HDTenEightyP/NetNews huggingface dataset:
From: erich@eye.com (Eric Haines)
Newsgroups: sci.image.processing
Subject: Errata for Wolberg's "Digital Image Warping"
Date: Wed, 26 Aug 92 13:08:51 GMT
Summary: wuarchive.wustl.edu:graphics/graphics/books/Digital-Image-Warping...
Organization: 3D/EYE, Inc.  Ithaca, NY

Errata for George Wolberg's book "Digital Image Warping" is now available on
wuarchive.wustl.edu in graphics/graphics/books/Digital-Image-Warping.errata,
in case you missed George's posting of it a few months back.

Eric Haines
Newsgroups: sci.image.processing
From: ttcain@vangogh.lerc.nasa.gov (Maureen Cain)
Subject: Image Segmentation
Organization: NASA Lewis Research Center
Distribution: usa
Date: Fri, 28 Aug 1992 22:39:22 GMT

	I have an image that was taken through a microscope of a lead sample.
It clearly (to the eye) has two distinct regions, one light and one dark.
However, there is a lot of noise in both regions.  I want to extract the
contours from the image defining the light and dark regions.  However, 
because of the noise, standard edge detection algorithms (Canny, Sobel, etc)
do not work very well, even after the image has been preprocessed with a 
median filter.  I need to get a closed contour from the image as if I took a
pencil and traced it off the image.  

	Is there a way to segment the image using contrast and variance 
stats rather than using and edge detector? Or a combination of both?  Any
help would be appreciated.  Thanks

Maureen Cain
ttcain@vangogh.lerc.nasa.gov
(216) 433-8928

-- 
-----------------------------------------------------------------------------
Maureen Cain			phone: 216-433-4000
NASA Lewis Research Center
Cleveland, Ohio  44135        email: ttcain@vangogh.lerc.nasa.gov
From: psomu@pilot.njin.net (Prabhakar Somu)
Newsgroups: sci.image.processing
Subject: Background correction
Keywords: background,non-uniform lighting,flat-field
Date: 30 Aug 92 18:26:50 GMT
Organization: Rutgers Univ., New Brunswick, N.J.

Hi,

   I am interested in methods of performing background correction
   (flat field correction). In the imaging setup that we use,
   the light source which illuminates the object that we are imaging,
   is non-uniform, as one moves away from the center towards the
   boundary of the field of view. As a result of this, the 'grabbed' 
   image also exhibits the same kind of non-uniformity. 
   (ie, regions in the middle of the field of view are brighter than 
   regions towards the boundary). Can anybody suggest any methods
   for doing this sort of correction or refer me to appropriate 
   publications?

   Thanks,

   psomu@pilot.njin.net
	or
   somu@mirage.umdnj.edu
Newsgroups: sci.image.processing
From: dak@kaa.informatik.rwth-aachen.de (David Kastrup)
Subject: Re: 2D Fourier Transforms, in English.
Organization: Rechnerbetrieb Informatik  /  RWTH Aachen
Date: 27 Aug 92 22:56:10 GMT

singlis@waikato.ac.nz writes:

>Thanks to all who replied about FFT segmentation.

>A Fourier Transform in 1D is a breakdown into the waveform component
>sin/cos terms. This sin/cos waves when added approximate (v. closely) the
>original waveform.

>How is the 2D case explained? Is the 2D transform a sum of "planar" sin/cos
>"waves", or is it more of a breakdown of the horizontal groups of vertical
>transforms?

Due to the separability, you can do row and column transforms after each
other, but you can do it the other way round as well, so this would seem
to be missing the point some way.

Another possibility is to view the term (xu+yv) in the exponential as
a dot product (x,y).(u,v).
If you view (u,v) as a vector, you can interpret the
value at index (u,v) as a complex exponential wave crossing the picture
in direction (1/u,1/v) (or was that (v,u)?) with frequency |u,v|. A real-
valued interpretation (phase and amplitude) would have to take index
(-u,-v) into consideration as well.
From: popat@image.mit.edu (Kris Popat)
Newsgroups: sci.image.processing
Subject: Re: Spread spectrum
Date: 26 Aug 92 23:12:12 GMT
Organization: MIT Advanced Television Research Program


The following reference will be of interest:

    AUTHOR:       Schreiber, William F.
    TITLE:        Spread-Spectrum Television Broadcasting.

