Markovian segmentation and parameter estimation on graphics hardware

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Markovian segmentation and parameter estimation on graphics hardware
In this paper, we show how Markovian strategies used to solve well-known segmentation problems such as motion estimation, motion detection, motion segmentation, stereovision, and color segmentation can be significantly accelerated when implemented on programmable graphics hardware. More precisely, we expose how the parallel abilities of a standard graphics processing unit usually devoted to image synthesis can be used to infer the labels of a segmentation map. The problems we address are stated in the sense of the maximum a posteriori with an energy-based or probabilistic formulation, depending on the application. In every case, the label field is inferred with an optimization algorithm such as iterated conditional mode (ICM) or simulated annealing. In the case of probabilistic segmentation, mixture parameters are estimated with the K-means and the iterative conditional estimation (ICE) procedure. For both the optimization and the parameter estimation algorithms, the graphics processor...
Pierre-Marc Jodoin, Max Mignotte
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2006
Where JEI
Authors Pierre-Marc Jodoin, Max Mignotte
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