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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.
収録刊行物
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- Memoirs of the Faculty of Engineering, Okayama University
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Memoirs of the Faculty of Engineering, Okayama University 44 50-59, 2010-01
Faculty of Engineering, Okayama University
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詳細情報
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- CRID
- 1390290699580251520
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- NII論文ID
- 120002308986
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- NII書誌ID
- AA12014085
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- ISSN
- 13496115
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- DOI
- 10.18926/19959
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- IRDB
- CiNii Articles
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