correlation_score — REPRCONV `score` op

• Data kinds: voxel × voxel → score

• Call: import reprconv; reprconv.correlation_score(voxel_a, voxel_b) (or opsreprconv.get("correlation_score"))

Usage

Two `voxel → a normalised cross-correlation score` volume. The only entry into this type.

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

`score は refine_peak_newton` が食う型だが、**台帳のどの op も

`score` を産まなかった**(実測。生成器の種を置いてようやく到達していた)。

ここでは FFT による循環相互相関を返す:

score[s] = sum_x (a[x] - mean_a) * (b[x + s] - mean_b) / (N * std_a * std_b)

したがって `b が a を s0 だけ np.roll` したものなら、

ピークは厳密に `s0` に立つ(閉形式の真値。テストがこれを使う)。

循環相関なので端は巻き込む —— 打ち切り相関ではないことを明記しておく。

Args:

voxel_a: (D, H, W)。

voxel_b: (D, H, W)、`voxel_a` と同形。

Returns:

(D, H, W) float64、値域は概ね [-1, 1] (完全一致で 1.0)。

Raises:

ValueError: 3-D でない / 形が違う / 定数体積(標準偏差 0)/ 非有限。

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)

• representation_conversion — py -3.11 examples/representation_conversion.py

• representation_roundtrip — py -3.11 examples/representation_roundtrip.py

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

score_to_position · score_to_image2d

Same category (`score`)

score_to_position · score_to_image2d

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

correlation_score — REPRCONV score op