Robust Techniques in Least Squares-Based Motion Estimation Problems

  • Raúl Montoliu
  • Filiberto Pla
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2905)

Abstract

In the literature of computer vision and image processing, motion estimation and image registration problems are usually formulated as parametric fitting problems. Least Squares techniques have been extensively used to solve them, since they provide an elegant, fast and accurate way of finding the best parameters that fit the data. Nevertheless, it is well known that least squares estimators are vulnerable to the presence of outliers. Robust techniques have been developed in order to cope with the presence of them in the data set. In this paper some of the most popular robust techniques for motion estimation problems are reviewed and compared. Experiments with synthetic image sequences have been done in order to test the accuracy and the robustness of the methods studied.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bad-Hadiashar, A., Suter, D.: Robust optic flow computation. International Journal on Computer Vision 29(1), 59–77 (1998)CrossRefGoogle Scholar
  2. 2.
    Bergen, J.R., Burt, P.J., Hingorani, R., Peleg, S.: A three-frame algorithm for estimating two-component image motion. PAMI 14(9), 886–896 (1992)Google Scholar
  3. 3.
    Bober, M., Kittler, J.V.: Estimation of complex multimodal motion: An approach based on robust statistics and hough transform. IVC 12(10), 661–668 (1994)Google Scholar
  4. 4.
    Danuser, G., Stricker, M.: Parametric model-fitting: From inlier characterization to outlier detection. PAMI 20(3), 263–280 (1998)Google Scholar
  5. 5.
    Irani, M., Rousso, B., Peleg, S.: Computing occluding and transparent motion. IJVC 12(1), 5–16 (1994)Google Scholar
  6. 6.
    Montoliu, R., Pla, F.: Multiple parametric motion model estimation and segmentation. In: ICIP 2001, 2001 International Conference on Image Processing, October 2001, vol. II, pp. 933–936 (2001)Google Scholar
  7. 7.
    Montoliu, R., Traver, V.J., Pla, F.: Log-polar mapping in generalized least-squares motion estimation. In: Proccedings of 2002 IASTED International Conference on Visualization, Imaging, and Image Processing (VIIP 2002), September 2002, pp. 656–661 (2002)Google Scholar
  8. 8.
    Zhang, Z.: Parameter-estimation techniques: A tutorial with application to conic fitting. Image and Vision Computing 15(1), 59–76 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Raúl Montoliu
    • 1
  • Filiberto Pla
    • 1
  1. 1.Dept. Lenguajes y Sistemas InformáticosJaume I UniversityCastellónSpain

Personalised recommendations