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.
Camera Calibration Using Principal-Axes Aligned Conics
Asian Conference on Computer Vision ; 2007 ; Tokyo, Japan November 18, 2007 - November 22, 2007
01.01.2007
11 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Camera calibration , Generalized eigenvalue decomposition , Principal-axes aligned conics , Image of the absolute conic Computer Science , Image Processing and Computer Vision , Computer Imaging, Vision, Pattern Recognition and Graphics , Pattern Recognition , Artificial Intelligence , Biometrics , Algorithm Analysis and Problem Complexity
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