Research of image matching based on a fast normalized cross correlation algorithm

In grayscale image matching, the normalized cross correlation algorithm has a lot of advantages such as robust, high precision, fitting for implementing on hardware and so on. But its application is limited because of high algorithm complication and low computation speed. Therefore, a fast normalized cross correlation algorithm was proposed based on iterative idea. At computing the energy of sub-based image, the sum of all the pixels in sub-based image is fastly computed by adding and subtracting several pixels' values to reduce the algorithm complication. The experimented results show that, the fast normalized cross correlation algorithm has the same matching precision as that of the original one, but its implementation time is just 1/9 of that of the original.