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The Journal of Supercomputing

Erschienen in:

05.09.2023

CF-lines: a fusing contour features optimization method for line segment detector

verfasst von: Runsheng Liu, Wencong Cai, Junyang Zhang, Xiaoling Wu, Lilin Yang, Kaiqing Luo

Erschienen in: The Journal of Supercomputing | Ausgabe 3/2024

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Abstract

Aiming at the problem that the existing line segment detectors will detect overdense meaningless textures, this paper proposes a fusing contour features optimization method for line segment detector, called CF-Lines. We define a new line segment attribute, called "line segment associate contour(LAC)" attribute, which includes the contour features, the length and the angle of line segment. After using existing algorithms to detect contours and line segments, CF-Lines calculates the LAC of all line segments. When the LAC is greater than the threshold and passes the quantitative verification, the line segment is removed as overdense meaningless texture. Using the YorkUrban and Wireframe datasets, the CF-Lines is tested and compared with original detectors. Experimental results show that CF-Lines performs better than the original detectors in average precision, F-score, average length of line segments and average number of line segments.

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Titel: CF-lines: a fusing contour features optimization method for line segment detector
verfasst von: Runsheng Liu
Wencong Cai
Junyang Zhang
Xiaoling Wu
Lilin Yang
Kaiqing Luo
Publikationsdatum: 05.09.2023
Verlag: Springer US
Erschienen in: The Journal of Supercomputing / Ausgabe 3/2024
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-023-05615-3

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