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Abstract:
A figure-ground segregation network is proposed based on a
novel boundary pair representation. Nodes in the network are
boundary segments obtained through local grouping. Each node is
excitatorily coupled with the neighboring nodes that belong to the
same region, and inhibitorily coupled with the corresponding paired
node. Gestalt grouping rules are incorporated by modulating
connections. The status of a node represents its probability being
figural and is updated according to a differential equation. The
system solves the figure-ground segregation problem through
temporal evolution. Different perceptual phenomena, such as modal
and amodal completion, virtual contours, grouping and shape
decomposition are then explained through local diffusion. The
system eliminates combinatorial optimization and accounts for many
psychophysical results with a fixed set of parameters.
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