{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00091722","sets":["934:1162:7067"]},"path":["7067"],"owner":"11","recid":"91722","title":["Hyperaccurate Correction of Maximum Likelihood for Geometric Estimation"],"pubdate":{"attribute_name":"公開日","attribute_value":"2013-04-11"},"_buckets":{"deposit":"3f8bfc06-a4f0-457e-aa59-82d94c151aba"},"_deposit":{"id":"91722","pid":{"type":"depid","value":"91722","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"Hyperaccurate Correction of Maximum Likelihood for Geometric Estimation","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Hyperaccurate Correction of Maximum Likelihood for Geometric Estimation"},{"subitem_title":"Hyperaccurate Correction of Maximum Likelihood for Geometric Estimation","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[Regular Paper - Pesearch Paper] geometric estimation,maximum likelihood,bias estimation,hyperaccurate correction","subitem_subject_scheme":"Other"}]},"item_type_id":"3","publish_date":"2013-04-11","item_3_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Department of Computer Science, Okayama University"},{"subitem_text_value":"Department of Computer Science and Engineering, Toyohashi University of Technology"}]},"item_3_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 Computer Science and Engineering, Toyohashi University of Technology","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"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/91722/files/IPSJ-TCVA0500003.pdf"},"date":[{"dateType":"Available","dateValue":"2015-04-11"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-TCVA0500003.pdf","filesize":[{"value":"534.2 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"5"},{"tax":["include_tax"],"price":"0","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"20"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"f64abea9-d7b4-4428-8798-e4f4d48d6966","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2013 by the Information Processing Society of Japan"}]},"item_3_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Kenichi, Kanatani"},{"creatorName":"Yasuyuki, Sugaya"}],"nameIdentifiers":[{}]}]},"item_3_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Kenichi, Kanatani","creatorNameLang":"en"},{"creatorName":"Yasuyuki, Sugaya","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_3_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-6695","subitem_source_identifier_type":"ISSN"}]},"item_3_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"The best known method for optimally computing parameters from noisy data based on geometric constraints is maximum likelihood (ML). This paper reinvestigates “hyperaccurate correction” for further improving the accuracy of ML. In the past, only the case of a single scalar constraint was studied. In this paper, we extend it to multiple constraints given in the form of vector equations. By detailed error analysis, we illuminate the existence of a term that has been ignored in the past. Doing simulation experiments of ellipse fitting, fundamental matrix, and homography computation, we show that the new term does not effectively affect the final solution. However, we show that our hyperaccurate correction is even superior to hyper-renormalization, the latest method regarded as the best fitting method, but that the iterations of ML computation do not necessarily converge in the presence of large noise.","subitem_description_type":"Other"}]},"item_3_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"The best known method for optimally computing parameters from noisy data based on geometric constraints is maximum likelihood (ML). This paper reinvestigates “hyperaccurate correction” for further improving the accuracy of ML. In the past, only the case of a single scalar constraint was studied. In this paper, we extend it to multiple constraints given in the form of vector equations. By detailed error analysis, we illuminate the existence of a term that has been ignored in the past. Doing simulation experiments of ellipse fitting, fundamental matrix, and homography computation, we show that the new term does not effectively affect the final solution. However, we show that our hyperaccurate correction is even superior to hyper-renormalization, the latest method regarded as the best fitting method, but that the iterations of ML computation do not necessarily converge in the presence of large noise.","subitem_description_type":"Other"}]},"item_3_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"29","bibliographic_titles":[{"bibliographic_title":"IPSJ Transactions on Computer Vision and Applications （CVA）"}],"bibliographicPageStart":"19","bibliographicIssueDates":{"bibliographicIssueDate":"2013-04-11","bibliographicIssueDateType":"Issued"},"bibliographicVolumeNumber":"5"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"id":91722,"updated":"2025-01-21T15:19:43.364848+00:00","links":{},"created":"2025-01-18T23:40:55.044848+00:00"}