Code

用matlab和VC实现图像匹配拼接的sift算法(VC implementation using matlab and the image matching algorithm sift Splicing)

CRectTracker（俗称“橡皮筋”类）是一个非常有意思的类，这是一个详细的的使用说明(CRectTracker (commonly known as the rubber band ) is a very interesting class, this is a detailed instructions for the use of MSWord file)

　　编写一通用的预测法分析程序，要求有一定的错误处理能力，出错后能够使程序继续运行下去，直到分析过程结束。可通过不同的文法（通过数据表现）进行测试。(Write a generic prediction analysis procedure that requires a certain degree of error-handling capabilities, enabling the program to continue after an error to run it until the end of the analysis process. Can be tested by different grammar (by performance data).)

说明：  蚁群算法原理及其应用PFD，希望对你们有帮助啊(Principle and Application of ant colony algorithm PFD, you would like to help ah)

说明：  足球联赛采用主客场双循环赛制，胜一场得3分，平局各得1分，负一场得0分，联赛排名以积分多者在前，当两队（或多队）积分相同时，则净胜球（即进球数与失球数之差）多者在前，若净胜球相同，则进球数多者在前，若仍相同，则抽签或踢附加赛决定名次（这在联赛结束后进行，联赛未结束则两队名次并列，本程序不做这方面要求）。试编一程序统计最近一轮比赛后，各队积分及排名。(SOCCER PLAYER)

QAM16调制的实现 matlab源程序源源码 星座图的输出与结果都正确 可直接使用。 (Matlab source source source constellation of QAM16 modulation output results are correct can be used directly.)

说明：  two wheeler inventory Management

内含visual C++和MATLAB代码，利用高斯呼喝模型进行背景建模，自适应确定高斯分量的个数，由Z.Zivkovic开发，相关文章是 Improved adaptive Gausian mixture model for background subtraction(Containing visual C++ and MATLAB code, the use of Gaussian background modeling feeding model, adaptive determine the number of Gaussian components, developed by Z.Zivkovic related article Improved adaptive Gausian mixture model for background subtraction)

用MATLAB实现带状矩阵A的LU分解，参数为A的主对角线元素组成的列向量(A MATLAB implementation by the LU decomposition of banded matrix, the parameters for the A' s main diagonal elements of the column vector)

Hidden_Markov_model_for_automatic_speech_recognition This code implements in C++ a basic left-right hidden Markov model and corresponding Baum-Welch (ML) training algorithm. It is meant as an example of the HMM algorithms described by L.Rabiner (1) and others. Serious students are directed to the sources listed below for a theoretical description of the algorithm. KF Lee (2) offers an especially good tutorial of how to build a speech recognition system using hidden Markov models.