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Divergence-Free Motion Estimation

  • Isabelle Herlin
  • Dominique Béréziat
  • Nicolas Mercier
  • Sergiy Zhuk
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7575)

Abstract

This paper describes an innovative approach to estimate motion from image observations of divergence-free flows. Unlike most state-of-the-art methods, which only minimize the divergence of the motion field, our approach utilizes the vorticity-velocity formalism in order to construct a motion field in the subspace of divergence free functions. A 4DVAR-like image assimilation method is used to generate an estimate of the vorticity field given image observations. Given that vorticity estimate, the motion is obtained solving the Poisson equation. Results are illustrated on synthetic image observations and compared to those obtained with state-of-the-art methods, in order to quantify the improvements brought by the presented approach. The method is then applied to ocean satellite data to demonstrate its performance on the real images.

Keywords

State Vector Image Sequence Data Assimilation Motion Estimation Image Observation 
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 2012

Authors and Affiliations

  • Isabelle Herlin
    • 1
    • 2
  • Dominique Béréziat
    • 3
  • Nicolas Mercier
    • 1
    • 2
  • Sergiy Zhuk
    • 4
  1. 1.INRIALe ChesnayFrance
  2. 2.CEREA, Joint Laboratory ENPC - EDF R&DUniversité Paris-EstMarne la Vallée Cedex 2France
  3. 3.Université Pierre et Marie CurieParisFrance
  4. 4.IBM ResearchDublin Tech. CampusDublinIreland

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