A Novel Linelet-Based Representation for Line Segment Detection

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 nu...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on pattern analysis and machine intelligence Jg. 40; H. 5; S. 1195 - 1208
Hauptverfasser: Nam-Gyu Cho, Yuille, Alan, Seong-Whan Lee
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.05.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Benchmark testing
Digital images
Digital imaging
Electronic mail
Estimation
Image detection
Image edge detection
Image segmentation
Intrinsic properties of digital line
line segment validation
Mathematical models
Nonlinear programming
Numerical methods
probabilistic line segment representation
Representations
State of the art
Visualization
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2017.2703841