Berkeley CSUA MOTD:Entry 11648
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2025/05/26 [General] UID:1000 Activity:popular
5/26    

2004/1/2-5 [Consumer/Camera, Computer/Companies/Google, Computer/Theory] UID:11648 Activity:nil
1/2     Computer science question for you PHDs out there.  What would be
        the feasibility of a program that, given a set of images that form
        a mosaic, produce the "most correct" composite image, based upon some
        definition of correctness that could be supplied in advance (color
        compatibility, smoothness of lines, etc)?  For instance, such a
        mosaic could be a 360 series of photos that form a panoramic
        photograph.  Such a program wouldn't necessarily need to be perfect,
        and any of amount of "hints" could be given to the program as well
        as the input images.  This might already exist, or it might be solving
        the halting problem - I don't know.  I'm asking the question for a
        materials science post-doc friend of mine that is working with
        crystal lattice images.  --lye
        \_ Does the camera rotate as it takes pictures?  Is there overlap
           between individual images?  At its most general, this problem
           involves object recognition and so is vision-hard.
           There are some papers on this problem, google for obvious things
           to find them.
        \_ such programs exist. if the camera undergoes pure rotation
           and no translation about the optical center, the problem is
           very easy to solve (assuming overlap between the views). other-
           wise it's harder and you have to rely on some kind of
           approximation because you need to know the 3D geometry of the
           scene. other things that help: if you know the exact motion of
           the camera, the problem is easy again. -ali
        \_ There is a ton of literature on this kind of problem, which is
           known as "registration." A standard approach is to define some
           kind of error function (distance between edges, or distance
           between overlapping pixels in color space, etc) and try to minimize
           it over the space of transformations. An algorithm that works well
           for a lot of problems of this type in the pairwise case is called
           "iterative closest point," due to Besl and McKay. If you don't
           know an approximate solution to start with, it is a lot
           more difficult. -lewis
           \_ homeslide, iterative closest point requires you to know
              the 3D geometry of the scene to perform registration.
              altneratively, you need some kind of parametric transformation
              model for your images.
ERROR, url_link recursive (eces.Colorado.EDU/secure/mindterm2) 2025/05/26 [General] UID:1000 Activity:popular
5/26    

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