Optical Flow Estimation with Prior Models Obtained from Phase Correlation

  • Alfonso Alba
  • Edgar Arce-Santana
  • Mariano Rivera
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6453)


Motion estimation is one of the most important tasks in computer vision. One popular technique for computing dense motion fields consists in defining a large enough set of candidate motion vectors, and assigning one of such vectors to each pixel, so that a given cost function is minimized. In this work we propose a novel method for finding a small set of adequate candidates, making the minimization process computationally more efficient. Based on this method, we present algorithms for the estimation of dense optical flow using two minimization approaches: one based on a classic block-matching procedure, and another one based on entropy-controlled quadratic Markov measure fields which allow one to obtain smooth motion fields. Finally, we present the results obtained from the application of these algorithms to examples taken from the Middlebury database.


Motion Vector Motion Estimation Prior Model Block Match Phase Correlation 
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.
    Lucas, B.D., Kanade, T.: An Iterative Image Registration Technique with an Application to Stereo Vision. In: Proc. of Imaging Understanding Workshop, pp. 121–130 (1981)Google Scholar
  2. 2.
    Horn, B.K.P., Schunck, B.G.: Determining Optical Flow. Artificial Intelligence 17, 185–203 (1981)CrossRefGoogle Scholar
  3. 3.
    Essannouni, F., Haj Thami, R.O., Salam, A., Aboutajdine, D.: An efficient fast full search block matching algorithm using FFT algorithms. International Journal of Computer Science and Network Security 6, 130–133 (2006)Google Scholar
  4. 4.
    Barron, J., Fleet, D., Beauchemin, S.: Performance of optical flow techniques. International Journal of Computer Vision 12, 43–77 (1994)CrossRefGoogle Scholar
  5. 5.
    Baker, S., Scharstein, D., Lewis, J.P., Roth, S., Black, M.J., Szeliski, R.: A database and evaluation methodology for optical flow. Technical Report MSR-TR-2009-179, Microsoft Research (2009)Google Scholar
  6. 6.
    Baker, S., Scharstein, D., Lewis, J.P., Roth, S., Black, M., Szeliski, R.: Middlebury stereo vision page (2007), http://vision.middlebury.edu/flow/
  7. 7.
    Sun, D., Roth, S., Black, M.J.: Secrets of Optical Flow Estimation and Their Principles. In: Proc. IEEE CVPR 2010. IEEE, Los Alamitos (2010)Google Scholar
  8. 8.
    Rannacher, J.: Realtime 3D motion estimation on graphics hardware. Bachelor thesis, Heidelberg University (2009)Google Scholar
  9. 9.
    De Castro, E., Morandi, C.: Registration of Translated and Rotated Images Using Finite Fourier Transforms. IEEE Transactions on Pattern Analysis and Machine Intelligence 9, 700–703 (1987)CrossRefGoogle Scholar
  10. 10.
    Reddy, B.S., Chatterji, B.N.: An FFT-Based Technique for Translation, Rotation, and Scale-Invariant Image Registration. IEEE Transactions on Image Processing 5, 1266–1271 (1996)CrossRefGoogle Scholar
  11. 11.
    Keller, Y., Averbuch, A., Moshe, I.: Pseudopolar-based estimation of large translations, rotations, and scalings in images. IEEE Transactions on Image Processing 14, 12–22 (2005)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Keller, Y., Shkolnisky, Y., Averbuch, A.: The angular difference function and its application to image registration. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 969–976 (2005)CrossRefGoogle Scholar
  13. 13.
    Chien, L.H., Aoki, T.: Robust Motion Estimation for Video Sequences Based on Phase-Only Correlation. In: 6th IASTED International Conference on Signal and Image Processing, pp. 441–446 (2004)Google Scholar
  14. 14.
    Takita, K., Muquit, M.A., Aoki, T., Higuchi, T.: A Sub-Pixel Correspondence Search Technique for Computer Vision Applications. IECIE Trans. Fundamentals E87-A, 1913–1923 (2004)Google Scholar
  15. 15.
    Muquit, M.A., Shibahara, T., Aoki, T.: A High-Accuracy Passive 3D Measurement System Using Phase-Based Image Matching. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. E89-A, 686–697 (2006)CrossRefGoogle Scholar
  16. 16.
    Foroosh, H., Zerubia, J.B., Berthod, M.: Extension of phase correlation to subpixel registration. IEEE Transactions on Image Processing 11, 188–200 (2002)CrossRefGoogle Scholar
  17. 17.
    Shimizu, M., Okutomi, M.: Sub-pixel estimation error cancellation on area-based matching. International Journal of Computer Vision 63, 207–224 (2005)CrossRefGoogle Scholar
  18. 18.
    Viola, P., Jones, M.: Robust Real-time Object Detection. International Journal of Computer Vision 57, 137–154 (2002)CrossRefGoogle Scholar
  19. 19.
    Charbonnier, P., Blanc-Féraud, L., Aubert, G., Barlaud, M.: Deterministic Edge-Preserving Regularization in Computed Imaging. IEEE Transactions on Image Processing 6, 298–311 (1997)CrossRefGoogle Scholar
  20. 20.
    Rivera, M., Ocegueda, O., Marroquin, J.L.: Entropy-Controlled Quadratic Markov Measure Field Models for Efficient Image Segmentation. IEEE Trans. Image Process. 16, 3047–3057 (2007)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Bradski, G., Kaehler, A.: Learning OpenCV: Computer Vision with the OpenCV Library. O’Reilly Media, Sebastopol (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Alfonso Alba
    • 1
  • Edgar Arce-Santana
    • 1
  • Mariano Rivera
    • 2
  1. 1.Facultad de CienciasUniversidad Autónoma de San Luis PotosíSan Luis PotosíMexico
  2. 2.Centro de Investigacion en Matematicas A.C.Mexico

Personalised recommendations