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Article: A Self-calibration Algorithm Based on a Unified Framework for Constraints on Multiple Views

TitleA Self-calibration Algorithm Based on a Unified Framework for Constraints on Multiple Views
Authors
KeywordsEuclidean reconstruction
Multiple views
Projective reconstruction
Self-calibration
Structure and motion
Uncalibrated images
Issue Date2012
PublisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0924-9907
Citation
Journal Of Mathematical Imaging And Vision, 2012, v. 44 n. 3, p. 432-448 How to Cite?
AbstractIn this paper, we propose a new self-calibration algorithm for upgrading projective space to Euclidean space. The proposed method aims to combine the most commonly used metric constraints, including zero skew and unit aspect-ratio by formulating each constraint as a cost function within a unified framework. Additional constraints, e.g., constant principal points, can also be formulated in the same framework. The cost function is very flexible and can be composed of different constraints on different views. The upgrade process is then stated as a minimization problem which may be solved by minimizing an upper bound of the cost function. This proposed method is non-iterative. Experimental results on synthetic data and real data are presented to show the performance of the proposed method and accuracy of the reconstructed scene. © 2012 The Author(s).
Persistent Identifierhttp://hdl.handle.net/10722/147102
ISSN
2021 Impact Factor: 1.627
2020 SCImago Journal Rankings: 0.504
ISI Accession Number ID
References

Chen, G.Q., Medioni, G.G.: Practical algorithms for stratified structure-from-motion. Image Vis. Comput. 20, 103–123 (2002) doi: 10.1016/S0262-8856(01)00090-7

Fusiello, A.: A new autocalibration algorithm: experimental evaluation. In: Skarbek, W. (ed.) Computer Analysis of Images and Patterns. LNCS, vol. 2124, pp. 717–724. Springer, Berlin (2001) doi: 10.1007/3-540-44692-3_86

Gurdjos, P., Bartoli, A., Sturm, P.: Is dual linear self-calibration artificially ambiguous? In: IEEE Int. Conf. Computer Vision, Kyoto, Japan, pp. 88–95 (2009) doi: 10.1109/ICCV.2009.5459152

Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004). ISBN: 0521540518 doi: 10.1017/CBO9780511811685

Hung, Y.S., Tang, W.K.: Projective reconstruction from multiple views with minimization of 2D reprojection error. Int. J. Comput. Vis. 66(3), 305–317 (2006) doi: 10.1007/s11263-005-3675-0

Maybank, S.J., Faugeras, O.D.: A theory of self-calibration of a moving camera. Int. J. Comput. Vis. 8(2), 123–151 (1992) doi: 10.1007/BF00127171

Pollefeys, M., Gool, L.V.: Stratified self-calibration with the modulus constraint. IEEE Trans. Pattern Anal. Mach. Intell. 21(8), 707–724 (1999) doi: 10.1109/34.784285

Sainz, M., Bagherzadeh, N., Susin, A.: Recovering 3d metric structure and motion from multiple uncalibrated cameras. In: Int. Conference on Information Technology: Coding and Computing, pp. 268–273 (2002) doi: 10.1109/ITCC.2002.1000399

Seo, Y., Heyden, A.: Auto-calibration by linear iteration using the dac equation. Image Vis. Comput. 22, 919–926 (2004) doi: 10.1016/j.imavis.2004.05.004

Tang, W.K., Hung, Y.S.: A column-space approach to projective reconstruction. Comput. Vis. Image Underst. 101(3), 166–176 (2006) doi: 10.1016/j.cviu.2005.07.007

Tang, W.K., Hung, Y.S.: A subspace method for projective reconstruction from multiple images with missing data. Image Vis. Comput. 54(5), 515–524 (2006) doi: 10.1016/j.imavis.2006.02.003

Tomasi, C., Kanade, T.: Shape and motion from image streams under orthography: a factorization method. Int. J. Comput. Vis. 9(2), 137–154 (1992) doi: 10.1007/BF00129684

 

DC FieldValueLanguage
dc.contributor.authorTang, AWKen_HK
dc.contributor.authorHung, YSen_HK
dc.date.accessioned2012-05-25T07:50:48Z-
dc.date.available2012-05-25T07:50:48Z-
dc.date.issued2012en_HK
dc.identifier.citationJournal Of Mathematical Imaging And Vision, 2012, v. 44 n. 3, p. 432-448en_HK
dc.identifier.issn0924-9907en_HK
dc.identifier.urihttp://hdl.handle.net/10722/147102-
dc.description.abstractIn this paper, we propose a new self-calibration algorithm for upgrading projective space to Euclidean space. The proposed method aims to combine the most commonly used metric constraints, including zero skew and unit aspect-ratio by formulating each constraint as a cost function within a unified framework. Additional constraints, e.g., constant principal points, can also be formulated in the same framework. The cost function is very flexible and can be composed of different constraints on different views. The upgrade process is then stated as a minimization problem which may be solved by minimizing an upper bound of the cost function. This proposed method is non-iterative. Experimental results on synthetic data and real data are presented to show the performance of the proposed method and accuracy of the reconstructed scene. © 2012 The Author(s).en_HK
dc.languageengen_US
dc.publisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0924-9907en_HK
dc.relation.ispartofJournal of Mathematical Imaging and Visionen_HK
dc.rightsThe Author(s)en_US
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.en_US
dc.subjectEuclidean reconstructionen_HK
dc.subjectMultiple viewsen_HK
dc.subjectProjective reconstructionen_HK
dc.subjectSelf-calibrationen_HK
dc.subjectStructure and motionen_HK
dc.subjectUncalibrated imagesen_HK
dc.titleA Self-calibration Algorithm Based on a Unified Framework for Constraints on Multiple Viewsen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://www.springerlink.com/link-out/?id=2104&code=KJ322H7256M77158&MUD=MPen_US
dc.identifier.emailHung, YS:yshung@eee.hku.hken_HK
dc.identifier.authorityHung, YS=rp00220en_HK
dc.description.naturepublished_or_final_versionen_US
dc.identifier.doi10.1007/s10851-012-0336-0en_HK
dc.identifier.scopuseid_2-s2.0-84866045322en_HK
dc.identifier.hkuros205889-
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dc.relation.referencesdoi: 10.1016/S0262-8856(01)00090-7en_US
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dc.relation.referencesHartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004). ISBN: 0521540518en_US
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dc.publisher.placeUnited Statesen_HK
dc.description.otherSpringer Open Choice, 25 May 2012en_US
dc.identifier.scopusauthoridTang, AWK=55213419700en_HK
dc.identifier.scopusauthoridHung, YS=8091656200en_HK
dc.identifier.citeulike10694804-
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