marshall@marshall.cs.unc.edu (Jonathan Marshall) (03/14/91)
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Papers available, hardcopy only.
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ADAPTIVE NEURAL METHODS FOR MULTIPLEXING ORIENTED EDGES
Jonathan A. Marshall
Department of Computer Science
University of North Carolina at Chapel Hill
Edge linearization operators are often used in computer vision and in
neural network models of vision to reconstruct noisy or incomplete
edges. Such operators gather evidence for the presence of an edge at
various orientations across all image locations and then choose the
orientation that best fits the data at each point. One disadvantage
of such methods is that they often function in a winner-take-all
fashion: the presence of only a single orientation can be represented
at any point; multiple edges cannot be represented where they
intersect. For example, the neural Boundary Contour System of
Grossberg and Mingolla implements a form of winner-take-all
competition between orthogonal orientations at each spatial location,
to promote sharpening of noisy, uncertain image data. But that
competition may produce rivalry, oscillation, instability, or mutual
suppression when intersecting edges (e.g., a cross) are present. This
"cross problem" exists for all techniques, including Markov Random
Fields, where a representation of a chosen favored orientation
suppresses representations of alternate orientations.
A new adaptive technique, using both an inhibitory learning rule and
an excitatory learning rule, weakens inhibition between neurons
representing poorly correlated orientations. It may reasonably be
assumed that neurons coding dissimilar orientations are less likely to
be coactivated than neurons coding similar orientations. Multiplexing
by superposition is ordinarily generated: combinations of intersecting
edges become represented by simultaneous activation of multiple
neurons, each of which represents a single supported oriented edge.
Unsupported or weakly supported orientations are suppressed. The
cross problem is thereby solved.
[to appear in Proceedings of the SPIE Conference on Advances in
Intelligent Systems, Boston, November 1990.]
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Also available:
J.A. Marshall, "A Self-Organizing Scale-Sensitive Neural Network."
In Proceedings of the International Joint Conference on
Neural Networks, San Diego, June 1990, Vol.III., pp.649-654.
J.A. Marshall, "Self-Organizing Neural Networks for Perception of
Visual Motion." Neural Networks, 3, pp.45-74 (1990).
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= Jonathan A. Marshall marshall@cs.unc.edu =
= Department of Computer Science =
= CB 3175, Sitterson Hall =
= University of North Carolina Office 919-962-1887 =
= Chapel Hill, NC 27599-3175, U.S.A. Fax 919-962-1799 =
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