xcv_watershed_markers — 2D `segmentation` op

• Data kinds: image → region

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

• HALCON equivalent: watersheds (the HALCON reference is a useful guide to its meaning and parameters)

Usage

Boundary extraction via marker-controlled watershed.

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

HALCON の `watersheds`(ウォーターシェッドと分水嶺盆地を抽出する)に

相当するが、ここでは盆地のラベルではなく 分水嶺の境界線 だけを

region として返す(近似)。

手順は古典的な OpenCV レシピ: Otsu 二値化 -> 膨張で「確実な背景」推定

-> 距離変換のしきい値で「確実な前景」推定 -> 両者の差分を「不明」領域

とし、確実な前景の連結成分をマーカーに `cv2.watershed` を実行。

`a` が確実な前景を決めるしきい値を距離変換最大値の 30%〜70% の範囲

で振る(大きいほど前景マーカーが小さく・保守的になり、過分割/過統合

の傾向が変わる)。`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).*

xcv_watershed_markers — 2D segmentation op