Statistical optimization for geometric fitting: theoretical accuracy bound and high order error analysis

DOI10.1007/s11263-007-0098-0zbMath1477.68383OpenAlexW2013131044MaRDI QIDQ847462

Ken-Ichi Kanatani

Publication date: 16 February 2010

Published in: International Journal of Computer Vision (Search for Journal in Brave)

Full work available at URL: http://ousar.lib.okayama-u.ac.jp/14087

zbMATH Keywords

parameter estimationerror analysisgeometric fittinghyperaccuracyKCR lower bound

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Image analysis in multivariate analysis (62H35) Machine vision and scene understanding (68T45)

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Cites Work

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• Statistical efficiency of curve fitting algorithms
• Performance evaluation of iterative geometric fitting algorithms
• Information geometry of estimating functions in semi-parametric statistical models
• A Riemannian framework for tensor computing
• Consistent Estimates Based on Partially Consistent Observations
• Rationalising the renormalisation method of Kanatani
• Heteroscedastic regression in computer vision: Problems with bilinear constraint
• A new look at the statistical model identification