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徐贵力

教授 博士生导师

招生学科专业:
仪器科学与技术 -- 【招收博士、硕士研究生】 -- 自动化学院
电子信息 -- 【招收博士、硕士研究生】 -- 自动化学院

性别:男

学位:工学博士学位

所在单位:自动化学院

办公地点:2-316

联系方式:13851714597 guilixu2002@163.com,guilixu@nuaa.edu.cn

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Contour detection model using linear and non-linear modulation based on non-CRF suppression

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所属单位:自动化学院

发表刊物:IET IMAGE PROCESSING

关键字:edge detection modulation image texture contour detection model nonlinear modulation linear modulation nonCRF suppression neurophysiological investigations psychophysical investigations human visual system primary visual cortex nonclassical receptive field region CRF region suppression mechanism inhibition term texture suppression contour protection spatial summation properties retinal X cells visual information processing X-Y channel contour detector machine vision

摘要:Psychophysical and neurophysiological investigations on the human visual system show that most neurons in the primary visual cortex (V1) possess a non-classical receptive field (nCRF) region in addition to the CRF region. The nCRF has a modulatory, normally inhibitory, effect on the responses to visual stimuli generated within the CRF. In computational terms, this mechanism suppresses the response to edges in the presence of similar edges in the surroundings. Many computational techniques have been proposed to address the surround suppression mechanism. These methods introduce an inhibition term that is required to suppress the textures and protect the contours. Several studies have found that the spatial summation properties over the receptive fields of retinal X cells are approximately linear, while they are non-linear for Y cells. Inspired by the visual information processing in the X-Y channel and spatial summation properties of X and Y cells, the authors propose a contour detector using linear and non-linear modulations based on nCRF suppression. Extensive experimental evaluations demonstrate that their contour detector significantly outperforms other algorithms. The methods proposed in this study are expected to facilitate the development of efficient computational models in the field of machine vision.

ISSN号:1751-9659

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发表时间:2018-06-01

合写作者:Lin, Chuan,Cao, Yijun

通讯作者:徐贵力

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