Visual segmentation by contextual influences via intra-cortical interactions in the primary visual cortex.

Stimuli outside classical receptive fields have been shown to exert a significant influence over the activities of neurons in the primary visual cortex. We propose that contextual influences are used for pre-attentive visual segmentation. The difference between contextual influences near and far from region boundaries makes neural activities near region boundaries higher than elsewhere, making boundaries more salient for perceptual pop-out. The cortex thus computes global region boundaries by detecting the breakdown of homogeneity or translation invariance in the input, using local intra-cortical interactions mediated by the horizontal connections. This proposal is implemented in a biologically based model of V1, and demonstrated using examples of texture segmentation and figure-ground segregation. The model is also the first that performs texture or region segmentation in exactly the same neural circuit that solves the dual problem of the enhancement of contours, as is suggested by experimental observations. The computational framework in this model is simpler than previous approaches, making it implementable by V1 mechanisms, though higher-level visual mechanisms are needed to refine its output. However, it easily handles a class of segmentation problems that are known to be tricky. Its behaviour is compared with psycho-physical and physiological data on segmentation, contour enhancement, contextual influences and other phenomena such as asymmetry in visual search.