Using a model of the human visual system to identify and enhance object contours in natural images

Segmentation of natural images depends on the ability to identify continuous contours that define the boundaries between objects. However, in many natural images (especially those captured in environments where the illumination is largely ambient) continuous contours can be difficult to identify. In spite of this, the human visual system efficiently perceives the contours along the boundaries of occluding objects. In fact, optical illusions, such as the Kanizsa triangle, demonstrate that the human visual system can `see' object boundaries even when spatial intensity contrasts are totally absent from an image. In searching for the mechanism that generates these `subjective contours' neurological researchers have found that the 2D image on the retina is mapped onto Layer 4 of the primary visual cortex (V1) and that there are lateral connections within the 6 layers of V1 that might subserve contour completion. This paper builds on a previous model of the early visual system (including the retina, the LGN and the simple cells of V1) by adding lateral interconnections to demonstrate how these interconnections might provide contour completion. Images are presented to show how this model enhances the detection of continuous spatial contours, thus contributing to the segmentation of natural images.