Robust Factorization Methods Using a Gaussian/Uniform Mixture Model

TL;DR

This paper introduces a robust factorization framework using a Gaussian/Uniform mixture model and EM algorithm, improving shape and motion estimation robustness against outliers in affine and perspective models.

Contribution

It presents a novel robust factorization method that integrates a Gaussian/Uniform mixture model with EM, applicable to affine and perspective camera models, enhancing outlier robustness.

Findings

Outperforms existing methods in robustness to outliers

Effective in both affine and perspective factorization

Validated through extensive experiments

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.