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Projective reconstruction with missing data combining 2D reprojection error and subspace method

  • Guang Yu Luan*
  • , Dong Ye
  • , Ren Sheng Che
  • *Corresponding author for this work
  • Harbin Institute of Technology

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

Abstract

Factorization algorithms widely used in projective reconstruction handle all of images uniformly without preferential treatment for any image. However, they require that all the points are visible in all views. Due to reasons, such as occlusion, the demand can not be satisfied. For the factorization method to be better applicable, we propose a new factorization-based algorithm combining 2D reprojection error and subspace method for projective reconstruction with missing data, which estimates projective shape, projection matrices, projective depths and missing data iteratively. Estimation problems of projective shape and projection matrices are formulated in terms of the minimization of 2D reprojection error. The subspace method is used to estimate projective depths. Experimental results using both synthetic data and real images are provided to illustrate the performance of the proposed method.

Original languageEnglish
Title of host publicationProceedings - 2009 International Conference on Information Engineering and Computer Science, ICIECS 2009
DOIs
StatePublished - 2009
Event2009 International Conference on Information Engineering and Computer Science, ICIECS 2009 - Wuhan, China
Duration: 19 Dec 200920 Dec 2009

Publication series

NameProceedings - 2009 International Conference on Information Engineering and Computer Science, ICIECS 2009

Conference

Conference2009 International Conference on Information Engineering and Computer Science, ICIECS 2009
Country/TerritoryChina
CityWuhan
Period19/12/0920/12/09

Keywords

  • 2D reprojection error
  • Missing data
  • Projective reconstruction
  • Subspace

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