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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)
AbstractCompared 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.
Other Abstract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。

KeywordPrimary Visual System Biological Mechanism Contour Detection Prior Filtering Uniform Sampling Sparse Coding
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
Indexed BySCI
Funding OrganizationNational Natural Science Foundation of China(61403375) ; CETC Joint Fund(6141B08010103)
WOS Research AreaMathematical & Computational Biology ; Neurosciences & Neurology
WOS SubjectMathematical & Computational Biology ; Neurosciences
WOS IDWOS:000431133100001
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Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Corresponding AuthorCeng Y(曾毅)
Affiliation1.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
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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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