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

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dc.contributor.authorUelker, Erkan
dc.contributor.authorArslan, Ahmet
dc.date.accessioned2020-03-26T17:37:57Z
dc.date.available2020-03-26T17:37:57Z
dc.date.issued2009
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractReverse engineering transforms real parts into engineering concepts or models. First, sampled points are mapped from the object's surface by using tools such as laser scanners or cameras. Then, the sampled points are fitted to a free-form surface or a standard shape by using one of the geometric modeling techniques. The curves on the surface have to be modeled before surface modeling. In order to obtain a good B-spline curve model from large data, the knots are usually respected as variables. A curve is then modeled as a continuous, nonlinear and multivariate optimization problem with many local optima. For this reason it is very difficult to reach a global optimum. In this paper, we convert the original problem into a discrete combinatorial optimization problem like in Yoshimoto et al. [F. Yoshimoto, M. Moriyama. T. Harada, Automatic knot placement by a genetic algorithm for data fitting with a spline, in: Proceedings of the International Conference on Shape Modeling and Applications, IEEE Computer Society Press, 1999, pp. 162-169] and Sarfraz et al. [M. Sarfraz, S.A. Raza, Capturing outline of fonts using genetic algorithm and splines, in: Fifth International Conference on Information Visualisation (IV'01), 2001, pp. 738-743]. Then, we suggest a new method that solves the converted problem by artificial immune systems. We think the candidates of the locations of knots as antibodies. We define the affinity measure benefit from Akaike's Information Criterion (AIC). The proposed method determines the appropriate location of knots automatically and simultaneously. Furthermore, we do not need any subjective parameter or good population of initial location of knots for a good iterative search. Some examples are also given to demonstrate the efficiency and effectiveness of our method. (c) 2008 Elsevier Inc. All rights reserved.en_US
dc.description.sponsorshipScientific Research Projects of Selcuk UniversitySelcuk Universityen_US
dc.description.sponsorshipThis study has been supported by the Scientific Research Projects of Selcuk Universityen_US
dc.identifier.doi10.1016/j.ins.2008.11.037en_US
dc.identifier.endpage1494en_US
dc.identifier.issn0020-0255en_US
dc.identifier.issn1872-6291en_US
dc.identifier.issue10en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1483en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.ins.2008.11.037
dc.identifier.urihttps://hdl.handle.net/20.500.12395/23314
dc.identifier.volume179en_US
dc.identifier.wosWOS:000265079600009en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherELSEVIER SCIENCE INCen_US
dc.relation.ispartofINFORMATION SCIENCESen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectArtificial immune systemen_US
dc.subjectB-spline curve approximationen_US
dc.subjectClonal selectionen_US
dc.subjectKnot adjustmenten_US
dc.titleAutomatic knot adjustment using an artificial immune system for B-spline curve approximationen_US
dc.typeArticleen_US

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