このエントリーをはてなブックマークに追加
ID 30071
フルテキストURL
著者
Kanatani, Kenichi Department of Computer Science, Okayama University
抄録

We give a formal definition of geometric fitting in a way that suits computer vision applications. We point out that the performance of geometric fitting should be evaluated in the limit of small noise rather than in the limit of a large number of data as recommended in the statistical literature. Taking the KCR lower bound as an optimality requirement and focusing on the linearized constraint case, we compare the accuracy of Kanatani's renormalization with maximum likelihood (ML) approaches including the FNS of Chojnacki et al. and the HEIV of Leedan and Meer. Our analysis reveals the existence of a method superior to all these.

キーワード
computer vision
maximum likelihood estimation
surface fitting
備考
Digital Object Identifier: 10.1109/3DIM.2005.49
Published with permission from the copyright holder. This is the institute's copy, as published in 3-D Digital Imaging and Modeling, 2005. 3DIM 2005. Fifth International Conference on, 13-16 June 2005, Pages 2-13.
Publisher URL:http://dx.doi.org/10.1109/3DIM.2005.49
Copyright © 2005 IEEE. All rights reserved.
発行日
2005-6
出版物タイトル
Proceedings of the Fifth International Conference on 3-D Digital Imaging and Modeling
出版者
IEEE Computer Society
開始ページ
2
終了ページ
13
ISSN
1550-6185
NCID
BA75362958
資料タイプ
会議発表論文
言語
English
著作権者
IEEE
イベント
Fifth International Conference on 3-D Digital Imaging and Modeling
イベント地
Ottawa, Ontario, Canada
イベント開催日
2005-6
論文のバージョン
publisher
査読
有り
DOI
Submission Path
industrial_engineering/112