Enhanced feature-based path-independent initial value estimation for robust point-wise digital image correlation

With the increased complexity in various applications of Digital Image Correlation (DIC), the requirement for its performances as a key technique of optical metrologies becomes more and more crucial, especially in terms of accuracy, efficiency, and robustness. However, the existing DIC methods mainly rely on a calculation path to conduct the DIC analysis, which limits their further improvement in the measurement accuracy, computational efficiency, and robustness to complex deformation. To promote the implementation of a real-time and wide-applied DIC system, this study presents an automatic, fast and robust point-wise DIC (PW-DIC) algorithm. This work introduces an enhanced feature-based path-independent initial value estimation (PI-IVE) method for an accurate subpixel iterative algorithm, which is achieved by a variant of PatchMatch algorithm in the feature image generated from the speckle pattern image. The feature image is derived from DAISY descriptor, which extracts the descriptor value at every pixel effectively rather than some detected keypoints. This method calculates reliable full-field initial values for all points of interest (POIs) simultaneously without an initial guess transfer path, which is path-independent. It cooperates with the state-of-the-art inverse compositional Gauss-Newton (IC-GN) algorithm to perform an accurate DIC analysis for each POI independently. Moreover, the proposed PW-DIC method can solve the problem of low calculation efficiency in path-dependent DIC methods. Due to the high accuracy and flexibility of the proposed PW-DIC method, it outperforms the standard reliability-guided DIC (RG-DIC) methods for large, discontinuous or rotational deformations. Extensive experiments and analysis are conducted to demonstrate the accuracy, effectiveness, and robustness of the proposed PW-DIC method.