Parallel vision for perception and understanding of complex scenes: methods, framework, and perspectives

doi:10.1007/s10462-017-9569-z

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 平行智能技术与系统团队

	Parallel vision for perception and understanding of complex scenes: methods, framework, and perspectives
	Wang, Kunfeng1 ; Gou, Chao1 ; Zheng, Nanning 2; Rehg, James M.3; Wang, Fei-Yue1,4
发表期刊	ARTIFICIAL INTELLIGENCE REVIEW
	2017-10-01
卷号	48 期号:3 页码:299-329
文章类型	Article
摘要	; In the study of image and vision computing, the generalization capability of an algorithm often determines whether it is able to work well in complex scenes. The goal of this review article is to survey the use of photorealistic image synthesis methods in addressing the problems of visual perception and understanding. Currently, the ACP Methodology comprising artificial systems, computational experiments, and parallel execution is playing an essential role in modeling and control of complex systems. This paper extends the ACP Methodology into the computer vision field, by proposing the concept and basic framework of Parallel Vision. In this paper, we first review previous works related to Parallel Vision, in terms of synthetic data generation and utilization. We detail the utility of synthetic data for feature analysis, object analysis, scene analysis, and other analyses. Then we propose the basic framework of Parallel Vision, which is composed of an ACP trilogy (artificial scenes, computational experiments, and parallel execution). We also present some in-depth thoughts and perspectives on Parallel Vision. This paper emphasizes the significance of synthetic data to vision system design and suggests a novel research methodology for perception and understanding of complex scenes.
关键词	Visual Perception Complex Scenes Parallel Vision Acp Methodology Computer Graphics Image Synthesis
WOS标题词	Science & Technology ; Technology
DOI	10.1007/s10462-017-9569-z
关键词[WOS]	PEDESTRIAN DETECTION ; VIDEO SURVEILLANCE ; DOMAIN ADAPTATION ; CAMERA NETWORKS ; COMPUTER VISION ; VIRTUAL WORLDS ; RECOGNITION ; IMAGES ; ALGORITHMS ; MACHINES
收录类别	SCI
语种	英语
项目资助者	National Natural Science Foundation of China(61533019 ; 71232006)
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000412658700001
引用统计	被引频次：94[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/173211/20082
专题	多模态人工智能系统全国重点实验室_平行智能技术与系统团队
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Xi An Jiao Tong Univ, IAIR, Xian 710049, Shaanxi, Peoples R China 3.Georgia Inst Technol, Sch Interact Comp, Atlanta, GA 30332 USA 4.Natl Univ Def Technol, Res Ctr Computat Expt & Parallel Syst Tech, Changsha 410073, Hunan, Peoples R China
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Wang, Kunfeng,Gou, Chao,Zheng, Nanning,et al. Parallel vision for perception and understanding of complex scenes: methods, framework, and perspectives[J]. ARTIFICIAL INTELLIGENCE REVIEW,2017,48(3):299-329.
APA	Wang, Kunfeng,Gou, Chao,Zheng, Nanning,Rehg, James M.,&Wang, Fei-Yue.(2017).Parallel vision for perception and understanding of complex scenes: methods, framework, and perspectives.ARTIFICIAL INTELLIGENCE REVIEW,48(3),299-329.
MLA	Wang, Kunfeng,et al."Parallel vision for perception and understanding of complex scenes: methods, framework, and perspectives".ARTIFICIAL INTELLIGENCE REVIEW 48.3(2017):299-329.

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