A Fast Contour Detection Model Inspired by Biological Mechanisms in Primary Vision System

doi:10.3389/fncom.2018.00028

	A Fast Contour Detection Model Inspired by Biological Mechanisms in Primary Vision System
	Kang, Xiaomei1,2 ; Kong, Qingqun1,2 ; Zeng, Yi1,2,3,4 ; Xu, Bo1,2,3 ; Ceng Y(曾毅)
发表期刊	FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
	2018-04-30
卷号	12 期号:12 页码:28
文章类型	Article
摘要	Compared to computer vision systems, the human visual system is more fast and accurate. It is well accepted that V1 neurons can well encode contour information. There are plenty of computational models about contour detection based on the mechanism of the V1 neurons. Multiple-cue inhibition operator is one well-known model, which is based on the mechanism of V1 neurons' non-classical receptive fields. However, this model is time-consuming and noisy. To solve these two problems, we propose an improved model which integrates some additional other mechanisms of the primary vision system. Firstly, based on the knowledge that the salient contours only occupy a small portion of the whole image, the prior filtering is introduced to decrease the running time. Secondly, based on the physiological finding that nearby neurons often have highly correlated responses and thus include redundant information, we adopt the uniform samplings to speed up the algorithm. Thirdly, sparse coding is introduced to suppress the unwanted noises. Finally, to validate the performance, we test it on Berkeley Segmentation Data Set. The results show that the improved model can decrease running time as well as keep the accuracy of the contour detection.
其他摘要	相比于计算机视觉系统，人的视觉系统更加快速和准确。神经科学的研究表明，在人的视觉系统中，V1区神经元能够很好地编码轮廓信息。基于V1区的编码机制，文献中出现了很多计算模型，其中比较公认的模型是MCI模型，它是基于V1区神经元的非经典感受野特性而提出的，但是它存在耗时长、提取的轮廓噪声多等不足。针对这些问题，本文融合初级视觉系统其他生物学机理，提出了一种快速的轮廓提取计算模型。首先，基于目标轮廓在图像中所占比例较小这一观察，我们引入了先验过滤来减少运行时间。然后，基于相邻神经元响应具有很强相关性，因此响应包含有很多冗余信息的这一生理依据，我们在模型中引入了均匀采样，以此来加速算法；其次，根据V1区神经元的响应具有稀疏性这一特点，在模型中引入了稀疏编码，抑制场景中非真实轮廓的噪声；最后，为了验证模型效果，我们将该计算模型在公开的数据集BSDS500上进行测试。实验结果表明，我们的模型一方面能够降低算法的运行时间，另一方面能够保证提取轮廓准确性。
关键词	Primary Visual System Biological Mechanism Contour Detection Prior Filtering Uniform Sampling Sparse Coding
WOS标题词	Science & Technology ; Life Sciences & Biomedicine
DOI	10.3389/fncom.2018.00028
关键词[WOS]	PRIMARY VISUAL-CORTEX ; RECEPTIVE-FIELD INHIBITION ; EDGE-DETECTION ; SUPPRESSION ; EXTRACTION ; NEURONS ; MACAQUE ; COLOR ; CUES ; V1
收录类别	SCI
语种	英语
项目资助者	National Natural Science Foundation of China(61403375) ; CETC Joint Fund(6141B08010103)
WOS研究方向	Mathematical & Computational Biology ; Neurosciences & Neurology
WOS类目	Mathematical & Computational Biology ; Neurosciences
WOS记录号	WOS:000431133100001
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/20915
专题	脑图谱与类脑智能实验室_类脑认知计算
通讯作者	Ceng Y(曾毅)
作者单位	1.Chinese Acad Sci, Inst Automat, Res Ctr Brain Inspired Intelligence, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai, Peoples R China 4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
第一作者单位	类脑智能研究中心
推荐引用方式 GB/T 7714	Kang, Xiaomei,Kong, Qingqun,Zeng, Yi,et al. A Fast Contour Detection Model Inspired by Biological Mechanisms in Primary Vision System[J]. FRONTIERS IN COMPUTATIONAL NEUROSCIENCE,2018,12(12):28.
APA	Kang, Xiaomei,Kong, Qingqun,Zeng, Yi,Xu, Bo,&曾毅.(2018).A Fast Contour Detection Model Inspired by Biological Mechanisms in Primary Vision System.FRONTIERS IN COMPUTATIONAL NEUROSCIENCE,12(12),28.
MLA	Kang, Xiaomei,et al."A Fast Contour Detection Model Inspired by Biological Mechanisms in Primary Vision System".FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 12.12(2018):28.

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