Abstract
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 language | English |
|---|---|
| Pages (from-to) | 1195-1208 |
| Number of pages | 14 |
| Journal | IEEE Transactions on Pattern Analysis and Machine Intelligence |
| Volume | 40 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 2018 May 1 |
Keywords
- image edge detection
- Intrinsic properties of digital line
- 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
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Cite this
Cho, N. G., Yuille, A., & Lee, S. W. (2018). A Novel Linelet-Based Representation for Line Segment Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(5), 1195-1208. https://doi.org/10.1109/TPAMI.2017.2703841