图像的正交变换，傅立叶变换，离散余弦变换，沃尔什变换，哈达玛变换...

彩色BMP图像->光栅处理->编码灰度图像->傅氏变换->滤波->逆傅氏变换->彩色合成->零级融合-color BMP image-gt; Grating processing-gt; Coding gray image-gt; Fourier Transform- gt; Filter- gt; Inverse Fourier Transform- gt; Synthesis of color-gt; Zero-integration

基于雅可比变换的空间可变的纹理映射-Jacobian transform based on the space variable Texture Mapping

本站以C语言和Java语言、嵌入式开发、算法实现为主，源码数量达几十万个，有200多个分类，很多都是本站会员的私家珍藏，互联网上其他地方是找不到的 -Site in C language and Java language, the development, the algorithm embedded, hundreds of thousands of a number of source, have more than 200 classification, many are private collection, members of the site on the Internet is not found elsewhere

简单的VC++环境的应用-simple application of the VC

mandelbrot 图案生成程序-Mandelbrot pattern generation process

详细给出了一个完整的人脸检测算法。对于实际应用有一定的参考价值-detail given a complete face detection algorithm. For practical application of a certain reference value

数字图象处理- The digital image processes

数字识别代码 第一步：训练网络。使用训练样本进行训练。（此程序中也可以不训练，因为笔者已经将训练好的网络参数保存起来了，读者使用时可以直接识别）第二步：识别。 首先，打开图像（256色） 再次，进行归一化处理。点击“一次性处理” 最后，点击“R”或者使用菜单找到相应项来进行识别识别的结果显示在屏幕上，同时也输出到文件result.txt中 该系统的识别率一般为90％ 另外，也可以单独对打开的图片一步一步进行图像预处理工作。但要注意，每一步工作只能执行一遍，而且要按顺序执行。步骤为：“256色位图转为灰度图”“灰度图二值化”“去噪”“倾斜校正”“分割”“标准化尺寸”“紧缩重排” 另外注意，要识别的图片，要与win.dat、whi.dat位于同一目录。此两文件保存训练后网络的权值参数 -digital identification code first step : training network. Use of training samples for training. (These procedures also can not training because I have trained network parameters saved, Readers can direct identification) second step : identification. First, open the Image (256 colors) again, to a treatment. Click on the "one-stop" Finally, Click on the "R" or use the menu item corresponding to find the identification of the identification results on the screen, also result.txt output to a file which the system"s recognition rate of 90% for the other, can be individually opened to the step-by-step pictures for image prep

VC汉字识别系统源码与个体软件改进文档-VC Chinese character recognition system with individual source software to improve document

利用LBP纹理模型进行纹理分类，其论文发表在2009年Pattern Recognition，效果很不错。-Texture classification by LBP texture model. The related paper has been published in Pattern Recognition 2009.