@InProceedings{6952617,
  author = {B. Galerne and A. Leclaire and L. Moisan},
  booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
  title = {A texton for fast and flexible Gaussian texture synthesis},
  year = {2014},
  pages = {1686-1690},
  abstract = {Gaussian textures can be easily simulated by convolving an image sample with a white noise. However, this procedure is not very flexible (it does not allow for non-uniform grids in particular), and becomes computationally heavy for very large domains. We here propose an algorithm that summarizes a texture sample into a synthesis-oriented texton, that is, a small image for which the discrete spot noise simulation (summed and normalized randomly-shifted copies of the texton) is more efficient than the classical convolution algorithm. Using this synthesis-oriented texture summary, Gaussian textures can be generated on-demand in a faster, simpler, and more flexible way.},
  keywords = {convolution;Gaussian processes;image texture;white noise;classical convolution algorithm;normalized randomly-shifted texton copy;discrete spot noise simulation;synthesis-oriented texton;white noise;flexible Gaussian texture synthesis;fast Gaussian texture synthesis;Computational modeling;Noise;Kernel;Approximation methods;Approximation algorithms;Convolution;Convergence;Spot noise;texton;Gaussian texture;texture synthesis;error reduction algorithm},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569925559.pdf},
}

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Moisan},\n booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},\n title = {A texton for fast and flexible Gaussian texture synthesis},\n year = {2014},\n pages = {1686-1690},\n abstract = {Gaussian textures can be easily simulated by convolving an image sample with a white noise. However, this procedure is not very flexible (it does not allow for non-uniform grids in particular), and becomes computationally heavy for very large domains. We here propose an algorithm that summarizes a texture sample into a synthesis-oriented texton, that is, a small image for which the discrete spot noise simulation (summed and normalized randomly-shifted copies of the texton) is more efficient than the classical convolution algorithm. 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