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Concurrent computation of topological watershed on shared memory parallel machines

机译:共享内存并行机上拓扑分水岭的并行计算

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The watershed transform is considered as the most appropriate method for image segmentation in the field of mathematical morphology. In the following paper, we present an adapted topological watershed algorithm suited for a rapid and effective implementation on Shared Memory Parallel Machine (SMPM). The introduced algorithm allows a parallel watershed computing while preserving the given topology. No prior minima extraction is needed, nor the use of any sorting step or hierarchical queue. The strategy that guides the parallel watershed computing, labeled SDM-Strategy (equivalent to Split-Distributes and Merge), is also presented. Experimental analyses such as execution time, performance enhancement, cache consumption, efficiency and scalability are also presented and discussed. (C) 2017 Elsevier B.V. All rights reserved.
机译:分水岭变换被认为是数学形态学领域中最合适的图像分割方法。在下面的文章中,我们提出了一种适合在共享内存并行机(SMPM)上快速有效实现的适应性拓扑分水岭算法。引入的算法允许并行分水岭计算,同时保留给定的拓扑。无需事先提取最小值,也无需使用任何排序步骤或分层队列。还介绍了指导并行分水岭计算的策略,称为SDM-Strategy(等效于Split-Distributes和Merge)。还介绍并讨论了实验分析,例如执行时间,性能增强,缓存消耗,效率和可伸缩性。 (C)2017 Elsevier B.V.保留所有权利。

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