Date: 15 Sep 90 08:50:58-PST
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Vision-List Digest	Sat Sep 15 08:50:58 PDT 90

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Today's Topics:

 grouping
 Locating edges in a field of view
 Industrial Vision Metrology Conference
 VISION and NN; special issue of IJPRAI
 available document
 MAC AI demos

----------------------------------------------------------------------

Date: 11 Sep 90 14:57:28+0200
From: Tilo Messer <messer@suncog1.informatik.tu-muenchen.dbp.de>
Subject: grouping

I am interested in grouping regions (not edges!) for increasing the
performance of an object identification system. It is part of a planned
real-time interpretation system of scenes taken from a moving camera.

I found a few articles and papers about grouping of egdes (Lowe et. al.), but
these don't fit. Is anybody else interested in this topic or does anybody
know some theoretical and practical work in this area?

I would be glad about some useful hints.

Thanks, Tilo

 | |\  /|					voice: ++ 49 89 48095 - 224
 | | \/ | FORWISS, FG Cognitive Systems		fax: ++ 49 89 48095 - 203
 | |    | Orleansstr. 34, D - 8000 Muenchen 80, Germany

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Date: Wed, 12 Sep 90 11:05:03 EDT
From: ICR - Mutual Group  <rjspitzi@watserv1.waterloo.edu>
Subject: Locating edges in a field of view

Here is an interesting real-world problem for you comp.ai.vision'aries
out there:

   I have built a scanning unit which basically produces a picture in  
memory of a 2-D object (such as a peice of paper) passing under the
scanning unit.  The image is made only of a series of points outlining
the object itself.  The object passing under the scanner is roughly
rectangular (i.e. four edges) but the edges can be somewhat bowed to
make slightly concave or convex edges.  There should be definate corners
however.

  The problem is this.  Given the limited information that I receive from
the image, I must locate the edges of the object and calculate each side's
length.  The result should be a *very* accurate estimate of the height and
width of the object and hence the area it covers.
 
   Oh ya, one other twist, the object can come through in any orientation.
There is no guarantee a corner will always be first.

   Any ideas you have for algorithms, or documents you could point me
toward would be greatedly appreciated!  Like I said, an interesting problem.

------------------------------

Date: 	Tue, 11 Sep 90 11:05:00 EDT
From: ELHAKIM@NRCCIT.NRC.CA
Subject: Industrial Vision Metrology Conference

                      ANNOUNCEMENT AND CALL FOR PAPERS

                         INTERNATIONAL CONFERENCE ON
                         INDUSTRIAL VISION METROLOGY

   Location: The Canadian Institute for Industrial Technology
             Winnipeg, Manitoba, Canada

   Date:     July 11-13, 1991

   Organized by:
        -International Society for Photogrammetry & Remote Sensing
         Commission V: Close-Range Photogrammetry and Machine Vision
         WG V/1: Digital and Real-time Close-range Photogrammetry Systems
        -National Research Council of Canada

   Proceeding published by:
         SPIE- The International Society for Optical Engineering

   Focusing on:
         Industrial applications of metric vision techniques

   Topics include:
        -Vision metrology techniques
        -Real-time systems
        -3-D object reconstruction
        -Decision algorithms
        -System calibration
        -Shop-floor metrology problems
        -Applications such as dimensional inspection

   500-1000 words abstracts are to be submitted before January 1, 1991 to:

        Dr. S. El-Hakim
        National Research Council
        435 Ellice Avenue
        Winnipeg, Manitoba, Canada R3B 1Y6
        tel:(204) 983-5056 / Fax:(204) 983-3154

------------------------------

Date: Thu, 13 Sep 90 15:29:49 PDT
From: skrzypek@CS.UCLA.EDU (Dr. Josef Skrzypek)
Subject: VISION and NN; special issue of IJPRAI

Because of repeat enquiries about the special issue of IJPRAI (Intl.
J. of Pattern Recognition and AI) I am posting the announcement again.

	    IJPRAI	 CALL FOR PAPERS	 IJPRAI

We are  organizing a  special  issue of  IJPRAI (Intl.   Journal of
Pattern Recognition  and Artificial Intelligence)  dedicated to the
subject   of neural networks in  vision   and pattern  recognition.
Papers  will be  refereed.  The  plan  calls for  the  issue  to be
published  in the fall of   1991.   I  would  like  to invite  your
participation.

   DEADLINE FOR SUBMISSION: 10th of December, 1990

   VOLUME TITLE: Neural Networks in Vision and Pattern Recognition

   VOLUME GUEST EDITORS: Prof. Josef Skrzypek and Prof. Walter Karplus
   Department of Computer Science, 3532 BH
   UCLA
   Los Angeles CA 90024-1596
   Email: skrzypek@cs.ucla.edu or karplus@cs.ucla.edu
   Tel: (213) 825 2381
   Fax: (213) UCLA CSD

		      DESCRIPTION

The capabilities    of   neural    architectures   (supervised  and
unsupervised learning,   feature  detection and   analysis  through
approximate pattern matching, categorization and self-organization,
adaptation, soft constraints,  and signal based processing) suggest
new approaches to solving problems in vision, image  processing and
pattern recognition as applied to  visual stimuli.  The purpose  of
this special issue  is to encourage further  work and discussion in
this area.

