Algebraic error analysis of collinear feature points for camera parameter estimation
Author
Urfalioglu, O.
Thormählen, T.
Broszio, H.
Mikulastik, P.
Çetin, A. Enis
Date
2011-01-04Source Title
Computer Vision and Image Understanding
Print ISSN
1077-3142
Publisher
Elsevier
Volume
115
Issue
4
Pages
467 - 475
Language
English
Type
ArticleItem Usage Stats
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Abstract
In general, feature points and camera parameters can only be estimated with limited accuracy due to noisy images. In case of collinear feature points, it is possible to benefit from this geometrical regularity by correcting the feature points to lie on the supporting estimated straight line, yielding increased accuracy of the estimated camera parameters. However, regarding Maximum-Likelihood (ML) estimation, this procedure is incomplete and suboptimal. An optimal solution must also determine the error covariance of corrected features. In this paper, a complete theoretical covariance propagation analysis starting from the error of the feature points up to the error of the estimated camera parameters is performed. Additionally, corresponding Fisher Information Matrices are determined and fundamental relationships between the number and distance of collinear points and corresponding error variances are revealed algebraically. To demonstrate the impact of collinearity, experiments are conducted with covariance propagation analyses, showing significant reduction of the error variances of the estimated parameters.
Keywords
Camera parameter estimationCramer-Rao bounds
Error analysis
ML-estimation
Camera parameter
Collinear
Collinearity
Covariance propagation
Error covariances
Error variance
Estimated parameter
Feature point
Fisher information matrices
Noisy image
Optimal solutions
Straight lines
Cameras
Fisher information matrix
Maximum likelihood estimation
Metal analysis
Parameter estimation
Permalink
http://hdl.handle.net/11693/21972Published Version (Please cite this version)
http://dx.doi.org/10.1016/j.cviu.20http://dx.doi.org/10.12.003Collections
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