Statistical Optimization for Geometric Fitting: TheoreticalAccuracy Bound and High Order Error Analysis
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Abstract
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.
Journal
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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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Details 詳細情報について
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- CRID
- 1390009224548474240
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- NII Article ID
- 120002308410
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- NII Book ID
- AA10699856
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- ISSN
- 04750071
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- DOI
- 10.18926/14087
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- Text Lang
- en
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- Data Source
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- JaLC
- IRDB
- CiNii Articles