multi_otsu

Builtin index · Images

result = multi_otsu(x, n_classes)

Input: x, n_classes · Output: result

What it is

x is an Image, Vec, or Signal; n_classes is a positive integer, the desired number of intensity classes. The n_classes - 1-threshold generalization of otsu_threshold.

Overloads

One call shape — this function is not overloaded.

Code	Input	Output	Description
`result = multi_otsu(x, n_classes)`	`x` Image, Vec, or Signal`x` is an `Image`, `Vec`, or `Signal`. `n_classes` number`n_classes` is a positive integer, the desired number of intensity classes.	`result` Vec	Returns a `Vec` of `n_classes - 1` cut points in ascending order. Compose with `multithreshold(x, multi_otsu(x, n))` to segment into `n` classes.

Examples

From the documentation, and run by the build -- the output and the figure below are what the engine actually produced. Simplest first.

m = [10, 20, 30, 220; 15, 25, 200, 230; 120, 130, 210, 225; 125, 135, 205, 215]
img = image_from_matrix(m)

seg = otsu(img)                             # compute Otsu's level and apply it

imshow(seg)

cuts = multi_otsu(img, 3)                   # two cut points -> three bands

banded = multithreshold(img, cuts)
imshow(banded)

k = kapur_threshold(img)                    # entropy-based single threshold

fg = foreground_mask(img, k, radius=1)      # threshold + speck cleanup

print(cuts, k, seg.width, banded.width, fg.width)

output

[30.625, 135.46875] 130.3125 4 4 4

from images

How it works

multi_otsu(x, n_classes) — the n_classes-1-threshold generalization of otsu_threshold, returned as a Vec in ascending order (mirrors otsu_threshold's "compute, don't apply" contract; compose with multithreshold(x, multi_otsu(x, n)) to actually segment).

From the note above this builtin in the interpreter, where the reasoning was written down as it was built.