CINNIC, a new computational algorithm for the modeling of early visual contour integration in humans.

In order to gain a better understanding of visual saliency, we have developed algorithm which simulates the phenomenon of contour integration for the purpose of visual saliency. The model developed consists of the classical butterfly pattern of connection between orientation selective neurons in the primary visual cortex. In addition, we also add a local group suppression gain control to eliminate extraneous noise and a fast plasticity term which helps to account for closure effect often observed in humans exposed to closed contour maps. Results from real world images suggest that our algorithm is effective at picking out reasonable contours from a scene. The results improved with the introduction of both the fast plasticity and group suppression. An addition of multi-scale analysis has also increased the effectiveness as well.