Details

Autor(en) / Beteiligte

• Zhang, Zhengyou

Titel

Parameter estimation techniques: a tutorial with application to conic fitting

Ist Teil von

• Image and vision computing, 1997, Vol.15 (1), p.59-76

Ort / Verlag

Oxford: Elsevier B.V

Erscheinungsjahr

1997

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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.