Deep Crisp Boundaries: From Boundaries to Higher-level Tasks
Wang, Yupei; Zhao, Xin; Li, Yin; Huang, Kaiqi
Source PublicationIEEE Transactions on Image Processing

Edge detection has made significant progress with the help of deep convolutional networks (ConvNet). These ConvNet-based edge detectors have approached human level performance on standard benchmarks. We provide a systematical study of these detectors’ outputs. We show that the detection results did not accurately localize edge pixels, which can be adversarial for tasks that require crisp edge inputs. As a remedy, we propose a novel refinement architecture to address the challenging problem of learning a crisp edge detector using ConvNet. Our method leverages a top-down backward refinement pathway, and progressively increases the resolution of feature maps to generate crisp edges. Our results achieve superior performance, surpassing human accuracy when using standard criteria on BSDS500, and largely outperforming the state-of-the-art methods when using more strict criteria. More importantly, we demonstrate the benefit of crisp edge maps for several important applications in computer vision, including optical flow estimation, object proposal generation, and semantic segmentation.

KeywordBoundary Detection, Deep Learning
Indexed BySCI
Document Type期刊论文
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wang, Yupei,Zhao, Xin,Li, Yin,et al. Deep Crisp Boundaries: From Boundaries to Higher-level Tasks[J]. IEEE Transactions on Image Processing,2019,28(3):1285-1298.
APA Wang, Yupei,Zhao, Xin,Li, Yin,&Huang, Kaiqi.(2019).Deep Crisp Boundaries: From Boundaries to Higher-level Tasks.IEEE Transactions on Image Processing,28(3),1285-1298.
MLA Wang, Yupei,et al."Deep Crisp Boundaries: From Boundaries to Higher-level Tasks".IEEE Transactions on Image Processing 28.3(2019):1285-1298.
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