Dense Parameter Fields from Total Least Squares

  • Hagen Spies
  • Christoph S. Garbe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2449)


A method for the interpolation of parameter fields estimated by total least squares is presented. This is applied to the study of dynamic processes where the motion and further values such as divergence or brightness changes are parameterised in a partial differential equation. For the regularisation we introduce a constraint that restricts the solution only in the subspace determined by the total least squares procedure. The performance is illustrated on both synthetic and real test data.


Source Term Dense Parameter Orthogonal Subspace Global Illumination Minimum Norm Solution 
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]
    Haußecker, H., Spies, H.: Motion. In: Handbook of Computer Vision and Applications. Academic Press (1999)Google Scholar
  2. [2]
    Haußecker, H., Garbe, C., Spies, H., Jähne, B.: A total least squares for low-level analysis of dynamic scenes and processes. In: DAGM, Bonn, Germany (1999) 240–249Google Scholar
  3. [3]
    Barron, J.L., Fleet, D.J., Beauchemin, S.: Performance of optical flow techniques. International Journal of Computer Vision 12 (1994) 43–77CrossRefGoogle Scholar
  4. [4]
    Horn, B.K.P., Schunk, B.: Determining optical flow. Artificial Intelligence 17 (1981) 185–204CrossRefGoogle Scholar
  5. [5]
    Schnörr, C., Weickert, J.: Variational image motion computation: Theoretical framework, problems and perspectives. In: DAGM, Kiel. Germany (2000) 476–487Google Scholar
  6. [6]
    Spies, H., Jähne, B., Barron, J.L.: Regularised range flow. In: ECCV, Dublin, Ireland (2000) 785–799Google Scholar
  7. [7]
    Haußecker, H., Fleet, D.J.: Computing optical flow with physical models of brightness variation. PAMI 23 (2001) 661–673Google Scholar
  8. [8]
    Garbe, C. S., Jähne, B.: Reliable estimates of the sea surface heat flux from image sequences. In: DAGM, Munich, Germany (2001) 194–201Google Scholar
  9. [9]
    Van Huffel, S., Vandewalle, J.: The Total Least Squares Problem: Computational Aspects and Analysis. SIAM, Philadelphia (1991)zbMATHGoogle Scholar
  10. [10]
    Mühlich, M., Mester, R.: The role of total least squares in motion analysis. In: ECCV, Freiburg, Germany (1998) 305–321Google Scholar
  11. [11]
    Schnörr, C.: On functionals with greyvalue-controlled smoothness terms for determining optical flow. PAMI 15 (1993) 1074–1079Google Scholar
  12. [12]
    Jähne, B.: Digital Image Processing. 3 edn. Springer, Germany (1995)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Hagen Spies
    • 1
  • Christoph S. Garbe
    • 2
  1. 1.ICG-III: Phytosphere, Research Center JülichJülichGermany
  2. 2.Interdisciplinary Center for Scientific ComputingUniversity of HeidelbergHeidelbergGermany

Personalised recommendations