Please don't apologize! That paper nicely bridged some issues I've been
wrestling with in my own work. I was experimenting with using a
PCA-based approach to extract unified data from 4 sensors: The SVD
approach outlined in the paper may be significantly faster.
I lurk on this list not just to learn about FC, but also to learn about
the wide array of situations to which it is being applied, and the tools
and methods used within each application.
Few lists have yielded as many gems as do the discussions on this list.
It's not just about drawing pictures or building a new canvas component
(FC2): It's about representing data, which means it's really about
getting data into a form that makes it presentable using FC.
Thanks!
- Bob Cunningham
PS: My $0.02 on coordinate transforms would be to include them in the
fabric of FC2 to ensure they are correctly implemented and easy to use,
and easy to use correctly! I've had to cope with many major and minor
errors in this area over the years (most of my own creation), and I've
had to reinvent the wheel several times along the way. That stopped
being fun over a decade ago. I believe it is time for a graphics
component to take ownership of this issue, and FC2 seems to me to be at
the right scope to do so.
Larry Meyn wrote:
Sorry for e-mailing a large attachment to the whole list. I didn't mean to,
I'm in the middle of packing for a trip and doing too much multitasking.
Larry
On 11/20/08 5:41 PM, "Larry Meyn" <[EMAIL PROTECTED]> wrote:
I would recommend using numpy, which includes an SVD function that is very
useful for these equations. As the attached paper shows the SVD can be used
for both calibration and determination of the 3D coordinates. At Georgia
Tech, they use four cameras and use SVD to get the least-squares best fit
for the 3D coordinates from up to 4 images.
Larry Meyn
NASA Ames Research Center
MS 210-15
Moffett Field CA 94035
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