pliman (version 3.1.0)

image_segment_kmeans: Image segmentation using k-means clustering

Description

Segments image objects using clustering by the k-means clustering algorithm

Usage

image_segment_kmeans(
  img,
  bands = 1:3,
  nclasses = 2,
  invert = FALSE,
  opening = FALSE,
  closing = FALSE,
  filter = FALSE,
  erode = FALSE,
  dilate = FALSE,
  fill_hull = FALSE,
  plot = TRUE
)

Value

A list with the following values:

• image The segmented image considering only two classes (foreground and background)

• clusters The class of each pixel. For example, if ncluster = 3, clusters will be a two-way matrix with values ranging from 1 to 3. masks A list with the binary matrices showing the segmentation.

Arguments

img

An Image object.

bands

A numeric integer/vector indicating the RGB band used in the segmentation. Defaults to 1:3, i.e., all the RGB bands are used.

nclasses

The number of desired classes after image segmentation.

invert

Invert the segmentation? Defaults to FALSE. If TRUE the binary matrix is inverted.

erode, dilate, opening, closing, filter

Morphological operations (brush size)

• dilate puts the mask over every background pixel, and sets it to foreground if any of the pixels covered by the mask is from the foreground.

• erode puts the mask over every foreground pixel, and sets it to background if any of the pixels covered by the mask is from the background.

• opening performs an erosion followed by a dilation. This helps to remove small objects while preserving the shape and size of larger objects.

• closing performs a dilatation followed by an erosion. This helps to fill small holes while preserving the shape and size of larger objects.

• filter performs median filtering in the binary image. Provide a positive integer > 1 to indicate the size of the median filtering. Higher values are more efficient to remove noise in the background but can dramatically impact the perimeter of objects, mainly for irregular perimeters such as leaves with serrated edges.

Hierarchically, the operations are performed as opening > closing > filter. The value declared in each argument will define the brush size.

fill_hull

Fill holes in the objects? Defaults to FALSE.

plot

Plot the segmented image?

References

Hartigan, J. A. and Wong, M. A. (1979). Algorithm AS 136: A K-means clustering algorithm. Applied Statistics, 28, 100–108. tools:::Rd_expr_doi("10.2307/2346830")

Examples

if (interactive() && requireNamespace("EBImage")) {
img <- image_pliman("la_leaves.jpg", plot = TRUE)
seg <- image_segment_kmeans(img)
seg <- image_segment_kmeans(img, fill_hull = TRUE, invert = TRUE, filter = 10)
}