基于视通路多感受野朝向性关联的轮廓检测方法

doi:10.3969/j.issn.0258-8021. 2017. 01.001

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Abstract Based on characteristics of receptive fields on different layers of human visual path and their correlation between every adjacent layer, a new method of image contour detection was proposed in this paper. Using the receptive feature of neurons with radial dendrite, a retina neuron network that has the capability to detect spatial difference was constructed for pre-coding of contour information. Taking the variety between adjacent layers into account, the adjustment mechanism for non-classical receptive field of LGN neurons was built to make a global adjustment on the pre-coding result. Next,a model of simple cell on primary visual cortex named orientation correlation of multiple receptive fields was proposed to realize the orientation selection of simple cells. At last,the fused contour response of all orientations was processed with non-maximum suppression and hysteresis thresholding to acquire the contour detecting result. In this paper, 40 images in the RuG library were taken for processing, and the mean value of measure P between detecting result and ground truth was 0.43, indicating that method in this paper could highlight principal contour and enhance the discrimination between contour and textured region. In this study, the correlate characteristics of multiple receptive fields were utilized to code and detect image contour, providing a new idea for the image processing and visual cognition of high-level visual cortex.

Key words： contour detection multiple receptive fields pre-coding global adjustment orientative correlation

Received: 08 June 2016

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R318

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	Articles by authors
	Li Kangqun
	Fan Yingle
	Gan Haitao
	Wu Wei

Cite this article:

Li Kangqun,Fan Yingle,Gan Haitao, et al. A Contour Detection Method Based on Correlation of Orientation for Multiple Receptive Fields in Visual Pathway[J]. Chinese Journal of Biomedical Engineering, 2017, 36(1): 1-11.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021. 2017. 01.001 OR http://cjbme.csbme.org/EN/Y2017/V36/I1/1

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