From: tedwards@src.umd.edu (Thomas Grant Edwards)
Subject: Re: Eyes and Mexican Hats (was Re: brain rom and all that... )
Organization: Systems Research Center, Maryversity of Uniland, College Park
Date: Fri, 4 Sep 1992 16:00:22 GMT

In article <4SEP199201252068@juliet.caltech.edu> tychay@juliet.caltech.edu (Chay, Terrence Y.) writes:
>  the "eye" i saw did edge detection also.  isn't most of the processing of
>edge-detection performed in the primary visual cortex (V1).  I can't see
>how RGC's with a mexican hat response can find edges.  is lining up RGC
>neurons (proposed by H&W) considered the "best" way of performing edge
>detection? does V1 use alternate methods also to enhance edge detection?

Mexican-hat responses really "enhance" edges, they do not detect them.
Although, the guys working on the retina at JHU claim when they turn up
the lateral inhibition all the way, the only thing they see on the output
is edges...

But yes, in general, edge "detection" occurs in visual cortex.  H+W
discovered neurons which would respond to lines.  Some required lines of
a certain orientation.  Others required lines to of a certain length also.
Others required lines to be moving in certain direction.  
(Lots of sliced up cats+monkeys for that research!)

>  isn't the mexican hat operator (or difference of gaussians DOG) a
>biological compromise between two competing ideals:
>   1) The sinc function- an attempt to reconstruct the function from discrete
>      sampling to the maximum possible spatial frequency via the nyquist
>      theorem. (function = intensity of image)
>   2) A gaussian- which removes "noise" in a non-biased way... absolutely
>      necessary in "edge-detection" because noise will cause numerous spurious
>      "zero-crossings" in the second derivative.  (a zero-crossing in the 
>      second derivative of an intensity function is an edge)

You're making me think! :)  

OK, the retinal lateral inhibiton serves to do a couple of important
things.  It increases the dynamic range across your retina, so that
with one pupil aperture, you can still see detail in light and dark
regions.

It also serves to enhance edges, as mentioned above, a bit of pre-cortical
frequency processing, although as I think you are trying to say,
the real frequency transforms (probably a variant of wavelet transforms)
are occuring in cortex.

>  in what ways can eyes we build be better than the eyes we have?  what are
>some practical uses of the VLSI eye? 

Better eyes?  Well, I think we are a long, long way from getting
better resolution eyes.  Those little photoreceptors are really packed
in there.  They are highly efficient in turning light into impulses.
Of course, it would be nice to see in non-visible wavelengths.

>  HELL, has anyone heard of any practical/commercial implementations of a
>neural network?

Yeah, neural nets are being used with infrared focal plane arrays.
The detectors on these arrays tend to be hideously mismatched in terms
of offset and slope of response to light.  A guy at Naval Research Lab
was using neural networks to self-adapt the responses of the photodetectors
to produce a useful image.  This was running on the Connection Machine,
although the thought was to put it into analog circuitry on-chip as soon
as reasonable.  The pictures were quite dramatic...uncompensated, the
image looked like random noise.  After a few iterations, you could start 
making out roads, then buildings, then cars, and soon the mismatch
was fully compensated.

There are also neural nets used for a chemical sniffer for checking for
bombs going on to airplanes, as well as for reading handwriting.

Some DSP processing uses basically single-layer perceptrons for
adaptive filtering.

-Thomas



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