shoudongquyu

mfc图像处理，实现图像的配准，自己编写，可以应用(mfc image processing, image registration)

说明：  php图像处理，用php实现，进行简单的操作，编辑，修改等功能(Intervention Image is a PHP image handling and manipulation library providing an easier and expressive way to create, edit, and compose images. The package includes ServiceProviders and Facades for easy Laravel integration.)

说明：  我主要做的是图像分割算法的研究，这个是图像分割的程序，主要用神经网络！(I mainly do is to image segmentation algorithm, this is the image segmentation procedure, the main neural network!)

指纹识别的MATLAB程序，包含了特征点抽取，特征点匹配，识别。(Fingerprint of the MATLAB program, including the feature point extraction, feature point matching, identification)

说明：  基于surf算法的复制移动检测，此方法适合图像伪造研究者初级实验(Replication mobile detection based on surf algorithm)

介绍了混合高斯背景建模法提取运动目标的过程，自己写的，比较好理解，付视频一个，用自己的视频也可以(Describes the process of Gaussian mixture background modeling method to extract moving target, write your own, is better understood, paid a video, you can use your own videos)

说明：  图像的重心区域检测 matlab程序实现 可以检测重心区域并设置成希望颜色，对单景物背景图像效果极佳(regional focus image detection Matlab program can detect regional focus and set hope that the color of single features excellent background images)

数字图像处理，边缘提取，分割，湖面面积计算matlab程序(Image processing, edge detection, segmentation, the lake area)

它所描述的是不同色彩在整幅图像中所占的比例，而并不关心每种色彩所处的空间位置，即无法描述图像中的对象或物体。颜色直方图特别适于描述那些难以进行自动分割的图像。(It describes the proportion of different colors in the entire image, and does not care about the spatial location of each color, that is, it can not describe objects or objects in the image. Color histograms are especially suitable for describing images that are hard to segment automatically.)

程序设计步骤： 1、去均值 2、计算协方差矩阵及其特征值和特征向量 3、计算协方差矩阵的特征值大于阈值的个数 4、降序排列特征值 5、去掉较小的特征值 6、去掉较大的特征值(一般没有这一步） 7、合并选择的特征值 8、选择相应的特征值和特征向量 9、计算白化矩阵 10、提取主分量(Programming steps: 1 to mean 2, calculation of covariance matrix and its eigenvalues and eigenvectors 3, the calculation of covariance matrix eigenvalues greater than the threshold number of 4, in descending order of eigenvalue 5, remove the smaller eigenvalue 6, remove the larger eigenvalue (usually not this step) 7, the combined choice of eigenvalues 8, select the appropriate eigenvalues and eigenvectors 9, computing whitening matrix 10, principal component extraction)