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ID 19959
Eprint ID
19959
フルテキストURL
著者
金谷 健一 Department of Computer Science Okayama University
Niitsuma Hirotaka Department of Computer Science Okayama University
Rangrajan Prasanna Department of Electrical Engineering Southern Methodist University
抄録
We present highly accurate least-squares (LS) alternatives to the theoretically optimal maximum likelihood (ML) estimator for homographies between two images. Unlike ML, our estimators are non-iterative and yield solutions even in the presence of large noise. By rigorous error analysis, we derive a “hyperaccurate” estimator which is unbiased up to second order noise terms. Then, we introduce a computational simplification, which we call “Taubin approximation”, without incurring a loss in accuracy. We experimentally demonstrate that our estimators have accuracy surpassing the traditional LS estimator and comparable to the ML estimator.
発行日
2010-01
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
44巻
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
50
終了ページ
59
ISSN
1349-6115
NCID
AA12014085
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