Vision-Based Structural Modal Identification Using Hybrid Motion Magnification
As a promising alternative to conventional contact sensors, vision-based technologies for a structural dynamic response measurement and health monitoring have attracted much attention from the research community. Among these technologies, Eulerian video magnification has a unique capability of analy...
Saved in:
Published in | Sensors (Basel, Switzerland) Vol. 22; no. 23; p. 9287 |
---|---|
Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Switzerland
MDPI AG
29.11.2022
MDPI |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | As a promising alternative to conventional contact sensors, vision-based technologies for a structural dynamic response measurement and health monitoring have attracted much attention from the research community. Among these technologies, Eulerian video magnification has a unique capability of analyzing modal responses and visualizing modal shapes. To reduce the noise interference and improve the quality and stability of the modal shape visualization, this study proposes a hybrid motion magnification framework that combines linear and phase-based motion processing. Based on the assumption that temporal variations can represent spatial motions, the linear motion processing extracts and manipulates the temporal intensity variations related to modal responses through matrix decomposition and underdetermined blind source separation (BSS) techniques. Meanwhile, the theory of Fourier transform profilometry (FTP) is utilized to reduce spatial high-frequency noise. As all spatial motions in a video are linearly controllable, the subsequent phase-based motion processing highlights the motions and visualizes the modal shapes with a higher quality. The proposed method is validated by two laboratory experiments and a field test on a large-scale truss bridge. The quantitative evaluation results with high-speed cameras demonstrate that the hybrid method performs better than the single-step phase-based motion magnification method in visualizing sound-induced subtle motions. In the field test, the vibration characteristics of the truss bridge when a train is driving across the bridge are studied with a commercial camera over 400 m away from the bridge. Moreover, four full-field modal shapes of the bridge are successfully observed. |
---|---|
AbstractList | As a promising alternative to conventional contact sensors, vision-based technologies for a structural dynamic response measurement and health monitoring have attracted much attention from the research community. Among these technologies, Eulerian video magnification has a unique capability of analyzing modal responses and visualizing modal shapes. To reduce the noise interference and improve the quality and stability of the modal shape visualization, this study proposes a hybrid motion magnification framework that combines linear and phase-based motion processing. Based on the assumption that temporal variations can represent spatial motions, the linear motion processing extracts and manipulates the temporal intensity variations related to modal responses through matrix decomposition and underdetermined blind source separation (BSS) techniques. Meanwhile, the theory of Fourier transform profilometry (FTP) is utilized to reduce spatial high-frequency noise. As all spatial motions in a video are linearly controllable, the subsequent phase-based motion processing highlights the motions and visualizes the modal shapes with a higher quality. The proposed method is validated by two