High-Accuracy and Robust Localization of Large Control Markers for Geometric Camera Calibration

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 31; no. 2; pp. 376 - 383
• Main Authors: Douxchamps, D., Chihara, K.
• Format: Journal Article
• Language: English
• Published: Los Alamitos, CA IEEE 01.02.2009; IEEE Computer Society; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Applied sciences; Artificial Intelligence; Calibration; Camera calibration; Cameras; Computer science; control theory; systems; Distortion measurement; Exact sciences and technology; high resolution; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; image measurement; imaging geometry; Imaging, Three-Dimensional - methods; Localization; Markers; Noise; Noise measurement; Noise robustness; Nonlinear distortion; Pattern Recognition, Automated - methods; Pattern recognition. Digital image processing. Computational geometry; Photogrammetry - instrumentation; Photogrammetry - methods; Position (location); Position measurement; ray tracing; Reproducibility of Results; Robust control; Sensitivity and Specificity; Size control; Solid modeling; subpixel; Camera calibration; Imaging geometry; High resolution; High precision; Artefact; Modeling; Discretization; Imaging; Ray tracing; noise; Illumination; Robustness; Localization; Pattern analysis; blurring image; Video cameras; Calibration; Marker; Robust control; subpixel; Image analysis; Luminance; image measurement; Position measurement; Non linear distortion; Artificial intelligence; imaging geometry
• ISSN: 0162-8828; 1939-3539
• DOI: 10.1109/TPAMI.2008.214

Accurate measurement of the position of features in an image is subject to a fundamental compromise: The features must be both small, to limit the effect of nonlinear distortions, and large, to limit the effect of noise and discretization. This constrains both the accuracy and the robustness of image measurements, which play an important role in geometric camera calibration as well as in all subsequent measurements based on that calibration. In this paper, we present a new geometric camera calibration technique that exploits the complete camera model during the localization of control markers, thereby abolishing the marker size compromise. Large markers allow a dense pattern to be used instead of a simple disc, resulting in a significant increase in accuracy and robustness. When highly planar markers are used, geometric camera calibration based on synthetic images leads to true errors of 0.002 pixels, even in the presence of artifacts such as noise, illumination gradients, compression, blurring, and limited dynamic range. The camera parameters are also accurately recovered, even for complex camera models.