“Measurement Matrix Interpolation Based on Projective Reconstruction for Factorization Method”

Shigeharu Kamijima, Hideo Saito

Research output: Contribution to journalArticlepeer-review


The factorization method by Tomasi and Kanade simultaneously recovers camera motion and object shape from an image sequence. This method is robust because the solution is linear by assuming the orthographic camera model. However, the only feature points that are tracked throughout the image sequence can be reconstructed, it is difficult to recover whole object shape by the factorization method. In this paper, we propose a new method to interpolate feature tracking so that even the loci of unseen feature points can be used as inputs of the factorization for object shape reconstruction. In this method, we employ projective reconstruction to interpolate un-tracked feature points. All loci of all detected feature points throughout the input image sequence provide correct reconstructed shape of the object via the factorization. The results of reconstruction are evaluated by the experiment using synthetic images and real images.

Original languageEnglish
Pages (from-to)576-585
Number of pages10
JournalJournal of the Institute of Image Electronics Engineers of Japan
Issue number3
Publication statusPublished - 2004


  • 3D Shape Reconstruction
  • Factorization Method
  • Projected Position Estimation
  • Projective Reconstruction

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Electrical and Electronic Engineering


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