CASIA OpenIR
ITERATIVE RESIDUAL NETWORK FOR STRUCTURED EDGE DETECTION
Wang, Yupei; Zhao, Xin; Huang, Kaiqi
2018
Conference NameIEEE International Conference on Image Processing
Conference DateOctober 7-10, 2018
Conference PlaceAthens, Greece
Abstract

Edge detection aims to find visually distinctive edges or boundaries in input images. Edge detection has made significant progress with the help of deep Convolutional Networks (ConvNet). Most ConvNet-based edge detectors predict each pixel independently and ignore the inherent correlations between pixels. However, structured cues in input images are critical to learn a good edge detector. To this end, we propose a novel Iterative Residual Holistically-nested Edge Detection (IRHED) network. IRHED incorporates multi-scale features from the hierarchy of the network, and learns to iteratively refine the output boundary map in a deeply supervised manner. In this way, global structural cues, such as object shape, are learned implicitly, thus edges can be effectively distinguished. Extensive experiments demonstrate that IRHED achieves state-of-the-art results on the widely used BSDS500 dataset. We also show the benefit of structured edge map for higher-level task, such as object proposal generation.

Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23352
Collection中国科学院自动化研究所
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wang, Yupei,Zhao, Xin,Huang, Kaiqi. ITERATIVE RESIDUAL NETWORK FOR STRUCTURED EDGE DETECTION[C],2018.
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