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Tabu search-based method for bézier curve parameterization

Autor Gálvez, Akemi; Iglesias, Andrés; Cabellos, Luis
Fecha de publicación 2013
EditorScience and Engineering Research Support Center
Citación International Journal of Software Engineering and its Applications 7: 283-296 (2013)
ResumenA very important issue in many applied fields is to construct the fitting curve that approximates a given set of data points optimally in the sense of least-squares. This problem arises in a number of areas, such as computer-aided design & manufacturing (CAD/CAM), virtual reality, medical imaging, computer graphics, computer animation, and many others. This is also a hard problem, because it is highly nonlinear, over-determined and typically involves a large number of unknown variables. A critical step in this process is to obtain a suitable parameterization of the data points. In this context, this paper introduces a new method to obtain an optimal solution for the parameterization problem of the least-squares fitting Bézier curve. Our method is based on a local search metaheuristic approach for optimization problems called tabu search. The method is applied to some simple yet illustrative examples for the cases of 2D and 3D curves. The proposed method is simple to understand, easy to implement and can be applied to any kind of smooth data points. Our experimental results show that the presented method performs very well, being able to fit the data points with a high degree of accuracy.
Versión del editorhttp://dx.doi.org/10.14257/ijseia.2013.7.5.25
URI http://hdl.handle.net/10261/109980
Identificadoresdoi: 10.14257/ijseia.2013.7.5.25
issn: 1738-9984
Aparece en las colecciones: (IFCA) Artículos
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