灰度共生矩阵法是图像纹理分析中一种十分重要的方法。
Gray level co-occurrence matrix is a very important method in image texture analysis.
利用软阈值和循环共生矩阵技术，对经典共生矩阵算法进行了改进。
In this paper, we improve the classic cooccurrence matrix using soft threshold and circular cooccurrence matrix.
介绍了一种基于色彩共生矩阵提取颜色-纹理特征的图像检索方法。
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.
将目标模型和候选目标的核共生矩阵规整到同一常数以提高计算精度；
Second, the KCMs of the target model and the target candidate were normalized to a same integer to improve calculation accuracy.
共生矩阵及其在特征提取中的应用，用于灰度或二值图象的特征提取中。
Symbiotic Matrix and feature extraction in the application for two or gray value image feature extraction.
针对图像区域复制—粘贴篡改，提出了一种基于灰度共生矩阵的检测算法。
With regard to the copy-move forgery of image region, this paper proposes a detection algorithm based on gray level co-occurrence matrix.
提出了一种基于灰度-梯度共生矩阵模型和最大熵原理的自动阈值化方法。
In this paper, an automatic approach for thresholding based on gray level gradient co occurrence matrix model and the maximum entropy principles is proposed.
实验结果表明，彩色子块共生矩阵描述子能够有效地结合颜色和纹理特征，具有良好的检索性能。
Experiments demonstrated that the color block co-occurrence matrix descriptor considerably improves the retrieval performance and can combine the feature of color and texture effectively.
它包括二维匹配滤波预处理以增强血管的灰度，以及用灰度-梯度共生矩阵的最大熵阈值化方法。
Entropy thresholding method is an automatic technique for thresholding of digital images based on gray level-gradient co-occurrence matrix and the maximum entropy principle.
首先计算文本区域的灰度-梯度共生矩阵，并根据目标函数快速地找到分割的灰度和梯度最佳阈值；
The gray-gradient co-occurrence matrix of the text region is calculated, and the optimum thresholds of segmented grayscale and gradient are found quickly according to the objective function.
根据求得的对应关系和具体图像的灰度共生矩阵的熵，能自动获取适合该图像的高斯空间系数的值。
Optimum Gassian space coefficient Of LOG operator can be self-adaptable acquired basing on the entropy of the concrete image and the relation.
在总结和分析共生矩阵算法的基础上，提出了一个快速获取目标图像纹理特征，进而实现图像检索的方法。
Base on generalization and analysis of co-occurrence matrix algorithms, a method is proposed that could make fast acquisition of target image texture features and thus implement image retrieval.
该系统先对粮虫图像进行小波边缘提取，根据灰度共生矩阵和局部统计方法提取小波分割后的图像纹理特征。
Edge detction based on wavelet multi-scale identity is made. The statistics features based on regional gray and the co-occurrence matrix of gray level are taken as performing image segmentation.
灰度共生矩阵法能够从像元的灰度相关性上对纹理特征进行描述，而分形维数反映了纹理的结构自相似特征。
GLCM describes texture features from pixel correlation of gray and the fractal dimension reflects the structure of self-similar.
传统的灰度共生矩阵是一种有效的纹理图像分析方法，它在图像理解和计算机视觉研究领域已得到了广泛的应用。
The traditional cooccurrence matrix is an effective approach in texture image analysis. It is widely used in the research sense of image comprehension and computer vision.
提出并应用改进的几何模型法对可见光云图日食阴影做订正处理，并计算出指定区域处理前、后的灰度共生矩阵；
This paper gives an improved method to eliminating eclipse shadow, and has been evaluated through an actual data experiment with gray level concurrence matrix and image texture feature quantities.
另外，还使用了灰度共生矩阵(GLCM)的方法对SHG图像分析，GLCM是一种对皮肤胶原组织评分的通用方法。
In addition, a pattern analysis of SHG images using grey-level co-occurrence matrix (GLCM) was carried out, a well-established method for scoring collagen organization in skin.
在此基础上，采用一种改进的基于纹理基元的共生矩阵来获取纹理特征，并结合纹理基元的形状直方图来进行图像检索。
On the basis of which, an improved co-occurrence matrix and histogram are developed to extract the texture and shape features for the image retrieval.
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.
在建筑物候选区域的寻找阶段，本文主要采用了一种基于灰度共生矩阵的方法对图像进行分割，从分割结果中得到建筑物的候选区域。
In the candidate building area searching step, an image segmentation method based on grey level co-occurrence matrix (GLCM) is adopted to get candidate building areas in an urban image.
在纹理特征提取方面，针对不同纹理特点分别采用了基于共生矩阵的统计纹理分析和基于小波变换的频谱纹理分析两种方法予以实现。
Similarly, the work of texture feature extraction is obtained by using co-occurrence matrix or frequency analysis based on wavelet transform depending on different characteristics of images.
研究了灰度共生矩阵和分形维特征相结合的综合特征，并将其应用于文物图像处理，实验表明基于该综合特征的分类优于基于单一特征的分类。
A combination of GLCM (Gray Level Co-occurrence Matrix) and Fractal features has been used: With the result of experiment we conclude that the combination feature is better than single feature.
针对这种情况，提出了一种改进的特征提取方法，将基于原图像的灰度级共生矩阵提取的纹理特征与滤波后图像的灰度特征进行组合用于分类。
Accordingly, we propose an improved feature extraction scheme, adopting the tone of filtered image combined with the texture features based on the GLCM of unfiltered image to form the feature vector.
首先运用灰度共生矩阵提取图像的纹理特征，然后用EBP - OP算法对提取的纹理特征进行分类，并在此基础上实现一组纹理图像的检索，实验证明这种方法是有效的。
First selects texture features based on the gray level co-occurrence Matrix and then EBP-OP neural network is used as a classifier. The experimental results show that this method is very effective.
首先运用灰度共生矩阵提取图像的纹理特征，然后用EBP - OP算法对提取的纹理特征进行分类，并在此基础上实现一组纹理图像的检索，实验证明这种方法是有效的。
First selects texture features based on the gray level co-occurrence Matrix and then EBP-OP neural network is used as a classifier. The experimental results show that this method is very effective.
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