Preattentive processing in vision
Computer Vision, Graphics, and Image Processing
Three-dimensional object recognition from single two-dimensional images
Artificial Intelligence
Trace Inference, Curvature Consistency, and Curve Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Information processing strategies and pathways in the primate retina and visual cortex
An introduction to neural and electronic networks
3-D Shape Recovery Using Distributed Aspect Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Perceptual Organization for Scene Segmentation and Description
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object discrimination based on depth-from-occlusion
Neural Computation
The Rapid Recovery of three-Dimensional Orientation from Line Drawings
The Rapid Recovery of three-Dimensional Orientation from Line Drawings
Intermediate-level visual representations and the construction of surface perception
Journal of Cognitive Neuroscience
Pattern segmentation in associative memory
Neural Computation
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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.