Open AccessContour detection model based on neuron behaviour in primary visual cortex
Author(s) -
Lin Chuan,
Xu Guili,
Cao Yijun
Publication year - 2018
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2017.0661
Subject(s) - receptive field , visual cortex , artificial intelligence , computer science , neuron , computer vision , surround suppression , simple cell , neuroscience , pattern recognition (psychology) , binocular neurons , filter (signal processing) , cortex (anatomy) , visual perception , psychology , perception
In the mammalian primary visual cortex, the response of the classical receptive field (CRF) to visual stimuli can be suppressed by inhibition of non‐CRF (nCRF) neurons. Although many biologically plausible models based on these centre–surround interaction properties have been proposed, most of these models have failed to account for two important behaviours of neurons in the primary visual cortex (V1). First, saturation properties of neuron response. Second, the properties of fixational eye movements (FEyeMs). In the present study, the authors proposed a biologically motivated counter detection approach based on these properties. The authors’ work is significant in that they utilised a simple threshold method to ensure that CRF responses were observed within a meaningful range, and multichannel filter bank was proposed to simulate the influence of FEyeMs on nCRF. Both methods effectively preserved object contours and inhibition isolated textures. Extensive experiments indicated that the authors’ model can preserve more object contours and suppress more textures than previous biologically based models.