diedafa

以混合高斯模型为基础，利用背景差分法获得目标图像并有阴影检测的方法进行目标检测(The Gaussian mixture model based on the use of the background difference method to obtain the target image and a shadow detection method for target detection)

说明：  matlab最小二乘法曲面拟合程序可以得到函数具体解析式(Matlab least squares surface fitting program can get the specific analytic expression of the function)

sift图像处理的一个测试的代码，他是用C++写的 用到了opencv中的一些函数(sift image processing as a test of the code, he wrote with C++ uses some of the opencv function)

click energie of image , we can use it to calculate markov field ... and other

灰色预测模型matlab,灰色模型GM(1,1)程序二次拟合和等维新陈代谢改进预测程(Matlab of gray prediction model, gray model GM (1,1) procedures for the second fitting and the same dimension metabolism to improve the forecasting process )

彩色图像处理，RGB分通道彩色显示，灰度显示，直方图均衡，假彩色图像合成(Color image processing, RGB color display sub-channel, grayscale display, histogram equalization, false color image synthesis)

说明：  人工智能 caffe 用与ssd yolo(Artificial intelligence SSD Yolo)

在逆向工程中通过测量仪器得到的产品外观表面的点数据集合也称之为点云，通常使用三维坐标测量机所得到的点数量比较少点与点的间距也比较大，叫稀疏点云；而使用三维激光扫描仪或照相式扫描仪得到的点云点数量比较大并且比较密集，叫密集点云。(Data collection point in the surface appearance of the product obtained by reverse engineering measurement instruments, also known as point clouds, usually spaced three-dimensional coordinate measuring machine number of points obtained in relatively small dots are relatively large, called sparse point cloud while the number of point cloud using the three-dimensional laser scanner or camera scanner get bigger and more intensive, called dense point clouds.)

基于TensorFlow图像处理的小测试，里面的代码自己尝试过都可以运行。(Based on the TensorFlow image processing test, the code can run if it has tried.)

视网膜血管检测的Gabor变换和机器学习，教程 本教程将演示如何Gabor变换和广义 的线性模型（GLM）可用于视网膜血管检测 图像。 ，我们将尝试检测视网膜血管从 的训练图像，首先，Gabor滤波器与图像卷积。 GLM将使用Gabor变换的图像特征确定 （独立变量）和容器的位置 为结果（因变量）。( Retinal Vessel Detection by Gabor Transform and Machine Learning, a Tutorial This tutorial will demonstrate how Gabor transforms and generalized linear model (GLM) can be used for detection of retinal vessels in images. Specifically, we will attempt to detect the retinal vessels a training image , by first, convoluting multiple Gabor filters with the image. A GLM will be determined using the Gabor transformed images as features (the independent variables), and the locations of the vessels as the outcome (the dependent variable).)