Keynote Address: Improving Contour Detection by Surround Suppression of Texture

Various effects show that the visual perception of an edge or line can be influenced by other such stimuli in the surroundings. Such effects can be related to nonclassical receptive field (non-CRF) inhibition, also called surround suppression, that is found in most of the orientation selective neurones in the primary visual cortex. A mathematical model of non-CRF inhibition is presented. Non-CRF inhibition acts as a feature contrast computation for oriented stimuli: the response to an edge at a given position is suppressed by other edges in the surround. Consequently, it strongly reduces the responses to texture edges while scarcely affecting the responses to isolated contours. The biological utility of this neural mechanism might thus be that of improving contour (vs. texture) detection. The results of computer simulations based on the proposed model explain perceptual effects, such as orientation contrast pop-out, ‘social conformity’ of lines embedded in gratings, reduced saliency of contours surrounded by textures and decreased visibility of letters embedded in band-limited noise. The insights into the biological role of non-CRF inhibition can be utilised in machine vision. The proposed model is employed in a contour detection algorithm. Applied on natural images it outperforms previously known such algorithms in computer vision.