Deep-learning-based object-level contour detection with CCG and CRF optimization

By Yuan Liao, Songping Fu, Xiaoqing Lu, Chengcui Zhang, Zhi Tang. 2017

16 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering, Media Technology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Haibin Ling
2. Shiliang Zhang
3. Tiejun Huang
4. Yi Yang
5. Dacheng Tao
6. Ming-Hsuan Yang
7. Yaping Huang
8. Qingji Guan

In Industrial and Manufacturing Engineering

1. Haibin Ling

In Media Technology

1. Yaping Huang
2. Shiliang Zhang
3. Haibin Ling
4. Tiejun Huang
5. Qingji Guan
6. Ming Yang
7. Mengyang Pu

Citations in the corpus

• EDTER: Edge Detection with Transformer (2022, methodology): “DeepEdge [3] CVPR’15 0.753 0.772 0.807 CSCNN [24] ArXiv’15 0.756 0.775 0.798 MSC [51] PAMI’15 0.756 0.776 0.787 DeepContour [48] CVPR’15 0.757 0.776 0.800 HFL [4] ICCV’15 0.767 0.788 0.795 HED [65] ICCV’15 0.788 0.808 0.840 Deep Boundary [29] ICLR’15 0.813 0.831 0.866 CEDN [67] CVPR’16 0.788 0.804 -”
• Bi-Directional Cascade Network for Perceptual Edge Detection (2019, methodology): “Performance on BSDS500: We compare our approach with recent deep learning based methods including CED [47], RCF [30], DeepBoundary [23], DCD [27], [40] .739 .758 .773 PMI [20] .741 .769 .799 OEF [17] .746 .770 .820 DeepContour [42] .757 .776 .800 HFL [3] .767 .788 .795 HED [49] .788 .808 .840 CEDN [”