HDTenEightyP/NetNews huggingface dataset:
From: mek@hydrox.enet.dec.com (Mark Klamerus)
Subject: ROIs/Segmentation for Bar Codes
Date: Mon, 12 Oct 92 13:13:15 PDT
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X-To: sci.image.processing.usenet
X-Apparently-To: sci.image.processing.usenet


	Help,

	I'm working on a program to process images which have had
	bar code labels attached.  I need to find the bar code data
	in the midst of the scanned in images.  Once I find the piece
	of the image with the Bar Code I have no problem in orienting
	it and decoding it.  My problem is in finding the barcode.

	This appears to be a segmentation/feature extraction/roi
	problem of sorts.  I'm looking for input on an image feature
	I might use to search for a barcode and on a filter/processing
	for searching for that feature.  These images are bitonal (~200 dpi).

	Some things which come to mind are using a neighborhood thresholding.
	For instance taking a 5x5 region and mapping the percentage of pixels
	which are black ( x pixels / 25 pixels ) to a 256 level of gray image
	and doing blobs.  I've been getting enormous processing times in doing
	this.  A 7x7 filter calculating thresholds for every pixel in a 8.5x11
	(1700x2200 pixel) image took nearly five minutes.  And this was on
	a DECstation 5000/200.  I was hoping for < 20 seconds (maybe < 10).

	I've read some articles on texel (texture elements) and have thought
	about them or perhaps an FFT (because of the regular nature of bars).

	Does anyone have any ideas on some sort of processing which might
	help here?

	thanks,

	Mark Klamerus
	imaging systems consultant
Newsgroups: sci.image.processing
From: meier@thumper (Christopher Meier)
Subject: Re: SBus/VME imaging board info needed.
Organization: Image Manipulation Systems Inc.
Date: Tue, 13 Oct 1992 15:37:01 GMT

Sorry folks, I accidentily sent that to the net and not via email
to the requestor.

Chris
-- 

@ Christopher  M.  Meier                    Image Manipulation Systems
@ Voice: (612) 753-5602 Fax: -5603          17595 Partridge St NW
@ meier%thumper@src.honeywell.com           Andover, MN 55304-1456
Newsgroups: sci.image.processing
From: pete@cs.curtin.edu.au (Pete Staples)
Subject: optical flow (again maybe *sorry*)
Organization: Curtin University of Technology
Date: Fri, 9 Oct 1992 02:45:10 GMT


(sorry if this has reappeared, I wanted to make sure it was in over the weekend)

I am interested in the methods of determining optical flow ivestigated
in articles such as:

Campani and Verri
"Motion analysis from first order properties of optical flow" 
CVGIP vol 56 No 1 pp 90-107 1992

Nelson and Polana
"Qualitative recognition of Motion using Temporal texture"
CVGIP vol 56 No 1 pp 78-89  1992

If someone has produced code to evaluate the optical flow of images, perhaps
also the characteristic curves (of singular points), and they are willing to
"share" them with me I would be particularly grateful.

Thanks in advance.

Peter.

email pete@cs.curtin.edu.au
From: francis@mpl.ucsd.edu (Francis C Felizardo)
Newsgroups: sci.image.processing
Subject: Need help on an image processing problem
Date: 11 Oct 1992 00:32:15 GMT
Organization: Scripps Institution of Oceanography
NNTP-Posting-Host: kiko.ucsd.edu


I have about 100 hours of VCR tapes of a camera looking at a spot on the 
ocean surface. Depending on the wind an wave conditions, you can see 
breaking wave whitecaps going across the TV screen. It would be nice if 
I could use those tapes to count the number of events (number of breaking 
waves moving across the screen), perhaps do statistics on the 
characteristics of those whitecaps and maybe even examine the evolution of  
breaking wave whitecaps. 

I'm looking for software and hardware that is fast enough to grab and 
process TV frames in real time. I've seen software (Jandel's JAVA) that will 
take an image and count objects, measure areas, etc. It would have been 
perfect except it cannot do this in real time.

