A neural network model of object segmentation and feature binding in visual cortex

Paul Sajda, L. H. Finkel

The authors present neural network simulations of how the visual cortex may segment objects and bind attributes based on depth-from-occlusion. They briefly discuss one particular subprocess in the occlusion-based model most relevant to segmentation and binding: determination of the direction of figure. They propose that the model allows addressing a central issue in object recognition: how the visual system defines an object. In addition, the model was tested on illusory stimuli, with the network’s response indicating the existence of robust psychophysical properties in the system.

Accepted 7 June 1992
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