A model has been presented of a neural process that segments the visual field into spatially disjoint regions, each region characterized by a specific feature such as a texture or color. The neural connectivity hypothesized to be necessary for the segmentation process has been formulated in mathematical terms and the corresponding neural network has been simulated on the digital computer. The properties of the network that result from the postulated patterns of excitatory and inhibitory connectivity have been investigated. It is shown that the required connectivity is that of excitatory connections only between neurons detecting similar features and inhibitory connections between all feature-detecting neurons. The resulting segmentation model is used to model the phenomenon of stereopsis as investigated through the use of random-dot stereograms. The process of depth perception through stereopsis can be viewed as a segmentation process with each segment, that is, each surface at a specific depth, characterized by a specific retinal disparity. It is shown that the segmentation model suffices to detect the different depth surfaces embedded in the random-dot patterns.
Visual perception is based on the detection and recognition of a set of features, such as contours, color and texture, that characterize the object or the scene. In general, there are many objects present in the visual field and, to recognize any one of these objects, the recognition procedure should be applied to the subset of visual features generated by the object. The method of selection of such a subset is a problem that remains a thorny one in fields ranging from neurophysiology to artificial intelligence. One possible solution is that a single feature or a small group of features acts as a cue to interact with long-term memory so as to generate a "hypothesis" concerning the identity of the object (Gregory, 1969; Axbib, 1972). Repeated comparisons between the input features and the continually updated hypothesis serves to