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TGGLinesPlus: A robust topological graph-guided computer vision algorithm for line detection from images

Salvato in:
Pubblicato in:arXiv.org (Mar 26, 2024), p. n/a
Autore principale: Yang, Liping
Altri autori: Driscol, Joshua, Gong, Ming, Wang, Shujie, Potts, Catherine G
Pubblicazione:
Cornell University Library, arXiv.org
Soggetti:
Algorithms
Vector processing (computers)
Computer vision
Image processing
Sea ice
Robustness
Satellite imagery
Topology
Accesso online:Citation/Abstract
Full text outside of ProQuest
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022 |a 2331-8422 
035 |a 3014333325 
045 0 |b d20240326 
100 1 |a Yang, Liping 
245 1 |a TGGLinesPlus: A robust topological graph-guided computer vision algorithm for line detection from images 
260 |b Cornell University Library, arXiv.org  |c Mar 26, 2024 
513 |a Working Paper 
520 3 |a 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. 
653 |a Algorithms 
653 |a Vector processing (computers) 
653 |a Computer vision 
653 |a Image processing 
653 |a Sea ice 
653 |a Robustness 
653 |a Satellite imagery 
653 |a Topology 
700 1 |a Driscol, Joshua 
700 1 |a Gong, Ming 
700 1 |a Wang, Shujie 
700 1 |a Potts, Catherine G 
773 0 |t arXiv.org  |g (Mar 26, 2024), p. n/a 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3014333325/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch 
856 4 0 |3 Full text outside of ProQuest  |u http://arxiv.org/abs/2403.18038