# Stereo Digital Image Correlation in MATLAB
## 1 Introduction  
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. The accuracy of DIC results is greatly influenced by the quality of the speckle pattern [1], the internals of the DIC algorithm [2; 3], the appropriateness of the chosen processing parameters [4] and the quality of the calibration process [5; 6; 7], among others. However, despite this, DIC, a non-interferometric technique, is more robust in withstanding vibrations and ambient light variations than interferometric techniques, such as Electronic Speckle Pattern Interferometry and Moire interferometry, allowing it to be used outside the confines of a laboratory. Additionally, its non-contact, full-field nature; its ability to function at a range of scales (from scanning electron microscopy images [8; 9] to satellite images [10]); and the rapid improvement of cameras has resulted in it becoming one of the most favored techniques in experimental solid mechanics. Although DIC has received the most attention and thus development within experimental solid mechanics, it has found widespread use across many fields. Some applications of DIC include: (i) investigating mechanical properties of biological tissues [11; 12; 13; 14; 15]; (ii) deflection measurement and structural health monitoring of bridges [16; 17; 18; 19; 20; 21; 22]; (iii) investigating cracks in masonry structures [23; 24; 25]; (iv) landslide monitoring [26; 27; 28; 29]; (v) determining material properties of woven fabrics (such as cotton) [30; 31]; vibrational analysis of components [32; 33; 34]; and (vii) in situ damage detection of components and structures [35; 36; 37] to name a few. Following the introduction of two-dimensional (2D) DIC in 1982 by [PERSON] and [PERSON] [38] it was extended to three dimensions (Stereo-DIC) by [PERSON] et al. [39] in 1993 and to digital volume correlation (DVC) by [PERSON] et al. [40] in 1999. The correlation aspect of DIC has undergone significant refinement over the years. Current state-of-the-art DIC algorithms as identified by [PERSON] [41] make use of: (i) bi-quintic b-spline interpolation which was shown by [PERSON] et al. [3] to produce the most accurate sub-pixel displacements; (ii) the first and second-order shape functions (SFs) proposed by [PERSON] et al. [42] and [PERSON] [43] respectively; (iii) the zero-mean normalized sum of squared difference (ZNSSD) correlation criterion suggested by [PERSON] [44]; (iv) Gaussian pre-filtering of images to reduce bias in the displacements caused by high frequency noise in the images as proposed by [PERSON] [45]; and (v) the inverse compositional Gauss-Newton (IC-GN) optimization method introduced by [PERSON] and [PERSON] [46]. Although the IC-GN optimization method is theoretically equivalent to the Newton-Raphson method [46], in practice it has greater efficiency, accuracy and robustness to withstand noise [47] making it preferable. Although DIC is a complex process, the papers proposed by [PERSON] et al. [48], [PERSON] et al. [49], [PERSON] et al. [50], and [PERSON] et al. [51] provide an in-depth explanation of the theory of the DIC process, which coupled with the Good Practices Guide for DIC [52], enables newcomers to the field of DIC to gain the necessary knowledge to effectively apply it in established use cases. However, in order for a newcomer to contribute to the field of DIC by developing new algorithms which improve its capabilities or adapting it for new applications, he or she must gain a deep understanding of DIC. Gaining such a deep understanding is cumbersome due to the lack of resources which directly bridge the gap between the theory of DIC and its coded implementation.