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ID 14086
Eprint ID
14086
FullText URL
Author
Sugaya, Yasuyuki
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 ellipse fitting, which reveals different characteristics of each method.
Published Date
2007-01
Publication Title
Memoirs of the Faculty of Engineering, Okayama University
Volume
volume41
Issue
issue1
Publisher
Faculty of Engineering, Okayama University
Publisher Alternative
岡山大学工学部
Start Page
63
End Page
72
ISSN
0475-0071
NCID
AA10699856
Content Type
Departmental Bulletin Paper
language
英語
File Version
publisher
Refereed
False
Eprints Journal Name
mfe