The traditional gray-level co-occurrence matrix (GLCM) was computationally intensive and discriminatively insufficient.
分析
的灰度共
矩阵在计算纹理特征时计算量大，且分辨能力差的缺点。
The traditional gray-level co-occurrence matrix (GLCM) was computationally intensive and discriminatively insufficient.
分析的灰度共矩阵在计算纹理特征时计算量大，且分辨能力差的缺点。
The structure of the network enhanced as well as the training efficiency of the network.A practical example by changing the training number to a dynagraph has been given.
对示功图图像进行对比度增强
灰度变换、平滑、锐化、大小归一化以及二值化和分块化处理。
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，部分未经过人工审核，其表达内容亦不代表本软件的观点；若发现问题，欢迎向我们指正。
