Kernel based Digital Image Correlation

Digital image correlation (DIC) is an effective displacement field measurement method featuring non-contact and full-field, which has been successfully applied in lots of fields, especially in the field of experimental mechanics. Unfortunately, the traditional DIC technique (TDIC) depends on the output values of image sensor heavily that usually noised in practical imaging system. So, TDIC cannot deal with bias error introduced by the image noise effectively. In this paper, a kernel based DIC is proposed to help reduce the bias error by establishing and optimizing a weighted correlation function. Compared with the methods of low-pass filtering, KDIC preserves the high frequency information well but reduces the bias error caused by image noise effectively. To demonstrate, two kinds of kernel function are analyzed. One is Epanechnikov kernel, with which KDIC is equivalent to TDIC. The other is Gaussian kernel, which can be used to improve the anti-noise performance of TDIC and get more accurate subpixel displacements. Both simulation analyses and experimental results validate the effectiveness of this new method.