Geodesic Regression for Image Time-Series

  • Marc Niethammer
  • Yang Huang
  • François-Xavier Vialard
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6892)


Registration of image-time series has so far been accomplished (i) by concatenating registrations between image pairs, (ii) by solving a joint estimation problem resulting in piecewise geodesic paths between image pairs, (iii) by kernel based local averaging or (iv) by augmenting the joint estimation with additional temporal irregularity penalties. Here, we propose a generative model extending least squares linear regression to the space of images by using a second-order dynamic formulation for image registration. Unlike previous approaches, the formulation allows for a compact representation of an approximation to the full spatio-temporal trajectory through its initial values. The method also opens up possibilities to design image-based approximation algorithms. The resulting optimization problem is solved using an adjoint method.


Image Registration Image Pair Initial Image Reproduce Kernel Hilbert Space Adjoint 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 2011

Authors and Affiliations

  • Marc Niethammer
    • 1
    • 2
  • Yang Huang
    • 1
  • François-Xavier Vialard
    • 3
  1. 1.UNCChapel HillUSA
  2. 2.BRICDenmark
  3. 3.Imperial CollegeLondonUK

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