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      • Department of Electrical and Electronics Engineering
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      Algebraic error analysis of collinear feature points for camera parameter estimation

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      Author
      Urfalioglu, O.
      Thormählen, T.
      Broszio, H.
      Mikulastik, P.
      Çetin, A. Enis
      Date
      2011-01-04
      Source Title
      Computer Vision and Image Understanding
      Print ISSN
      1077-3142
      Publisher
      Elsevier
      Volume
      115
      Issue
      4
      Pages
      467 - 475
      Language
      English
      Type
      Article
      Item Usage Stats
      132
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      92
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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 estimation
      Cramer-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/11693/21972
      Published Version (Please cite this version)
      http://dx.doi.org/10.1016/j.cviu.20http://dx.doi.org/10.12.003
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      • Department of Electrical and Electronics Engineering 3524
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