Pardon the rather naive question but is what I want to do possible?
Does anyone here know of a software and frame grabber combination  
for PCs (I have a Dell 486D50 ISA bus) that can do the job? My budget is 
about $3K.

Thank you.

   - Francis
-------------------------------------------------------------------------
Francis C Felizardo				francis@mpl.ucsd.edu
Scripps Institution of Oceanography, 0213	      (619) 534 1085
La Jolla, CA 92093-0213
Newsgroups: sci.cognitive
From: noykman%peruvian.utah.edu@cs.utah.edu (Nathan Dykman)
Subject: Newbie questions
Date: 14 Oct 92 15:04:57 MDT
Reply-To: noykman%peruvian.utah.edu@cs.utah.edu
Organization: University of Utah, CS Dept., Salt Lake City

Hello fellow net people! I have just recently gained a interest in
cognitive science and would like a couple of questions answered concering
this confusing(for me)subject.

I. What is the goal of cognitive in relation to human congition. Is it 
defining it, comprehending it, or actively modeling it?

II. What are some good readings on this subject?

III. What parts of the below subjects fall under the domain of congitive
science?

lingusitics/pragmatics
psychology(other than cognitive psychology)
mathematics
computer sciences
neurophysiology
philosophy
theology/morals
music/other arts.

and finally:

IV. What would be a good undergraduate background if you were planning 
graduate studies in cognitive science

Many thanks,

Nathan Dykman
(noykman@peruvian.cs.utah.edu)

Newsgroups: sci.cognitive
From: A5711DAA@vm.univie.ac.at
Subject: Re: Problems with AI
Organization: University of Vienna
Date: Thu, 8 Oct 1992 11:30:33 GMT

------------------------- Original Article -------------------------
References::<5101@calmasd.Prime.COM>
    <1992Oct5.011143.8020@hellgate.utah.edu>

> In article <1992Oct5.011143.8020@hellgate.utah.edu> tolman%asylum.cs.utah.ed>
>> What, in your opinion, are the fundamental problems with artificial
>> Intelligence research today?
>>

> I think that there are two basic problems in the area of computer
> emulation of human intelligence.  The first is simply a lack of
> appreciation of the computational requirements.  We are really talking
> about many hundreds of millions of neurons, each of which may be connected
> to thousands of other neurons with many differnt types of neurons and
> connections, all functioning in parallel.  Models of associative
> memory suggest that the resolution of associations is directly proportional
> to the number of synapses participating so small scale simulations
> should not be expected to perform particularly well.

> The other problem has to do with unrealistic expectations.  The assumption
> is that computers should be able to perform AI tasks, such as pattern
> recognition and natural language translation, 'better' than humans
> once we are able to 'solve' these AI problems.  But think about it.
> It will take HUGE massively parallel computer systems to come close
> to the performance of a human child on these tasks.  And the computer
> will not be able to do it 'better' than the child because the computer's
> use of an 'associative filter' to emulate the child's neural mechanisms...
> / Jerry,    CalmaSD UNIX SysAdmin,   +1 619 587 3065

In this discussion Jerry seems to believe that problems should be solved
with connectionism, if they are solved at all. I do not believe that this is
the right way - we need both, symbolic and distributed knowledge and
computation, at least as long as we haven't solved the symbol grounding
affair (and I do not see any solution at the horizon till now).

Specially in language we cannot abandon symbols.
The problem in natural language processing in my point of view is that we
don't really konw what is going on there, so how could we implement it?
There are a lot of matters to solve, before we can make a real natural
(and cognitive adaquat) language processing, e.g. modularism of knowledge/
processes vs. holism; the representation of the lexicon - it's connection
to our concepts of the world.
The brain model on the whole: do we really have a short and a long term
memory (there are alternative concepts) - then to decide with problem
solving algorithm is appropriate for language processing - to we need
frames to fill, or are we processing a whole search tree?

