Robust Camera Calibration using Discrete Optimization
Camera calibration is an indispensable step for augmented
reality or image guided applications where quantitative information
should be derived from the images. Usually, a camera
calibration is obtained by taking images of a special calibration object
and extracting the image coordinates of projected calibration marks
enabling the calculation of the projection from the 3d world coordinates
to the 2d image coordinates. Thus such a procedure exhibits
typical steps, including feature point localization in the acquired
images, camera model fitting, correction of distortion introduced by
the optics and finally an optimization of the model-s parameters. In
this paper we propose to extend this list by further step concerning
the identification of the optimal subset of images yielding the smallest
overall calibration error. For this, we present a Monte Carlo based
algorithm along with a deterministic extension that automatically
determines the images yielding an optimal calibration. Finally, we
present results proving that the calibration can be significantly
improved by automated image selection.
[1] Edward Mikhail Chris McGlone. Manual of Photogrammetry. ASPRS,
5 edition, 2004.
[2] M. A. Fischler and R.C. Bolles. Random sampling consensus: A
paradigm for model fitting with applications to image analysis and
automated cartography. Comm. of the ACM, 24:381 - 395, 1981.
[3] Janne Heikkil¨a and Olli Silv'en. A four-step camera calibration procedure
with implicit image correction. IEEE Conference on Computer Vision
and Pattern Recognition, pages 1106-1112, June 1997.
[4] Janne Heikkil¨a and Olli Silv'en. Geometric camera calibration using
circular control points. IEEE Transactions on Pattern Analysis and
Machine Intelligence, 22(10):1066 - 1077, October 2000.
[5] G.G. Mateos. A camera calibration technique using targets of circular
features. 5th Ibero-America Symposium On Pattern Recognition
(SIARP), 2000.
[6] Jean-Nicolas Ouellet and Patrick H'ebert. Developing assistant tools for
geometric camera calibration: Assessing the quality of input images. In
ICPR (4), pages 80-83, 2004.
[7] Rangaraj M. Rangayyan, N. M. El-Faramawy, J. E. Leo Desautels, and
Onsy Abdel Alim. Measures of acutance and shape for classification of
breast tumors. IEEE Trans. Med. Imaging, 16(6):799-810, 1997.
[8] Peter Sturm and Steve Maybank. On plane-based camera calibration:
A general algorithm, singularities, applications. In IEEE Conference on
Computer Vision and Pattern Recognition, pages 432-437, June 1999.
[9] Roger Y. Tsai. A versatile camera calibration technique for highaccuaricy
3d machine vision metrology using off-the-shelf tv cameras
and lenses. IEEE Transactions on Robotics and Automation, 4:323 -
344, August 1987.
[10] Zhengyou Zhang. A flexible new technique for camera calibration.
IEEE Transactions on Pattern Analysis and Machine Intelligence,
22(11):1330-1334, 2000.
[1] Edward Mikhail Chris McGlone. Manual of Photogrammetry. ASPRS,
5 edition, 2004.
[2] M. A. Fischler and R.C. Bolles. Random sampling consensus: A
paradigm for model fitting with applications to image analysis and
automated cartography. Comm. of the ACM, 24:381 - 395, 1981.
[3] Janne Heikkil¨a and Olli Silv'en. A four-step camera calibration procedure
with implicit image correction. IEEE Conference on Computer Vision
and Pattern Recognition, pages 1106-1112, June 1997.
[4] Janne Heikkil¨a and Olli Silv'en. Geometric camera calibration using
circular control points. IEEE Transactions on Pattern Analysis and
Machine Intelligence, 22(10):1066 - 1077, October 2000.
[5] G.G. Mateos. A camera calibration technique using targets of circular
features. 5th Ibero-America Symposium On Pattern Recognition
(SIARP), 2000.
[6] Jean-Nicolas Ouellet and Patrick H'ebert. Developing assistant tools for
geometric camera calibration: Assessing the quality of input images. In
ICPR (4), pages 80-83, 2004.
[7] Rangaraj M. Rangayyan, N. M. El-Faramawy, J. E. Leo Desautels, and
Onsy Abdel Alim. Measures of acutance and shape for classification of
breast tumors. IEEE Trans. Med. Imaging, 16(6):799-810, 1997.
[8] Peter Sturm and Steve Maybank. On plane-based camera calibration:
A general algorithm, singularities, applications. In IEEE Conference on
Computer Vision and Pattern Recognition, pages 432-437, June 1999.
[9] Roger Y. Tsai. A versatile camera calibration technique for highaccuaricy
3d machine vision metrology using off-the-shelf tv cameras
and lenses. IEEE Transactions on Robotics and Automation, 4:323 -
344, August 1987.
[10] Zhengyou Zhang. A flexible new technique for camera calibration.
IEEE Transactions on Pattern Analysis and Machine Intelligence,
22(11):1330-1334, 2000.
@article{"International Journal of Information, Control and Computer Sciences:49589", author = "Stephan Rupp and Matthias Elter and Michael Breitung and Walter Zink and Christian Küblbeck", title = "Robust Camera Calibration using Discrete Optimization", abstract = "Camera calibration is an indispensable step for augmented
reality or image guided applications where quantitative information
should be derived from the images. Usually, a camera
calibration is obtained by taking images of a special calibration object
and extracting the image coordinates of projected calibration marks
enabling the calculation of the projection from the 3d world coordinates
to the 2d image coordinates. Thus such a procedure exhibits
typical steps, including feature point localization in the acquired
images, camera model fitting, correction of distortion introduced by
the optics and finally an optimization of the model-s parameters. In
this paper we propose to extend this list by further step concerning
the identification of the optimal subset of images yielding the smallest
overall calibration error. For this, we present a Monte Carlo based
algorithm along with a deterministic extension that automatically
determines the images yielding an optimal calibration. Finally, we
present results proving that the calibration can be significantly
improved by automated image selection.", keywords = "Camera Calibration, Discrete Optimization, Monte
Carlo Method.", volume = "2", number = "7", pages = "2309-5", }