imgIsoData: Image clustering

In matiasb/biOps: Image processing and analysis

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

imgIsoData (imgdata, k, min_dist=1, min_elems=1, split_sd=0.1, iter_start=5, max_merge=2, max_iter=10)

Arguments

imgdata	The image
k	Number of clusters
min_dist	Minimum distance between cluster centroids
min_elems	Minimum elements per cluster
split_sd	Standard deviation threshold for splitting operation
iter_start	Maximum number of forgy iterations
max_merge	Maximum of merge operations per iteration
max_iter	Maximum number of iterations

Value

return an imagedata object, the result of the classification

See Also

imgKMeans imgEKMeans imgKDKMeans

Examples

	## Not run: 
		x <- readJpeg(system.file("samples", "violet.jpg", package="biOps"))
		y <- imgIsoData(x, 4)
	
## End(Not run)

matiasb/biOps documentation built on May 21, 2019, 12:55 p.m.