Level-line Guided Edge Drawing for Robust Line Segment Detection
This work addresses the need for more robust line segment detection in computer vision applications, offering incremental improvements over existing edge drawing methods.
The paper tackled the problem of robust line segment detection in computer vision by proposing a level-line guided edge drawing method, which improved detection accuracy and efficiency, achieving superior performance compared to state-of-the-art methods in numerical experiments.
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.