% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Gtz:845250,
      author       = {Götz, Markus and Cavallaro, Gabriele and Geraud, Thierry
                      and Book, Matthias and Riedel, Morris},
      title        = {{P}arallel {C}omputation of {C}omponent {T}rees on
                      {D}istributed {M}emory {M}achines},
      journal      = {IEEE transactions on parallel and distributed systems},
      volume       = {29},
      number       = {11},
      issn         = {1045-9219},
      address      = {New York, NY},
      publisher    = {IEEE},
      reportid     = {FZJ-2018-02533},
      pages        = {2582 -},
      year         = {2018},
      abstract     = {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.},
      cin          = {JSC},
      ddc          = {620},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {512 - Data-Intensive Science and Federated Computing
                      (POF3-512) / PhD no Grant - Doktorand ohne besondere
                      Förderung (PHD-NO-GRANT-20170405)},
      pid          = {G:(DE-HGF)POF3-512 / G:(DE-Juel1)PHD-NO-GRANT-20170405},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000447046200014},
      doi          = {10.1109/TPDS.2018.2829724},
      url          = {https://juser.fz-juelich.de/record/845250},
}