Abstract
A novel algorithm to detect semantic lines is proposed in this paper. We develop three networks: detection network with mirror attention (D-Net) and comparative ranking and matching networks (R-Net and M-Net). D-Net extracts semantic lines by exploiting rich contextual information. To this end, we design the mirror attention module. Then, through pairwise comparisons of extracted semantic lines, we iteratively select the most semantic line and remove redundant ones overlapping with the selected one. For the pairwise comparisons, we develop R-Net and M-Net in the Siamese architecture. Experiments demonstrate that the proposed algorithm outperforms the conventional semantic line detector significantly. Moreover, we apply the proposed algorithm to detect two important kinds of semantic lines successfully: dominant parallel lines and reflection symmetry axes. Our codes are available at https://github.com/dongkwonjin/Semantic-Line-DRM.
Original language | English |
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Title of host publication | Computer Vision – ECCV 2020 - 16th European Conference 2020, Proceedings |
Editors | Andrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 119-135 |
Number of pages | 17 |
ISBN (Print) | 9783030585648 |
DOIs | |
Publication status | Published - 2020 |
Event | 16th European Conference on Computer Vision, ECCV 2020 - Glasgow, United Kingdom Duration: 2020 Aug 23 → 2020 Aug 28 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 12365 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 16th European Conference on Computer Vision, ECCV 2020 |
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Country/Territory | United Kingdom |
City | Glasgow |
Period | 20/8/23 → 20/8/28 |
Bibliographical note
Publisher Copyright:© 2020, Springer Nature Switzerland AG.
Keywords
- Attention
- Line detection
- Matching
- Ranking
- Semantic lines
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science