Bio‐inspired contour detection model based on multi‐bandwidth fusion and logarithmic texture inhibition. Issue 12 (17th September 2019)

• Record Type: Journal Article
• Authors: Lin, Chuan; Li, Fuzhang; Cao, Yijun; Zhao, Haojun
• Is Part Of: IET image processing. Volume 13:Issue 12(2019)
• Page Start: 2304
• Page End: 2313
• Publication Date: 2019-09-17
• Subjects: pattern recognition -- visual perception -- object detection -- Gabor filters -- computer vision -- image texture; bio‐inspired contour detection model -- multibandwidth fusion -- logarithmic texture inhibition -- visual stimuli -- primary visual cortex -- CRF responses -- dynamic characteristics -- visual mechanisms -- nCRF inhibition kernel -- detection algorithm -- bio‐inspired contour detection models -- multi‐bandwidth Gabor filter -- non‐CRF inhibition; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.0214
• Languages: English
• ISSNs: 1751-9659

Abstract

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.