A Fluid Motion Estimator for Schlieren Image Velocimetry

  • Elise Arnaud
  • Etienne Mémin
  • Roberto Sosa
  • Guillermo Artana
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3951)


In this paper, we address the problem of estimating the motion of fluid flows that are visualized through a Schlieren system. Such a system is well known in fluid mechanics as it enables the visualization of unseeded flows. As the resulting images exhibit very low photometric contrasts, classical motion estimation methods based on the brightness consistency assumption (correlation-based approaches, optical flow methods) are completely inefficient. This work aims at proposing a sound energy based estimator dedicated to these particular images. The energy function to be minimized is composed of (a) a novel data term describing the fact that the observed luminance is linked to the gradient of the fluid density and (b) a specific div curl regularization term. The relevance of our estimator is demonstrated on real-world sequences.


Particle Image Velocimetry Flow Visualization Regularization Term Data Term Schlieren Image 
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 2006

Authors and Affiliations

  • Elise Arnaud
    • 1
  • Etienne Mémin
    • 2
  • Roberto Sosa
    • 3
  • Guillermo Artana
    • 3
  1. 1.DisiUniversità di GenovaGenovaItaly
  2. 2.IRISAUniversité de Rennes 1RennesFrance
  3. 3.Facultad de IngenieríaUniversitad de Buenos AiresBuenos AiresArgentina

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