laboratory experiments and a field test on a large-scale truss bridge. The quantitative evaluation results with high-speed cameras demonstrate that the hybrid method performs better than the single-step phase-based motion magnification method in visualizing sound-induced subtle motions. In the field test, the vibration characteristics of the truss bridge when a train is driving across the bridge are studied with a commercial camera over 400 m away from the bridge. Moreover, four full-field modal shapes of the bridge are successfully observed. As a promising alternative to conventional contact sensors, vision-based technologies for a structural dynamic response measurement and health monitoring have attracted much attention from the research community. Among these technologies, Eulerian video magnification has a unique capability of analyzing modal responses and visualizing modal shapes. To reduce the noise interference and improve the quality and stability of the modal shape visualization, this study proposes a hybrid motion magnification framework that combines linear and phase-based motion processing. Based on the assumption that temporal variations can represent spatial motions, the linear motion processing extracts and manipulates the temporal intensity variations related to modal responses through matrix decomposition and underdetermined blind source separation (BSS) techniques. Meanwhile, the theory of Fourier transform profilometry (FTP) is utilized to reduce spatial high-frequency noise. As all spatial motions in a video are linearly controllable, the subsequent phase-based motion processing highlights the motions and visualizes the modal shapes with a higher quality. The proposed method is validated by two laboratory experiments and a field test on a large-scale truss bridge. The quantitative evaluation results with high-speed cameras demonstrate that the hybrid method performs better than the single-step phase-based motion magnification method in visualizing sound-induced subtle motions. In the field test, the vibration characteristics of the truss bridge when a train is driving across the bridge are studied with a commercial camera over 400 m away from the bridge. Moreover, four full-field modal shapes of the bridge are successfully observed.As a promising alternative to conventional contact sensors, vision-based technologies for a structural dynamic response measurement and health monitoring have attracted much attention from the research community. Among these technologies, Eulerian video magnification has a unique capability of analyzing modal responses and visualizing modal shapes. To reduce the noise interference and improve the quality and stability of the modal shape visualization, this study proposes a hybrid motion magnification framework that combines linear and phase-based motion processing. Based on the assumption that temporal variations can represent spatial motions, the linear motion processing extracts and manipulates the temporal intensity variations related to modal responses through matrix decomposition and underdetermined blind source separation (BSS) techniques. Meanwhile, the theory of Fourier transform profilometry (FTP) is utilized to reduce spatial high-frequency noise. As all spatial motions in a video are linearly controllable, the subsequent phase-based motion processing highlights the motions and visualizes the modal shapes with a higher quality. The proposed method is validated by two laboratory experiments and a field test on a large-scale truss bridge. The quantitative evaluation results with high-speed cameras demonstrate that the hybrid method performs better than the single-step phase-based motion magnification method in visualizing sound-induced subtle motions. In the field test, the vibration characteristics of the truss bridge when a train is driving across the bridge are studied with a commercial camera over 400 m away from the bridge. Moreover, four full-field modal shapes of the bridge are successfully observed. |
