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Proceedings Paper

Structural deformation measurement via efficient tensor polynomial calibrated electro-active glass targets
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Paper Abstract

This paper describes the physical setup and mathematical modelling of a device for the measurement of structural deformations over large scales, e.g., a mining shaft. Image processing techniques are used to determine the deformation by measuring the position of a target relative to a reference laser beam. A particular novelty is the incorporation of electro-active glass; the polymer dispersion liquid crystal shutters enable the simultaneous calibration of any number of consecutive measurement units without manual intervention, i.e., the process is fully automatic. It is necessary to compensate for optical distortion if high accuracy is to be achieved in a compact hardware design where lenses with short focal lengths are used. Wide-angle lenses exhibit significant distortion, which are typically characterized using Zernike polynomials. Radial distortion models assume that the lens is rotationally symmetric; such models are insufficient in the application at hand. This paper presents a new coordinate mapping procedure based on a tensor product of discrete orthogonal polynomials. Both lens distortion and the projection are compensated by a single linear transformation. Once calibrated, to acquire the measurement data, it is necessary to localize a single laser spot in the image. For this purpose, complete interpolation and rectification of the image is not required; hence, we have developed a new hierarchical approach based on a quad-tree subdivision. Cross-validation tests verify the validity, demonstrating that the proposed method accurately models both the optical distortion as well as the projection. The achievable accuracy is e ≤ ±0.01 [mm] in a field of view of 150 [mm] x 150 [mm] at a distance of the laser source of 120 [m]. Finally, a Kolmogorov Smirnov test shows that the error distribution in localizing a laser spot is Gaussian. Consequently, due to the linearity of the proposed method, this also applies for the algorithm's output. Therefore, first-order covariance propagation provides an accurate estimate of the measurement uncertainty, which is essential for any measurement device.

Paper Details

Date Published: 6 March 2013
PDF: 9 pages
Proc. SPIE 8661, Image Processing: Machine Vision Applications VI, 86610F (6 March 2013); doi: 10.1117/12.2003679
Show Author Affiliations
Christoph Gugg, Montan Univ. Leoben (Austria)
Matthew Harker, Montan Univ. Leoben (Austria)
Paul O'Leary, Montan Univ. Leoben (Austria)


Published in SPIE Proceedings Vol. 8661:
Image Processing: Machine Vision Applications VI
Philip R. Bingham; Edmund Y. Lam, Editor(s)

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