基于ORB与K-means聚类的图像匹配算法  

Image matching algorithm based on ORB and K-means clustering

作　　者：邢艺馨 田爱奎[1] 张立晔 常春红 郝本利 XING Yixin;TIAN Aikui;ZHANG Liye;CHANG Chunhong;HAO Benli(School of Computer Science and Technology,Shandong University of Technology,Zibo Shangdong 255000,China)

机构地区：[1]山东理工大学计算机科学与技术学院,山东淄博255000

出　　处：《智能计算机与应用》2020年第9期77-80,共4页Intelligent Computer and Applications

基　　金：山东省自然科学基金(ZR2019BF022)。

摘　　要：图像处理技术在计算机视觉等领域中发挥着重要作用,然而在目前的双目视觉领域中的图像匹配水平,实现高配准率和高实时性的技术仍需进一步探索。ORB(oriented FAST and rotated BRIEF)特征点匹配算法相比SIFT算法提高了检测速度,可以实时准确地检测目标。为了提高匹配特征准确性的要求,本文提出了一种基于ORB与K-means(K-均值)聚类的图像匹配算法,可在减少耗时的基础上,有效地提升双目图像特征点的匹配正确率。在公开的双目图像数据集上进行实验对比,实验结果表明该算法匹配更精确、性能更优越。Image processing technology plays an important role in computer vision and other fields.However,in the current binocular vision of the image matching level,However,in the current field of binocular vision image matching level,to achieve high matching rate and high real-time technology still need to be further explored.Compared with SIFT algorithm,ORB(oriented FAST and rotated BRIEF)feature point matching algorithm can improve the detection speed,and can detect the target accurately in real time.In order to improve the accuracy of matching features,this paper proposes an image matching algorithm based on ORB and K-means(K-means)clustering,which can effectively improve the accuracy of image feature point location and the accuracy and efficiency of image feature matching,and reduce the time consumption.The experimental results show that the algorithm is more accurate and has better performance.

关 键 词：ORB K-MEANS 图像匹配 双目视觉 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]