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Re: More on perception

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  • red_cell_op
    Here s a quote from Robert O Shea s webpage, at: http://psy.otago.ac.nz/r_oshea/ I have so many interests in human visual perception it makes my eyes blur.
    Message 1 of 14 , Jul 4, 2002
      Here's a quote from Robert O'Shea's webpage, at:
      http://psy.otago.ac.nz/r_oshea/

      I have so many interests in human visual perception it makes my eyes
      blur. Currently I'm working on projects on: Binocular rivalry
      (including
      spread of rivalry, the nature of rivalry suppression, and rivalry in
      split-brain observers); Early history of binocular vision; Interocular
      transfer of aftereffects; Meteorological optics (including why we
      perceive the bowl of the sky, perception of sun rays and their linear
      perspective, and effects of height on perceived eye level); Size and
      depth perception over large distances; Spatial frequency, blur,
      contrast, and luminance as depth cues; Colour constancy with
      reflected and emitted light; Kinetic depth effect; Perception of
      contrast and blur in the peripheral visual field; Colour spreading in
      the McCollough effect; and Vernier acuity with opposite-contrast and
      dichoptic figures.

      At the same page, you can also download the PDF of an article
      describing
      some of the work covered by the talk:

      O'Shea, R. P., & Corballis, P. M. (2001).
      Binocular rivalry between complex stimuli in split-brain observers.
      Brain and Mind, 2, 151-160.

      You can check out the webpage of O'Shea's collaborator, Paul
      Corballis,
      here:
      http://www.dartmouth.edu/~cogneuro/corballis.html

      Raj
      ---------- Forwarded message ----------
      Date: Tue, 02 Jul 2002 16:01:14 -0400
      From: George Alvarez <geoalvarez@w...>
      To: VisionLabTalks <geoalvarez@w...>
      Subject: Harvard Vision Lab Talk Wednesday: Robert P. O'Shea,
      Wednesday,
      July 3rd

      *************************************************************

      Harvard Vision Lab Seminar Series Announcement

      *************************************************************

      Binocular rivalry in split-brain observers

      Robert P. O'Shea & Paul M. Corballis
      Department of Psychology, University of Otago

      Wednesday, July 3rd
      12:00 noon
      Rm 765, William James Hall, Harvard University
      33 Kirkland Street, Cambridge

      A split-brain observer has had the corpus callosum, the major tract
      between the left and the right hemispheres, cut to relieve epilepsy.
      One can selectively stimulate the left or right hemisphere by
      presenting stimuli to the right or left of fixation respectively.
      Likewise, one can elicit responses from the left or right hemisphere
      by requiring the observer to press keys with the right or left hand
      respectively. On many tasks, these fascinating individuals behave as
      though each hemisphere is acting independently of the other. Are there
      differences in rivalry between the isolated hemispheres? To answer
      this, we have studied two split-brain observers, VP and JW.

      We first trained split-brain and intact-brain observers to respond to
      real alternations between nonrival stimuli by pressing keys with the
      ipsilateral hand. When we presented rival stimuli to the isolated
      hemispheres of split-brain observers, their key presses showed that
      their experiences of rivalry were similar to those of intact-brain
      observers. When we presented stimuli to the left hemisphere of the
      split-brain observers, they were also able to describe the chaotic
      appearance of rivalry alternations.

      Over many experiments, mainly on JW, we conclude that rivalry is
      essentially normal when processed in each isolated hemisphere,
      although periods of dominance are slower from the left hemisphere than
      from the right. Rivalry is normal from stimuli such as sinusoidal
      gratings, coloured faces, random dots, and Diaz-Caneja displays. The
      distributions of periods of dominance follow the classical gamma
      shape. The only case in which lacking a corpus callosum made a
      difference was that the synchronization of rivalry in two regions of
      the visual field did not happen when the two regions were processed by
      different hemispheres. We think that the longer rivalry periods from
      the left hemisphere reflect only its response bias. We conclude from
      the qualitative similarity of rivalry in the two isolated hemispheres
      that the rivalry mechanism is low in the visual system.

      Wednesday, July 3rd
      12:00 noon
      Rm 765, William James Hall, Harvard University
      33 Kirkland Street, Cambridge
    • red_cell_op
      http://64.4.22.250/cgi-bin/linkrd? _lang=EN&lah=74d76f6202fc28863821a712ea0cd4a7&lat=1025813685&hm___acti on=http%3a%2f%2fcgi%2ezdnet%2ecom%2fslink%3f182325
      Message 2 of 14 , Jul 4, 2002
        http://64.4.22.250/cgi-bin/linkrd?
        _lang=EN&lah=74d76f6202fc28863821a712ea0cd4a7&lat=1025813685&hm___acti
        on=http%3a%2f%2fcgi%2ezdnet%2ecom%2fslink%3f182325
      • red_cell_op
        Young: Actually, film works at 24 frames per second, and TV at 25/sec. Your eyes, however, work at about 1000 fps. Your brain is cognitive of these
        Message 3 of 14 , Jul 4, 2002
          Young:> Actually, film works at 24 frames per second, and TV at
          25/sec. Your > eyes, however, work at about 1000 fps. Your brain is
          cognitive of > these pictures in a vastly different way, so that
          a "frames per> second" rating becomes meaningless.
          >

