Pre-attentive segmentation in the primary visual cortex

Stimuli outside classical receptive elds have been shown to exert signi cant in uence over the activities of neurons in primary visual cortex. We propose that contextual in uences are used for pre-attentive visual segmentation, in a new framework called segmentation without classi cation. This means that segmentation of an image into regions occurs without classi cation of features within a region or comparison of features between regions. This segmentation framework is simpler than previous computational approaches, making it implementable by V1 mechanisms, though higher level visual mechanisms are needed to re ne its output. However, it easily handles a class of segmentation problems that are tricky in conventional methods. The cortex 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. The di erence between contextual in uences near and far from region boundaries makes neural activities near region boundaries higher than elsewhere, making boundaries more salient for perceptual pop-out. This proposal is implemented in a biologically based model of V1, and demonstrated using examples of texture segmentation and gure-ground segregation. The model performs segmentation in exactly the same neural circuit that solves the dual problem of the enhancement o f c o n tours, as is suggested by experimental observations. Its behavior is compared with psychophysical and physiological data on segmentation, contour enhancement, and contextual in uences. We discuss the implications of segmentation without classi cation and the predictions of our V1 model, and relate it to other phenomena such as asymmetry in visual search.