Abstract: The structure extraction task is analyzed. The co-occurrence matrices (CMs) are the popular basis for this goal. We show that binary preparation of arbitrary texture preserves its structure. This transformation decreases the computation time of analysis and the required memory in dozens times. A number of features for detecting displacement vectors on binarized images are compared. We suggest to use CM elements jointly as the united feature for this goal. We have shown that it is a stable detector for noisy images and simpler than well- known $chi$+2$/ and $kappa statistics.!12
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机译：摘要：分析了结构提取任务。共现矩阵（CM）是实现此目标的流行基础。我们表明，任意纹理的二进制准备保留了其结构。这种转换将分析的计算时间和所需的内存减少了数十倍。比较了用于检测二值化图像上的位移矢量的许多功能。我们建议为此目的将CM元素一起用作统一功能。我们已经表明，它是用于噪点图像的稳定检测器，并且比众所周知的$ chi $ + 2 $ /和$ kappa统计信息更简单。！12
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