Image Registration, Optical Flow, and Local Rigidity

  • Martin Lefébure
  • Laurent D. Cohen
Conference paper
Part of the Lecture Notes in Computer Science 2106 book series (LNCS, volume 2106)


We address the theoretical problems of optical flow estimation and image registration in a multi-scale framework in any dimension. We start by showing, in the translation case, that convergence to the global minimum is made easier by applying a low pass filter to the images hence making the energy “convex enough”. In order to keep convergence to the global minimum in the general case, we introduce a local rigidity hypothesis on the unknown deformation. We then deduce a new natural motion constraint equation (MCE) at each scale using the Dirichlet low pass operator. This allows us to derive sufficient conditions for convergence of a new multi-scale and iterative motion estimation/registration scheme towards a global minimum of the usual nonlinear energy instead of a local minimum as did all previous methods. We then use an implicit numerical approach. We illustrate our method on synthetic and real examples (Motion, Registration, Morphing).


Global Minimum Optical Flow Motion Estimation Image Registration Residual Motion 
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 2001

Authors and Affiliations

  • Martin Lefébure
    • 1
  • Laurent D. Cohen
    • 2
  1. 1.Poseidon TechnologiesNeuilly Sur SeineFrance
  2. 2.CEREMADEUniversité Paris-DauphineParisFrance

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