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From: Gehua Yang <yangg2@rp...>  20050510 22:16:30

> > maziyar wrote: > >> I am using a 16x3 matrix. The data is the following: >> >> 78.317 81.539 85.283 >> 134.87 135.07 136.71 >> 137.84 136.2 147.03 >> 83.278 82.835 93.192 >> 87.246 86.773 80.599 >> 143.79 138.87 132.94 >> 147.76 147.82 141.45 >> 91.214 95.244 91.233 >> 150.22 144.2 154.16 >> 140.3 141.93 136.84 >> 172.05 171.58 164.5 >> 179.99 174.78 180.63 >> 190.9 198.19 196.92 >> 182.96 181.72 182.31 >> 212.72 211.98 210.58 >> 220.66 225.64 228.5 >> >> This data is yielding me different answers. Thus, as per my earlier >> email, I am transposing this then finding the covariance matrix. >> Then the resulting eigenvector produced using the VNL libraries >> differs from the results produced in matlab. >> Besides Kang's comments, a couple thoughts: You mentioned that this 16x3 matrix is a covariance matrix. However, as covariance matrix is defined as Covar(x) = E[ (xmean_x)(xmean_x)^T ], which is always square and symmetric, you may want doublecheck it. On the other hand, to decompose this 16x3 matrix, Singular Value Decomposition(SVD) is more desired. Gehua 
From: Kongbin Kang <kk@le...>  20050510 21:35:14

Your data is a 16x3 matrix and the cov(data.') will produce a 16x16 matrix which is a degenerate matrix (you only have 3 samples in a 16 dimensional space). The eigenvectors cooresponding to 13 zero (or almost zero) eigenvalues span a 13 dimension subspace space. Any vector in this subspace is a eigenvector of the matrix cov(data.'). Therefore it is normal to have different eigenvectors in any algorithms because the eigenvector is not unique. Hope it helps, Kongbin maziyar wrote: >I am using a 16x3 matrix. The data is the following: > >78.317 81.539 85.283 >134.87 135.07 136.71 >137.84 136.2 147.03 >83.278 82.835 93.192 >87.246 86.773 80.599 >143.79 138.87 132.94 >147.76 147.82 141.45 >91.214 95.244 91.233 >150.22 144.2 154.16 >140.3 141.93 136.84 >172.05 171.58 164.5 >179.99 174.78 180.63 >190.9 198.19 196.92 >182.96 181.72 182.31 >212.72 211.98 210.58 >220.66 225.64 228.5 > >This data is yielding me different answers. Thus, as per my earlier email, I >am transposing this then finding the covariance matrix. Then the resulting >eigenvector produced using the VNL libraries differs from the results produced >in matlab. > >Thanks, > >Maz Khorasani > > 
From: Kongbin Kang <kk@le...>  20050510 20:04:48

Amitha Perera wrote: >On Tue 10 May 2005, maziyar wrote: > > >>I have tried using: >> >>vnl_symmetric_eigensystem<double> eig(cov(data.transpose())); >>vcl_cerr << eig.V << vcl_endl; >>vcl_cerr << eig.D << vcl_endl; >> >>to find the eigenvalues and the eigenvectors for a covariance matrix. When >>comparing my results to those of Matlab, I am producing similiar eigenvalues, >>however, my eigenvectors are not the same. >> >>Is this an expected result due to the different algorithms used by the two >>systems, or should they yield the same result. >> >> > > > If the matrix is nonedegenerate, the eigenvectors of the matrix should be unique upto a scale. It means matlab or vxl should be different only up to the machine accuracy. But for degenerate matrix, the solution to eigenvector is not unique so that it is normal to have different eigenvectors. If you can post the eigen values, eigenvectors and your matrix, we may find out whether it is normal or not to have difference between vxl and matlab. 
From: Amitha Perera <perera@cs...>  20050510 19:42:55

On Tue 10 May 2005, maziyar wrote: > I have tried using: > > vnl_symmetric_eigensystem<double> eig(cov(data.transpose())); > vcl_cerr << eig.V << vcl_endl; > vcl_cerr << eig.D << vcl_endl; > > to find the eigenvalues and the eigenvectors for a covariance matrix. When > comparing my results to those of Matlab, I am producing similiar eigenvalues, > however, my eigenvectors are not the same. > > Is this an expected result due to the different algorithms used by the two > systems, or should they yield the same result. In general, Matlab algorithms are not the same as those used in vxl, so the results could be different. If the results from vxl actually are eigenvectors, then I don't see a problem. If they aren't, well, something needs to be done. Amitha. 
From: maziyar <maziyar@ua...>  20050510 15:25:35

I have tried using: vnl_symmetric_eigensystem<double> eig(cov(data.transpose())); vcl_cerr << eig.V << vcl_endl; vcl_cerr << eig.D << vcl_endl; to find the eigenvalues and the eigenvectors for a covariance matrix. When comparing my results to those of Matlab, I am producing similiar eigenvalues, however, my eigenvectors are not the same. Is this an expected result due to the different algorithms used by the two systems, or should they yield the same result. In matlab, i am simply using: [v,d] = eig(cov(data.')). Thanks, Maz Khorasani 
From: Peter Vanroose <peter_vanroose@ya...>  20050510 15:16:25

> /usr/local/vxl/vxl1.0.0/core/vgui/vgui_section_buffer.cxx:279: > void vgui_section_buffer::apply(const vil1_image&): > Assertion `image.planes() == 1' failed. Apparently, the vil1 VIFF loader created a multiplanar colour image, while the vgui interface to vil1 only supports singleplane (also colour) images. Try using the vil VIFF loader instead. vil1 is no longer actively supported.  Peter. 
From: Marc Anderson <yulq2004@ya...>  20050510 14:03:30

Hi vxl guys, xcv fails to open viff image files I just download from http://www.tnt.unihannover.de/project/eu/distima/images/ (DISTIMA image pair 'aqua') The error message is: xcv: /usr/local/vxl/vxl1.0.0/core/vgui/vgui_section_buffer.cxx:279: void vgui_section_buffer::apply(const vil1_image&): Assertion `image.planes() == 1' failed.Aborted Any help appreciated! Marc. Yahoo! Mail Stay connected, organized, and protected. Take the tour: http://tour.mail.yahoo.com/mailtour.html 
From: Angel Todorov <angel.todorov@ae...>  20050510 07:10:41

Hi, Suppose we have two uncalibrated cameras, and we are processing some image sequence  in the example contrib\oxl\mvl\examples\compute_FMatrix_example.cxx , the point coordinates are normalized with w=1.0, i.e we are basically computing the essential matrix, but, my question is, given that we have no knowledge of the homogeneous coordinates, is it still possible to obtain a meaningful error by computing F (with w=1.0 set), and then try to find the camera matrices by some other method (like bundle adjustment) ? The purpose is to refine matches by fitting them to epipolar lines in the beginning, and I was wondering if this is a sensible approach provided that the matched points' coordinates are normalized, and we know nothing about the camera calibration matrices as well (and we don't do explicit calibration)? In some publications, unknown cameras are also assumed, but it's not very clear to me if, while first computing F or T, coordinates are normalized or not. (Otherwise, as far as I know, we can obtain the essential matrix, if knowing the calibration matrices: E = K'*F*K.) Thank you very much for your help. Regards, Angel 