MATLAB6.x-for-process-signal

Visual C++/MATLAB图像处理与识别实用案例精选 一书的所有程序代码(Visual C++/MATLAB image processing and recognition of practical cases selected all the code book)

现代数值计算方法所附光盘源码第四部分（福建师范大学）马昌凤, 林伟川编著 科学出版社(Modern numerical methods accompanying CD-ROM Source Part IV (Fujian Normal University) MA Chang-Feng, LIN Wei-chuan edited by Science Press)

pscad的具体实例，适合初学者，能尽快熟悉pscad的运行等。(pscad examples)

该文介绍了我们基于 m 删 模 型语音信号识别原理开发的连续数字音识别软件包及其程序实 现 。(this WenJie shaoxing us based on m delete mode of speech signal the principle of general don t open a few words sound hair general don t software package and its program real now)

Matlab code for The Labor-Supply Elasticity and Borrowing Constraints: Why Estimates are Biased 2

说明：  MATLAB通信仿真及应用实例详解,matlb学习资料(MATLAB通信仿真及应用实例详解)

Copula理论及应用实例 从xls文件中读取数据 绘制频率直方图 计算偏度和峰度 正态性检验 求经验分布函数值 核分布估计 求Copula中参数的估计值 绘制Copula的密度函数和分布函数图(sample for copula theory)

模式识别留一法模式识别留一法模式识别留一法模式识别留一法模式识别留一法(Pattern Recognition Pattern Recognition leave-one-leave-one-leave-one pattern recognition pattern recognition pattern recognition leave-one-leave-one-)

matlab绘图命令，描述了各种绘图用工具，对matlab新手有很好的指导作用(matlab drawing commands, described in a variety of drawing tools, good for novice matlab guide)

基于奇异值分解的人脸识别方法 梁毅雄 龚卫国 潘英俊 李伟红 刘嘉敏 张红梅 提出了一种将傅里叶变换和奇异值分解相结合的人脸自动识别方法.首先对人脸图像进行傅里叶变换,得到其具有位移不变特性的振幅谱表征.其次,从所有训练图像样本的振幅谱表征中给定标准脸并对其进行奇异值分解,求出标准特征矩阵,再将人脸的振幅谱表征投影到标准特征矩阵后得到的投影系数作为该人脸的模式特征.然后,对经典的最近邻分类器算法进行了改进,并采用模式特征之间的欧式距离作为相似性度量,从而完成对未知人脸的识别.采用ORL (Olivetti Research Laboratory)人脸库对本文提出的人脸识别方法进行验证,获得了100.00 的识别率.实验结果表明,本方法优于现有的基于奇异值分解的人脸识别方法,且对表情、姿态变换等具有一定的鲁棒性. (Face recognition based on singular value decomposition method Deliberate simultaneously Gong Weiguo Li Wei Hung Stephen Lau, Hong-Mei Zhang Ying-Jun Pan Paper, a Fourier transform and singular value decomposition of the combination of automatic face recognition. First of all, the face image by Fourier transformation, it has the same characteristics of the displacement amplitude spectra. Secondly, all training The amplitude spectrum of the sample images given in standard face representation and its singular value decomposition, find the standard characteristic matrix, then the amplitude of spectral characterization of human faces projected onto the standard characteristic matrix of projection coefficients obtained as the face of the model features . Then, the classical nearest neighbor classifier is improved, and the use of Euclidean distance between pattern features as the similarity measure, thus completing the identification of unknown human faces. using ORL (Olivetti Research La)