ch8_4_2

二维相位展开算法 引导相位展开方法相质量运行QualityGuidedUnwrap2D的。 运行戈尔茨坦枝剪相位展开方法GoldsteinUnwrap2D。 （3D实施质量的制导方法，可根据要求提供）(2D PHASE UNWRAPPING ALGORITHMS Run QualityGuidedUnwrap2D for the phase quality guided phase unwrapping method. Run GoldsteinUnwrap2D for Goldstein s branch cut phase unwrapping method. (3D implementation of the quality guided method available on request))

对图形进行二维傅里叶变化后的频谱进行中心化(Centralization of the spectrum after two-dimensional Fourier transform)

Random Walker图像分割Matlab源代码，需要安装图形分析工具箱，地址http://eslab.bu.edu/software/graphanalysis/，给出了一个应用例子。(Random Walker Image Segmentation Matlab source code, need to install the graphical analysis toolbox, address http://eslab.bu.edu/software/graphanalysis/, gives an application example.)

说明：  图像特征提取，不变矩。hu矩。f1~f7.用于物体识别。(hu)

骨骼图像增强，对应于冈萨雷斯数字图像处理教程上的五个步骤(Bone image enhancement, digital image processing corresponding to Gonzalez' s five-step Tutorial)

位图操作图形选取源码，例程程序结合易语言位图操作支持库，填充小图形的背景颜色。(Select the source bitmap graphics operations , routine program combining bitmap operations easy language support library , filled with small graphic background color.)

对任意一副灰度图像进行拉普拉斯金字塔分解。(For any gray-scale image of a Laplacian pyramid decomposition.)

通过遗传算法选择道路图象分割的最佳阈值,阈值的选择采用最大类间方差的办法,程序运行结果达到相当理想的效果(Through the genetic algorithm to choose the best road image segmentation threshold, the threshold was chosen as the maximum between-cluster variance approach, the program results achieved the desired results rather)

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)

实现了迭代盲反卷积的图像去模糊，自己写的代码，没有调用matlab已有函数(Iterative blind deconvolution of images blurred, write your own code, without calling matlab existing function)