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.


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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

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