I have implemented an iterative gaussian smoothing approach that is
working well for my purposes. My approach uses a median filter to
populate the initial values and then runs a few passes with gaussian
smoothing. This works very well for the missing values that I care
about within the
Thanks for all the ideas. I think I will look into the
scikits.delaunay, Rbf, or gaussian smoothing approach. My best idea
is similar to the Gaussian smoothing. Anyway, all of the missing data
gaps seem to be small enough that I expect any of these methods to
accomplish my purpose.
I am working on a face recognition using 3D data from a special 3D
imaging system. For those interested the data comes from the FRGC
2004 dataset. The problem I am having is that for some pixels the
scanner fails to capture depth information. The result is that the
image has missing
2009/1/16 Robert Kern robert.k...@gmail.com:
of the missing region into the center. This is roughly akin to solving
a PDE over the missing region using the known pixels as boundary
conditions. I have no particular references for this approach, but I
imagine you can dig up something in the
2009/1/16 Robert Kern robert.k...@gmail.com:
On Thu, Jan 15, 2009 at 16:55, David Bolme bolme1...@comcast.net wrote:
I am working on a face recognition using 3D data from a special 3D
imaging system. For those interested the data comes from the FRGC
2004 dataset. The problem I am having is
