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href="https://www.academia.edu/85631955/Background_Image_Generation_by_Analyzing_Long_Term_Images_for_Outdoor_Fixed_Camera">Background Image Generation by Analyzing Long-Term Images for Outdoor Fixed Camera</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="948400" href="https://jp.academia.edu/YasutomoKawanishi">Yasutomo Kawanishi</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2011</p><p class="ds-related-work--abstract ds2-5-body-sm">a) E-mail: kawanishi@mm.media.kyoto-u.ac.jp ることは難しいため,これまでに観測した過去の観測画像をもとにそのシーンで起きている変動を再現する様々な手法が提案されている. 過去に観測した画像を利用して背景を推定する代表的な手法として,直近の観測画像に対する統計的なアプローチが多く用いられている.これまでに観測した複数枚の画像に対し,画素ごとの時間平均やメディアンを求めることで背景を推定し,それをもとに背景画像を生成する手法 [5]~[7]は最も単純なものである.島井ら [8] はロバスト統計の手法として知られる M 推定を用いて適応的に背景を推定する手法を提案している.一方,直近の観測期間中にシーン中で起きている変動を選び出し,こうした変動を陽にモデル化してそのパラメータを推定するアプローチがある.吉村ら [9] は色情報のうちの明るさをパラメータ化した明るさ可変背景モデルを用い,カルマンフィルタを用いて逐次的に日照成分のパラメータ推定を行うことによって背景を推定する手法を提案している.また,Liu ら [10] は画像中の背景,前景の変動を ECD (Effect Components Description)としてモデル化し,Mean Shift によって短期間における背景物体の変動を推定することで背景画像を生成する手法を提案している. これらの手法では,処理に用いる画像の枚数が少ないと,その期間内に起こっていない変化が起きたときに</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Background Image Generation by Analyzing Long-Term Images for Outdoor Fixed Camera","attachmentId":90270493,"attachmentType":"pdf","work_url":"https://www.academia.edu/85631955/Background_Image_Generation_by_Analyzing_Long_Term_Images_for_Outdoor_Fixed_Camera","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" 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