{"links":{},"id":193836,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00193836","sets":["1164:4619:9659:9661"]},"path":["9661"],"owner":"44499","recid":"193836","title":["ニューラルネットワークを用いた物体画像から把持方法候補の想起"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-01-10"},"_buckets":{"deposit":"fd5b3adc-39b8-44f2-a424-d874f7496d6d"},"_deposit":{"id":"193836","pid":{"type":"depid","value":"193836","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"ニューラルネットワークを用いた物体画像から把持方法候補の想起","author_link":["454920","454922","454921","454919"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"ニューラルネットワークを用いた物体画像から把持方法候補の想起"}]},"item_type_id":"4","publish_date":"2019-01-10","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"立命館大学情報理工学研究科"},{"subitem_text_value":"立命館大学情報理工学部"},{"subitem_text_value":"立命館大学情報理工学部"},{"subitem_text_value":"立命館大学情報理工学部"}]},"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/193836/files/IPSJ-CVIM19215011.pdf","label":"IPSJ-CVIM19215011.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CVIM19215011.pdf","filesize":[{"value":"1.4 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"20"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"2394e746-f0b0-4354-9b74-2d2b53c5d798","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2019 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG.\n"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"眞田, 慎"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"松尾, 直志"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"島田, 伸敬"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"白井, 良明"}],"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_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8701","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"人間がロボットに物体に対する単純な動作指示を出すことで，ロボットが物体に対して想定される複数の動作から指示に沿った最適な動作を選択し，実行することが求められている．しかし，一つの物体の把持方法は複数あり，最適な動作の決定には物体に対して複数の把持方法の推定を行うことが重要となる．本研究では，一枚の物体画像から複数の把持位置や把持手形状を推定することを目標とする．学習時には，一枚の物体画像に対して複数の把持方法がある場合には，その中から一度につき一つを選んで提示する．これを一枚の物体画像の入力に対して複数の把持方法を別チャンネルで出力するネットワークで学習する．学習過程では似た物体形状に対する似た把持方法が自動的にクラスタリングされ，把持方法候補が別チャンネルに出力されるようになる．実験例を用いて本手法の有用性を示す．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"5","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":"2019-01-10","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"11","bibliographicVolumeNumber":"2019-CVIM-215"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T00:58:58.575633+00:00","updated":"2025-01-19T23:48:18.745153+00:00"}