Scalable data parallel algorithms for texture synthesis using Gibbs random fields

@article{Bader1995ScalableDP,
  title={Scalable data parallel algorithms for texture synthesis using Gibbs random fields},
  author={David A. Bader and Joseph J{\'a}J{\'a} and Rama Chellappa},
  journal={IEEE transactions on image processing : a publication of the IEEE Signal Processing Society},
  year={1995},
  volume={4 10},
  pages={
          1456-60
        }
}
This article introduces scalable data parallel algorithms for image processing. Focusing on Gibbs and Markov random field model representation for textures, we present parallel algorithms for texture synthesis, compression, and maximum likelihood parameter estimation, currently implemented on Thinking Machines CM-2 and CM-5. The use of fine-grained, data parallel processing techniques yields real-time algorithms for texture synthesis and compression that are substantially faster than the… CONTINUE READING
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