imgEKMeans: Image clustering
In matiasb/biOps: Image processing and analysis
Description
Usage
Arguments
Value
See Also
Examples
Description
This function performs an unsupervised classification through the k-means algorithm.
It is an enhanced implementation, that avoid some comparisons based on kept information
about distances and centroids of previous iterations.
Usage
1
imgEKMeans (imgdata, k, maxit=10)
Arguments
imgdata
The image
k
Number of clusters
maxit
Max number of iterations
Value
return an imagedata object, the result of the classification
See Also
imgKMeans
imgKDKMeans
imgIsoData
Examples
1
2
3
4
5
## Not run:
x <- readJpeg(system.file("samples", "violet.jpg", package="biOps"))
y <- imgEKMeans(x, 4)
## End(Not run)
matiasb/biOps documentation built on May 21, 2019, 12:55 p.m.
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Description Usage Arguments Value See Also Examples
Description
This function performs an unsupervised classification through the k-means algorithm. It is an enhanced implementation, that avoid some comparisons based on kept information about distances and centroids of previous iterations.
Usage
1 | imgEKMeans (imgdata, k, maxit=10)
|
Arguments
imgdata |
The image |
k |
Number of clusters |
maxit |
Max number of iterations |
Value
return an imagedata object, the result of the classification
See Also
imgKMeans
imgKDKMeans
imgIsoData
Examples
1 2 3 4 5 | ## Not run:
x <- readJpeg(system.file("samples", "violet.jpg", package="biOps"))
y <- imgEKMeans(x, 4)
## End(Not run)
|
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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