Detecting Markov Random Fields Hidden in White Noise

@article{AriasCastro2015DetectingMR,
  title={Detecting Markov Random Fields Hidden in White Noise},
  author={Ery Arias-Castro and S'ebastien Bubeck and G'abor Lugosi and Nicolas Verzelen},
  journal={arXiv: Statistics Theory},
  year={2015}
}
  • Ery Arias-Castro, S'ebastien Bubeck, +1 author Nicolas Verzelen
  • Published 2015
  • Mathematics
  • arXiv: Statistics Theory
  • Motivated by change point problems in time series and the detection of textured objects in images, we consider the problem of detecting a piece of a Gaussian Markov random field hidden in white Gaussian noise. We derive minimax lower bounds and propose near-optimal tests. 

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 89 REFERENCES

    Classification of textures using Gaussian Markov random fields

    Man-made structure detection in natural images using a causal multiscale random field

    • Sanjiv Kumar, Martial Hebert
    • Computer Science
    • 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings.
    • 2003
    VIEW 2 EXCERPTS

    Near-optimal detection of geometric objects by fast multiscale methods

    VIEW 3 EXCERPTS

    Markov Random Field Texture Models