图像处理入门者的一本好书！

TC256图形模式下实现，模拟机械表的走时，以及中文汉字的显示。希望对大家理解C语言有所帮助

这是一个播放音乐的系统，只要你打开所要播放的歌曲就可以播放了，不信试试看-This is a music player, if you are to play the opening song on the player can not believe that she

该程序显示了如何绘制各种形状、线条和文本-the program shows how to draw various shapes, lines and text

matlab DSP（数字信号处理） 库文件-matlab DSP (digital signal processing) library file

a slide show design-a slide show design.........

三维科学图形包括三维网线图形；三维线状图形；三维坐标设置；填充网状图形；填充颜色的层数；颜色分层的数值条；向顶部和底部投影；边框设置；背景颜色设置；实体图形；等高线图形。给定zij=f(xi,yj)离散数据点，三维图形控件可以绘制出各种精美的图形。三维 图形控件包含的文件有：mesh45.ocx，olch3d32.dll，meshxyz.dll。-Three-dimensional graphics, including three-dimensional network of scientific line graphics linear three-dimensional graphics three-dimensional coordinates set filled mesh graphics fill color layers color layering Numerical Article to the top and bottom of the projector border settings background color settings graphical entities contour graphics. Given zij = f (xi, yj) of discrete data points, three-dimensional graphics can control a variety of beautifully drawn graphics. Three-dimensional graphical controls contained in the documents: mesh45.ocx, olch3d32.dll, meshxyz.dll.

一个完整的人脸识别程序,分别用了几种比较通用的算法。-A code for face identification which contain some common.

视频图像处理，在视频上加入上浮文字-video image processing, video, text to float

VC/MFC 常见的几种图像显示的方法: 1.使用CBitMap,在OnPaint 中绘制 2.用picturn控件绘制 3.用背景图 4.用KoDak控件(需要已经安装) -VC/MFC common image shows several ways : 1. CBitMap use. Drawing the OnPaint 2. Controls used picturn drawing three. use background Figure 4. charged with KoDak pieces (need to have installed)

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.