Fast inverse compositional image alignment with missing data and re-weighting

Vincent Wen Han Lui, Dinesh Srikantha Gamage, Thomas William Drummond

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

This paper proposes a novel method of performing inverse compositional image
alignment which elegantly deals with missing data and re-weighting, and does not require the Jacobians and Hessian to be re-computed at every iteration. We show how missing data and re-weighting can be handled through preconditioning. We propose a few preconditioning techniques and analyse how each technique models the effects of missing data and re-weighting for inverse composition. We show through extensive experiments on different applications that our method improves the convergence rate of the conventional re-weighted inverse compositional method while remaining robust to outliers. We also show that the update parameters are usually underestimated and how this can be used to further speed up convergence of image alignment methods.
Original languageEnglish
Title of host publicationProceedings of the British Machine Vision Conference 2015
EditorsXianghua Xie, Mark W Jones, Gary K L Tam
Place of PublicationDurham UK
PublisherBMVA Press
Pages1 - 12
Number of pages12
ISBN (Print)1901725537
DOIs
Publication statusPublished - 2015
EventBritish Machine Vision Conference 2015 - Swansea, United Kingdom
Duration: 7 Sep 201510 Sep 2015
Conference number: 26th

Conference

ConferenceBritish Machine Vision Conference 2015
Abbreviated titleBMVC 2015
CountryUnited Kingdom
CitySwansea
Period7/09/1510/09/15

Cite this

Lui, V. W. H., Gamage, D. S., & Drummond, T. W. (2015). Fast inverse compositional image alignment with missing data and re-weighting. In X. Xie, M. W. Jones, & G. K. L. Tam (Eds.), Proceedings of the British Machine Vision Conference 2015 (pp. 1 - 12). Durham UK: BMVA Press. https://doi.org/10.5244/C.29.54
Lui, Vincent Wen Han ; Gamage, Dinesh Srikantha ; Drummond, Thomas William. / Fast inverse compositional image alignment with missing data and re-weighting. Proceedings of the British Machine Vision Conference 2015. editor / Xianghua Xie ; Mark W Jones ; Gary K L Tam. Durham UK : BMVA Press, 2015. pp. 1 - 12
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title = "Fast inverse compositional image alignment with missing data and re-weighting",
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Lui, VWH, Gamage, DS & Drummond, TW 2015, Fast inverse compositional image alignment with missing data and re-weighting. in X Xie, MW Jones & GKL Tam (eds), Proceedings of the British Machine Vision Conference 2015. BMVA Press, Durham UK, pp. 1 - 12, British Machine Vision Conference 2015, Swansea, United Kingdom, 7/09/15. https://doi.org/10.5244/C.29.54

Fast inverse compositional image alignment with missing data and re-weighting. / Lui, Vincent Wen Han; Gamage, Dinesh Srikantha; Drummond, Thomas William.

Proceedings of the British Machine Vision Conference 2015. ed. / Xianghua Xie; Mark W Jones; Gary K L Tam. Durham UK : BMVA Press, 2015. p. 1 - 12.

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

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N2 - This paper proposes a novel method of performing inverse compositional imagealignment which elegantly deals with missing data and re-weighting, and does not require the Jacobians and Hessian to be re-computed at every iteration. We show how missing data and re-weighting can be handled through preconditioning. We propose a few preconditioning techniques and analyse how each technique models the effects of missing data and re-weighting for inverse composition. We show through extensive experiments on different applications that our method improves the convergence rate of the conventional re-weighted inverse compositional method while remaining robust to outliers. We also show that the update parameters are usually underestimated and how this can be used to further speed up convergence of image alignment methods.

AB - This paper proposes a novel method of performing inverse compositional imagealignment which elegantly deals with missing data and re-weighting, and does not require the Jacobians and Hessian to be re-computed at every iteration. We show how missing data and re-weighting can be handled through preconditioning. We propose a few preconditioning techniques and analyse how each technique models the effects of missing data and re-weighting for inverse composition. We show through extensive experiments on different applications that our method improves the convergence rate of the conventional re-weighted inverse compositional method while remaining robust to outliers. We also show that the update parameters are usually underestimated and how this can be used to further speed up convergence of image alignment methods.

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Lui VWH, Gamage DS, Drummond TW. Fast inverse compositional image alignment with missing data and re-weighting. In Xie X, Jones MW, Tam GKL, editors, Proceedings of the British Machine Vision Conference 2015. Durham UK: BMVA Press. 2015. p. 1 - 12 https://doi.org/10.5244/C.29.54