一个dos下的c语言仿windows界面，用在一个pII+128MRAM+dos+640*480*8+pc104总线工控机上，主要用了自己写的几个画图函数,技术

包含两篇论文：《基于LLE算法的人脸识别方法》，《一种改进的LLE方法》； 以及相关源码-Contains two papers:

最近邻分类器（KNN）的C＋＋源码，适合模式识别、图像处理开发者用！-nearest neighbor classifier (KNN) of the C++ source code, suitable for pattern recognition, image processing developers use!

北京大学遥感所的研究生图像处理讲义高级遥感数字图像处理（硕士生课程）之第十七章 图像压缩-Peking University

较全面的图像处理与分析软件源程序-more comprehensive image processing and analysis software source

又一种关于matlab的数字水印技术的系统实现，有比较详细的说明-on Matlab also a digital watermarking technology systems, a more detailed explanation

OpenGL高级编程与可视化系统开发（高级编程篇）的随书源码-4，学习OpenGL的好东西-OpenGL Programming and Advanced Visualization System (Advanced Programming Part 1) the source-4 with books, learning the good stuff OpenGL -OpenGL high-level programming and visualization systems development (high-level programming chapter)-4 of the source with the book, learning OpenGL good things-OpenGL Programming and Advanced Visualization System (Advanced Programming Part 1) the source-4 with books, learning the good stuff OpenGL

图像采集处理程序-image acquisition and processing procedures

切换屏幕在原始尺寸与全屏中切换，而且向你演示即使全屏文字也不会按比例放大，还有一个退出动画的按钮；不过你可能要DOWN回去之后才能看到它的效果-Switch the screen size and full-screen in the original switch, but also to the presentation you will not even full-screen text in proportion to enlarge, there is a quit button animation but you may have to go back DOWN to see it after the effects of

联机手写数字识别，可以运行的 (2005-1-14，VC,69KB,下载1-online handwritten numeral recognition, can run (2005-1-14, VC, 69KB download a

Graph matching (GM) is a fundamental problem in computer science, and it plays a central role to solve correspondence problems in computer vision. GM problems that incorporate pairwise constraints can be formulated as a quadratic assignment problem (QAP). Although widely used, solving the correspondence problem through GM has two main limitations: (1) the QAP is NP-hard and difficult to approximate; (2) GM algorithms do not incorporate geometric constraints between nodes that are natural in computer vision problems. To address aforementioned problems, this paper proposes factorized graph matching (FGM). FGM factorizes the large pairwise affinity matrix into smaller matrices that encode the local structure of each graph and the pairwise affinity between edges. Four are the benefits that follow from this factorization: (1) There is no need to compute the costly (in space and time) pairwise affinity matrix; (2) The factorization allows the