Audience | Academic |
Author | Zhu, Andong Hou, Wenhui Zhang, Dashan Liu, Lu Wang, Yuwei |
AuthorAffiliation | 1 College of Engineering, Anhui Agricultural University, Hefei 230036, China 2 Anhui Province Engineering Laboratory of Intelligent Agricultural Machinery and Equipment, Anhui Agricultural University, Hefei 230036, China |
AuthorAffiliation_xml | – name: 1 College of Engineering, Anhui Agricultural University, Hefei 230036, China – name: 2 Anhui Province Engineering Laboratory of Intelligent Agricultural Machinery and Equipment, Anhui Agricultural University, Hefei 230036, China |
Author_xml | – sequence: 1 givenname: Dashan orcidid: 0000-0002-2416-1058 surname: Zhang fullname: Zhang, Dashan – sequence: 2 givenname: Andong orcidid: 0000-0002-5876-5597 surname: Zhu fullname: Zhu, Andong – sequence: 3 givenname: Wenhui surname: Hou fullname: Hou, Wenhui – sequence: 4 givenname: Lu orcidid: 0000-0001-8137-671X surname: Liu fullname: Liu, Lu – sequence: 5 givenname: Yuwei orcidid: 0000-0003-4282-9821 surname: Wang fullname: Wang, Yuwei |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36501990$$D View this record in MEDLINE/PubMed |
BookMark | eNptkktv1DAQxy1URNuFA18ArcQFDmkdPxL7glQqoCu1cIBytSZ-BK-ycWsnSP32DLtl1VbIkm2Nf_P3vI7JwZhGT8jrmp5wrulpYYxxzVT7jBzVgolKMUYPHtwPyXEpa0oZ51y9IIe8kbTWmh6Rrz9jiWmsPkLxbvl9yrOd5gzD8io53FfOj1MM0cKE1PK6xLFfXtx1OToktrYr6Mc98ZI8DzAU_-r-XJDrz59-nF9Ul9--rM7PLisrqZqqICCoRoBtaE0t1bwB6TWAbJ1j3oJknVRBMluHoKzkLWXglfWWCkodUL4gq52uS7A2NzluIN-ZBNFsDSn3BvIU7eBNq1sZmHKMCiWUa7rOiQ5ooxWXwLEiC_Jhp3UzdxvvLGaMBXgk-vhljL9Mn34b3WLRRY0C7-4FcrqdfZnMJhbrhwFGn-ZiWCs5r1mtWkTfPkHXac4jlgopoaTUWgmkTnZUD5hAHEPCfy0u5zfRYu9DRPtZKxrJBVMNOrx5mMI-9n99RuD9DrA5lZJ92CM1NX9nyOxnCNnTJ6yN07a7GEUc_uPxB7XexwE |
CitedBy_id | crossref_primary_10_1016_j_istruc_2024_107414 crossref_primary_10_3390_electronics13224499 crossref_primary_10_1016_j_ymssp_2024_111681 crossref_primary_10_1016_j_ymssp_2025_112552 |
Cites_doi | 10.1061/(ASCE)ST.1943-541X.0002203 10.1109/TIM.2022.3216413 10.1016/j.ymssp.2021.107870 10.1016/j.ymssp.2018.07.026 10.1063/1.4961979 10.3390/rs13173375 10.3390/rs13183668 10.1073/pnas.1703715114 10.3390/ma15165658 10.1016/j.jsv.2015.01.024 10.1016/j.engstruct.2012.06.015 10.1016/j.engstruct.2021.112728 10.1016/j.measurement.2020.108538 10.1016/j.engstruct.2017.11.018 10.1364/AO.22.003977 10.1061/(ASCE)BE.1943-5592.0000747 10.3390/rs13173471 10.1016/j.engstruct.2020.110183 10.1145/2461912.2461966 10.3390/rs14133133 10.1016/j.jsv.2020.115586 10.1145/3015573 10.1016/j.compstruc.2004.09.009 10.1016/j.ymssp.2020.106995 10.1016/j.ymssp.2016.08.041 10.1016/S0888-3270(03)00086-4 10.1109/TIP.2012.2214050 10.1002/stc.1852 10.1061/(ASCE)BE.1943-5592.0000765 10.1145/2185520.2185561 10.1016/j.ymssp.2021.107871 10.1016/j.measurement.2018.02.059 10.1007/s00034-016-0374-8 10.1109/TPAMI.2016.2622271 10.1016/S0143-8166(02)00071-4 10.1016/j.jsv.2016.11.034 |
ContentType | Journal Article |
Copyright | COPYRIGHT 2022 MDPI AG 2022 by the authors. 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/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2022 by the authors. 2022 |
