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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 [ Please reply to the group, return address is invalid ] |
#2
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Matching images from different sources.
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: http://www.google.com/search?hl=en&q=image+fusion You just missed the image fusion conference but maybe you can get proceedings, or go next year: http://www.iqpc.com/cgi-bin/template...9&event=11435& Hoping this posts (please Google!!!) ImageAnalyst On Feb 22, 6:59 pm, Roberto Waltman wrote: 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 [ Please reply to the group, return address is invalid ] |
#3
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Matching images from different sources.
Roberto Waltman wrote:
Looking for information, algorithms, etc. on how to match images of the same object obtained from different sources. MCammarano wrote: This problem is often called multimodal registration. It crops up in medical imaging (align an MRI with a CT scan, for example) and aerial/satellite imaging (line up images acquired in different spectral bands). ... You might find a technique called "alignment by maximization of mutual information" helpful. ... Thanks for the pointers, a first Google search is bringing more relevant hits than what I was able to find before. "ImageAnalyst" wrote: ... building up feature vectors. ... 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. Ditto. ... You just missed the image fusion conference but maybe you can get proceedings, or go next year: http://www.iqpc.com/cgi-bin/template...9&event=11435& 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 .... Hoping this posts (please Google!!!) 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 http://news.individual.net/ as a usenet provider. (Not free, but only 10 euros per year). Thanks again, Roberto Waltman [ Please reply to the group, return address is invalid ] |
#4
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Matching images from different sources.
"ImageAnalyst" wrote in message ps.com... 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: http://www.google.com/search?hl=en&q=image+fusion You just missed the image fusion conference but maybe you can get proceedings, or go next year: http://www.iqpc.com/cgi-bin/template...9&event=11435& Hoping this posts (please Google!!!) ImageAnalyst On Feb 22, 6:59 pm, Roberto Waltman wrote: 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 [ Please reply to the group, return address is invalid ] 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 http://www.atnf.csiro.au/computing/software/karma/ Terry B |
#5
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Matching images from different sources.
On Feb 22, 6:59 pm, Roberto Waltman wrote:
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. 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, http://spiff.rit.edu/match/ |
#6
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Matching images from different sources.
Terry B wrote:
If the aim is pretty pics than I don't know the answer. 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. 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 http://www.atnf.csiro.au/computing/software/karma/ 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 [ Please reply to the group, return address is invalid ] |
#7
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Matching images from different sources.
"Stupendous_Man" 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, http://spiff.rit.edu/match/ 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 [ Please reply to the group, return address is invalid ] |
#8
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Matching images from different sources.
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: http://www.cs.jhu.edu/~cis/cista/746...nfo_survey.pdf . Good luck! P. On Mar 13, 8:45 pm, Roberto Waltman wrote: "Stupendous_Man" 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, http://spiff.rit.edu/match/ 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 [ Please reply to the group, return address is invalid ] |
#9
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Matching images from different sources.
"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 ] |
#10
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Matching images from different sources.
I would go for SIFT, try:
http://en.wikipedia.org/wiki/Scale-i...ture_transform Roberto Waltman כתב: "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 ] |
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