I fact we know nearly nothing of what is going on in our heads.

Benke Gertraud (A5711daa@awiuni11.edvz.univie.ac.at)
From: heinzl@elroy (Heinz Lottermoser)
Newsgroups: sci.cognitive
Subject: Re: Do neurons fatigue?
Date: 12 Oct 92 16:37:51 GMT
Distribution: sci
X-Newsreader: Tin 1.1 PL4

hucka@engin.umich.edu (Michael Hucka) writes:
: Hello,
: 
: I would like to try to find out whether there is evidence for fatigue in
: neurons.  Specifically, I'm interested in finding out whether there is
: evidence for fatigue effects in non-receptor neurons in vertebrates, in
: particular in cortical cells.  By fatigue I mean a decrease in responsiveness
: of a particular cell as a result of continued stimulation, and not a process
: of habituation.
: 
: If any of you know about research that deals with this topic, please let me
: know via email.
: --
: -- Mike
: _____________________________________________________________________________
: Mike Hucka                     | Internet: hucka@engin.umich.edu    
: University of Michigan AI Lab  | 1101 Beal Ave., Ann Arbor, MI 48109-2110

Yes.  Lookup the electrolyte mechanics involved rapid, or repeated firing of
the neuron.  Exhaustion(reduction actually) of electrolytes is possible, thus
retarding the firing thresholds.
From: gdh@calmasd.Prime.COM (Gerald Hall)
Newsgroups: sci.cognitive
Subject: Re: Problems with AI
Date: 7 Oct 92 18:18:28 GMT
Organization: Computervision, San Diego, CA

In article <1992Oct5.011143.8020@hellgate.utah.edu> tolman%asylum.cs.utah.edu@cs.utah.edu (Kenneth Tolman) writes:
>What, in your opinion, are the fundamental problems with artificial 
>intelligence research today?
>
Of course AI (I prefer the term 'Computer Intelligence') is many different
things.  My favorite definition, from one of my Profs at UCSC was that
AI was the study of the "really hard" problems.

I think that there are two basic problems in the area of computer
emulation of human intelligence.  The first is simply a lack of
appreciation of the computational requirements.  We are really talking
about many hundreds of millions of neurons, each of which may be connected
to thousands of other neurons with many differnt types of neurons and
connections, all functioning in parallel.  Models of associative
memory suggest that the resolution of associations is directly proportional
to the number of synapses participating so small scale simulations
should not be expected to perform particularly well.

The other problem has to do with unrealistic expectations.  The assumption
is that computers should be able to perform AI tasks, such as pattern
recognition and natural language translation, 'better' than humans
once we are able to 'solve' these AI problems.  But think about it.
It will take HUGE massively parallel computer systems to come close
to the performance of a human child on these tasks.  And the computer
will not be able to do it 'better' than the child because the computer's
use of an 'associative filter' to emulate the child's neural mechanisms
will be subject to exactly the same 'pathologies' of mistaken recognitions
and mis-associations (Freudian slips) as the human subject exhibits.  These
are inherent in the process and are fundamental to human creativity but
they are not what we expect from AI applications.

It may be an interesting research project, and parts of it may have reasonable
applications, but it will never be anywhere near cost effective when the
computers will cost millions of dollars and the humans will work pretty cheap.
-- 
   /
  /	Jerry,	CalmaSD UNIX SysAdmin,	+1 619 587 3065
 /
Newsgroups: sci.cognitive
From: zelek@thunder.mcrcim.mcgill.edu (John S. Zelek)
Subject: What does 'near' and 'far' mean?
Organization: McGill Research Centre for Intelligent Machines
Date: Wed, 14 Oct 92 19:17:44 GMT

The following is a request for references, and/or 
information from people who may have done work in this
area.

