@techreport{oai:ipsj.ixsq.nii.ac.jp:00052416,
 author = {金谷, 健一 and Kenichi, Kanatani},
 issue = {4(2004-CVIM-147)},
 month = {Jan},
 note = {幾何学的当てはめはコンピュータビジョンの最も基本的な問題の一つである．筆者は以前これに対する精度の理論限界（KCR下界）を導き，最尤推定が統計的に最適であることを証明した．最近，Chernovらは，これが筆者の用いた仮定を弱めても成立することを証明している．本稿ではこれを紹介し，その筆者の定式化との相違や問題の背景，セミパラメトリックモデルなどの最近の話題や今後の課題を検討する．, Geometric fitting is one of the most fundamental problems of computer vision.  In the past, the author derived a theoretical accuracy bound (KCR lower bound) for the geometric fitting problem in general and proved that maximum likelihood estimation is statistically optimal. Recently, Chernov and Lesort proved a similar result, using weaker assumptions than the author's.  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 problems.},
 title = {最尤推定の最適性とKCR下界},
 year = {2005}
}