Integrated adaptive particle filtering-based color digital image correlation for large deformation measurement

Abstract

In large deformation measurement, digital image correlation (DIC¹) using functional forms can no longer describe the complex changes in grayscale information and morphology of sub-regions. To address this issue, particle filtering (PF) can be combined with color DIC (PFDIC²) to establish a color distribution model to describe sub-regions, but PFDIC has poor performance. Therefore, this paper proposes an integrated adaptive particle filtering-based color digital image correlation (APF-DIC³). This method first constructs a dynamic color distribution model based on ACFEF⁴ to describe the sub-region. It then introduces the SSKL⁵ correlation coefficient to measure the similarity between sub-regions, and establishes TAR⁶ to adaptively adjust the number of particles, ultimately achieving adaptive matching of sub-regions under complex deformations. Performance evaluation and simulation results show that APF-DIC significantly improves the accuracy, robustness, and computational efficiency of the algorithm. Real experimental results further verify the effectiveness of APF-DIC, demonstrating its excellent illumination invariance.