Max-tree Computation on GPUs

• Published in: IEEE transactions on parallel and distributed systems Vol. 33; no. 12; p. 1
• Main Authors: Carlinet, Edwin, Blin, Nicolas, Lemaitre, Florian, Lacassagne, Lionel, Geraud, Thierry
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-12-2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; component-trees; Computer networks; Distributed processing; Encoding; graph algorithms; Graphics processing units; hierarchical image representation; Image processing; Level set; Mathematical analysis; Mathematical morphology; max-tree; Morphology; Optimization; Pipelines; Pipelining (computers); Representations; Shape
• ISSN: 1045-9219; 1558-2183
• DOI: 10.1109/TPDS.2022.3158488

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 proved useful in numerous image processing applications. For the last decade, work has led to improving the construction time of this structure; mixing algorithmic optimizations, parallel and distributed computing. Nevertheless, there is still no algorithm that benefits 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.