Path: utzoo!attcan!uunet!tut.cis.ohio-state.edu!cs.utexas.edu!uwm.edu!ogicse!milton!forbis
From: forbis@milton.u.washington.edu (Gary Forbis)
Newsgroups: comp.ai.neural-nets
Subject: Re: Networks for pattern recognition problems?
Message-ID: <5309@milton.u.washington.edu>
Date: 20 Jul 90 15:14:48 GMT
References: <8462@ur-cc.UUCP> <5874@jhunix.HCF.JHU.EDU> <2809@mrsvr.UUCP>
Organization: University of Washington, Seattle
Lines: 18

In article <2809@mrsvr.UUCP> scott@isles.UUCP (Scott Otterson  x5117	) writes:
>In article <5856@jhunix.HCF.JHU.EDU> you write:
>>(I am aware of retina-like neural models which provide very
>> good contrast enhancement and CCD element calibration which
>> do work better than most "traditional" techniques.

>Are there any published references on this?  Sounds intesting.

I cannot cite any particular work but I can give you a place to start.

Carver Mead gave a lecture at the UW this spring.  He was touting a switch
to analog devices for computing.  He showed pictures of images generated by
a retina simulator.  Over time it corrected for flaws in manufacturing and
defects on the lens.  One interesting side effect was the device produced
after images.

I think a perusal of recent works by this interesting man would be a good place
to start.