Technical Section: Capturing outlines of planar images using Bézier cubics
Computers and Graphics
Technical Section: An efficient technique for capturing 2D objects
Computers and Graphics
An Artificial Immune System Approach for B-Spline Surface Approximation Problem
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part II
Automatic knot adjustment using an artificial immune system for B-spline curve approximation
Information Sciences: an International Journal
Using simulated annealing for knot placement for cubic spline approximation
MMES'10 Proceedings of the 2010 international conference on Mathematical models for engineering science
Efficient particle swarm optimization approach for data fitting with free knot B-splines
Computer-Aided Design
Computing optimized curves with NURBS using evolutionary intelligence
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and its Applications - Volume Part I
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Abstract: In order to obtain a good spline model from large measurement data, we frequently have to deal with knots as variables, which becomes a continuous, non-linear and multivariate optimization problem with many local optima. Hence, it is very difficult to obtain a global optima. In this paper, we present a method to convert the original problem into a discrete combinatorial optimization problem and solve it by a genetic algorithm. We also incorporate a corner detection algorithm to detect significant points which are necessary to capture a pleasant looking spline fitting for shapes such as fonts. A parametric B-Spline has been approximated to various characters and symbols. The chromosomes have been constructed by considering the candidates of the locations of knots as genes. The best model among the candidates is searched by using Akaike's Information Criterion (AIC). The method determines the appropriate number and location of knots automatically and simultaneously. Some examples are given to show the results obtained from the algorithm.