example.ls2w: Example of how the LS2W package can be used in texture...
In LS2W: Locally Stationary Two-Dimensional Wavelet Process Estimation Scheme
example.ls2w R Documentation
Example of how the LS2W package can be used in texture analysis
Description
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.
Usage
example.ls2w(n=25, size=64)
Arguments
n
The number of sub-images to be sampled from each texture type.
size
The number of rows-columns required for each sub-image.
Value
An object of class lda.
Author(s)
Idris Eckley
References
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/.
LS2W documentation built on Nov. 2, 2022, 1:06 a.m.
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| example.ls2w | R Documentation |
Example of how the LS2W package can be used in texture analysis
Description
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.
Usage
example.ls2w(n=25, size=64)
Arguments
n |
The number of sub-images to be sampled from each texture type. |
size |
The number of rows-columns required for each sub-image. |
Value
An object of class lda.
Author(s)
Idris Eckley
References
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/.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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