segmentation op• Data kinds: image → region
• Call: fullseye.apply(img, "sk_canny", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
• HALCON equivalent: edges_image (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).*
Canny edge detection (region version). Finds edge pixels through the classic multi-stage pipeline -- Gaussian smoothing -> gradient computation -> non-maximum suppression -> hysteresis thresholding -- and returns them as a boolean region.
> The detailed description below is the original text — the summary and the headings are translated.
HALCON の edges_image に相当。実装は `feature.canny(v, sigma=0.5+2.0*a)` —— a は前段のガウス平滑化の σ を 0.5〜2.5 に振る(大きいほど細かいノイズ由来のエッジが消え、太い輪郭だけ残る)。b は未使用 —— ヒステリシスの低/高しきい値は skimage が画像から自動推定した値のまま(明示指定していない)。
• 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.
sk_canny 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.