dolfin team mailing list archive

[A Navaei <axnavaei@xxxxxxxxxxxxxx>]

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