Robust Factorization Methods Using a Gaussian/Uniform Mixture Model

• URL: http://arxiv.org/abs/2012.08243v1
• Date: Tue, 15 Dec 2020 12:21:33 GMT
• Title: Robust Factorization Methods Using a Gaussian/Uniform Mixture Model
• Authors: Andrei Zaharescu and Radu Horaud
• Abstract summary: We introduce a Gaussian/uniform mixture model and its associated EM algorithm. We propose a robust technique that works with any affine factorization method and makes it robust to outliers. We carry out a large number of experiments to validate our algorithms and to compare them with existing ones.
• Score: 24.756003635916613
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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