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Error Characterization of the Factorization Approach to Shape and Motion Recovery

  • Zhaohui Sun
  • Visvanathan Ramesh
  • A. Murat Tekalp
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1883)

Abstract

This paper is focused on error characterization of the factorization approach to shape and motion recovery from image sequence using results from matrix perturbation theory and covariance propagation for linear models. Given the 2-D projections of a set of points across multiple image frames and small perturbation on image coordinates, first order perturbation and covariance matrices for 3-D affine/Euclidean shape and motion are derived and validated with the ground truth. The propagation of the small perturbation and covariance matrix provides better understanding of the factorization approach and its results, provides error sensitivity information for 3-D affine/Euclidean shape and moton subject to small image error. Experimental results are demonstrated to support the analysis and show how the error analysis and error measures can be used.

Keywords

Covariance Matrice Error Characterization Measurement Matrix Covariance Propagation Motion Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Zhaohui Sun
    • 1
  • Visvanathan Ramesh
    • 2
  • A. Murat Tekalp
    • 1
  1. 1.Dept. of Electrical and Computer EngineeringUniversity of RochesterRochesterUSA
  2. 2.Imaging & Visualization DepartmentSiemens Corporate ResearchPrincetonUSA

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