Subpixel Matching Using Double-Precision Gradient-Based Method for Digital Image Correlation

Abstract

Digital image correlation (DIC) for displacement and strain measurement has flourished in recent years. There are integer pixel and subpixel matching steps to extract displacement from a series of images in the DIC approach, and identification accuracy mainly depends on the latter step. A subpixel displacement matching method, named the double-precision gradient-based algorithm (DPG), is proposed in this study. After, the integer pixel displacement is identified using the coarse-fine search algorithm. In order to improve the accuracy and anti-noise capability in the subpixel extraction step, the traditional gradient-based method is used to analyze the data on the speckle patterns using the computer, and the influence of noise is considered. These two nearest integer pixels in one direction are both utilized as an interpolation center. Then, two subpixel displacements are extracted by the five-point bicubic spline interpolation algorithm using these two interpolation centers. A novel combination coefficient considering contaminated noises is presented to merge these two subpixel displacements to obtain the final identification displacement. Results from a simulated speckle pattern and a painted beam bending test show that the accuracy of the proposed method can be improved by four times that of the traditional gradient-based method that reaches the same high accuracy as the Newton–Raphson method. The accuracy of the proposed method efficiently reaches at 92.67%, higher than the Newton-Raphon method, and it has better anti-noise performance and stability.

Review of research progress and development trend of digital image correlation

“…Liu et al . (2021b) introduced the double precision gradient-based (DPG) method, as outlined in their work.…”

Damage Detection for Rotating Blades Using Digital Image Correlation with an AC-SURF Matching Algorithm

“…The conventional DIC is a subset-based image matching method that determines the target location by comparing the correlation degree between the reference and deformed subsets [ 31 ]. It is notable that it has the limited convergence range of a rotation angle; this is a decorrelation problem that appears when the deformed image is subjected to large-scale rotation [ 32 ].…”

Bayesian-Inference Embedded Spline-Kerneled Chirplet Transform for Spectrum-Aware Motion Magnification

“…Carrying out the analysis of global motion information in video remains a challenging task due to the millions of pixels’ respective temporal vibration signal exiting in video. In engineering application areas and stereo-vision systems [ 51 , 52 , 53 , 54 , 55 , 56 , 57 ], the three-dimensional digital image correlation (3D DIC) and three-dimensional point tracking (3DPT) techniques are used to extract the full-field dynamic displacements of the analytical structure, and temporal vibration signals are further analyzed to obtain the material properties [ 58 , 59 ]. These methods are appropriate for structural engineering due to the law of mechanics; however, limitations arise when analyzing an irregular video.…”