Parameter estimation techniques: a tutorial with application to conic fitting

摘要

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-correctd renor- malization; kalman filtering; and robust techniques (clustering, regression diagnostics, M-estimators, lest median of squares).