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A convolution model which accounts for neural activity dynamics in theprimary visual cortex is derived and used to detect visually salient contoursin images. Image inputs to the model are modulated by long-range horizontalconnections, allowing contextual effects in the image to determine visualsaliency, i.e. line segments arranged in a closed contour elicit a largerneural response than line segments forming background clutter. The model istested on 3 types of contour, including a line, a circular closed contour, anda non-circular closed contour. Using a modified association field to describehorizontal connections the model is found to perform well for differentparameter values. For each type of contour a different facilitation mechanismis found. Operating as a feed-forward network, the model assigns saliency byincreasing the neural activity of line segments facilitated by the horizontalconnections. Alternatively, operating as a feedback network, the model canachieve further improvement over several iterations through cooperativeinteractions. This model has no dynamical stability issues, and is suitable foruse in biologically-inspired neural networks.

Visually-salient contour detection using a V1 neural model with horizontal connections