Robust Factorization Methods Using A Gaussian/Uniform Mixture Model

In this paper we address the problem of building a class of robust factorization algorithms that solve for the shape and motion parameters with both affine (weak perspective) and perspective camera models. We introduce a Gaussian/uniform mixture model and its associated EM algorithm. This allows us to address robust parameter estimation within a data clustering approach. We propose a robust technique that works with any affine factorization method and makes it robust to outliers. In addition, we show how such a framework can be further embedded into an iterative perspective factorization scheme. We carry out a large number of experiments to validate our algorithms and to compare them with existing ones. We also compare our approach with factorization methods that use M-estimators.

@Article\{ZH09,
  author       = "Zaharescu, Andrei and Horaud, Radu P.",
  title        = "Robust Factorization Methods Using A Gaussian/Uniform Mixture Model",
  journal      = "International Journal of Computer Vision",
  number       = "3",
  volume       = "81",
  pages        = "240-258",
  month        = "March",
  year         = "2009",
  url          = "http://perception.inrialpes.fr/Publications/2009/ZH09"
}