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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.
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 47 1-18, 2013-01
Faculty of Engineering, Okayama University
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詳細情報 詳細情報について
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- CRID
- 1390009224822825344
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- NII論文ID
- 120005232372
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- NII書誌ID
- AA12014085
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- ISSN
- 13496115
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- DOI
- 10.18926/49320
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- NDL書誌ID
- 025619163
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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
- NDL
- CiNii Articles