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High-accuracy calibration of cameras without depth of field and target size limitations
Author(s) -
Feng Yan,
Zhen Liu,
Xiao Pan,
Yuan Shen
Publication year - 2020
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.402826
Subject(s) - computer science , artificial intelligence , computer vision , calibration , camera resectioning , feature (linguistics) , depth of field , camera lens , projection (relational algebra) , field of view , point (geometry) , field (mathematics) , light field , lens (geology) , optics , mathematics , algorithm , physics , statistics , linguistics , philosophy , geometry , pure mathematics
During camera calibration, targets need to be placed in the depth of field of the lens to ensure clear imaging, and they should take up proper proportions in the image. These requirements cause difficulty in many calibration scenarios, such as those involving large-field-of-view, shallow-depth-of-field, or online operation cameras. In view of the above-mentioned problems, this study proposes a high-accuracy camera calibration method, which can overcome the influence of image blur and noise and is not limited by depth of field and target size. First, a high-accuracy light-spot small target is placed closely in front of the camera, so that the target image can take up a large proportion in the whole image. In case of defocus blur, the adaptive multi-scale method is used to extract feature point coordinates at first to ensure accuracy, and the location variance of each feature point is estimated concurrently. Finally, the high-accuracy intrinsic and extrinsic parameters of the camera under test are obtained by nonlinear optimization where re-projection errors are normalized by location variances according to the Gauss-Markov theorem. Simulation and physical experiments validate the effectiveness of the proposed method.

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