              In:   SMPTE journal
                    AUG 01 1992 v 101 n 8
            Page:   538

---------------------------------------------------------------
Kris Popat
MIT Rm E15-355   Cambridge, MA 02139
From: bokkie@uctvax.uct.ac.za
Newsgroups: sci.cognitive
Subject: Re:Re:Cognitive Science Programs?
Date: 27 Aug 92 06:55:10 +0200
Organization: University of Cape Town

OOops! The correct address is reinhard@hpscdc.scd.hp.com
Newsgroups: sci.cognitive
From: ashwin@gatech.edu (Ashwin Ram)
Subject: Re: Cognitive Science Programmes
Reply-To: ashwin@cc.gatech.edu (Ashwin Ram)
Organization: Georgia Institute of Technology, College of Computing
Distribution: na
Date: Tue, 25 Aug 1992 18:57:18 GMT

I can tell you a little bit about research in Artificial Intelligence and
Cognitive Science at Georgia Tech.  The AI research group in the College of
Computing presently consists of six full-time academic faculty members:
Professors Ronald Arkin, Kurt Eiselt, Ashok Goel, Janet Kolodner, Daryl
Lawton, and Ashwin Ram.  As a group, we cover a fairly large portion of the
Artificial Intelligence research spectrum, ranging from use of knowledge in
problem solving, language processing, explanation, perception, and robotics;
to representation and organization of knowledge; to acquisition of knowledge,
learning, and instruction.  Much of our research has a strong cognitive
flavor to it.  Some of it is inspired by cognition while some explicitly
models cognitive processes.

Beyond our group, several academic and research units of Georgia Tech are
engaged in active and collaborative research in applied Artificial
Intelligence and Cognitive Science.  Professors Dorrit Billman, Susan Bovair,
Richard Catrambone and Tony Simon in the School of Psychology are working on
several of the issues mentioned above.  The Man-Machine Systems Research
Center in the School of Industrial and Systems Engineering has a strong
ongoing research program in cognitive engineering.  The Georgia Tech Research
Institute, similarly, has a large research and development program in several
aspects of applied AI and CS including expert systems, cognitive engineering,
and neural networks.  The Graphics, Visualization and Graphics group in the
College of Computing conducts research in cognitive aspects of Human-Computer
Interfaces.

The College of Computing also offers a Cognitive Science certificate program
in collaboration with our colleagues in other academic units.  This is an
interdisciplinary program dealing with fundamental problems in cognition.
All of our Artificial Intelligence faculty are involved in this program, as
are also faculty from Psychology, English, Industrial and Systems
Engineering, and other disciplines.

The Summer 91 issue of AI Magazine (vol. 12, no. 2) describes some of our
ongoing projects in AI.  You can get a tech report describing the AI and
Cognitive Science research in more detail by sending e-mail to
<ai-secretary@cc.gatech.edu>.  Some AI tech reports are anonymously FTP'able
from ftp.cc.gatech.edu:/pub/ai (see the README file).  To find out more about
the graduate programs in the College of Computing, and to request application
materials and research brochures, send e-mail to <grad-study@cc.gatech.edu>,
or write to the Office of Student Services, College of Computing, Georgia
Institute of Technology, Atlanta, Georgia 30332-0280.

Ashwin Ram <ashwin@cc.gatech.edu>
Assistant Professor and AI Coordinator, College of Computing
Georgia Institute of Technology, Atlanta, Georgia 30332-0280
Xref: sparky sci.cognitive:368 comp.ai.neural-nets:3308
From: karls@ztivax.UUCP (Ingolf Karls)
Newsgroups: sci.cognitive,comp.ai.neural-nets
Subject: Re: selective attention
Date: 24 Aug 92 07:33:56 GMT
Organization: Siemens A.G of Munich, Germany

>In sci.cognitive rosauer@fzi.de (Bernd Rosauer) writes:
>
>Is there a good introductory/review/survey paper on selective attention?
>
>--Bernd

Although somewhat terse, the following paper covers (and references)
most of the important psychophysical/neurophysiological findings:

  Posner, M.I., and Petersen, S.E.  The Attention System of the Human Brain.
  Annual Rev. Neurosci., 13:25-42, 1990.

My thesis also spends two chapters reviewing this stuff in relation
to a connectionist model.  Below I've also included some other references 
to work that discusses connectionist models of selective attention:

  ** Ahmad, S. (1991) VISIT: An Efficient Computational Model Of 
  Human Visual Attention. PhD Thesis, University of Illinois at
  Urbana-Champaign. Also: Technical Report TR 91-049, International
  Computer Science Institute, Berkeley, CA.

  Ahmad, S. (1992) VISIT: A Neural Model of Covert Visual Attention.
  In: J.E. Moody and S.J. Hanson and R.P. Lippman (eds), "Advances 
  in Neural Information Processing Systems 4", Morgan Kaufmann Publishers.

