xsk_random_walker — 2D `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])

Usage

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。

Detailed usage guide

• gallery2d_segmentation family guide

References (sample data, literature)

• 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.

Runnable examples (verified samples that actually call this op)

• gallery2d_segmentation — py -3.11 examples/gallery2d_segmentation.py

Ops the type connects to (they accept `region` as input)

identity · reg_erode · reg_dilate · reg_open · reg_close · fill_holes · select_largest · remove_small

Same category (`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).*

xsk_random_walker — 2D segmentation op