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Vanishing Point Detection Method Based on Constrained Classification for Checkpoints on Urban Roads
To solve the problems of computational complexity and inaccuracy in classical vanishing point detection algorithms, such as the cascaded Hough transform, a vanishing point detection method based on constrained classification is proposed. First, the short line data are filtered to avoid interference...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9289364/ https://www.ncbi.nlm.nih.gov/pubmed/35860326 http://dx.doi.org/10.3389/fbioe.2022.920329 |
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author | Gong, Guoqiang Liu, Junqing Li, Zhengxiao |
author_facet | Gong, Guoqiang Liu, Junqing Li, Zhengxiao |
author_sort | Gong, Guoqiang |
collection | PubMed |
description | To solve the problems of computational complexity and inaccuracy in classical vanishing point detection algorithms, such as the cascaded Hough transform, a vanishing point detection method based on constrained classification is proposed. First, the short line data are filtered to avoid interference in straight line detection, and then, the line segment is screened and classified by hierarchical clustering according to the image characteristics of the line segment and the variation pattern of angle similarity. Subsequently, Three types of straight line segments with the most significant angle differences are acquired. To prevent the optimization algorithm from getting stuck in the “wrong” local optimum neighborhood or failing to locate the global optimum, a set of constraints are set to further restrict the search. Afterward, the classified line segments are projected into a finite rhombic space, which are then quantified. The point with the maximum vote is eventually identified as the vanishing point. Experimental results show that the proposed method not only greatly reduces the computational complexity of vanishing points but also largely improves the accuracy of vanishing point detection. |
format | Online Article Text |
id | pubmed-9289364 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92893642022-07-19 Vanishing Point Detection Method Based on Constrained Classification for Checkpoints on Urban Roads Gong, Guoqiang Liu, Junqing Li, Zhengxiao Front Bioeng Biotechnol Bioengineering and Biotechnology To solve the problems of computational complexity and inaccuracy in classical vanishing point detection algorithms, such as the cascaded Hough transform, a vanishing point detection method based on constrained classification is proposed. First, the short line data are filtered to avoid interference in straight line detection, and then, the line segment is screened and classified by hierarchical clustering according to the image characteristics of the line segment and the variation pattern of angle similarity. Subsequently, Three types of straight line segments with the most significant angle differences are acquired. To prevent the optimization algorithm from getting stuck in the “wrong” local optimum neighborhood or failing to locate the global optimum, a set of constraints are set to further restrict the search. Afterward, the classified line segments are projected into a finite rhombic space, which are then quantified. The point with the maximum vote is eventually identified as the vanishing point. Experimental results show that the proposed method not only greatly reduces the computational complexity of vanishing points but also largely improves the accuracy of vanishing point detection. Frontiers Media S.A. 2022-07-04 /pmc/articles/PMC9289364/ /pubmed/35860326 http://dx.doi.org/10.3389/fbioe.2022.920329 Text en Copyright © 2022 Gong, Liu and Li. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Bioengineering and Biotechnology Gong, Guoqiang Liu, Junqing Li, Zhengxiao Vanishing Point Detection Method Based on Constrained Classification for Checkpoints on Urban Roads |
title | Vanishing Point Detection Method Based on Constrained Classification for Checkpoints on Urban Roads |
title_full | Vanishing Point Detection Method Based on Constrained Classification for Checkpoints on Urban Roads |
title_fullStr | Vanishing Point Detection Method Based on Constrained Classification for Checkpoints on Urban Roads |
title_full_unstemmed | Vanishing Point Detection Method Based on Constrained Classification for Checkpoints on Urban Roads |
title_short | Vanishing Point Detection Method Based on Constrained Classification for Checkpoints on Urban Roads |
title_sort | vanishing point detection method based on constrained classification for checkpoints on urban roads |
topic | Bioengineering and Biotechnology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9289364/ https://www.ncbi.nlm.nih.gov/pubmed/35860326 http://dx.doi.org/10.3389/fbioe.2022.920329 |
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