Methods and datasets on semantic segmentation: A review
文献类型：期刊论文
| 作者 | Yu HS(余洪山); Yang, Zhengeng; Tan, Lei; Wang YN(王耀南); Sun, Wei; Sun, Mingui; Tang YD(唐延东) |
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2018 |
| 卷号 | 304页码:82-103 |
| 关键词 | Semantic segmentation Convolutional neural network Markov random fields Weakly supervised method 3D point clouds labeling |
| ISSN号 | 0925-2312 |
| 产权排序 | 5 |
| 通讯作者 | Yu HS(余洪山) |
| 中文摘要 | 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. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence |
| 研究领域[WOS] | Computer Science |
| 关键词[WOS] | MARKOV RANDOM-FIELDS ; IMAGE SEGMENTATION ; OBJECT RECOGNITION ; ENERGY MINIMIZATION ; POINT CLOUDS ; FEATURES ; VISION ; CONTEXT ; MODEL ; ALGORITHMS |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000432492800006 |
| 源URL | [http://ir.sia.cn/handle/173321/21879]  |
| 专题 | 沈阳自动化研究所_光电信息技术研究室 |
| 作者单位 | 1.Shenzhen Research Institute of Hunan University, Shenzhen, Guangdong 518057, China 2.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China 3.Laboratory for Computational Neuroscience, University of Pittsburgh, Pittsburgh, USA 4.Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA 5.National Engineering Laboratory for Robot Visual Perception and Control Technology, College of Electrical and Information Engineering, Hunan University, Changsha, China |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：沈阳自动化研究所
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