*2.1. Basic Theory*

The stereo vision system shown in Figure 1, or a two-camera system with different perspectives, plays an important role in the DIC measurement system. A space point *p* is recorded simultaneously by two cameras on the image plane with a different perspective. The points *p*1 and *p*2 on the image plane of the two cameras are then matched using the DIC matching algorithm, and the 3D coordinates of point *p* can be recovered based on the geometry of the stereo-vision system.

The core idea of the DIC method is to measure displacement and strain information on an object's surface by matching and tracking feature points in the natural texture or artificial speckle pattern before and after deformation. The DIC matching process obtains the coordinate on the target image after deformation corresponding to a known coordinate position on the reference image taken before deformation. It is usually necessary to divide the image into multiple grids, or subsets, and the deformation of the subset is used to represent the deformation of the local area of the object. Figure 2 shows this principle. On the reference image, a rectangle with a size of (2*M* + 1) pixel × (2*M* + 1) pixel and point *p*(*x*0, *y*0) at the center is taken as the reference subset. A correlation function is then used to find a subset on the target image with a similar distribution to that of the reference subset. The subset with the largest correlation coefficient is considered the deformed subset. The sum squared difference correlation criterion (SSD) is commonly used to evaluate the similarity between subsets [20]. Assuming that the intensity distribution of the two facets is represented as *F*(*x*, *y*) and *G*(*x* ′ , *y* ′ ), the SSD function has the following form:

(, ) (

(, ) (

′ , ′ )

′ , ′ )

= ∑ (, ) × (

= ∑ (, ) × (

′ , ′ )

′ , ′ )

**Figure 1.** Schematic of stereo-vision system.

**Figure 2.** Basic principle of digital image correlation.
