Connected Morphological Attribute Filters on Distributed Memory Parallel Machines

  • Jan J. Kazemier
  • Georgios K. Ouzounis
  • Michael H. F. Wilkinson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10225)


We present a new algorithm for attribute filtering of extremely large images, using a forest of modified max-trees, suitable for distributed memory parallel machines. First, max-trees of tiles of the image are computed, after which messages are exchanged to modify the topology of the trees and update attribute data, such that filtering the modified trees on each tile gives exactly the same results as filtering a regular max-tree of the entire image. On a cluster, a speed-up of up to 53\(\times \) is obtained on 64, and up to 100\(\times \) on 128 single CPU nodes. On a shared memory machine a peak speed-up of 50\(\times \) on 64 cores was obtained.


Shared Memory Attribute Filter Merging Algorithm Communication Load Morphological Profile 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We would like to thank the Center for Information Technology of the University of Groningen for their support and for providing access to the Peregrine high performance computing cluster. The 64 core Opteron machine was obtained by funding for the HyperGAMMA project from the Netherlands Organisation for Scientific Research (NWO) under project number 612.001.110.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jan J. Kazemier
    • 1
  • Georgios K. Ouzounis
    • 2
  • Michael H. F. Wilkinson
    • 1
  1. 1.Johann Bernoulli InstituteUniversity of GroningenGroningenThe Netherlands
  2. 2.DigitalGlobe, Inc.WestminsterUSA

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