Naturalization and evaluation of synthetic random stripes pattern for digital image correlation

Abstract

Abstract The morphology of speckle patterns commonly used in the Digital Image Correlation (DIC) community can be categorized into artificially generated free shapes and computer-synthesized circular dots. Due to the non-repeatability of manually sprayed patterns, researchers tend to favour code-controlled dot patterns, which exist in two forms: “white dots on a black background” and “black dots on a white background”. However, from the perspective of biological evolution, these two dot patterns can be hybridized “in silico” using the Turing model to create an intermediate form—the “striped-like” pattern. This hybridization significantly improves species identification and environmental adaptability. Although there have been reports on producing stripe patterns based on the kernel-based Turing model, there is no specific speckle pattern generation specification or comprehensive evaluation research for DIC applications. This paper naturalizes a novel striped speckle pattern and a corresponding generation approach. The pattern quality was assessed and compared with circular dots and hand-sprayed speckles. The stripes pattern outperformed the other two forms regarding Mean Intensity Gradient (MIG), q-factor, systematic bias, and random error. Sub-pixel displacement simulation and actual translation test results demonstrate that the proposed striped pattern offers higher precision and robustness in displacement and static strain measurements. Therefore, this striped pattern provides a preferred alternative for DIC technicians.