  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.
