A Novel Linelet-Based Representation for Line Segment Detection

Nam Gyu Cho, Alan Yuille, Seong Whan Lee

Research output: Contribution to journalArticlepeer-review

68 Citations (Scopus)


This paper proposes a method for line segment detection in digital images. We propose a novel linelet-based representation to model intrinsic properties of line segments in rasterized image space. Based on this, line segment detection, validation, and aggregation frameworks are constructed. For a numerical evaluation on real images, we propose a new benchmark dataset of real images with annotated lines called YorkUrban-LineSegment. The results show that the proposed method outperforms state-of-the-art methods numerically and visually. To our best knowledge, this is the first report of numerical evaluation of line segment detection on real images.

Original languageEnglish
Pages (from-to)1195-1208
Number of pages14
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number5
Publication statusPublished - 2018 May 1

Bibliographical note

Publisher Copyright:
© 1979-2012 IEEE.


  • Intrinsic properties of digital line
  • image edge detection
  • line segment validation
  • probabilistic line segment representation

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


Dive into the research topics of 'A Novel Linelet-Based Representation for Line Segment Detection'. Together they form a unique fingerprint.

Cite this