ID | 14155 |
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Sort Key | 10
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FullText URL | |
Author | |
Abstract | Geometric fitting is one of the most fundamental problems of computer vision. In [8], the author derived a theoretical accuracy bound (KCR lower bound) for geometric fitting in general and proved that maximum likelihood (ML) estimation is statistically optimal. Recently, Chernov and Lesort [3] proved a similar result, using a weaker assumption. In this paper, we compare their formulation with the author’s and describe the background of the problem. We also review recent topics including semiparametric models and discuss remaining issues.
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Publication Title |
Memoirs of the Faculty of Engineering, Okayama University
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Published Date | 2005-01
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Volume | volume39
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Issue | issue1
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Publisher | Faculty of Engineering, Okayama University
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Publisher Alternative | 岡山大学工学部
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Start Page | 63
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End Page | 70
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ISSN | 0475-0071
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NCID | AA10699856
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Content Type |
Departmental Bulletin Paper
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OAI-PMH Set |
岡山大学
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language |
English
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File Version | publisher
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NAID | |
Eprints Journal Name | mfe
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