Director Field Model of the Primary Visual Cortex for Contour Detection

Abstract

We aim to build the simplest possible model capable of detecting long, noisy contours in a cluttered visual scene. For this, we model the neural dynamics in the primate primary visual cortex in terms of a continuous director field that describes the average rate and the average orientational preference of active neurons at a particular point in the cortex. We then use a linear-nonlinear dynamical model with long range connectivity patterns to enforce long-range statistical context present in the analyzed images. The resulting model has substantially fewer degrees of freedom than traditional models, and yet it can distinguish large contiguous objects from the background clutter by suppressing the clutter and by filling-in occluded elements of object contours. This results in high-precision, high-recall detection of large objects in cluttered scenes. Parenthetically, our model has a direct correspondence with the Landau - de Gennes theory of nematic liquid crystal in two dimensions.

Contour Integration in Dynamic Scenes: Impaired Detection Performance in Extended Presentations

“…Several psychophysical (Kellman and Shipley, 1991 ; Mullen et al, 2000 ; Li and Gilbert, 2002 ), neurophysiological (Bosking et al, 1997 ; Gilbert et al, 2000 ) and computational studies (Li, 1998 ; Ernst et al, 2012 ; Singh et al, 2014 ; Sarti and Citti, 2015 ) have helped to create a comprehensive picture of contour integration encompassing different levels of understanding. From these studies we have learned much about which physical properties elements in a scene must have, and how they must be positioned in relation to each other, in order to induce the perception of a contour.…”