Path: utzoo!utgpu!watmath!clyde!att!rutgers!ucsd!ucbvax!ADS.COM!Vision-List-Request From: Vision-List-Request@ADS.COM (Vision-List moderator Phil Kahn) Newsgroups: comp.ai.vision Subject: Vision-List delayed redistribution Message-ID: <8812030020.AA04138@deimos.ads.com> Date: 3 Dec 88 00:16:37 GMT Sender: daemon@ucbvax.BERKELEY.EDU Reply-To: Vision-List@ADS.COM Distribution: inet Organization: The Internet Lines: 112 Approved: vision-list@ads.com 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 ---------------------------------------------------------------------- Date: Thu, 24 Nov 88 15:47:37 IST From: Shelly Glaser 011 972 3 5450119Subject: 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 ------------------------------ 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... ] ------------------------------ 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. ------------------------------ End of VISION-LIST ********************