Parallel vision for perception and understanding of complex scenes: methods, framework, and perspectives
Wang, Kunfeng1; Gou, Chao1; Zheng, Nanning2; Rehg, James M.3; Wang, Fei-Yue1,4
Source PublicationARTIFICIAL INTELLIGENCE REVIEW
2017-10-01
Volume48Issue:3Pages:299-329
SubtypeArticle
Abstract
; 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.
KeywordVisual Perception Complex Scenes Parallel Vision Acp Methodology Computer Graphics Image Synthesis
WOS HeadingsScience & Technology ; Technology
DOI10.1007/s10462-017-9569-z
WOS KeywordPEDESTRIAN DETECTION ; VIDEO SURVEILLANCE ; DOMAIN ADAPTATION ; CAMERA NETWORKS ; COMPUTER VISION ; VIRTUAL WORLDS ; RECOGNITION ; IMAGES ; ALGORITHMS ; MACHINES
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61533019 ; 71232006)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000412658700001
Citation statistics
Cited Times:14[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20082
Collection复杂系统管理与控制国家重点实验室_先进控制与自动化
Affiliation1.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
Recommended Citation
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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