k-means-for-image-segmentation

图像处理.最全的压缩编码与解码.非常值得学习(Image processing most complete compression encoding and decoding. Worth learning)

该程序是对原图像进行梯度加掩模的三次分水岭分割(That the procedure was in the original image mask plus three gradient watershed segmentation)

VS 环境下利用opencv 实现图像的傅里叶变换，输入图片，保存输出结果。(Under VS environment use opencv to achieve image Fourier transform, the input image and save the output.)

边界跟踪程序，可以跟踪闭合的边界和非闭合的边界。读者可以根据自己需要存储其跟踪方向以及跟踪路径。(Boundary tracking program, you can track the closed borders and the non-closed borders. Readers can track the direction of their own need to store and track the path.)

分析了合成孔径雷达的模糊度问题及其对图像质量的影响, 详细描述了模糊度的 表达式, 分析了不同系统参数对模糊度的影响, 对所谓的重影现象作了定量分析(Analysis of synthetic aperture radar ambiguity problems and their impact on image quality, a detailed description of the fuzzy degree of expression of the different system parameters on the degree of influence on the so-called ghost phenomenon analyzed quantitatively)

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)

图像滤波处理程序(matlab 代码)。(A classic image filtering process（Matlab code).)

提取GIST特征的matlab实现源码，初学者实用(Extract GIST features matlab achieve source beginners practical)

图像背景提取，Matlab写的，效果还不错。(Image background extraction)

数字图像处理 opencv 一些基本例子和源码(opencv code)