StereoRegion

适合初学小波的同志，小波图像增强原理底层。严格高低频分解重构，没有使用小波工具库。(Suitable for beginners wavelet comrades, the underlying principle of wavelet image enhancement. Strict high-low-frequency decomposition of reconstruction, did not use the wavelet tool library.)

说明：  图像高斯滤波,再边缘检测中,首先要用到高斯滤波(images Gaussian filtering, edge detection again, the first use of the Gaussian filter! !)

说明：  图像融合程序大全 （有图）包括IHS PCA 拉普拉斯 PCNN 小波 超好！(the total of image fusion (pictures))

matlab,图像分割手势，不是用摄像头的，是将图片输入进去识别使用MATLAB进行图像分割(Matlab, image segmentation gesture, not with the camera, is to input pictures to identify the use of MATLAB image segmentation)

说明：  通过拟合出一条曲线，对这个曲线进行结构函数法的分形维数计算，得到其粗糙度(By fitting a curve, the fractal dimension of the curve is calculated by structural function method, and its roughness is obtained)

图像纹理分析的一种源代码 希望对大家有用(Image processing+ CD-ROM content online is also good under the procedures)

说明：  用于特征提取，比较传统的一个方法，应用范围广，适合初学者(For feature extraction)

可以在harris角点检测和ncc粗匹配之后实现精确准确的角点匹配，为下一步配准做准备(Can the Harris corner detection and NCC coarse matching after achieve precise accurate angular point matching, preparing for the next registration )

说明：  基于ADMM的TV正则化最小化稀疏重建算法(TV Minimization Sparse Reconstruction Algorithms Based on ADMM)

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)