Learning to detect contours in natural images via biologically motivated schemes

A model for detecting contours in natural images is presented by combining the visual perceptual mechanisms and machine learning. The surround stimuli will enhance the response of the central stimulus if they can form a precise spatial configuration. On the other hand, surround inhibition will reduce the responses to homogeneous elements. Facilitation and inhibition activities in the primary visual cortex (V1) are used to enhance the well-organized structures and to reduce the non-meaningful distractors engendering from texture fields, respectively. We approach the task of facilitatory and inhibitory cue integration as a supervised learning problem using the logistic regression model. Our experiments demonstrate that the model can dramatically reduce texture edges and spurious contours, and meanwhile can to some extent avoid ground-truth contours missed by the detector.