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Iterative data reweighting for balanced model learning
Iterative data reweighting for balanced model learning
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机译:迭代数据重新加权以实现平衡模型学习
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摘要
Aspects of the present invention include systems and methods for forming generative models, for utilizing those models, or both. In embodiments, an object model fitting system can be developed comprising a 3D active appearance model (AAM) model. The 3D AAM comprises an appearance model comprising a set of subcomponent appearance models that is constrained by a 3D shape model. In embodiments, the 3D AAM may be generated using a balanced set of training images. The object model fitting system may further comprise one or more manifold constraints, one or more weighting factors, or both. Applications of the present invention include, but are not limited to, modeling and/or fitting face images, although the teachings of the present invention can be applied to modeling/fitting other objects.
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机译:本发明的方面包括用于形成生成模型,用于利用那些模型或两者的系统和方法。在实施例中,可以开发包括3D活动外观模型(AAM)模型的对象模型拟合系统。 3D AAM包括外观模型,该外观模型包括受3D形状模型约束的一组子组件外观模型。在实施例中,可以使用平衡的一组训练图像来生成3D AAM。对象模型拟合系统可以进一步包括一个或多个歧管约束,一个或多个加权因子或两者。尽管本发明的教导可以应用于建模/拟合其他对象,但是本发明的应用包括但不限于建模和/或拟合面部图像。
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