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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
2015 Impact Factor: 1.461
2015 SCImago Journal Rankings: 0.901
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.rightsCreative Commons: Attribution 3.0 Hong Kong Licenseen_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-
dc.relation.referencesChen, G.Q., Medioni, G.G.: Practical algorithms for stratified structure-from-motion. Image Vis. Comput. 20, 103–123 (2002)en_US
dc.relation.referencesdoi: 10.1016/S0262-8856(01)00090-7en_US
dc.relation.referencesFaugeras, O.D., Luong, Q.-T., Maybank, S.J.: Camera self-calibration: theory and experiments. In: European Conf. on Computer Vision, SantaMargerita, Italy, pp. 321–334 (1992). citeseer.nj.nec.com/faugeras92camera.htmlen_US
dc.relation.referencesFusiello, 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)en_US
dc.relation.referencesdoi: 10.1007/3-540-44692-3_86en_US
dc.relation.referencesGantmacher, F.R.: The Theory of Matrices, vol. I. Chelsea, London (1959)en_US
dc.relation.referencesGurdjos, P., Bartoli, A., Sturm, P.: Is dual linear self-calibration artificially ambiguous? In: IEEE Int. Conf. Computer Vision, Kyoto, Japan, pp. 88–95 (2009)en_US
dc.relation.referencesdoi: 10.1109/ICCV.2009.5459152en_US
dc.relation.referencesHartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004). ISBN: 0521540518en_US
dc.relation.referencesdoi: 10.1017/CBO9780511811685en_US
dc.relation.referencesHeyden, A.: Reconstruction from image sequences by means of relative depths. In: IEEE Int. Conf. Computer Vision, pp. 1058–1063 (1995).en_US
dc.relation.referencesHung, 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)en_US
dc.relation.referencesdoi: 10.1007/s11263-005-3675-0en_US
dc.relation.referencesMaybank, S.J., Faugeras, O.D.: A theory of self-calibration of a moving camera. Int. J. Comput. Vis. 8(2), 123–151 (1992)en_US
dc.relation.referencesdoi: 10.1007/BF00127171en_US
dc.relation.referencesPollefeys, M., Gool, L.V.: Stratified self-calibration with the modulus constraint. IEEE Trans. Pattern Anal. Mach. Intell. 21(8), 707–724 (1999)en_US
dc.relation.referencesdoi: 10.1109/34.784285en_US
dc.relation.referencesPollefeys, M., Koch, R., Gool, L.V.: Self-calibration and metric reconstruction inspite of varying and unknown intrinsic camera parameters. Int. J. Comput. Vis. 32(1), 7–25 (1999).en_US
dc.relation.referencesPonce, J.: On computing metric upgrades of projective reconstructions under the rectangular pixel assumption. In: Proc. of the SMILE 2000 Workshop on 3D Structure from Multiple Images of Large-Scale Environments. LNCS, vol. 2018, pp. 52–67 (2000). http://www.springerlink.com/content/kut09cu6qtykf3aq/en_US
dc.relation.referencesSainz, 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)en_US
dc.relation.referencesdoi: 10.1109/ITCC.2002.1000399en_US
dc.relation.referencesSchaffalitzky, F.: Direct solution of modulus constraints. In: Proc. Indian Conf. on Computer Vision, Graphics and Image Processing, pp. 314–321 (2000). http://www.robots.ox.ac.uk/~vgg/vggpapers/Schaffalitzky2000b.ps.gzen_US
dc.relation.referencesSeo, Y., Heyden, A.: Auto-calibration by linear iteration using the dac equation. Image Vis. Comput. 22, 919–926 (2004)en_US
dc.relation.referencesdoi: 10.1016/j.imavis.2004.05.004en_US
dc.relation.referencesSturm, P.: A case against Kruppa’s equations for camera self-calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1199–1204 (2000).en_US
dc.relation.referencesTang, W.K., Hung, Y.S.: A column-space approach to projective reconstruction. Comput. Vis. Image Underst. 101(3), 166–176 (2006)en_US
dc.relation.referencesdoi: 10.1016/j.cviu.2005.07.007en_US
dc.relation.referencesTang, 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)en_US
dc.relation.referencesdoi: 10.1016/j.imavis.2006.02.003en_US
dc.relation.referencesTomasi, C., Kanade, T.: Shape and motion from image streams under orthography: a factorization method. Int. J. Comput. Vis. 9(2), 137–154 (1992)en_US
dc.relation.referencesdoi: 10.1007/BF00129684en_US
dc.relation.referencesBougnoux, S.: From projective to Euclidean space under any practical situation, a criticism of self-calibration. In: IEEE Int. Conf. Computer Vision, pp. 790–796 (1998)en_US
dc.relation.referencesFaugeras, O.D.: Three-Dimensional Computer Vision: A Geometric Viewpoint. MIT Press, Cambridge (1993)en_US
dc.relation.referencesHan, M., Kanade, T.: Scene reconstruction from multiple uncalibrated views. Tech. Rep. CMU-RI-TR-00-09, Robotics Institute, Carnegie Mellon University (2000)en_US
dc.relation.referencesHartley, R.I.: Euclidean reconstruction from uncalibrated views. In: European Conf. on Computer Vision, pp. 579–587 (1994)en_US
dc.relation.referencesHeyden, A., Åström, K.: Euclidean reconstruction from image sequences with varying and unknown focal length and principal point. In: IEEE Int. Conf. on Computer Vision & Pattern Recognition, San Juan, Puerto Rico, pp. 438–443 (1997)en_US
dc.relation.referencesMendonça, P., Cipolla, R.: A simple technique for self-calibration. In: IEEE Int. Conf. on Computer Vision & Pattern Recognition, vol. I, pp. 500–505 (1999)en_US
dc.relation.referencesPollefeys, M.: Self-calibration and metric 3d reconstruction from uncalibrated image sequences. Ph.D. thesis, ESAT-PSI, KU Leuven (1999)en_US
dc.relation.referencesPollefeys, M., Koch, R., Van Gool, L.: Self-calibration and metric reconstruction in spite of varying and unknown internal camera parameters. In: IEEE Int. Conf. Computer Vision, pp. 90–95 (1998)en_US
dc.relation.referencesSeo, Y., Heyden, A.: Auto-calibration from the orthogonality constraints. In: IEEE Int. Conf. Pattern Recognition, Barcelona, Spain, pp. 67–71 (2000)en_US
dc.relation.referencesSeo, Y., Hong, K.-S.: A linear metric reconstruction by complex eigen-decomposition. IEICE Trans. Inf. Syst. E84-D(12), 1626–1632 (2001)en_US
dc.relation.referencesSturm, P.: Critical motion sequences for monocular self-calibration and uncalibrated Euclidean reconstruction. In: IEEE Int. Conf. on Computer Vision & Pattern Recognition, Puerto Rico, pp. 1100–1105 (1997)en_US
dc.relation.referencesTriggs, B.: Autocalibration and the absolute quadric. In: IEEE Int. Conf. on Computer Vision & Pattern Recognition, pp. 609–614 (1997)en_US
dc.relation.referencesZeller, C., Faugeras, O.: Camera self-calibration from video sequences: the Kruppa equations revisited. Tech. Rep. 2793, INRIA (1996)en_US
dc.identifier.spage1en_HK
dc.identifier.epage17en_HK
dc.identifier.eissn1573-7683en_US
dc.identifier.isiWOS:000307772900015-
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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