Modeling contextual modulation in the primary visual cortex

Contextual modulation is a universal phenomenon in the primary visual cortex (V1). It is often allocated to the two categories of suppression and facilitation, which are either weakened or strengthened by the contextual stimuli, respectively. A number of experiments in neurophysiology have elucidated their important functions in visual information processing, such as contour integration, figure-ground segregation, saliency map and so on. A computational model, inspired by visual cortical mechanisms of contextual modulation, is presented in this paper. We first give separate models for surround suppression (SS), collinear facilitation (CF) and cross-orientation facilitation (COF), respectively, then unify them to a mixed model. Model behavior has then been tested using synthetical images and nature images, and is consistent with the data of physiological experimentation. We achieve fine results using the model to extract salient structures and contours from images. This work develops a computational model, using the perceptual mechanisms in V1, and provides a biologically plausible strategy for computer vision.