Cognitive Template-Clustering Improved LineMod for Efficient Multi-object Pose Estimation

doi:10.1007/s12559-020-09717-5

	Cognitive Template-Clustering Improved LineMod for Efficient Multi-object Pose Estimation
	Zhang, Tielin1 ; Yang, Yang 5; Zeng, Yi1,2,3,4 ; Zhao, Yuxuan1
发表期刊	COGNITIVE COMPUTATION
ISSN	1866-9956
	2020-03-17
页码	10
通讯作者	Zhang, Tielin(tielin.zhang@ia.ac.cn)
摘要	Various types of theoretical algorithms have been proposed for 6D pose estimation, e.g., the point pair method, template matching method, Hough forest method, and deep learning method. However, they are still far from the performance of our natural biological systems, which can undertake 6D pose estimation of multi-objects efficiently, especially with severe occlusion. With the inspiration of the Muller-Lyer illusion in the biological visual system, in this paper, we propose a cognitive template-clustering improved LineMod (CT-LineMod) model. The model uses a 7D cognitive feature vector to replace standard 3D spatial points in the clustering procedure of Patch-LineMod, in which the cognitive distance of different 3D spatial points will be further influenced by the additional 4D information related with direction and magnitude of features in the Muller-Lyer illusion. The 7D vector will be dimensionally reduced into the 3D vector by the gradient-descent method, and then further clustered by K-means to aggregately match templates and automatically eliminate superfluous clusters, which makes the template matching possible on both holistic and part-based scales. The model has been verified on the standard Doumanoglou dataset and demonstrates a state-of-the-art performance, which shows the accuracy and efficiency of the proposed model on cognitive feature distance measurement and template selection on multiple pose estimation under severe occlusion. The powerful feature representation in the biological visual system also includes characteristics of the Muller-Lyer illusion, which, to some extent, will provide guidance towards a biologically plausible algorithm for efficient 6D pose estimation under severe occlusion.
关键词	Muller-Lyer illusion Cognitive template-clustering Brain-inspired computation LineMod 6D pose estimation
DOI	10.1007/s12559-020-09717-5
收录类别	SCI
语种	英语
资助项目	Beijing Natural Science Foundation[4184103] ; National Natural Science Foundation of China[61806195] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB32070100] ; Beijing Municipality of Science and Technology[Z181100001518006] ; CETC Joint Fund[6141B08010103] ; Beijing Academy of Artificial Intelligence (BAAI)
项目资助者	Beijing Natural Science Foundation ; National Natural Science Foundation of China ; Strategic Priority Research Program of Chinese Academy of Sciences ; Beijing Municipality of Science and Technology ; CETC Joint Fund ; Beijing Academy of Artificial Intelligence (BAAI)
WOS研究方向	Computer Science ; Neurosciences & Neurology
WOS类目	Computer Science, Artificial Intelligence ; Neurosciences
WOS记录号	WOS:000520697600002
出版者	SPRINGER
七大方向——子方向分类	类脑模型与计算
引用统计	被引频次：9[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/173211/38693
专题	脑图谱与类脑智能实验室_类脑认知计算
通讯作者	Zhang, Tielin
作者单位	1.Chinese Acad Sci, Inst Automat, Res Ctr Brain Inspired Intelligence, Beijing, Peoples R China 2.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai, Peoples R China 3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 4.Univ Chinese Acad Sci, Beijing, Peoples R China 5.Peking Univ, Sch Software & Microelect, Beijing, Peoples R China
第一作者单位	类脑智能研究中心
通讯作者单位	类脑智能研究中心
推荐引用方式 GB/T 7714	Zhang, Tielin,Yang, Yang,Zeng, Yi,et al. Cognitive Template-Clustering Improved LineMod for Efficient Multi-object Pose Estimation[J]. COGNITIVE COMPUTATION,2020:10.
APA	Zhang, Tielin,Yang, Yang,Zeng, Yi,&Zhao, Yuxuan.(2020).Cognitive Template-Clustering Improved LineMod for Efficient Multi-object Pose Estimation.COGNITIVE COMPUTATION,10.
MLA	Zhang, Tielin,et al."Cognitive Template-Clustering Improved LineMod for Efficient Multi-object Pose Estimation".COGNITIVE COMPUTATION (2020):10.