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Nonparametric joint shape learning for customized shape modeling.

机译:用于定制形状建模的非参数关节形状学习。

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摘要

We present a shape optimization approach to compute patient-specific models in customized prototyping applications. We design a coupled shape prior to model the transformation between a related pair of surfaces, using a nonparametric joint probability density estimation. The coupled shape prior forces with the help of application-specific data forces and smoothness forces drive a surface deformation towards a desired output surface. We demonstrate the usefulness of the method for generating customized shape models in applications of hearing aid design and pre-operative to intra-operative anatomic surface estimation.
机译:我们提出一种形状优化方法,以在定制的原型应用中计算患者特定的模型。我们使用非参数联合概率密度估计来设计耦合模型,然后对相关的一对表面之间的转换建模。结合形状的先验力借助专用数据力和平滑力将表面变形推向所需的输出表面。我们证明了在助听器设计和术前术中解剖表面估计的应用中生成定制形状模型的方法的有用性。

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