Heas11a

P. Héas, C. Herzet, E. Mémin. Robust optic-flow estimation with Bayesian inference of model and hyper-parameters. In Proc. Conf. Scale-Space and Variational Meth. (SSVM'11), Israel, June 2011.

Download [help]

Download paper: Adobe portable document (pdf)

Copyright notice:

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

Selecting optimal models and hyper-parameters is crucial for accurate optic-flow estimation. This paper solves the problem in a generic variational Bayesian framework. The method is based on a conditional model linking the image intensity function, the velocity field and the hyper-parameters characterizing the motion model. Inference is performed at three levels by considering maximum a posteriori problem of marginalized probabilities. We assessed the performance of the proposed method on image sequences of fluid flows and of the “Middle- bury” database. Experiments prove that applying the proposed inference strategy on very simple models yields better results than manually tuning smoothing parameters or discontinuity preserving cost functions of classical state-of-the-art methods.

Contact

Cédric Herzet
Etienne Mémin

BibTex Reference

@InProceedings{Heas11a,
   Author = {Héas, P. and Herzet, C. and Mémin, E.},
   Title = {Robust optic-flow estimation with Bayesian inference of model and hyper-parameters},
   BookTitle = {Proc. Conf. Scale-Space and Variational Meth. (SSVM'11)},
   Address = {Israel},
   Month = {June},
   Year = {2011}
}

EndNote Reference [help]

Get EndNote Reference (.ref)

This page has been automatically generated by bib2html.