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.
@article{arxiv.1812.04826,
title = {Spatial-Temporal Digital Image Correlation: A Unified Framework},
author = {Yuxi Chi and Bing Pan},
journal= {arXiv preprint arXiv:1812.04826},
year = {2019}
}
Comments
20 pages, 7 figures v2:add figures and revise the contents