          Borgia: I will re-check my notes about 25/sec shots(of the human
          eye)....but this comparison of shots/second is moot--that is only
          tangent to the point i was trying to make, which is: isolating each
          frames and executing algorithms on them in _real time_. I have
          forgotten about the name of a super-fast cameras that can take
          thousands of shots/second--much faster, and with better resolution
          than the human eye. The rate is not all that important as much as the
          computing power that will process the _stills_ in real time. On how
          the brain process these visual data, is irrelevant in my view--we
          don't have to simulate the brain. Just let us do something that
          works, brain-imitation or no brain-imitation, regardless.

          > > On _depth perception_ ...
          >
          > Maybe the lasers that land surveyors use to judge distance would be
          > easier and more accurate than running an algorithm on an image.

          Borgia: The _depth perception_ part of the post _the search for om_
          has nothing to do with "running algorithm on an image". I do not
          recall typing "running algorithm on an image to determine depth
          perception", what i recalled doing was proposing two approaches from
          physics for depth perception. Thanks for the thought though.

          > > If a robot is ordered to find an exit out of a room;
          > > and the robot circles about looking for a door or
          > > opening and happens to find none--the room is walled
          > > completely. How does the robot improvise on its own?
          > > E.g. Break down a wall.
          >
          YOung:> A good question. I guess that it would either be programmed
          to > destroy objects in it's way, or it would have to learn to do
          that by > itself. How, though, I'm not sure!


          Borgia:From my own personal experience i think creativity =
          integration. You see an apple falling down and then integrate that
          visual data with other data to get Newton's gravitation. Think about
          how we CREATIVELY solve problems, it seems to be one and only one
          way: integrating relevant but seemily disparate data into a new
          synthesis. Can a robot on seeing a woman slicing an apple on a street
          corner break down this visual input into some version of this crude
          formalization:"sharp object(of certain characteristics y) + force +
          an object(of certain characteristics x))--> a split x + object y"??.
          Then, how can one write algorithms that will attempt to match this
          _solution pattern_ with a _problem pattern_(e.g. getting out of a
          walled room)?. To find a solution to something, first, the problem
          has to be defined. Is an algorithm capable of partially formalizing
          environmental events, feasible?
          After the problem of the walled room has been formalized:
          (a)Get from point_A_(inside the walled enclosure) to point _B_(out of
          the walled enclosure)
          (b)How? Tranverse a sectional area without walls
          (c)There is no such area.
          [The problem has been determined: no such area. Any "~x" that
          fustrates acquiring an objective "z":is a problem.

          Can the robot then formalize this scenario into this format:
          X(passage) + Y(robot; moving) = Z(objective--get from A to B)?

          Since the room is walled, then there is no X, only ~X. "~X"
          being "walls".

          Can the robot then proceed to this stage: ~X + Y = ~Z.--this will now
          be the _state of events_ in the robots' cpu, There are logically two
          options: Opt for ~Z--and not leave the room(that will be against its
          instruction, thus the robot can't do that) Or two, eliminate ~X--this
          fits with its instructions.
          How do you eliminate ~X?
          Scan ~X(the walls), from the scanning the robot will gather some
          scientific data from its scans--physical and chemical properties of
          ~X.
          Then, First priority:(i)how do you eliminate walls or things bearing
          close resemblance to the physical/chemical properties of walls as
          determined by the robot's scans?
          Second priority:(ii)How don you eliminate any object?

          Inorder to answer these questions by itself(the robot), algorithms
          then prompt the robot to conduct memory search for visual data
          involving scenes of any form of separation involving physical
          objects?--cutting, slicing, dicing, twisting, cracking, burning, ,
          chemical dissolution in a degree relevantly close to the
          physical/chemical objects of walls and digressing from that point
          away. etcetera. Then, formalize these scenes into a X + Y = Z, partly
          using _cause and effect_/physics, and attempt to implement the
          _formalization_ on the walls so as to create a passage way out of a
          walled room?

          Does this makes any sense?

          --Borgia, C.
        • red_cell_op
          Can a chip help computers see in 3D? 09:07 Wednesday 3rd July 2002 Stephen Shankland, CNET News.com A Silicon Valley start-up believes it can give stereo
          Message 4 of 14 , Jul 4, 2002
            Can a chip help computers see in 3D?
            09:07 Wednesday 3rd July 2002
            Stephen Shankland, CNET News.com


            A Silicon Valley start-up believes it can give stereo vision to video
            cameras by encoding a processing scheme into a custom chip. It could
            ready the way for robots with depth perception
            A Silicon Valley start-up believes it can improve computer vision by
            combining a custom-designed chip with the way humans see.

            Human brains judge how far away objects are by comparing the slightly
            different view each eye sees. Tyzx hopes to build this stereo vision
            process into video cameras.



            The Palo Alto, California-based start-up has encoded a processing
            scheme into a custom chip called DeepSea, allowing the processor to
            determine not only the color of each tiny patch of an image but also
            how far away that patch is from the camera.

