A_particle_filter_based_fusion_framework_for_video

说明：  python实现双目立体匹配SGBM算法(SGBM algorithm for binocular stereo matching based on Python)

经典的数字图像处理的形态学处理，包含源代码(Digital image processing morphological processing)

通过稀疏重构一幅自然图像，使得该图像重新生成，同时找出稀疏系数。(One kind of denoising signal sparse representation)

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)

基于Gabor的特征提取,gabor滤波器的构造 包含中科院的虹膜库(base on gabor zhongkeyuandehongmoku)

给图像加椒盐和高斯噪声，以及空域滤波（中值和均值滤波）！(to Image pretzels and Gaussian noise, and the spatial filter (mean values and filter)!)

VIBE用于视频背景建模，经典算法，C++和matlab代码都有，文件是全的。(VIBE for video background modeling, classical algorithms, C++ and matlab code has a file is complete.)

图片压缩到最小源码，例程程序结合易语言图像格式转换支持库，将快照的BMP图片转换成JPG图片，再将众多JPG图片压缩到一个压缩文件中，达到减小图片体积的效果。(Image compression to a minimum source , routine program combined image format conversion easy language support library , a snapshot of BMP images into JPG image , and then a large number of JPG images into a compressed file , to reduce the effect of image volume .)

说明：  多种刚性和非刚性配准算法，带有举例说明，非常好用(Multiple Rigid and Non-Rigid Registration Algorithms)

说明：  powell刚性图像配准，c++代码，通过查找非相关方向进行配准.(Polwell rigid image registration, c++ code, by looking for non-related directions for registration.)