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)
*The figure is the real output on a synthetic 128×128 input. Left: input, right: output (a non-image return value is shown as the value itself).*
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。
• 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.
xcv_watershed_markers 0.50 0.50
▸ Load this pipeline · Load & run
• 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.