摘要
文中对尺度不变特征变换(SIFT)算法进行分析研究,针对原算法中128维的高维描述子提出60维方形邻域描述子,统计邻域梯度信息.方形邻域描述子较原算法增加了邻域像素统计范围,增强了关键点的邻域信息;在配准阶段采用欧氏距离作为度量函数,用次临近与最邻近之比来对60维描述子进行匹配.通过实验证实,改进算法的匹配时间是原算法的30%～60%,配准精度与原算法相近,对于复杂图像的配准精度较原算法有所提高,适用于对实时性要求较高的图像配准场合.
The principal of SIFT algorithm is researched in this paper.Due to the descriptor that one feature point needs 128 dimensions,a 60-dimension-square descriptor based on statistic local gradient information is taken forth.Comparing with the orignal one,the new descriptor expands the scope of neighborhood pixels;the ratio between the first and second closest distance is used to match the 60-dimension descriptors.According to the experiment,the results show that matching time is greatly shortened by 30%～60%;and the new descriptor is competitive with SIFT descriptor in effectiveness.Further more,the new descriptor exhibits good performance in more complicated images.Therefore,it is more suitable in real-time applications.
出处
《微电子学与计算机》
CSCD
北大核心
2011年第5期184-188,共5页
Microelectronics & Computer
