Journal ArticleDOI
A flexible new technique for camera calibration
TLDR
A flexible technique to easily calibrate a camera that only requires the camera to observe a planar pattern shown at a few (at least two) different orientations is proposed and advances 3D computer vision one more step from laboratory environments to real world use.Abstract:
We propose a flexible technique to easily calibrate a camera. It only requires the camera to observe a planar pattern shown at a few (at least two) different orientations. Either the camera or the planar pattern can be freely moved. The motion need not be known. Radial lens distortion is modeled. The proposed procedure consists of a closed-form solution, followed by a nonlinear refinement based on the maximum likelihood criterion. Both computer simulation and real data have been used to test the proposed technique and very good results have been obtained. Compared with classical techniques which use expensive equipment such as two or three orthogonal planes, the proposed technique is easy to use and flexible. It advances 3D computer vision one more step from laboratory environments to real world use.read more
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Multiple View Geometry in Computer Vision.
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Proceedings ArticleDOI
Flexible camera calibration by viewing a plane from unknown orientations
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References
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Journal ArticleDOI
Self-Calibration of a Moving Camera from PointCorrespondences and Fundamental Matrices
TL;DR: It is shown that point correspondences between three images, and the fundamental matrices computed from these point correspondence are sufficient to recover the internal orientation of the camera, the motion parameters, and to compute coherent perspective projection matrices which enable us to reconstruct 3-D structure up to a similarity.
Proceedings ArticleDOI
Metric rectification for perspective images of planes
David Liebowitz,Andrew Zisserman +1 more
TL;DR: The novel contributions are that in a stratified context the various forms of providing metric information can be represented as circular constraints on the parameters of an affine transformation of the plane, providing a simple and uniform framework for integrating constraints.
Book ChapterDOI
Autocalibration from Planar Scenes
TL;DR: The theory and a practical algorithm for the autocalibration of a moving projective camera, from m ≥ 5 views of a planar scene, which generalizes Hartley's method for the internal calibration of a rotating camera to allow camera translation and to provide 3D as well as calibration information.
Book ChapterDOI
Self-calibration from multiple views with a rotating camera
TL;DR: There is no epipolar structure since all images are taken from the same point in space and determination of point matches is considerably easier than for images taken with a moving camera, since problems of occlusion or change of aspect or illumination do not occur.