What I am looking for is a definition of the spatial
prepositions 'near' and 'far'.  I am looking for what
factors (e.g. task, context, object sizes,..) influence 
what these defintions may be.  The goal is to quantify
the definitions, and also to determine what the variables
are.
Thank-you in advance for whoever replies.
John Zelek
(zelek@mcrcim.mcgill.edu)
-- 
===============================================================================
John Zelek       		McGill Research Centre for Intelligent Machines
zelek@mcrcim.mcgill.ca          McGill University, Montreal
===============================================================================
Newsgroups: sci.cognitive
From: cs91mjw@brunel.ac.uk (Martin Watson)
Newsgroups: sci.cognitive
Subject: Re: Memory-Based NLP
Date: 15 Oct 92 15:53:05 GMT
Organization: Brunel University, Uxbridge, UK
X-Newsreader: TIN [version 1.1 PL6]

I am far from an expert in this field , but I think you may find the following
book useful:

In-Depth Understanding

A computer Model of Integrated Processing for Narrative Comprehension

By Michael G.Dyer

ISBN 0-262-04073-5

pages 143- in particular deal with "Process intergration and memory interactions"
Newsgroups: sci.cognitive
From: "norman danner" <ndanner@kiwi.ucs.indiana.edu>
Subject: Re: Re: Problems with AI
Organization: Indiana University, Bloomington
Date: Fri, 9 Oct 1992 13:29:04 -0500

In article <1b44bgINN7fb@bigboote.WPI.EDU> mikecap@planet10.WPI.EDU (Michael Vince Caprio Jr) writes:
>...We know that the brain does not work
>by using 0's and 1's to communicate

Do we know this?  According to the basic neurophysiology I've had, at
least a very important class of neurons have all-or-nothing firing
schemes.  They don't send of graded signals as a function of the integration
of input.  They either fire or they don't.  Seems somewhat binary
to me.  Forgive me if I'm totally out of it here--I've just started
studying the subject.
-- 
Norman Danner                    | The only difference between mathematicians
"The guy with the hair."         |  and philosophers is that mathematicians
ndanner@indiana.edu              |      throw away their scratch paper.
-----------------------------------------------------------------------------
Newsgroups: sci.cognitive
From: hucka@engin.umich.edu (Michael Hucka)
Subject: Do neurons fatigue?
Reply-To: hucka@engin.umich.edu
Organization: University of Michigan AI Lab
Distribution: sci
Date: Mon, 12 Oct 1992 06:14:16 GMT

Hello,

I would like to try to find out whether there is evidence for fatigue in
neurons.  Specifically, I'm interested in finding out whether there is
evidence for fatigue effects in non-receptor neurons in vertebrates, in
particular in cortical cells.  By fatigue I mean a decrease in responsiveness
of a particular cell as a result of continued stimulation, and not a process
of habituation.

If any of you know about research that deals with this topic, please let me
know via email.
--
-- Mike
_____________________________________________________________________________
Mike Hucka                     | Internet: hucka@engin.umich.edu    
University of Michigan AI Lab  | 1101 Beal Ave., Ann Arbor, MI 48109-2110
From: novak@vitruvius.ar.utexas.edu (Marcos Novak)
Newsgroups: sci.cognitive
Subject: ANNOUNCING: New Graduate Program in Architecture+Art+Technology
Date: 9 Oct 92 06:49:34 GMT
Organization: School of Architecture, University of Texas at Austin
X-UserAgent: Nuntius v1.1b3


Newsgroups: sci.cognitive,comp.ai
From: gat@robotics.jpl.nasa.gov (Erann Gat)
Subject: Re: The semantics of 'some'
Organization: Jet Propulsion Laboratory
Date: Wed, 14 Oct 1992 21:01:51 GMT

In article <1992Oct13.093008.20942@news.unige.ch> afzal@divsun.unige.ch writes:
>pautler@ils.nwu.edu (David Pautler) writes
>
>>Is there a simple way of rendering propositions like:
>>
>>	"John believes someone broke into the bank."
>>
>>	(John believe E(x):(x break-into bank))
>>
>>in a form usable by a simple unifier?  Note that this has
>>a different meaning than:
>>
>>	"There is a particular someone who John believes
>>	 broke into the bank."
>>
>>	E(x):(John believes (x break-into bank))
>
>This is one of the standard problems of quantification and belief. When
>skolemising the two sentences they end up mapping into one sentence, hence
>the difference between them is lost.

