绘图

我自己写的一个程序，决没有重复啊！主要是一些基本的图像处理的算法，希望同大家交流。-I write a procedure, and must not repeat ah! Mainly some basic image processing algorithm, we hope that with the exchange.

RushHour ======== C + + 编写的规划求解 RushHour 拼图 此代码将解析任何种类的 RushHour 拼图与 m 行和 n 列，与不同的长度，不只是车 标准游戏。它基于公开为闭的集和优先级队列使用哈希表 AStar 搜索 设置。为启发式函数，用的阻塞的汽车的数量为红车加一。 一辆汽车在任何方向的一个或多个单一步骤代表只有一个动作。 称为"out.txt"文件中打印最少的移动次数和每次更改的审计委员会。 输入的数据是从称为"in.txt"文件中读取。谜题是给在 ASCII 模式中。 执行时间是低于 2 秒，对于难的难题。对于容易，0.5 秒的时间是足够多。

在Visual C++环境下读写bmp文件。该程序可用于图像处理，并对bmp格式的文件进行读写和处理。-In the Visual C++ environment to read and write bmp files. The program can be used for image processing, and bmp format for reading and writing and processing.

MFC实例，有将近20个实例，非常好，希望大家顶一下-MFC example

Hough变换 Hough变换 -Hough transform Hough transform Hough transform Hough transform

Visual c++ 图像识别 Visual c++ 图像识别 很好用的哦-it is very good!

C语言的体系，大量使用指针运算速度可以保证，内含先进的多种插值算法。 另外独有的支持meta exif信息的读取。该库最大的特点就是比较简练，只把重点放在对各种格式图像的读取写入支持上，没有显示部分，实际编程的时候还是需要调用API函数进行显示。 -FreeImage is an Open Source library project for developers who would like to support popular graphics image formats like PNG, BMP, JPEG, TIFF and others as needed by today s multimedia applications. FreeImage is easy to use, fast, multithreading safe, compatible with all 32-bit versions of Windows, and cross-platform (works both with Linux and Mac OS X).

一个显示磁盘文件的例子，类似资源管理器左半部分-a disk file shows examples similar resource-management part of the Left

最小二乘法拟合曲面的程序，个人认为很好用了，希望大家喜欢-Surface least squares fitting procedure, individuals spent a good think, I hope everyone likes

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