sar_data_and_process

说明：  基于matlab 的rbf神经网络计算，下载解压缩后直接运行(The rbf neural network-based computing matlab, download directly after extracting run)

矢量匹配算法，关于曲线理和的问题，能克服有理匹配的相关缺陷(vectfiting )

时域有限差分法-2维自由空间传输电磁场分布(Finite Difference Beam Propagation Method)

信息隐藏的一种算法,使用matlab实现.mycompress.m JPEG有损压缩 mycrop.m 剪切 myhighpass.m 高通滤波器 mylowpass.m 低通滤波器 mynoise.m 高斯，椒盐噪声 myresize.m 放缩 myrotate.m 旋转 mywiener.m 维纳滤波 myattack.m 以上所有攻击的集合(information hidden an algorithm, using Matlab to achieve. JPEG Compression mycompress.m mycrop.m shear myhighpass.m high pass filter mylowpass.m lowpass filter mynoise.m Gaussian, salt and pepper noise myresize.m Zooming myrotate.m peacekeeping rotation mywiener.m Filtering myattack.m satisfied all of the above attacks pool)

A short and simple gravity-driven LBM solver based on the code snippets in Sukop and Thorne s Lattice Boltzmann Modeling

用matlab实现的四类基本滤波器的设计代码，可运行(Matlab implementation of the four basic types of filter design code can be run)

Function H for Method upwind, Lax_Friedrichs and Lax_Wendroff for equation of advection 1d in Matlab

fruit recongnize is importart understand the code beefore of play

边缘是图像最基本的特征，是图像分割的第一步。经典的边缘检测方法如：Roberts，Sobel，Prewitt，Kirsch，Laplace等方法，基本都是对原始图像中象素的小邻域构造边缘检测算子，进行一阶微分或二阶微分运算，求得梯度最大值或二阶导数的过零点，最后选取适当的阀值提取边界。由于这些算法涉及梯度的运算，因此均存在对噪声敏感、计算量大等缺点。在实践中，发现SUSAN算法只基于对周边象素的灰度比较，完全不涉及梯度的运算，因此其抗噪声能力很强，运算量也比较小。并将SUSAN算法用于多类图像的边缘检测中，实验证明该算法非常适合含噪图像的边缘检测。 2 SUSAN边缘检测简介 (err)

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)