segmentation op• Data kinds: image → region
• Call: fullseye.apply(img, "xsk_random_walker", 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 real output on a synthetic 128×128 input. Left: input, right: output (a non-image return value is shown as the value itself).*
Graph-based region segmentation using the random walker method.
> The detailed description below is the original text — the summary and the headings are translated.
skimage.segmentation.random_walker で、明暗の閾値から自動生成した
2 クラスのシード(marker)を出発点に、各画素がどちらのシードへ拡散
伝播しやすいかを解いて 2 値ラベルに分割し、そのラベル境界を返す
(返り値は領域そのものではなく、領域の境界線 region)。
`a がシードの閾値幅を振る(暗側シード < 0.3+0.2a`、明側シード
`> 0.7-0.2a。a` が大きいほどシードが広がり不定領域が減る)。
`b は拡散のしやすさを決める beta`(10〜210)を振り、大きいほど
エッジをまたいだ伝播が抑えられ境界がシャープになる。
• 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.
xsk_random_walker 0.50 0.50
▸ Load this pipeline · Load & run
• gallery2d_segmentation — py -3.11 examples/gallery2d_segmentation.py
region as input)identity · reg_erode · reg_dilate · reg_open · reg_close · fill_holes · select_largest · remove_small
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.