segmentation op• Data kinds: image → region
• Call: fullseye.apply(img, "sk_slic", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
*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).*
Superpixel segmentation by SLIC (Simple Linear Iterative Clustering). Performs k-means clustering in a combined color (here, gray value) and coordinate space, dividing the image into regions of roughly equal size. Here it returns the boundaries of the segmentation result as a region.
> The detailed description below is the original text — the summary and the headings are translated.
HALCON に直接対応するものは無い。実装は `segmentation.find_boundaries(segmentation.slic(v, n_segments=int(10+80*a), channel_axis=None))` —— a はおおよそのセグメント数を 10〜90 に振る(大きいほど細かく分割される)。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.
sk_slic 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.