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 |
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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
- 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