直方图的绘画和图片的显示，并且与数据库联接，是毕业设计作的...

用 matlab 图像处理与 3 pic 和比较它们的圆检测 1.taranslate 到灰色 2.转换为二进制 3.反相 blob 算法的使用

实现四个功能：一、图和图素1． 定义屏幕坐标2． 在屏幕中心作下列图素：点 • ； 小方块 ■ ；大方块 ■ ； 圆点 ● 。以上述图素画线二、3． 用DDA法画线段；4． 用DDA法画圆。三、已知如图所示的多面体，其各棱边长均相等。要求1． 用步进法（即+1比较法）编写AB线段生成程序；2． 用法线法编写消隐程序；3． 编写让多面体绕其中心轴线旋转30o、45o、60o的程序四.在三的基础上自动绘制各棱边，自由旋转任意角度-realization of the four functions : 1, maps and map-1. Two definition screen coordinates. In the center of the screen-map for the following : Point# 8226; ; small chunks; large chunks; dots. The above map-line drawing two, three. DDA law with painting segment; 4. With DDA Act Circle. 3, as shown in the figure known polyhedron, its edge longer being equal. Requirements 1. Walking France (a comparison) prepared AB segment generated procedures; 2. Usage prepared blanking line method; 3. To prepare for polyhedron around its central axis rotation 30o, 45o, 60o procedures 4. The three on the basis of automatic drawing the edge, the Liberal arbitrary rotation angle

用Delphi开发的比较图片的代码-developed using Delphi code comparison Photo

C++builder实现图象的垂直交错效果，分别由上下两端出现的较淡图形向屏幕中央移动，直到完全清楚为止。-C builder Image staggered effect of the vertical, from the lower sections of more lightly-screen graphics to the central mobile until it is completely clear so far.

一个用vc制作的绘图软件，可以很好的学习图形算法-vc produced using a mapping software can be a good learning algorithm Graphics

DXF图形文件的读取和存储,可以完全加载到CAD中去.-DXF graphics files to read and store, can be completely loaded into the CAD go.

CGAL计算几何算法库-Computational geometry algorithms library CGAL

分形几何生成器 -Fractal geometry generator

用于读取IGES文件-IGES files for reading

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.