Unsupervised Markovian Segmentation on Graphics Hardware

Pierre-Marc Jodoin, Jean-Francois St-Amour, Max Mignotte
Universite de Montreal, Departement d’Informatique et de Recherche Operationnelle (DIRO), P.O. Box 6128, Studio Centre-Ville, Montreal, Quebec, H3C 3J7
Pattern Recognition and Image Analysis (2005), pp. 444-454


   title={Unsupervised markovian segmentation on graphics hardware},

   author={Jodoin, P.M. and St-Amour, J.F. and Mignotte, M.},

   journal={Pattern Recognition and Image Analysis},





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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.
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