MatlabTest

LabWindows /CVI 大学实验指导书，难度循序渐进，一套下来，基本操作都会了。推荐一下(LabWindows/CVI University experiment instructions, the difficulty of step-by-step, set down, the basic operation will be. You recommend)

说明：  实现了基于关键像素点的FLICM图像分割算法，关键像素由局部最大值方法提取。(A FLICM image segmentation algorithm based on key pixels is implemented. Key pixels are extracted by local maximum method.)

自适应双边滤波，利用空域低通滤波和图像灰度信息的非线性组合，自适应的对背景进行抑制。(Adaptive bilateral filtering, the use of the airspace low-pass filter and image grey scale information of nonlinear combination, adaptive to suppress background.)

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)

LBP 一种描述子，常用于人脸识别，自己写的，包括实验程序和LBP函数(LBP descriptor)

使用基于词典的稀疏表示高光谱图像分类,多任务联合稀疏表示和逐步MRF优化的高光谱图像分类(Dictionary-based sparse representation hyperspectral image classification, multi-task joint sparse representation and stepwise MRF optimized hyperspectral image classification)

此代码为显著性检测代码，显著性检测效果不错(This code is a significant detection code to detect significant good results)

基于块的OMP算法，相对传统的OMP算法速度很快！(Batch-based OMP algorithm,execution quickly than the comman OMP algorithm.)

介绍图像处理技在AMD上的GPU的处理方案，加速高清，快速度(The processing scheme, image processing technology on AMD GPU accelerated HD, speed)

说明：  各种分割算法的实验 包括阈值分割，多阈值分割，二维最大熵分割等(various experiments, including the threshold segmentation, multi-threshold segmentation, 2D Maximum Entropy segmentation)