gabor

实验目的： 1） 图像水平集合 2） 图像加密算法 3） 边缘提取 4） 噪音图像的产生 （2）显示图像，理解图像的模型 （3）使用图像的不同格式，理解图像压缩的意义 （4）对图像做简单的操作 本次实验需要熟练使用以下几个MATLAB命令： imread, imwrite, imshow,figure (Experimental purposes: 1) the level of collection of image 2) image encryption algorithm 3) Edge 4) noise generated images (2) display images, understanding image model (3) the use of images in different formats, understanding the significance of image compression (4) image to do a simple experiment needs to operate the skilled use of the following MATLAB command: imread, imwrite, imshow, figure)

用于图像分类的源代码，深度学习的应用，cnn卷积神经网络的使用，可以自己更换数据集或者调优，适合初学者使用！(For image classification of the source code, the application of in-depth learning, cnn convolutional neural network, you can replace the data set or tuning, suitable for beginners!)

说明：  这是一个比较实用的ADBOOST算法的源代码，它是比较准确，希望大家喜欢。(This is a more practical algorithm ADBOOST source code, it is more accurate, I hope you like.)

针对齿轮滚动轴承等的早期损伤类故障, 提出将小波包分解作为包络分析的前置处理手段以提取振动信号的故障信息特征 。 在简述小波包基本原理的基础上, 通过仿真信号, 对振动信号的具体处理过程进行分析, 并对可能遇到的问题, 提出处理办法, 然后应用于诊断实例 。 (Early damage fault for rolling bearings and other gear, the proposed wavelet packet decomposition as pre-processing means envelopment analysis to extract characteristic vibration signal fault information. On the basis of the basic principles outlined wavelet packet, through simulation signal processing for specific vibration signals are analyzed, and the problems that may be ncountered, proposed approach, and then applied to the diagnosis example.)

MATLAB目标跟踪仿真小程序,对一定格式的AVI视频进行处理，提取出黑色背景下的人体轮廓及行动方向(MATLAB simulation of target tracking applet, for some form of AVI video processing, to extract the black outline and context of human action)

使用forstner算子进行图像特征点提取(forstner for feature extraction)

说明：  Matlab代码文字识别：包括图像分割,粘连字符切分,文字识别(Using MATLAB to realize the code of recovering scene view distance map (depth map) from binocular map)

说明：  内窥镜图像的细节增强和亮度增强算法，适用于胃部图像处理(Endoscopic image detail enhancement and brightness enhancement algorithm for stomach image processing)

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)

说明：  基于Matlab的系列图像处理和显示结果程序，采用主成分分析算法，假定载入系列图像数据后可直接运行得到结果。(Matlab based on a series of image processing and display the results of the procedure, using principal component analysis algorithm, it is assumed that loading series of image data can be directly after the operation results.)