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ID 46952
フルテキストURL
著者
Kanatani, Kenichi Department of Information Technology, Okayama University
抄録
We investigate the meaning of "statistical methods" for geometric inference based on image feature points. Tracing back the origin of feature uncertainty to image processing operations, we discuss the implications of asymptotic analysis in reference to "geometric fitting" and "geometric model selection", We point out that a correspondence exists between the standard statistical analysis and the geometric inference problem. We also compare the capability of the "geometric AIC" and the "geometric MDL' in detecting degeneracy. Next, we review recent progress in geometric fitting techniques for linear constraints, describing the "FNS method", the "HEIV method", the "renormalization method", and other related techniques. Finally, we discuss the "Neyman-Scott problem" and "semiparametric models" in relation to geometric inference. We conclude that applications of statistical methods requires careful considerations about the nature of the problem in question.
発行日
2004-03
出版物タイトル
Memoirs of the Faculty of Engineering, Okayama University
38巻
1-2号
出版者
Faculty of Engineering, Okayama University
開始ページ
39
終了ページ
59
ISSN
0475-0071
NCID
AA10699856
資料タイプ
紀要論文
言語
English
論文のバージョン
publisher
査読
無し
Eprints Journal Name
mfe