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Algorithm for feature point matching is an effective algorithm to realize the target recognition. At present, algorithm for the image feature points matching is time-consuming, and there are wrong matching points in the matching process. Therefore, a kind of improved algorithm based on SURF algorithm was proposed. In process of detecting initial feature points, Gaussian Pyramid Layers was built dynamically according to the image size to improve real-time and accuracy. And initial feature points were optimized by setting the threshold, which reduced the matching time. In the stage of feature points’ matching, false matching points can be removed by the difference of coordinate value between false matching points and right matching points. Experimental results show that in the image size of 800×600, the SURF algorithm extracts 225 feature points, which takes 92.499 ms, Octave 3; the feature point matching rate is 97.50%, which takes 349.716 ms. The improved method is more simple and effective, reduces the error of feature point matching, and can effectively shorten the image registration time.