This paper presents an improved method based on Speed up Robust Features (SURF) algorithm to achieve fast image stitching. As the variability of scenes lead to instability of features, expecting to obtain accurate number of features is pretty difficult and time-consuming. support vector machine (SVM) applied in this paper to predict primary threshold of determinant of Hessian matrix can conspicuously reduce detected feature points and simplify the process of features matching. This paper also combines an optimized method of image preprocessing-cylindrical projection and image interpolation to weigh the final quality of stitching image and stitching time. Several experiments are conducted to verify the performance of improved SURF.
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