ksvdbox13

计算机视觉与应用，详细介绍图像，计算机视觉理论(application of computer vision, detailed image, computer vision theory)

一个基于MFC窗口的具有图形图像处理的功能的MFC程序，可对图像灰度，滤波等处理好功能(MFC program has a function of image processing based on MFC window can be a good feature for grayscale images, filtering and other processing)

一个短小精悍的分水岭算法,用Matlab实现,程序简单易懂(A dapper watershed algorithm, using Matlab implementation, the program easy-to-read)

基于NCC（归一化互相关）的图像配准算法(NCC (normalized cross-correlation), image registration algorithm)

求解l1范式的值，用于压缩感知中的稀疏表示。进行分类(Solving the value of l1 paradigm for compressed sensing of sparse representation. Classification)

说明：  有数字1和2的图片各四十张，利用mat lab读取图片并将其二值化，得到只由0和1表示的矩阵，根据数字的不同特征，选择两个特征值，用来对分类器进行训练，最后用剩下的5个1和5个2来进行测试，看分类器是否可以正确的对数据进行分类。(Figures 1 and 2 pictures of the 40, using mat lab picture and secondly, the value of reading, and received only by 0 and 1, said matrix, according to the number of different features, select the two characteristic values, used to classifier training, and finally with the remaining 5 1 and 5 2 to carry out tests to see whether the classifier can correctly classify the data.)

图像融合的各种Q值评价指标的代码，包括Q0,Qe,Qw,Qabf,vif等，非常适合图像融合的质量评价(Q values ​ ​ of various uation code image fusion, including Q0, Qe, Qw, Qabf, vif, etc., is very suitable for quality uation of image fusion)

1、对观测数据进行中心化，； 2、使它的均值为0，对数据进行白化—>Z； 3、选择需要估计的分量的个数m，设置迭代次数p<-1 4、选择一个初始权矢量（随机的W，使其维数为Z的行向量个数）； 5、利用迭代W(i,p)=mean(z(i,:).*(tanh((temp) *z)))-(mean(1-(tanh((temp)) *z).^2)).*temp(i,1)来学习W （这个公式是用来逼近负熵的） 6、用对称正交法处理下W 7、归一化W(:,p)=W(:,p)/norm(W(:,p)) 8、若W不收敛，返回第5步 9、令p=p+1，若p小于等于m,返回第4步 剩下的应该都能看懂了 基本就是基于负熵最大的快速独立分量分析算法(1, on the center of the observation data, 2, making a mean of 0, the data to whitening-> Z 3, select the number of components to be estimated m, setting the number of iterations p < -1 4, select an initial weight vector (random W, so that the Z dimension of the row vectors of numbers) 5, the use of iteration W (i, p) = mean (z (i, :).* (tanh ((temp) ' * z)))- (mean (1- (tanh ((temp)) ' * z). ^ 2)).* temp (i, 1) to learn W (This formula is used to approximate the negative entropy) 6 with symmetric orthogonal treatments W 7, normalized W (:, p) = W (:, p)/norm (W (:, p)) 8, if W does not converge, return to step 5 9 , so that p = p+1, if p less than or equal m, return to step 4 should be able to read the rest of the basic is based on negative entropy of the largest fast independent component analysis algorithm)

利用surf1函数绘制函数图形的三维光照图形，其中图形函数表达式为peaks(Use surf1 function to draw three-dimensional graphics of functional graphics, in which the graphical function expression is peaks)

LBP（局部二值模式）特征提取，用于人脸识别，(LBP feature extraction for face recognition. . .)