            The technology could be a boon for surveillance systems,
            strengthening the ability to track people in banks, stores or
            airports. But stereo vision could have wider uses as well, helping
            focus a computer's attention and cutting down on the amount of data
            that needs to be crunched.

            For instance, a vacuuming robot trying to discern a table leg through
            pattern recognition could avoid getting caught up in examining the
            wallpaper in the background. Similarly, vehicles could use the
            technology to detect obstacles in their path while filtering out
            visual noise.

            "The biggest value is the segmentation. It separates out the portion
            of the image that interests you," said Takeo Kanade, a stereo vision
            computing pioneer at Carnegie Mellon University and a member of an
            independent Tyzx advisory board. "You have not only appearance but
            also distance to each point. That makes the subsequent processing,
            such as object detection and recognition, significantly easier."

            Tyzx's first customers are mostly research labs, with other potential
            business partners evaluating the technology, chief executive Ron Buck
            said in an interview. Those who have bought the systems include MD
            Robotics, the company that makes the robotic arm for the Space
            Shuttle and, in the future, for the International Space Station. And
            ChevronTexaco is employing the equipment for "augmented reality"
            work -- supplementing what ordinary people see with computer imagery
            for tasks such as operating oil platform cranes in bad weather.

            The company hopes to win customers in the military and surveillance
            industries, and, as costs go down, to expand into
            broader "intelligent environments" where, for example, doors could
            open automatically or a house could send a medical alert if someone
            has been sitting still for an unusually long time. But Tyzx faces a
            solid challenge translating the idea into a workable product.

            "I believe it's a great idea," Kanade said. "Conceptually it's easy,
            but computationally it's not."

            Tyzx is backed by Vulcan Ventures, the investment firm of Microsoft
            co-founder Paul Allen. It has less than 20 employees, some of whom
            have years of experience in the field.

            John Woodfill and Gaile Gordon launched the company in early 2001,
            but much of their work precedes that date. A key formula used in the
            custom chip dates back to 1990, and Tyzx has had prototype chips for
            about a year, Buck said. It's only recently, though, that Tyzx's
            ideas have become economically feasible.

            Eyes on the prize
            Stereo vision may indeed be a leap ahead for computers, but there's
            still a long way to go before machines can achieve the sophistication
            of human sight.

            "Because vision comes so naturally to us, we don't appreciate the
            problem intuitively," said David Touretzky, a computational
            neuroscientist at Carnegie Mellon. "I don't think we got that
            appreciation until people started trying to build computer systems to
            see."

            A large fraction of the brains of primates such as monkeys, apes and
            humans is devoted to processing visual information, Touretzky said.
            There are more than 20 different specialised areas for tasks such as
            recognizing motion, color, shapes and spatial relationships between
            objects.

            "These areas are all interconnected in ways not fully understood
            yet," Touretzky said, but together these parts of the brain can
            discern the difference between the edge of a shadow and the edge of
            an object or compensate for color shifts that occur when the sun
            comes out.

            Tyzx isn't the only company trying to capitalize on stereo computer
            vision. Microsoft Research is working on technology that extracts 3D
            information from 2D pictures. Point Grey Research already has cameras
            on the market, though its processing algorithms require a full-
            fledged computer.

            In Japan, a company called ViewPlus is working in collaboration with
            Point Grey Research. Its products, though, combine as many as 60
            cameras into a spherical system that produces 20 simultaneous video
            information streams.

            These other companies are taking a fundamentally different approach
            to Tyzx in one respect: Their systems compare more than two images.

            Carnegie Mellon's Kanade said it might seem that comparing three
            images would be a harder computational task, but in fact having more
            data to work with can actually make the process simpler.

            DeepSea processing
            The key development at Tyzx is its custom chip, which runs an
            algorithm called census correspondence that quickly finds
            similarities across two streams of video images broken up into a
            square grid of 512 pixels, or picture elements. The chip can perform
            this comparison 125 times per second with a video image measuring 512
            by 512 pixels, but the 33MHz DeepSea consumes much less power than
            full-fledged processors such as Intel's Pentium.

            "It allows incredibly compute-intensive searching for matching pixels
            to happen very fast at a very low price. It allows us to bring stereo
            vision to computers," chief executive Buck said.

            Another important development needed to reach Tyzx's low-price
            targets is camera sensors built using the comparatively inexpensive
            complimentary metal-oxide semiconductor (CMOS) technology -- the same
            process used to build most computer chips, Buck said. Digital cameras
            today use more elaborate -- but more expensive -- "charge-coupled
            devices", or CCDs.

            Kanade has an appreciation for the difficulties involved. About 10
            years ago he built an expensive but pioneering stereo vision system
            with many processors that could determine range information by
            comparing the images from multiple cameras.

            Since then, more powerful computer processing abilities have elevated
            the potential of the field, which Kanade believes will take off once
            stereo cameras are as cheap as today's ordinary video cameras.

            "I'm very impressed with the various attempts which made real-time
            stereo possible. I think the Tyzx effort may be one of the eventual
            successes," Kanade said.



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