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Linear 3-D object pose estimation with dense sample images : Discussions about limitation of parameter estimation ability by the linear regressions
https://hiroshima.repo.nii.ac.jp/records/2001017
https://hiroshima.repo.nii.ac.jp/records/2001017eb50180f-ebb9-4f6c-8a4e-d26fa3b37a3c
名前 / ファイル | ライセンス | アクション |
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Item type | デフォルトアイテムタイプ_(フル)(1) | |||||||||
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公開日 | 2023-03-18 | |||||||||
タイトル | ||||||||||
タイトル | Linear 3-D object pose estimation with dense sample images : Discussions about limitation of parameter estimation ability by the linear regressions | |||||||||
言語 | en | |||||||||
作成者 |
Amano, Toshiyuki
× Amano, Toshiyuki
× Tamaki, Toru
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アクセス権 | ||||||||||
アクセス権 | open access | |||||||||
アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||||
権利情報 | ||||||||||
権利情報 | Copyright (c) 2009 MVA Conference Committee | |||||||||
主題 | ||||||||||
主題Scheme | NDC | |||||||||
主題 | 540 | |||||||||
内容記述 | ||||||||||
内容記述 | In the image parameter estimation by the linear regression, it has very high degrees of freedom for the decision of regression coefficients, because the dimension of image vector is huge high. In this paper, we discuss its potential by the learning of the dense samples. For the learning process, we employed a sequential regression coefficient calculation algorithm and realize its calculation for dense samples with reasonable computational cost. Through the experimental result, we discuss about the limitation of parameter estimation ablity by the linear regression. | |||||||||
言語 | en | |||||||||
出版者 | ||||||||||
出版者 | Machine Vision and Application Organization | |||||||||
言語 | ||||||||||
言語 | eng | |||||||||
資源タイプ | ||||||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_5794 | |||||||||
資源タイプ | conference paper | |||||||||
出版タイプ | ||||||||||
出版タイプ | VoR | |||||||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||
開始ページ | ||||||||||
開始ページ | 287 | |||||||||
書誌情報 |
Proceedings of MVA 2009 : IAPR Conference on Machine Vision Applications Proceedings of MVA 2009 : IAPR Conference on Machine Vision Applications p. 287-290, 発行日 2009-05 |
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旧ID | 26462 |