xsk3_threshold_local_median — 2D `segmentation` op

• Data kinds: image → region

• Call: fullseye.apply(img, "xsk3_threshold_local_median", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])

Usage

Local median adaptive binarization (skimage `filters.threshold_local with method='median'`). Uses the median of each block as the local threshold and binarizes by whether the pixel value exceeds it. More robust to outliers (noise pixels) than the version using local mean.

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

`a はブロックサイズ(2*int(a*6)+3 で 3〜15 の奇数)を振る。b` は未使用。

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).*

xsk3_threshold_local_median — 2D segmentation op