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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Zaharescu, A., Horaud, R. Robust Factorization Methods Using a Gaussian/Uniform Mixture Model. Int J Comput Vis 81, 240–258 (2009). https://doi.org/10.1007/s11263-008-0169-x
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DOI: https://doi.org/10.1007/s11263-008-0169-x