ID | 46952 |
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Sort Key | 7
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FullText URL | |
Author | |
Abstract | We investigate the meaning of "statistical methods" for geometric inference based on image feature points. Tracing back the origin of feature uncertainty to image processing operations, we discuss the implications of asymptotic analysis in reference to "geometric fitting" and "geometric model selection", We point out that a correspondence exists between the standard statistical analysis and the geometric inference problem. We also compare the capability of the "geometric AIC" and the "geometric MDL' in detecting degeneracy. Next, we review recent progress in geometric fitting techniques for linear constraints, describing the "FNS method", the "HEIV method", the "renormalization method", and other related techniques. Finally, we discuss the "Neyman-Scott problem" and "semiparametric models" in relation to geometric inference. We conclude that applications of statistical methods requires careful considerations about the nature of the problem in question.
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Publication Title |
Memoirs of the Faculty of Engineering, Okayama University
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Published Date | 2004-03
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Volume | volume38
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Issue | issue1-2
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Publisher | Faculty of Engineering, Okayama University
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Start Page | 39
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End Page | 59
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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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