Spatial-Temporal Digital Image Correlation: A Unified Framework
This work provides a systematic tool for researchers in experimental mechanics and imaging to customize DIC algorithms, but it is incremental as it builds on existing subset-based DIC methods.
The authors introduced a unified framework for spatial-temporal digital image correlation (DIC) that decouples shape function, correlation criterion, and optimization algorithm, enabling flexible algorithm combinations. Results from subpixel translation and simulated image series showed noise suppression and velocity compatibility, with an application to mitigate heat haze air disturbance.
A comprehensive and systematic framework for easily extending and implementing the subset-based spatial-temporal digital image correlation (DIC) algorithm is presented. The framework decouples the three main factors (i.e. shape function, correlation criterion, and optimization algorithm) involved in algorithm implementation of DIC and represents different algorithms in a uniform form. One can freely choose and combine the three factors to meet his own need, or freely add more parameters to extract analytic results. Subpixel translation and a simulated image series with different velocity characters are analyzed using different algorithms based on the proposed framework, confirming the merit of noise suppression and velocity compatibility. An application of mitigating air disturbance due to heat haze using spatial-temporal DIC is given to demonstrate the applicability of the framework.