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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Unsupervised Markovian Segmentation on Graphics Hardware
verfasst von : Pierre-Marc Jodoin, Jean-François St-Amour, Max Mignotte
Erschienen in: Pattern Recognition and Image Analysis
Verlag: Springer Berlin Heidelberg
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This contribution shows how unsupervised Markovian segmentation techniques can be accelerated when implemented on graphics hardware equipped with a Graphics Processing Unit (GPU). Our strategy exploits the intrinsic properties of local interactions between sites of a Markov Random Field model with the parallel computation ability of a GPU. This paper explains how classical iterative site-wise-update algorithms commonly used to optimize global Markovian cost functions can be efficiently implemented in parallel by
fragment shaders
driven by a
fragment processor
. This parallel programming strategy significantly accelerates optimization algorithms such as ICM and simulated annealing. Good acceleration are also achieved for parameter estimation procedures such as
K
-means and ICE. The experiments reported in this paper have been obtained with a mid-end, affordable graphics card available on the market.