The traditional gray-level co-occurrence matrix (GLCM) was computationally intensive and discriminatively insufficient.
了传统的灰度共生矩阵在计算纹理特征时计算量大,且
辨能力差的缺点。
The traditional gray-level co-occurrence matrix (GLCM) was computationally intensive and discriminatively insufficient.
了传统的灰度共生矩阵在计算纹理特征时计算量大,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、词性类均由互联网资源自动生成,
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理特征时计算量大,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、词性
类均由互联网资源自动生成,部
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,部分未经过人工审核,其表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.
分析了灰度共
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。
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纹理特征
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
及二值化和分块化处理。
声:
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。
声明:以上例句、词性分类均由互联网资源自,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,部分未经过人工,
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。