paves the way for a new kind of
artificial vision.
strength is in capturing movement,
for example the milk drops.
The retina is an enormously powerful
tool. It sorts through massive
amounts of data
while operating on
only a fraction of the power that a
conventional digital camera and
computer would require to do the
same task.
Now, engineers at a company called
iniLabs in Switzerland are applying
lessons from biology in an effort to
build a more efficient digital camera
inspired by the human retina.
Like the individual neurons in our
eyes, the new camera—named the
Dynamic Vision Sensor (DVS)—
responds only to changes in a given
scene. This approach eliminates large
swaths of redundant data and could
be useful for many fields, including
surveillance, robotics, and microscopy.
“Your eye and my eye are digital
cameras too. [They’re] just a different
kind of digital camera,” says Tobi
Delbruck, the chief scientific officer at
iniLabs. “We had machine vision that
was as good as possible with existing
architecture and hardware. But
compared to biology, machine vision
is pathetically poor.”
An ordinary camera will take in
everything it sees, storing the
information to be processed later.
This uses up a lot of power and a lot
of space. Neurons in the eye,
however, fire only when they sense a
change—such as when a particular
part of a scene gets brighter or
dimmer. The DVS mimics that
selectivity, transmitting information
only in response to a shift in the
scene. That takes less power and
leaves less information processed.
Artificial retina: The Dynamic Vision
Sensor (DVS) responds only to
changes in the scene, eliminating
large swaths of irrelevant data.
This feature could be especially useful
for recording scenes that do not
change often. For example, when
sleep researchers videotape their
subjects, they are later forced to comb
through hours of uneventful footage.
With a sensor like the DVS, important,
active portions of the data are
automatically highlighted.
The pixels in the DVS also mimic the
way an individual eye neuron will
calibrate itself to a particular location:
that cell and those responsible for
another area will respond to incoming
data in slightly different ways, so one
neuron might be very sensitive to
input while another takes more
stimulation to fire. Similarly, each pixel
of the DVS adjusts its own exposure.
This allows the DVS to handle uneven
lighting conditions, though it also
requires enormous pixels that are 10
times the size of those in a modern
cell-phone camera.
The DVS is built to work with IBM’s
new TrueNorth computer architecture
(see “IBM Researchers Show
Blueprints for Brainlike Computing
”). TrueNorth is a programming
approach that mimics biology—
information is stored, processed, and
shared in a network of
“neuromorphic” computer chips,
inspired by the neural networks in the
brain.
“What we’re talking about—the
cameras sending information when
something changes—is actually a very
central theme to how the brain works,
or at least how neuroscientists think it
works,” says Nabil Imam, a computer
scientist at Cornell University, who is
part of the Cornell team that helped
IBM develop its neuromorphic chips.
“We’re capturing brain features at a
high level.”
By combining their camera with
TrueNorth architecture, Delbruck and
his team believe, they will achieve a
device that’s better at handling
dynamic, real-time problems.
The DVS is available for purchase for
about $2,700 and has been used in
several research projects, including
one that recorded traffic and another
that involved tracking particles in a
fluid. The team plans to continue
improving the device. The next goals
are to add color sensitivity and to
enlarge the camera’s retina from its
current resolution of 240x180.
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