barcode1_12

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)

联合贝叶斯人脸验证python代码，机器学习(Joint Bayes face verification)

小波算法。机械化的提高我们的效率。让我们减少错误率。大家要对她有性心。(Wavelet algorithm. Mechanization to improve our efficiency. Let us reduce the error rate. We have to have sex to her heart.)

对图像分块,计算每个块的均值和方差,并给出了结果的图象化表示,易于观察(Image sub-blocks, each block calculated the mean and variance, and gives the results of the image that is easy to observe)

matlab做的标定程序，包括角点检测与张正友标定算法做的，有界面 (matlab to do the calibration interface, including the corner detection algorithm and Zhang Zheng-you do calibration)

说明：  图片马赛克效果图片马赛克效果图片图片马赛克效果马赛克效果(Photo mosaic picture image photo mosaic mosaic mosaic)

说明：  经典的Tarel去雾算法，可用于图像去雾的指标性能对比。(It is the classical dehazing algorithm, and it is can be used comparison with other algorithms.)

几幅SAR图像。对SAR图像处理的童鞋很有用的。(Pieces of SAR images. Of SAR image processing is useful for students.)

Image Mosaicing interesting application in computer vision

介绍了不同非线性模型在随机共振中的应用，像饱和非线性，阈值非线性等(Describes the different nonlinear model of stochastic resonance in the application, such as nonlinear saturation threshold nonlinearity. . . .)