摘要
由于待匹配图像存在视场差别且像素灰度存在非线性差异,异源图像在匹配过程中存在特征点稳定性弱、分布不均匀、匹配质量差等问题,基于此,提出了一种基于尺度不变特征变换(SIFT)算法的图像特征点匹配算法。首先,特征点检测时,在尺度空间设置权重系数对各层图片分别设置网格,并结合图像的相位响应强度图采用四叉树的方法筛选出均匀分布且稳定的特征点。其次,重新构建了描述子,并以标准化欧氏距离代替欧氏距离对特征描述符进行度量,采用双向匹配策略进行粗匹配。最后,以随机抽样一致性(RANSAC)算法进行提纯。实验结果表明,所提算法可以提取到异源图像间可靠稳定的特征,提高特征点匹配的准确性。
Some fundamental problems such as weak stability of feature points, uneven distribution, and poor matching quality arise in the matching process of heterogeneous images owing to the difference in the field of view of the image to be matched and the nonlinear difference in pixel gray. To mitigate these issues, an image feature point matching algorithm based on scale-invariant feature transform(SIFT) algorithm is proposed herein. First, in the feature point detection, the weight coefficient was set in the scale space and the grid was set for each layer of images.Combined with the phase response intensity map of the image, the evenly distributed and stable feature points were selected using the quadtree method. Second, the descriptor was reconstructed and the normalized Euclidean distance was used to measure the feature descriptor instead of Euclidean distance. Furthermore, a two-way matching strategy was used for rough matching. Finally, the random sample consensus(RANSAC) algorithm was used for purification. Experimental results show that the proposed algorithm can extract reliable and stable features between heterogeneous images and improve the accuracy of feature point matching.
作者
于子雯
张宁
潘越
张越
王瑀萱
Yu Ziwen;Zhang Ning;Pan Yue;Zhang Yue;Wang Yuxuan(School of Opto-Electronic Engineering,Changchun University of Science and Technology,Changchun 130022,Jilin,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2022年第12期204-215,共12页
Laser & Optoelectronics Progress
基金
国家自然科学基金(61903048)
吉林省教育厅科学技术研究项目(JJKH20210824KJ)。
关键词
成像系统
异源图像
图像匹配
尺度不变特征变换算法
特征点
imaging systems
heterologous image
image matching
scale-invariant feature transform algorithm
feature point
