首页> 外文会议>European Conference on Computer Vision(ECCV 2004) pt.4; 20040511-20040514; Prague; CZ >Unifying Approaches and Removing Unrealistic Assumptions in Shape from Shading: Mathematics Can Help
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Unifying Approaches and Removing Unrealistic Assumptions in Shape from Shading: Mathematics Can Help

机译:统一方法并从阴影中消除形状中不切实际的假设:数学可以提供帮助

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This article proposes a solution of the Lambertian Shape Prom Shading (SFS) problem by designing a new mathematical framework based on the notion of viscosity solutions. The power of our approach is twofolds: 1) it defines a notion of weak solutions (in the viscosity sense) which does not necessarily require boundary data. Note that, in the previous SFS work of Rouy et al., Falcone et al., Prados et al., the characterization of a viscosity solution and its computation require the knowledge of its values on the boundary of the image. This was quite unrealistic because in practice such values are not known. 2) it unifies the work of Rouy et al., Falcone et al. , Prados et al., based on the notion of viscosity solutions and the work of Dupuis and Oliensis dealing with classical (C~1) solutions. Also, we generalize their work to the "perspective SFS" problem recently introduced by Prados and Faugeras. Moreover this article introduces a "generic" formulation of the SFS problem. This "generic" formulation summarizes various (classical) formulations of the Lambertian SFS problem. In particular it unifies the orthographic and the perspective SFS problems. This "generic" formulation significantly simplifies the formalism of the problem. Thanks to this generic formulation, a single algorithm can be used to compute numerical solutions of all these previous SFS formulations. Finally we propose two algorithms which provide numerical approxima-tions of the new weak solutions of the "generic SFS" problem. These provably convergent algorithms are quite robust and do not necessarily require boundary data.
机译:本文通过基于粘度解决方案的概念设计新的数学框架,提出了Lambertian形状舞会着色(SFS)问题的解决方案。我们方法的功效有两个方面:1)它定义了弱解的概念(在粘度意义上),它不一定需要边界数据。注意,在Rouy等人,Falcone等人,Prados等人的先前SFS工作中,粘度溶液的表征及其计算需要了解其在图像边界上的值。这是非常不现实的,因为在实践中这种值是未知的。 2)它统一了Rouy等人,Falcone等人的工作。 ,Prados等人,基于粘性溶液的概念以及Dupuis和Oliensis处理经典(C〜1)溶液的工作。另外,我们将他们的工作概括为最近由Prados和Faugeras提出的“透视SFS”问题。此外,本文介绍了SFS问题的“通用”表述。这种“通用”表述总结了Lambertian SFS问题的各种(经典)表述。特别是,它统一了正交和透视SFS问题。这种“通用的”表述大大简化了问题的形式化。由于采用了这种通用公式,因此可以使用单个算法来计算所有这些以前的SFS公式的数值解。最后,我们提出了两种算法,它们提供了“通用SFS”问题的新弱解的数值近似。这些可证明的收敛算法非常健壮,不一定需要边界数据。

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