Cited by 15,004Optical measurement and interference techniquesAdvanced Vision and ImagingImage and Object Detection Techniques

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A flexible new technique for camera calibration

Zhang, Z.·2000·IEEE Transactions on Pattern Analysis and Machine Intelligence

DOI 10.1109/34.888718

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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.

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Matrix Computations
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses
Tsai, R. (1987) · IEEE Journal on Robotics and Automation · doi:10.1109/jra.1987.1087109 · Cited by 5,890
The Levenberg-Marquardt algorithm: Implementation and theory
Morè, J. J. (1978) · Lecture notes in mathematics · doi:10.1007/bfb0067700 · Cited by 5,796
Three-dimensional computer vision: a geometric viewpoint
(1994) · Choice Reviews Online · doi:10.5860/choice.32-0357 · Cited by 3,032
Camera calibration with distortion models and accuracy evaluation
Weng, J., Cohen, P. R., & Herniou, M. (1992) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.159901 · Cited by 1,767
Camera self-calibration: Theory and experiments
Faugeras, O. D., Luong, Q.-T., & Maybank, S. J. (1992) · Lecture notes in computer science · doi:10.1007/3-540-55426-2_37 · Cited by 957
Manual of photogrammetry
Slama, C. C., Theurer, C. B., & Henriksen, S. W. (1980) · American Society of Photogrammetry eBooks · Cited by 856
A theory of self-calibration of a moving camera
Maybank, S. J., & Faugeras, O. D. (1992) · International Journal of Computer Vision · doi:10.1007/bf00127171 · Cited by 831
Using vanishing points for camera calibration
Caprile, B., & Torre, V. (1990) · International Journal of Computer Vision · doi:10.1007/bf00127813 · Cited by 715
Algebraic Projective Geometry.
Atchison, W. F., Semple, J. G., & Kneebone, G. T. (1953) · American Mathematical Monthly · doi:10.2307/2307420 · Cited by 708

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Research works printed but not linked: 2

brown, 1971, close-range camera calibration, Photogrammetric Eng, 37, 855
faugeras, 1986, the calibration problem for stereo, Proc IEEE Conf Computer Vision and Pattern Recognition, 15

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Self-citation
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Self-citation
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Audio–Visual Active Speaker Tracking in Cluttered Indoors Environments
Talantzis, F., Pnevmatikakis, A., & Constantinides, A. G. (2008) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2008.922063 · Cited by 57
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Authors: Zhang, Z.
Affiliations: Microsoft (United States) (US)
Corresponding author: Zhang, Z.
ORCID: Zhang, Z.
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2000
Volume: 22
Issue: 11
Pages: 1330-1334
ISSN: 0162-8828 (ISSN-L)
DOI: 10.1109/34.888718
OpenAlex: W2167667767
Cited by: 15,004
References: 30
Citations by year: 2026: 858 · 2025: 1043 · 2024: 1031 · 2023: 1035 · 2022: 941 · 2021: 898 · 2020: 840 · 2019: 897 · 2018: 814 · 2017: 771 · 2016: 707 · 2015: 650 · 2014: 595 · 2013: 616 · 2012: 502
FWCI: 79.80
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations: IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Optical measurement and interference techniques
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Object Detection Techniques
Keywords: camera calibration, planar pattern, radial distortion, analytical solution, maximum likelihood refinement, 3D vision

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A flexible new technique for camera calibration

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