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Parameter Estimation Techniques: A Tutorial with Application to Conic Fitting

Zhengyou Zhang 1
1 ROBOTVIS - Computer Vision and Robotics
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Almost all problems in computer vision are related in one form or another to the problem of estimating parameters from noisy data. In this tutorial, we present what is probably the most commonly used techniques for parameter estimation. These include linear least-squares (pseudo-inverse and eigen analysis); orthogonal least-squares; gradient-weighted least-squares; bias-corrected renormalization; Kalman filtering; and robust techniques (clustering, regression diagnostics, M-estimators, least median of squares). Particular attention has been devoted to discussions about the choice of appropriate minimization criteria and the robustness of the different techniques. Their application to conic fitting is described.
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Submitted on : Friday, June 8, 2012 - 4:47:11 PM
Last modification on : Thursday, January 20, 2022 - 5:30:29 PM
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  • HAL Id : inria-00074015, version 1



Zhengyou Zhang. Parameter Estimation Techniques: A Tutorial with Application to Conic Fitting. [Research Report] RR-2676, INRIA. 1995. ⟨inria-00074015⟩



Les métriques sont temporairement indisponibles