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ID 49320
フルテキストURL
著者
Kanatani, Kenichi Department of Computer Science, Okayama University
抄録
We summarize techniques for optimal geometric estimation from noisy observations for computer vision applications. We first discuss the interpretation of optimality and point out that geometric estimation is different from the standard statistical estimation. We also describe our noise modeling and a theoretical accuracy limit called the KCR lower bound. Then, we formulate estimation techniques based on minimization of a given cost function: least squares (LS), maximum likelihood (ML), which includes reprojection error minimization as a special case, and Sampson error minimization. We describe bundle adjustment and the FNS scheme for numerically solving them and the hyperaccurate correction that improves the accuracy of ML. Next, we formulate estimation techniques not based on minimization of any cost function: iterative reweight, renormalization, and hyper-renormalization. Finally, we show numerical examples to demonstrate that hyper-renormalization has higher accuracy than ML, which has widely been regarded as the most accurate method of all. We conclude that hyper-renormalization is robust to noise and currently is the best method.
発行日
2013-01
出版物タイトル
Memoirs of the Faculty of Engineering, Okayama University
出版物タイトル(別表記)
岡山大学工学部紀要
47巻
出版者
Faculty of Engineering, Okayama University
開始ページ
1
終了ページ
18
ISSN
1349-6115
NCID
AA12014085
資料タイプ
紀要論文
言語
English
OAI-PMH Set
岡山大学
著作権者
Copyright © by the authors
論文のバージョン
publisher
査読
無し
Sort Key
2
Eprints Journal Name
mfe