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A Novel Biologically Inspired Structural Model for Feature Correspondence | |
Lu, Yan-Feng1,2![]() ![]() ![]() ![]() ![]() | |
发表期刊 | IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
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ISSN | 2379-8920 |
2023-06-01 | |
卷号 | 15期号:2页码:844-854 |
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. |
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 |
DOI | 10.1109/TCDS.2022.3188333 |
关键词[WOS] | OBJECT RECOGNITION |
收录类别 | SCI |
语种 | 英语 |
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 |
项目资助者 | National Key Research and Development Plan of China ; Beijing Natural Science Foundation ; National Natural Science Foundation of China ; Youth Innovation Promotion Association CAS |
WOS研究方向 | Computer Science ; Robotics ; Neurosciences & Neurology |
WOS类目 | Computer Science, Artificial Intelligence ; Robotics ; Neurosciences |
WOS记录号 | WOS:001005746000046 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 人工智能基础理论 |
国重实验室规划方向分类 | 人工智能基础前沿理论 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/53652 |
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.Chinese Acad Sci, Inst Automat, Natl Key Lab Multimodal Artificial Intelligence Sy, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Nanchang Univ, Sch Informat Engn, Nanchang 330031, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
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. |
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