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A robust deformed image matching method for multi-source image matching
Infrared Physics & Technology ( IF 3.3 ) Pub Date : 2021-03-04 , DOI: 10.1016/j.infrared.2021.103691
Guili Xu , Quan Wu , Yuehua Cheng , Fuju Yan , Zhenhua Li , Qida Yu

Multi-source image matching is a challenging task due to the presence of image distortion, as well as significant intensity changes between image pairs in corresponding regions. In addition, the influences of variant scales and multiplicative noises will also have an adverse effect on the matching accuracy. In this paper, a combination of feature descriptor called “histogram of angle and maximal edge orientation distribution” (HAED) is proposed for multi-source image matching. First, the contour segment feature, which extracts the image information using both the angle and edge orientation distribution, presents the accurate correspondence between multi-source images. Second, the similarity calculated by using Fréchet distance metric between curves is defined as a weight parameter of each contour segment histogram to improve the matching performance. Finally, a precise bilateral matching rule is used to perform the matching between the corresponding contour segments. Infrared–visible image data sets in different environments are used for experiments. The results demonstrate that the proposed algorithm achieves a more accurate matching performance than other multi-source image matching algorithms.



中文翻译:

用于多源图像匹配的鲁棒变形图像匹配方法

由于存在图像失真以及相应区域中图像对之间的显着强度变化,因此多源图像匹配是一项具有挑战性的任务。另外,可变比例和乘性噪声的影响也将对匹配精度产生不利影响。本文提出了一种称为“角度直方图和最大边缘方向分布”(HAED)的特征描述符的组合,用于多源图像匹配。首先,轮廓片段特征使用角度和边缘方向分布提取图像信息,从而在多源图像之间呈现出精确的对应关系。第二,通过使用曲线之间的Fréchet距离度量计算出的相似度被定义为每个轮廓线段直方图的权重参数,以提高匹配性能。最后,使用精确的双边匹配规则来执行相应轮廓线段之间的匹配。实验中使用了不同环境中的红外可见图像数据集。结果表明,与其他多源图像匹配算法相比,所提算法具有更高的匹配性能。

更新日期:2021-03-15
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