icmlc08_code

背景差分法运动目标检测，建立背景模型，然后使用当前模型与背景模型相减，再和阈值相比较，比阈值大的即判断为有运动目标，比阈值小的即判断为没有运动目标，阈值大小可以自己适当调试，根据经验值设置最佳阈值。(Background difference method is used for moving target detection and setting up background model, then using current model and background model subtracting, and comparing with threshold, it is judged to have moving target compared with the threshold value, which is smaller than threshold. The size of threshold can be adjusted properly, and the best threshold is set according to experience value.)

RINGSDK包含界面库和图象库,我编译过，可以使用(RINGSDK is a interface lib )

Arnold变换和Logistic混沌映射的数字图像加密算法MATLAB源代码。将第一行图片路径换位要加密的图片运行即可。(Digital Image Encrypti Algorithm based on Arnold Tranform and Logistic Chaotic Maps)

说明：  分块直方图均衡化处理图像 用户输入分块的x,y方向的分块个数，即可将图形分成各个小块进行直方图均衡化。 大津法处理图形，包括一维大津法和二维大津法的实现 (the realization of the Histogram equalization algorithm. the realization of otsu algorithm to process the images)

实现dicom和bmp之间的转换。适合医学图像中的最初学习。(transformation between dicom and bmp image.)

非局部平均，用来去除灰度图里面的噪声，效果明显(Probability Non-Local Means)

传统图形细化算法，每条代码有详细注解，适合新手。(Traditional graphics thinning algorithm, each code have detailed notes. )

根据厄米高斯光束的振幅分布物理公式来画厄米高斯光束的强度分布图(plot the intensity distribution of the HermiteGaussian Beams based on the physical formulars of the Hermite-Gaussian Beams )

FK滤波分析，设计窗口！！！！！！！！！！！(FK filtering analysis, design window! ! ! ! ! ! ! ! ! ! !)

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)