能实现实时图象旋转

利用图像库开发屏幕保护-image for the development of screen protection

数字图像处理（朱志刚）-Digital Image Processing (Zhu Zhigang)

CODE128条码

一款开源的完整矢量绘图控件源码,支持直线、弧线、矩形、圆角矩形、椭圆、多边形、星形、文本和图片等的绘制，支持旋转、缩放、选择、排列等操作，总的说来功能还是不错的！其实如果稍微懂点计算机图形学的知识要做出比这个还要完善的矢量绘图软件应该是没有问题的。 VB6本身对于绘图的支持是非常弱的，因此一般直接通过其内置方法实现的绘图效率都成很大的问题。解决方法之一是采用Windows API来进行底层GDI的绘制，更好的方法是采用GDI+来绘图，这样效果会更好。 -A complete open source vector graphics controls in support of a straight line, arc, rectangle, rounded rectangle, ellipse, polygon, star, text and pictures, such as rendering, support for rotation, zooming, selection, arrangement and other operations, the total to function or that good! In fact, if a little point of computer graphics to understand the knowledge to make than that perfect vector drawing software should not be any problem. VB6 their support for the graphics are very weak, so the general method of directly through its built-in graphics to achieve efficiency into a big problem. One solution is the use of Windows API for the underlying GDI rendering, and better way is to adopt GDI+ To graphics, so the effect will be even better.

把JPEG图片加载到BMP图片后面，形成资源文件-JPEG images to BMP images loaded into the back to form a resource file

Double Click Editor Reactor The sample of programm works in AutoCAD and Bricscad.

位图文件读写综述-bitmap file on literacy

用CorelDraw9制作的金属机械钟，每个零件均可拆分，可供初学者参考。-CorelDraw9 produced using metal mechanical clock, each part can be split, for advanced users.

在visuanl c++平台下编写的一个2D螺旋矩阵，基本实现功能-In visuanl c++ platform written in a spiral 2D matrix, the basic realization of functions

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.