1) gray level co-occurrence matrix
灰度共生矩阵
1.
Texture Image Retrival Based on Wavelet Decomposition and Gray Level Co-occurrence Matrix;
基于小波分解和灰度共生矩阵的纹理图像检索
2.
Texture segmentation on the scale co-occurrence matrix and gray level co-occurrence matrix;
基于尺度和灰度共生矩阵的纹理图像分割
3.
A Self-adaptable Method to Detect Edge in Images Based on Gray level co-occurrence matrix;
基于灰度共生矩阵的自适应图像边缘检测
2) gray-level Co-occurrence matrix
灰度共生矩阵
1.
Study on identification of fabric texture based on gray-level co-occurrence matrix;
基于灰度共生矩阵的织物组织结构差异分析
2.
Then texture features are extracted based on gray-level co-occurrence matrix.
然后基于灰度共生矩阵实现纹理特征的提取,结合实际现状筛选出较好的纹理特征图像。
3.
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。
3) GLCM
灰度共生矩阵
1.
Wood Texture Classification and Recognition Based on Spatial GLCM;
基于空间灰度共生矩阵木材纹理分类识别的研究
2.
Research on Building Method of GLCM Suitable to Wood Texture;
适于描述木材纹理的灰度共生矩阵构造方法研究
3.
First,GLCM was used to extract the feature parameters of the wood textures.
首先,应用灰度共生矩阵提取了木材的纹理特征参数;其次,在此特征参数体系下,应用 BP 神经网络对木材纹理进行了分类研究,识别率达89%。
4) grey level co-occurrence matrix
灰度共生矩阵
1.
In this paper,the methods for CBIR is based on color co-occurrence matrix-a new conception which proposed on the basis of grey level co-occurrence matrix.
在灰度共生矩阵方法的基础上,发展出色彩共生矩阵方法,解决了灰度共生矩阵方法不能有效处理真彩色图像的缺陷,并从色彩共生矩阵中提取颜色和纹理特征用于图像检索。
2.
Seven feature parameters of grey level co-occurrence matrix(GLCM) and ten shape feature formulae of Hu invariant moments were combined when discussing the parameters of Panthera tigris altaica individual recognition.
在探讨东北虎个体识别的参数时,本文把灰度共生矩阵的7个特征参数和Hu不变矩抽象的10个形状特征公式相结合,根据相关系数分析选择其中的7个特征参数,最后利用BP神经网络进行分类识别,证明按照上述规则生成的7个特征参数有效,可以用于东北虎的个体识别。
3.
This paper investigates the collection of textural parameters according to the analysis of grey level co-occurrence matrix and classification of Brodatz texture database by means of BP neural network ensemble.
通过对灰度共生矩阵的分析,提取图像的纹理特征参数,并用BP神经网络集成的方法对Brodatz纹理库图像进行分类,仿真结果显示,其分类效果优于单一的BP神经网络,可有效提高分类识别率。
5) co-occurrence matrix
灰度共生矩阵
1.
This paper integrates the advantages of the traditional co-occurrence matrix and generalized image co-occurrence matrix and proposes an improved method based on generalized image co-occurrence matrix.
综合考虑了传统灰度共生矩阵法与基于广义图像灰度共生矩阵法各自的优点,提出了改进的基于广义图像灰度共生矩阵的图像检索方法。
2.
Based on gray level co-occurrence matrix, we got the features of an image.
该文提出一种新的道路分类方法,在灰度共生矩阵的基础上得到图像的纹理特征,并通过决策树模型得到道路图像分类的决策树。
3.
This paper studies the extraction of texture features of remote sensing images using gray-scale extraction of co-occurrence matrix,realized the classification and analysis for panchromatic remote sensing image using the shortest distance classifier and filter of the supervised classification methods under MATLAB.
研究了利用灰度共生矩阵提取遥感影像的纹理特征,实现了MATLAB下采用监督分类方法应用最短距离分类器及滤波完成了全色遥感影像的分类分析。
6) Gray co-occurrence Matrix
灰度共生矩阵
1.
In order to recognize document images (DIs) more efficiently, this paper analyzes the blind point of the gray co-occurrence matrix based features in document image recognition, and presents novel features based on rectangle frame histogram (RFH) for united recognition of document images.
为了更好地识别文本图像,分析了基于灰度共生矩阵的特征在文本图像识别中的盲点,提出利用矩形框直方图来形成新的特征,以此联合识别文本图像。
2.
Considering the spatial relations of grayscale intensity in an image, five parameters, including angularity second order moment, contrast, correlation, inverse difference moment and entropy, are also obtained based on a gray co-occurrence matrix.
在建立灰度共生矩阵的基础上,得到了像素平面分布关系的5个纹理特征参数(角2阶矩、对比度、相关度、逆差矩、熵)。
补充资料：不共生
【不共生】
谓六根六尘和合名之为共。前云不自生，则是根不能生；又云不他生，则是尘不能生。根尘各各既不能生，根尘相共又焉得生，故名不共生。
谓六根六尘和合名之为共。前云不自生，则是根不能生；又云不他生，则是尘不能生。根尘各各既不能生，根尘相共又焉得生，故名不共生。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条