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73462655TextureAnlysisfcm
- 用FCM聚类算法对纹理图像的分割,纹理使用的灰度纹理矩阵 计算四个方向的灰度共生矩阵-FCM clustering algorithm used for texture image segmentation, texture matrix used in calculating the four gray-scale texture of the gray-level co-occurrence matrix direction
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
texture_extraction
- 灰度共生矩阵和灰度梯度共生矩阵提取纹理特征,用matlab实现的-Graylevel co-occurrence matrix and grayscale gradient co-occurrence matrix extraction means
Texture_anlysis
- 使用灰度共生矩阵,对图像进行处理,进而提取图像的纹理特征-Texuere anlysis using SGLDM
Gray-symbiotic-matrix
- 灰度共生矩阵和纹理分析功能,能很好的对SAR图像进行分析和提取目标地物。-Gray symbiotic matrix and texture analysis function, can be a very good image of SAR analysis and extraction target features.
000
- 支持向量机(svM)是一种新的机器学习技术。本文采用一对一方法构建多分类SVM 分类器。利用常用的灰度共生矩阵方法提取图像纹理特征,组成特征向量,输入构建好的SVM 多分类器中进行分类。对从Brodatz纹理库中选取的4张纹理图像进行了分类实验,取得较好的 分类结果-Support vector machine (svM) is a new machine learning techniques. In this paper, one way to build a multi-cla
tezhengtiqu
- 在matlab中利用灰度共生矩阵提取图像的纹理特征,实现图像识别。-GLCM to extract image texture features of image recognition in matlab.
GLCM
- 计算图像的灰度共生矩阵,并且提取图像的纹理特征-Computing image s GLCM and extracting texture characteristics
classification
- 基于主成分分析与灰度(颜色)、纹理(灰度共生矩阵)的图像分类matlab程序(附示例图像)。-Image classification matlab program based on principal component analysis and gray (color), texture (GLCM) (with sample images).
GLCM-method-to-extract-features
- 基于灰度共生矩阵提取图像纹理特征,FC为生成的特征向量-This is a method to extract features from an image by GLCM
gongshengjuzheng
- 用灰度共生矩阵提取五个纹理特征 包括:熵、均匀度、相关性、能量、对比度-Extraction of five GLCM texture characteristics,Include: entropy, uniformity, correlation, energy, contrast
Calculates-cooccurrence-matrix
- 基于纹理特征提取灰度共生矩阵用于纹理判断-Extract gray level co-occurrence matrix based texture features for texture judgment
statmoments
- 灰度共生矩阵提取图像纹理特征的需要,这里有部分纹理特征的提取程序代码。-GLCM the need to extract image texture features, part of the texture feature extraction code.
GLDM
- 基于灰度共生矩阵的纹理特征提取。特征参数为:熵,能量,-GLCM-based texture feature extraction. The characteristic parameters: entropy, energy,
graycopropswy
- 利用灰度共生矩阵提取SAR图像纹理特征,可提取:熵‘对比度、同质性、能量等特征。-GLCM to extract SAR image texture features can be extracted: entropy ' contrast, homogeneity, energy and other features.
GTTexturer
- 计算灰度共生矩阵的Matlab程序,参考《基于颜色空间和和纹理特征的图像检索》,输入图像数据,返回八维纹理特征行向量 -Computing to GLCM the Matlab program, the reference input color space and texture feature-based image retrieval, image data, return to the eight-dimensional texture feature row vector
T_Features
- 使用OPENCV实现的Tamura纹理特征,基于灰度共生矩阵的纹理特征-Use the OPENCV to achieve Tamura texture features based on GLCM texture features
texture-GLCM
- 利用灰度共生矩阵(GLCM)方法提取图像纹理。-GLCM to extract image texture.
co_occurrence-matrix
- 灰度共生矩阵 在提取纹理特征时存在的问题,提出一种基于方块编码(BTC)的图像纹理特征的检索算法。 -gray level co-occurrence matrix A novel image retrieval method based on block truncation coding(BTC) is proposed to solve the problems of gray level co-occurrence matrix