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2223222
- 我们给出一个模板 和一幅图象 。不难发现原图中左边暗，右边亮，中间存在着一条明显的边界。进行模板操作后的结果如下： 。 可以看出，第3、4列比其他列的灰度值高很多，人眼观察时，就能发现一条很明显的亮边，其它区域都很暗，这样就起到了边沿检测的作用。 为什么会这样呢？仔细看看那个模板就明白了，它的意思是将右邻点的灰度值减左邻点的灰度值作为该点的灰度值。在灰度相近的区域内，这么做的结果使得该点的灰度值接近于0；而在边界附近，灰度值有明显的跳变，这么做的结果使得该点的灰度值很大，这样
texturefeature
- 纹理特征提取算法，灰度共生矩阵源代码，给出了0度45度90度和135度的矩阵
vtex
- 基于图像灰度共生矩阵的特征提取代码，程序实现0 45 90 135度4个方向的特征提取-GLCM based on image feature extraction code, the program achieved 04,590,135 degrees in four directions of the feature extraction
textture-feature
- 基于共生矩阵纹理特征提取，d=1,θ=0°,45°,90°,135°共四个矩阵,所用图像灰度级均为256-Co-occurrence matrix based texture feature extraction, d = 1, θ = 0 °, 45 °, 90 °, 135 ° total of four matrices, the use of gray-scale images are 256
untitled
- 基于图像灰度共生矩阵的特征提取代码，程序实现0 45 90 135度4个方向的特征提取-GLCM based on image feature extraction code, the program achieved 04,590,135 degrees in four directions of the feature extraction
Glcm-untitled
- 基于图像灰度共生矩阵的特征提取代码，程序实现0 45 90 135度4个方向的特征提取-GLCM based on image feature extraction code, the program achieved 04,590,135 degrees in four directions of the feature extraction
VC6.0
- 在VC++6.环境下实现灰度图像的0度，45度，90度，135度四个方向的纹理信息统计-In VC++6 Environmental gray-scale image of 0 degrees, 45 degrees, 90 degrees, 135 degrees of the four directions of the texture statistics
edge-detection
- 基于数学形态学的多方向结构元素的边缘检测，方向分别为0°，45°，90°，135°。-Multi-directional structural elements based on mathematical morphology, edge detection, direction 0,45,90,135.
mygaborfilter
- 4方向滤波器gabor 实现0°、45°、 90°、 135°方向滤波 - The Gabor filter is basically a Gaussian (with variances sx and sy along x and y-axes respectively) modulated by a complex sinusoid (with centre frequencies U and V along x and y-axes respectively)
Texture1
- 基于共生矩阵纹理特征提取，d=1,θ=0°,45°,90°,135°共四个矩阵-Extract texture features based on the gray level co-occurrence matrix, d = 1, θ = 0, 45, 90, 135, a total of four matrix
135
- matlab程序，图像应用处理，可实现散焦模糊篡改图像的定位和分离，实现模糊图像真实性的检测-matlab program, image processing applications can be realized defocus blurred image tampering localization and separation, to achieve authenticity blurred image detection
wenli
- 基于灰度共生矩阵纹理特征提取，d=1,θ=0°,45°,90°,135°共四个矩阵-Based on GLCM texture feature extraction, d = 1, θ = 0,45,90,135 of four matrix
SobelEdge
- Sobel算子实现水平边缘检测、垂直边缘检测；45度、135度角边缘检测 -Using Sobel operator to realize horizon and vertical edge detection 45 degree, 135 degree angle edge detection
bianyuanjiance
- 利用sobel算子实现边缘检测，包括水平方向，垂直方向，以及45度，135度检测。-Using edge detection sobel operator, including horizontal and vertical directions, as well as 45 degrees, 135 degrees is detected
glcm24
- Matlab实现灰度共生矩阵算法，0,45,90,135四个方向的，每个方向各6个特征，因而一幅图像总共24个特征。在图像纹理识别方面挺有效。-Matlab implementation of the gray level co-occurrence matrix algorithm, 0,45,90135 four directions, each direction of the 6 characteristics, and thus a total of 24 images of the
GongSheng
- % 图像检索——纹理特征 %基于共生矩阵纹理特征提取，d=1,θ=0°,45°,90°,135°共四个矩阵 %所用图像灰度级均为256 %参考《基于颜色空间和纹理特征的图像检索》(% image retrieval - texture features % based on co-occurrence matrix texture feature extraction, d=1, theta, =0 degrees, 45 degrees, 90 degrees, 135 degrees