Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

August 15, 1996, Vol. 8, No. 6, Pages 1203-1225
(doi: 10.1162/neco.1996.8.6.1203)
© 1996 Massachusetts Institute of Technology
Neural Network for Dynamic Binding with Graph Representation: Form, Linking, and Depth-from-Occlusion
Article PDF (1.21 MB)
Abstract

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 creates 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.