sg_slic_superpixels — 2D `segment` op

• Data kinds: image → region

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

Usage

Returns the boundary grid of a SLIC superpixel over-segmentation (`skimage.segmentation.slic`; no corresponding HALCON operator).

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

`a（0〜1）がセグメント数とコンパクトネスの両方を振る: 大きいほどセグメント数が少なく（n_seg = max(2, round(6+(1-a)*40))）、コンパクトネスが高くなる（compactness = 0.01 + a*0.2、形が四角く整う）。b` は未使用。戻り値はスーパーピクセルの境界線（外側の輪郭）をなぞった region で、境界はコンパクトネスが低いほど輝度エッジにスナップしやすい。

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 (`segment`)

sg_felzenszwalb · sg_gmm_segment · sg_kmeans_intensity · sg_region_growing_seeded · sg_normalized_cut_2 · sg_watershed_gradient

*Provenance: ops.py — 2D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

sg_slic_superpixels — 2D segment op