A parallel approach to the picture restoration algorithm of Geman and Geman on an SIMD machine
Image and Vision Computing
The theory and practice of Bayesian image labeling
International Journal of Computer Vision
Performance of optical flow techniques
International Journal of Computer Vision
Neural networks for pattern recognition
Neural networks for pattern recognition
Real-Time Rendering
Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware
The Cg Tutorial: The Definitive Guide to Programmable Real-Time Graphics
The Cg Tutorial: The Definitive Guide to Programmable Real-Time Graphics
Linear algebra operators for GPU implementation of numerical algorithms
ACM SIGGRAPH 2003 Papers
OpenGL(R) Shading Language
Real-time DSP implementation for MRF-based video motion detection
IEEE Transactions on Image Processing
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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.