1) scale co-occurrence matrix
尺度共生矩阵
1.
This paper proposed a new image segmentation method,which integrated the theory of tree-structured frame-wavelet transform,scale co-occurrence matrix(SCM),principal component analysis and self-organizing neural network,and applied them to the clinical ultrasonic image finishing image segmentation.
本研究将图像树型框架小波变换、尺度共生矩阵、KL变换主分量分析和自组织神经网络聚类相结合应用于医学超声图像,提出一种分割新方法。
2.
This paper claims that the dynamic information-reflected scale co-occurrence matrix is combined with static information-reflected gray level co-occurrence matrix,and the texture images are segmented.
将能反映纹理空间尺度变化信息的尺度共生矩阵(动态信息)和反映纹理信息的灰度共生矩阵(静态信息)相结合,进行纹理特征抽取,对纹理图像进行分割,再对分割结果进行滤波,去除分割结果中存在的误分像素,结果表明,能够获得良好的分割效果。
3.
This paper proposes a new image segmentation method,which integrates the theory of tree-structured frame-wavelet transform,scale co-occurrence matrix(SCM),and self-organizing neural network and applies them to the clinical ultrasonic image finish segment.
文中将图像树型框架小波变换、尺度共生矩阵和自组织神经网络聚类相结合应用于超声图像提出一种分割方法。
2) scale-based concurrent matrix of frame wavelet
小波尺度共生矩阵
3) dimension matrix
尺度矩阵
4) 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
5) 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%。
6) 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。
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。
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