Improved Edge Representation via Early Recurrent Inhibition

Abstract

This paper describes a biologically motivated computational model, termed as early recurrent inhibition, to improve edge representation. The computation borrows the idea from the primate visual system that visual features are calculated in the two main visual pathways with different speeds and thus one can positively affect the other via early recurrent mechanisms. Based on the collected results, we conclude such a recurrent processing from area MT to the ventral layers of the primary visual area (V1) may be at play, and hypothesize that one effect of this recurrent mechanism is that V1 responses to high-spatial frequency edges are suppressed by signals sent from MT, leading to a cleaner edge representation. The operation is modeled as a weighted multiplicative inhibition process. Depending on the weighting methods, two types of inhibition are investigated, namely isotropic and anisotropic inhibition. To evaluate the inhibited edge representation, our model is attached to a contour operator to generate binary contour maps. Using real images, we quantitatively compared contours calculated by our work with those by a well-known biologically motivated model. Results clearly demonstrate that early recurrent inhibition has a positive and consistent influence on edge detection.