Abstract
Digital Image Correlation (DIC) has found widespread use in measuring full-field displacements and deformations experienced by a body from images captured of it. Stereo-DIC has received significantly more attention than two-dimensional (2D) DIC since it can account for out-of-plane displacements. Although many aspects of Stereo-DIC that are shared in common with 2D DIC are well documented, there is a lack of resources that cover the theory of Stereo-DIC. Furthermore, publications which do detail aspects of the theory do not detail its implementation in practice. This literature gap makes it difficult for newcomers to the field of DIC to gain a deep understanding of the Stereo-DIC process, although this knowledge is necessary to contribute to the development of the field by either furthering its capabilities or adapting it for novel applications. This gap in literature acts as a barrier thereby limiting the development rate of Stereo-DIC. This paper attempts to address this by presenting the theory of a subset-based Stereo-DIC framework that is predominantly consistent with the current state-of-the-art. The framework is implemented in practice as a 202 line MATLAB code. Validation of the framework shows that it performs on par with well-established Stereo-DIC algorithms, indicating it is sufficiently reliable for practical use. Although the framework is designed to serve as an educational resource, its modularity and validation make it attractive as a means to further the capabilities of DIC.
Highlights
Digital Image Correlation (DIC) is an optical metrology technique capable of determining full-field displacements and deformations experienced by a body from images captured of the body’s surface
If VLFeat library is not setup, return an error; Compute keypoint locations and descriptors for F and G using vl_sift; Determine vector KptsInVacinity identifying the keypoints of F which fall within the perimeter of the square subsets, equivalent in size to that specified for the subset under consideration, of F; Eliminate keypoints of F which do not fall within the perimeter of any of the subsets; Determine matching keypoints using vl_ubcmatch; Determine the 20 nearest keypoints for each subset stored in matrix relevantKpts; Define anonymous functions RansacModel and RansacError for determining affine transformation parameters (of Equation (29)) and evaluating Equation (30) respectively; Initialise P as NaNs; for subset number q = 1 to number of subsets, do try
The theory of a subset-based, Stereo-DIC framework, that is predominantly consistent with current state-of-the-art techniques, and its implementation as a modular 202 line
Summary
Digital Image Correlation (DIC) is an optical metrology technique capable of determining full-field displacements and deformations experienced by a body from images captured of the body’s surface. Some publications release code that is consistent with the theory presented [50,51], these codes focus on robustness and ease of use which, despite making them more suitable for real world applications, results in a complex code which is ineffective as a learning resource This lack of resources acts as a barrier to newcomers intending to further the capabilities of DIC, thereby limiting the development rate of the field. It is for this reason that the authors published a paper bridging the gap between the theory of a 2D DIC framework (ADIC2D) and its practical implementation as a modular. This modularity allows the code to be altered, urging readers to further the capabilities of DIC
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