derivative op• Data kinds: gradient → voxel
• Call: import fullseye as fs; fs.ledger.gradient_coherence(gradient, sigma=1.5, eps=1e-12) (to call the implementation directly, import reprconv; reprconv.gradient_coherence(gradient, sigma=1.5, eps=1e-12); from the registry, opsreprconv.get("gradient_coherence"))
• GPU: none (CPU reference implementation only)
3-D gradient → the structure tensor's sheet-likeness (coherence) voxel, range [0, 1].
> The detailed description below is the original text — the summary and the headings are translated.
各 voxel で勾配の外積 g gᵀ をガウス sigma で平均した構造テンソル J を作り、固有値 μ1 ≥ μ2 ≥ μ3 ≥ 0 から (μ1 − μ2) / (μ1 + μ2 + μ3 + eps) を返す(Westin の線形異方性を構造テンソルに当てたもの。Bigün & Granlund 1987、Westin 2002)。近傍の勾配が 1 方向にそろう(層・板・界面)と 1 に近づき、向きがばらばら(等方的なざらつき・塊)だと 0 に近づく。勾配の大きさには依らない(比なので)。平らで勾配の無い所は分母が eps だけになり 0 を返す。
gradient 型を一級の入力として受けるので、勾配の出どころ(gradient3d / sobel3d / 外部の微分可能レンダラ)を問わない。
Args:
gradient: gradient3d の (gmag, gvec) か sobel3d の (gz, gy, gx)。
sigma: 構造テンソルを平均するガウスの標準偏差 [voxel]。> 0。
eps: 分母の下駄(勾配ゼロの所で 0 を返すため)。> 0。
Returns:
(D, H, W) float64、[0, 1]。
Raises:
ValueError: 形が違う / 非有限 / sigma <= 0 / eps <= 0。
• 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.
• type_connectivity_tour — py -3.11 examples/type_connectivity_tour.py
voxel as input)correlation_score · voxel_to_hessian
derivative)gradient_magnitude · voxel_to_hessian · hessian_determinant · hessian_vesselness
*Provenance: reprconv.py — REPRCONV 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.