threshold_multiotsu(image, classes=3, nbins=256)
The threshold values are chosen to maximize the total sum of pairwise variances between the thresholded graylevel classes. See Notes and for more details.
This implementation relies on a Cython function whose complexity is $O\left(\frac{Ch^{C-1}}{(C-1)!}\right)$ , where $h$ is the number of histogram bins and $C$ is the number of classes desired.
The input image must be grayscale.
Grayscale input image.
Number of classes to be thresholded, i.e. the number of resulting regions.
Number of bins used to calculate the histogram. This value is ignored for integer arrays.
If image
contains less grayscale value then the desired number of classes.
Array containing the threshold values for the desired classes.
Generate :None:None:`classes`-1 threshold values to divide gray levels in :None:None:`image`.
>>> from skimage.color import label2rgbSee :
... from skimage import data
... image = data.camera()
... thresholds = threshold_multiotsu(image)
... regions = np.digitize(image, bins=thresholds)
... regions_colorized = label2rgb(regions)
The following pages refer to to this document either explicitly or contain code examples using this.
skimage.filters.thresholding.threshold_multiotsu
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