| Re: x-y offsets [message #70950 is a reply to message #70949] |
Thu, 20 May 2010 08:01   |
Gray
Messages: 253
Registered: February 2010
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Senior Member |
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Jeremy, Craig,
Thanks for the great suggestions! I've tried both methods, and here
are my comments.
First, some more information about the problem - I wanted to ask it in
as general a way as possible because I think it would be useful for
others to have the answer for this question. However, what I really
want is to find the offset, remove it, and match pairs to use as pins
for poly-warping. As I said above, I couldn't figure out how to find
a good match radius, and removing the offset should shrink the radius
to something manageable. But, Craig's method could work too for
finding my match radius, in which case I wouldn't need to find an
offset at all (in all cases, I'm using MATCH_2D to match sources).
Neither method worked completely - I have a particular data set which
broke both methods, so here's what we have (I can give out the actual
numbers if you like):
IDL> print, minmax(x1), minmax(y1)
2.24139 128.413
2.91512 122.837
IDL> print, minmax(x2), minmax(y2)
0.949352 127.562
7.84978 127.785
IDL> print, n_elements(x1), n_elements(x2)
82 47
First I tried the cross-correlation/gauss2dfit method. I tweaked the
algorithm very slightly - the change I'm most proud of (which was
completely negligible) was to replace the periodicity for-loop with
the line:
refinedindex -= imagesize * (refinedindex gt imagesize/2)
I did one pass only, using as my coarse binsize my desired match
radius (this seemed intuitive, if someone can think of a better way to
choose a binsize, let me know), and for a number of datasets it worked
beautifully. However, about half the datasets failed to converge for
the gaussian fit. In general this gave reasonable results (I
think...), but for the dataset above the x offset was ~3e12, so I
added a reasonableness check: if the offset x values are all greater
or all less than the x-range (same for y), then use the simple
max_index result. However, the points still didn't match up - there
was still a systematic x offset that caused the matching to fail.
Maybe doing another pass would fix that, but I haven't tried yet.
Then I tried Craig's distance-histogram (or "distogram", if you will)
suggestion. First problem is that there's no guarantee that the
"preferred" offset is actually the maximum of the full histogram -
there's a predicable peak around half of the maximum distance between
two points (for these sets, around 75). So, I have to pick a
histogram range, but I don't know what it is likely to be a priori
(which is the whole point of this exercise). However, let's assume
that it's somewhere between a 0 pixel and 20 pixel shift, and then the
distogram max should be the actual offset. With that in mind, there's
a couple things I tried, both of which fail for the same reason.
First was to just use MATCH_2D with that distance as the match radius;
the other was to use reverse_indices to pick out the distance pairs
that fell in that bin, then compute a mean offset. However, both
methods run into the problem of multiple matches. MATCH_2D allows
multiple mapping from x1/y1 onto x2/y2 (though it prevents the
reverse) in the case of a large radius, which this is (~12 for this
dataset), and the reverse_indices method does the same. Even if you
try to discard multiple matches, there's no way to discriminate
between them because there's no guarantee that the minimum distance
match is the right one.
All in all, I think we're close, but no cigar yet. Thoughts?
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