基于双尺度SIFT描述符及搜索区域？は拗频耐枷衿ヅ渌惴？
A image matching algorithm based on SIFT descriptors with double scales and limited searching region

针对尺度不变特征变换(SIFT)算法的匹配结果存在大量的错误匹配点对, 提出一种基于双尺度SIFT描述符及搜索区域限制的图像匹配算法(DSLSR-SIFT). 该方法使用双尺度描述符来计算初始匹配点集, 然后加入局部搜索区域限制条件在初始匹配点集中剔除偏离区域限制条件较大的点对从而得到提炼的匹配结果. 最后, 利用随机抽样一致性(RANSAC)算法进行评估两种算法的匹配结果. 实验结果表明, 本方法比SIFT算法在匹配正确率上平均提高了17%左右, 显著地提高了匹配精度.
Since the matching result of scale invariant feature transform(SIFT) algorithm has many false matching points, a image matching algorithm based on SIFT descriptors with double scales and limited searching region(DSLSR SIFT) is proposed. The method uses double scales descriptors to calculate the preliminary matching points set, then local limited searching region condition is added in the preliminary matching points set to remove the points which are far away from the limited region condition and obtains refined matching result. Finally, the random sample consensus (RANSAC) algorithm is used to evaluate the matching results. Experiment results show that the correct rate of this algorithm is better than SIFT algorithm on average of about 17%, which significantly improves the matching accuracy