Advertisement

Error Analysis for Lucas-Kanade Based Schemes

  • Patricia Márquez-Valle
  • Debora Gil
  • Aura Hernàndez-Sabaté
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7324)

Abstract

Optical flow is a valuable tool for motion analysis in medical imaging sequences. A reliable application requires determining the accuracy of the computed optical flow. This is a main challenge given the absence of ground truth in medical sequences. This paper presents an error analysis of Lucas-Kanade schemes in terms of intrinsic design errors and numerical stability of the algorithm. Our analysis provides a confidence measure that is naturally correlated to the accuracy of the flow field. Our experiments show the higher predictive value of our confidence measure compared to existing measures.

Keywords

Optical flow Confidence measure Lucas-Kanade Cardiac Magnetic Resonance 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Sun, D., Roth, S., Black, M.J.: Secrets of optical flow estimation and their principles. In: CVPR, pp. 2432–2439 (2010)Google Scholar
  2. 2.
    Zimmer, H., Bruhn, A., Weickert, J.: Optical flow in harmony. IJCV 93, 368–388 (2011)MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Barron, J.L., Fleet, D.J., Beauchemin, S.S.: Performance of optical flow techniques. IJCV 12(1), 43–77 (1994)CrossRefGoogle Scholar
  4. 4.
    Bruhn, A., Weickert, J.: A confidence measure for variational optic flow methods. In: Geometric Properties for Incomplete Data, pp. 283–298 (2006)Google Scholar
  5. 5.
    Simoncelli, E., Adelson, E., Heeger, D.: Probability distributions of optical flow. In: CVPR, pp. 310–315 (1991)Google Scholar
  6. 6.
    Kondermann, C., Mester, R., Garbe, C.: A Statistical Confidence Measure for Optical Flows. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 290–301. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Lucas, B., Kanade, T.: An iterative image registration technique with an application to stereovision. In: DARPA IU Workshop, pp. 121–130 (1981)Google Scholar
  8. 8.
    Bruhn, A., Weickert, J., Schnörr, C.: Lucas/Kanade meets Horn/Schunck: Combining local and global optic flow methods. IJCV 61(2), 221–231 (2005)Google Scholar
  9. 9.
    Baraldi, P., Sarti, A., Lamberti, C., Prandini, A., Sgallari, F.: Evaluation of differential optical flow techniques on synthesized echo images. IEEE Trans. Biomed. Eng. 43(3), 259–272 (1996)CrossRefGoogle Scholar
  10. 10.
    Duan, Q., Angelini, E., Homma, S., Laine, A.: Tracking endocardium using optical flow along iso-value curve. In: EMBS, pp. 707–710 (2006)Google Scholar
  11. 11.
    Leung, K., Danilouchkine, M., van Stralen, M., de Jong, N., van der Steen, A., Bosch, J.: Artifact aware tracking of left ventricular contours in 3D ultrasound. In: SPIE, vol. 7623 (2010)Google Scholar
  12. 12.
    Jähne, B.: Spatio-Temporal Image Processing. LNCS, vol. 751. Springer, Heidelberg (1993) ISSN:0302-9743zbMATHCrossRefGoogle Scholar
  13. 13.
    Trefethen, L., Bau, D.: Numerical Linear Algebra. SIAM, USA (1997)zbMATHCrossRefGoogle Scholar
  14. 14.
    Baker, S., Scharstein, D., Lewis, J.P., Roth, S., Black, M.J., Szeliski, R.: A database and evaluation methodology for optical flow. IJCV 92(1), 1–31 (2011)CrossRefGoogle Scholar
  15. 15.
    Myers, J.L., Well, A.D.: Research Design and Statistical Analysis, 2nd edn. Lawrence Erlbaum Associates, New Jersey (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Patricia Márquez-Valle
    • 1
  • Debora Gil
    • 1
  • Aura Hernàndez-Sabaté
    • 1
  1. 1.Computer Vision Center and Computer Science DepartmentCampus UABBellaterraSpain

Personalised recommendations