fusion

这是一个地震数据多尺度分析和处理的文章.内容相当丰富,不仅仅增加地质方面的知识,更重要的是可以学到很多数据处理和小波分析方面的知识.(This is a multi-scale seismic data analysis and processing of the article. Fairly rich, is not only increasing geological knowledge, the more important it is to learn a lot of data processing and wavelet analysis knowledge.)

过分割后区域合并代码。思想是在多尺度下，基于区域的色彩，纹理，形状信息的合并方法。(After the merger of over-segmentation region code. Thinking is in a multi-scale, region-based color, texture, shape information of the combined method.)

说明：  最大熵双阈值分割算法，基于遗传算法优化的快速最大熵双阈值分割算法。(Maximum Entropy Double Threshold Segmentation and Fast Maximum Entropy Double Threshold Segmentation Based on Genetic Algorithms.)

应用MSER检测子检测图像局部区域来估计图像尺度信息，非常好用！(Applying MSER to detect the local region in images, then estimate the image scale information, very easy to use!)

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)

这是对图像进行分块的代码，分块后提取LBP特征，站中有LBP代码，可以直接调用这个代码进行分块LBP特征提取(This is to block of code of image, block LBP feature extracting, after standing in LBP code, you can directly invoke the code block LBP feature extraction)

光谱微分（一阶、二阶微分）对高光谱遥感图像进行最佳波段选择 (Spectral radiant brightness)

使用 RANSAC 算法计算仿射变换矩阵(RANSAC algorithm using an affine transformation matrix)

基于马尔科夫链的图像分割，较以往经典图像分割性能有很大提高(Markov chain-based image segmentation, image segmentation performance than the classical past has greatly improved)

此篇论文是利用稀疏低秩矩阵分解来实验的鲁棒图片的矫正。(This paper is the use of sparse low rank matrix decomposition to experimental robust image correction。 )