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An Image Stereo Matching Algorithm with Multi-Spectral Attention Mechanism

With the advancement of artificial intelligence technology and computer hardware, the stereo matching algorithm has been widely researched and applied in the field of image processing. In scenarios such as robot navigation and autonomous driving, stereo matching algorithms are used to assist robots...

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Detalles Bibliográficos
Autores principales: Quan, Zhenhua, Wu, Bin, Luo, Liang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10574877/
https://www.ncbi.nlm.nih.gov/pubmed/37837009
http://dx.doi.org/10.3390/s23198179
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author Quan, Zhenhua
Wu, Bin
Luo, Liang
author_facet Quan, Zhenhua
Wu, Bin
Luo, Liang
author_sort Quan, Zhenhua
collection PubMed
description With the advancement of artificial intelligence technology and computer hardware, the stereo matching algorithm has been widely researched and applied in the field of image processing. In scenarios such as robot navigation and autonomous driving, stereo matching algorithms are used to assist robots in acquiring depth information about the surrounding environment, thereby improving the robot’s ability for autonomous navigation during self-driving. In this paper, we address the issue of low matching accuracy of stereo matching algorithms in specular regions of images and propose a multi-attention-based stereo matching algorithm called MANet. The proposed algorithm embeds a multi-spectral attention module into the residual feature-extraction network of the PSMNet algorithm. It utilizes different 2D discrete cosine transforms to extract frequency-specific feature information, providing rich and effective features for cost computation in matching. The pyramid pooling module incorporates a coordinated attention mechanism, which not only maintains long-range dependencies with directional awareness but also captures more positional information during the pooling process, thereby enhancing the network’s representational capacity. The MANet algorithm was evaluated on three major benchmark datasets, namely, SceneFlow, KITTI2015, and KITTI2012, and compared with relevant algorithms. Experimental results demonstrated that the MANet algorithm achieved higher accuracy in predicting disparities and exhibited stronger robustness against specular reflections, enabling more accurate disparity prediction in specular regions.
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spelling pubmed-105748772023-10-14 An Image Stereo Matching Algorithm with Multi-Spectral Attention Mechanism Quan, Zhenhua Wu, Bin Luo, Liang Sensors (Basel) Article With the advancement of artificial intelligence technology and computer hardware, the stereo matching algorithm has been widely researched and applied in the field of image processing. In scenarios such as robot navigation and autonomous driving, stereo matching algorithms are used to assist robots in acquiring depth information about the surrounding environment, thereby improving the robot’s ability for autonomous navigation during self-driving. In this paper, we address the issue of low matching accuracy of stereo matching algorithms in specular regions of images and propose a multi-attention-based stereo matching algorithm called MANet. The proposed algorithm embeds a multi-spectral attention module into the residual feature-extraction network of the PSMNet algorithm. It utilizes different 2D discrete cosine transforms to extract frequency-specific feature information, providing rich and effective features for cost computation in matching. The pyramid pooling module incorporates a coordinated attention mechanism, which not only maintains long-range dependencies with directional awareness but also captures more positional information during the pooling process, thereby enhancing the network’s representational capacity. The MANet algorithm was evaluated on three major benchmark datasets, namely, SceneFlow, KITTI2015, and KITTI2012, and compared with relevant algorithms. Experimental results demonstrated that the MANet algorithm achieved higher accuracy in predicting disparities and exhibited stronger robustness against specular reflections, enabling more accurate disparity prediction in specular regions. MDPI 2023-09-29 /pmc/articles/PMC10574877/ /pubmed/37837009 http://dx.doi.org/10.3390/s23198179 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Quan, Zhenhua
Wu, Bin
Luo, Liang
An Image Stereo Matching Algorithm with Multi-Spectral Attention Mechanism
title An Image Stereo Matching Algorithm with Multi-Spectral Attention Mechanism
title_full An Image Stereo Matching Algorithm with Multi-Spectral Attention Mechanism
title_fullStr An Image Stereo Matching Algorithm with Multi-Spectral Attention Mechanism
title_full_unstemmed An Image Stereo Matching Algorithm with Multi-Spectral Attention Mechanism
title_short An Image Stereo Matching Algorithm with Multi-Spectral Attention Mechanism
title_sort image stereo matching algorithm with multi-spectral attention mechanism
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10574877/
https://www.ncbi.nlm.nih.gov/pubmed/37837009
http://dx.doi.org/10.3390/s23198179
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