Performance evaluation of iterative geometric fitting algorithms
The convergence performance of typical numerical schemes for geometric fitting for computer vision applications is compared. First, the problem and the associated KCR lower bound are stated. Then, three well-known fitting algorithms are described: FNS, HEIV, and renormalization. To these, we add a special variant of GaussNewton iterations. For initialization of iterations, random choice, least squares, and Taubin's method are tested. Simulation is conducted for fundamental matrix computation and ellipse fitting, which reveals different characteristics of each method.
geometric fitting convergence performance kcr lower bound fundamental matrix ellipse fitting