Experimental Analysis of the Errors due to Polynomial Interpolation in Digital Image Correlation

Digital image correlation attempts to estimate displacement fields by digitally correlating two images acquired before and after motion. To do so, pixel intensity has to be interpolated at non-integer locations. The ideal interpolator is the sinc, but as it requires infinite support, it is not normally used and is replaced by polynomials. Polynomial interpolation produces visually appealing results but introduces positional errors in the signal, thus causing the digital image correlation algorithms to converge to incorrect results. In this work, an experimental campaign is described, that aims to characterise the errors introduced by interpolation, focusing in particular on the systematic error and the standard deviation of displacements.

[1]  Thomas Martin Deserno,et al.  Survey: interpolation methods in medical image processing , 1999, IEEE Transactions on Medical Imaging.

[2]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[3]  D. M. Freeman,et al.  Statistics of subpixel registration algorithms based on spatiotemporal gradients or block matching , 1998 .

[4]  M. Sutton,et al.  High-temperature deformation measurements using digital-image correlation , 1996 .

[5]  Sven Bossuyt,et al.  Quality assessment of speckle patterns for digital image correlation , 2006 .

[6]  Hugh Alan Bruck,et al.  Quantitative Error Assessment in Pattern Matching: Effects of Intensity Pattern Noise, Interpolation, Strain and Image Contrast on Motion Measurements , 2009 .

[7]  Dennis C. Ghiglia,et al.  Two-Dimensional Phase Unwrapping: Theory, Algorithms, and Software , 1998 .

[8]  Phillip L. Reu,et al.  Experimental and Numerical Methods for Exact Subpixel Shifting , 2011 .

[9]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[10]  Simon Baker,et al.  Lucas-Kanade 20 Years On: A Unifying Framework , 2004, International Journal of Computer Vision.

[11]  Akram Aldroubi,et al.  B-spline signal processing. II. Efficiency design and applications , 1993, IEEE Trans. Signal Process..

[12]  Pierre Jacquot,et al.  Phase Extraction in Dynamic Speckle Interferometry by Empirical Mode Decomposition , 2007 .

[13]  Hugh Alan Bruck,et al.  Digital image correlation using Newton-Raphson method of partial differential correction , 1989 .

[14]  M. A. Sutton,et al.  Systematic errors in digital image correlation caused by intensity interpolation , 2000 .

[15]  Michael Unser,et al.  B-spline signal processing. I. Theory , 1993, IEEE Trans. Signal Process..

[16]  Michael A. Sutton,et al.  Error Assessment in Stereo-based Deformation Measurements , 2011 .

[17]  Michael Unser,et al.  Fast B-spline Transforms for Continuous Image Representation and Interpolation , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Shaopeng Ma,et al.  The systematic error in digital image correlation induced by self-heating of a digital camera , 2012 .

[19]  M. Grédiac,et al.  Assessment of Digital Image Correlation Measurement Errors: Methodology and Results , 2009 .

[20]  Zhaoyang Wang,et al.  Equivalence of digital image correlation criteria for pattern matching. , 2010, Applied optics.

[21]  Antonio Baldi Phase unwrapping by region growing. , 2003, Applied optics.

[22]  Timothy J. Miller,et al.  On Error Assessment in Stereo-based Deformation Measurements , 2011 .