Am 13.02.2011 16:25, schrieb kfj:
On 13 Feb., 11:13, Jeffrey Martin<[email protected]> wrote:
I was thinking also...
for panos, the centers of images usually do not overlap.
isn't searching the WHOLE of images a big waste?
There is another aspect which becomes more important with fisheyes and
stereographic images: if you feed the CPG with a partial image, it
can't decipher the warp to adapt it's feature point detector: the
image geometry is quite different from center to margin. So you can't
just use partial images, but you have to use the whole images and mask
them appropriately. Masking means introduction of additional data into
the process, either in the shape of a separate mask or as an alpha
channel. Using an alpha channel might be a reasonalbly inexpensive and
transparent (hah) way of doing the needful, but I'm not sure if the
current CPGs honour alpha channels - I rather doubt it.
cpfind does honor alpha channels and masks defined in the pto file.
It removes any matches found in the alpha channels (this is needed for
remapped images, otherwise one could get "false" matches at the
corners/edges of a remapped image.)
It will still analyse all parts of the image, though, so there is not
much computational benefit. I think the typical savings are not really
worth the coding effort.
I am currently toying with this mechanism for use in another demo
plugin, but I want to throw in rewarping of the parts of the images
that correspond to the ROIs to a common projection to make them
geometrically as similar as possible, thus improving CPG performance
especially with fisheye images and avoiding warp-related CPG problems
- some sort of high-end matching which would produce very good
quality, well-distributed CPs, particularly for applications like lens
calibration.
Yes, something like that would be very nice. Or one could try Least
squares matching. (estimates local affine transformation as part of the
matching process, is typically accurate to ~ 0.1 pixel). However, it
needs good initialisations (1-2 pixels error, good estimate for the
initial affine transform).
ciao
Pablo
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