Jason, thanks. That makes a great deal of sense.
"J" <jason@[EMAIL PROTECTED]
> wrote in message
news:42978c44-01c5-45e9-b7ba-c2ffaef4309f@[EMAIL PROTECTED]
> 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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