The traditional gray-level co-occurrence matrix (GLCM) was computationally intensive and discriminatively insufficient.
分析了传统
灰度共生矩阵在计算纹理特征时计算量大，且分辨能力差
。
The traditional gray-level co-occurrence matrix (GLCM) was computationally intensive and discriminatively insufficient.
分析了传统灰度共生矩阵在计算纹理特征时计算量大，且分辨能力差。
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、锐化、大小归一化以
化和分块化处理。
声明：以上例句、词性分类均由互联网资源自动生成，部分未经过人工审核，其表达内容亦不代表本软件
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