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Date: 3 Dec 88 00:16:37 GMT
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Vision-List Digest	Fri Dec 02 16:16:37 PDT 88

 - Send submissions to Vision-List@ADS.COM
 - Send requests for list membership to Vision-List-Request@ADS.COM

Today's Topics:

 DeScreening
 dissemination of sharware for Image Processing and Computer Vision
 Robotics Seminar

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Date:         Thu, 24 Nov 88 15:47:37 IST
From: Shelly Glaser  011 972 3 5450119 
Subject:      DeScreening

Please publish the following question in the Vision Newsletter:

     I  am looking  for information  on practical  solutions to  the
     "de-screening" problem: taking a half-toned image (like the one
     in printed book or magazine)  and removing the half-tone screen
     so we get a true continuous-gray-scale image (as opposed to the
     binary  pulse area  modulated  half-tone  image).  The  obvious
     solution, low-pass filtering, often kills  too much of the fine
     details  in the  image,  so  I am  looking  for something  more
     sophisticated.

     Many thanks,
                             Sincerely Yours,
                                                       Shelly Glaser

                    Department of Electronic, Communication, Control
                                                and Computer Systems
                                              Faculty of Engineering
                                                 Tel-Aviv University
                                                    Tel-Aviv, Israel

                                                   FAX: 972 3 419513
                               Computer network: GLAS@TAUNIVM.BITNET

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Date: 26 Nov 88 18:58:00 GMT
From: annala%neuro.usc.edu@oberon.usc.edu (A J Annala)
Subject: possible use of comp.ai.vision
Organization: University of Southern California, Los Angeles, CA


There has been some discussion in comp.graphics about using comp.ai.vision
as the home for discussions about andf distribution of image processing
software.  I personally suspect that this would not be an appropriate use
of the comp.ai.vision group; however, I would appreciate email to my user
account (which I will summarize) on this issue.

Thanks, AJ Annala ( annala%neuro.usc.edu@oberon.usc.edu )

[ Discussions on IP sofware are most definitely appropriate for the Vision
  List and comp.ai.vision.  Yet, as with other SIG networks, it is not 
  appropriate to submit the code in this forum.  Rather, if there is 
  shareware IP and CV software which should be disseminated, then a
  new network newsgroup entitled something like COMP.BINARIES.VISION
  should be established.  This requires a site and moderator for this new
  net which can establish and manage this new facility.  Any volunteers?

			phil...  	]

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Date: Tue, 29 Nov 88 19:30:46 PST
From: binford@Boa-Constrictor.Stanford.EDU.stanford.edu (Tom Binford)
Subject: Robotics Seminar

			 Robotics Seminar
			Monday, Dec 7, 1988
		4:15pm Cedar Hall Conference Room


            SOLVING THE STEREO CONSTRAINT EQUATION

		Stephen Barnard
		Artificial Intelligence Center
		SRI International

The essential problem of stereo vision is to find a disparity map
between two images in epipolar correspondence. The stereo constraint
equation, in any of its several forms, specifies a function of disparity
that is a linear combination of photometric error and the first order
variation of the map.  This equation can also be interpreted as the
potential energy of a nonlinear, high dimensional dynamic system.  By
simulating either the deterministic newtonian dynamics or the
statistical thermodynamics of this system one can find approximate
ground states (i.e., states of minimum potential energy), thereby
solving the stereo constraint equation while constructing a dense
disparity map.

The focus of this talk will be a stochastic method that uses a
microcanonical version of simulated annealing.  That is, it explicitly
represents the heat in the system with a lattice of demons, and it cools
the system by removing energy from this lattice.  Unlike the
``standard'' Metropolis version of simulated annealing, which simulates
the canonical ensemble, temperature emerges as a statistical property of
the system in this approach.  A scale-space image representation is
coupled to the thermodynamics in such a way that the higher frequency
components come into play as the temperature decreases.  This method has
recently been implemented on a Connection Machine.


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End of VISION-LIST
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