Max-Tree Computation on GPUs

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Abstract

In Mathematical Morphology, the max-tree is a region-based representation that encodes the inclusion relationship of the threshold sets of an image. This tree has been proven useful in numerous image processing applications. For the last decade, works have been led to improve the building time of this structure; mixing algorithmic optimizationsparallel and distributed computing. Nevertheless, there is still no algorithm that takes benefit from the computing power of the massively parallel architectures. In this work, we propose the first GPU algorithm to compute the max-tree. The proposed approach leads to significant speed-ups, and is up to one order of magnitude faster than the current State-of-the-Art parallel CPU algorithms. This work paves the way for a max-tree integration in image processing GPU pipelines and real-time image processing based on Mathematical Morphology. It is also a foundation for porting other image representations from Mathematical Morphology on GPUs.

Documents

Bibtex (lrde.bib)

@Article{	  blin.22.tpds,
  author	= {Nicolas Blin and Edwin Carlinet and Florian Lemaitre and
		  Lionel Lacassagne and Thierry G\'eraud},
  title		= {Max-Tree Computation on {GPU}s},
  journal	= {IEEE Transactions on Parallel and Distributed Systems},
  month		= mar,
  year		= {2022},
  abstract	= {In Mathematical Morphology, the max-tree is a region-based
		  representation that encodes the inclusion relationship of
		  the threshold sets of an image. This tree has been proven
		  useful in numerous image processing applications. For the
		  last decade, works have been led to improve the building
		  time of this structure; mixing algorithmic optimizations,
		  parallel and distributed computing. Nevertheless, there is
		  still no algorithm that takes benefit from the computing
		  power of the massively parallel architectures. In this
		  work, we propose the first GPU algorithm to compute the
		  max-tree. The proposed approach leads to significant
		  speed-ups, and is up to one order of magnitude faster than
		  the current State-of-the-Art parallel CPU algorithms. This
		  work paves the way for a max-tree integration in image
		  processing GPU pipelines and real-time image processing
		  based on Mathematical Morphology. It is also a foundation
		  for porting other image representations from Mathematical
		  Morphology on GPUs.},
  doi		= {10.1109/TPDS.2022.3158488}
}