Calibration Method Based on the Image of the Absolute Quadratic Curve
In this paper, a new camera calibration method based on the image of the absolute quadratic curve (IAC) is proposed, and a new target is designed for this method, which is both convenient and flexible. It first extracts the characteristic points and the characteristic lines of the target and finds o...
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doaj-3f772b8533694bd8870c2c5bc12bd8972021-03-29T22:22:46ZengIEEEIEEE Access2169-35362019-01-017298562986810.1109/ACCESS.2019.28936608616767Calibration Method Based on the Image of the Absolute Quadratic CurveWenlei Liu0https://orcid.org/0000-0001-6425-9466Sentang Wu1Xiaolong Wu2Hongbo Zhao3School of Automation Science and Electrical Engineering, Beihang University, Beijing, ChinaSchool of Automation Science and Electrical Engineering, Beihang University, Beijing, ChinaNavigation and Control Technology Institute of NORINCO Group, Beijing, ChinaSchool of Automation Science and Electrical Engineering, Beihang University, Beijing, ChinaIn this paper, a new camera calibration method based on the image of the absolute quadratic curve (IAC) is proposed, and a new target is designed for this method, which is both convenient and flexible. It first extracts the characteristic points and the characteristic lines of the target and finds out the vanishing point and the vanishing line. The radial and tangential distortion coefficients are obtained by using the cross ratio invariance to correct the target image distortion. Then, the four internal parameters of the camera are obtained by IAC. The influence of the skew parameters is ignored. The rotation matrix is then calculated by the orthogonal characteristic of the coordinate system, and the translation vector is calculated by the center coordinates of the camera. In this way, the internal and external parameters of the camera can be obtained. The internal and external parameters are taken as initial values, and the optimal results are obtained by nonlinear optimization using the reprojection method. Finally, the relative position between different target images can be obtained by using the fundamental matrix, namely, the rotation angle. In the process of solving, the normalization method is used to improve the accuracy of data processing. Not requiring any prior information of the camera, the method has a wide range of applications.https://ieeexplore.ieee.org/document/8616767/Camera calibrationimage of the absolute quadratic curve (IAC)fundamental matrixcross ratio invariancevanishing pointvanishing line |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Wenlei Liu Sentang Wu Xiaolong Wu Hongbo Zhao |
spellingShingle |
Wenlei Liu Sentang Wu Xiaolong Wu Hongbo Zhao Calibration Method Based on the Image of the Absolute Quadratic Curve IEEE Access Camera calibration image of the absolute quadratic curve (IAC) fundamental matrix cross ratio invariance vanishing point vanishing line |
author_facet |
Wenlei Liu Sentang Wu Xiaolong Wu Hongbo Zhao |
author_sort |
Wenlei Liu |
title |
Calibration Method Based on the Image of the Absolute Quadratic Curve |
title_short |
Calibration Method Based on the Image of the Absolute Quadratic Curve |
title_full |
Calibration Method Based on the Image of the Absolute Quadratic Curve |
title_fullStr |
Calibration Method Based on the Image of the Absolute Quadratic Curve |
title_full_unstemmed |
Calibration Method Based on the Image of the Absolute Quadratic Curve |
title_sort |
calibration method based on the image of the absolute quadratic curve |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
In this paper, a new camera calibration method based on the image of the absolute quadratic curve (IAC) is proposed, and a new target is designed for this method, which is both convenient and flexible. It first extracts the characteristic points and the characteristic lines of the target and finds out the vanishing point and the vanishing line. The radial and tangential distortion coefficients are obtained by using the cross ratio invariance to correct the target image distortion. Then, the four internal parameters of the camera are obtained by IAC. The influence of the skew parameters is ignored. The rotation matrix is then calculated by the orthogonal characteristic of the coordinate system, and the translation vector is calculated by the center coordinates of the camera. In this way, the internal and external parameters of the camera can be obtained. The internal and external parameters are taken as initial values, and the optimal results are obtained by nonlinear optimization using the reprojection method. Finally, the relative position between different target images can be obtained by using the fundamental matrix, namely, the rotation angle. In the process of solving, the normalization method is used to improve the accuracy of data processing. Not requiring any prior information of the camera, the method has a wide range of applications. |
topic |
Camera calibration image of the absolute quadratic curve (IAC) fundamental matrix cross ratio invariance vanishing point vanishing line |
url |
https://ieeexplore.ieee.org/document/8616767/ |
work_keys_str_mv |
AT wenleiliu calibrationmethodbasedontheimageoftheabsolutequadraticcurve AT sentangwu calibrationmethodbasedontheimageoftheabsolutequadraticcurve AT xiaolongwu calibrationmethodbasedontheimageoftheabsolutequadraticcurve AT hongbozhao calibrationmethodbasedontheimageoftheabsolutequadraticcurve |
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1724191721282076672 |