  ** Ahmad, S. and Omohundro, S. (1991) Efficient Visual Search: A 
  Connectionist Solution. In: Proceedings of the 13th Annual meeting 
  of the Cognitive Science Society, Chicago, pages 293-298.

  Chapman, D. (1990) Vision, Instruction, and Action. PhD
  Thesis, MIT. (Book form available from MIT Press)

  Mozer, M. (1991) The Perception of Multiple Objects: A 
  Connectionist Approach, MIT Press.

  Olshausen, B., Anderson, C., and Van Essen, D. (1992) A
  Neural Model of Visual Attention and Invariant Pattern
  Recognition. Computation and Neural Systems Memo 18, Caltech.

  Sandon, P.A. (1990) Simulating Visual Attention. Journal of
  Cognitive Neuroscience, 2(3): 213-231

The two reports marked ** are available by anonymous ftp 
as files tr-91-040.ps.Z and tr-91-049.ps.Z from
ftp.icsi.berkeley.edu in directory pub/techreports

--------------
Subutai Ahmad
Siemens AG, ZFE ST SN61,                        Phone: +49 89 636-3532
Otto-Hahn-Ring 6,                                 FAX: +49 89 636-2393
W-8000 Munich 83, Germany           E-mail: ahmad@bsun7.zfe.siemens.de
Newsgroups: sci.cognitive
Subject: Re: Consciousness
From: barryp@otago.ac.nz
Date: 24 Aug 92 09:44:52 +1300
Followup-To: sci.cognitive
Distribution: sci
Organization: University of Otago, Dunedin, New Zealand

In article <1992Aug23.014900.16178@news.Hawaii.Edu>, lady@uhunix.uhcc.Hawaii.Edu (Lee Lady) writes:
> 
> It seems to me that one of the things that distinquishes humans from
> other animals is that humans (at least in most cultures) have a real
> sense of time, a sense of the past and future.  While certainly other
> animals have memory in some sense, I don't think they have a real sense
> of the past.  When a dog's owner comes back from being away at college,
> for instance, and the dog seems totally overjoyed, I think it is only our
> anthropomorphism that interprets this as meaning that the dog has been
> aware that the owner was gone a very long time.  I realize that this is
> pretty fuzzy speculation on my part, but there it is, for what it's worth. 
> 
> weeks or even years in the future.  Birds flying south for the winter or
> building a nest in the spring don't seem to have this same sort of
> conscious intentionality, although we anthropomorphically attribute it to
> them.  Do beavers building a dam have some sort of awareness that it will
> be nice to have a dam, or do they just enjoy dam building as an
> activity?  I guess one could figure out experiments to test this.  
> 
One could figure out experiments but teh problem with animals is that we can't
ask them what there reasons for their actions are.  My feeling is that the
time sense is more developed in humans than most other animals but it is
impossible to argue that it is totally lacking elsewhere.  A good point to 
remember is that humans are just animals and any faculty found in humans is
found elsewhere in nature.  Dogs probably think something similar about 
humans.

Barry Phease
From: pluto@cs.ucsd.edu (Mark Plutowksi)
Newsgroups: sci.cognitive
Subject: Re: Neural connections, getting smarter, and TV
Date: 1 Sep 92 19:33:46 GMT

tbeaudoin@vax1.umkc.edu writes:


>	Please excuse my momentary instrusion on this erudite list,
>but i am a high school teacher looking for a tiny bit of help.  I am 
>interested in explaining to my students how using their brain can 
>actually help them use their brain better; that is, that learning can 
>actually increase the number of potential neuron interactions in the brain.
>I am also interested in the ways TV encourages neural connections that make
>it difficult to think critically.  
>	Thanks very much in advance for any help you can give me, 
>either by way of more info/elaboration on my own limited knowledge,
>or by way of references.  


>Thomas Beaudoin
>Lee's Summit High School
>International Baccalaureate Program
>Lee's Summit, MO


Although not quite an answer to what you asked, 
a related result by a U. of Illinois neuropsychologist
(William Greenough)  shows that using your BODY helps
develop your brain.  Reported in the Proc. Nat'l Academy 
of Sciences, Greenough's experiment (with rats of course)
showed a 20% increase in synaptic connections due to learning
to perform certain tasks, resulting in a massive rewiring
of the brain.  

According to Greenough,
when skilled movements are to be learned, alterations
in synaptic connections occur.  When simple repetitive
tasks are learned, vasculature support is altered to 
handle the metabolic load. In the experiments, an increase in blood
vessels occured in the cerebellum of exercising rats, whereas none
occured in lazy rats.

So, tell your students to play hard as well as think!