Copyright_xml | – notice: COPYRIGHT 2022 MDPI AG – notice: 2022 by the authors. 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/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2022 by the authors. 2022 |
DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7X7 7XB 88E 8FI 8FJ 8FK ABUWG AFKRA AZQEC BENPR CCPQU DWQXO FYUFA GHDGH K9. M0S M1P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS 7X8 5PM DOA |
DOI | 10.3390/s22239287 |
DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central ProQuest One ProQuest Central Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) ProQuest Health & Medical Collection Medical Database ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Central China ProQuest Central ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Health & Medical Research Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
DatabaseTitleList | CrossRef MEDLINE - Academic MEDLINE Publicly Available Content Database |
Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 4 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Engineering |
EISSN | 1424-8220 |
ExternalDocumentID | oai_doaj_org_article_7975f28d204848d6bbd4ba069835a323 PMC9739241 A746534286 36501990 10_3390_s22239287 |
Genre | Journal Article |
GrantInformation_xml | – fundername: National Natural Science Foundation of China grantid: 51905005 – fundername: National Natural Science Foundation of China grantid: 51805006 – fundername: National Natural Science Foundation of China grantid: 51805006; 51905005 |
GroupedDBID | --- 123 2WC 53G 5VS 7X7 88E 8FE 8FG 8FI 8FJ AADQD AAHBH AAYXX ABDBF ABUWG ACUHS ADBBV ADMLS AENEX AFKRA AFZYC ALIPV ALMA_UNASSIGNED_HOLDINGS BENPR BPHCQ BVXVI CCPQU CITATION CS3 D1I DU5 E3Z EBD ESX F5P FYUFA GROUPED_DOAJ GX1 HH5 HMCUK HYE IAO ITC KQ8 L6V M1P M48 MODMG M~E OK1 OVT P2P P62 PHGZM PHGZT PIMPY PQQKQ PROAC PSQYO RNS RPM TUS UKHRP XSB ~8M 3V. ABJCF ARAPS CGR CUY CVF ECM EIF HCIFZ KB. M7S NPM PDBOC 7XB 8FK AZQEC DWQXO K9. PJZUB PKEHL PPXIY PQEST PQUKI PRINS 7X8 5PM PUEGO |
ID | FETCH-LOGICAL-c508t-f4af864ac6010c0936a5e9aa57dd2eca52b58f52c1ff8c53702ae8cec0400da03 |
IEDL.DBID | M48 |
ISSN | 1424-8220 |
IngestDate | Wed Aug 27 01:32:14 EDT 2025 Thu Aug 21 18:39:03 EDT 2025 Fri Jul 11 06:59:49 EDT 2025 Fri Jul 25 20:39:31 EDT 2025 Tue Jul 01 05:43:28 EDT 2025 Wed Feb 19 02:26:21 EST 2025 Thu Apr 24 23:11:06 EDT 2025 Tue Jul 01 01:19:34 EDT 2025 |
IsDoiOpenAccess | true |
IsOpenAccess | true |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 23 |
Keywords | modal shapes visualization operational modal analysis vision-based measurement hybrid motion magnification temporal and spatial denoising |
Language | English |
License | 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/). |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c508t-f4af864ac6010c0936a5e9aa57dd2eca52b58f52c1ff8c53702ae8cec0400da03 |
Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
ORCID | 0000-0002-2416-1058 0000-0003-4282-9821 0000-0001-8137-671X 0000-0002-5876-5597 |
OpenAccessLink | http://journals.scholarsportal.info/openUrl.xqy?doi=10.3390/s22239287 |
PMID | 36501990 |
PQID | 2748559984 |
PQPubID | 2032333 |
ParticipantIDs | doaj_primary_oai_doaj_org_article_7975f28d204848d6bbd4ba069835a323 pubmedcentral_primary_oai_pubmedcentral_nih_gov_9739241 proquest_miscellaneous_2753312187 proquest_journals_2748559984 gale_infotracacademiconefile_A746534286 pubmed_primary_36501990 crossref_primary_10_3390_s22239287 crossref_citationtrail_10_3390_s22239287 |
ProviderPackageCode | CITATION AAYXX |
PublicationCentury | 2000 |
PublicationDate | 20221129 |
