Discrete Orthogonal Decomposition and Variational Fluid Flow Estimation

  • Jing Yuan
  • Paul Ruhnau
  • Etinne Mémin
  • Christoph Schnörr
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3459)


The decomposition of motion vector fields into components of orthogonal subspaces is an important representation for both the analysis and the variational estimation of complex motions. Common finite differencing or finite element methods, however, do not preserve the basic identities of vector analysis. Therefore, we introduce in this paper the mimetic finite difference method for the estimation of fluid flows from image sequences. Using this discrete setting, we represent the motion components directly in terms of potential functions which are useful for motion pattern analysis. Additionally, we analyze well-posedness which has been lacking in previous work. Experimental results, including hard physical constraints like vanishing divergence of the flow, validate the theory.


Orthogonal Subspace Motion Vector Field Subspace Correction Real Image Sequence Scale Space Method 
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 2005

Authors and Affiliations

  • Jing Yuan
    • 1
  • Paul Ruhnau
    • 1
  • Etinne Mémin
    • 2
  • Christoph Schnörr
    • 1
  1. 1.Department of Mathematics and Computer Science, Computer Vision, Graphics, and Pattern Recognition GroupUniversity of MannheimMannheimGermany
  2. 2.IRISA RennesRennes CedexFrance

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