Optical Flow with Non-local Weight and Fractional Order Regularization: A Variational Model with Superpixel Algorithm for Various Application Oriented Spectrum.
Saved in:
| Title: | Optical Flow with Non-local Weight and Fractional Order Regularization: A Variational Model with Superpixel Algorithm for Various Application Oriented Spectrum. |
|---|---|
| Authors: | Singh, Bhavana1 (AUTHOR) 213104004@stu.manit.ac.in, Kumar, Pushpendra1 (AUTHOR) pkumarfma@manit.ac.in |
| Source: | Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ). Oct2025, Vol. 50 Issue 19, p15297-15327. 31p. |
| Subjects: | Optical flow, Motion estimation (Signal processing), Variational approach (Mathematics), Computer vision, Mathematical regularization, Image processing |
| Abstract: | The motion estimation has gained significant prominence in the field of computer vision due to its wide range of applications. In general, motion estimation is performed in terms of optical flow, which is represented by a vector plot and color maps. The objective of the work is to combine the global and local optical flow, and provide the dense and discontinuity preserving robust optical flow in various application-oriented spectra. A non-local weighted fractional order variational optical flow (NLW-FOOF) model is presented using the Marchaud fractional derivative and the superpixel algorithm. The global nature of the model provides the information about the overall motion present in the scene, while the local nature focuses on the individual motion of each object within the image frame. Thus, the fusion of local–global model along with superpixel algorithm yield the dense flow field, and the Marchaud fractional derivative deals with the textures and edge discontinuities effectively and also provides a significant robustness against outliers. An ablation study is conducted to show the significance of each component of the proposed model. The variational functional is minimized using Euler-Lagrange equations. The Marchaud derivative is numerically discretized through Grünwald-Letnikov derivative method. The resulting system of equations is solved using an efficient iteration technique. The experiments are performed on heterogeneous datasets, and results are evaluated both qualitatively and quantitatively. The performance of the proposed model is shown by comparing it with existing models. The robustness of the NLW-FOOF model is elaborated by estimating optical flow under four different noises. [ABSTRACT FROM AUTHOR] |
| Copyright of Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ) is the property of Springer Nature 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. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 188475227 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Optical Flow with Non-local Weight and Fractional Order Regularization: A Variational Model with Superpixel Algorithm for Various Application Oriented Spectrum. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Singh%2C+Bhavana%22">Singh, Bhavana</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 213104004@stu.manit.ac.in</i><br /><searchLink fieldCode="AR" term="%22Kumar%2C+Pushpendra%22">Kumar, Pushpendra</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> pkumarfma@manit.ac.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Arabian+Journal+for+Science+%26+Engineering+%28Springer+Science+%26+Business+Media+B%2EV%2E+%29%22">Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. )</searchLink>. Oct2025, Vol. 50 Issue 19, p15297-15327. 31p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Optical+flow%22">Optical flow</searchLink><br /><searchLink fieldCode="DE" term="%22Motion+estimation+%28Signal+processing%29%22">Motion estimation (Signal processing)</searchLink><br /><searchLink fieldCode="DE" term="%22Variational+approach+%28Mathematics%29%22">Variational approach (Mathematics)</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+regularization%22">Mathematical regularization</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The motion estimation has gained significant prominence in the field of computer vision due to its wide range of applications. In general, motion estimation is performed in terms of optical flow, which is represented by a vector plot and color maps. The objective of the work is to combine the global and local optical flow, and provide the dense and discontinuity preserving robust optical flow in various application-oriented spectra. A non-local weighted fractional order variational optical flow (NLW-FOOF) model is presented using the Marchaud fractional derivative and the superpixel algorithm. The global nature of the model provides the information about the overall motion present in the scene, while the local nature focuses on the individual motion of each object within the image frame. Thus, the fusion of local–global model along with superpixel algorithm yield the dense flow field, and the Marchaud fractional derivative deals with the textures and edge discontinuities effectively and also provides a significant robustness against outliers. An ablation study is conducted to show the significance of each component of the proposed model. The variational functional is minimized using Euler-Lagrange equations. The Marchaud derivative is numerically discretized through Grünwald-Letnikov derivative method. The resulting system of equations is solved using an efficient iteration technique. The experiments are performed on heterogeneous datasets, and results are evaluated both qualitatively and quantitatively. The performance of the proposed model is shown by comparing it with existing models. The robustness of the NLW-FOOF model is elaborated by estimating optical flow under four different noises. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ) is the property of Springer Nature 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=188475227 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s13369-024-09777-x Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 31 StartPage: 15297 Subjects: – SubjectFull: Optical flow Type: general – SubjectFull: Motion estimation (Signal processing) Type: general – SubjectFull: Variational approach (Mathematics) Type: general – SubjectFull: Computer vision Type: general – SubjectFull: Mathematical regularization Type: general – SubjectFull: Image processing Type: general Titles: – TitleFull: Optical Flow with Non-local Weight and Fractional Order Regularization: A Variational Model with Superpixel Algorithm for Various Application Oriented Spectrum. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Singh, Bhavana – PersonEntity: Name: NameFull: Kumar, Pushpendra IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 2193567X Numbering: – Type: volume Value: 50 – Type: issue Value: 19 Titles: – TitleFull: Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ) Type: main |
| ResultId | 1 |
