Experimental Evaluation of Geometric Fitting Algorithms

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Abstract

The convergence performance of typical numerical schemes for geometric fitting for computer vision applications is compared. First, the problem and the associated KCR lower bound are stated. Then, three well known fitting algorithms are described: FNS, HEIV, and renormalization.To these, we add a special variant of Gauss-Newton iterations. For initialization of iterations, random choice, least squares, and Taubin's method are tested. Numerical simulations and real image experiments and conducted for fundamental matrix computation and ellipsefitting, which reveals different characteristics of each method.

Journal

  • Memoirs of the Faculty of Engineering, Okayama University

    Memoirs of the Faculty of Engineering, Okayama University 41(1), 63-72, 2007-01

    Faculty of Engineering, Okayama University

Codes

  • NII Article ID (NAID)
    120002308585
  • NII NACSIS-CAT ID (NCID)
    AA10699856
  • Text Lang
    ENG
  • Article Type
    departmental bulletin paper
  • ISSN
    0475-0071
  • Data Source
    IR 
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