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ID 14086
JaLCDOI
Sort Key
7
フルテキストURL
著者
金谷 健一 Department of Computer Science, Okayama University Kaken ID publons researchmap
菅谷 保之 Department of Information and Computer Sciences Toyohashi University of Technology
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.
出版物タイトル
Memoirs of the Faculty of Engineering, Okayama University
発行日
2007-01
41巻
1号
出版者
Faculty of Engineering, Okayama University
出版者(別表記)
岡山大学工学部
開始ページ
63
終了ページ
72
ISSN
0475-0071
NCID
AA10699856
資料タイプ
紀要論文
OAI-PMH Set
岡山大学
言語
English
論文のバージョン
publisher
NAID
Eprints Journal Name
mfe