基于四叉树的SIFT与K-D树融合的图像匹配研究  

Research on image matching based on quadtree fusion of SIFT and K-D tree

作　　者：张学峰 马行[1,2] 穆春阳 Zhang Xuefeng;Ma Xing;Mu Chunyang(Ningxia Key Laboratory of Advanced Data Processing,North Minzu University,Yinchuan 750021,China;College of Electrical and Information Engineering,North Minzu University,Yinchuan 750021,China;College of Mechanical Engineering,North Minzu University,Yinchuan 750021,China)

机构地区：[1]北方民族大学宁夏智能信息与大数据处理重点实验室,银川750021 [2]北方民族大学电气信息工程学院,银川750021 [3]北方民族大学机电工程学院,银川750021

出　　处：《电子测量技术》2021年第22期121-127,共7页Electronic Measurement Technology

基　　金：宁夏自然科学基金项目(2020AAC03201,2019AAC03121);宁夏回族自治区重点研发计划项目(2021BEE03002);2019年自治区大学本科教育和教学改革研究与实践项目(nxbjgZD-5);宁夏智能信息与大数据处理重点实验室开放课题(2019KLBD002);2018年研究生教学改革研究与实践项目(YJG201858)资助。

摘　　要：为了解决SIFT算法在立体视觉中耗时长、误匹配度高以及提取的特征点扎堆的问题,提出一种基于四叉树的SIFT与K-D树融合的快速特征匹配算法。该方法采用一种结合了自适应阈值的快速特征点提取算法提取关键点,由于所提取的关键点具有扎堆现象,故提出一种四叉树结构并将其应用到图像匹配中。利用改进的K-D树与随机一致性算法进行关键点的粗匹配与提纯。实验证明,该改进算法的平均匹配速率相比SIFT算法提高了3.35倍,匹配正确率由86.18%提高到97.53%,同时该改进算法比SIFT算法在视角、模糊、光照以及尺度变化方面更具有优越性,所以该算法能够满足高匹配率、实时性好且特征点均匀化的要求。In order to solve the problems of SIFT algorithm in stereo vision,such as long time consuming,high degree of mismatch and feature points clustering,a fast feature matching algorithm based on quadtree fusion of SIFT and K-D tree is proposed.This method uses a fast feature point extraction algorithm combined with adaptive threshold to extract the key points.Because the extracted key points have the phenomenon of clustering,a quadtree structure is proposed and applied to image matching.The improved K-D tree and random consistency algorithm are used to rough match and purify the key points.Experimental results show that the average matching rate of the improved algorithm is 3.35 times higher than that of SIFT algorithm,and the matching accuracy is improved from 86.18%to 97.53%.At the same time,the improved algorithm has more advantages than SIFT algorithm in view angle,blur,illumination and scale change,so the algorithm can meet the requirements of high matching degree,good real-time performance and uniform feature points.

关 键 词：尺度不变性 立体视觉 图像匹配 快速特征提取算法 四叉树 K-D树 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]