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GLCM--texture-extraction
- 对图像的纹理特征进行提取，主要基于灰度共生矩阵，然后计算二次统计量（即特征）-Texture features for image extraction, mainly based on gray level co-occurrence matrix, and then calculate the amount of secondary statistics (ie features)
horizontal
- 提取图像的GLCM矩阵，并计算出Haralick建议的14个特征值-Extract images GLCM matrix, and calculate the 14 Haralick features of the proposed values
Texture
- 用灰度共生矩阵对共生矩阵归一化和计算能量、熵、惯性矩、相关4个纹理参数。-On the symbiosis with the GLCM matrix normalization and calculation of energy, entropy, moment of inertia, related to four texture parameters.
GrayMatrix
- Visual C++ 实现的灰度共生矩阵-The Visual C++ implementation GLCM
GLCM
- segmentation code in image processing
imageProcessing
- glcm examples in matlab
graymatrix
- 灰度共生矩阵相关资料，包括生成灰度共生矩阵matlab代码，Matlab7工具箱中缺少的graycomatrix.m文件，以及一个通过灰度共生矩阵提取特征的matlab程序（共20多个特征），可以根据他的方法来从灰度共生矩阵中提取你需要的特征。-GLCM relevant information, including generating GLCM matlab code, Matlab7 toolbox graycomatrix.m missing documents, and a gray
Analysisoftexturefeatureextractedbygraylevelco2occ
- 为使灰度共生矩阵(GLCM)提取的特征值较好地表达纹理信息,对Brodatz纹理库图片进行了大量实验。 首先测试了各构造参数对关键特征统计量的影响,给出了特征值随参数变化的规律,确立了构造参数的合理取值 然 后测试了图像旋转和大小变化对所提取特征值的影响-In order to grayscale co-occurrence matrix (GLCM) features extracted texture to express the value of good information
Textural_defect_detection_based_on_label_co-occurr
- 基于类别共生矩阵的纹理疵点检测方法 邹超 朱德森 肖力 摘要：根据有规则纹理的特点，提出了基于类别的共生矩阵来描述纹理特征，从而很好地将正常纹理与疵点区分开。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．为了克服灰度共生矩 阵在计算量和分辨能力上的缺点，定义了类别共生矩阵．在类别共生矩阵的算法中，首先学习纹理的一些基本特征以确定类别共生矩阵的一些关键参数。如纹理的概率密度分布、纹理的主方向和周期，以及分类准则等重要参数，然后计算类别共生矩阵并提取白疵点增强、黑疵
GLCM_Features4
- GLCM gray level cooccurrence matrix
Calculates-cooccurrence-matrix
- 灰度共生矩阵的MATLAB实现，为调用子函数的源码。-GLCM MATLAB implementation of the source code for the subroutine call.
Gray-Level-Co-Occurrence-Matrix
- 基于OpenCV和VS2008的图像灰度共生矩阵特征提取实现。文件中还有matlab版本的-Images based on OpenCV and VS2008 GLCM feature extraction achieved. Matlab version of the file there
graycomatrix
- 计算灰度共生矩阵的Matlab程序，提取图像灰度共生矩阵- glcm image segmentation matlab GLCM program segmentation glcm GLCM Matlab Code image segmentation GLCM matrix
glcm
- 利用灰度共生矩阵提取纹理特征图像，证明了灰度共生矩阵提取的纹理特征对图像分类精度起到了提高的作用-Gray Level Co-occurrence matrix extraction method is more important texture feature extraction method, using matlab realize the
glcm.py
- Grey level cooccurence matrix
gray-matrix
- 用matlab编写的关于灰度共生矩阵的源代码，对于纹理特征的研究有重要意义。-Written using matlab the GLCM source code is important for the study of texture features.
GLCM+train
- 利用灰度共生矩阵提取图像特征。。。。。。(Extracting image features using gray level co-occurrence matrix)
glcm
- 灰度共生矩阵用于提取图像的特征，用于图像识别和分类(Gray level co-occurrence matrix is used to extract the features of images, and is used for image recognition and classification)
glcm
- ??为了能更直观地以共生矩阵描述纹理状况，从共生矩阵导出一些反映矩阵状况的参数，典型的有以下几种(In order to describe the state of texture with a symbiotic matrix more intuitively, some parameters reflecting the matrix condition are derived from the symbiotic matrix, typical of the following sever
GLCM-OpenCV-master
- 由于纹理是由灰度分布在空间位置上反复出现而形成的，因而在图像空间中相隔某距离的两象素之间会存在一定的灰度关系，即图像中灰度的空间相关特性。灰度共生矩阵就是一种通过研究灰度的空间相关特性来描述纹理的常用方法。 Gray-level co-occurrence matrix from an image 图像的灰度共生矩阵(Because the texture is formed by the repeated appearance of the gray distribution in the