biaozhunchaandxinxishang

高通高斯滤波，LBP，LDP的matlab源代码，用于图像的增强，特征提取。(High-pass Gaussian filter, LBP, LDP matlab source code for image enhancement, feature extraction.)

图形voronoi图的自动生成算法，用vc做，是基于一般图形voronoi图的算法，结合数字图形处理里的轮廓跟踪算法，使一般图形voronoi图能够根据不同的生成元总动生成。(Graph voronoi diagram automatic generation algorithm, using vc do is based on the general graph voronoi diagram algorithm, combined with digital image processing in the contour tracking algorithm, the general graph voronoi diagram to the total depending on the generator automatically generated.)

用奇异值分解的方法实现图像压缩，有助于初学者了解奇异值分解的应用(Using singular value decomposition method to achieve image compression will help beginners understand the application of singular value decomposition)

方向梯度直方图的全部代码，广泛应用于行人检测行人检测(Histogram of Oriented Gradients code Pedestrian Detection)

运动检测 检测人体的头部位置、整体位置、滤除影子(Motion detection detect the body and head position, the overall position of the shadow filter)

平时工作中常会遇到需要处理图片的工作，这个是判断并修改图片分辨率的python方法。(Usually work the Central Standing Committee met to deal with the work of the picture, this is a python method to determine and modify the image resolution.)

说明：  本算法是matlab代码，通过icp点云匹配算法实现三维点云的配准(The algorithm is matlab code, and the registration of three-dimensional point cloud is realized by ICP point cloud matching algorithm.)

快速剪切波变换的源程序，用于图像处理，运行速度快(Fast shear wave transform source, used for image processing, fast running)

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)

用MATLAB读取一幅图像，并且计算其图像信噪比(Using MATLAB to read an image and calculate the image SNR)