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Robust Factorization Methods Using a Gaussian/Uniform Mixture Model

Authors
  • Zaharescu, Andrei1
  • Horaud, Radu1
  • 1 INRIA Grenoble Rhône-Alpes, 655, avenue de l’Europe, Montbonnot, 38330, France , Montbonnot (France)
Type
Published Article
Journal
International Journal of Computer Vision
Publisher
Springer-Verlag
Publication Date
Sep 13, 2008
Volume
81
Issue
3
Identifiers
DOI: 10.1007/s11263-008-0169-x
Source
Springer Nature
Keywords
License
Yellow

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 parameter estimation within a data clustering approach. We propose a robust technique that works with any affine factorization method and makes it resilient 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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