ID | 46952 |
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Sort Key | 7
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フルテキストURL | |
著者 |
Kanatani, Kenichi
Department of Information Technology, Okayama University
Kaken ID
publons
researchmap
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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.
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出版物タイトル |
Memoirs of the Faculty of Engineering, Okayama University
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発行日 | 2004-03
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巻 | 38巻
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号 | 1-2号
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出版者 | Faculty of Engineering, Okayama University
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開始ページ | 39
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終了ページ | 59
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ISSN | 0475-0071
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NCID | AA10699856
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資料タイプ |
紀要論文
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OAI-PMH Set |
岡山大学
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言語 |
英語
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論文のバージョン | publisher
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NAID | |
Eprints Journal Name | mfe
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