[Insight-users] About the metric derivative of image registration

[Karthik Krishnan]

Simplex optimizers as well (amoeba)..

But as Dan mentioned, you could try a finite differencing scheme.

On Tue, May 5, 2009 at 1:14 PM, Dan Mueller <dan.muel at gmail.com> wrote:

> Hi Huanxiang,
>
> > I’d like to know if there is an optimizer in ITK which is derivative
> free.
> OnePlusOneEvolutionaryOptimizer does not use the gradient.
>
>
> > Or is there any other way to calculate the derivative value?
> Have you tried a simple finite difference approach?
> For an example have a look at the function fdgrad(..)
>      Utilities\vxl\core\vnl\vnl_cost_function.cxx
>
> Hope this helps.
>
> Regards, Dan
>
> 2009/5/5 Huanxiang Lu <huanxiang.lu at artorg.unibe.ch>:
> > Hello,
> >
> >
> >
> > I am creating a new metric using CUDA implementation. So far I found it
> is
> > very difficult to get the derivative value from my implementation. I’d
> like
> > to know if there is an optimizer in ITK which is derivative free. Or is
> > there any other way to calculate the derivative value?
> >
> >
> >
> > Thanks a lot!
> >
> >
> >
> > Huanxiang Lu
> >
> >
> >
> >
> >
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-- 
Karthik Krishnan
R&D Engineer,
Kitware Inc.
Ph: 518 881 4919
Fax: 518 371 4573
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