Parameter Estimation Techniques: A Tutorial with Application to Conic Fitting - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Reports (Research Report) Year : 1995

Parameter Estimation Techniques: A Tutorial with Application to Conic Fitting

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.
Fichier principal
Vignette du fichier
RR-2676.pdf (358.97 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

inria-00074015 , version 1 (08-06-2012)

Identifiers

  • HAL Id : inria-00074015 , version 1

Cite

Zhengyou Zhang. Parameter Estimation Techniques: A Tutorial with Application to Conic Fitting. [Research Report] RR-2676, INRIA. 1995. ⟨inria-00074015⟩
479 View
45142 Download

Share

Gmail Facebook Twitter LinkedIn More