The normalized cross correlation algorithm is a classic image matching algorithm. The method is simple and has a high matching probability. However, its disadvantage is enormous computation, which is difficult to meet the requirements of real-time. In this paper, first, a method for reducing the computation is proposed. By constructing two searching matrix from the reference image, the searching path of the real-time image is simplified. It completes the matching just along one translation direction and can avoid repetitious computation of the image energy easily based on an iterative operation between the two adjacent sub-based images. Then, the fast matching algorithm is proposed by combining the method and the bounded partial correlation (BPC) algorithm. It improves the computational speed of the algorithm by reducing computation from two aspects. Final, the experiment results show the superiorities of the algorithm.