| Re: Image registration [message #31212 is a reply to message #25081] |
Wed, 19 June 2002 07:45  |
thompson
Messages: 584
Registered: August 1991
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Senior Member |
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carsten@rad.uni-kiel.de (Carsten Liess) writes:
> Dear all,
> I am fairly new to IDL, so bear with me please.
> I have two images of the same object, only with different intensities.
> I want to create an image that displays the ratio of the two input
> images. I programmed that and it works.
> Problem is that the object I am imaging might have moved by up to 10
> pixels (at a 256*256 matrix) in between, so I need to match the
> position of these two images before I calculate their ratio. The
> movement might include translation as well as rotation.
> Is there a routine in IDL that does such an operation? Alternatively,
> does any among you have any clever suggestion how to go about this?
> Many thanks!
Carsten:
The way I've handled this problem in the past is to make a cross correlation
matrix of one image against the other, and looking for where the correlation
coefficient is highest. Throwing in rotation as well makes it more
complicated, but in essence the problem is the same. The basic difference is
that one is working with three dimensions instead of two.
In the classic problem with translation only, one shifts one image relative to
the other by one pixel at a time, and then calculates the cross correlation
coefficient between them (e.g. with CORRELATE). For example, if you think that
the two images could be shifted relative to each other by a maximum of 10
pixels, you would build up a matrix of size (21,21) representing shifts of -10
to +10 pixels in each direction. If you display this matrix as an image, you
should be able to see a clear peak representing the best match between the two
images. On my data, I've been able to fit the area around the peak to get down
to subpixel resolution--your mileage may vary.
If you add rotation into the mix, I would guess that the best place to proceed
would be to build up an array of (N,N,M) of cross correlation coefficients,
where N is the number of pixel shifts (21 in the above example), and M is the
number of angular rotations you wish to explore. For each angle position
(K=0,M-1), I would rotate one of the images (e.g. with ROT) and then calculate
the NxN cross correlation matrix for that rotation. You would then have a
three dimensional volume to examine for the peak correlation.
I hope this helps,
Bill Thompson
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