sk_slic — 2D `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])

Usage

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。

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).*

sk_slic — 2D segmentation op