A Hybrid Parametrization Method for B-Spline Curve Interpolation via Supervised Learning

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dc.contributor.authorSong, Tianyuen_US
dc.contributor.authorShen, Tongen_US
dc.contributor.authorGe, Linlinen_US
dc.contributor.authorFeng, Jieqingen_US
dc.contributor.editorChen, Renjieen_US
dc.contributor.editorRitschel, Tobiasen_US
dc.contributor.editorWhiting, Emilyen_US
dc.date.accessioned2024-10-13T18:09:03Z
dc.date.available2024-10-13T18:09:03Z
dc.date.issued2024
dc.description.abstractB-spline curve interpolation is a fundamental algorithm in computer-aided geometric design. Determining suitable parameters based on data points distribution has always been an important issue for high-quality interpolation curves generation. Various parameterization methods have been proposed. However, there is no universally satisfactory method that is applicable to data points with diverse distributions. In this work, a hybrid parametrization method is proposed to overcome the problem. For a given set of data points, a classifier via supervised learning identifies an optimal local parameterization method based on the local geometric distribution of four adjacent data points, and the optimal local parameters are computed using the selected optimal local parameterization method for the four adjacent data points. Then a merging method is employed to calculate global parameters which align closely with the local parameters. Experiments demonstrate that the proposed hybrid parameterization method well adapts the different distributions of data points statistically. The proposed method has a flexible and scalable framework, which can includes current and potential new parameterization methods as its components.en_US
dc.description.number7
dc.description.sectionheadersCurve and Surface Modeling
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume43
dc.identifier.doi10.1111/cgf.15240
dc.identifier.issn1467-8659
dc.identifier.pages12 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.15240
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf15240
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectCCS Concepts: Computing methodologies → Parametric curve and surface models; Supervised learning by classification
dc.subjectComputing methodologies → Parametric curve and surface models
dc.subjectSupervised learning by classification
dc.titleA Hybrid Parametrization Method for B-Spline Curve Interpolation via Supervised Learningen_US
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