密码算法的C # implemenstation净。

代码充满了哟！代码都是哟！

可是化语言IDL的，绘图的鼠标处理程序。用欲对鼠标的控制，分析。控制事件

二维码识别  包括文件：Ø  EnCodePdf.dll           //PDF417编码动态链接库Ø  EnCodeQr.dll             //Qr_Code编码动态链接库Ø  EnDataMatrix.dll              //DataMatrix编码动态链接库Ø  Wcomp.dll                 //图像压缩动态链接库 2)        支持操作系统Ø  Windows 95/98Ø  Windows NTØ  Windows 2000 Professinal / Server 3)        支持开发环境有：Ø  Microsoft Visual C++ 6.0 / .NetØ  Microsoft Visual Basic 5.0/6.0Ø  Microsoft Visual Foxpro 5.0/6.0Ø  PowerBuilderØ  Delphi

各种图像边缘提取算法的C语言实现及一些测试用图像。这些算法包括：Marr、Canny、shen-Castan、Sobel等等。（注：这些代码是《Algorithms for Image Processing And Computer Vision》一书中的内容，要想理解代码最好参看原书）。-various Image Edge Extraction Algorithm in C language and some test images. These algorithms include : Marr, Canny, Shen-Castan, Sobel and so on. (Note : the code is "Image Processing Algorithms for Computer Vision And" the contents of a book, in order to see the best understanding of the original code book).

本程序整合了 OSG 大部分的基础功能，程序代码编写采用了模块化思想，有效代码在 1000 行左右，注释比较清晰，对于程序的修改和功能的扩展非常方便。-This procedure combines the foundation for most functional OSG, procedures used to prepare the code modular thinking, effective code line in about 1000, the Notes are more well-defined, for the process to modify and extend the very convenient features.

利用matlab所编程的许多例子，帮组大家学习matlab-Matlab programming by using many examples to help everyone learn matlab Group

凸包算法好东西哦-convex hull algorithm Oh good things

在VB的图形窗口中平滑滚动地图，源码运行没有闪烁-in VB graphics window smoothing rolling map source operating without blinking

Abstract We present a component-based, trainable system for detecting frontal and near-frontal views of faces in still gray images. The system consists of a two-level hierarchy of Support Vector Machine (SVM) classifiers. On the first level, component classifiers independently detect components of a face. On the second level, a single classifier checks if the geometrical configuration of the detected components in the image matches a geometrical model of a face. We propose a method for automatically learning components by using 3-D head models. This approach has the advantage that no manual interaction is required for choosing and extracting components. Experiments show that the componentbased system is significantly more robust against rotations in depth than a comparable system trained on whole face patterns.

vp中的特效 功能 及基本操作 框架都可以看到 帮助出学者-vp effects in the function and basic operation of the framework can be seen to help the scholars