The traditional gray-level co-occurrence matrix (GLCM) was computationally intensive and discriminatively insufficient.
分析了传统的灰度共生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.
分析了传统的灰度共生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.
对示功图图像进行了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。
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。
The traditional gray-level co-occurrence matrix (GLCM) was computationally intensive and discriminatively insufficient.
分析了传统的灰度共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.
对示功图图像进行了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。
声明:以上例句、词性分类均网资源自动
成,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。
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
纹理特征时
量大,且
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.
对示功图图像进行了对比度增强灰度变换、平滑、锐化、大小归一化以及二值化和
块化处理。
声明:以上例句、词性由互联网资源自动生成,部
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。
The traditional gray-level co-occurrence matrix (GLCM) was computationally intensive and discriminatively insufficient.
分析了传统的灰度共生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.
对像进行了对比度增强
灰度变换、平滑、锐
、大小
以及二值
和分块
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的观点;若发现问题,欢迎向我们指正。
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
特征时计算量
,
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.
对示功图图像进行了对比度增强灰度变换、平滑、锐
、
小归一
以及二值
和
块
。
声明:以上例句、词性类均由互联网资源自动生成,部
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。
The traditional gray-level co-occurrence matrix (GLCM) was computationally intensive and discriminatively insufficient.
分析传统的灰度共生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.
示功图图像进行
度增强
灰度变
、
、锐化、大小归一化以及二值化和分块化处理。
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的观点;若发现问题,欢迎向我们指正。
The traditional gray-level co-occurrence matrix (GLCM) was computationally intensive and discriminatively insufficient.
分析了传统的灰度共生矩阵在计算纹特征时计算
,
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.
对示功图图像进行了对比度增强灰度变换、平滑、锐化、
小归一化以及二值化和分块化
。
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的观点;若发现问题,欢迎向我们指正。
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
特征时计算量
,
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.
对示功图图像进行了对比度增强灰度变换、平滑、锐
、
小归一
以及二值
和
块
。
声明:以上例句、词性类均由互联网资源自动生成,部
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。
The traditional gray-level co-occurrence matrix (GLCM) was computationally intensive and discriminatively insufficient.
分析了传统共生矩阵在计算纹理特征时计算量大,且分辨能力差
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增强
变换、平滑、锐化、大小归一化以及二值化和分块化处理。
声明:以上例句、词性分类均由互联网动生成,部分未经过人工审核,其表达内容亦不代表本软件
观点;若发现问题,欢迎向我们指正。