Digital image correlation (DIC) technique is one of the most widely used methods for shape, motion and deformation measurements [1]. The DIC technique typically works by comparing and matching the grayscale images of an object captured from different views, at different time, or at different stages of deformation. Through tracking every few pixels of interest in the reference and target images, followed by carrying out data interpolation, the DIC technique can determine the whole-field two-dimensional (2D) and three-dimensional (3D) shape, deformation and motion vector fields as well as their gradient maps. Because of its primary advantages of easy implementation and wide range of measurement sensitivity and resolution, the DIC technique has found numerous applications in many fields [2].

The DIC technique generally employs a correlation criterion to detect the best image matching for a group of pixels (named subset) centred at each interrogated pixel. There exist a variety of correlation criteria for the DIC analysis. For instance, a simple yet robust criterion named the parametric sum of squared difference criterion can be written as [3]

- (1)

where *a* is a scale factor, *b* is an offset of intensity and *f*(*x*_{i},*y*_{i}) and *g*(*x*′_{i},*y*′_{i}) indicate the intensity values at the *i*th pixel in the reference subset and the matching pixel in the target subset, respectively. The task of the correlation analysis is to minimise the coefficient *C* in Equation (1) to find the best matching. For a representative pixel (*x*_{0},*y*_{0}) to be analysed in the reference image, a square pattern of *N* = (2*M* + 1) × (2*M* + 1) pixels with its centre located at (*x*_{0},*y*_{0}) is usually chosen as the reference subset. The corresponding subset in the target image, i.e., the target subset, is often of irregular shape. Denoting the shift amount between the centres of the two matching subset patterns as (*ξ*,*η*), the shape mapping function for the entire reference and target subsets can be expressed as

- (2)

where *ξ*_{x}, *ξ*_{y}, *η*_{x} and *η*_{y} are the coefficients of the shape function. To determine all the six unknowns of shape function (*ξ*,*η*,*ξ*_{x},*ξ*_{y},*η*_{x},*η*_{y}) as well as the scale and offset parameters (*a*,*b*) involved in Equation (1), the DIC technique often employs an iterative algorithm such as the Newton–Raphson or the Levenberg–Marquardt method to carry out the correlation optimization. The iterative algorithm is capable of providing very fast and highly accurate correlation analysis [4]. The downside, however, is that a reasonably good initial guess for the six unknowns of the shape function on the starting or seed point is required.

When the shape change and the rotation of the target image with respect to the reference image are relatively small, the initial values of *ξ*_{x}, *ξ*_{y}, *η*_{x} and *η*_{y} can be set to zeros. In this case, the initial values of *ξ* and *η* can be automatically detected by a full-field subset scanning process [5, 6]. On the contrary, when the shape change and/or the rotation are relatively large, the initial guess normally has to be conducted by manually selecting three pairs of matching pixels in the reference and target images through human–computer interaction, which yields six equations to solve for the six unknowns in Equation (2) as an initial guess. Another notable case where a manual initial guess is usually demanded is when each of the reference and target images has multiple identical or nearly identical regions; the reason is that the existing automatic initial guess methods may not be able to detect the correct matches in those regions. It is also noted that the shape mapping function in Equation (2) may have more than six parameters to include higher-order terms; nevertheless, the higher-order terms are negligible for the initial guess purpose.

Manual selection of points for the initial guess in the DIC analysis has hampered the fully automatic analysis feature of the technique. The situation can become worse when there are multiple regions of interest, each requiring a reliable initial guess. In this paper, a robust scheme combining the concepts of a scale-invariant feature transform (SIFT) algorithm and an improved random sample consensus (iRANSAC) algorithm is employed to achieve an automated fast initial guess for the DIC technique. The SIFT algorithm is widely used in computer vision to detect and describe local features in images, and it has been recently applied to deformation measurements [7, 8]. Despite that, the advantages of the SIFT algorithm for the DIC measurements have not been fully utilised. The novel approach to be presented has the ability to accurately and automatically detect a number of matching points from two images even though the deformation and rotation are large or the images have periodic and identical patterns.