholar 

  9. Caselles, V., Coll, B., Morel, J.-M.: Topographic maps and local contrast changes in natural images. IJCV 33(1), 5–27 (1999)

    Article  MathSciNet  Google Scholar 

  10. Chen, H., Belhumeur, P., Jacobs, D.: In search of illumination invariants. In: Int. Conf. on Computer Vision and Pattern Recognition, pp. 254–261 (2000)

    Google Scholar 

  11. Cremers, D., Soatto, S.: Motion competition: a variational approach to piecewise parametric motion segmentation. IJCV (2004)

    Google Scholar 

  12. Davatzikos, C.: Spatial transformation and registration of brain images using elastically deformable models. CVIU (1997)

    Google Scholar 

  13. Droske, M., Rumpf, M.: A variational approach to non-rigid morphological image registration. SIAM Journal Applied Mathematics (2004)

    Google Scholar 

  14. Dugelay, J.L., Sanson, H.: Differential methods for the identification of 2D and 3D motion models in image sequences. Image Communication 7, 105–127 (1995)

    Google Scholar 

  15. Farid, H., Simoncelli, E.P.: Differentiation of discrete multidimensional signals. IEEE Trans. on IP 13(4) (April 2004)

    Google Scholar 

  16. Keys, R.G.: Cubic convolution interpolation for digital image processing. IEEE Trans. on ASSP 29(6), 1153–1160 (1981)

    Article  MATH  MathSciNet  Google Scholar 

  17. Morel, J.M., Solimini, S.: Variational methods in image segmentation. Birkhäuser Verlag, Basel (1995)

    Google Scholar 

  18. Obobez, J.M., Bouthemy, P.: Robust multiresolution estimation of parametric motion models applied to complex scenes. JVCIR 6(4), 348–365 (1995)

    Article  Google Scholar 

  19. Pluim, J.P.W., Maintz, J.B.A., Viergever, M.A.: Mutual-information-based registration of medical images. IEEE Trans. on MI 22(8), 986–998 (2003)

    Article  Google Scholar 

  20. Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C: the art of scientific computing. Cambridge Univ. Press, Cambridge (1992)

    Google Scholar 

  21. Serra, J.: Image Analysis and Mathematical Morphology. Academic Press, London (1982)

    MATH  Google Scholar 

  22. Stiller, C., Konrad, J.: Estimating motion in image sequences. In: IEEE Signal Processing Magazine, pp. 70–91 (July 1999)

    Google Scholar 

  23. Tekalp, A.M.: Digital Video Processing. Prentice-Hall, Englewood Cliffs (1995)

    Google Scholar 

  24. Unser, M., Aldroubi, A., Eden, M.: Enlargement or reduction of digital images with minimum loss of information. IEEE Trans. on IP 4(3), 247–258 (1995)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Caselles, V., Garrido, L., Igual, L. (2005). A Contrast Invariant Approach to Motion Estimation. In: Kimmel, R., Sochen, N.A., Weickert, J. (eds) Scale Space and PDE Methods in Computer Vision. Scale-Space 2005. Lecture Notes in Computer Science, vol 3459. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11408031_21

Download citation

Keywords

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.

Publish with us

Policies and ethics