DIFFERENCE-SETS

MATLAB的GUI设计，共35个例子，适合初学者。文件来源：mathworks(MATLAB-GUI design, a total of 35 examples, for beginners. Document Source: mathworks)

图像处理 蛇行法处理图像问题 Using Dynamic Programming for Solving Variational Problems in Vision(1)书中的一个例子(Image processing image processing problems hunting law)

遗传算法的算法实例，我是一个初学者，通过研究这些现成的代码，感觉对自己挺有帮助(It is helpful for those who study MATLAB first time)

这篇论文可帮助大家快速的学习matlab编程(This paper will help study of U.S. Express matlab programming)

DsimOrder Calculate the Dissimilarity index based on order pattern analysis.Input: two time series Output: Dissimilarity index between inputs permutation entropy

一个关于GMM实现的MATLAB的训练代码！很有用，用来检测纹理。(using to detection texture.)

用matlab实现kalman滤波器condensation滤波器跟踪自由落体的小球(Using matlab to achieve condensation filter kalman filter tracking of free-falling ball)

matlab实现小波变换,图象处理应用中最基础的matlab编程,希望对大家有用(matlab realization of wavelet transform, image processing applications, the most basic matlab programming, in the hope that useful)

EMD的Toolbox及使用方法 经验模态分解（Empirical Mode Decomposition, 简称EMD）是由美国NASA的黄锷博士提出的一种信号分析方法.它依据数据自身的时间尺度特征来进行信号分解, 无须预先设定任何基函数。这一点与建立在先验性的谐波基函数和小波基函数上的傅里叶分解与小波分解方法具有本质性的差别。正是由于这样的特点, EMD 方法在理论上可以应用于任何类型的信号的分解, 因而在处理非平稳及非线性数据上, 具有非常明显的优势。所以, EMD方法一经提出就在不同的工程领域得到了迅速有效的应用, 例如用在海洋、大气、天体观测资料与地震记录分析、机械故障诊断、密频动力系统的阻尼识别以及大型土木工程结构的模态参数识别方面。本工具书主要分以下几个部分：导论、非稳态信号介绍、一维时频信号分解、二维时频信号分解、时频影像信息提取等。 (Time frequency toolbox for use with MATLAB The Time-Frequency Toolbox has been mainly developed under the auspices of the French CNRS (Centre National de la Recherche Scientifique). It results from a research effort conducted within its Groupements de Recherche ”Traitement du Signal et Images” (O. Macchi) and ”Information, Signal et Images” (J.-M. Chassery). Parts of the Toolbox have also been developed at Rice University, when one of the authors (PG) was visiting the Department of Electrical and Computer Engineering, supported by NSF. Supporting institutions are gratefully acknowledged, as well as M. Guglielmi, M. Najim, R. Settineri, R.G. Baraniuk, M. Chausse, D. Roche, E. Chassande-Mottin, O. Michel and P. Abry for their help at different phases of the development.)

THIS IS GOOD MATLAB ARTICLE ABOUT FACE DETECTION