Statistical Optimization for Geometric Fitting: TheoreticalAccuracy Bound and High Order Error Analysis
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抄録
A rigorous accuracy analysis is given to various techniques for estimating parameters of geometric models from noisy data for computer vision applications. First, it is pointed out that parameter estimation for vision applications is very different in nature from traditional statistical analysis and hence a different mathematical framework is necessary in such a domain. After general theories on estimation and accuracy are given, typical existing techniques are selected, and their accuracy is evaluated up to higher order terms. This leads to a “hyperaccurate” method that outperforms existing methods.
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 41 (1), 73-92, 2007-01
Faculty of Engineering, Okayama University
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詳細情報
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- CRID
- 1390009224548474240
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- NII論文ID
- 120002308410
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- NII書誌ID
- AA10699856
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- ISSN
- 04750071
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- DOI
- 10.18926/14087
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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