L2

图像工程作业3：基于视词袋模型的场景识别 (Scene recognition with bag of words)(Image Engineering Job 3: Scene Recognition Based visual bag of words (Scene recognition with bag of words))

基于像素点的全局阈值法求图像的分割 实现方法有五种： 最小极值法，最优阈值法，最大方差方法，最大熵法，迭代法(Pixel-based global threshold method for image segmentation method there are five: the smallest extreme value method, the optimal threshold method, the largest variance method, the maximum entropy method, iterative method)

基于openCV的gabor函数,一位牛人编写的。希望对大家有用(gabor function based on openCV)

说明：  toolbox_calib图像畸变矫正工具箱(Image distortion correction toolbox)

暗原色先验的视频去雾算法 matlab实现(the code of defog using dark-chanel prior algorithm)

视觉测量中编码点与非编码点的匹配的方法实现(Vision measuring, coding point and the coding point matching )

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)

C#处理数字图像集锦，有图像旋转、平移等变换，有图像编码，有图像解码，有数字图像处理那本书里的各种算法(C# processing the digital image gallery, image rotation, translation, transformation, and image coding, image decoding, digital image processing various algorithms in the book)

MAP超分辨率重建算法,实现图像的超分辨率重建。(the MAP code for super-resolution)

说明：  将已知的灰度图像逆时针旋转60度，矩阵运算，图像旋转(Rotate the known grayscale image 60 degrees counterclockwise, matrix operation, image rotation)