{"created":"2025-01-18T23:16:40.308105+00:00","updated":"2025-01-22T06:49:49.162599+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00052318","sets":["1164:4619:4637:4639"]},"path":["4639"],"owner":"1","recid":"52318","title":["遷移ネットワークに基づく複雑背景下での手指ジェスチャの認識"],"pubdate":{"attribute_name":"公開日","attribute_value":"2005-09-05"},"_buckets":{"deposit":"368cb72d-6105-463f-b167-dcf2ca96a424"},"_deposit":{"id":"52318","pid":{"type":"depid","value":"52318","revision_id":0},"owners":[1],"status":"published","created_by":1},"item_title":"遷移ネットワークに基づく複雑背景下での手指ジェスチャの認識","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"遷移ネットワークに基づく複雑背景下での手指ジェスチャの認識"},{"subitem_title":"Hand Gesture Recognition under Complex Backgrounds Based on Transition Network","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2005-09-05","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部知能情報学科"},{"subitem_text_value":"立命館大学情報理工学部知能情報学科"},{"subitem_text_value":"立命館大学情報理工学部知能情報学科"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Dept. of Human and Computer Intelligence Ritsumeikan University","subitem_text_language":"en"},{"subitem_text_value":"Dept. of Human and Computer Intelligence Ritsumeikan University","subitem_text_language":"en"},{"subitem_text_value":"Dept. of Human and Computer Intelligence Ritsumeikan 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/52318/files/IPSJ-CVIM05150002.pdf"},"date":[{"dateType":"Available","dateValue":"2007-09-05"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CVIM05150002.pdf","filesize":[{"value":"562.5 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":"da76d455-513a-4d7c-9de1-1d6a59413802","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2005 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"浜田, 康志"},{"creatorName":"島田, 伸敬"},{"creatorName":"白井, 良明"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Yasushi, Hamada","creatorNameLang":"en"},{"creatorName":"Nobutaka, Shimada","creatorNameLang":"en"},{"creatorName":"Yoshiaki, Shirai","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":"一般に手指ジェスチャは衣服や顔などを含む複雑背景下で形状が提示される。我々は学習用画像列中の典型的な手指形状と可能な形状遷移から遷移ネットワークを作成し、形状遷移を追跡することで手指形状を推定する手法を提案した。本稿ではこの遷移ネットワークを拡張し、ジェスチャを羅織する手法を提案する。遷移ネットワークの生成では全てのジェスチャの学習用画像列を用いて学習することで一つの大規模なネットワークが生成され、各ジェスチャは遷移ネットワークの部分経路として学習される。そこで入力画像列の各フレームで推定される手指形状の系列から遷移ネットワーク上の経路を求め、ジェスチャの部分経路を通過したことを検出し、ジェスチャを認識する。","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In hand gesture recognition, hand shapes are generally shown under complex backgrounds including cloths and a face. We proposed a method to estimate hand shapes based on a transition network. The transition network is generated from training sequences in a leaming phase, and a hand shape is tracked by using the network in an estimation phase. In this paper, we propose a method to recognize hand gestures from shape estimation results by using the enhanced transition network. Each gesture is registered as a partial path on the transition network by gesture learning. Also, Shape estimation results construct paths on the network. Since a gesture is recognized by detecting the gesture path which is passed by the estimation results.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"16","bibliographic_titles":[{"bibliographic_title":"情報処理学会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":"9","bibliographicIssueDates":{"bibliographicIssueDate":"2005-09-05","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"88(2005-CVIM-150)","bibliographicVolumeNumber":"2005"}]},"relation_version_is_last":true,"weko_creator_id":"1"},"id":52318,"links":{}}