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Photometric stereo with coherent outlier handling and confidence estimation

10 years 3 months ago
Photometric stereo with coherent outlier handling and confidence estimation
In photometric stereo a robust method is required to deal with outliers, such as shadows and non-Lambertian reflections. In this paper we rely on a probabilistic imaging model that distinguishes between inliers and outliers, and formulate the problem as a Maximum-Likelihood estimation problem. To signal which imaging model to use a hidden binary inlier map is introduced, which, to account for the fact that inlier/outlier pixels typically group together, is modelled as a Markov Random Field. To make inference of model parameters and hidden variables tractable a mean field Expectation-Maximization (EM) algorithm is used. If for each pixel we add the scaled normal, i.e. albedo and normal combined, to the model parameters, it would not be possible to obtain a confidence estimate in the result. Instead, each scaled normal is added as a hidden variable, the distribution of which, approximated by a Gaussian, is also estimated in the EM algorithm. The covariance matrix of the recovered approx...
Frank Verbiest, Luc J. Van Gool
Added 19 Oct 2010
Updated 19 Oct 2010
Type Conference
Year 2008
Where CVPR
Authors Frank Verbiest, Luc J. Van Gool
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