I addressed this problem in my master's thesis (tech report 87-36, Virginia
Polytechnic Institute, Blacksburg, VA 24060).  The solution I proposed is
to introduce distinguished operators to designate operators preceded by
exitential quantifiers.  So, for example, John believes someone broke
into the bank becomes (BELIEVE JOHN (BROKE-INTO-BANK X)) while the
other version would be (BELEEV JOHN (BROKE-INTO-BANK X)).  This, together
with a set of deduction rules for distinguished operators forms a naive
but workable solution.  (I have a small demonstration theorem prover in
common lisp which illustrates this if anyone is interested.)  The solution
is naive because it cannot deal with multiple variables whose scopes are
different (e.g. Exists(x):BELIEVE(JOHN,Exists(y):FOO(x,y))...  There may
be a way to extend my solution to this case, but I haven't thought about
it in about five years.

Erann Gat
gat@robotics.jpl.nasa.gov
From: cs91mjw@brunel.ac.uk (Martin Watson)
Newsgroups: sci.cognitive
Subject: Re: Problems with AI
Date: 13 Oct 92 21:24:37 GMT
Organization: Brunel University, Uxbridge, UK
X-Newsreader: TIN [version 1.1 PL6]

>What, in your opinion, are the fundamental problems with artificial
>intelligence research today?

>  Perhaps that computers are not interconnected enough?  Not enough is
> understood about the nervous system is to effectively simulate it?  That all
> the research is concentrated in too small an area? What?

I am new to this group & this field in general and would any comments/
correction/criticsms on the following.

I feel that there are two major problems:

i.) Inadequate hardware.

 The brain is estimated to contain somewhere between 10^11 and 10^12 neurons
each with upto 10000 connections.Simulating 10 milliseconds of activity for
1 neuron can take 2-3 minutes on a sparc.Our hardware is not remotely upto
this kind of computational performance even on the latest supercomputers.

 I know that this looking at the problem from a connectionist viewpoint but 
when it comes to tasks like visual recognition do you really think we can
do it much more efficiently (regardless of how its implemented) , remember
evolution had millions of years to perfect a  solution.

ii.) Incompatibility of systems.

 I think an even bigger problem however is that our existing systems just cant
work with each other.A simple example:

Two of the most advanced systems I know are:

1.) BORIS , a story understander
2.) Khonens speech recogniser using ordered neural Maps

BUT if I where to take the output from the second system and feed it into the first I would not get a system that can understand spoken short stories.I would
instead get havoc when BORIS was fed a word that had been misrecognised e.g.
Beech instead of Speech.

People do not work this way , instead they unify multiple sources of data &
knowledge in order to perform a task.

Martin Watson cs91mjw@brunel.ac.uk
Newsgroups: sci.cognitive
From: rowe@engin.umich.edu (Steven Rowe)
Subject: Cell Assemblies
Date: Mon, 12 Oct 92 13:55:19 EDT
Organization: University of Michigan Engineering, Ann Arbor
Originator: rowe@muenster.engin.umich.edu


Can anyone provide me with a list of references of both clinical
and modelled assemblies of 100 - 1000 neurons?  I am interested
in any data on the behaviour of highly interconnected neuron groups
in biologically plausible models (i.e., no feed-forward, backprop).
Of special interest to me are data on the activity level of the
cell assembly with respect to time, and the topology of any known
natural cell assemblies.

Please send e-mail to the address above or to steve_rowe@35.1.1.43
Thanks.


