A Weighted Integral Method for Parametrically Describing Local Image Appearance

• Item type: Trans(1)
• 公開日: 2013-07-29
• タイトル: A Weighted Integral Method for Parametrically Describing Local Image Appearance
• タイトル / 言語: en
• タイトル: A Weighted Integral Method for Parametrically Describing Local Image Appearance
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [Regular Paper - Express Paper] feature description, parameter estimation problem, scale analysis, weighted integral method
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Nagoya Institute of Technology
• 著者所属: Nagoya Institute of Technology
• 著者所属(英): en
• 著者所属(英): Nagoya Institute of Technology
• 著者所属(英): en
• 著者所属(英): Nagoya Institute of Technology
• 著者名: Hidetoshi, Goto; Hidekata, Hontani
• 著者名(英): Hidetoshi, Goto; Hidekata, Hontani
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: We propose a new method that efficiently and accurately estimates the parameters of the Gaussian function that describes the given local image profiles. The Gaussian function is non-linear with respect to the parameters to be estimated, and this non-linearity makes their efficient and accurate estimation difficult. In our proposed method, the weighted integral method is introduced to linearize the parameter estimation problem: A system of differential equations is firstly derived that is satisfied by the Gaussian function and that is linear with respect to the parameters. The system is then converted to that of integral equations. Given a local sub-window of the image, one can obtain the system of integral equations and estimate the parameters of the Gaussian that describe the appearance in the sub-window by solving the linear system of the parameters. Experimental results showed that our proposed method estimates the parameters more efficiently and accurately than existing state-of-the-art methods.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: We propose a new method that efficiently and accurately estimates the parameters of the Gaussian function that describes the given local image profiles. The Gaussian function is non-linear with respect to the parameters to be estimated, and this non-linearity makes their efficient and accurate estimation difficult. In our proposed method, the weighted integral method is introduced to linearize the parameter estimation problem: A system of differential equations is firstly derived that is satisfied by the Gaussian function and that is linear with respect to the parameters. The system is then converted to that of integral equations. Given a local sub-window of the image, one can obtain the system of integral equations and estimate the parameters of the Gaussian that describe the appearance in the sub-window by solving the linear system of the parameters. Experimental results showed that our proposed method estimates the parameters more efficiently and accurately than existing state-of-the-art methods.
• 書誌情報: IPSJ Transactions on Computer Vision and Applications （CVA）; 巻 5, p. 70-74, 発行日 2013-07-29
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-6695
• 出版者 / 言語: ja
• 出版者: 情報処理学会