{"_buckets": {"deposit": "bb8b89da-0b54-403f-8832-cc47af34bfe5"}, "_deposit": {"id": "21549", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "21549"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00021549", "sets": ["314"]}, "author_link": ["63028", "63029", "63030", "63031", "63032", "63033", "63034", "63035"], "item_10_alternative_title_19": {"attribute_name": "その他のタイトル", "attribute_value_mlt": [{"subitem_alternative_title": "Estimation of procedure in laparoscopic surgery by subspace method with color information", "subitem_alternative_title_language": "en"}]}, "item_10_biblio_info_6": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2011-01", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "364", "bibliographicPageEnd": "4", "bibliographicPageStart": "1", "bibliographicVolumeNumber": "110", "bibliographic_titles": [{"bibliographic_title": "電子情報通信学会技術研究報告. MI, 医用画像", "bibliographic_titleLang": "ja"}]}]}, "item_10_description_4": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "本稿では腹腔鏡手術映像に対し,色情報を用いた部分空間法により手術シーンを自動分類する手法について報告する.腹腔鏡手術では体内に挿入したカメラの視野が狭く,複雑な鉗子操作が必要となるなど,術者に高度な技術が要求される.本研究では腹腔鏡手術における知的支援・手術評価の基礎的検討として,腹腔鏡カメラにより撮影されたビデオ映像の各フレームを「臓器」・「手術器具」といったシーンに自動分類することを試みる.この分類によって得られた結果は,進行状況情報の共有化や手術手技の評価・採点にも利用できると考える.本手法では,ビデオ映像の各フレームの色情報をもとに部分空開法を利用し,腹腔鏡手術映像の自動シーン分類をおこなった.実験により,分類成功率が79.8%という結果を得た. ", "subitem_description_language": "ja", "subitem_description_type": "Abstract"}, {"subitem_description": "This paper presents an automated method for classifying scenes of laparoscope videos based on the subspace method with color information. Because laparoscopic surgery requires more collaborative work in the narrow perspective of the operator, are strongly expected to develop a navigation system that supports collaboration. This paper presents the unction for automated scene classification of laparoscopic videos as a preliminary study for achieving the intelligent assistance based on recognition of the progress of surgery and surgical evaluation. The subspace of each category is created from training dataset with color information. We applied the proposed method to a sequence of a laparoscopic video. 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