Image registration research based on SUSAN-SURF algorithm

Image registration is an important technology of image processing and has a broad application. Good adaptability, computing speed and registration accuracy are the basic requirements in image registration. In recent years, the image registration algorithm based on local invariant features is becoming a hot spot of research, such as the SIFT method is one of them. But the traditional SIFT algorithm has computational complexity and the high real-time performance. SURF algorithm as a new feature extraction method, possess a small amount of calculation and quick speed, but the large amount of feature points are create false match easily. To solve these problems, this paper proposed an effective combination of SUSAN-SURF algorithm, which retain the high efficiency of SURF and the outline information of SUSAN. And then SURF algorithm is effectively improved by using KNN to speed up image matching. Finally the algorithm was verified by experiment, SUSAN-SURF can under the premise in accuracy, improve the real-time performance.