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Bayesian texture segmentation of weed and crop images using reversible jump Markov chain Monte Carlo methods

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  • Ian L. Dryden
  • Mark R. Scarr
  • Charles C. Taylor

Abstract

A Bayesian method for segmenting weed and crop textures is described and implemented. The work forms part of a project to identify weeds and crops in images so that selective crop spraying can be carried out. An image is subdivided into blocks and each block is modelled as a single texture. The number of different textures in the image is assumed unknown. A hierarchical Bayesian procedure is used where the texture labels have a Potts model (colour Ising Markov random field) prior and the pixels within a block are distributed according to a Gaussian Markov random field, with the parameters dependent on the type of texture. We simulate from the posterior distribution by using a reversible jump Metropolis-Hastings algorithm, where the number of different texture components is allowed to vary. The methodology is applied to a simulated image and then we carry out texture segmentation on the weed and crop images that motivated the work. Copyright 2003 Royal Statistical Society.

Suggested Citation

  • Ian L. Dryden & Mark R. Scarr & Charles C. Taylor, 2003. "Bayesian texture segmentation of weed and crop images using reversible jump Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(1), pages 31-50.
  • Handle: RePEc:bla:jorssc:v:52:y:2003:i:1:p:31-50
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    Cited by:

    1. N. Friel & A. N. Pettitt, 2008. "Marginal likelihood estimation via power posteriors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(3), pages 589-607.
    2. Van Lieshout, M.N.M. & Stoica, R.S., 2010. "A note on pooling of labels in random fields," Statistics & Probability Letters, Elsevier, vol. 80(17-18), pages 1431-1436, September.
    3. Thon, Kevin & Rue, Håvard & Skrøvseth, Stein Olav & Godtliebsen, Fred, 2012. "Bayesian multiscale analysis of images modeled as Gaussian Markov random fields," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 49-61, January.

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