example.ls2w | R Documentation |

This function provides an example of how LS2W can be used to discriminate between three different textures. The approach provided simply consists of (i) sampling a number of subimages from the specified data sets; (ii) estimating the local wavelet spectrum properties for each sub-image; (iii) summarising this information in a feature vector; (iv) using all feature vectors to identify whether it is possible to discriminate between the different image types. Linear Discriminant Analysis is the approach which we adopt in this example.

example.ls2w(n=25, size=64)

`n` |
The number of sub-images to be sampled from each texture type. |

`size` |
The number of rows-columns required for each sub-image. |

An object of class `lda`

.

Idris Eckley

Eckley, I.A., Nason, G.P. and Treloar, R.L. (2010) Locally stationary wavelet fields with application to the modelling and analysis of image texture. Journal of the Royal Statistical Society (Series C), 59, 595 - 616.

Eckley, I.A. and Nason, G.P. (2011). LS2W: Implementing the Locally Stationary 2D Wavelet Process Approach in R, Journal of Statistical Software, 43(3), 1-23. URL http://www.jstatsoft.org/v43/i03/.

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