segment op• Data kinds: image → region
• Call: fullseye.apply(img, "sg_gmm_segment", 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).*
Fits a 2-class Gaussian mixture model (EM algorithm) to the intensity values and returns the brighter cluster as the region (a self-contained EM implementation independent of `skimage`; no corresponding HALCON operator).
> The detailed description below is the original text — the summary and the headings are translated.
25/75 パーセンタイルで初期化した決定論的 EM で 1 次元 2 成分ガウス混合をフィットし、平均が高い方を「明るいクラス」とする。`a（0〜1）は事後確率のしきい値 t = 0.25 + 0.5*a を振り、大きいほど「確実に明るい」と判定された画素だけを拾うようになる。b` は未使用。乱数を使わないので再現性がある。
• 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.
sg_gmm_segment 0.50 0.50
▸ Load this pipeline · Load & run
• gallery2d_segmentation — py -3.11 examples/gallery2d_segmentation.py
• poc_nuclei_ploidy — py -3.11 examples/poc_nuclei_ploidy.py
region as input)identity · reg_erode · reg_dilate · reg_open · reg_close · fill_holes · select_largest · remove_small
segment)sg_slic_superpixels · sg_felzenszwalb · 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).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.