Calibration Method for Stereovision Measurement of High-Temperature Components Using Two Infrared Cameras

Le Song; Zi-Hui Zhang

doi:10.20965/ijat.2013.p0163

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IJAT Vol.7 No.2 pp. 163-170

doi: 10.20965/ijat.2013.p0163

(2013)

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Calibration Method for Stereovision Measurement of High-Temperature Components Using Two Infrared Cameras

Le Song and Zi-Hui Zhang

State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, No.92, Weijin Rd., Tianjin, China

Received:

October 30, 2012

Accepted:

February 4, 2013

Published:

March 5, 2013

Keywords:

infrared image, stereovision, ceramic spherical calibration, edge detection

Abstract

The infrared vision measurement method has some advantages over visible vision in the measurement of high-temperature components. Infrared imaging is based on different imaging principles, however, making it unreasonable to adopt a conventional visiblelight camera calibration method directly. The present study proposes a dual infrared-camera calibration program in which we use the infrared imaging principle to measure high-temperature components threedimensionally. We use two ceramic balls as calibration targets against the background of an external hightemperature radiation source. Multiple feature points are generated from the precise movement of these targets. In order to improve the accuracy of the calibration method, we took the following approaches: A highly accurate edge-detection algorithm is realized by using a fuzzy neural network that is self-learning, self-adaptive, and utilizes fuzzy processing. We thus achieve a low signal-to-noise ratio and low contrast in infrared images. The distance between these two ceramic targets is used as a calibration reference to further reduce the temperature effect and to improve calibration accuracy and efficiency. The calibration results we got are an average residual error of 6.4134 µm and a variance of 2.9205 µm.

Cite this article as:

L. Song and Z. Zhang, “Calibration Method for Stereovision Measurement of High-Temperature Components Using Two Infrared Cameras,” Int. J. Automation Technol., Vol.7 No.2, pp. 163-170, 2013.

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References

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[7] F. M. Zhang, X. H. Qu, H. Y. Wu, et al., “Method for improving measurement precision of large-Scale circular section,” Nanotechnology and Precision Engineering, Vol.7, No.4, pp. 342-345, 2009. (in Chinese)
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[10] Z. Q. Liu, J. W. Zou, J. Zhang, et al, “The Homomorphic enhancement of infrared image in fuzzy field,” Laser & Infrared. Vol.37, No.1, pp. 87-90, 2007. (in Chinese)
[11] F. X. Guo, J. Liang, and X.Wang, “Edge detection for noisy images based on fuzzy reasoning,” Computer Engineering, Vol.36, No.15, pp. 194-195, 2010. (in Chinese)
[12] Q. H. Tian and Y. X. Du, “The evaluation for the performance of mechanical products based on fuzzy neural network,” Mie of China, Vol.33, No.9, pp. 124-126, 2004. (in Chinese)
[13] H. G. Luo, L. M. Zhu, and H. Ding, “Camera calibration with coplanar calibration board near parallel to the imaging plane,” Sensors and Actuators, A, Vol.132, pp. 480-486, 2006.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] Q. H. Liu, “Theoretical and Experimental Research on 3D Size Measurement of High-temperature Components Based on Infrared Vision,” Tianjin: College of Precision Instrument and Optoelectronics engineering, Tianjin University, 2011. (in Chinese)

[2] [2] B. P. Amavasai, F. Caparrelli, A. Selvan, et al., “Machine vision methods for autonomous micro-robotic systems,” Kybernetes, Vol.34, No.9/10, pp. 1421-1439, 2005.

[3] [3] D. B. Gennery, “Generalized camera calibration including fish-eye lenses,” Int. J. of Computer Vision, Vol.68, No.3, pp. 239-266, 2006.

[4] [4] F. J. Zou, X. Y. Su, and M. J. Li, “Camera linear calibration method with a coplanar target,” Opto-Electronic Engineering, Vol.32, No.4, pp. 70-74, 2005. (in Chinese)

[5] [5] J. Pan and W. M. Li, “Algorithm of implementing 3D calibration board-based camera calibration,” Machinery & Electronics, Vol.5, pp. 3-5, 2007. (in Chinese)

[6] [6] Z. Zhang, “A flexible new technique for camera calibration,” IEEE Trans. on Pattern Analysis andMachine Intelligence, Vol.22, No.11, pp. 1330-1334, 2000.

[7] [7] F. M. Zhang, X. H. Qu, H. Y. Wu, et al., “Method for improving measurement precision of large-Scale circular section,” Nanotechnology and Precision Engineering, Vol.7, No.4, pp. 342-345, 2009. (in Chinese)

[8] [8] R. L. Duan, Q. X. Li, and Y. H. Li, “Summary of image edge detection,” Optical Technique, Vol.31, No.3, pp. 415-419, 2005. (in Chinese)

[9] [9] P. D. Zhou, M. Y. Fu, and C. J. Zhang, “An approach to extract thermal image edge,” J. of Computer-Aided Design & Computer Graphics, Vol.16, No.8, pp. 8-13, 2004. (in Chinese)

[10] [10] Z. Q. Liu, J. W. Zou, J. Zhang, et al, “The Homomorphic enhancement of infrared image in fuzzy field,” Laser & Infrared. Vol.37, No.1, pp. 87-90, 2007. (in Chinese)

[11] [11] F. X. Guo, J. Liang, and X.Wang, “Edge detection for noisy images based on fuzzy reasoning,” Computer Engineering, Vol.36, No.15, pp. 194-195, 2010. (in Chinese)

[12] [12] Q. H. Tian and Y. X. Du, “The evaluation for the performance of mechanical products based on fuzzy neural network,” Mie of China, Vol.33, No.9, pp. 124-126, 2004. (in Chinese)

[13] [13] H. G. Luo, L. M. Zhu, and H. Ding, “Camera calibration with coplanar calibration board near parallel to the imaging plane,” Sensors and Actuators, A, Vol.132, pp. 480-486, 2006.