Parallel Attribute Computation for Distributed Component Forests

Component trees are powerful image processing tools to analyze the connected components of an image. One attractive strategy consists in building the nested relations at first and then deriving the components' attributes afterward, such that the user can switch between different attribute funct...

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Veröffentlicht in:Proceedings - International Conference on Image Processing S. 601 - 605
Hauptverfasser: Gazagnes, Simon, Wilkinson, Michael H.F.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 16.10.2022
Schlagworte:
Buildings
Component trees
Connected filters
Data visualization
Filtering
Forestry
Image processing
Image representation
Mathematical morphology
Parallel computing
Switches
Three-dimensional displays
ISSN:2381-8549
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Zusammenfassung:Component trees are powerful image processing tools to analyze the connected components of an image. One attractive strategy consists in building the nested relations at first and then deriving the components' attributes afterward, such that the user can switch between different attribute functions without having to re-compute the entire tree. Only sequential algorithms allow such an approach, while no parallel algorithm is available. In this paper, we extend a recent method using distributed memory techniques to enable posterior attribute computation in a parallel or distributed manner. This novel approach significantly reduces the computational time needed for combining several attribute functions interactively in Giga and Tera-Scale data sets.
ISSN:2381-8549
DOI:10.1109/ICIP46576.2022.9897660