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中文摘要：
针对图像特征提取算法-SIFT,特征描述器维数较高,特征匹配耗费时间较长,匹配过程中存在相同图像不能匹配和不同图像能够匹配等问题,提出了一种改进SIFT算法与KD-tree搜索匹配算法相结合的新方法.采用KD-Tree算法替代传统链表式搜索方法降低特征点匹配时间;把特征点间距离和特征描述子内积同时作为匹配标准,加入相应匹配阈值减少匹配错误率,并通过理论和实验证明采用欧几里德距离作为相似性度量具有更高的匹配成功率.实验结果表明,在图像特征匹配中,该算法能够有效减少特征匹配错误率,大幅度降低匹配时间,具有较好的实时性和鲁棒性.
英文摘要：
The scale invariant feature transform（SIFT） algorithm is widely used in the 3D reconstruction,image registration and object recognition,etc.There are some problems in the study of SIFT,its characterization instruments have high dimensions,which increase the time-consuming; and another problem is that different images can match but the same image can not match.According to this,this paper chooses k-dimension search instead of the traditional chain table search method,proves by experiments that using the Euclidean distance as the similarity measurement is more accurate and reliable,and the improved SIFT joined distance and inner product matching threshold can solve mismatch.The results indicates that the improved SIFT algorithm can solve the fitting errors,and has less time consuming,higher accuracy and strong robustness.
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