On Jul 23, 2:36 pm, "Fredo" <fr...@[EMAIL PROTECTED]
> wrote:
> I have an image that I'm told I need to do a best fit linear filter on,
but
> I'm not exactly sure what that means and searches on it keep leading me
to
> linear regression, which I don't think is what I want, but maybe it is.
>
> Can someone give me some help here? If it's a linear regression and the
> pixel brightness (they're 8-bit greyscale) is X, what is Y?
>
> Thanks.
The goal is probably to remove the global (lowest frequency) lighting
effects from your face data set. I would think that any sort of
recursive ultra low pass filter is the ideal tool for the job for
quickly estimating the global lighting. It is possible to write a
recursive IIR filter that produces the linear least squares fit to
data (used in target tracking, parameter estimation), which may be
what you have been recommended. I would have thought any low-pass
IIR filter would do the job. If you run one of these filters on an
image (possibly from left-right then back again, plus top-bottom and
back again), then you get a very low-pass image which can be
subtracted from the original. This enhances the features which are
relevant for face recognition, and partially removes the global
lighting features which are irrelevant.
Regards
Jason
Vision Experts Ltd
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