Level-line Guided Edge Drawing for Robust Line Segment Detection
Xinyu Lin Yingjie Zhou Yipeng Liu Ce Zhu
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.