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说明：  用Visual C++实现模糊图像恢复处理程序实例(Using Visual C++ fuzzy image restoration processing examples)

C++，人脸变形，选择图片，在图片中选择一个四边形窗口（人物脸部），然后再在内部选择一个四边形窗口，逆时针选择，然后点击变形直接呈现变形结果，点击动画可以查看人脸变形过程(C++ program, select a picture, choose a quadrilateral window (human faces) in the picture, and then select a quadrilateral inside the window, counter-clockwise to select, then click on the deformation presented directly deformation results, click animated facial deformation process can be viewed)

VC图像编辑软件 就要卷积模糊和卷积模糊加噪 以不同方法（大于等于2）实现图像的模糊效果（局部或全局）。并以BMP格式存储。(soft)

该代码用于对错误匹配点进行过滤操作，即去除错误的匹配点(The code is used to remove the wrong match points,and make sure the right matching.)

RAw格式图片的显示，边缘检测，包括SOBEL算子，具有不同模板的拉普拉斯算子边缘检测，以及一些简单的滤波方式(sadfsadf asdfasdf asdf)

实现图像的锐化浮雕等效果。当然，这是我自己弄了很多次之后的。我也忘了它实现的是什么效果了，反正就是大概图片处理一类的。(To achieve the effect of image sharpening and other relief. Of course, I get many times. I also forget to realize what effect, anyway, is about image processing class.)

精心收集的几个去噪算法，包括基于视觉的非线性去噪、BLS_GSM算法、NL-Bayes图像去噪、KSVD去噪等，内容为核心代码，需要配置完善。 (Denoising algorithm of several carefully collected, including visual based nonlinear denoising, BLS_GSM algorithm, NL-Bayes image denoising, KSVD denoising, the content as the core code, the need to improve the allocation of. )

：植物种类识别方法主要是根据叶片低维特征进行自动化鉴定。然而，低维特征不能全面描述叶片信息，识别准确率低，本文提 出一种基于多特征降维的植物叶片识别方法。首先通过数字图像处理技术对植物叶片彩色样本图像进行预处理，获得去除颜色、虫洞、 叶柄和背景的叶片二值图像、灰度图像和纹理图像。然后对二值图像提取几何特征和结构特征，对灰度图像提取 Hu不变矩特征、灰 度共生矩阵特征、局部二值模式特征和 Gabor 特征，对纹理图像提取分形维数，共得到 2183 维特征参数。再采用主成分分析与线性 评判分析相结合的方法对叶片多特征进行特征降维，将叶片高维特征数据降到低维空间。使用降维后的训练样本特征数据对支持向量 机分类器进行训练(plant species identification method is mainly based on blade automatic identification of low dimensional characteristics.However, can not fully describe blade low-dimensional feature information, identification accuracy is low, in this paper A kind of plant leaves recognition method based on multiple feature dimension reduction.First by digital image processing technology to the plant leaf color sample image preprocessing, obtain background color removal, wormhole, petioles, and the blades of a binary image, gray image and texture image.Then the binary image to extract the geometric characteristics and characteristics of structure and characteristics of gray image extraction Hu moment invariants, gray co-occurrence matrix feature, local binary pattern features and Gabor, to extract the fractal dimension of texture image, get 2183 d characteristic parameters.By principal component analysis and linear uation analysis method of combining the characteristics of blade more feature dimensi)

基于opencv实时图像的2值化处理，一定要有摄像头(Opencv-based real-time image processing of two values, there must be a camera)

基于meanshift/camshift的视频运动目标跟踪实现，通过鼠标手动选择跟踪目标。基于HSV空间模型，可手动调节H S V的阈值，显示前景、背景、反响投影图。开发工具是VS2008、OPENCV.(Based meanshift/camshift video moving target tracking achieved manually using the mouse to select the target track. HSV space model, you can manually adjust the threshold HSV, showing foreground, background, reaction projection. Development tools are VS2008, OPENCV)