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Dryden, Ian L.; Scarr, Mark R.; Taylor, Charles C.

Bayesian Texture Segmentation of Weed and Crop Images Using Reversible Jump Markov Chain Monte Carlo Methods

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  • Media type: E-Article
  • Title: Bayesian Texture Segmentation of Weed and Crop Images Using Reversible Jump Markov Chain Monte Carlo Methods
  • Contributor: Dryden, Ian L.; Scarr, Mark R.; Taylor, Charles C.
  • Published: Oxford University Press (OUP), 2003
  • Published in: Journal of the Royal Statistical Society Series C: Applied Statistics, 52 (2003) 1, Seite 31-50
  • Language: English
  • DOI: 10.1111/1467-9876.00387
  • ISSN: 1467-9876; 0035-9254
  • Origination:
  • Footnote:
  • Description: SummaryA 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.