DIC Uncertainty Estimation from Statistical Analysis of Correlation Values

Abstract

The uncertainty of a Digital Image Correlation (DIC) displacement field is estimated using a generic post-processing method based on statistical analysis of the intensity patterns. First the second image is dewarped back onto the first one using the computed displacement field which provides two almost perfectly matching images. Differences are analyzed regarding the effect on shifting the minimum of the correlation function. A relationship is derived between the standard deviation of intensity differences over a local region (subset or facet size) and the expected asymmetry of the correlation peak, which is then converted to the uncertainty of the displacement vector. This procedure is tested with synthetic data for various types of noise (random Gaussian noise, photon shot noise, image degradation) and provides accurate estimate of the true error. Finally the technique is applied to experimental data where the true error is estimated independently by other means. The proposed technique provides in many cases a reliable uncertainty estimate for different error sources related to variation in surface pattern as well as illumination and viewing angle changes.