Robust factorization methods using a Gaussian/uniform mixture model. (English) Zbl 1477.68445
Summary: 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.
MSC:
| 68T45 | Machine vision and scene understanding |
| 62H12 | Estimation in multivariate analysis |
| 62H30 | Classification and discrimination; cluster analysis (statistical aspects) |
Keywords:
robust factorization; 3-D reconstruction; multiple camera calibration; data clustering; expectation-maximization; EM; M-estimators; outlier rejectionReferences:
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