Abstract
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.
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Acknowledgment
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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Kazemier, J.J., Ouzounis, G.K., Wilkinson, M.H.F. (2017). Connected Morphological Attribute Filters on Distributed Memory Parallel Machines. In: Angulo, J., Velasco-Forero, S., Meyer, F. (eds) Mathematical Morphology and Its Applications to Signal and Image Processing. ISMM 2017. Lecture Notes in Computer Science(), vol 10225. Springer, Cham. https://doi.org/10.1007/978-3-319-57240-6_29
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DOI: https://doi.org/10.1007/978-3-319-57240-6_29
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