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

© The Institution of Engineering and Technology
Received 23/02/2019, Accepted 29/07/2019, Revised 25/04/2019, Published 07/08/2019

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.

Inspec keywords: Gabor filters; visual perception; object detection; image texture; pattern recognition; computer vision

Other keywords: CRF responses; multibandwidth fusion; visual stimuli; logarithmic texture inhibition; primary visual cortex; multi-bandwidth Gabor filter; non-CRF inhibition; nCRF inhibition kernel; bio-inspired contour detection models; detection algorithm; dynamic characteristics; bio-inspired contour detection model; visual mechanisms

Subjects: Filtering methods in signal processing; Optical, image and video signal processing; Computer vision and image processing techniques

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