A self-adaptive sampling digital image correlation algorithm for accurate displacement measurement

Digital image correlation (DIC) is nowadays widely applied to many engineering areas as an effective optical displacement measurement technique. To minimize the potential effect of spatial sampling locations on full-field displacement measurement, this paper develops a self-adaptive sampling DIC algorithm for accurate and reliable displacement computation over entire specimen surfaces. Depending on local deformation states, the algorithm can automatically optimize spatial distribution of sampling points over specimen surfaces in a self-adaptive manner in combination with the well-developed DIC algorithm with Gaussian windows. Both a series of well-designed computer-simulated speckle images and actual cell-substrate deformation ones are employed to verify the feasibility and effectiveness of the proposed algorithm, which demonstrates that the set of self-adaptive sampling algorithm is capable of recovering more accurate and precise full-field displacements compared to the conventional DIC algorithm with equidistant sampling.

Duke Scholars

Author Fan Yuan Biomedical Engineering