Efficient binary morphological algorithms on a massively parallel processor

Svolos, Andreas; Konstantopoulos, Charalampos; Kaklamanis, Christos

doi:10.1109/ipdps.2000.845997

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…In Section 4, we propose a parallel algorithm to compute the proposed distance maps, hence the morphological operators of [3]. Parallel and/or separable algorithms for morphological operators and distance maps on images have been widely studied [10,8,2,5,11,9,1,7]. Based on the regular structure of the space, such computations use a static partitioning of the image into rows, columns or blocks processed in parallel.…”

Section: Introductionmentioning

confidence: 99%

Parallelization Strategy for Elementary Morphological Operators on Graphs

Youkana

Cousty

Saouli

et al. 2016

Discrete Geometry for Computer Imagery

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International audienceThis article focuses on the graph-based mathematical morphology operators presented in [J. Cousty et al, Morphological ltering on graphs, CVIU 2013]. These operators depend on a size parameter that species 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 thresh-olding) 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

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Section: Introductionmentioning

confidence: 99%

Parallelization Strategy for Elementary Morphological Operators on Graphs

Youkana

Cousty

Saouli

et al. 2016

Discrete Geometry for Computer Imagery

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show abstract

“…The processing elements of the massively parallel processor employed in this paper are interconnected via a hypercube network. The hypercube is a generalpurpose network proven to be efficient in a large number of applications, especially in image processing (2D-FFT, Binary Morphology) [7]. It has the ability to efficiently compute the gray level pairs in an analyzed image region for any displacement vector.…”

Section: The Parallel Algorithm For the Co-occurrence Matrix Computationmentioning

confidence: 99%

A Parallel Solution in Texture Analysis Employing a Massively Parallel Processor

Svolos

Konstantopoulos

Kaklamanis

2002

Euro-Par 2002 Parallel Processing

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Texture is a fundamental feature for image analysis, classification, and segmentation. Therefore, the reduction of the time needed for its description in a real application environment is an important objective. In this paper, a texture description algorithm running over a hypercube massively parallel processor, is presented and evaluated through its application in real texture analysis. It is also shown that its hardware requirements can be tolerated by modern VLSI technology.

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“…Section 5 introduces a parallel algorithm to compute the proposed distance maps, hence the morphological operators of [9]. Parallel and/or separable algorithms for morphological operators and distance maps on images have been widely studied [31,28,5,20,32,30,4,25]. Based on the regular structure of the space, such computations use a static partitioning of the image into rows, columns or blocks processed in parallel.…”

Section: Introductionmentioning

confidence: 99%

Parallelization Strategy for Elementary Morphological Operators on Graphs: Distance-Based Algorithms and Implementation on Multicore Shared-Memory Architecture

et al. 2017

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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 O(λ.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 O(n) 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. 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. Keywords Graph-based mathematical morphology • distance maps • parallel algorithm • multicores/multithreaded architectures.

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Efficient binary morphological algorithms on a massively parallel processor

Cited by 6 publications

References 11 publications

Parallelization Strategy for Elementary Morphological Operators on Graphs

Parallelization Strategy for Elementary Morphological Operators on Graphs

A Parallel Solution in Texture Analysis Employing a Massively Parallel Processor

Parallelization Strategy for Elementary Morphological Operators on Graphs: Distance-Based Algorithms and Implementation on Multicore Shared-Memory Architecture

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