灰度共生矩阵

然后再利用灰度共生矩阵算法提取图像的特征，以此作为分类的依据和前提。

Then use GLCM algorithm for image feature extraction , as the basis and prerequisite for classification .

灰度共生矩阵描述了图像的方向性、邻近空间关系、方差的变化范围。

GLCM of image reflects information about direction , adjacency spacing relationship , and the range of variance change .

基于灰度共生矩阵和BP神经网络集成的纹理图像分类

Texture Image Classification Based on Gray Level Co-occurrence Matrix and BP Neural Network Ensemble

基于185例肺小结节CT图像灰度共生矩阵纹理特征的多水平模型研究

The Multilevel Model Based on CT Images ' Texture Features of 185 Small Solitary Pulmonary Nodules Patients Using Gray Level Co-occurrence Matrix

面向装配自动化的产品设计方法利用小波尺度共生矩阵和灰度共生矩阵的SAR图像分类

A classification method of SAR image based on the scale-based concurrent matrix and the gray-level co-occurrence matrix

基于灰度共生矩阵的Julia集的纹理研究

Study on Image Texture of Julia Set Based on Gray Level Co-occurrence Matrix

纹理特征方面，主要使用灰度共生矩阵法和基于Gabor小波纹理特征提取两种方法。

We use gray level co-occurrence matrix and Gabor wavelet transform to extract texture feature .

灰度共生矩阵与Sobel算子结合的图像检索方法

An Image Retrieval Algorithm Using GLCM and Sobel Operator

该方法将树型小波中颇纹理能量特征、灰度共生矩阵特征、树型小波滤波后的灰度组成的特征矢量对SAR图像进行分类。

The feature vector is composed of wavelet texture energy features , texture features based on the gray-level co-occurrence matrix and the tone of filtered SAR image by using tree wavelet .

提出了一种基于灰度共生矩阵的C-均值聚类算法，用于对合成孔径雷达（SAR）图像的分类。

Then a novel C-mean clustering algorithm , which is based on the gray-level co-matrix and can be used for synthetic aperture radar ( SAR ) image classification , was proposed .

而基于灰度共生矩阵提取纹理图像的统计量组成的特征向量，并在Fisher判别准则的基础上设计一种线性分类器来对皮革纹理图像进行分类。

This paper achieves characteristic of texture images based on co-occurrence matrix and designs a linear classifier based on Fisher criterion to classify leather images .

实验结果表明，结构谱方法具有较好的光照不变性，对麻点、夹杂、结疤等缺陷的识别率要高于灰度共生矩阵、Laws纹理能量、傅里叶功率谱等其他纹理分析方法。

Compared with other textural features such as gray level co-occurrence matrix , Laws texture energy , and Fourier power spectrum , higher classification rates were made by structure spectrum for classification of pits , scars and inclusions .

首先根据HOS假设检验处理帧差图，判定像素点是否属于运动区域，阈值通过灰度共生矩阵获得，考虑了背景纹理的慢变化。

Additionally , the threshold is found by gray level co-occurrence matrix considering the background texture change .

基于LVQ神经网络和灰度共生矩阵的遥感图像分类及其应用

Image Classification of Remote Sensing and Its Application Based on LVQ Neural Network and Gray Level Co-occurrence Matrix

提出了一种新的图像分类算法，首先利用灰度共生矩阵方法提取出图像的纹理特征，然后结合遗传算法优化的BP神经网络进行网络训练和样本分类。

A new image classification algorithm is proposed . Firstly , the GLCM is adopted to describe texture feature of an image , combine the neural network optimized by the genetic algorithm for network training and sample classification .

定义了基于灰度共生矩阵的参数R，给出了纹理特征参数R的提取方法，提出了基于图像处理的纹理特征参数R的电缆故障信号的识别方法。

R parameter based on gray-level co-occurrence matrix is defined , the method of texture features R parameter extraction is given , and the cable fault signal recognition method based on R parameters of texture features image processing is proposed .

论文通过对纹理图像灰度共生矩阵的分析，提取出图像的纹理特征参数，并将提取的特征参数作为Fourier权函数神经网络分类器的输入，对纹理图像进行分类识别。

Four sets of texture parameters has been extracted according to the analysis of grey level co-occurrence matrix , which are used for textural image classification and recognition as the input of Fourier weight function neural network .

