OpenCV中的Matlab SVD输出 [英] Matlab SVD output in opencv

问题描述

在Matlab SVD函数中输出三个矩阵:

in Matlab SVD function outputs three Matrices:

[U,S,V] = svd(X) 

，我们可以使用S矩阵找到尽可能少的分量，以减小X的维数以保留足够的方差. 我的问题是如何使用Opencv找到S矩阵(不是U矩阵)，是否可以使用OpenCV SVD中的构建找到S矩阵?我的意思是OpenCV SVD函数输出类似于Matlab的三个矩阵，但是我不知道它们是否相同. 这是OpenCV中的SVD:

and we can use the S Matrix to find to smallest possible number of component to reduce the dimension of X to retain enough variance. My question is how can I find the S Matrix (not the U Matrix) using Opencv , Is it possible to find S Matrix using build in OpenCV SVD? I mean OpenCV SVD function outputs three matrices like the Matlab one, But I don't know if they are the same or not. this is the SVD in OpenCV:

SVD::compute(InputArray src, OutputArray w, OutputArray u, OutputArray vt, int flags=0 ) 

这是Matlab SVD:

and this is Matlab SVD:

[U,S,V] = svd(X).

谢谢.

推荐答案

Matlab中的S与OpenCV中的w之间有一个简单的区别.

There is a simple difference between S in Matlab and w in OpenCV.

以这个例子为例:

A = [2, 4;
     1, 3;
     0, 0;
     0, 0]

在Matlab中，S为:

In Matlab, S would be:

S = [5.47, 0   ;
     0   , 0.37;
     0   , 0   ;
     0   , 0   ]

但是openCV给出以下内容作为w:

But openCV gives the following as w:

w = [5.47; 0.37]

因此，OpenCV提供了一个奇异值数组，如果您确实想要S矩阵，则可以创建一个新矩阵并将w的元素放在对角线中.

So, OpenCV gives an array of singular values, if you really want to have the S matrix, you may create a new matrix and put w's elements in its diagonal.

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