Parameter estimation techniques: a tutorial with application to conic fitting

Almost all problems in computer vision are related in one form or an other to the problem of estimating parameters from noisy data. In this tutorial, we present what is probably the most commonly used techniques for parameter es timation. These include linear least-squares (pseudo-inverse and eigen analysis); orthogonal least-squares; gradient-weighted least-squares; bias-corrected renormal ization; Kalman ltering; 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 di erent techniques. Their application to conic tting is described. Key-words: Parameter estimation, Least-squares, Bias correction, Kalman lter ing, Robust regression Updated on April 15, 1996 To appear in Image and Vision Computing Journal, 1996