Contour Detection by Simulating the Curvature Cell in the Visual Cortex and its Application to Object Classification

Chen, Zekun; Cai, Ruixian

doi:10.1109/access.2020.2988496

Cited by 4 publications

(1 citation statement)

References 41 publications

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“…Neural responses in the visual cortex are associated with various visual functions, including the contour matching, contour detection, the associative processing, the orientation selectivity [24]- [28]. For the primary visual cortex, the biological background of binocular rivalry has been modeled by spiking networks [9], [17], [29].…”

Section: Introductionmentioning

confidence: 99%

Depth Perception With Interocular Blur Differences Based on a Spiking Network

Liu

2022

IEEE Access

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Visual depth perception is the basic function of the visual nervous system. To a stimulus in the stereo space, the visual nervous system could generate a perception about depth of its position. Experimental data have demonstrated that interocular blur differences could lead to illusory perceptions about depths of moving stimuli. However, to stimuli with interocular blur differences, influences of different factors on illusory depth perceptions are still unclear. To explore these influences, this paper constructs a plastic two-layer k-winner-take-all (k-WTA) spiking network, simulating primary visual cortical responses. With incompatible stimuli presented into two eyes in experiments, binocular rivalry could occur in the primary visual cortex and interact with depth perception. To simulate binocular rivalry, the network consists of two parallel visual channels driven by left-eye and right-eye stimuli and competing with each other through mutual inhibition. In simulations, the horizontally moving stimulus is filtered with different Gaussian filters to generate paired monocular stimuli with interocular blur differences. The blurry strength, the moving direction and the moving speed perform as varying factors of moving stimuli. The network updates its dynamics through probabilistic inference, reflecting impacts of each factor on both neural responses and binocular rivalry. The modified responses could simulate illusory depth perceptions of stimuli as observed in experiments. To stimuli with interocular blur differences, varying factors could modify binocular rivalry in the network, inducing distinguishing illusory depth perceptions. Based on probabilistic inference, our model could provide possible explanations to illusory depth perceptions with interocular blur differences.INDEX TERMS Depth perception, Binocular rivalry, Winner-take-all, Spiking neural networks.

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Section: Introductionmentioning

confidence: 99%

Depth Perception With Interocular Blur Differences Based on a Spiking Network

Liu

2022

IEEE Access

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Contour detection based on binocular parallax perception mechanism

Wei

Fang

et al. 2022

SIViP

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Automatic Recognition for Fat Reduction Motion Image Landmark Using Computer Vision

Yang

Wang

et al. 2022

Mobile Information Systems

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To solve the problems existing in the traditional algorithm, such as image quality, the ability to predict the coordinate of the motion fat reduction image, the Intersection over Union (IOU) of the estimated coordinate of the landmark, the peak signal-to-noise ratio (PSNR) of the image, and the recognition accuracy of the image landmark, an automatic recognition algorithm for fat reduction motion image landmark using computer vision is proposed. The OV7725 camera is used to obtain the fat reduction motion image, and the histogram equalization method is used to enhance the fat reduction motion image. After that, the estimated coordinates of the landmark are obtained by the automatic recognition model of the landmarks of the fat reduction motion image. According to the coordinates of the landmark, after using a field programmable gate array to segment the image of the fat reduction motion image, the incremental updated freeman chain code extracts the contour of the landmark of the fat reduction motion image through 8-neighborhood retrieval, so as to realize the automatic recognition of landmark. The results show that the fat reduction motion image enhanced by the algorithm is clear and the contrast is obvious. The ability to predict the coordinates of the landmark of the fat reduction image is strong. The IOU of the estimated landmark coordinates is about 0.96. The processed image has a high PSNR, which can effectively recognize the landmark of the fat reduction motion image, and the recognition accuracy is about 97%, which has certain value in the field of image recognition and can improve the fat reduction movement normality.

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Contour Detection by Simulating the Curvature Cell in the Visual Cortex and its Application to Object Classification

Cited by 4 publications

References 41 publications

Depth Perception With Interocular Blur Differences Based on a Spiking Network

Depth Perception With Interocular Blur Differences Based on a Spiking Network

Contour detection based on binocular parallax perception mechanism

Automatic Recognition for Fat Reduction Motion Image Landmark Using Computer Vision

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