Parameter estimation techniques: a tutorial with application to conic fitting
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Publication:2573061
DOI10.1007/s10851-005-0482-8zbMath1478.65011OpenAlexW2038967717MaRDI QIDQ2573061
Publication date: 7 November 2005
Published in: Journal of Mathematical Imaging and Vision (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10851-005-0482-8
Numerical smoothing, curve fitting (65D10) Pattern recognition, speech recognition (68T10) Computer graphics; computational geometry (digital and algorithmic aspects) (68U05) Numerical aspects of computer graphics, image analysis, and computational geometry (65D18)
Related Items (16)
Subpixel determination of imperfect circles characteristics ⋮ Is the best fitting curve always unique? ⋮ Robust fitting of circle arcs ⋮ Robust fitting of ellipsoids by separating interior and exterior points during optimization ⋮ Error analysis for circle fitting algorithms ⋮ Does the best-fitting curve always exist? ⋮ Further statistical analysis of circle fitting ⋮ Real-time accurate circle fitting with occlusions ⋮ Method of determining the cohesion and adhesion parameters in the Shan-Chen multicomponent multiphase lattice Boltzmann models ⋮ An algorithm for fitting circular arcs to data using the \(l_1\) norm ⋮ Fast and numerically stable circle fit ⋮ Monte Carlo maximum likelihood circle fitting using circular density functions ⋮ Fitting circles to scattered data: parameter estimates have no moments ⋮ Automatic radial distortion estimation from a single image ⋮ Fitting circles to data with correlated noise ⋮ Statistical analysis of curve fitting methods in errors-in-variables models
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- A method for the solution of certain non-linear problems in least squares
- Least-squares fitting of circles and ellipses
- Rationalising the renormalisation method of Kanatani
- Least-squares orthogonal distances fitting of circle, sphere, ellipse, hyperbola, and parabola
- Heteroscedastic regression in computer vision: Problems with bilinear constraint
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