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mahalanobis
- 一种新的计算马氏距离的算法，算法采用二次协方差矩阵操作，进而使距离考加入相对性因素。-a new Mahalanobis distance calculation algorithm, the algorithm using quadratic covariance matrix operations, thereby enabling the relative distance to take factors.
maximum_likelihood_classification
- 经典的最大似然法分类法的C语言实现，有助于深入了解遥感分类原理。-This program implements the maximum likelihood classification procedure. ouput:1.classified image, and 2. probability file. Note: For constructong variance-covariance matrix must be generic binary file.
jiaozheng
- 这是一个阵列校正的程序，通过计算协方差矩阵，利用矩阵元素间的关系计算出阵列的幅相误差-This is an array of calibration procedures, by calculating the covariance matrix, the relationship between the matrix elements of the array to calculate the amplitude and phase errors
StraightFlight_3D
- 探测三维冲突概率算法，通过对误差协方差矩阵的运算，对其进行一事实上的矩阵变换使算法进行简化，-Three-dimensional probabilistic conflict detection algorithm, through the error covariance matrix of computing, its a matter of fact so that the matrix transformation algorithm can be simplified,
COV2K
- 极化SAR图像处理中，将协方差矩阵转换为STOKES矩阵或Mueller矩阵的程序-Polarization SAR image processing, the covariance matrix will be converted to matrix or Mueller matrix STOKES procedures
pauli cov
- 极化SAR图像中的将协方差矩阵数据进行PAULI分解的程序-Polarization SAR images will covariance matrix data Pauli decomposition process
KLtransform
- （1）应用9×9的窗口对上述图象进行随机抽样，共抽样200块子图象； （2）将所有子图象按列相接变成一个81维的行向量； （3）对所有200个行向量进行KL变换，求出其对应的协方差矩阵的特征向量和特征值，按降序排列特征值以及所对应的特征向量； （4）选择前40个最大特征值所对应的特征向量作为主元，将原图象块向这40个特征向量上投影，所获得的投影系数就是这个子块的特征向量。 （5）求出所有子块的特征向量。 -(1) the application of 9 × 9 window
pcaProgram
- PCA算法程序设计步骤： 1、取均值 2、计算协方差矩阵及其特征值和特征向量 3、计算协方差矩阵的特征值大于阈值的个数 4、降序排列特征值 5、去掉较小的特征值 6、去掉较大的特征值(一般没有这一步） 7、合并选择的特征值 8、选择相应的特征值和特征向量 9、计算白化矩阵 10、提取主分量 -PCA algorithm programming steps: 1, access means 2, the calculation of
generate_mean_covariance
- 本程序编程语言为C，主要用来对遥感训练数据进行处理，得到covariance矩阵。-This program is used to generate 3 files:mean file,covariance matrix of the training set, and inverse covariance matrix for training set.
hw2
- 1.对一个256*256的图像进行DCT变换得到图像D，将D得斜下角数值置为零，然后进行DCT反变换. 2.对源图像进行K-L转换 1和2比较-1.Get a grey level image which size is N*N. (For example, 256*256, however, N = ), and partition to 8*8 sub images. 2.. Apply DCT to these sub images, and get the transfo
coviance
- 一个关于协方差矩阵的例子 改成自己的图片即可 运行-An example on the covariance matrix into its own image to run
000
- Mahalanobis距離是一個可以準確找出資料分布上面極端值(Outliers)的統計方法,使用線性迴歸的概念,也就是說他使用的是共變數矩陣以及該資料分布的平均數來找尋極端值的產生,而可以讓一群資料系統具有穩健性(Robust),去除不必要的雜訊訊息,這邊拿前面共變數矩陣的資料為例,並且新增了兩個點座標向量來做Mahalanobis距離的比較-Mahalanobis distance is the information that can accurately identify the dis
Multi_gp
- 用来产生多变量高斯过程的MATLAB源程序。-MULTI_GP generates a multivariate Gaussian random process with mean vector m (column vector) and covariance matrix C。
compression_3
- 在神经网络中用求协方差矩阵的方法对人脸图像进行压缩及恢复-In the neural network covariance matrix using the method of face image compression and restoration
Mutilface
- 这是一个多重脸识别的程序，代码采用k_L变换和奇异值分解，利用协方差矩阵获得人脸图像的特征脸。-This is a multi-face recognition program, the code used k_L transform and singular value decomposition, the covariance matrix can be obtained by using the face image features the face.
image-retrieval-technology-research
- 基于内容的图像检索技术的关键在于特征提取，是利用图像的颜色、形状、纹理、轮廓、对象的空间关系等客观独立的存在于图像中的基本视觉特征作为图像的索引，计算查询图像和目标图像的相似距离，按相似度匹配进行检索。综合国内外研究现状，可将基于内容的图像检索技术分为如下几种类型:基于颜色特征的检索、基于纹理特征的检索、基于形状及区域的检索、基于空间约束关系的检索。-Based on comparing various affine invariant regional basis, selection of
gongbian
- 利用共变矩阵，扫描数组，估计潜伏期变化。-Covariance matrix using scanning arrays, it is estimated latency change.
Covariance
- An imlementation in MATLAB of fast algorithm for calculate covariance matrix, which is widely used in image processingimlementation of fast algorithm for calculate covariance matrix, which is widely used in image processing
covariance-tracking
- 协方差跟踪，将视频变成帧读入，计算协方差矩阵，并通过遍历找到最相似点区域进行跟踪，并生成轨迹-The covariance tracking, the video frame into read, calculate the covariance matrix, and find the similarities by traversing the area for tracking, trajectory generation and
exp_2_1
- Iris数据集，计算协方差矩阵和相关系数矩阵和kl变换(The goal of this programming experiment is to: Calculate the covariance matrix and the correlation coefficient matrix of the Iris data set. Perform the Karhunen-Loeve transform on this data set.)