Long range connections in primary visual cortex: a large scale model applied to edge detection in gray-scale images

The primary visual cortex (V1) in primates is known to perform edge analysis. A new neural network model of V1 is proposed that integrates three of the most prominent features of V1 architecture-complex-cells, long-range horizontal connections formed by Hebbian learning, and feature maps. The utility of the model is demonstrated on the problem of extracting edges from gray-scale photographs. This biologically based model outperforms the Canny edge operator with hysteresis when tested on a variety of gray-scale photographs with the local edge coherence metric of Kitchen and Rosenfeld (1981)