Optimized patterns for digital image correlation

This work presents theoretical background on a novel class of strain sensor patterns. A combination of morphological image processing and Fourier analysis is used to characterize gray-scale images, according to specific criteria, and to synthesize patterns that score particularly well on these criteria. The criteria are designed to evaluate, with a single digital image of a pattern, the suitability of a series of images of that pattern for full-field displacement measurements by digital image correlation (DIC). Firstly, morphological operations are used to flag large featureless areas and to remove from consideration features too small to be resolved. Secondly, the autocorrelation peak sharpness radius en the autocorrelation margin are introduced to quantify the sensitivity and robustness, respectively, expected when using these images in DIC algorithms. For simple patterns these characteristics vary in direct proportion to each other, but it is shown how to synthesize a range of patterns with wide autocorrelation margins even though the autocorrelation peaks are sharp. Such patterns are exceptionally well-suited for DIC measurements.