Corner localization and camera calibration from imaged lattices
Date
2023
Authors
Stadnik, Andrii
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Abstract
This thesis proposes a model-based approach to improve the detection of calibration board fiducials from calibration imagery taken by wide-angle or fisheye lenses.
From a single image, we estimate the camera model and project the calibration board
into the image to guide the search for missed detections and reject spurious detections. In addition, we propose a classifier to label ambiguous detections that are
geometrically plausible given the estimated camera model and imaged board. The
proposed method addresses shortcomings of the state-of-the-art, which struggle to
reliably detect board fiducials at the extents of the image, where the lens distortion
is most observable. The proposed method recovers additional corners that can be
used to place additional constraints on the non-convex camera calibration problem,
which improves the likelihood of convergence to a global minimum.
The code for this paper is available on GitHub.
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Citation
Stadnik Andrii. Corner localization and camera calibration from imaged lattices. Ukrainian Catholic University, Faculty of Applied Sciences, Department of Computer Sciences. Lviv 2023, 33 p.