Bio-inspired contour detection model based on multi-bandwidth fusion and logarithmic texture inhibition

Abstract

Relevant physiological studies have revealed that the response of the classical receptive field (CRF) to visual stimuli could be suppressed by non-CRF (nCRF) inhibition of the kernel in the primary visual cortex (V1). Based on this mechanism, many bio-inspired contour detection models have been proposed, which are mainly achieved through CRF responses and nCRF surround inhibition calculation. In fact, the dynamic characteristics of neurons play an important role in contour detection in biological vision. Inspired by these visual mechanisms, the authors propose a contour detection model that emulates these dynamic characteristics. By introducing a multi-bandwidth Gabor filter, according to the target image, they can effectively adjust the weight ratios of the filter to protect the contours and filter the background textures in the calculation of CRF responses. Additionally, they logarithmically modulate the nCRF inhibition kernel to make texture suppression more flexible and effective, thus improving the accuracy of detection algorithm as a whole. Compared with existing bio-inspired contour detection models, the proposed model is more effective at contour detection, which will aid engineering applications that utilise pattern recognition in machine vision.