{"updated":"2025-01-21T19:55:24.676169+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00080156","sets":["1164:4619:6650:6651"]},"path":["6651"],"owner":"11","recid":"80156","title":["楕円当てはめの精度比較：最小二乗法から超精度くりこみ法まで"],"pubdate":{"attribute_name":"公開日","attribute_value":"2012-01-12"},"_buckets":{"deposit":"f96173aa-a2dd-4aaa-8c1d-983367b8293f"},"_deposit":{"id":"80156","pid":{"type":"depid","value":"80156","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"楕円当てはめの精度比較：最小二乗法から超精度くりこみ法まで","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"楕円当てはめの精度比較：最小二乗法から超精度くりこみ法まで"},{"subitem_title":"Accuracy Comparison of Ellipse Fitting: From Least Squares to Hyper-Renormalization","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2012-01-12","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"岡山大学大学院自然科学研究科"},{"subitem_text_value":"豊橋技術科学大学情報工学系"},{"subitem_text_value":"豊橋技術科学大学情報工学系"},{"subitem_text_value":"岡山大学大学院自然科学研究科"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Department of Computer Science, Okayama University","subitem_text_language":"en"},{"subitem_text_value":"Department of Information and Computer Sciences, Toyohashi University of Technology","subitem_text_language":"en"},{"subitem_text_value":"Department of Information and Computer Sciences, Toyohashi University of Technology","subitem_text_language":"en"},{"subitem_text_value":"Department of Computer Science, Okayama University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/80156/files/IPSJ-CVIM12180024.pdf"},"date":[{"dateType":"Available","dateValue":"2014-01-12"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CVIM12180024.pdf","filesize":[{"value":"979.9 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"20"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"4c276420-3452-4411-92b1-10157b556f19","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2012 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"横田, 健太"},{"creatorName":"村田, 和洋"},{"creatorName":"菅谷, 保之"},{"creatorName":"金谷, 健一"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Kenta, Yokota","creatorNameLang":"en"},{"creatorName":"Kazuhiro, Murata","creatorNameLang":"en"},{"creatorName":"Yasuyuki, Sugaya","creatorNameLang":"en"},{"creatorName":"Kenichi, Kanatani","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11131797","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"画像から抽出した点列に楕円を当てはめる手法として，「最小二乗法」とそれを反復的に改善する「重み反復法」，「Taubin法」とそれを反復的に改善する「くりこみ法」，「超精度最小二乗法」とそれを反復的に改善する「超精度くりこみ法」，再投影誤差を最小にする「最尤推定」とそれを事後的に補正する「超精度補正」をまとめる．そして，これらの精度を実験的に比較し，次のことを示す．1. 従来から最尤推定が最も高精度であるとみなされていたが，新たに提案された超精度くりこみ法はそれよりさらに精度が高い．2. 最も精度が高いのは超精度補正であるが，超精度くりこみ法との差は非常にわずかである．3. 最尤推定解の計算はノイズが大きいと収束しないことがあるのに対して，超精度くりこみ法はノイズに対してロバストである．これらの結果から，実用的には超精度くりこみ法が最も優れた方法であることを結論する．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"We summarize the following techniques for fitting an ellipse to a point sequence extracted from an image: “least squares” and its update by “iterative reweight”, the “Taubin method” and its iterative update by “renormalization”, “HyperLS” and its iterative update by “hyper-renormalization”, “maximum likelihood (ML)” which minimize the reprojection error and its a posteriori “hyperaccurate correction”. We experimentally compare their accuracy and show the following: 1. Newly proposed hyper-renormalization is more accurate than ML, which has been widely regarded as the most accurate. 2. The most accurate is the hyperaccurate correction of ML, but the difference from hyper-renormalization is very small. 3. While iterations for computing ML may not always converge in the presence of large noise, Hyper-renormalization is more robust that ML. From these, we conclude that hyper-renormalization is the best method in practical situations.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告コンピュータビジョンとイメージメディア（CVIM）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2012-01-12","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"24","bibliographicVolumeNumber":"2012-CVIM-180"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"created":"2025-01-18T23:34:41.405183+00:00","id":80156,"links":{}}