{"updated":"2025-01-20T01:52:45.592069+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00188928","sets":["6504:9465:9482"]},"path":["9482"],"owner":"6748","recid":"188928","title":["主成分分析を用いた火星ダストストーム領域の自動検出"],"pubdate":{"attribute_name":"公開日","attribute_value":"2018-03-13"},"_buckets":{"deposit":"cd677bf2-a2d2-4426-a681-7912421f898b"},"_deposit":{"id":"188928","pid":{"type":"depid","value":"188928","revision_id":0},"owners":[6748],"status":"published","created_by":6748},"item_title":"主成分分析を用いた火星ダストストーム領域の自動検出","author_link":["429054","429053","429052","429051"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"主成分分析を用いた火星ダストストーム領域の自動検出"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"ネットワーク","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2018-03-13","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"滋賀県立大"},{"subitem_text_value":"滋賀県立大"},{"subitem_text_value":"滋賀県立大"},{"subitem_text_value":"滋賀県立大"}]},"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/188928/files/IPSJ-Z80-2U-07.pdf","label":"IPSJ-Z80-2U-07.pdf"},"date":[{"dateType":"Available","dateValue":"2018-05-07"}],"format":"application/pdf","filename":"IPSJ-Z80-2U-07.pdf","filesize":[{"value":"521.9 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"5bd3d47b-9bf6-42d1-b3e7-5c4096c2398a","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2018 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"義忠, 隆生"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"小郷原, 一智"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"畑中, 裕司"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"砂山, 渡"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本研究では,火星の衛星画像からダストストーム領域を自動検出することを目的とした.小領域パッチに分割し,パッチ画像のパターンに基づいてダストストームを自動検出するアルゴリズムを構築した.パッチ画像は特徴次元が大きすぎるために,主成分分析によって抽出された基底を用いることで,次元削減を行った.こうして低次元にしたパッチ画像の特徴量を用いて機械学習(Neural Networkを訓練)し,未知画像からの検出を行った.評価する際には未知画像を5枚用意し,ROC曲線を用いて評価した.その結果AUC=0.975という数値が得られ,高い精度で検出することに成功した.今後はパッチサイズやデータ数の検討を行い,精度の向上を目指す.","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"270","bibliographic_titles":[{"bibliographic_title":"第80回全国大会講演論文集"}],"bibliographicPageStart":"269","bibliographicIssueDates":{"bibliographicIssueDate":"2018-03-13","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2018"}]},"relation_version_is_last":true,"weko_creator_id":"6748"},"created":"2025-01-19T00:55:01.915393+00:00","id":188928,"links":{}}