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Deformed contour segment matching for multi-source images
Pattern Recognition ( IF 8 ) Pub Date : 2021-04-01 , DOI: 10.1016/j.patcog.2021.107968
Quan Wu , Guili Xu , Yuehua Cheng , Zhengsheng Wang , Zhenhua Li

Robust and accurate multi-source matching is a difficult task due to significant nonlinear radiometric differences, background clutter, and geometric deformation in corresponding regions. Motivated by these existing problems, a discriminating yet robust combined descriptor for multi-source image matching, called deformed contour segment similarity (DCSS), is proposed in this work. First, the proposed DCSS, which is constructed by histogram of the combined contour features rather than the commonly used corner point and gradient, presents the accurate correspondence between image pairs and improves the descriptive ability to radiometric differences. Second, the deformed curve is presented via a finite-dimensional matrix Lie group to determine the similarity metric with an explicit geodesic solution. The geodesic distance, which indicates the nearest distance between curves in fluid space, is defined as the weight coefficient of the constructed histogram to enhance the robustness of the descriptor. The proposed algorithm utilizes the holistic contour information for the scoring and ranking of the shape similarity hypothesis, which can effectively reduce the influence of partially missing contours. Finally, a precise bilateral matching rule is used to perform the matching between the corresponding contour segments. Some experiments are carried out on various infrared-visible image data sets. The results demonstrate that the proposed DCSS achieves more robust and accurate matching performance than many popular multi-source image matching methods.



中文翻译:

多源图像的变形轮廓线段匹配

由于存在明显的非线性辐射差异,背景杂波和相应区域的几何变形,因此稳健而准确的多源匹配是一项艰巨的任务。受这些现有问题的影响,在这项工作中提出了一种可区分而又健壮的多源图像匹配组合描述符,称为变形轮廓线段相似度(DCSS)。首先,提出的DCSS由组合轮廓特征的直方图而不是常用的拐角点和坡度构造而成,它表示图像对之间的准确对应关系,并提高了对辐射差异的描述能力。其次,通过有限维矩阵李群展示变形曲线,以确定具有明确测地线解的相似性度量。测地距离 表示流体空间中曲线之间最接近的距离,其表示为所构造直方图的权重系数,以增强描述符的鲁棒性。所提出的算法利用整体轮廓信息对形状相似性假设进行评分和排序,可以有效地减少部分缺失轮廓的影响。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。在各种红外可见图像数据集上进行了一些实验。结果表明,与许多流行的多源图像匹配方法相比,提出的DCSS实现了更鲁棒和准确的匹配性能。定义为构造的直方图的权重系数,以增强描述符的鲁棒性。所提出的算法利用整体轮廓信息对形状相似性假设进行评分和排序,可以有效地减少部分缺失轮廓的影响。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。在各种红外可见图像数据集上进行了一些实验。结果表明,与许多流行的多源图像匹配方法相比,提出的DCSS实现了更鲁棒和准确的匹配性能。定义为构造的直方图的权重系数,以增强描述符的鲁棒性。所提出的算法利用整体轮廓信息对形状相似性假设进行评分和排序,可以有效地减少部分缺失轮廓的影响。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。在各种红外可见图像数据集上进行了一些实验。结果表明,与许多流行的多源图像匹配方法相比,提出的DCSS实现了更鲁棒和准确的匹配性能。可以有效减少轮廓部分缺失的影响。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。在各种红外可见图像数据集上进行了一些实验。结果表明,与许多流行的多源图像匹配方法相比,提出的DCSS实现了更鲁棒和准确的匹配性能。可以有效减少轮廓部分缺失的影响。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。在各种红外可见图像数据集上进行了一些实验。结果表明,与许多流行的多源图像匹配方法相比,提出的DCSS实现了更鲁棒和准确的匹配性能。

更新日期:2021-04-19
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