Neural network model of the visual system: binding form and motion
Neural Networks - 1996 Special issue: four major hypotheses in neuroscience
Contour fragment grouping and shared, simple occluders
Computer Vision and Image Understanding
Integration of form and motion within a generative model of visual cortex
Neural Networks - 2004 Special issue Vision and brain
Restoring partly occluded patterns: a neural network model
Neural Networks
Surrounding Suppression and Facilitation in the Determination of Border Ownership
Journal of Cognitive Neuroscience
Intermediate-level visual representations and the construction of surface perception
Journal of Cognitive Neuroscience
Neural Network Capable of Amodal Completion
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
A computational model that enables global amodal completion based on V4 neurons
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
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When some parts of a pattern are occluded by other objects, the visual system can often estimate the shape of occluded contours from visible parts of the contours. This paper proposes a neural network model capable of such function, which is called amodal completion. The model is a hierarchical multi-layered network that has bottom-up and top-down signal paths. It contains cells of area V1, which respond selectively to edges of a particular orientation, and cells of area V2, which respond selectively to a particular angle of bend. Using the responses of bend-extracting cells, the model predicts the curvature and location of the occluded contours. Missing contours are gradually extrapolated and interpolated from the visible contours. Computer simulation demonstrates that the model performs amodal completion to various stimuli in a similar way as observed by psychological experiments.