= Mark
Newsgroups: sci.cognitive
From: pautler@ils.nwu.edu (David Pautler)
Subject: Re: Similarity in Problem-Solving
Organization: The Institute for the Learning Sciences
Date: Wed, 26 Aug 1992 21:40:36 GMT

In article <GHN.92Aug26135350@PTERO.SOAR.CS.CMU.EDU>, ghn@CS.CMU.EDU (Greg Nelson) writes:
> In article <seWmin_00WBK86=W9a@andrew.cmu.edu> Daniel Read <dr3u+@andrew.cmu.edu> writes:
> > 
> > PREVIOUS CLAIM: [by me, DP]
> > 
> > There is little collaboration or agreement between the cognitive psychology
> > researchers I mention in the first paragraph and the CBR folks.  The primary
> > point of disagreement, I believe, is that CBR researchers believe similarity
> > judgements are highly goal-dependent, while the cognitive psychologists
> > (and Hofstadter) think they are highly automatic.
> > 
> > RESPONSE:
> > 
> > There are two curious elements in this posting.  First, the notion that
> > automaticity and goal-dependence are opposite!  They are, as should be
> > evident on even a moments reflection, orthogonal dimensions. 
> 
> I would agree.  Having worked with Thagard and Holyoak for five years
> (not any longer, however, and I can't speak for other researchers) I
> think they'd probably agree too.  It depends somewhat on how you
> define "automaticity" -- if you're using Gentner, Ratterman, etc. as
> your cognitive psychologists, then you must be thinking about "surface
> similarity."  The Holyoak and Thagard analogical mapping model (ACME),
> the Thagard, Holyoak, Nelson, and Gochfeld retrieval model (ARCS), and
> their later work, all acknowledge that both surface similarity and
> goal relevance play important roles in these and other processes.

In email to Dan Read, I admitted that I probably fudged the meaning of
"goal-dependent" to mean "dependent on goals to initiate and guide the
process", not "includes goals as part of the base representation";
I should have used the term "strategic".  I agree that goals can be part
of the representations mapped in the process of judging similarity.  The
argument is over the extent to which goals guide retrieval and mapping.

And my argument isn't limited to "surface similarity", either.  Abstract
remindings, like realizing _West Side Story_ resembles _Romeo and Juliet_,
don't appear to be the result of any strategy and are handled by Gentner's
Structure-Mapping Theory very well.

I don't doubt that some remindings can be goal-relevant, but I don't think
the remindings occur in service of that goal.  They just happen as a product
of our normal flow of processing (which can include reflection on goals), and
only after we notice them can they they be said to be goal-relevant.

	-dp-
Newsgroups: sci.cognitive
From: ghn@CS.CMU.EDU (Greg Nelson)
Subject: Re: Similarity in Problem Solving
Originator: ghn@PTERO.SOAR.CS.CMU.EDU
Organization: School of Computer Science, Carnegie Mellon University
	<KPC.92Aug11172545@zog.arc.nasa.gov>
Date: Wed, 26 Aug 1992 18:53:50 GMT

In article <KPC.92Aug11172545@zog.arc.nasa.gov> kpc@pluto.arc.nasa.gov (k p c) writes:

>   there is also a surprisingly large literature in clustering, which
>   could be construed as finding ways to group nodes in a similarity
>   network.  the encyclopedia of ai discusses it.  does anybody out there
>   work in clustering or apply its results?
>
>   my question is: how does this research relate to CBR?

Basically, my feeling is, it doesn't.  Not that it shouldn't, but
these are two very disparate research communities and they don't have
much to say to each other.  A few are trying to combine both methods
into the same system, but are still keeping them as distinct methods.

In article <seWmin_00WBK86=W9a@andrew.cmu.edu> Daniel Read <dr3u+@andrew.cmu.edu> writes:
> 
> PREVIOUS CLAIM:
> 
> There is little collaboration or agreement between the cognitive psychology
> researchers I mention in the first paragraph and the CBR folks.  The primary
> point of disagreement, I believe, is that CBR researchers believe similarity
> judgements are highly goal-dependent, while the cognitive psychologists
> (and Hofstadter) think they are highly automatic.
> 
> RESPONSE:
> 
> There are two curious elements in this posting.  First, the notion that
> automaticity and goal-dependence are opposite!  They are, as should be
> evident on even a moments reflection, orthogonal dimensions. 

I would agree.  Having worked with Thagard and Holyoak for five years
(not any longer, however, and I can't speak for other researchers) I
think they'd probably agree too.  It depends somewhat on how you
define "automaticity" -- if you're using Gentner, Ratterman, etc. as
your cognitive psychologists, then you must be thinking about "surface
similarity."  The Holyoak and Thagard analogical mapping model (ACME),
the Thagard, Holyoak, Nelson, and Gochfeld retrieval model (ARCS), and
their later work, all acknowledge that both surface similarity and
goal relevance play important roles in these and other processes.

