ID | 14086 |
JaLCDOI | |
Sort Key | 7
|
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.
|
Publication Title |
Memoirs of the Faculty of Engineering, Okayama University
|
Published Date | 2007-01
|
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
|
OAI-PMH Set |
岡山大学
|
language |
English
|
File Version | publisher
|
NAID | |
Eprints Journal Name | mfe
|