{"updated":"2025-01-19T15:07:35.045265+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00218555","sets":["1164:3206:10884:10949"]},"path":["10949"],"owner":"44499","recid":"218555","title":["定義域分割法とRidge回帰を用いた高精度Rolling Guidance画像フィルタ"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-06-20"},"_buckets":{"deposit":"710f931d-2a07-4a0f-90ac-dc5faefb6230"},"_deposit":{"id":"218555","pid":{"type":"depid","value":"218555","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"定義域分割法とRidge回帰を用いた高精度Rolling Guidance画像フィルタ","author_link":["568484","568482","568483","568485"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"定義域分割法とRidge回帰を用いた高精度Rolling Guidance画像フィルタ"},{"subitem_title":"Accurate Rolling Guidance Image Filter via Domain Splitting and Ridge Regression Techniques","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2022-06-20","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京理科大学／理化学研究所"},{"subitem_text_value":"理化学研究所"},{"subitem_text_value":"東京理科大学"},{"subitem_text_value":"理化学研究所"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Tokyo University of Science / RIKEN","subitem_text_language":"en"},{"subitem_text_value":"RIKEN","subitem_text_language":"en"},{"subitem_text_value":"Tokyo University of Science","subitem_text_language":"en"},{"subitem_text_value":"RIKEN","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/218555/files/IPSJ-CG22186002.pdf","label":"IPSJ-CG22186002.pdf"},"date":[{"dateType":"Available","dateValue":"2024-06-20"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CG22186002.pdf","filesize":[{"value":"15.1 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"28"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"0fde4ca3-d84c-4f08-8766-e6d5c55a9332","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"深津, 美薫"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"吉澤, 信"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"竹村, 裕"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"横田, 秀夫"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10100541","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":"2188-8949","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"デジタル画像中のテクスチャーは様々な大きさで構成されており，顕著な特徴を保持しながら小さい構造を除去することは幅広い CGVI 応用にて有用である．一方，既存法では畳み込み計算に由来するアーティファクトや収束性の問題が知られている．そこで本稿では，高品質なスケール対応画像フィルタ結果を生成する効率的な計算法を提案する．提案法は，Ridge 回帰の再帰適用で構成し，その畳み込み計算には，L1 ガウス関数を高速かつ高精度に畳み込む独自の定義域分割法を適応する．既存法と計算速度，近似精度，収束性に関して数値実験により比較を行い，約 7 桁～ 10 桁近似精度が高い高品質な結果を得た．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Texture in a digital image consists of various scales, and removing its small structures while preserving salient image features is useful in a variety of CGVI applications. Conventional scale-aware image filters have some artifacts and convergence problems mainly caused by their inaccurate approximations of fast image convolutions. In this paper, we propose a novel, fast, and accurate computational framework to obtain high-quality scale-aware image filtering results. The framework is based on a recursive process of weighted ridge regressions and our domain-splitting technique which accurately approximates the L1 Gaussian convolution quickly. We numerically examined our framework by comparing the existing filters in terms of speed, accuracy, and convergence rate, and achieved high-quality filtering results efficiently.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"11","bibliographic_titles":[{"bibliographic_title":"研究報告コンピュータグラフィックスとビジュアル情報学（CG）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2022-06-20","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"2","bibliographicVolumeNumber":"2022-CG-186"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:18:55.342437+00:00","id":218555,"links":{}}