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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Verbiest, F. Van Gool, L. |
| Copyright Year | 2008 |
| Description | Author affiliation: KU Leuven, Leuven (Verbiest, F.; Van Gool, L.) |
| Abstract | 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 approximate Gaussian distribution serves as a confidence estimate of the scaled normal. We demonstrate experimentally the effectiveness or our approach. |
| Starting Page | 1 |
| Ending Page | 8 |
| File Size | 579836 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424422425 |
| ISSN | 10636919 |
| DOI | 10.1109/CVPR.2008.4587712 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-06-23 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Photometry Lighting Robustness Pixel Gaussian distribution Markov random fields Equations Geometry Smoothing methods Optical reflection |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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