Experimental Evaluation of Geometric Fitting Algorithms

• Kanatani Kenichi

• Sugaya Yasuyuki

抄録

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 ellipse fitting, which reveals different characteristics of each method.

収録刊行物

• 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

詳細情報

• CRID: 1390572174501899392
• NII論文ID: 120002308585
• NII書誌ID: AA10699856
• ISSN: 04750071
• DOI: 10.18926/14086
• Web Site: https://ousar.lib.okayama-u.ac.jp/14086
• 本文言語コード: en
• データソース種別:
  • JaLC
  • IRDB
  • CiNii Articles