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Matching images from different sources.



 
 
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  #11  
Old May 4th 07, 12:36 PM posted to sci.image.processing,sci.astro.ccd-imaging,comp.graphics.algorithms
Rob[_3_]
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Posts: 29
Default Matching images from different sources.


"Roberto Waltman" wrote in message
...

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.


Roberto,

Have you come across AstroWave in your searches?

"AstroWave is a program that calculates the linear transformation
details needed to register one image with another, ie. rotation and
scaling."

http://www.planetsi.plus.com/AstroWave/AstroWave.html

HTH,
--
Rob


  #12  
Old May 5th 07, 02:16 AM posted to sci.image.processing,sci.astro.ccd-imaging,comp.graphics.algorithms
pixel.to.life
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Posts: 2
Default Matching images from different sources.

On Mar 22, 1:33 pm, Roberto Waltman wrote:
"pixel.to.life" wrote:
If your problem is solely because of intensity inconsistencies
in the images,


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

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:
http://www.cs.jhu.edu/~cis/cista/746...nfo_survey.pdf.


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

[ Please reply to the group,
return address is invalid ]


Hi,

I thought this post might be useful to you:

http://groups.google.com/group/medic...1c88659894ba/#

In addition, you can also search for material on 'Fourier Mellin
transform based registration'.

There is more than one ways to try to estimate the 'perfect' alignment
between two images, so they comney the most information when overlaid.
The choice depends on criteria such as

- source of images (direct [digital camers etc.], indirect [medical
scans])
- noise tolerance
- overlap between similar objects when images are overlaid without
registration
- degrees of freedom (objects imaged are affected by which of scaling,
rotation, translation, shear, or deformation)
- performance (speed and memory consumption)

Ultimately, it boils down to optimization of a multi-variate function
that would explain similarity between two given images given a
transform.
In medical image registration, people use Normalized mutual
information as a function of given degrees of freedom of the alignment
transform.
Some people use Chamfer matching.. where you could use chamfer score
as the function to optimize given a transform.
Some people use fourier domain registration. e.g. 'Fourier Mellin
transform based registration', but mostly only for rigid/affine
transform estimation.
There are others too.. geometric model based registration, or pointset
based registration, where secondary features are extracted from
subject images and registered.

The choise is yours.. based on what is more important to you from the
given criteria. But I think you will have to choose

(1) A metric (e.g. mutual information, cross correlation, or chamfer
match score)
(2) An optimizer (gradient descent, simplex etc.)

at the very least to begin with.

Good luck,

Pixel.To.Life

[ http://groups.google.com/group/medicalimagingscience ]


 




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