Robust Factorization Methods Using a Gaussian/Uniform Mixture Model
This work addresses the problem of robust parameter estimation in factorization for computer vision researchers, particularly for handling outliers in shape and motion recovery.
This paper introduces a Gaussian/uniform mixture model and an EM algorithm to develop robust factorization methods for shape and motion parameters under both affine and perspective camera models. The proposed technique enhances any affine factorization method to be robust to outliers and can be integrated into an iterative perspective factorization scheme.
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.