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一种CSS-SIFT复合图像配准算法

李培华 章盛 刘玉莉 钱名思

李培华, 章盛, 刘玉莉, 钱名思. 一种CSS-SIFT复合图像配准算法[J]. 红外技术, 2021, 43(1): 26-36.
引用本文: 李培华, 章盛, 刘玉莉, 钱名思. 一种CSS-SIFT复合图像配准算法[J]. 红外技术, 2021, 43(1): 26-36.
LI Peihua, ZHANG Sheng, LIU Yuli, QIAN Mingsi. CSS-SIFT Composite Image Registration Algorithm[J]. INFRARED TECHNOLOGY, 2021, 43(1): 26-36.
Citation: LI Peihua, ZHANG Sheng, LIU Yuli, QIAN Mingsi. CSS-SIFT Composite Image Registration Algorithm[J]. INFRARED TECHNOLOGY, 2021, 43(1): 26-36.

一种CSS-SIFT复合图像配准算法

基金项目: 

安徽省科技重大专项项目 17030901053

详细信息
    作者简介:

    李培华(1982-),男,山东潍坊人,硕士研究生,工程师,主要从事微控制器、嵌入式和图形处理方面的研究

    通讯作者:

    章盛(1989-),男,安徽芜湖人,硕士研究生,主要从事嵌入式、图像配准和人机交互方面的研究。E-mail:18365393973@163.com

  • 中图分类号: TP391

CSS-SIFT Composite Image Registration Algorithm

  • 摘要: 针对SIFT算法的图像配准耗时长的问题,提出一种CSS-SIFT复合图像配准算法。CSS-SIFT算法首先使用CSS算法检测图像特征,然后,使用优化的SIFT算法生成并降维图像特征描述子,最后,使用基于欧式距离和曼哈顿距离的优化双向匹配算法对图像特征进行匹配。仿真实验条件是通过计算机中仿真软件进行仿真实验,统计图像特征数目、匹配数目、正确匹配数目、配准准确率、配准时间与配准时间下降率共6个指标数据,统计结果表明,CSS-SIFT算法在图像配准准确度方面与传统SIFT算法、传统SURF算法、Forstern-SIFT算法、Harris-SIFT算法、Trajkovic-SIFT算法相当,但在图像配准耗时方面分别降低了58.45%、10.68%、14.84%、16.21%与4.63%,为图像配准提供了一种解决方案。
  • 图  1  CSS-SIFT算法的流程框图

    Figure  1.  Flow chart of the CSS-SIFT algorithm

    图  2  实验一待配准图像

    Figure  2.  Registration images of the experiment one

    图  3  源图像的特征检测结果图

    Figure  3.  The results graphs of source image feature detection

    图  4  右视图像的特征检测结果图

    Figure  4.  The results graphs of right image feature detection

    图  5  下视图像的特征检测结果图

    Figure  5.  The results graphs of down image feature detection

    图  6  形变图像的特征检测结果图

    Figure  6.  The results graphs of deformation image feature detection

    图  7  旋转图像的特征检测结果图

    Figure  7.  The result graphs of rotated image feature detection

    图  8  基于传统SIFT算法的图像特征匹配结果图

    Figure  8.  The result graphs of image feature matching based on the traditional SIFT algorithm

    图  9  基于传统SURF算法的图像特征匹配结果图

    Figure  9.  The result graphs of image feature matching based on the traditional SURF algorithm

    图  10  基于Forstner-SIFT算法的图像特征匹配结果图

    Figure  10.  The result graphs of image feature matching based on the Forstner-SIFT algorithm

    图  11  基于Harris-SIFT算法的图像特征匹配结果图

    Figure  11.  The result graphs of image feature matching based on the Harris-SIFT algorithm

    图  12  基于Trajkovic-SIFT算法的图像特征匹配结果图

    Figure  12.  The result graphs of image feature matching based on the Trajkovic-SIFT algorithm

    图  13  基于CSS-SIFT算法的图像特征匹配结果图

    Figure  13.  The result graphs of image feature matching based on the CSS-SIFT algorithm

    图  14  图像配准的对比分析图

    Figure  14.  Comparison analysis graphs of image registration

    T: Tsukuba visible images; K: Kaptein infrared images; M: MR_T1 medical images;
    SIFT: Traditional SIFT algorithm; SURF: Traditional SURF algorithm; F-SIFT: Forstern-SIFT algorithm;
    H-SIFT: Harris-SIFT algorithm; T-SIFT: Trajkovic-SIFT algorithm; C-SIFT: CSS-SIFT algorithm

    表  1  不同规模图像集的图像配准时间、准确率比较

    Table  1.   The comparison of the image registration time, accuracy of different scale image sets

    100 200 300 400
    time/s accuracy/% time/s accuracy /% time/s accuracy /% time/s accuracy /%
    SIFT 735.6 92.28 1502.6 92.10 2305.9 91.89 2985.9 91.96
    SURF 335.9 92.52 685.2 92.46 1008.3 92.09 1388.9 92.33
    F-SIFT 344.8 93.02 700.3 92.33 1076.3 92.56 1456.8 93.01
    H-SIFT 360.7 92.86 730.4 92.54 1082.6 93.03 1480.6 92.48
    T-SIFT 318.6 91.89 640.1 92.36 960.7 92.99 1300.8 92.91
    C-SIFT 303.7 92.26 618.9 92.37 930.3 92.38 1240.6 92.56
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-03-15
  • 修回日期:  2020-12-27
  • 刊出日期:  2021-01-20

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