Non-classical receptive field inhibition and contour detection


N Petkov, M Westenberg, C Grigorescu (Institute of Mathematics and Computing Science, University of Groningen, Postbus 800, 9700 AV Groningen, Netherlands; email:; website: )


Various visual effects show that the perception of an edge or line can be influenced by other such stimuli in the surroundings. Such effects can be related to non-classical receptive field (non-CRF) inhibition that is found in 80% of the orientation selective neurons in the primary visual cortex [Nothdurft et al, 1999 Visual Neuroscience 16 15-34].


A mathematical model of non-CRF inhibition is presented in which the response of an orientation selective cell is suppressed by the responses of other such cells beyond its classical (excitatory) receptive field. Non-CRF inhibition acts as a feature contrast computation for oriented stimuli: the response to an optimal stimulus over the receptive field is suppressed by similar stimuli 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 contour (vs. texture) detection. Two types of inhibition are considered: isotropic and anisotropic that, respectively, do and do not depend on the orientation difference of center and surround stimuli.


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 [Petkov and Westenberg, 2003 Biological Cybernetics 88 236-246].


The insights into the biological role of non-CRF inhibition can be utilized in machine vision. The proposed model is employed in a contour detection algorithm that substantially outperforms previously known such algorithms in computer vision [Grigorescu et al, 2003 IEEE Transactions on Image Processing 12 729-739].