Naturalization and Evaluation of Synthetic Random Stripes Pattern for Digital Image Correlation

• Published in: Measurement science & technology 2025-01, Vol.36 (1), p.15028
• Main Authors: Ye, Meitu, Guo, Min, Xie, Meilin, Tian, Guangyuan, Shi, Heng, Tian, Yan
• Format: Article
• Language: English
• ISSN: 0957-0233; 1361-6501
• DOI: 10.1088/1361-6501/ad8941

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 favor 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, 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.