关键词: 尺度不变特征变换（SIFT）描述子, 图像匹配, 图像配准, 随机抽样一致性, 顺序概率比测试

Abstract: There have been great advances in object recognition and image registration, through the match of invariant local image feature. With the feature of invariance to affine, 3D projection, scaling and rotation, illumination changes, image translation, the Scale Invariant Features Transform（SIFT） is commonly used in object recognition. The RANdom Sample Consensus（RANSAC） is widely used as robust estimator, as a standard in the field of computer vision. In the process of R-RANSAC, the Randomized（hypothesis evaluation） RANSAC, a modification is made to RANSAC by checking data points sequentially, while the standard RANSAC check all the data points in the model verification step. Own to this improvement, hypotheses with low support can be rejected before all points are considered. In addition, the Sequential Probability Ratios Test（SPRT） is used to minimize R-RANSAC runtime.

Key words: Scale Invariant Features Transform（SIFT） descriptor, image recognition, image registration, Randomized RANdom Sample Consensus（R-RANSAC）, Sequential Probability Ratios Test（SPRT）