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Camera Calibration Using Principal-Axes Aligned Conics

  • Xianghua Ying
  • Hongbin Zha
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4843)

Abstract

The projective geometric properties of two principal-axes aligned (PAA) conics in a model plane are investigated in this paper by utilized the generalized eigenvalue decomposition (GED). We demonstrate that one constraint on the image of the absolute conic (IAC) can be obtained from a single image of two PAA conics even if their parameters are unknown. And if the eccentricity of one of the two conics is given, two constraints on the IAC can be obtained. An important merit of the algorithm using PAA is that it can be employed to avoid the ambiguities when estimating extrinsic parameters in the calibration algorithms using concentric circles. We evaluate the characteristics and robustness of the proposed algorithm in experiments with synthetic and real data.

Keywords

Camera calibration Generalized eigenvalue decomposition Principal-axes aligned conics Image of the absolute conic 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Xianghua Ying
    • 1
  • Hongbin Zha
    • 1
  1. 1.National Laboratory on Machine Perception, Peking University, Beijing, 100871P.R. China

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