摘要
针对传统SIFT匹配算法复杂、特征冗余点多、难以满足实时性等问题,本文提出了一种具有局部自适应阈值的SIFT快速图像匹配算法。首先,所提方法在SIFT算法的基础上,对构建的高斯金字塔进行了优化,通过减少金字塔层数来消除冗余特征点以提高检测效率,并根据图像局部对比度来自适应提取FAST算法中的阈值从而实现高质量的特征点检测,筛选出鲁棒性较强的特征点进行更准确的匹配;其次,采用高斯圆形窗口建立32维降维特征向量,提高算法运行效率;最后,根据匹配特征点对之间的几何一致性对特征点进行提纯,有效减少误匹配。实验结果表明,本文方法在匹配精度和运算效率方面的综合表现均优于SIFT算法及其他对比匹配算法,相比传统的SIFT算法,匹配精度提高了约10%,算法运行时间缩短了约49%。在图像发生尺度、旋转以及光照变化的情况下,正确匹配率在93%以上。
Aiming at the problems of complex traditional SIFT matching algorithm,many feature redundancy points,and difficulty in meeting real-time performance,this paper proposes a SIFT fast image matching algorithm with local adaptive threshold.Based on the SIFT algorithm,the proposed method optimizes the construction of Gaussian pyramids,eliminates redundant feature points by reducing the number of pyramid layers to improve the detection efficiency.The threshold in the FAST algorithm is extracted according to the local contrast of the image,so as to achieve high-quality feature point detection.The feature points with strong robustness are screened out for more accurate matching.Secondly,a Gaussian circular window is used to establish a 32-dimensional dimensionality reduction feature vector to improve the operation efficiency of the algorithm.Finally,the feature points are purified according to the geometric consistency between the matching feature point pairs,which effectively reduces the false matching.The experimental results show that the comprehensive performance of the proposed method in terms of matching accuracy and computational efficiency is better than that of SIFT algorithm and other comparative matching algorithms,and the matching accuracy is improved by about 10%and the algorithm execution time is shortened by about 49%compared with the traditional SIFT algorithm.The correct matching rate is above 93%in the case of image scale,rotation and lighting change.
作者
汪崟
蒋峥
刘斌
WANG Yin;JIANG Zheng;LIU Bin(College of Information Science and Engineering,Wuhan University of Science and Technology,Wuhan 430080,China;Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education,Wuhan University of Science and Technology,Wuhan 430080,China)
出处
《液晶与显示》
CAS
CSCD
北大核心
2024年第2期228-236,共9页
Chinese Journal of Liquid Crystals and Displays
基金
国家自然科学基金(No.61902286)。
作者简介
通信联系人:汪崟,男,硕士研究生,2019年于湖北工程学院获得学士学位,研究方向为图像处理。E-mail:2650518115@qq.com;蒋峥,男,博士,副教授,2005年于浙江大学获得博士学位,研究方向为模式识别、智能控制、嵌入式系统。E-mail:1047438629@qq.com。
