Adaptive neural methods for multiplexing oriented edges

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