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Interior and Exterior Shape Representations Using the Screened Poisson Equation

机译:使用筛选的泊松方程式的内部和外部形状表示

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Shape classification is a required task in many systems for image and video understanding. Implicit shape representations, such as the solutions to the Eikonal or Poisson equations defined on the shape, have been shown to be particularly effective for generating features that are useful for classification. The Poisson-based shape representation can be derived at each point inside the shape as the expected time for a particle undergoing Brownian motion to hit the shape boundary. This representation has no natural generalization when considering points outside of a shape, however, because the corresponding Brownian motion would have infinite expected hitting time. In this article, we modify the Brownian motion model by introducing an exponential lifetime for the particle, yielding a random variable whose expected value satisfies a screened Poisson equation that can be solved at points both interior and exterior to the shape. We then show how moments of this new random variable can be used to improve classification results on experiments with natural silhouettes and handwritten numerals.
机译:形状分类是许多用于图像和视频理解的系统中所需的任务。已经显示出在形状上定义的eikonal或泊松方程的解剖形式表示,用于产生可用于分类的特征特别有效。基于泊松的形状表示可以在形状内的每个点衍生,作为粒子接受棕色运动以击中形状边界的预期时间。然而,当考虑形状之外的点时,该表示没有自然的概括,因为相应的布朗运动将具有无限的预期打击时间。在本文中,我们通过向粒子引入指数寿命来修改布朗运动模型,产生一个随机变量,其预期值满足屏蔽泊松方程,该方程可以以点内部和外部求解。然后,我们展示了这种新的随机变量的瞬间可用于改善具有自然剪影和手写数字的实验的分类结果。

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