灰度共生矩阵----提取纹理信息_envigldm

前段时间一直在出差，各处跑，没有多少时间写博客；

灰度共生矩阵(GLDM)的统计方法是20世纪70年代初由R.Haralick等人提出的，它是在假定图像中各像素间的空间分布关系包含了图像纹理信息的前提下，提出的具有广泛性的纹理分析方法。

• The statistical method of grayscale symbiosis matrix (GLDM) was proposed by R.Haralick et al., in the early 1970s. It is a generalized texture analysis method on the premise that the spatial distribution relation among pixels in an image contains the texture information of the image.

灰度共生矩阵被定义为从灰度为i的像素点出发，离开某个固定位置（相隔距离为d，方位为）的点上灰度值为的概率，即，所有估计的值可以表示成一个矩阵的形式，以此被称为灰度共生矩阵。对于纹理变化缓慢的图像，其灰度共生矩阵对角线上的数值较大；而对于纹理变化较快的图像，其灰度共生矩阵对角线上的数值较小，对角线两侧的值较大。由于灰度共生矩阵的数据量较大，一般不直接作为区分纹理的特征，而是基于它构建的一些统计量作为纹理分类特征。Haralick曾提出了14种基于灰度共生矩阵计算出来的统计量：即：能量、熵、对比度、均匀性、相关性、方差、和平均、和方差、和熵、差方差、差平均、差熵、相关信息测度以及最大相关系数。

• The gray co-occurrence matrix is defined as the probability of gray value is at the point away from a fixed position (distance D, azimuth) starting from the pixel point with gray level I, that is, all estimated values can be expressed as a matrix, which is called gray co-occurrence matrix.For images with slow texture change, the values on the diagonal of grayscale co-occurrence matrix are larger.For images with fast texture change, the values on the diagonal of the grayscale co-occurrence matrix are smaller, while the values on both sides of the diagonal are larger.Due to the large amount of data of gray co-occurrence matrix, it is generally not directly used as texture distinguishing features, but some statistics constructed based on it as texture classification features.Haralick once proposed 14 kinds of statistics calculated based on gray level co-existence matrix: namely, energy, entropy, contrast, uniformity, correlation, variance, and average, and variance, and entropy, difference variance, difference average, difference entropy, correlation information measure and maximum correlation coefficient.