PublicationDateYYYYMMDD | 2022-11-29 |
PublicationDate_xml | – month: 11 year: 2022 text: 20221129 day: 29 |
PublicationDecade | 2020 |
PublicationPlace | Switzerland |
PublicationPlace_xml | – name: Switzerland – name: Basel |
PublicationTitle | Sensors (Basel, Switzerland) |
PublicationTitleAlternate | Sensors (Basel) |
PublicationYear | 2022 |
Publisher | MDPI AG MDPI |
Publisher_xml | – name: MDPI AG – name: MDPI |
References | Wadhwa (ref_15) 2016; 60 Wadhwa (ref_20) 2017; 114 Qin (ref_27) 2017; 36 Silva (ref_21) 2020; 487 Wu (ref_13) 2012; 31 Xiao (ref_35) 2021; 160 Yang (ref_17) 2017; 390 Yang (ref_22) 2020; 207 Wadhwa (ref_14) 2013; 32 Feng (ref_32) 2015; 20 Eitner (ref_23) 2021; 146 Zhang (ref_12) 2021; 173 Davis (ref_19) 2017; 39 Xu (ref_28) 2019; 116 Feng (ref_34) 2015; 20 Patil (ref_11) 2018; 122 Khuc (ref_5) 2017; 24 Feng (ref_6) 2018; 156 Hermanns (ref_33) 2005; 83 Zuo (ref_3) 2012; 45 Xiao (ref_36) 2022; 162 Cakar (ref_2) 2005; 19 Yang (ref_18) 2017; 85 Siringoringo (ref_24) 2021; 244 Chen (ref_25) 2018; 144 Berryman (ref_30) 2003; 39 ref_1 Zhang (ref_26) 2016; 87 ref_9 Chen (ref_16) 2015; 345 ref_8 Wang (ref_10) 2022; 71 Takeda (ref_29) 1983; 22 Mittal (ref_31) 2012; 21 ref_4 ref_7 |
References_xml | – volume: 144 start-page: 04018207 year: 2018 ident: ref_25 article-title: Camera-Based Vibration Measurement of the World War I Memorial Bridge in Portsmouth, New Hampshire publication-title: J. Struct. Eng. doi: 10.1061/(ASCE)ST.1943-541X.0002203 – volume: 71 start-page: 1 year: 2022 ident: ref_10 article-title: Nonlinear Correction for Fringe Projection Profilometry with Shifted-Phase Histogram Equalization publication-title: IEEE Trans. Instrum. Meas. doi: 10.1109/TIM.2022.3216413 – volume: 160 start-page: 107870 year: 2021 ident: ref_35 article-title: A stochastic analysis method of transient responses using harmonic wavelets, part 1: Time-invariant structural systems publication-title: Mech. Syst. Signal Proc. doi: 10.1016/j.ymssp.2021.107870 – volume: 116 start-page: 585 year: 2019 ident: ref_28 article-title: Enhanced sparse component analysis for operational modal identification of real-life bridge structures publication-title: Mech. Syst. Signal Proc. doi: 10.1016/j.ymssp.2018.07.026 – volume: 87 start-page: 198 year: 2016 ident: ref_26 article-title: Note: Sound recovery from video using SVD-based information extraction publication-title: Rev. Sci. Instrum. doi: 10.1063/1.4961979 – ident: ref_1 doi: 10.3390/rs13173375 – ident: ref_4 doi: 10.3390/rs13183668 – volume: 114 start-page: 11639 year: 2017 ident: ref_20 article-title: Motion microscopy for visualizing and quantifying small motions publication-title: Proc. Natl. Acad. Sci. USA doi: 10.1073/pnas.1703715114 – ident: ref_9 doi: 10.3390/ma15165658 – volume: 345 start-page: 58 year: 2015 ident: ref_16 article-title: Modal identification of simple structures with high-speed video using motion magnification publication-title: J. Sound Vibr. doi: 10.1016/j.jsv.2015.01.024 – volume: 45 start-page: 117 year: 2012 ident: ref_3 article-title: A model of pedestrian-induced bridge vibration based on full-scale measurement publication-title: Eng. Struct. doi: 10.1016/j.engstruct.2012.06.015 – volume: 244 start-page: 112728 year: 2021 ident: ref_24 article-title: Noncontact operational modal analysis of light poles by vision-based motion-magnification method publication-title: Eng. Struct. doi: 10.1016/j.engstruct.2021.112728 – volume: 173 start-page: 108538 year: 2021 ident: ref_12 article-title: Efficient subpixel image registration algorithm for high