Hyperaccurate Correction of Maximum Likelihood for Geometric Estimation

• Item type: Trans(1)
• 公開日: 2013-04-11
• タイトル: Hyperaccurate Correction of Maximum Likelihood for Geometric Estimation
• タイトル / 言語: en
• タイトル: Hyperaccurate Correction of Maximum Likelihood for Geometric Estimation
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [Regular Paper - Pesearch Paper] geometric estimation,maximum likelihood,bias estimation,hyperaccurate correction
• 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: Department of Computer Science, Okayama University
• 著者所属: Department of Computer Science and Engineering, Toyohashi University of Technology
• 著者所属(英): en
• 著者所属(英): Department of Computer Science, Okayama University
• 著者所属(英): en
• 著者所属(英): Department of Computer Science and Engineering, Toyohashi University of Technology
• 著者名: Kenichi, Kanatani; Yasuyuki, Sugaya
• 著者名(英): Kenichi, Kanatani; Yasuyuki, Sugaya
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: The best known method for optimally computing parameters from noisy data based on geometric constraints is maximum likelihood (ML). This paper reinvestigates “hyperaccurate correction” for further improving the accuracy of ML. In the past, only the case of a single scalar constraint was studied. In this paper, we extend it to multiple constraints given in the form of vector equations. By detailed error analysis, we illuminate the existence of a term that has been ignored in the past. Doing simulation experiments of ellipse fitting, fundamental matrix, and homography computation, we show that the new term does not effectively affect the final solution. However, we show that our hyperaccurate correction is even superior to hyper-renormalization, the latest method regarded as the best fitting method, but that the iterations of ML computation do not necessarily converge in the presence of large noise.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: The best known method for optimally computing parameters from noisy data based on geometric constraints is maximum likelihood (ML). This paper reinvestigates “hyperaccurate correction” for further improving the accuracy of ML. In the past, only the case of a single scalar constraint was studied. In this paper, we extend it to multiple constraints given in the form of vector equations. By detailed error analysis, we illuminate the existence of a term that has been ignored in the past. Doing simulation experiments of ellipse fitting, fundamental matrix, and homography computation, we show that the new term does not effectively affect the final solution. However, we show that our hyperaccurate correction is even superior to hyper-renormalization, the latest method regarded as the best fitting method, but that the iterations of ML computation do not necessarily converge in the presence of large noise.
• 書誌情報: IPSJ Transactions on Computer Vision and Applications （CVA）; 巻 5, p. 19-29, 発行日 2013-04-11
• 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