Improved contour detection model with spatial summation properties based on nonclassical receptive field

Chuan Lin; Guili Xu; Yijun Cao; Chenghua Liang; Ya Li

doi:10.1117/1.JEI.25.4.043018

2 August 2016 Improved contour detection model with spatial summation properties based on nonclassical receptive field

Chuan Lin, Guili Xu, Yijun Cao, Chenghua Liang, Ya Li

Author Affiliations +

Journal of Electronic Imaging, Vol. 25, Issue 4, 043018 (August 2016). https://doi.org/10.1117/1.JEI.25.4.043018

Abstract

The responses of cortical neurons to a stimulus in a classical receptive field (CRF) can be modulated by stimulating the non-CRF (nCRF) of neurons in the primary visual cortex (V1). In the very early stages (at around 40 ms), a neuron in V1 exhibits strong responses to a small set of stimuli. Later, however (after 100 ms), the neurons in V1 become sensitive to the scene’s global organization. As per these visual cortical mechanisms, a contour detection model based on the spatial summation properties is proposed. Unlike in previous studies, the responses of the nCRF to the higher visual cortex that results in the inhibition of the neuronal responses in the primary visual cortex by the feedback pathway are considered. In this model, the individual neurons in V1 receive global information from the higher visual cortex to participate in the inhibition process. Computationally, global Gabor energy features are involved, leading to the more coherent physiological characteristics of the nCRF. We conducted an experiment where we compared our model with those proposed by other researchers. Our model explains the role of the mutual inhibition of neurons in V1, together with an approach for object recognition in machine vision.

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Chuan Lin, Guili Xu, Yijun Cao, Chenghua Liang, and Ya Li "Improved contour detection model with spatial summation properties based on nonclassical receptive field," Journal of Electronic Imaging 25(4), 043018 (2 August 2016). https://doi.org/10.1117/1.JEI.25.4.043018

Published: 2 August 2016

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