sk_sauvola — 2D `segmentation` op

• Data kinds: image → region

• Call: fullseye.apply(img, "sk_sauvola", 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)

Usage

Sauvola's local adaptive thresholding. Determines the threshold locally from the local mean and standard deviation around each pixel -- designed for binarizing (text extraction from) document images with uneven illumination.

> The detailed description below is the original text — the summary and the headings are translated.

HALCON の var_threshold(Threshold an image by local mean and standard deviation analysis.)に相当。実装は `v > filters.threshold_sauvola(v, window_size=2*int(a*6)+3)` —— a は局所窓のサイズを 3〜15(奇数)に振る(小さいほど照明ムラに強いが計算が細かくノイズにも敏感)。b は未使用。k, r パラメータは skimage の既定値のまま。

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_niblack

*Provenance: ops.py — 2D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

sk_sauvola — 2D segmentation op