Content area

Abstract

Line detection is a classic and essential problem in image processing, computer vision and machine intelligence. Line detection has many important applications, including image vectorization (e.g., document recognition and art design), indoor mapping, and important societal challenges (e.g., sea ice fracture line extraction from satellite imagery). Many line detection algorithms and methods have been developed, but robust and intuitive methods are still lacking. In this paper, we proposed and implemented a topological graph-guided algorithm, named TGGLinesPlus, for line detection. Our experiments on images from a wide range of domains have demonstrated the flexibility of our TGGLinesPlus algorithm. We also benchmarked our algorithm with five classic and state-of-the-art line detection methods and the results demonstrate the robustness of TGGLinesPlus. We hope our open-source implementation of TGGLinesPlus will inspire and pave the way for many applications where spatial science matters.

Details

1009240
Title
TGGLinesPlus: A robust topological graph-guided computer vision algorithm for line detection from images
Publication title
arXiv.org; Ithaca
Publication year
2024
Publication date
Mar 26, 2024
Section
Computer Science
Publisher
Cornell University Library, arXiv.org
Source
arXiv.org
Place of publication
Ithaca
Country of publication
United States
University/institution
Cornell University Library arXiv.org
e-ISSN
2331-8422
Source type
Working Paper
Language of publication
English
Document type
Working Paper
Publication history
 
 
Online publication date
2024-03-28
Milestone dates
2024-03-26 (Submission v1)
Publication history
 
 
   First posting date
28 Mar 2024
ProQuest document ID
3014333325
Document URL
https://www.proquest.com/working-papers/tgglinesplus-robust-topological-graph-guided/docview/3014333325/se-2?accountid=208611
Full text outside of ProQuest
Copyright
© 2024. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2024-03-30
Database
ProQuest One Academic