Matching images from different sources.

Looking for information, algorithms, etc. on how to match images of
the same object obtained from different sources.

(Also on what would be the proper terminology to describe this
problem. I'm sure I am doing a poor job here. )

For example, I may take pictures of a cloud formation using three
cameras sensible to the visible, infrared and ultraviolet spectra.
The cameras, although close to each other, may be located far enough
to introduce parallax errors, they may have different resolutions, the
images capture may not be simultaneous, so the cloud shapes may change
slightly from one image to the next, etc.

By 'matching' I mean scaling and rotating the images so that they can
be overlaid in such a way that all the data in any area of the screen
is coming from the same 'region' in the physical world.

The matching process should be based only in the images, I may not
have enough information about the cameras physical location and
orientation.

I understand that in the most general case the images could be so
different that this problem is unsolvable, but I still expect to be
able to find (partial) solutions when some minimal correlation level
exists.

Thanks,


Roberto Waltman

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Roberto:
I'm not sure why my lengthy reply from yesterday isn't there. I've
seen this once before from Google Groups in the past month - where it
says it posted successfully but then it never shows up. Anyway, it
was something about building up feature vectors. But I had another
thought. In some fields (medical, remote sensing, military) they have
a problem such as yours. The terms you want to search for are "image
fusion" or "data fusion" and have to do with aligning images from
different modalities, like how can you overlap corresponding physical
slices from a CT image and an MRI image. I've never really had to do
fusion this myself but I know it was (and maybe still is) a hot topic
in medical imaging in the 90's.
Try this:

[Registered users can see links. ]

You just missed the image fusion conference but maybe you can get
proceedings, or go next year:
[Registered users can see links. ]

Hoping this posts (please Google!!!)
ImageAnalyst

On Feb 22, 6:59 pm, Roberto Waltman <[Registered users can see links. ]> wrote:

>Roberto Waltman wrote:

MCammarano wrote:

Thanks for the pointers, a first Google search is bringing more
relevant hits than what I was able to find before.

"ImageAnalyst" wrote:

Ditto.


That would be very nice, but at this time this is just a thought
experiment. Even if it wasn't, nobody is going to pay me to attend
such a conference. Well, maybe if I reincarnate somewhere in academia
....

Going off topic, I use Google extensively for searching, never for
posting. The best environment I found after trying a few different
things is Forte's Agent as a usenet reader (Windows, Linux under Wine)
and [Registered users can see links. ] as a usenet provider. (Not free, but
only 10 euros per year).

Thanks again,

Roberto Waltman

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"ImageAnalyst" <[Registered users can see links. ]> wrote in message
news:1172233654.160967.217560@8g2000cwh.googlegrou ps.com...
Roberto
It depends what your aiming to achieve.
If the aim is pretty pics than I don't know the answer.
If the aim is multifrequency analysis then there are varoius programs to do
this. I used "Karma" in the past but I am sure others also exist.
See [Registered users can see links. ]

Terry B

On Feb 22, 6:59 pm, Roberto Waltman <[Registered users can see links. ]> wrote:

If you can reduce your images into a list of discrete
point sources (or tie points, call them what you will),
there are plenty of routines devised by astronomers
to match up the lists and compute the geometric
transformation between the two lists. For example,

[Registered users can see links. ]

Terry B wrote:

In a sense, it is. I am interested on the non-visual spectra, but I
want to be able to easily correlate the data with a visual reference.


Thanks for the info. From a quick glance it looks as I could use a lot
from that library, (mostly non-image related.)

Roberto Waltman

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"Stupendous_Man" wrote:

No point sources in my case, I am looking mainly for ill-defined areas
at different temperature. Still, may be I can generate "points of
interest" based on local peaks, etc. and apply some of these
techniques. Thanks!


Roberto Waltman

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Hi, Roberto,

If your problem is solely because of intensity inconsistencies in the
images, try
using normalized mutual information based image fusion. It is widely
used to fuse medical images from different modality sources (e.g.
CT-MRI, CT-PET etc.) , so the final alignment is largely overlap and
intensity-difference independent. Heres the link to a survey article:
[Registered users can see links. ] .

Good luck!

P.



On Mar 13, 8:45 pm, Roberto Waltman <[Registered users can see links. ]> wrote:

"pixel.to.life" wrote:

Unfortunately not. The images are different in
other ways, but hopefully there is enough of a
common structure to allow to correlate then
somehow.


Thanks, will take a look. I am only getting
started on this area, so any additional
information is both interesting and potentially
useful.

Roberto Waltman

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I would go for SIFT, try:

[Registered users can see links. ]

Roberto Waltman כתב: