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Widening residual skipped network for semantic segmentation
Su, Wen1,2; Wang, Zengfu1,2
2017-10-01
Source PublicationIET IMAGE PROCESSING
Volume11Issue:10Pages:880-887
AbstractOver 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.
SubtypeArticle
WOS HeadingsScience & Technology ; Technology
Funding OrganizationNational Natural Science Foundation of China(61472393) ; National Natural Science Foundation of China(61472393)
DOI10.1049/iet-ipr.2017.0070
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61472393) ; National Natural Science Foundation of China(61472393)
WOS Research AreaComputer Science ; Engineering ; Imaging Science & Photographic Technology
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology
WOS IDWOS:000413198200010
Citation statistics
Document Type期刊论文
Identifierhttp://ir.hfcas.ac.cn:8080/handle/334002/33828
Collection中科院合肥智能机械研究所
Affiliation1.Chinese Acad Sci, Inst Intelligent Machines, Hefei, Anhui, Peoples R China
2.Univ Sci & Technol China, Dept Automat, Hefei, Anhui, Peoples R China
Recommended Citation
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.
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