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Ranks for Pairs of Spatial Fields via Metric Based on Grayscale Morphological Distances

机译:基于灰度形态距离的度量空间对对等级

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Based on a set of morphological distances computed between the grayscale images (spatial fields) of similar size specifications, the ratios of selected morphological distances, and the ratios of areas of infima and suprema of grayscale images, a new metric to quantify the degree of similarity between the grayscale images is proposed. We denote the two spatial fields (grayscale images), respectively, with and , and the infima and suprema of these spatial fields with and . The three morphology-based distances include: 1) dilation distance ; 2) erosion distance ; and 3) median-based distance . By employing these parameters, which play vital role in construction of parameter-specific interaction matrices, we provide a metric to designate every possible pair of images that can be considered out of a database consisting of a huge number of images. We demonstrate the whole approach on: 1) synthetic spatial fields; 2) a set of 12 similar-sized grayscale images representing cloud-top temperatures of a specific region for 12 different time instants; and 3) four spatial elevation fields to rank possible pairs of images.
机译:基于一组相似尺寸规格的灰度图像(空间场)之间计算出的一组形态学距离,所选形态学距离的比率以及灰度图像的图像上界与上界的比率,这是一种量化相似度的新指标提出了灰度图像之间的关系。我们用和分别表示两个空间场(灰度图像),用和表示这些空间场的图像和上界。基于形态学的三种距离包括:1)膨胀距离; 2)侵蚀距离;和3)基于中位数的距离。通过使用这些参数,这些参数在构建特定于参数的交互矩阵中起着至关重要的作用,我们提供了一种度量标准,用于指定可以从包含大量图像的数据库中考虑的每对可能的图像。我们将在以下方面展示整个方法:1)综合空间场; 2)一组12个相似尺寸的灰度图像,代表特定区域12个不同时刻的云顶温度;和3)四个空间标高场,以对可能的图像对进行排名。

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