Image Segmentation
Basic image segmentation like connected components labelling, blob extraction and fuzzy c-means
image_connect(image, connectivity = 4) image_split(image, keep_color = TRUE) image_fuzzycmeans(image, min_pixels = 1, smoothing = 1.5)
image |
magick image object returned by |
connectivity |
number neighbor colors which are considered part of a unique object |
keep_color |
if TRUE the output images retain the color of the input pixel. If FALSE all matching pixels are set black to retain only the image mask. |
min_pixels |
the minimum number of pixels contained in a hexahedra before it can be considered valid (expressed as a percentage) |
smoothing |
the smoothing threshold which eliminates noise in the second derivative of the histogram (higher values gives smoother second derivative) |
image_connect Connect adjacent pixels with the same pixel intensities to do blob extraction
image_split Splits the image according to pixel intensities
image_fuzzycmeans Fuzzy c-means segmentation of the histogram of color components
image_connect performs blob extraction by scanning the image, pixel-by-pixel from top-left to bottom-right where regions of adjacent pixels which share the same set of intensity values get combined.
# Split an image by color
img <- image_quantize(logo, 4)
layers <- image_split(img)
layers
# This returns the original image
image_flatten(layers)
# From the IM website
objects <- image_convert(demo_image("objects.gif"), colorspace = "Gray")
objects
# Split image in blobs of connected pixel levels
if(magick_config()$version > "6.9.0"){
objects %>%
image_connect(connectivity = 4) %>%
image_split()
# Fuzzy c-means
image_fuzzycmeans(logo)
logo %>%
image_convert(colorspace = "HCL") %>%
image_fuzzycmeans(smoothing = 5)
}Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.