Template matching algorithms consider all possible transformations

such as rotation

scaling

and affine transformations.Template matching algorithms are commonly used to find the corresponding image regions between image pairs.However

the two following issues negatively affect its accuracy.1) When the photography baseline increases

the effective information of the area to be matched in the target image decreases and the matching accuracy gradually decreases.2) The choice of matching areas significantly influences the matching accuracy;thus

the matching results may differ considerably by selecting different regions as matching templates.We propose a template selection and matching algorithm for image matching to resolve these problems. First

sampling vector normalized cross-correlation (SV-NCC) is proposed to measure the regional consistency between two images by multichannel features.The proposed method discards two parameters

i.e.

Δ and

which play an important role in suppressing the interference of light and noise in the CSAD method but reduce matching accuracy.The NCC method is introduced using the "mean" and "cross-correlation" to inhibit the effect of light and noise to solve this problem.Template matching is conducted in Lab color space to better reduce the influence of the change in illumination.When computing the color similarity of two images

if the Euclidean distance of two color components