Fast normalized cross-correlation image matching based on multiscale edge information

In order to overcome the Large computation of cross correlation matching, we propose a method of quick cross correlation matching. In traditional cross correlation matching all the pixels take part in computing, so the speed of matching is slow down. In this paper, we use multi-scale edge information which was extracted by improved Laplacian pyramid(ILP) as feather temple; to convert the gray information matching to the feature information matching; take advantage of the feature template and the image to be matched to establish indirect similarity measure to achieve speed-up, anti-geometric distortion. The multi-scale features of the method can meet multiple needs of the matching accuracy and speed. Experiments show, the method improves the speed of cross correlation matching, and has certain robustness.