Re: More on perception
- View Sourcehttp://220.127.116.11/cgi-bin/linkrd?
- View SourceYoung:> 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_ ...Borgia: The _depth perception_ part of the post _the search for om_
> Maybe the lasers that land surveyors use to judge distance would be
> easier and more accurate than running an algorithm on an image.
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;YOung:> A good question. I guess that it would either be programmed
> > 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.
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"
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
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
Does this makes any sense?
- View SourceCan 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
"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
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
"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
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-
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
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.
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.