yundongjianceGUI

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)

用于显著性检测的对比算法SP的MATLAB版本，比较新的是2013年的，亲测可用(MatLAB version of SP, a contrast algorithm for saliency detection, is relatively new in 2013, and pro-test is available.)

说明：  matlab实现三维重建，代码书写规范，运行稳定(Matlab to achieve three-dimensional reconstruction, code writing standard, stable operation)

模拟退火MATLAB程序，可直接运行，或修改参数做其他用途(MATLAB monituihuo program, you can use it solve many problem.)

本程序主要实现暗背景下含高斯噪声的光斑滤除噪声，实现光斑中心提取。采用了sobel算子和梯度倒数加权滤噪、最大类间方差法（Otsu）确定阈值、质心法提取光斑中心坐标。(The program is mainly realized under a dark background Gaussian noise spot containing noise filtering, to achieve spot center extraction. Using sobel operator and gradient inverse weighting filter noise, Otsu method (Otsu) to determine the threshold, the center coordinates of the spot centroid extraction.)

比较新的图像处理类，2010版，并且有DEMO,方便应该(Relatively new class of image processing, 2010 Edition, and has DEMO, should facilitate)

说明：  基于局部Fisher准则的非线性核Fisher辨别分析，应用于有监督的特征提取与高维数据的有效降维。(Kernel Local Fisher Discriminant Analysis for Supervised Dimensionality Reduction.)

本文是基于最新的模糊C均值彩色图像分割方法改进得来的新算法(This article is based on the fuzzy c-means clustering color image segmentation method of the latest paper)

CT（computer tomography ）图像重建算法，FBP，是目前商用CT的主流重建算法(computer tomography reconstruction algrithm)

利用 svt算法解决 Rpca算法的放缩问题，牛刀小试，大家见谅！(Solves the Robust PCA relaxation)