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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Main Authors: Wenlei Liu, Sentang Wu, Xiaolong Wu, Hongbo Zhao
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8616767/
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spelling 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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