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Message #12320
Re: DOLFIN-based variational image processing now available
This looks very good!!
I could not find this demo in the svn repository ?
Is debiosee a separate project?
How do you go from the functional to the weak form, differentiate ?
Kent
On ti., 2009-02-24 at 00:08 +0000, A Navaei wrote:
> Finally after all those long discussions on the best way of
> architecturing variational image processing problems based on dolfin,
> a minimal demo showing how to solve a classical motion estimation PDE
> is available now -- thanks for all the support. Detailed explanation
> is given here:
>
> http://code.google.com/p/debiosee/wiki/DemosOptiocFlowHornSchunck
>
> Currently, the c++ implementation is done and the python wrapper is on the way.
>
> I have tried to perform sub-classing as much as possible and leave the
> rest to be implemented outside of the main classes. While this works,
> there is a tiny problem which I'm not quite happy about. Here is
> what's happening:
>
> [ITK-backend]
> |
> v
> [GenericImage]-------\
> | |
> v |
> [ImageToMesh] |
> | |
> v |
> [FunctionSpace] |
> | |
> v |
> [ImageFunction]<-----/
>
> If you still cannot see what's happening, get a monospace font :) What
> I don't like is the right branch connecting GenericImage to
> ImageFunction. There should be a way of making sure that the two
> branches are initiated from the same image source, or this could be a
> source of error. Simply encapsulating this in a class takes away the
> freedom of defining a general problem away from the user.
>
> Another problem is converting images back from functions. To do that,
> we need image dimension, size and data pointer. Of these, it seems
> that there is no way to re-construct image size from a given function
> (any Function, not just ImageFunction, eg the solution of a PDE)
> asuming that the corresponding mesh is a structured grid. For
> instance, does UnitSquare store the input parameters from which the
> image size can be restored?
>
>
> -Ali
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