precision visual vibrometry publication-title: Measurement doi: 10.1016/j.measurement.2020.108538 – volume: 156 start-page: 105 year: 2018 ident: ref_6 article-title: Computer vision for SHM of civil infrastructure: From dynamic response measurement to damage detection—A review publication-title: Eng. Struct. doi: 10.1016/j.engstruct.2017.11.018 – volume: 22 start-page: 3977 year: 1983 ident: ref_29 article-title: Fourier transform profilometry for the automatic measurement of 3-D object shapes publication-title: Appl. Optics doi: 10.1364/AO.22.003977 – volume: 20 start-page: 04015023.1 year: 2015 ident: ref_32 article-title: Nontarget Vision Sensor for Remote Measurement of Bridge Dynamic Response publication-title: J. Bridge Eng. doi: 10.1061/(ASCE)BE.1943-5592.0000747 – ident: ref_7 doi: 10.3390/rs13173471 – volume: 207 start-page: 110183 year: 2020 ident: ref_22 article-title: Blind, simultaneous identification of full-field vibration modes and large rigid-body motion of output-only structures from digital video measurements publication-title: Eng. Struct. doi: 10.1016/j.engstruct.2020.110183 – volume: 32 start-page: 1 year: 2013 ident: ref_14 article-title: Phase-Based Video Motion Processing publication-title: ACM Trans. Graph. doi: 10.1145/2461912.2461966 – ident: ref_8 doi: 10.3390/rs14133133 – volume: 487 start-page: 115586 year: 2020 ident: ref_21 article-title: Nonnegative matrix factorization-based blind source separation for full-field and high-resolution modal identification from video publication-title: J. Sound Vibr. doi: 10.1016/j.jsv.2020.115586 – volume: 60 start-page: 87 year: 2016 ident: ref_15 article-title: Eulerian video magnification and analysis publication-title: Commun. ACM doi: 10.1145/3015573 – volume: 83 start-page: 793 year: 2005 ident: ref_33 article-title: Efficient computation of the pressures developed during high-speed train passing events publication-title: Comput. Struct. doi: 10.1016/j.compstruc.2004.09.009 – volume: 146 start-page: 106995 year: 2021 ident: ref_23 article-title: Effect of broad-band phase-based motion magnification on modal parameter estimation publication-title: Mech. Syst. Signal Proc. doi: 10.1016/j.ymssp.2020.106995 – volume: 85 start-page: 567 year: 2017 ident: ref_18 article-title: Blind identification of full-field vibration modes from video measurements with phase-based video motion magnification publication-title: Mech. Syst. Signal Proc. doi: 10.1016/j.ymssp.2016.08.041 – volume: 19 start-page: 87 year: 2005 ident: ref_2 article-title: Elimination of transducer mass loading effects from frequency response functions publication-title: Mech. Syst. Signal Proc. doi: 10.1016/S0888-3270(03)00086-4 – volume: 21 start-page: 4695 year: 2012 ident: ref_31 article-title: No-Reference Image Quality Assessment in the Spatial Domain publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2012.2214050 – volume: 24 start-page: e1852 year: 2017 ident: ref_5 article-title: Completely contactless structural health monitoring of real-life structures using cameras and computer vision publication-title: Struct. Control. Health Monit. doi: 10.1002/stc.1852 – volume: 20 start-page: 04015019 year: 2015 ident: ref_34 article-title: Model Updating of Railway Bridge Using In Situ Dynamic Displacement Measurement under Trainloads publication-title: J. Bridge Eng. doi: 10.1061/(ASCE)BE.1943-5592.0000765 – volume: 31 start-page: 1 year: 2012 ident: ref_13 article-title: Eulerian Video Magnification for Revealing Subtle Changes in the World publication-title: ACM Trans. Graph. doi: 