Widening residual skipped network for semantic segmentation
文献类型：期刊论文
| 作者 | Su, Wen1,2; Wang, Zengfu1,2
|
| 刊名 | IET IMAGE PROCESSING
 |
| 出版日期 | 2017-10-01 |
| 卷号 | 11期号:10页码:880-887 |
| DOI | 10.1049/iet-ipr.2017.0070 |
| 文献子类 | Article |
| 英文摘要 | Over the past two years deep convolutional neural networks have pushed the performance of computer vision systems to soaring heights on semantic segmentation. In this study, the authors present a novel semantic segmentation method of using a deep fully convolutional neural network to achieve image segmentation results with more precise boundary localisation. The above segmentation engine is trainable, and consists of an encoder network with widening residual skipped connections and a decoder network with a pixel-wise classification layer. Here the encoder network with widening residual skipped connections allows the combination of shallow layer features and deep layer semantic features, and the decoder network with classification layer maps the low-resolution encoder features to full resolution image with pixel-wise classification. The experimental results on PASCAL VOC 2012 semantic segmentation dataset and Cityscapes dataset show that the proposed method is effective and competitive. |
| WOS研究方向 | Computer Science ; Engineering ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000413198200010 |
| 资助机构 | National Natural Science Foundation of China(61472393) ; National Natural Science Foundation of China(61472393) ; National Natural Science Foundation of China(61472393) ; National Natural Science Foundation of China(61472393) ; National Natural Science Foundation of China(61472393) ; National Natural Science Foundation of China(61472393) ; National Natural Science Foundation of China(61472393) ; National Natural Science Foundation of China(61472393) |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/33828]  |
| 专题 | 合肥物质科学研究院_中科院合肥智能机械研究所 |
| 作者单位 | 1.Chinese Acad Sci, Inst Intelligent Machines, Hefei, Anhui, Peoples R China 2.Univ Sci & Technol China, Dept Automat, Hefei, Anhui, Peoples R China |
| 推荐引用方式 GB/T 7714 | Su, Wen,Wang, Zengfu. Widening residual skipped network for semantic segmentation[J]. IET IMAGE PROCESSING,2017,11(10):880-887. |
| APA | Su, Wen,&Wang, Zengfu.(2017).Widening residual skipped network for semantic segmentation.IET IMAGE PROCESSING,11(10),880-887. |
| MLA | Su, Wen,et al."Widening residual skipped network for semantic segmentation".IET IMAGE PROCESSING 11.10(2017):880-887. |
入库方式： OAI收割
来源：合肥物质科学研究院
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