半导体光电, 2017, 38 (4): 571, 网络出版: 2017-08-30  

一种基于聚类分析的红外图像配准算法

An Infrared Image Registration Algorithm Based on Clustering Analysis
尹丽华 1,2,3,4,*李范鸣 1,2,4刘士建 1,2,4王霄 1,2,3
作者单位
1 中国科学院上海技术物理研究所, 上海 200083
2 中国科学院大学, 北京 100049
3 上海科技大学, 上海 200031
4 中国科学院红外探测与成像技术重点实验室, 上海 200083
摘要
为了提高红外图像匹配的精度和效率, 提出了一种将Harris-Laplace关键点提取和旋转不变LBP特征描述算子相结合的局部特征检测新算法, 该算法不仅在图像的尺度、光照和角度发生变化时, 仍然能够得到很好的检测效果, 而且能很好地描述图像的局部纹理特征。特征向量描述完成后, 为了进一步提高红外图像特征点匹配的正确率, 提出了一种基于K-means聚类分析的图像匹配策略。先利用Cosine余弦相关匹配策略实现特征点的初步粗匹配, 接着采用K-means聚类分析匹配策略剔除图像中大部分的错误匹配。实验表明: 提出的算法表现出良好的鲁棒性, 关键点提取的重复率(Repeatability)提高了9.2%。与传统的匹配算法相比, 采用基于K-means聚类分析的匹配策略匹配精度可以提高5.05%, 匹配时间可以缩短0.068s。该特征描述算法和基于K-means聚类分析的匹配算法满足了红外图像配准的高精度性和高实时性的要求。
Abstract
In order to improve the accuracy and efficiency of infrared image matching, a new local feature detection algorithm based on combination of Harris-Laplace and the rotation invariant LBP was proposed. The algorithm could not only get good detection effect when image scale, the light, the angle changed, but also a good description of the local texture features of images. After completion of feature vectors described, in order to further improve the accuracy of infrared image feature points matching, this paper presented an image matching strategy based on K-means clustering analysis. Firstly, Cosine correlation matching strategy was used to achieve initial coarse matching feature points; Secondly, using K-means clustering analysis excluded most of the matching strategy image mismatching points. Experimental results show that the characterization algorithm maintained a good robustness and repetition rate (Repeatability) improved by 9.2%. Compared with the traditional matching algorithm, matching precision matching strategy based K-means clustering analysis can be increased by 5.05%, matching time can be reduced by 0.068s. In this paper, the feature description algorithm and matching algorithm based on K-means clustering analysis could meet the high precision and high real-time requirements of image registration.

尹丽华, 李范鸣, 刘士建, 王霄. 一种基于聚类分析的红外图像配准算法[J]. 半导体光电, 2017, 38(4): 571. YIN Lihua, LI Fanming, LIU Shijian, WANG Xiao. An Infrared Image Registration Algorithm Based on Clustering Analysis[J]. Semiconductor Optoelectronics, 2017, 38(4): 571.

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