mlsanityData to 14 Oct 2026

Level-line Guided Edge Drawing for Robust Line Segment Detection

arXiv 2305.05883 ↗First posted 10 May 2023cs.CVcited by 1

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Abstract (arXiv)

Line segment detection plays a cornerstone role in computer vision tasks. Among numerous detection methods that have been recently proposed, the ones based on edge drawing attract increasing attention owing to their excellent detection efficiency. However, the existing methods are not robust enough due to the inadequate usage of image gradients for edge drawing and line segment fitting. Based on the observation that the line segments should locate on the edge points with both consistent coordinates and level-line information, i.e., the unit vector perpendicular to the gradient orientation, this paper proposes a level-line guided edge drawing for robust line segment detection (GEDRLSD). The level-line information provides potential directions for edge tracking, which could be served as a guideline for accurate edge drawing. Additionally, the level-line information is fused in line segment fitting to improve the robustness. Numerical experiments show the superiority of the proposed GEDRLSD algorithm compared with state-of-the-art methods.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 22 references that are in the map. First posted on arXiv 10 May 2023.

Builds on

8 of 22 references.

References · 8

  1. 2015ORB-SLAM: a Versatile and Accurate Monocular SLAM Systemcited by 988
  2. 2017HPatches: A benchmark and evaluation of handcrafted and learned local descriptorscited by 264
  3. 2019End-to-End Wireframe Parsingcited by 72
  4. 2020Learning to Parse Wireframes in Images of Man-Made Environmentscited by 71
  5. 2020Deep Hough-Transform Line Priorscited by 44
  6. 2021Towards Light-weight and Real-time Line Segment Detectioncited by 33
  7. 2021Fully Convolutional Line Parsingcited by 24
  8. 2021ELSED: Enhanced Line SEgment Drawingcited by 23

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