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INRIA Research Report No.2676, October 1995
PDF version available here
appeared in Image and Vision Computing Journal, Vol.15, No.1, pages 59-76, 1997
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.
Keywords: Parameter estimation, Least-squares, Bias correction, Kalman filtering, Robust regression