Digital Image Correlation with Self-Adaptive Gaussian Windows

A novel subpixel registration algorithm with Gaussian windows is put forward for accurate deformation measurement in digital image correlation technique. Based on speckle image quality and potential deformation states, this algorithm can automatically minimize the influence of subset sizes by self-adaptively tuning the Gaussian window shapes with the aid of a so-called weighted sum-of-squared difference correlation criterion. Numerical results of synthetic speckle images undergoing in-plane sinusoidal displacement fields demonstrate that the proposed algorithm can significantly improve displacement and strain measurement accuracy especially in the case with relatively large deformation.

[1]  Jing Fang,et al.  Determination of cellular tractions on elastic substrate based on an integral Boussinesq solution. , 2009, Journal of biomechanical engineering.

[2]  Sun Yaofeng,et al.  Study of optimal subset size in digital image correlation of speckle pattern images , 2007 .

[3]  Jun Ma,et al.  Accuracy enhancement of digital image correlation with B-spline interpolation. , 2011, Optics letters.

[4]  Hubert W. Schreier,et al.  Image Correlation for Shape, Motion and Deformation Measurements: Basic Concepts,Theory and Applications , 2009 .

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

[6]  W. Tong.  An Evaluation of Digital Image Correlation Criteria for Strain Mapping Applications , 2005 .

[7]  H. Lu,et al.  Deformation measurements by digital image correlation: Implementation of a second-order displacement gradient , 2000 .

[8]  Jing Fang,et al.  High-efficiency cell–substrate displacement acquisition via digital image correlation method using basis functions , 2010 .

[9]  Dimitri Debruyne,et al.  Study of systematic errors in strain fields obtained via DIC using heterogeneous deformation generated by plastic FEA , 2010 .

[10]  H. Haddadi,et al.  Use of rigid-body motion for the investigation and estimation of the measurement errors related to digital image correlation technique , 2008 .

[11]  Jing Fang,et al.  Cellular traction force recovery: An optimal filtering approach in two-dimensional Fourier space. , 2009, Journal of theoretical biology.

[12]  Anand Asundi,et al.  Two-dimensional digital image correlation for in-plane displacement and strain measurement: a review , 2009 .

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

[14]  G. Vendroux,et al.  Submicron deformation field measurements: Part 2. Improved digital image correlation , 1998 .

[15]  Dimitri Debruyne,et al.  Assessment of measuring errors in DIC using deformation fields generated by plastic FEA , 2009 .

[16]  M. Sutton,et al.  Systematic errors in digital image correlation due to undermatched subset shape functions , 2002 .

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

[18]  Wilfried Philips,et al.  Improved Newton-Raphson digital image correlation method for full-field displacement and strain calculation. , 2010, Applied optics.

[19]  Olivier Dalverny,et al.  Study of image characteristics on digital image correlation error assessment , 2010 .

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

[21]  Jing Fang,et al.  A high-efficiency digital image correlation method based on a fast recursive scheme , 2010 .

[22]  Franccois Hild,et al.  Digital Image Correlation: from Displacement Measurement to Identification of Elastic Properties – a Review , 2006 .

[23]  K. Qian,et al.  Study on subset size selection in digital image correlation for speckle patterns. , 2008, Optics express.

[24]  Wei Tong.  Subpixel image registration with reduced bias. , 2011, Optics letters.