edge image segmentation procedures, the use of vb programming, with the hope of...

运用matlab的形态学，把一幅图片的豆子数目数出来的程序，自己编的，希望和大家分享！-Morphology using matlab to a number of pictures a few beans out of the procedure, its own series, and would like to share!

《抓图实践》的源代码- "Stresses Chart Practice" source code

现在非codebehind方式下不能生成png图形，大家以后要用png就用codebehind方式吧-now under way not to be generated png graphics, since we used to use png codebehind style

图形学和数据结构的一些算法 还有图形学的一些算法的演示-graphics and data structure of the algorithm there are some graphics algorithm demo

能够浏览yuv文件- Can glance over the yuv document

程序运行平台为AutoCAD2000简体中文版。 开发平台VC6.0及ObjectARX3.0(本程序所涉及的文件已包含在安装程序中)。 1. 解压缩source.zip到C盘，自动生成c:source目录。 2.在VC6下打开工程c:sourceMyModelessSheet.dsw。 3.编译生成c:/source/debug/MyModelessSheet.arx文件。(用Rebuild All命令) 4.启动 AutoCAD2000。选择主菜单“工具”下“加载应用程序”菜单项。 弹出加载/卸载应用程序对话框，选中c:/source/debug/MyModelessSheet.arx 单击加载按扭。关闭对话框。 5.在AutoCAD命令行输入命令MODELESSSHEET，即可显示一个无模式标签对话框。 其中属性页1和属性页2为基本子标签，扩展标签为动态扩展的。 首次运行编辑控件使用初始数据，以后运行使用注册数据，调整对话框尺寸 则编辑窗口的位置自动调整。-platform for running AutoCAD English version. VC6.0 development platform and AutoCAD2000 (the procedures involved in the document has been included in the installation procedure). 1. Decompress source.zip to C plates, automatic generation c : source directory. 2. The VC6 open the project c : source MyModelessSheet.dsw. 3. C Compiler Generation :/source/debug/MyModelessSheet.arx document. (With Rebuild All orders) 4. Start AutoCAD2000. The main menu option "Tools" under "application loading" menu item. Pop loading/unloading applicatio

自编各种插值，包括最近邻插值，双线性插值，还有图像拼接程序-Composing a variety of interpolation, including the nearest neighbor interpolation, bilinear interpolation, as well as image stitching program

opengl 的C++源程序代码实例 可直接运行 没有错误-opengl the C++ source code examples can be run without error

自己写的图像边缘抽取，有krish,sobel还一种名字忘了，主要为提取一些类似字的边缘。-himself wrote from the Edge, krish, segmentation also forgot a name, Extracting some of the main characters similar to the brink.

运用pca算法降维，提取主要特征值，从而达到降维目的-Dimensionality reduction using pca algorithm, extract the main features of the value of