Study on subset size selection in digital image correlation for speckle patterns.
By Bing Pan, Huimin Xie, Zhaoyang Wang, Kemao Qian, Zhiyong Wang. Optics Express, 2008
632 citations recorded by the source corpus.
Digital Image Correlation (DIC) is a flexible and effective technique to measure the displacements on specimen surfaces by matching the reference subsets in the undeformed image with the target subsets in the deformed image. With the existing DIC techniques, the user must rely on experience and intuition to manually define the size of the reference subset, which is found to be critical to the accuracy of measured displacements. In this paper, the problem of subset size selection in the DIC technique is investigated. Based on the Sum of Squared Differences (SSD) correlation criterion as well as the assumption that the gray intensity gradients of image noise are much lower than that of speckle image, a theoretical model of the displacement measurement accuracy of DIC is derived. The theoretical model indicates that the displacement measurement accuracy of DIC can be accurately predicted based on the variance of image noise and Sum of Square of Subset Intensity Gradients (SSSIG). The model further leads to a simple criterion for choosing an optimal subset size for the DIC analysis. Numerical experiments have been performed to validate the proposed concepts, and the calculated results show good agreements with the theoretical predictions.
Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/5a19a26ab1605bac2d4549b79acd7553fa7ed2e4
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
In Computer Vision and Pattern Recognition
In Mechanical Engineering
Citations in the corpus
- Vermiculate artefacts in image analysis of granular materials (2016, methodology): “All of the MIG, SSSIG and σIs measures for such an image would also indicate highquality image texture was present, since the intensity gradients (and the standard deviations within the subsets) would be near maximal.”
- Theoretical Analysis on the Measurement Errors of Local 2D DIC: Part I Temporal and Spatial Uncertainty Quantification of Displacement Measurements (2016, methodology): “In the field of DIC, the Monte Carlo method has been adopted in some researches, e.g. References [49, 50].”
- Digital image correlation for surface deformation measurement: historical developments, recent advances and future goals (2018, methodology): “The SSSIG was then advocated by Pan et al to evaluate the quality of the speckle pattern within individual subsets [119].”
- The subpixel resolution of optical-flow-based modal analysis (2017, methodology): “Since the first implementations of optical flow [16] it is known that high contrasts are required for more accurate results and various research was done to determine the adequacy of the patterns painted on the observed surface [24, 25].”
- A practical approach to optimizing the preparation of speckle patterns for digital-image correlation (2014, background): “In the literature there are different studies that suggest the importance of the quality of the speckle pattern for DIC applications, especially concerning the average speckle size and variability [1, 6, 8].”