Uncertainty Modeling and Geometric Inference

• Kanatani Kenichi

抄録

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.

収録刊行物

• Memoirs of the Faculty of Engineering, Okayama University

  Memoirs of the Faculty of Engineering, Okayama University 38 (1-2), 39-59, 2004-03

  Faculty of Engineering, Okayama University

詳細情報

• CRID: 1390290699799536768
• NII論文ID: 80016889442
• NII書誌ID: AA10699856
• ISSN: 04750071
• DOI: 10.18926/46952
• Web Site: https://ousar.lib.okayama-u.ac.jp/46952
• 本文言語コード: en
• データソース種別:
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