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基于相位一致性的异源图像匹配方法
Heterogonous image matching method based on phase congruency

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文摘 异源图像由于亮度和对比度差异较大,采用基于灰度和梯度信息的局部特征匹配方法匹配正确率较低。针对该问题,提出一种基于相位一致性和梯度方向直方图的异源图像匹配方法。该方法首先采用具有亮度和对比度不变性的相位一致性方法提取异源图像特征点和边缘图像,并以特征点为中心,选取100 × 100的边缘图像作为特征区域,统计梯度方向直方图,生成64维特征描述符;然后,选用归一化相关函数作为匹配测度,采用双点匹配方法选取一个特征点的两个较优的候选匹配点,并采用 RANSAC 方法进行匹配点提纯;最后,基于局部归一化互信息方法和最优化方法进行匹配点精确定位,提高匹配精度。实验结果表明,该方法在可见光、近红外、中波红外和长波红外等异源图像匹配中具有较好的匹配性能,平均匹配正确率高达88%,是 SURF 匹配方法的3.4倍。
其他语种文摘 The common local feature matching methods based on gradient histogram are difficult to match correctly due to the difference of contrast and luminance of heterogonous image. For solving this problem,the heterogonous image matching method was proposed based on the phase congruency and histograms of oriented gradients. Firstly,feature points and edge image of heterogonous images were extracted by using phase congruency method which has invariance of luminance and contrast,and then the 64 - dimensional feature descriptor was generated by counting histograms of oriented gradients of squared feature area whose size is 100 × 100. Secondly,for matching heterogonous image pair,normalized correlation function is selected as similarity measure,and two better candidate matching point pairs of one feature point was first selected by using dual - point matching method,then matching point pair was purified by using RANSAC method. Finally,the location of matching point was refined using optimization method and local normalized mutual information. The experimental results indicate that the proposed method can achieve higher performance in heterogonous image matching,the average matching correct rate is up to 88% and is 3. 4 times of SURF matching method.
来源 激光与红外 ,2014,44(10):1174-1178 【核心库】
关键词 异源图像匹配 ; 相位一致性 ; 梯度方向直方图 ; 归一化互相关 ; 归一化互信息
地址

中国科学院沈阳自动化研究所, 中国科学院光电信息处理重点实验室, 辽宁, 沈阳, 110016

语种 中文
文献类型 研究性论文
ISSN 1001-5078
学科 自动化技术、计算机技术
基金 中国科学院光电信息处理重点实验室开放基金项目
文献收藏号 CSCD:5266118

参考文献 共 10 共1页

1.  Fan Bin. Registration of optical and SAR satellite images by exploring the spatial relationship of the improved SIFT. IEEE Geoscience and Remote Sensing Letters,2013,10(4):657-661 被引 22    
2.  陈冰. 一种新的光电成像末制导景象匹配方法. 光学学报,2010,30(1):163-168 被引 9    
3.  黄杰贤. 互信息熵与区域特征结合的图像匹配研究. 激光与红外,2013,43(1):98-103 被引 5    
4.  Bodensteiner C. Local multi-modal image matching based on self-similarity. Proceedings of 2010 IEEE 17th International Conference on Image Processing,2010:937-940 被引 1    
5.  闫钧华. 基于多尺度红外与可见光图像配准研究. 激光与红外,2013,43(3):329-333 被引 4    
6.  Lowe D. Distinctive Image Features from scale-invariant keypoints. International Journal of Computer Vision,2004,60(2):91-110 被引 4626    
7.  Bay H. SURF:speeded up robust features. Proceeding of European Conference on Computer Vision,2006:404-417 被引 2    
8.  Mikolajczyk K. A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(10):1615-1630 被引 633    
9.  Kovesi P. Phase congruency:a low-level image invariant. Psychological research,2000,64(2):136-148 被引 25    
10.  Kovesi P. Phase congruency detects corners and edges. The Australian Pattern Recognition Society Conference:DICTA,2003:309-318 被引 3    
引证文献 1

1 宋睿 改进Hausdorff距离和粒子群算法在激光制导中的应用 激光与红外,2017,47(12):1535-1540
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