基于改进SIFT算法的图像匹配方法

Image registration method based on improved SIFT algorithm

针对尺度不变特征变换(SIFT)算法中128维的高维度特征描述符导致耗时长,实时性差的问题,首先提出了一种非线性的基于局部线性嵌入(LLE)法的降维方法,尽可能地保留原数据空间中的非线性信息,缩短算法运行时间,提高匹配精度。其次,针对匹配过程中使用欧氏距离计算量大的问题,提出使用曼哈顿距离计算参考图与待匹配图的相似度,进一步减少算法所耗时间。实验结果表明,该算法在具有较高匹配率的同时,提高了匹配速度。

Aiming at the problem that the 128-dimensional feature descriptors in scale-invariant feature transform(SIFT)algorithm results in long time-consuming and poor real-time performance,an nonlinear improved SIFT algorithm based on local linear embedding(LLE)is proposed,it is possible to preserve nonlinear information in the original data space,shorten the running time of the algorithm,and improve the matching precision.Secondly,aiming at the problem of large calculation quantity of Euclidean distance in the registration process,use the Manhattan distance to calculate the similarity between reference map and the image to be matched to further reduce the time spent by the algorithm.The experimental results show that the algorithm has high matching speed,while has high matching rate.