关键词: 尺度不变特征变换, 随机抽样一致性, 渐入渐出融合, 图像拼接

Abstract: An improved SIFT algorithm is proposed for the problem of SIFT（Scale Invariant Feature Transform） algorithm which has high computational complexity and long running time. In order to solve this problem, the improved SIFT algorithm expands the range of extremum points to reduce the number of extreme points, in order to increase the speed of operation. Furthermore, the improved algorithm uses a circular window of 12 rings instead of the traditional square window and simplifies the construction of SIFT feature descriptors with generating 78-dimensional SIFT feature descriptors, which further improve the operation speed of the algorithm. For the best experimental results, the Best Bin First（BBF） method and the Random Sample Consensus（RANSAC） are used in this improved algorithm. BBF is applied in the initial registration search between feature point pairs, and RANSAC is used to perform secondary processing on the feature point registration pairs to eliminate mismatching. Finally, this paper combines the improved SIFT algorithm with the gradual image fusion algorithm to realize the mosaic and fusion of time series images. To evaluate the experimental effect, this paper uses partial block detection to evaluate after image mosaic and fusion. The experimental results show that the algorithm has fast computation speed, high robustness and good fusion effect.

Key words: Scale Invariant Feature Transform（SIFT）, random sample consensus, gradual image fusion, image mosaic