Academic Journal
Multimodal Fusion Image Stabilization Algorithm for Bio-Inspired Flapping-Wing Aircraft.
| Τίτλος: | Multimodal Fusion Image Stabilization Algorithm for Bio-Inspired Flapping-Wing Aircraft. |
|---|---|
| Συγγραφείς: | Wang, Zhikai, Wang, Sen, Hu, Yiwen, Zhou, Yangfan, Li, Na, Zhang, Xiaofeng |
| Πηγή: | Biomimetics (2313-7673); Jul2025, Vol. 10 Issue 7, p448, 25p |
| Θεματικοί όροι: | Image stabilization, Multisensor data fusion, Inertial navigation systems, Long short-term memory, Ornithopters, Camera movement |
| Περίληψη: | This paper presents FWStab, a specialized video stabilization dataset tailored for flapping-wing platforms. The dataset encompasses five typical flight scenarios, featuring 48 video clips with intense dynamic jitter. The corresponding Inertial Measurement Unit (IMU) sensor data are synchronously collected, which jointly provide reliable support for multimodal modeling. Based on this, to address the issue of poor image acquisition quality due to severe vibrations in aerial vehicles, this paper proposes a multi-modal signal fusion video stabilization framework. This framework effectively integrates image features and inertial sensor features to predict smooth and stable camera poses. During the video stabilization process, the true camera motion originally estimated based on sensors is warped to the smooth trajectory predicted by the network, thereby optimizing the inter-frame stability. This approach maintains the global rigidity of scene motion, avoids visual artifacts caused by traditional dense optical flow-based spatiotemporal warping, and rectifies rolling shutter-induced distortions. Furthermore, the network is trained in an unsupervised manner by leveraging a joint loss function that integrates camera pose smoothness and optical flow residuals. When coupled with a multi-stage training strategy, this framework demonstrates remarkable stabilization adaptability across a wide range of scenarios. The entire framework employs Long Short-Term Memory (LSTM) to model the temporal characteristics of camera trajectories, enabling high-precision prediction of smooth trajectories. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Complementary Index |
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| Header | DbId: edb DbLabel: Complementary Index An: 186982280 RelevancyScore: 1007 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1007.33386230469 |
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| Items | – Name: Title Label: Title Group: Ti Data: Multimodal Fusion Image Stabilization Algorithm for Bio-Inspired Flapping-Wing Aircraft. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Zhikai%22">Wang, Zhikai</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Sen%22">Wang, Sen</searchLink><br /><searchLink fieldCode="AR" term="%22Hu%2C+Yiwen%22">Hu, Yiwen</searchLink><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Yangfan%22">Zhou, Yangfan</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Na%22">Li, Na</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Xiaofeng%22">Zhang, Xiaofeng</searchLink> – Name: TitleSource Label: Source Group: Src Data: Biomimetics (2313-7673); Jul2025, Vol. 10 Issue 7, p448, 25p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Image+stabilization%22">Image stabilization</searchLink><br /><searchLink fieldCode="DE" term="%22Multisensor+data+fusion%22">Multisensor data fusion</searchLink><br /><searchLink fieldCode="DE" term="%22Inertial+navigation+systems%22">Inertial navigation systems</searchLink><br /><searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22Ornithopters%22">Ornithopters</searchLink><br /><searchLink fieldCode="DE" term="%22Camera+movement%22">Camera movement</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper presents FWStab, a specialized video stabilization dataset tailored for flapping-wing platforms. The dataset encompasses five typical flight scenarios, featuring 48 video clips with intense dynamic jitter. The corresponding Inertial Measurement Unit (IMU) sensor data are synchronously collected, which jointly provide reliable support for multimodal modeling. Based on this, to address the issue of poor image acquisition quality due to severe vibrations in aerial vehicles, this paper proposes a multi-modal signal fusion video stabilization framework. This framework effectively integrates image features and inertial sensor features to predict smooth and stable camera poses. During the video stabilization process, the true camera motion originally estimated based on sensors is warped to the smooth trajectory predicted by the network, thereby optimizing the inter-frame stability. This approach maintains the global rigidity of scene motion, avoids visual artifacts caused by traditional dense optical flow-based spatiotemporal warping, and rectifies rolling shutter-induced distortions. Furthermore, the network is trained in an unsupervised manner by leveraging a joint loss function that integrates camera pose smoothness and optical flow residuals. When coupled with a multi-stage training strategy, this framework demonstrates remarkable stabilization adaptability across a wide range of scenarios. The entire framework employs Long Short-Term Memory (LSTM) to model the temporal characteristics of camera trajectories, enabling high-precision prediction of smooth trajectories. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Biomimetics (2313-7673) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/biomimetics10070448 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 448 Subjects: – SubjectFull: Image stabilization Type: general – SubjectFull: Multisensor data fusion Type: general – SubjectFull: Inertial navigation systems Type: general – SubjectFull: Long short-term memory Type: general – SubjectFull: Ornithopters Type: general – SubjectFull: Camera movement Type: general Titles: – TitleFull: Multimodal Fusion Image Stabilization Algorithm for Bio-Inspired Flapping-Wing Aircraft. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Zhikai – PersonEntity: Name: NameFull: Wang, Sen – PersonEntity: Name: NameFull: Hu, Yiwen – PersonEntity: Name: NameFull: Zhou, Yangfan – PersonEntity: Name: NameFull: Li, Na – PersonEntity: Name: NameFull: Zhang, Xiaofeng IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 23137673 Numbering: – Type: volume Value: 10 – Type: issue Value: 7 Titles: – TitleFull: Biomimetics (2313-7673) Type: main |
| ResultId | 1 |