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Robust Factorization Methods Using A Gaussian/Uniform Mixture Model

Andrei Zaharescu 1 Radu Horaud 1
1 PERCEPTION - Interpretation and Modelling of Images and Videos
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
Abstract : 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.
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Submitted on : Thursday, January 14, 2010 - 10:38:14 AM
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Andrei Zaharescu, Radu Horaud. Robust Factorization Methods Using A Gaussian/Uniform Mixture Model. International Journal of Computer Vision, Springer Verlag, 2009, 81 (3), pp.240-258. ⟨10.1007/s11263-008-0169-x⟩. ⟨inria-00446987⟩



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