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 f
>
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.
> 2009/1/16 Robert Kern :
> On Thu, Jan 15, 2009 at 16:55, David Bolme 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 that for some pixels the
>> scan
2009/1/16 Robert Kern :
> 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 literature about PDE-b
On Thu, Jan 15, 2009 at 16:55, David Bolme 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 that for some pixels the
> scanner fails to capture depth inform
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 va
