psf

超分辨率图像重建matlab 源码，实现了超分辨率图像重建算法(super-resolution image reconstruction)

在图像处理中实现带通滤波器的功能的matlab实现代码(Bandpass filter)

实时统计监控视频下路人人数，采用python(This is a demo made for tracking pedestrian)

1.能够对简单瑕疵点进行检测：正常线、断线、重线、跳线、平纹面料、平纹面料疵点、水印疵点、染色疵点 能够对不同的纹理布匹进行检测：平纹、斜纹等(翻译 1. Be able to perform simple point defect detection: the normal lines, broken, heavy lines, jumpers, plain weave, plain weave fabric faults, watermark defects, dyeing defects Able to detect different textures of cloth: plain weave, twill)

主要研究了彩色图像数字水印算法，在DCT域进行了水印的自适应嵌入和提取，并用Matlab进行了仿真实验，测试算法的性能(The main color image watermarking algorithm in the DCT domain adaptive watermark embedding and extraction, and with the Matlab simulation experiment to test the performance of algorithm)

用Graph cuts 来解决磁共振图像的 phase unwrapping。(Graph cuts used to solve the magnetic resonance images of the phase unwrapping.)

著名的LUCAS KANADE算法，用于图像匹配，跟踪，识别(LUCAS KANADE well-known algorithm for image matching, tracking, identification)

基于相位一致性与K-means方法的结合边缘检测(PC and K-means)

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)

根据图的邻接矩阵判断图的连通性并计算图的分块数(To determine the connectivity of the graph and computing graph blocks according to the adjacency matrix)