Parallelization strategy for elementary morphological operators on graphs: distance-based algorithms and implementation on multicore shared-memory architectureReport as inadecuate




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1 ESIEE PARIS 2 Université de Biskra 3 LIGM - Laboratoire d-Informatique Gaspard-Monge

Abstract : This article focuses on the unweighted 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, the algorithms running in Oλ.n time, where n is the size of the underlying graph and λ is 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 On 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 On-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. Then, implementations of the proposed algorithm on a shared-memory multicore architecture are described and assessed on datasets of 45 images and 6 textured 3-dimensional meshes, showing a reduction of the processing time by a factor up to 55 over the previously available implementations on a 8 core architecture.





Author: Imane Youkana - Jean Cousty - Rachida Saouli - Mohamed Akil -

Source: https://hal.archives-ouvertes.fr/



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