screen color extraction procedure code, the programming study images can look at...

在程序实际开发中，闪屏是我们经常用到的。如果程序初始化要一些时间，设计闪屏更加有必要，不但使你的程序也漂亮，还能在上面写上你的版本信息。好处是多多的。此源代码提供了一个很简单的类，示范了如何实现闪屏。这个类只有二个函数，结构很清晰。一看就明白，还有详细的注释。-procedures in the actual development, blinking screen is used frequently. If the program to initialize some time, blinking screen design more necessary, not only to make your procedures beautiful, but also write on your version information. A lot of the benefits. This source provides a very simple type, model of how to achieve blinking screen. The only two types of function, structure is very clear. One can understand it, there are detailed notes.

一个图象处理软件，包涵源代码-an image processsing software, source code included

一个分割视的程序，值得学习-A partition, as the procedure, it is worth learning

pcx图像读取、保存、显示等基本操作,功能完整，代码可靠。-pcx image to read, preserve, display and other basic operations, full-featured, reliable code.

图像浏览，支持放缩小、移动,request指定图片文件.-Image View to support narrow release, mobile, request specified photo paper.

数字图像处理的代码 内容非常全 千辛万苦得来啊-Source code of digital image process procedure,covered many technology,hard to find.

橡皮筋技术的实现，c语言实现，希望交流，多多指教-Rubber Band Technology, c language, hoping the exchange of exhibitions

在程序中添加灯光，背面灯光，镜面反射强度，镜面颜色反射比等-In the procedure to add lighting, back lighting, specular reflection intensity, and specular color reflectance ratio

一个dos下的电子表格软件，由于使用了svga驱动，界面比较精美，使用方法类似于Excel。 -a dos under electronic forms software, the use of svga-driven, more beautiful interface, similar to the use of Excel.

目前该手写体识别系统主要分为 预处理模块： 主要包括训练数据和识别数据的读取，归一化，二值化 特征提取模块：主要包括笔划方向特征和网格密度特征，还可以根据对识别率的要求继续增加其他特征 识别（分类器）模块：主要包括SVM方法和BP神经网络的方法，其中SVM方法的识别率较高，SVM＋网格密度特征， 在小字符集情况下，达到了识别率97％以上 采用OO思想编写，适合做二次开发-currently the handwriting recognition system consists of pretreatment modules : including training data and identification data read, in one, two values of feature extraction module : the main characteristics of the direction including strokes and grid density characteristics, but also based on the identification of the requirements to continue to increase other identification ( Categories) modules : include SVM methods and BP networks, which SVM method for the identification of high SVM+ mesh density characteristics, in a small set of characters to achieve a recognition rate of over 97% using OO ideological preparation, suitable for secondary development