Contour detection based on horizontal interactions in primary visual cortex

Abstract

Neurons in the primary visual cortex (V1) respond differently to a simple visual element presented in isolation from when it is embedded within a cluttered scene. The contextual influences are mediated by horizontal connections within V1. Inspired by these visual cortical mechanisms, a contour detection model is proposed. Different from previous researches, the facilitation and the suppression are unified modulated by surrounding elements in the image without separating regions of excitatory and inhibitory lateral connections specifically. The proposed model can selectively extract object contours while reducing non-meaningful texture edges and process angular visual features such as corners and T-junctions effectively. The experiments demonstrate the effectiveness of the proposed model.