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LMFD: lightweight multi-feature descriptors for image stitching

Image stitching is a fundamental pillar of computer vision, and its effectiveness hinges significantly on the quality of the feature descriptors. However, the existing feature descriptors face several challenges, including inadequate robustness to noise or rotational transformations and limited adap...

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Autores principales: Fan, Yingbo, Mao, Shanjun, Li, Mei, Kang, Jitong, Li, Ben
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10689729/
https://www.ncbi.nlm.nih.gov/pubmed/38036564
http://dx.doi.org/10.1038/s41598-023-48432-7
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author Fan, Yingbo
Mao, Shanjun
Li, Mei
Kang, Jitong
Li, Ben
author_facet Fan, Yingbo
Mao, Shanjun
Li, Mei
Kang, Jitong
Li, Ben
author_sort Fan, Yingbo
collection PubMed
description Image stitching is a fundamental pillar of computer vision, and its effectiveness hinges significantly on the quality of the feature descriptors. However, the existing feature descriptors face several challenges, including inadequate robustness to noise or rotational transformations and limited adaptability during hardware deployment. To address these limitations, this paper proposes a set of feature descriptors for image stitching named Lightweight Multi-Feature Descriptors (LMFD). Based on the extensive extraction of gradients, means, and global information surrounding the feature points, feature descriptors are generated through various combinations to enhance the image stitching process. This endows the algorithm with formidable rotational invariance and noise resistance, thereby improving its accuracy and reliability. Furthermore, the feature descriptors take the form of binary matrices consisting of 0s and 1s, not only facilitating more efficient hardware deployment but also enhancing computational efficiency. The utilization of binary matrices significantly reduces the computational complexity of the algorithm while preserving its efficacy. To validate the effectiveness of LMFD, rigorous experimentation was conducted on the Hpatches and 2D-HeLa datasets. The results demonstrate that LMFD outperforms state-of-the-art image matching algorithms in terms of accuracy. This empirical evidence solidifies the superiority of LMFD and substantiates its potential for practical applications in various domains.
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spelling pubmed-106897292023-12-02 LMFD: lightweight multi-feature descriptors for image stitching Fan, Yingbo Mao, Shanjun Li, Mei Kang, Jitong Li, Ben Sci Rep Article Image stitching is a fundamental pillar of computer vision, and its effectiveness hinges significantly on the quality of the feature descriptors. However, the existing feature descriptors face several challenges, including inadequate robustness to noise or rotational transformations and limited adaptability during hardware deployment. To address these limitations, this paper proposes a set of feature descriptors for image stitching named Lightweight Multi-Feature Descriptors (LMFD). Based on the extensive extraction of gradients, means, and global information surrounding the feature points, feature descriptors are generated through various combinations to enhance the image stitching process. This endows the algorithm with formidable rotational invariance and noise resistance, thereby improving its accuracy and reliability. Furthermore, the feature descriptors take the form of binary matrices consisting of 0s and 1s, not only facilitating more efficient hardware deployment but also enhancing computational efficiency. The utilization of binary matrices significantly reduces the computational complexity of the algorithm while preserving its efficacy. To validate the effectiveness of LMFD, rigorous experimentation was conducted on the Hpatches and 2D-HeLa datasets. The results demonstrate that LMFD outperforms state-of-the-art image matching algorithms in terms of accuracy. This empirical evidence solidifies the superiority of LMFD and substantiates its potential for practical applications in various domains. Nature Publishing Group UK 2023-11-30 /pmc/articles/PMC10689729/ /pubmed/38036564 http://dx.doi.org/10.1038/s41598-023-48432-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Fan, Yingbo
Mao, Shanjun
Li, Mei
Kang, Jitong
Li, Ben
LMFD: lightweight multi-feature descriptors for image stitching
title LMFD: lightweight multi-feature descriptors for image stitching
title_full LMFD: lightweight multi-feature descriptors for image stitching
title_fullStr LMFD: lightweight multi-feature descriptors for image stitching
title_full_unstemmed LMFD: lightweight multi-feature descriptors for image stitching
title_short LMFD: lightweight multi-feature descriptors for image stitching
title_sort lmfd: lightweight multi-feature descriptors for image stitching
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10689729/
https://www.ncbi.nlm.nih.gov/pubmed/38036564
http://dx.doi.org/10.1038/s41598-023-48432-7
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