When I call dsygv, I obtain the eigenvectors as a matrix. Are these eigenvectors along the columns or along the rows of the matrix ?
Along the columns
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I have one column that i need to divide by a numeric value and place in another column on that matrix. The matrix is filled with average and counts of a large table
In Linear Discriminant Analysis algorithm for face recognition, the between class scatter matrix and within class scatter matrix are both of size MxM (M=total number of images, C=number of classes). The fisherspace(matrix with eigenvectors as columns) consists of the eigenvectors corresponding to non-zero eigenvalues and hence has dimension Mx(C-1). How am I supposed to project the training phase images, each in N-dimensional space, onto the fisherspace.(Correct me if I am wrong). Can anybody help me figure this out?
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5256630
This is the research paper I followed to implement LDA. I am trying to implement it in OpenCV using C++
I'm trying to design a line detector in opencv, and to do that, I need to get the Gaussian matrix with variance σs.
The final formula should be
H=Gσs∗(Gσd')T, and H is the detector that I'm going to create, but I have no idea how am I supposed to create the matrix with the variance and furthermore calculate H finally.
Update
This is the full formula.where “T” is the transpose operation.Gσd' is the first-order derivative of a 1-D Gaussian function Gσd with varianceσd in this direction
****Update****
These are the two formulas that I want, I need H for further use so please tell me how to generate the matrix. thx!
As a Gaussian filter is quite common, OpenCV has a built-in operation for it: GaussianBlur.
When you use that function you can set the ksize argument to 0/0 to automatically compute the pixel size of the kernel from the given sigmas.
A Gaussian 2D filter kernel is separable. That means you can first apply a 1D filter along the x axis and then a 1D filter along the y axis. That is the reason for having two 1D filters in the equation above. It is much faster to do two 1D filter operations instead of one 2D.
Does boost or open CV contains dot product of vectors, matrix inversion, eigenvalues calculation of matrices?
Boost has that:
http://www.crystalclearsoftware.com/cgi-bin/boost_wiki/wiki.pl?LU_Matrix_Inversion
and for more information about ublas:
http://www.boost.org/doc/libs/1_54_0/libs/numeric/ublas/doc/overview.htm
Also you can find information about OpenCV operations using following links.
http://opencv.willowgarage.com/documentation/python/operations_on_arrays.html
http://note.sonots.com/OpenCV/MatrixOperations.html
Is there a way to calculate the normalized cross correlation of two arrays in OpenCV (C++)?
http://local.wasp.uwa.edu.au/~pbourke/miscellaneous/correlate/
I have a CvMat and I want to get a correlation matrix of all the cols.
I saw cvCalcCovarMatrix but I can't see a way to normalize it to get the correlation.
You should use cvMatchTemplate() with method=CV_TM_CCORR_NORMED.