Abstract
This paper proposes combining B-spline curved surface fitting with evolutionary algorithm to improve the fitting efficiency and precision. In the process of selecting curved surface control points, taking the minimum error sum of squares as the fitness standard, the optimal basis curved surface is obtained by optimizing control points constantly, which is partitioned into small blocks according to its precision. Control points of each piece are fast and precisely reversed, then x, y and z values of control point are used as gene chromosomes. Genetic operation is continuous reiteration until original curve surface reconstruction is achieved.
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© 2012 Springer-Verlag Berlin Heidelberg
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Yang, J., Liu, F., Tao, X., Wang, X., Cheng, J. (2012). The Application of Evolutionary Algorithm in B-Spline Curved Surface Fitting. In: Lei, J., Wang, F.L., Deng, H., Miao, D. (eds) Artificial Intelligence and Computational Intelligence. AICI 2012. Lecture Notes in Computer Science(), vol 7530. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33478-8_31
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DOI: https://doi.org/10.1007/978-3-642-33478-8_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33477-1
Online ISBN: 978-3-642-33478-8
eBook Packages: Computer ScienceComputer Science (R0)