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Density estimation of grey-level co-occurrence matrices for image texture analysis

机译:图像纹理分析灰度共生矩阵的密度估计

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摘要

The Haralick texture features are common in the image analysis literature, partly because of their simplicity and because their values can be interpreted. It was recently observed that the Haralick texture features are very sensitive to the size of the GLCM that was used to compute them, which led to a new formulation that is invariant to the GLCM size. However, these new features still depend on the sample size used to compute the GLCM, i.e. the size of the input image region-of-interest (ROI).
机译:Haralick纹理功能在图像分析文献中很常见,部分原因是他们的简单性,因为它们的值可以解释。 最近观察到,Haralick纹理功能对用于计算它们的GLCM的大小非常敏感,这导致了一种新的配方,其不变于GLCM尺寸。 但是,这些新功能仍然依赖于用于计算GLCM的样本大小,即输入图像的兴趣区域的大小(ROI)。

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