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A Self-Organizing Neural Network That Learns to Detect and
Represent Visual Depth from Occlusion Events
Jonathan A. Marshall
and
Richard Alley.
Department of Computer Science, CB 3175, Sitterson Hall
The University of North Carolina
Chapel Hill, NC 27599-3175, U.S.A.
{marshall,
alley}@cs.unc.edu
Abstract
Visual occlusion events constitute a major source of depth
information. We have developed a neural network model that learns to
detect and represent depth relations, after a period of exposure to
motion sequences containing occlusion and disocclusion events. The
network's learning is governed by a new set of learning and activation
rules. The network develops two parallel opponent channels or
``chains'' of lateral excitatory connections for every resolvable
motion trajectory. One channel, the ``On'' chain or
``visible'' chain, is activated when a moving stimulus is visible.
The other channel, the ``Off'' chain or ``invisible'' chain, is
activated when a formerly visible stimulus becomes invisible due to
occlusion. The On chain carries a predictive modal
representation of the visible stimulus. The Off chain carries a
persistent, amodal representation that predicts the motion of
the invisible stimulus. The new learning rule uses disinhibitory
signals emitted from the On chain to trigger learning in the
Off chain. The Off chain neurons learn to interact reciprocally with
other neurons that indicate the presence of occluders. The
interactions let the network predict the disappearance and
reappearance of stimuli moving behind occluders, and they let the
unexpected disappearance or appearance of stimuli excite the
representation of an inferred occluder at that location. Two results
that have emerged from this research suggest how visual systems may
learn to represent visual depth information. First, a visual system
can learn a nonmetric representation of the depth relations arising
from occlusion events. Second, parallel opponent On and Off channels
that represent both modal and amodal stimuli can also be learned
through the same process.
In Bowyer KW & Hall L (Eds.), Proceedings of the
AAAI Fall Symposium on Machine Learning and Computer Vision,
Research Triangle Park, NC, October 1993, pp. 70--74.
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