针对肤色图片的特点，比较了三种皮肤纹理检测算法：Gabor滤波法、灰度共生矩阵法和简单灰度统计法。

For color images , this paper compares the three types of skin texture detection algorithm : Gabor filtering , gray co-occurrence matrix method and the simple gray level statistics .

通过纹理分析，利用灰度共生矩阵惯性矩特征值能够反映图像灰度空间复杂度的特性，成功获取了LOG边缘检测算子最佳空间系数，抑制了图像中的大部分噪声。

Characteristic number of inertia moment of gray level co-occurrence matrix can reflect the feature of complexity of image gray space . Optimum space coefficient of LOG operator can be ac-quired effectively and most noise of images can be removed .

该算法首先对HSV颜色空间进行量化，提取主颜色特征；然后使用灰度共生矩阵和双树复小波变换进行纹理特征提取。

Firstly , quantify the HSV color space , extract the main color characteristics ; then use the GLCM and double tree complex wavelet transform for texture feature extraction .

提出了基于纹理特征的距离像识别算法，通过计算距离像的灰度共生矩阵，提取出13个纹理特征作为输入矢量训练RBF神经网络用于分类识别。

A recognition algorithm based on texture feature is proposed . Thirteen texture features based on Gray Level Co-occurrence Matrix are extracted from range image . Then the target is identified using the RBF neural network .

为了加快基于纹理的图像检索的速度，提出了一种灰度共生矩阵和Sobel算子结合的图像检索方法。

In order to speed up the image retrieval based on texture , an image retrieval method using gray-level co-occurrence matrix and Sobel operator was proposed .

图像的灰度共生矩阵（GLCM）已知被理论证明并且实验显示它在纹理分析中是一个很好的方法，广泛用于将灰度值转化为纹理信息。

The Gray Level Co-occurrence Matrix ( GLCM ) has been proved to be a promising method for image texture analysis .

最后在实验部分验证了采用空间灰度共生矩阵法提取皮肤纹理特征的准确性，并利用MATLAB中的神经网络工具箱对BP网络进行了训练和识别。

In the end , it validated the validity with spatial gray level co-occurrence matrix means extracting the skin texture feature in the experiment part , and made use of the neural network tools of MATLAB soft for BP network to train and recognize .

这种方法采用双树复小波高频模值子带Gamma分布与Lognormal分布参数组合特征、灰度共生矩阵特征组成的联合纹理特征作为遥感图像每一像素特征，然后通过K均值聚类完成遥感图像分割。

This method uses DT-CWT high-frequency sub-bands ' Gamma and Lognormal parameters and features of GLCM as the feature vector of remote sensing image pixels . Then , use the K-means clustering to complete remote sensing image segmentation .

该系统提取图像的颜色矩、Hu式不变矩和灰度共生矩阵的统计参数作为系统的三组特征值，采用同步组合的相似性度量结构。

The system extracts color moments , Hu invariant moments and the grey level grows matrix of statistical parameters of the image as three sets of eigenvalues of the system , it uses similarity measure architecture of synchronous composition .

该方法以小波变换的L1范数特征和灰度共生矩阵二次统计特征为基础，运用基于类距离的可分离性判据原理提取出可分离性特征向量。

The method is based on wavelet transform L1 norm feature and secondary statistic feature of gray level co-occurrence matrix , extracts the separable feature vector according to the separable criterion theory based on class distance .

本文作者首先用灰度共生矩阵的方法提取图像的各纹理特征，然后对各特征图像进行K均值聚类，同时讨论了各特征对不同目标的分类性能。

In this thesis , first , author investigates the performance of texture features derived from the gray-level co-occurrence matrix , then uses K-means clustering to achieve the pre-classification of SAR image , at the same time , discusses the difference between variant texture features in SAR image classification .

空域上，采用了灰度共生矩阵的二阶统计量作为特征：频域上，采用了Gabor滤波器对图像进行滤波，进而提取特征。

In the spatial domain , the second-order statistics of a gray level co-occurrence matrix are used as the features . In the frequency domain , Gabor filters are used for image filtering , and then textural features are extracted .

然后对图像纹理特征采用灰度共生矩阵法和LBP算法对图像进行检索实验，从实验中总结灰度共生矩阵法和LBP法的使用优缺点。

Then using gray level co-occurrence matrix of image texture feature and LBP algorithm to image retrieval experiments , concluded the use of the advantages and disadvantages from the experimental gray level co-occurrence matrix and LBP method .