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Parallelization Strategy for Elementary Morphological Operators on Graphs

机译:图上基本形态学算子的并行化策略

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This article focuses on the graph-based mathematical morphology operators presented in [J. Cousty et al., "Morphological filtering on graphs", CVIU 2013]. These operators depend on a size parameter that specifies the number of iterations of elementary dilations/erosions. Thus, the associated running times increase with the size parameter. In this article, we present distance maps that allow us to recover (by thresholding) all considered dilations and erosions. The algorithms based on distance maps allow the operators to be computed with a single linear-time iteration, without any dependence to the size parameter. Then, we investigate a parallelization strategy to compute these distance maps. The idea is to build iteratively the successive level-sets of the distance maps, each level set being traversed in parallel. Under some reasonable assumptions about the graph and sets to be dilated, our parallel algorithm runs in O(n/p + K log_2 p) where n, p, and K are the size of the graph, the number of available processors, and the number of distinct level-sets of the distance map, respectively.
机译:本文重点介绍[J. Cousty等人,“图形形态过滤”,CVIU 2013]。这些运算符取决于指定基本扩张/腐蚀迭代次数的size参数。因此,关联的运行时间随size参数而增加。在本文中,我们介绍了距离图,使我们能够(通过阈值化)恢复所有考虑的膨胀和腐蚀。基于距离图的算法允许通过单个线性时间迭代来计算算子,而无需依赖于大小参数。然后,我们研究一种并行化策略来计算这些距离图。这个想法是迭代地建立距离图的连续的水平集,每个水平集被并行遍历。在关于图和要扩张的集合的一些合理假设下,我们的并行算法在O(n / p + K log_2 p)中运行,其中n,p和K是图的大小,可用处理器的数量以及距离图的不同级别集的数量。

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