Parameter optimization for B-spline curve fitting using genetic algorithms

Abstract

Abstract-B-splines have today become the industry standard for CAD data representation. Freeforrn shape synthesis from point cloud data is an emerging technique. This predominantly involves B-spline curve / snrface fitting to the point cloud data to obtain the CAD definitions. Accurate curve a n d surface fitting from point clouds needs a good parameterization model, i.e. the determination of parameter values of the digitized points in order to perform least squares (LSQ) fitting. Numerous work have been done on selection of such parameters. Nevertheless, it is difficult with the present approaches to estimate better parameters particularly when the points are irregularly spaced and lie on a complex hase curve o r surface. There is a need to evolve from all the available parameterization solutions a n optimum set of parameters which in turn will generate curves / surfaces interpolating the given data closely. An approach based on genetic algorithms for parameter optimization is presented here. A novel population initialization scheme is proposed that ensures that the optimization procedure is both global in nature with less expensive convergence. The present study of parameterization is for Non Uniform B-spline curve fitting.

Automatic knot adjustment using an artificial immune system for B-spline curve approximation

“…In addition to these, a framework is presented for constitution of gene pools which include initial population in GA in Kumar et al [26]. To speed up our method, initial antibody population can be created to increase the convergence via this framework algorithm.…”

B-Spline Curve Approximation Using Pareto Envelope-Based Selection Algorithm – PESA

“…Specified processing steps are fulfilled according to AIC value obtained from equation 6and (7) or to the border of a particular generation.…”

“…Mamic and Bennamoun (2001) suggested a bayesian model to identify automatically node placement in spline modeling. An approach based on genetic algorithm for parameter optimization aiming B-spline curve fitting is proposed by Kumar et al(2003). The place of genetic algorithm in computer -aided design are deeply examined by Renner and Ekart (2003).…”

Regional Registration of Whole Slide Image Stacks Containing Major Histological Artifacts

“…The {θ, dx, dy} triplet which gives the least sum of squared difference is chosen and its corresponding transformation matrix is applied to the moving image: been proposed for parameter optimization for B-spline curve fitting [26,27]. Parameter optimization gets especially more challenging for the case of discontinuous control points [28] which can occur in the existence of major distortions.…”