segmentation op• Data kinds: image → image
• Call: fullseye.apply(img, "xsk2_multiotsu", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
*The figure is the actual output on a synthetic 128×128 input. Left: input, right: output. Point clouds are drawn as a top-down scatter (brightness = z), 1-D series as a line plot, volumes as the maximum-intensity projection along z, videos as the middle frame, complex images as magnitude; return values that are not pictures are shown as the values themselves.*
Sweeping knob a (0.1 / 0.5 / 0.9, the other knob at its default):
▸ xsk2_multiotsu: knob a sweep (docs site)
*Knob b does not change the output (measured: identical at 0.1 / 0.5 / 0.9).*
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ xsk2_multiotsu: other inputs (docs site)
*The 4th column is a colour (H,W,3) input. This op handles colour without crossing channels (it does not convolve the colour channel as a third spatial axis).*
Quantizes tonal levels using multi-level Otsu's method, an extension of Otsu's discriminant analysis method to multiple thresholds. Uses `skimage.filters.threshold_multiotsu`.
> The detailed description below is the original text — the summary and the headings are translated.
a はクラス数を 3 または 4 に切り替える（`3 + int(a > 0.5)`）。
5 クラス以上は実装していない——多値大津はしきい値の全探索コストが
`bins ** (classes - 1)` で増えるため、実測（128x128）で 3 クラス
0.0008 秒に対し 5 クラスは 2.435 秒（3239 倍）かかり、進化ループ 1 世代
だけで実行が止まって見えるほど遅い（画像サイズにはほぼ依らない）。
4 クラスなら 0.025 秒に収まる。b は未使用。しきい値で量子化した後
`(cls-1)` で割って [0,1] に正規化する。
Value comparability (measured): the output is normalised by that image's own maximum (the output maximum is always 1.0, and scaling the input leaves the output unchanged). Values therefore cannot be compared across images — a feature of the same strength takes a different value depending on what the strongest feature in that image happens to be, and in an image holding only weak features the noise is lifted to 1.0. To compare across images, rescale by a common reference.
• gallery2d_segmentation family guide
• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).
• Operator provenance and references — the sources of the research/methods this op family came from.
• The canonical algorithm (author, year) and its uses are named in the family usage guide above.
The program below has been verified to run (same input as the figure). In Studio's help this block becomes buttons that load and run it on the spot.
xsk2_multiotsu 0.50 0.50
▸ Load this pipeline · Load & run
• gallery2d_segmentation — py -3.11 examples/gallery2d_segmentation.py
• poc_mri_bias_field — py -3.11 examples/poc_mri_bias_field.py
image as input)identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter
segmentation)threshold · otsu · canny · adaptive_gauss_thresh · sk_otsu · sk_li · sk_yen · sk_sauvola
*Provenance: ops.py — 2D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.