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Feature Correspondences Increase and Hybrid Terms Optimization Warp for Image Stitching

Feature detection and correct matching are the basis of the image stitching process. Whether the matching is correct and the number of matches directly affect the quality of the final stitching results. At present, almost all image stitching methods use SIFT+RANSAC pattern to extract and match featu...

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Autores principales: Cong, Yizhi, Wang, Yan, Hou, Wenju, Pang, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9857804/
https://www.ncbi.nlm.nih.gov/pubmed/36673247
http://dx.doi.org/10.3390/e25010106
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author Cong, Yizhi
Wang, Yan
Hou, Wenju
Pang, Wei
author_facet Cong, Yizhi
Wang, Yan
Hou, Wenju
Pang, Wei
author_sort Cong, Yizhi
collection PubMed
description Feature detection and correct matching are the basis of the image stitching process. Whether the matching is correct and the number of matches directly affect the quality of the final stitching results. At present, almost all image stitching methods use SIFT+RANSAC pattern to extract and match feature points. However, it is difficult to obtain sufficient correct matching points in low-textured or repetitively-textured regions, resulting in insufficient matching points in the overlapping region, and this further leads to the warping model being estimated erroneously. In this paper, we propose a novel and flexible approach by increasing feature correspondences and optimizing hybrid terms. It can obtain sufficient correct feature correspondences in the overlapping region with low-textured or repetitively-textured areas to eliminate misalignment. When a weak texture and large parallax coexist in the overlapping region, the alignment and distortion often restrict each other and are difficult to balance. Accurate alignment is often accompanied by projection distortion and perspective distortion. Regarding this, we propose hybrid terms optimization warp, which combines global similarity transformations on the basis of initial global homography and estimates the optimal warping by adjusting various term parameters. By doing this, we can mitigate projection distortion and perspective distortion, while effectively balancing alignment and distortion. The experimental results demonstrate that the proposed method outperforms the state-of-the-art in accurate alignment on images with low-textured areas in the overlapping region, and the stitching results have less perspective and projection distortion.
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spelling pubmed-98578042023-01-21 Feature Correspondences Increase and Hybrid Terms Optimization Warp for Image Stitching Cong, Yizhi Wang, Yan Hou, Wenju Pang, Wei Entropy (Basel) Article Feature detection and correct matching are the basis of the image stitching process. Whether the matching is correct and the number of matches directly affect the quality of the final stitching results. At present, almost all image stitching methods use SIFT+RANSAC pattern to extract and match feature points. However, it is difficult to obtain sufficient correct matching points in low-textured or repetitively-textured regions, resulting in insufficient matching points in the overlapping region, and this further leads to the warping model being estimated erroneously. In this paper, we propose a novel and flexible approach by increasing feature correspondences and optimizing hybrid terms. It can obtain sufficient correct feature correspondences in the overlapping region with low-textured or repetitively-textured areas to eliminate misalignment. When a weak texture and large parallax coexist in the overlapping region, the alignment and distortion often restrict each other and are difficult to balance. Accurate alignment is often accompanied by projection distortion and perspective distortion. Regarding this, we propose hybrid terms optimization warp, which combines global similarity transformations on the basis of initial global homography and estimates the optimal warping by adjusting various term parameters. By doing this, we can mitigate projection distortion and perspective distortion, while effectively balancing alignment and distortion. The experimental results demonstrate that the proposed method outperforms the state-of-the-art in accurate alignment on images with low-textured areas in the overlapping region, and the stitching results have less perspective and projection distortion. MDPI 2023-01-04 /pmc/articles/PMC9857804/ /pubmed/36673247 http://dx.doi.org/10.3390/e25010106 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
Cong, Yizhi
Wang, Yan
Hou, Wenju
Pang, Wei
Feature Correspondences Increase and Hybrid Terms Optimization Warp for Image Stitching
title Feature Correspondences Increase and Hybrid Terms Optimization Warp for Image Stitching
title_full Feature Correspondences Increase and Hybrid Terms Optimization Warp for Image Stitching
title_fullStr Feature Correspondences Increase and Hybrid Terms Optimization Warp for Image Stitching
title_full_unstemmed Feature Correspondences Increase and Hybrid Terms Optimization Warp for Image Stitching
title_short Feature Correspondences Increase and Hybrid Terms Optimization Warp for Image Stitching
title_sort feature correspondences increase and hybrid terms optimization warp for image stitching
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9857804/
https://www.ncbi.nlm.nih.gov/pubmed/36673247
http://dx.doi.org/10.3390/e25010106
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