Just my two cents...

Greg Nelson             Disclaimer:  My opinions may reach high RPM's and
ghn+@cs.cmu.edu         temperatures without suffering a significant loss
                        of bogosity.  If anyone else, including CMU or the
                        School of Computer Science, agrees with them, it's
                        not my fault.
From: dmp@tigger.cs.bham.ac.uk (Donald Peterson)
Newsgroups: sci.cognitive
Subject: FINAL CALL : AISB'93
Date: 3 Sep 92 16:25:00 GMT
Organization: Birmingham University Computer Science


    ================================================================

                          AISB'93 CONFERENCE

                        FINAL CALL FOR PAPERS 

    Theme: "Prospects for AI as the General Science of Intelligence"

                       29 March --  2 April 1993 

                        University of Birmingham

    ================================================================

The Society for the Study of Artificial Intelligence and the
Simulation of Behaviour will hold its ninth bi-annual conference on
the dates above at the University of Birmingham.

This is a reminder that the deadline for submitted papers and posters
is 15 September 1992 (though a few days leeway will be allowed). 

Papers are invited in any of the normal areas represented at AI and
Cognitive Science conferences, including:

AI in Design,
AI in software engineering
Teaching AI and Cognitive Science,
Analogical and other forms of Reasoning
Applications of AI,
Automated discovery,
Control of actions,
Creativity,
Distributed intelligence,
Expert Systems,
Intelligent interfaces
Intelligent tutoring systems,
Knowledge representation,
Learning,
Methodology,
Modelling affective processes,
Music,
Natural language,
Naive physics,
Philosophical foundations,
Planning,
Problem Solving,
Robotics,
Tools for AI,
Vision,

The conference proceedings will be published. Long papers and invited
papers will definitely be included. Selected poster summaries may be
included if there is space.


                 PAPER LENGTH and SUBMISSION DETAILS

Full papers:
    10 pages maximum, A4 or 8.5"x11", no smaller than 12 point print
    size Times Roman or similar preferred, in letter quality print.

Poster submissions
    5 pages summary

Excessively long papers will be rejected without being reviewed.

All submissions should include

    1. Full names and addresses of all authors
    2. Electronic mail address if available
    3. Topic area
    4. Label: "Long paper" or "Poster summary"
    5. Abstract no longer than 10 lines.
    6. Statement certifying that the paper is not being
       submitted elsewhere for publication.
    7. An undertaking that if the paper is accepted at least
       one of the authors will attend the conference.

THREE copies are required.


                  EMAIL, PAPER MAIL, PHONE and FAX. 

Email:
    * aisb93-prog@cs.bham.ac.uk 
      (for communications relating to submission of papers to the programme)
    * aisb93-delegates@cs.bham.ac.uk 
      (for information on accommodation, meals, programme etc. as it 
       becomes available --- enquirers will be placed on a mailing list)

Address:
    AISB'93 (prog) or AISB'93 (delegates), 
    School of Computer Science,
    The University of Birmingham,
    Edgbaston,
    Birmingham,
    B15 2TT, 
    U.K. 

Phone:
    +44-(0)21-414-3711

Fax:
    +44-(0)21-414-4281

----------------------------------------------------------------------------
Newsgroups: sci.cognitive
Organization: K.U.Leuven - Academic Computing Center
Date: Wednesday, 26 Aug 1992 09:05:39 +02
From: Johan Van Rensbergen <FPAAS01@cc1.kuleuven.ac.be>
Newsgroups: sci.cognitive
Subject: Re: Cognitive Science Programmes
Distribution: na
 <ASHWIN.92Aug25135718@leo.gatech.edu>

========================================================================
The University of Leuven (Belgium, Europe) has an excellent International
Study Program in Artificial Intelligence and Cognitive Science.
To obtain the degree of Master of Artificial Intelligence and Cognitive
Science, applicants must hold a Master`s degree or its equivalent in
Bahavioral Sciences or Humanities.
This Master`s program can be finished in 1 year.  All courses are
taught in English.

More detailed information and application forms can be obtained from
Prof. G. d`Ydewalle
Laboratory of Experimental Psychology
Tiensestraat 102
B-3000 Leuven, Belgium.

E-mail :  gery at blekul11.earn

For students with a background in the field of engineering or sciences,
there is also a parallel program yielding the degree of Master of Engi-
neering in Artificial Intelligence and the degree of Master of A.I.
More information available at the same adress.