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![Thales Theorem: An inscribed angle in a semicircle is a right angle [16].](profile/Dong-Hoon-Lee-5/publication/221297003/figure/fig3/AS:305433114890241@1449832337236/Thales-Theorem-An-inscribed-angle-in-a-semicircle-is-a-right-angle-16.png)
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Camera calibration is an important step in obtaining 3D information from 2D im- ages. Vanishing points of parallel lines have proven to be useful features for self- calibration task. Most tasks using vanishing points estimate parameters using three orthogonal vanishing points (OVPs). However, in a real scene it is hard to find views that capture a...
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Citations
... Later on, Hartley and Zisserman [12], Cipolla et al. [6] or He [13] used VPs with the aim of extracting the camera parameters. Two similar works [10] [16], presented the method for finding the intrinsic parameters using the calibration sphere obtained from several images containing two VPs. The authors explained the relation between the calibration sphere and the image of the absolute conic, used for extracting the calibration matrix [12]. ...
The perspective projection models the way a 3D scene is transformed into a 2D image, usually through a camera or an eye. In a projective transformation, parallel lines intersect in a point called vanishing point. This paper presents in detail two calibration methods that exploit the properties of vanishing points. The aim of the paper is to offer a practical tool for the choice of the appropriate calibration method depending on the application and on the initial conditions. The methods, using two respectively three vanishing points, are presented in detail and are compared. First, the two models are analyzed using synthetic data. Finally, each method is tested in a real configuration and the results show the quality of the calibration.
In this paper, a new method is proposed to detect the pose of an object with two cameras. First, the intrinsic parameters of the cameras are self-calibrated with two pairs of parallel lines that are orthogonal. Then, the poses of the cameras relative to the parallel lines are deduced, and the rotational transformation between the two cameras is calculated. With the intrinsic parameters and the relative pose of the two cameras, a method is proposed to obtain the poses of a line, plane, and rigid object. Furthermore, a new visual-control method is developed using a pose detection rather than a three-dimensional reconstruction. Experiments are conducted to verify the effectiveness of the proposed method.
