sg_normalized_cut_2 — 2D `segment` op

• Data kinds: image → region

• Call: fullseye.apply(img, "sg_normalized_cut_2", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])

Usage

Splits the image into two regions (bright / dark) using normalized cut on the intensity graph (spectral 2-way partitioning, Shi & Malik; no corresponding HALCON operator).

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

計算量を抑えるため画像を `sdim = round(10+b*14) 程度まで間引いてから、輝度差と距離で重みを決めたアフィニティグラフを作り、一般化固有値問題 (D-W)y = lambda*D*y の第 2 固有ベクトル（Fiedler ベクトル）を中央値でしきい値化して 2 群に分ける。a（0〜1）は輝度方向の帯域幅sig_i = 0.05 + a*0.5 を振り、b`（0〜1）は間引きの解像度を振る。明るい方の群を最近傍で元解像度に拡大して region として返す。

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_slic_superpixels · sg_felzenszwalb · sg_gmm_segment · sg_kmeans_intensity · sg_region_growing_seeded · 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_normalized_cut_2 — 2D segment op