Hi !
Thank you for your taking some time on this.
I'll try to find some examples during the next week, and put a link to
download the individual images.
Le 13/02/2022 à 13:11, [email protected] a écrit :
I think that problem is important and ought to get some attention and
can be solved.
But I think your suggested solution would not be effective. Either the
user would need to know enough in setting the limits that they might
as well just set the value and not optimize the variable, or
automatically set limits would do more harm than good in other cases.
Based entirely on use, without yet even looking at the relevant part
of the code, I'm guessing both sources of a control point are
projected into their theoretical positions and compared there. That
approach would be inherently unstable and lead to finding wrong
solutions that have lower "error" than the right solution. That fits
the observed behavior, thus my guess.
Projecting one point to its theoretical position and then back to the
other image (and computing the error there) would be inherently much
more stable. But given varying zoom level across the input, that
could inappropriately weight control points. Effective zoom level
could vary within an image due to the original lens distortion as well
as varying original zoom in the images. But my guess is that the
weight problem could be solved more easily than other approaches to
fixing the original problem.
I might be 100% wrong about all of this. I hope to find time to look
at that part of the code and really understand why optimize is so
likely to find wrong answers. But in working on many other optimize
problems in many other domains, my experience tells me that stricter
limits are likely to be a bad cover up for a problem that needs to be
fixed elsewhere.
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