基于局部近邻图的特征描述与特征匹配算法研究

被引：4

作者：

谢宜婷

王爱平

邹海

机构：

[1] 安徽大学计算机科学与技术学院

来源：

计算机应用与软件 | 2017年 / 34卷 / 08期

关键词：

特征描述; 局部近邻图; 特征匹配; 相似性度量;

D O I：

暂无

中图分类号：

TP391.41 [];

学科分类号：

080203 ;

摘要：

特征描述和特征匹配是计算机视觉领域的重要组成部分。近年来,为了实现图像匹配上的可靠性和鲁棒性,许多特征描述算法被提出来,例如SIFT、SURF、DAISY和BRIEF等。然而,当图像发生平移、旋转、缩放等大视角变化时,这些描述符通常会失效。为了解决这个问题,在局部近邻图模型的基础上,提出一种新颖的特征描述和相似性度量方法(LNFM算法)。所提出的描述符和相似度可以很好地应用于各种流行的图像匹配算法。实验结果表明:在特征匹配过程中,该算法可以检测到可靠的匹配关系,性能较为优越。

引用

页码：185 / 190+196 +196

页数：7

共 21 条

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