cooc_feature_matrix — 2D `texture` op

• Data kinds: image → feature

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

• HALCON equivalent: cooc_feature_matrix (the HALCON reference is a useful guide to its meaning and parameters)

Usage

> This operator's description has not been translated yet. The original text follows as it is.

グレーレベル共起行列(GLCM、`skimage.feature.graycomatrix`、16 階調

に量子化、距離 `1+3*a`、角度 0°)から Haralick テクスチャ特徴量

`energy`(角二次モーメント、行列の値の集中度=テクスチャの均一性)を計算

する。HALCON の `cooc_feature_matrix`（Calculate gray value features

from a co-occurrence matrix.）に相当(HALCON は複数の特徴量・複数角度を

同時に返せるが、ここでは energy・角度 0° 固定に単純化)。

`a が共起を取る画素間距離を 1〜4 の範囲で振る。b` は未使用。

Detailed usage guide

• gallery2d_texture_freq 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_texture_freq — py -3.11 examples/gallery2d_texture_freq.py

Ops the type connects to (they accept `feature` as input)

identity

Same category (`texture`)

std_filter · gabor · sk_frangi · sk_meijering · sk_hessian · sk_gabor · sk_lbp · sk_entropy

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

cooc_feature_matrix — 2D texture op