imMosaic

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)

当前流行的机器学习算法之一：boosting的变体——Gentleboost(The current popular one of machine learning algorithms: boosting variants- Gentleboost)

通过统计直方图并作相应的处理完成了红外图像非均匀性校正，特别是针对红外图像中常见的条纹非均匀性(Through statistical histogram and processed accordingly infrared image non-uniformity correction, especially for the common striped infrared image non-uniformity)

背景音乐只是随便写了写,具体的可以改数组里的数字.有点闪的效果还是不太好看. (Background music just casually write write array of specific numbers can be changed. Bit flash effect is still quite nice.)

计算色差输出的小程序。可以输出两组颜色值之间的CIE色差(Calculation of small color difference output procedures. Can output two sets of color values of the CIE color difference between)

基于opencv实时图像的2值化处理，一定要有摄像头(Opencv-based real-time image processing of two values, there must be a camera)

西南交通大学出版社出版的《信号与图像的稀疏分解及初步应用》，作者是王建英老师，尹忠科老师和张春梅老师(Southwest Jiaotong University Press sparse signal and image decomposition and preliminary application, WANG Jian, teacher, Yin Zhongke teacher and Zhang Chunmei teacher)

说明：  带精英决策的多目标优化关键程序之遗传算法主程序(Main program of genetic algorithm for key program of multi-objective optimization with elite decision)

基于图像处理，vc++平台的水果识别。通过亮度调整，梯度处理，边缘图像，边界跟踪，计算颜色形状等，计算出结果。(Based on image processing, fruit recognition vc++ platform. Through the brightness adjustment, gradient, edge image, boundary tracking, calculation of color shape, the calculated results. )

首先对指纹图像进行预处理，包括图像的分割，增强，二值化，细化。对细化后的图像进行特征点的提取，最后通过细节特征匹配完成指纹的识别。(Firstly, the fingerprint image is preprocessed, including image segmentation, enhancement, binarization and thinning. The refined image is extracted by feature points, and the fingerprint recognition is completed by minutiae feature matching.)