Lateral and feedback schemes for the inhibition of false-positive responses in edge orientation channels

Object recognition is one of the most important applications of robotics. For object recognition, edge orientation is widely used as a primitive visual feature. However, a classical filter-based approach passes not only edges inside target orientation band but also edges outside. This can thus cause problem in the estimation of the true orientation of edge. This study proposes a filtering scheme to reduce the false-positive responses, i.e. edges outside target orientation band, and investigate a solution inspired by biological vision. Motivated by several psychophysical and neuro-physiological findings, we present a computational framework based on the basic mechanisms of cortical processing, i.e. feed-forward, lateral and feedback stages. In the feed-forward stage, our model uses a classical filter-based method to allow as many true orientation edges to pass through as possible. False responses in orientation channels are then inhibited by lateral interaction. The remaining undesired responses are suppressed through the feedback stage. We evaluated the performance of our model against classical filter-based methods such as Gabor and Neumann filtering using several artificial and natural images. The results validated the effectiveness of our approach.