segmentation op• Data kinds: image → region
• Call: fullseye.apply(img, "var_threshold", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
• HALCON equivalent: var_threshold (the HALCON reference is a useful guide to its meaning and parameters)
Sauvola's local adaptive binarization (`skimage.filters.threshold_sauvola, window size 2*int(a*6)+3). Since the threshold is determined using both the local mean and local standard deviation, it is more robust to uneven illumination and low-contrast documents than dyn_threshold. An approximation corresponding to HALCON's var_threshold` (Threshold an image by local mean and standard deviation analysis) -- the formulation follows a similar idea, but Sauvola's formula and HALCON's formula have different coefficients, so the numerical results are not identical.
> The detailed description below is the original text — the summary and the headings are translated.
`a が局所窓のサイズを振る。b` は未使用(Sauvola の k, r は skimage の
既定値に固定)。
• 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.
• 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.