The  volume will  include both invited  and submitted peer-reviewed
articles.  We are seeking submissions  from researchers in relevant
fields,   including,  natural  and  artificial vision,   scientific
computing,  artificial  intelligence, psychology, image  processing
and pattern recognition.  "We  encourage submission of: 1) detailed
presentations of  models   or  supporting  mechanisms,   2)  formal
theoretical analyses, 3)  empirical and methodological studies.  4)
critical  reviews   of neural  networks   applicability to  various
subfields of vision, image processing and pattern recognition.

Submitted    papers may  be    enthusiastic   or  critical  on  the
applicability   of  neural   networks  to   processing   of  visual
information.   The  IJPRAI   journal   would  like    to  encourage
submissions    from  both  , researchers  engaged   in analysis  of
biological      systems         such            as         modeling
psychological/neurophysiological data using neural networks as well
as from members of  the engineering  community who are synthesizing
neural network  models.  The number of  papers that can be included
in this special issue  will be limited.  Therefore,  some qualified
papers may be encouraged  for submission to  the regular issues  of
IJPRAI.
		       SUBMISSION PROCEDURE

Submissions should  be sent to  Josef Skrzypek, by 12-10-1990.  The
suggested length is  20-22 double-spaced  pages  including figures,
references,  abstract and  so  on. Format  details, etc.  will   be
supplied on request.

Authors  are strongly  encouraged  to  discuss  ideas  for possible
submissions with the editors.

The  Journal   is  published  by   the  World   Scientific and  was
established in 1986.

Thank you for your consideration.

------------------------------

Date: Wed, 5 Sep 90 13:21:08 +0200
From: ronse@prlb.philips.be
Subject: available document

The following unpublished working document is available. If you want a
copy of it, please send me:

- Your complete postal (snail mail) address, preferably formatted as on
an enveloppe (cfr. mine below); an e-mail address is useless in this
context.

- The title of the working document.

				Christian Ronse

Internet:			ronse@prlb.philips.be
BITNET:				ronse%prlb.philips.be@cernvax

				Philips Research Laboratory
				Avenue Albert Einstein, 4
				B-1348 Louvain-la-Neuve
				Belgium

				Tel: (32)(10) 470 611	(central)
				     (32)(10) 470 637	(direct line)
				Fax: (32)(10) 470 699

=========================================================================
A twofold model of edge and feature detection

C. Ronse

September 1990

ABSTRACT.

Horn's model of surface reflectance shows that edges in
three-dimensional surfaces lead to grey-level edges combining in various
ways sharp or rounded steps, lines and roofs. The perceptual analysis of
extended edges necessicates the localization not only of step and line
edges, but also of roof edges and Mach bands, and more generally of
discontinuities and sharp changes in the n-th derivative of the
grey-level. Arguments are given which indicate the inadequacy of
locating features at zero-crossings of any type of smooth operator
applied to the image, and the necessity of orientationally selective
operators. The null space of feature detection is defined; it contains
in particular all constant signals. Oriented local features are modelled
as the linear superposition of a featureless signal (in the null space),
an even-symmetric and/or an odd-symmetric feature, measured by
convolution with respectively even-symmetric and odd-symmetric
functions. Advantages of energy feature detectors are given.

KEY WORDS.
Edge types, zero-crossings and peaks, orientational selectivity, linear
processing, feature symmetry, energy feature detector.

------------------------------

Date: Wed, 12 Sep 90 02:41:16 GMT
From: pegah@pleiades.cps.msu.edu (Mahmoud Pegah)
Subject: MAC AI demos
Organization: Computer Science, Michigan State University, E. Lansing

Greetings;

I am trying to find freeware demos of AI that run on the MAC.  This
will be used in a classroom setting (not in a lab) and will be
projected on a large screen from the video on the MAC.

Demos having to do with search space techniques, natural language
processing, vision, neural nets, knowledge based systems... would all
be items I would like to FTP for use here.

These demos will be used in an intro grad level survey course in AI.

Reply to me directly, and indicate whether you would like your
demo to be listed in a catalogue of AI educational demos that
I will prepare from the mail I get. I will post the composed directory
back to the net in two weeks time.

Please indicate an FTP host (with internet number) from which your
demo can be FTPed.

Thanks in advance.

-Mahmoud Pegah				pegah@pleiades.cps.msu.edu
AI/KBS Group				pegah@MSUEGR.BITNET
Comp Sci Dept				... uunet!frith!pegah
Mich State Univ

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