Neural network for dynamic binding with graph representation: form, linking, and depth-from-occlusion.
A neural network is presented that explicitly represents form attributes and relations between them, thus solving the binding problem without temporal coding. Rather, the network create a graph representation by dynamically allocating nodes to code local form attributes and establishing arcs to link them. With this representation, the network selectively groups and segments in depth objects based on line junction information, producing results consistent with those of several recent visual search experiments. In addition to depth-from-occlusion, the network provides a sufficient framework for local line-labeling processes to recover other three-dimensional (3-D) variables, such as edge/surface contiguity, edge slant, and edge convexity.
Williamson
J R
JR
Center for Adaptive Systems, Boston University, MA 02215, USA.
D005556:Form Perception / Q000502:physiology*; D016571:Neural Networks, Computer; D010775:Photic Stimulation