Parallel Computation of Component Trees on Distributed Memory Machines

Component trees are region-based representations that encode the inclusion relationship of the threshold sets of an image. These representations are one of the most promising strategies for the analysis and the interpretation of spatial information of complex scenes as they allow the simple and effi...

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Vydáno v:IEEE transactions on parallel and distributed systems Ročník 29; číslo 11; s. 2582 - 2598
Hlavní autoři: Gotz, Markus, Cavallaro, Gabriele, Geraud, Thierry, Book, Matthias, Riedel, Morris
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.11.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Témata:
Algorithms
Component-trees
Computer memory
Computer Science
connected component labeling
Distributed memory
Distributed, Parallel, and Cluster Computing
Electronic mail
Flooding
Gray-scale
high-performance computing
hybrid application
Image acquisition
Image Processing
Image resolution
max-tree
Merging
Morphology
MPI
multithreading
Parallel processing
Remote sensing
Representations
Shape
Spatial data
threshold decomposition
Trees
Connected Component Labeling
Component-Trees
Multithreading
Threshold Decomposition
MPI
High-Performance Computing
Max-Tree
Hybrid Application
ISSN:1045-9219, 1558-2183
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Shrnutí:Component trees are region-based representations that encode the inclusion relationship of the threshold sets of an image. These representations are one of the most promising strategies for the analysis and the interpretation of spatial information of complex scenes as they allow the simple and efficient implementation of connected filters. This work proposes a new efficient hybrid algorithm for the parallel computation of two particular component trees-the max- and min-tree-in shared and distributed memory environments. For the node-local computation a modified version of the flooding-based algorithm of Salembier is employed. A novel tuple-based merging scheme allows to merge the acquired partial images into a globally correct view. Using the proposed approach a speed-up of up to 44.88 using 128 processing cores on eight-bit gray-scale images could be achieved. This is more than a five-fold increase over the state-of-the-art shared-memory algorithm, while also requiring only one-thirty-second of the memory.
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ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2018.2829724