Immunological Approach for Data Parameterization in Curve Fitting of Noisy Points with Smooth Local-Support Splines

This paper addresses the problem of computing the parameterization of a smooth local-support spline curve for data fitting of noisy points by using an immunological approach. Given an initial set (not necessarily optimal) of breakpoints, our method applies a popular artificial immune systems technique called clonal selection algorithm to perform curve parameterization. The resulting optimal data parameters are then used for further refinement of the breakpoints via the deBoor method. In this way, the original non-convex optimization method is transformed into a convex one, subsequently solved by least-squares singular value decomposition. The method is applied to two illustrative examples (human hand and ski goggles, each comprised of three curves) of two-dimensional sets of noisy data points with very good experimental results.

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