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 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).*
Graph-based region segmentation using the random walker method.
Using skimage.segmentation.random_walker, starting from 2-class seeds (marker) generated automatically from light/dark thresholds, it solves for which seed each pixel more easily diffuses and propagates to, splits the image into 2-valued labels, and returns the label boundary (the return value is not the regions themselves but a region of the boundary lines between them).
a sweeps the seed threshold width (dark seeds < 0.3+0.2a, bright seeds > 0.7-0.2a; the larger a is, the wider the seeds spread and the smaller the undecided area). b sweeps beta (10–210), which sets how easily diffusion occurs; the larger it is, the more propagation across edges is suppressed and the sharper the boundary becomes.
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.
• 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
• genspark_external_review — py -3.11 examples/genspark_external_review.py
• poc_segmentation_gauntlet — py -3.11 examples/poc_segmentation_gauntlet.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.