xsk_random_walker — 2D segmentation op

Data kinds: imageregion

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])

xsk_random_walker: input → output

*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):

xsk_random_walker: knob a sweep (docs site)

Sweeping knob b (0.1 / 0.5 / 0.9, the other knob at its default):

xsk_random_walker: knob b sweep (docs site)

On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):

xsk_random_walker: 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).*

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

`> 0.7-0.2aa` 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。

Blank frame (measured): feeding an image of uniform brightness gives an empty result — every pixel is background (0) — regardless of the brightness. Pure white and pure black behave alike; brightness by itself detects nothing.

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.

Try it in Studio

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

Runnable examples (verified samples that actually call this op)

gallery2d_segmentationpy -3.11 examples/gallery2d_segmentation.py

genspark_external_reviewpy -3.11 examples/genspark_external_review.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).*

© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.