3 research outputs found
Connectivity-Enforcing Hough Transform for the Robust Extraction of Line Segments
Global voting schemes based on the Hough transform (HT) have been widely used
to robustly detect lines in images. However, since the votes do not take line
connectivity into account, these methods do not deal well with cluttered
images. In opposition, the so-called local methods enforce connectivity but
lack robustness to deal with challenging situations that occur in many
realistic scenarios, e.g., when line segments cross or when long segments are
corrupted. In this paper, we address the critical limitations of the HT as a
line segment extractor by incorporating connectivity in the voting process.
This is done by only accounting for the contributions of edge points lying in
increasingly larger neighborhoods and whose position and directional content
agree with potential line segments. As a result, our method, which we call
STRAIGHT (Segment exTRAction by connectivity-enforcInG HT), extracts the
longest connected segments in each location of the image, thus also integrating
into the HT voting process the usually separate step of individual segment
extraction. The usage of the Hough space mapping and a corresponding
hierarchical implementation make our approach computationally feasible. We
present experiments that illustrate, with synthetic and real images, how
STRAIGHT succeeds in extracting complete segments in several situations where
current methods fail.Comment: Submitted for publicatio
Learning Regional Attraction for Line Segment Detection
This paper presents regional attraction of line segment maps, and hereby
poses the problem of line segment detection (LSD) as a problem of region
coloring. Given a line segment map, the proposed regional attraction first
establishes the relationship between line segments and regions in the image
lattice. Based on this, the line segment map is equivalently transformed to an
attraction field map (AFM), which can be remapped to a set of line segments
without loss of information. Accordingly, we develop an end-to-end framework to
learn attraction field maps for raw input images, followed by a squeeze module
to detect line segments. Apart from existing works, the proposed detector
properly handles the local ambiguity and does not rely on the accurate
identification of edge pixels. Comprehensive experiments on the Wireframe
dataset and the YorkUrban dataset demonstrate the superiority of our method. In
particular, we achieve an F-measure of 0.831 on the Wireframe dataset,
advancing the state-of-the-art performance by 10.3 percent.Comment: Accepted to IEEE TPAMI. arXiv admin note: text overlap with
arXiv:1812.0212