| Methods and datasets on semantic segmentation: A review | |
Yu HS(余洪山)1,2; Yang, Zhengeng1; Tan, Lei1,3; Wang YN(王耀南)1; Sun, Wei1; Sun, Mingui4; Tang YD(唐延东)5
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| Department | 工业控制网络与系统研究室 |
| Source Publication | NEUROCOMPUTING
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| ISSN | 0925-2312 |
| 2018 | |
| Volume | 304Pages:82-103 |
| Indexed By | SCI ; EI |
| EI Accession number | 20182105222020 |
| WOS ID | WOS:000432492800006 |
| Contribution Rank | 5 |
| Funding Organization | National Natural Science Foundation of China ; National Key Technology Support Program ; National Key Scientific Instrument and Equipment Development Project of China ; Hunan Key Laboratory of Intelligent Robot Technology in Electronic Manufacturing ; Science and Technology Plan Project of Shenzhen City ; Key Project of Science and Technology Plan of Guangdong Province ; Open foundation of State Key Laboratory of Robotics of China ; National Institutes of Health of the United States |
| Keyword | Semantic segmentation Convolutional neural network Markov random fields Weakly supervised method 3D point clouds labeling |
| Abstract | Semantic segmentation, also called scene labeling, refers to the process of assigning a semantic label (e.g. car, people, and road) to each pixel of an image. It is an essential data processing step for robots and other unmanned systems to understand the surrounding scene. Despite decades of efforts, semantic segmentation is still a very challenging task due to large variations in natural scenes. In this paper, we provide a systematic review of recent advances in this field. In particular, three categories of methods are reviewed and compared, including those based on hand-engineered features, learned features and weakly supervised learning. In addition, we describe a number of popular datasets aiming for facilitating the development of new segmentation algorithms. In order to demonstrate the advantages and disadvantages of different semantic segmentation models, we conduct a series of comparisons between them. Deep discussions about the comparisons are also provided. Finally, this review is concluded by discussing future directions and challenges in this important field of research. (c) 2018 Elsevier B.V. All rights reserved. |
| Language | 英语 |
| WOS Subject | Computer Science, Artificial Intelligence |
| WOS Keyword | MARKOV RANDOM-FIELDS ; IMAGE SEGMENTATION ; OBJECT RECOGNITION ; ENERGY MINIMIZATION ; POINT CLOUDS ; FEATURES ; VISION ; CONTEXT ; MODEL ; ALGORITHMS |
| WOS Research Area | Computer Science |
| Funding Project | National Natural Science Foundation of China[61573135] ; National Key Technology Support Program[2015BAF11B01] ; National Key Scientific Instrument and Equipment Development Project of China[2013YQ140517] ; Hunan Key Laboratory of Intelligent Robot Technology in Electronic Manufacturing[2018001] ; Science and Technology Plan Project of Shenzhen City[JCYJ20170306141557198] ; Key Project of Science and Technology Plan of Guangdong Province[2013B011301014] ; Open foundation of State Key Laboratory of Robotics of China[2013O09] ; National Institutes of Health of the United States[R01CA165255] ; National Institutes of Health of the United States[R21CA172864] |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.sia.cn/handle/173321/21902 |
| Collection | 工业控制网络与系统研究室 |
| Corresponding Author | Yu HS(余洪山) |
| Affiliation | 1.National Engineering Laboratory for Robot Visual Perception and Control Technology, College of Electrical and Information Engineering, Hunan University, Changsha, China; 2.Shenzhen Research Institute of Hunan University, Shenzhen, Guangdong 518057, China; 3.Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA; 4.Laboratory for Computational Neuroscience, University of Pittsburgh, Pittsburgh, USA; 5.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China |
| Recommended Citation GB/T 7714 | Yu HS,Yang, Zhengeng,Tan, Lei,et al. Methods and datasets on semantic segmentation: A review[J]. NEUROCOMPUTING,2018,304:82-103. |
| APA | Yu HS.,Yang, Zhengeng.,Tan, Lei.,Wang YN.,Sun, Wei.,...&Tang YD.(2018).Methods and datasets on semantic segmentation: A review.NEUROCOMPUTING,304,82-103. |
| MLA | Yu HS,et al."Methods and datasets on semantic segmentation: A review".NEUROCOMPUTING 304(2018):82-103. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| Methods and datasets（2926KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Application Full Text | |
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