10.1145/2185520.2185561 – volume: 162 start-page: 107871 year: 2022 ident: ref_36 article-title: A stochastic analysis method of transient responses using harmonic wavelets, part 2: Time-dependent vehicle-bridge systems publication-title: Mech. Syst. Signal Proc. doi: 10.1016/j.ymssp.2021.107871 – volume: 122 start-page: 358 year: 2018 ident: ref_11 article-title: A multi-view optical technique to obtain mode shapes of structures publication-title: Measurement doi: 10.1016/j.measurement.2018.02.059 – volume: 36 start-page: 1569 year: 2017 ident: ref_27 article-title: Sparse Component Analysis Based on Hierarchical Hough Transform publication-title: Circuits Syst. Signal Process. doi: 10.1007/s00034-016-0374-8 – volume: 39 start-page: 732 year: 2017 ident: ref_19 article-title: Visual Vibrometry: Estimating Material Properties from Small Motions in Video publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2016.2622271 – volume: 39 start-page: 35 year: 2003 ident: ref_30 article-title: A theoretical comparison of three fringe analysis methods for determining the three-dimensional shape of an object in the presence of noise publication-title: Opt. Lasers Eng. doi: 10.1016/S0143-8166(02)00071-4 – volume: 390 start-page: 232 year: 2017 ident: ref_17 article-title: Blind identification of full-field vibration modes of output-only structures from uniformly-sampled, possibly temporally-aliased (sub-Nyquist), video measurements publication-title: J. Sound Vibr. doi: 10.1016/j.jsv.2016.11.034 |
SSID | ssj0023338 |
Score | 2.397461 |
Snippet | As a promising alternative to conventional contact sensors, vision-based technologies for a structural dynamic response measurement and health monitoring have... |
SourceID | doaj pubmedcentral proquest gale pubmed crossref |
SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
StartPage | 9287 |
SubjectTerms | Bridges Decomposition Fourier Analysis hybrid motion magnification Laboratories modal shapes visualization Motion Noise control Nondestructive testing operational modal analysis Sensors Sound Technology application temporal and spatial denoising Vibration vision-based measurement |
SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3NT90wDI8mTuwwAfug40PdhMQuFW2aNMkR0NDTpMdlgLhFbj4ACfUhwQ7899hpX9UnkLjs0kPig-PYtd3aPzN2ANIrjOppepkXhYiqKowRpoghNKoMGtpIzcnz82Z2Kf5cy-vJqC-qCevhgXvBHSmjZOTaE8Cs0L5pWy9aKBuDoQPUPOF8os9bJlNDqlVj5tXjCNWY1B89khc0nOrmJt4ngfS_fhVPfNFqneTE8ZxtsE9DxJgf95xusg-h22IfJziCn9n5VeoQL07QJfn8b4KEJTiNfL7w-OybcePwdS5PVQL57Jl6tZAirc3hphspvrDLs98Xp7NiGJRQOIyvnoooIOpGgKPsypWmbkAGAyCV9zw4kLyVOkruqhi1k7UqOQTtgiML9lDWX9lat-jCNsuFCtwFKaKrpUD5Ania_N166V2rQGTs11KA1g0o4jTM4t5iNkGytqOsM_ZzJH3ooTPeIjqhWxgJCO06LaAO2EEH7Hs6kLFDukNLNonMOBhaC_BIhG5ljxWhyGGi1WRsd3nNdjDWR4uJuSbgNY2n-zFuo5nRvxPowuIf0WBcXGE8hBx_67Vi5LnGKLdCr54xtaIvK4da3enubhOUt1EoBVF9_x9S2GHrnHozqqrgZpetobKFPYyYntr9ZBwv-yAURg priority: 102 providerName: Directory of Open Access Journals – databaseName: ProQuest Technology Collection dbid: 8FG link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1Lb9QwELagXOBQ8SZQUEBIcLGa-BHbJ9QilhXS9gJFvVmOHy0SSkq3HPj3zDjesCsQlxycOYztGc-MPfMNIa-dDAq8euxeFgQVSbXUGGFoirFTTdSuT1icvDrplqfi05k8Kxdu65JWuTkT80EdRo935IcQPWlEx9Li3eUPil2j8HW1tNC4SW61YGkwpUsvPs4BF4f4a0IT4hDaH67RFhqG2XNbNihD9f99IG9ZpN1syS3zs7hL9ovfWB9NG32P3IjDfXJnC03wATn5muvE6TEYplB_zsCwCKpRr8YA36kkN5U7ujrnCtTLX1ixBRR5bOXOh5niITldfPjyfklLuwTqwcu6pkm4pDvhPMZYvjG8czIa56QKgUXvJOulTpL5NiXtJVcNc1H76FGPg2v4I7I3jEN8QmqhIvNRiuS5FI4z5wL2_-6DDL5XTlTk7WYBrS9Y4tjS4ruFmALX2s5rXZFXM-nlBKDxL6Jj3IWZADGv88B4dW6LCllllExMB4QaFjp0fR9E75rOgBMJPPKKvME9tKiZwIx3pcAApoQYV_ZIIZYchFtdRQ4222yLyq7tHwGryMv5NygbvqC4IY4_kQa84xa8IuD48SQVM88cfN0WbHtF1I687Exq98_w7SIDehsFqyDap_9n6xm5zbD2om0pMwdkD8QoPgeP6Lp_kcX-N33kDOI priority: 102 providerName: ProQuest |
