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...

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Vydané v:	IEEE transactions on pattern analysis and machine intelligence Ročník 40; číslo 5; s. 1195 - 1208
Hlavní autori:	Nam-Gyu Cho, Yuille, Alan, Seong-Whan Lee
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.05.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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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.
AbstractList	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.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. 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.
Author	Nam-Gyu Cho Yuille, Alan Seong-Whan Lee
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SubjectTerms	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
Title	A Novel Linelet-Based Representation for Line Segment Detection
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