Selection of Local Optical Flow Models by Means of Residual Analysis

  • Björn Andres
  • Fred A. Hamprecht
  • Christoph S. Garbe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4713)


This contribution presents a novel approach to the challenging problem of model selection in motion estimation from sequences of images. New light is cast on parametric models of local optical flow. These models give rise to parameter estimation problems with highly correlated errors in variables (EIV). Regression is hence performed by equilibrated total least squares. The authors suggest to adaptively select motion models by testing local empirical regression residuals to be in accordance with the probability distribution that is theoretically predicted by the EIV model. Motion estimation with residual-based model selection is examined on artificial sequences designed to test specifically for the properties of the model selection process. These simulations indicate a good performance in the exclusion of inappropriate models and yield promising results in model complexity control.


Motion Estimation Residual Analysis Total Little Square Motion Segmentation Model Selector 
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 2007

Authors and Affiliations

  • Björn Andres
    • 1
  • Fred A. Hamprecht
    • 1
  • Christoph S. Garbe
    • 1
  1. 1.Interdisciplinary Center for Scientific Computing, University of Heidelberg, 69120 HeidelbergGermany

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