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DeshadowNet: A Multi-context Embedding Deep Network for Shadow Removal
Qu JQ(屈靓琼); Tian JD(田建东); He SF(何盛丰); Tang YD(唐延东); Lau, Rynson W.H.
作者部门机器人学研究室
会议名称30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017)
会议日期July 21-26, 2017
会议地点Honolulu, USA
会议录名称30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017)
出版者IEEE
出版地New York
2017
页码2308-2316
收录类别EI ; CPCI(ISTP)
EI收录号20181304947741
WOS记录号WOS:000418371402040
产权排序1
ISSN号1063-6919
ISBN号978-1-5386-0457-1
摘要Shadow removal is a challenging task as it requires the detection/annotation of shadows as well as semantic understanding of the scene. In this paper, we propose an automatic and end-to-end deep neural network (DeshadowNet) to tackle these problems in a unified manner. DeshadowNet is designed with a multi-context architecture, where the output shadow matte is predicted by embedding information from three different perspectives. The first global network extracts shadow features from a global view. Two levels of features are derived from the global network and transferred to two parallel networks. While one extracts the appearance of the input image, the other one involves semantic understanding for final prediction. These two complementary networks generate multi-context features to obtain the shadow matte with fine local details. To evaluate the performance of the proposed method, we construct the first large scale benchmark with 3088 image pairs. Extensive experiments on two publicly available benchmarks and our large-scale benchmark show that the proposed method performs favorably against several state-of-the-art methods.
语种英语
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被引频次:1[WOS]   [WOS记录]     [WOS相关记录]
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/21346
专题机器人学研究室
通讯作者Tian JD(田建东)
作者单位1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
3.City University of Hong Kong
4.South China University of Technology
推荐引用方式
GB/T 7714
Qu JQ,Tian JD,He SF,et al. DeshadowNet: A Multi-context Embedding Deep Network for Shadow Removal[C]. New York:IEEE,2017:2308-2316.
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