md51

提出了一种基于小波变换可抗击几何攻击算法。在水印嵌人 部分# 首先对宿主图像进行小波分解 、 嵌人水印, 再通过余弦变换求得该水印图像的一个特征向量, 利用该特征向量和水印 信息通过12 31 函数生成一个二值逻辑系列, 将该序列作为密钥/ 水印提取部分为# 对待测的图像进行小波变换, 再通过余 弦变换求得待测图像的一个特征向量, 利用该特征向量和密钥通过1 2 4 1 函数提取出水印。(Proposed based on wavelet transform algorithm can be against geometric attacks.# Watermark embedded in the first part of the host image wavelet decomposition, embedded watermark, and then through the cosine transform of the watermarked image obtained by a feature vector, using the feature vector and the watermark information through 1231 to generate a binary logic function series, the sequence as the key/watermark extraction part is# treated transform measured image, and then obtained through the cosine transform the image of a feature vector under test, using the feature vector and the key function by 1241 to extract the watermark .)

采用Clewlow and Strickland 单要素日现货模型，利用Monte Carlo方法模拟商品现货价格的Matlab程序。(Commodity Model One Factor Spot Model：This code simulates commodity spot prices using the Clewlow and Strickland one factor daily spot model using a Monte Carlo approach. The derived stochastic differential equations (SDEs) are solved using several finite difference schemes.)

efficient LBP Local binary patterns (LBP) is a type of feature used for classification in computer vision. LBP is the particular case of the Texture Spectrum model proposed in 1990.[1][2] LBP was first described in 1994.[3][4] It has since been found to be a powerful feature for texture classification it has further been determined that when LBP is combined with the Histogram of oriented gradients (HOG) descriptor, it improves the detection performance considerably on some dataset

FCMTHRESH Thresholding by 3-class fuzzy c-means clustering [bw,level]=fcmthresh(IM,sw) outputs the binary image bw and threshold level of image IM using a 3-class fuzzy c-means clustering. It often works better than Otsu s methold which outputs larger or smaller threshold on fluorescence images. sw is 0 or 1, a switch of cut-off position. sw=0, cut between the small and middle class sw=1, cut between the middle and large class Contributed by Guanglei Xiong (xgl99@mails.tsinghua.edu.cn) at Tsinghua University, Beijing, China. Please try testfcmthresh.m first!

说明：  CS_UKF当前统计模型无迹卡尔曼滤波跟踪算法CS_UKF当前统计模型无迹卡尔曼滤波跟踪算法(CS_UKF)

对helmholtz方程的matlab编程 (helmholtz)

The sourcecode of Universally-Convergent Squared-Operator Iteration Methods for fig5 using SOM

说明：  Some common algorithms

background subtraction using gmm

支持向量机工具箱，有两种的。支持向量机工具箱，有两种的。(svm toolbox)