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A Novel Biologically Inspired Structural Model for Feature Correspondence

文献类型：期刊论文

作者Lu, Yan-Feng1,2; Yang, Xu1,2; Li, Yi3; Yu, Qian1; Liu, Zhi-Yong1,2; Qiao, Hong1,2
刊名IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
出版日期2023-06-01
卷号15期号:2页码:844-854
关键词Visualization Biological system modeling Biology Brain modeling Biological information theory Task analysis Strain Appearance feature descriptor biologically inspired model feature correspondence feature representation graph matching (GM) graph structure
ISSN号2379-8920
DOI10.1109/TCDS.2022.3188333
通讯作者Yang, Xu(xu.yang@ia.ac.cn)
英文摘要Feature correspondence is an essential issue in computer science, which could be well formulated by graph matching (GM). However, traditional GM is susceptible to outliers, thus limiting the applications. To address the issue, we present a biologically inspired feature descriptor (BIFD) corresponding to the simple and complex cell layers in primary visual cortex, which shows robust performance against deformations. Furthermore, we propose a novel biologically inspired structural model (BISM) for feature correspondence by fusing the graph structural information and appearance information described by BIFD in the images. The proposed BIFD imitates the cortical pooling operation across multiscale and multiangle cell layers, which makes BISM robust to outliers and distortions. To demonstrate the validity of the proposed method, we evaluate it in feature correspondence tasks on the public databases. The experimental results on synthetic data prove the validity of the proposed method.
WOS关键词OBJECT RECOGNITION
资助项目National Key Research and Development Plan of China[2020AAA0105900] ; Beijing Natural Science Foundation[L211023] ; National Natural Science Foundation of China[91948303] ; National Natural Science Foundation of China[61973301] ; Youth Innovation Promotion Association CAS
WOS研究方向Computer Science ; Robotics ; Neurosciences & Neurology
语种英语
WOS记录号WOS:001005746000046
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Key Research and Development Plan of China ; Beijing Natural Science Foundation ; National Natural Science Foundation of China ; Youth Innovation Promotion Association CAS
源URL[http://ir.ia.ac.cn/handle/173211/53651]  
专题多模态人工智能系统全国重点实验室
通讯作者Yang, Xu
作者单位1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Key Lab Multimodal Artificial Intelligence Sy, Beijing 100190, Peoples R China
3.Nanchang Univ, Sch Informat Engn, Nanchang 330031, Peoples R China
推荐引用方式
GB/T 7714		 Lu, Yan-Feng,Yang, Xu,Li, Yi,et al. A Novel Biologically Inspired Structural Model for Feature Correspondence[J]. IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS,2023,15(2):844-854.
APA Lu, Yan-Feng,Yang, Xu,Li, Yi,Yu, Qian,Liu, Zhi-Yong,&Qiao, Hong.(2023).A Novel Biologically Inspired Structural Model for Feature Correspondence.IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS,15(2),844-854.
MLA Lu, Yan-Feng,et al."A Novel Biologically Inspired Structural Model for Feature Correspondence".IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 15.2(2023):844-854.

入库方式： OAI收割

来源：自动化研究所

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