Improving contour detection by surround suppression of texture

Prof. Nicolai Petkov

Bernoulli Institute of Mathematics, Computer Science and Artificial Intelligence University of Groningen, Netherlands

 Abstract: 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.

Bio: Nicolai Petkov was full professor of computer science (chair of Parallel Computing and Intelligent Systems) at the University of Groningen from 1991 till 2023. From 1998 till 2009 he was scientific director of the Institute for Mathematics and Computer Science. He has done research in parallel computing, pattern recognition, image processing, computer vision and applied machine learning. His current research interests as emeritus professor concern predictive analysis of financial time serie