segment_ga

Random ppm modulated analog signal under Complete class-based image processing, contains all of the source code, auto image, Correlation analysis process matlab method.

摄像标定法求T R矩阵的方法 摄像标定法求T R矩阵的方法 摄像标定法求T R矩阵的方法(how to calculate the matrix R and T using the Camera Calibration method)

基于Halcon开发的掩模 纯手工编辑(Mask editing based on Halcon)

改进的聚类光流法，聚类方法有kmeans和DBSCAN，光流法金字塔LK可以用，HS光流法占用内存太多。(Improved clustering optical flow method, clustering method kmeans and DBSCAN, pyramid LK optical flow method can be used, HS optical flow occupy too much memory.)

带通配符的字符串匹配,多重匹配,支持通配符“*”，“？”(String matching with wildcards, multiple matches, supports wildcards " *" , " ?" )

说明：  用jpeg对图像进行编码解码，里面包含界面，以及图片(Using JPEG to encode and decode the image)

DOG检测算子，能够构造高斯尺度空间，并检测其中的极值，从而得到有价值的特征点位置(DOG detection operator, the Gaussian scale space can be constructed, and tested one of the extremes, resulting in valuable feature position)

说明：  在VC中实现了使用Moravec算子对遥感影像进行特征点的提取，缺点是速度较慢。不过提取效果不错！(Implemented in VC using Moravec operator of remote sensing images feature point extraction, the disadvantage is slow. However, extracting good results!)

运用harr特征+SRC（稀疏表示）分类实现的一种车辆检测方法，文件中提供了训练和测试车辆图片。由于时间原因，所用haar特征没有优化，维度过高，导致滑窗框图过慢，本代码只输出效果统计数据，以供大家参考学习稀疏表示在车辆检测中的应用。(Using harr feature+SRC (sparse representation) classification to achieve a vehicle detection method, the paper provides a training and test vehicle picture. Due to time reasons, the use of haar feature is not optimized, high dimension, resulting in sliding sash figure is too slow, the effect of the code only output statistics for your reference learning sparse representation in the vehicle detection.)

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)