Title | Vision-Based Structural Modal Identification Using Hybrid Motion Magnification |
URI | https://www.ncbi.nlm.nih.gov/pubmed/36501990 https://www.proquest.com/docview/2748559984 https://www.proquest.com/docview/2753312187 https://pubmed.ncbi.nlm.nih.gov/PMC9739241 https://doaj.org/article/7975f28d204848d6bbd4ba069835a323 |
Volume | 22 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1Lj9QwDLb2cYED4k1hGRWEBJdCmyZNckBoB-0wQpoRAgbNLUrzWJBWHdhdJPbfY7edaipWXHpofXAcu_7c1J8BXljhJaJ6ml7mecajLDKtuc5iCJXMg7J1pObkxbKar_jHtVjvwXbGZm_Ai2tLO5ontTo_e_3n19U7DPi3VHFiyf7mgnKcRui_D4eYkCQNMljw4TCBlViGdaRCY_FRKmoZ-_99L-8kpvFPkztZaHYbbvXwMT3u9vsO7IXmLtzcIRW8B8tvbbt4NsX85NMvLT8scWuki43Ha9eZG_tPdWn7y0A6v6LGLZRo7y3saTNI3IfV7OTr-3nWT03IHIKtyyxyG1XFraNSy-W6rKwI2lohvWfBWcFqoaJgrohROVHKnNmgXHAUzt7m5QM4aDZNeAQpl4G5IHh0peC2ZNZ6GgNee-FdLS1P4NXWgMb1lOI02eLMYGlBtjaDrRN4Poj-7Hg0rhOa0i4MAkR93d7YnJ-aPpKM1FJEpjwxDnPlq7r2vLZ5pRFLoo5lAi9pDw25DCrjbN9ngEsiqitzLIlSDquuKoGj7TabreMZrNIVsbApXN2z4THGHB2k2CZsfpMMguQCwRFq_LDzikHnEiFvgSk-ATnyl9Gixk-aH99bXm8t0Qq8ePx_tZ7ADUYtGEWRMX0EB-hG4SkCo8t6AvtyLfGqZh8mcDg9WX76PGk_MkzagPgLkmISmw |
linkProvider | Scholars Portal |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwEB6VcgAOiDeBAgGB4GI1cew4OVSoBZYt7e6FFvVmHD8KEkoKW4T6p_iNncmLXYG49ZJDPIom47Hnm8TzDcBzI51CVE_dy5xgIqiUlaUoWfA-V4kvTBWoOHk2z6eH4sORPFqD30MtDB2rHPbEdqN2jaVv5JuYPRXEjlWI1yffGXWNor-rQwuNzi32_NkvTNkWW7tvcX5fcD55d_BmyvquAswiGDllQZhQ5MJYSkUsJvS5kb40RirnuLdG8koWQXKbhlBYmamEG19Yb8ndnUkyfO4luCwyjORUmT55PyZ4GeZ7HXsRDiabC4q9JafTeksxr20N8HcAWIqAq6czl8Ld5AZc73FqvN051k1Y8_UtuLbEXngb5p_aunS2g4HQxR9bIloi8YhnjcNrVwIc-m-CcXs2IZ6eUYUYSrT3Zua4HiXuwOGFGPIurNdN7e9DLJTn1ksRbCaFybgxjvqNV046WykjIng1GFDbnrucWmh805jDkK31aOsIno2iJx1hx7-EdmgWRgHi2G5vND-Odb9ktSqVDLxwRG0sCpdXlROVSfISQSvqmEXwkuZQ006AyljTFzTgKxGnlt5WxF2H6V0ewcYwzbrfIhb6j0NH8HQcxsVNf2xM7ZufJINoPEUUhhrf67xi1DlDbJ0ilohArfjLykutjtRfv7QE4qVCK4j0wf_VegJXpgezfb2_O997CFc51X2kKePlBqyjS_lHiMZOq8ftEojh80WvuXME9ErY |
linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9RADLbKVkJwQLwJFAgIBJdok8lMJnNAqEu72lJ2VQGteguTeRQklBS2CPWv8euw82JXIG695JBYkeOxx_bE_gzwTAsrMaqn6WWWR9zLJFKKq8g7l8nY5br01Jw8X2SzQ_72WBxvwK--F4bKKvs9sdmobW3ojHyM2VNO6Fg5H_uuLOJgZ_r69FtEE6ToT2s_TqNVkX13_hPTt-WrvR1c6-eMTXc_vplF3YSByGBgchZ5rn2ecW0oLTGY3GdaOKW1kNYyZ7Rgpci9YCbxPjcilTHTLjfOkOpbHaf43kuwKSkrGsHmZHdx8H5I91LM_losozRV8XhJnlgxqt1b8YDNoIC_3cGKP1yv1VxxftPrcK2LWsPtVs1uwIarbsLVFSzDW7A4arrUowm6RRt-aGBpCdIjnNcWr21DsO9OCMOmUiGcnVO_GFI09-b6pBoobsPhhYjyDoyqunL3IOTSMeME9yYVXKdMa0vTx0srrCml5gG87AVYmA7JnAZqfC0woyFZF4OsA3g6kJ628B3_IprQKgwEhLjd3Ki_nxSdARdSSeFZbgnomOc2K0vLSx1nCkNY5DEN4AWtYUH7AjJjdNfegJ9ECFvFtiQkO0z2sgC2-mUuug1jWfxR7wCeDI_R1On_ja5c_YNoMDZPMCZDju-2WjHwnGKknWBkEYBc05e1j1p_Un353MCJK4lS4Mn9_7P1GC6jvRXv9hb7D-AKoyaQJImY2oIRapR7iKHZWfmos4EQPl202f0GtbpQag |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Vision-Based+Structural+Modal+Identification+Using+Hybrid+Motion+Magnification&rft.jtitle=Sensors+%28Basel%2C+Switzerland%29&rft.au=Zhang%2C+Dashan&rft.au=Zhu%2C+Andong&rft.au=Hou%2C+Wenhui&rft.au=Liu%2C+Lu&rft.date=2022-11-29&rft.pub=MDPI+AG&rft.eissn=1424-8220&rft.volume=22&rft.issue=23&rft.spage=9287&rft_id=info:doi/10.3390%2Fs22239287&rft.externalDBID=HAS_PDF_LINK |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1424-8220&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1424-8220&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1424-8220&client=summon |