The traditional gray-level co-occurrence matrix (GLCM) was computationally intensive and discriminatively insufficient.
析了传统的灰度共生矩阵在计算纹理特征时计算量大，且辨
的缺点。
The traditional gray-level co-occurrence matrix (GLCM) was computationally intensive and discriminatively insufficient.
析了传统的灰度共生矩阵在计算纹理特征时计算量大，且辨的缺点。
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.
对示功图图像进行了对比度增强
灰度变换、平滑、锐
、大小归一以及二值
块处理。
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