IMR OpenIR
Methods and datasets on semantic segmentation: A review
Yu, Hongshan1,2; Yang, Zhengeng1; Tan, Lei1,3; Wang, Yaonan1; Sun, Wei1; Sun, Mingui4; Tang, Yandong5
Corresponding AuthorYu, Hongshan(yuhongshancn@hotmail.com)
2018-08-23
Source PublicationNEUROCOMPUTING
ISSN0925-2312
Volume304Pages:82-103
AbstractSemantic 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.
KeywordSemantic segmentation Convolutional neural network Markov random fields Weakly supervised method 3D point clouds labeling
Funding OrganizationNational 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
DOI10.1016/j.neucom.2018.03.037
Indexed BySCI
Language英语
Funding ProjectNational 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]
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000432492800006
PublisherELSEVIER SCIENCE BV
Citation statistics
Cited Times:55[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.imr.ac.cn/handle/321006/128471
Collection中国科学院金属研究所
Corresponding AuthorYu, Hongshan
Affiliation1.Hunan Univ, Coll Elect & Informat Engn, Natl Engn Lab Robot Visual Percept & Control Tech, Changsha, Hunan, Peoples R China
2.Hunan Univ, Shenzhen Res Inst, Shenzhen 518057, Guangdong, Peoples R China
3.Carnegie Mellon Univ, Robot Inst, Pittsburgh, PA 15213 USA
4.Univ Pittsburgh, Lab Computat Neurosci, Pittsburgh, PA USA
5.Chinese Acad Sci, Shenyang Inst Automat, Shenyang, Liaoning, Peoples R China
Recommended Citation
GB/T 7714
Yu, Hongshan,Yang, Zhengeng,Tan, Lei,et al. Methods and datasets on semantic segmentation: A review[J]. NEUROCOMPUTING,2018,304:82-103.
APA Yu, Hongshan.,Yang, Zhengeng.,Tan, Lei.,Wang, Yaonan.,Sun, Wei.,...&Tang, Yandong.(2018).Methods and datasets on semantic segmentation: A review.NEUROCOMPUTING,304,82-103.
MLA Yu, Hongshan,et al."Methods and datasets on semantic segmentation: A review".NEUROCOMPUTING 304(2018):82-103.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Yu, Hongshan]'s Articles
[Yang, Zhengeng]'s Articles
[Tan, Lei]'s Articles
Baidu academic
Similar articles in Baidu academic
[Yu, Hongshan]'s Articles
[Yang, Zhengeng]'s Articles
[Tan, Lei]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Yu, Hongshan]'s Articles
[Yang, Zhengeng]'s Articles
[Tan, Lei]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.