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ID 14155
JaLCDOI
Sort Key
10
フルテキストURL
著者
金谷 健一 Department of Information Technology, Okayama University Kaken ID publons researchmap
抄録
Geometric fitting is one of the most fundamental problems of computer vision. In [8], the author derived a theoretical accuracy bound (KCR lower bound) for geometric fitting in general and proved that maximum likelihood (ML) estimation is statistically optimal. Recently, Chernov and Lesort [3] proved a similar result, using a weaker assumption. In this paper, we compare their formulation with the author’s and describe the background of the problem. We also review recent topics including semiparametric models and discuss remaining issues.
出版物タイトル
Memoirs of the Faculty of Engineering, Okayama University
発行日
2005-01
39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
70
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