Digital image correlation as a tool for surface deformation measurements has found widespread use and acceptance in the field of experimental mechanics. The method is known to reconstruct displacements with subpixel accuracy and tangential surface strains in the sub-millistrain range.
Potential error sources, limiting the system resolution, are varied, including intrinsic noise of the acquired images, statistical and systematical errors introduced by the system calibration, subpixel effects resulting from a limited camera resolution, as well as intrinsic uncertainties of the correlation algorithm.
The impact on resulting quantities like contours, displacements, and strains depends essentially on the nature of the error source. A statistical random error distribution can be estimated from the results themselves, and the quality of the result can be further improved by proper filtering. In contrast, systematic errors, such as those introduced by an erroneous system calibration, cannot be reduced by post-processing and thus build a hard limit for the system resolution.