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
,
辨能力差的缺点。
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
,
辨能力差的缺点。
声:以上例句、词性
类
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.
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。
声: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
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矩阵在计算
征时计算量大,且分辨能力差的缺点。
声: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.
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。
声:以上例句、词
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。
声:以上
、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
达内容亦不
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
理特征时
量大,且
辨能力差的缺点。
声:以上例句、词性
类均由互联网资源自动生
,
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,
分辨能力差的缺点。
声:以上例句、词性分类均
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计算量大,且分辨能力差的缺点。
声:以上例句、词性分类均由互联网资
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。
声: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
软件的观点;若发现问题,欢迎向我们指正。