-- 
     These folks who don't believe in evolution
are the ones who look the most like they could use some.

                                   -Frank Allison
Newsgroups: sci.cognitive
From: ashwin@gatech.edu (Ashwin Ram)
Subject: Re: Memory-Based NLP
Reply-To: ashwin@cc.gatech.edu (Ashwin Ram)
Organization: Georgia Institute of Technology, College of Computing
Date: Fri, 16 Oct 1992 20:02:43 GMT

In article <61058@mimsy.umd.edu> waander@cs.umd.edu (Bill Andersen) writes:
>     I'm interested in seeing what sort of work has been done (in AI
>   and in Psychology) on the role of memory in natural language proces-
>   sing.  

It depends on what you mean by "memory" and "natural language processing".  I
assume "memory" means something like "episodic memory", since every system
uses some sort of lexical "memory" (i.e., dictionary).  If by "natural
language processing" you mean syntactic and/or semantic processing of natural
language sentences (i.e., "parsing"), Riesbeck and Martin's DMAP (and
Quillian's earlier TLC system that they refer to) is probably the place to
start.  If, however, you mean natural language "understanding", i.e., not
only the parsing of the sentences but also the building of conceptual
representations of those sentences, the building of explanations or coherent
connections between individual sentences in a larger text, and so on, you
might also look at Schank's dynamic memory, and the case-based and
memory-based reasoning literature on explanation and learning.  For example,
Lebowitz's IPP, Dyer's BORIS, and my AQUA system (among others) use episodic
memory to interpret, understand, explain, and/or learn from stories (see,
e.g., my article "A theory of questions and question asking" in the Journal
of the Learning Sciences, 1(3&4):273-318, 1991).

--
Ashwin Ram <ashwin@cc.gatech.edu>
Assistant Professor, College of Computing
Georgia Institute of Technology, Atlanta, Georgia 30332-0280
From: rjones@dip.eecs.umich.edu (Randolph M. Jones)
Newsgroups: sci.cognitive
Subject: Re: Re: Problems with AI
Date: 9 Oct 92 14:40:26 GMT
Organization: University of Michigan EECS Dept., Ann Arbor

In article <1b44bgINN7fb@bigboote.WPI.EDU> mikecap@planet10.WPI.EDU (Michael Vince Caprio Jr) writes:
>The entire basis of
>all computer process is based on the binary system - that in and of itself
>is enough to halt any more progress.  We know that the brain does not work
>by using 0's and 1's to communicate;

You don't like using just one bit at a time?  Then take two...they're small :-).
Seriously, I think Church and Turing would argue that it doesn't really
matter whether your hardware-level implementation uses 0's and 1's or the colors
of the rainbow, and I'm inclined to agree with them.  As long as the two
systems can be mapped onto each other, you should be okay.
From: novak@vitruvius.ar.utexas.edu (Marcos Novak)
Newsgroups: sci.cognitive
Subject: ANNOUNCING: New Graduate Program in Architecture+Art+Technology
Date: 9 Oct 92 06:01:58 GMT
Organization: School of Architecture, University of Texas at Austin
X-UserAgent: Nuntius v1.1b3
From: waander@cs.umd.edu (Bill Andersen)
Newsgroups: sci.cognitive
Subject: Memory-Based NLP
Date: 10 Oct 92 01:55:15 GMT
Article-I.D.: mimsy.61058
Organization: U of Maryland, Dept. of Computer Science, Coll. Pk., MD 20742


  I'm interested in seeing what sort of work has been done (in AI
and in Psychology) on the role of memory in natural language proces-
sing.  Other than the DMAP system of Riesbeck & Martin, I have seen
nothing in the AI world stressing the use of memory, either in parsing
or in understanding spoken or written language.

  I'd appreciate any response.

  ...bill

-- 
   / Bill Andersen (waander@cs.umd.edu) /
  / University of Maryland             /
 / Department of Computer Science     /
/ College Park, Maryland  20742      /