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Multi-Constrained Optimization Method of Line Segment Extraction Based on Multi-Scale Image Space

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journal contribution
posted on 2019-06-19, 14:06 authored by Yiyuan Sun, Qiang Wang, Kevin Tansey, Sana Ullah, Fan Liu, Haimeng Zhao, Lei Yan
Image-based line segment extraction plays an important role in a wide range of applications. Traditional line segment extraction algorithms focus on the accuracy and efficiency, without considering the integrity. Serious line segmentation fracture problems caused by image quality will result in poor subsequent applications. To solve this problem, a multi-constrained line segment extraction method, based on multi-scale image space, is presented. Firstly, using Gaussian down-sampling with a classical line segment detection method, a multi-scale image space is constructed to extract line segments in each image scale and all line segments are projected onto the original image. Then, a new line segment optimization and purification strategy is proposed with the horizontal and vertical distances and angle geometric constraint relationships between line segments to merge fracture line segments and delete redundant line segments. Finally, line segments with adjacent positions are optimized using the grayscale constraint relationship, based on normalized cross-correlation similarity criterion for realizing the second optimization of fracture line segments. Compared with mainstream line segment detector and edge drawing lines methods, experimental results (i.e., indoor, outdoor, and aerial images) indicate the validity and superiority of our proposed methods which can extract longer and more complete line segments.

History

Citation

ISPRS International Journal of Geo-Information, 2019, 8 (4), pp. 183-183

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/School of Geography, Geology and the Environment/GIS and Remote Sensing

Version

  • VoR (Version of Record)

Published in

ISPRS International Journal of Geo-Information

Publisher

MDPI

eissn

2220-9964

Acceptance date

2019-04-04

Copyright date

2019

Available date

2019-06-19

Publisher version

https://www.mdpi.com/2220-9964/8/4/183

Language

en

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