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How to Make nD Functions Digitally Well-Composed in a Self-dual Way

机译:如何以自我对偶的方式使数字功能组成良好

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Latecki et al. introduced the notion of 2D and 3D well-composed images, i.e., a class of images free from the "connectivities paradox" of digital topology. Unfortunately natural and synthetic images are not a priori well-composed. In this paper we extend the notion of "digital well-composedness" to nD sets, integer-valued functions (gray-level images), and interval-valued maps. We also prove that the digital well-composedness implies the equivalence of connectivities of the level set components in nD. Contrasting with a previous result stating that it is not possible to obtain a discrete nD self-dual digitally well-composed function with a local interpolation, we then propose and prove a self-dual discrete (non-local) interpolation method whose result is always a digitally well-composed function. This method is based on a sub-part of a quasi-linear algorithm that computes the morphological tree of shapes.
机译:Latecki等。引入了2D和3D精心组合的图像的概念,即一类不受数字拓扑的“连接性悖论”影响的图像。不幸的是,自然图像和合成图像的合成并不是先验的。在本文中,我们将“数字良好组合”的概念扩展到nD集,整数值函数(灰度级图像)和区间值映射。我们还证明,数字结构良好意味着nD中水平集组件的连通性相等。与先前的结果表明不可能通过局部插值获得离散的nD自对偶数字良好组合函数,然后我们提出并证明了一种自对偶的离散(非局部)插值方法,其结果始终是数字组成的功能。此方法基于计算形状形态树的准线性算法的子部分。

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