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| Hauptseite > Publikationsdatenbank > Parallel Computation of Component Trees on Distributed Memory Machines > print |
| Hauptseite > Publikationsdatenbank > Parallel Computation of Component Trees on Distributed Memory Machines > print |
| 001 | 845250 | ||
| 005 | 20220930130146.0 | ||
| 024 | 7 | _ | |a 10.1109/TPDS.2018.2829724 |2 doi |
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| 024 | 7 | _ | |a 2128/19786 |2 Handle |
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| 037 | _ | _ | |a FZJ-2018-02533 |
| 082 | _ | _ | |a 620 |
| 100 | 1 | _ | |a Götz, Markus |0 P:(DE-Juel1)162390 |b 0 |e Corresponding author |u fzj |
| 245 | _ | _ | |a Parallel Computation of Component Trees on Distributed Memory Machines |
| 260 | _ | _ | |a New York, NY |c 2018 |b IEEE |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a 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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| 773 | _ | _ | |a 10.1109/TPDS.2018.2829724 |g p. 1 - 1 |0 PERI:(DE-600)2027774-X |n 11 |p 2582 - |t IEEE transactions on parallel and distributed systems |v 29 |y 2018 |x 1045-9219 |
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